Reviewer #2 (Public Review):

This manuscript describes the functional and structural characterization of an anaerobic (Class III) ribonucleotide reductase (RNR) with an ATP cone domain from Prevotella copri (PcNrdD). Most significantly, the cryo-EM structural characterization revealed the presence of a flap domain that connects the ATP cone domain and the active site and provides structural insights about how nucleotides and deoxynucleotides bind to this enzyme. The authors also demonstrated the catalytic functions and the oligomeric states. However, many of the biochemical characterizations are incomplete, and it is difficult to make mechanistic conclusions from the reported structures. The reported nucleotide-binding constants may not be accurate because of the design of the assays, which complicates the interpretation of the effects of ATP and dATP on PcNrdD oligomeric states. Importantly, statistical information was missing in most of the biochemical data. Also, while the authors concluded that the dATP binding makes the GRD flexible based on the absence of cryo-EM density for GRD in the dATP-bound PcNrdD, no other supports were provided. There was also a concern about the relevance of the proposed GRD flexibility and the stability of Gly radical. Overall, the manuscript provides structural insights about Class III RNR with ATP cone domain and how it binds ATP and dATP allosteric effectors. However, ambiguity remains about the molecular mechanism by which the dATP binding to the ATP cone domain inhibits the Class III RNR activity.

Strengths:<br /> 1. The manuscript reports the first near-atomic resolution of the structures of Class III RNR with ATP domain in complex with ATP and dATP. These structures revealed the NxN flap domain proposed to form an interaction network between the substrate, the linker to the ATP cone domain, the GRD, and loop 2 important for substrate specificity. The structures also provided insights into how ATP and dATP bind to the ATP cone domain of Class III RNR. Also, the structures suggested that the ATP cone domain is directly involved in the tetramer formation by forming an interaction with the core domain in the presence of dATP. These observations serve as an important basis for future study on the mechanism of Allosteric regulation of Class III RNR.

2. The authors used a wide range of methodologies including activity assays, nucleotide binding assays, oligomeric state determination, and cryo-EM structural characterization, which were impressive and necessary to understand the complex allosteric regulation of RNR.

3. The activity assays demonstrated the catalytic function of PcNrdD and its ability to be activated by ATP and low-concentration dATP and inhibited by high-concentration dATP.

4. ITC and MST were used to show the ability of PcNrdD to bind NTP and dATP.

5. GEMMA was used successfully to determine the oligomeric state of PcNrdD, which suggested that PcNrdD exists in dimeric and tetrameric forms, whose ratio is affected by ATP and/or dATP.

Weaknesses:<br /> 1. Activity assays.<br /> The activity assays were performed under conditions that may not represent the nucleotide reduction activity. The authors initiated the Gly radical formation and nucleotide reduction simultaneously. The authors also showed that the amount of Gly radical formation was different in the presence of ATP vs dATP. Therefore, it is possible that the observed Vmax is affected by the amount of Gly radical. In fact, some of the data fit poorly into the kinetic model. Also, the number of biological and technical replicates was not described, and no statistical information was provided for the curve fitting.

2. Binding assays.<br /> The interpretation of the binding assays is complicated by the fact that dATP binds both a- and s-sites and ATP binds a- and active sites. dATP may also bind the active site as the product. It is unknown if ATP binds s-site in PcNrdD. Despite this complexity, the binding assays were performed under the condition that all the binding sites were available. Therefore, it is not clear which event these assays are reporting.

3. Oligomeric states.<br /> Due to the ambiguity in the kinetic parameters and the binding constants determined above, the effects of ATP and dATP on the oligomeric states are difficult to interpret. The concentrations of ATP used in these experiments (50 and 100 uM) were significantly lower than KL determined by the activity assays (780 uM), while it is close to the Kd values determined by ITC or MST (~25 uM). Since it is unclear what binding events ITC and MST are reporting, the data in Figure 3 does not provide support for the claimed effects of ATP binding. For the effects of dATP, the authors did not observe a significant difference in oligomeric states between 50 or 100 uM dATP alone vs 50 uM dATP and 100 uM CTP. The former condition has dATP ~ 2x higher than the Kd and KL (Figure 1b) and therefore could be considered as "inhibited". On the other hand, NrdD should be fully active under the latter condition. Therefore, these observations show no correlation between the oligomeric state and the catalytic activity.

4. Effects of dATP binding on GRD structure<br /> One of the key conclusions of this manuscript is that dATP binding induces the dissociation of GRD from the active site. However, the structures did not provide an explanation for how the dATP binding affects the conformation of GRD or whether the dissociation of GRD is a direct consequence of dATP binding or it is due to the absence of nucleotide substrate. Also, Gly radical is unlikely to be stable when it is not protected from the bulk solvent. Therefore, it is unlikely that the GRD dissociates from the active site unless the inhibition by dATP is irreversible. Further evidence is needed to support the proposed mechanism of inhibition by dATP.

5. Functional support for the observed structures.<br /> Evidence for connecting structural observations and mechanistic conclusions is largely missing. For example, the authors proposed that the interactions between the ATP cone domain and the core domain are responsible for tetramer formation. However, no biochemical evidence was provided to support this proposal. Similarly, the functional significance of the interaction through the NxN flap domain was not proved by mutagenesis experiments.

Reviewer #2 (Public Review):

In this work, Xin et al. describe cryo-EM structures of the native and carotenoid-depleted forms of RC-LH from R. castenholzii, attempting to reveal how differences in the carotenoid composition may result in the structural and functional differences in the RC-LH complex. Previously, the authors obtained the nRC-LH structure at 4.1 angstrom resolution. The current work extends the earlier moderate-resolution to a higher resolution (2.8 angstrom), which allowed them to identify 14 additional carotenoid molecules located at the external positions between adjacent LHs. These external carotenoids, together with bacteriochlorophylls, result in an impenetrable LH ring surrounding the RC, leaving only the LH opening shaped by subunit X and c-TM as the pathway for quinone exchange. They further solve the dRC-LH structure at 3.1 angstrom resolution, and find that while nRC-LH binds 15 internal and 14 external carotenoids, dRC-LH contains only five internal carotenoids, as well as a highly mobile c-TM, but no subunit X. Comparing the two types of complexes at both structural and biochemical levels, they show that these structural changes may result in the accelerated quinone exchange in dRC-LH than that in nRC-LH.<br /> The structural data in this work are solid. The cryo-EM structures are well discussed and presented by the authors to highlight the structural features that may arise from carotenoid depletion. The authors also measured the oxidation rate of the auracyanin to characterize the quinone exchange rate. The work carried out by the authors is useful in the understanding of the regulatory role of carotenoids in complex assembly and quinone exchange.

Reviewer #2 (Public Review):

This is a modeled analysis of the impact of disruptions in school-based HPV vaccination due to the COVID-19 pandemic. Different catch-up scenarios were considered, ranging from a rapid catch-up period to no catch-up vaccination, and the impact of these on future HPV-related malignancies was approximated. The approach in this study could shed light on strategies for catch-up vaccination due to disruptions caused by the COVID-19 pandemic.

Strengths:<br /> - Using the context of Australia, which has led the world in vaccination, allows us to consider a best-case scenario for the impact of disruptions in a well-running HPV vaccination program with good population coverage.<br /> - The model accounts for multiple factors, including HPV transmission dynamics, mitigation of disease development by screening

Suggested clarifications:<br /> - It could benefit from fleshing out concepts instead of using parentheses, particularly in the abstract.<br /> - There is space to expand on the results presented in Table 1, including an explanation of Affected cohorts 2008 vs Affected cohorts 2008-2009. It may also be useful to explain this analysis in the methods section.<br /> - Given that Australia is a best-case scenario and other countries have not had the same success in HPV vaccination coverage, in the discussion would it be possible to give a comparison of how these three scenarios would look different in a population with school-based vaccination but lower coverage volume, such that readers could understand how much of the success / failures of each of the three catch-up scenarios? It would be particularly helpful for readers who are not familiar with the modeling tool used in this analysis.

Reviewer #2 (Public Review):

The purpose of this study is unclear from the introduction. Additionally, the methods are incomplete and did not describe how data was collected and analyzed. The results do not describe the sample. Once these are described more clearly, further comments can be made about what the authors were trying to achieve and the impact of the work on the field.

Reviewer #2 (Public Review):

In studying the neural control of action generation there is a presumption that different nodes within a connected neural control circuit contribute differentially to the production of a given gesture. In many cases, these circuits also receive inputs that can bias ongoing motor commands to alter output and therefore the motor gesture itself. Showing the specific role that each of the different areas play in motor control and how inputs might bias motor output is challenging. Taking advantage of a precisely controlled error-correction learning task of adult birdsong, Tian et al. perform simultaneous neural recording in both the primary forebrain song motor output nucleus (RA) as well as in an input structure (LMAN) known to be necessary for biasing motor output during such learning tasks. By comparing the activity pattern and timing between recorded activity in both structures, they show that LMAN activity leads RA activity for each of the song syllables but that there is a preferential gain in activity level in LMAN after learning only during the precise time window (10 - 50 msec) associated with the specific syllable that is targeted during the error-correction paradigm. They then follow these recordings with short focal electrical stimulation in LMAN targeted to the precise time window that shows increased gain in the dual recording paradigm. This stimulation is intended to scramble the bias signal and they show that such manipulation, in a temporally specific manner, does indeed eliminate the acoustic bias imposed by LMAN.

The precise combination of dual recording and targeted stimulation, in my opinion, convincingly shows that LMAN provides a temporally precise command that can bias motor output in RA. It is assumed that LMAN inputs onto RA are mapped with some level of functional topography, especially given that RA is thought to have some degree of motor mapping. The more dorsal areas, for example, likely contribute more to respiratory control while the more ventral portion contributes to acoustic control with a possible acoustic motor map within that region. Unfortunately, the spatial precision of the recording electrodes in both RA as well as LMAN is rather coarse and a careful functional spatial mapping of spike timing correlation is not possible. Hopefully in future studies, more precise spatial mapping will provide correlations within these two structures that might be able to target subareas that encode the signal bias for subcomponents of the specific acoustic features that are being targeted in this error-correction learning paradigm.

Reviewer #2 (Public Review):

This study by Pentz et al. aims to understand how cellular attachments and/or development affect the fitness of the transition to undifferentiated multicellularity. This work has the potential to better understand why some types of multicellular development (e.g. clonal development) versus others (e.g. aggregative development) are more or less commonly observed in nature.

Presently, much of our understanding of these processes comes from observation and theoretical work. This work aims to bridge this gap by rewiring the evolutionary clock and testing if different selected undifferentiated multicellular developmental strategies are better or worse.

The authors compare the fitness of Snowflake and Floc yeast under settling-based selection. They find that Snowflake is fitter under these conditions than Floc. They augment these findings with a simplified mathematical model that supports these findings.

On their face, the findings seem interesting but have limitations in that the authors did not consider alternate selective conditions and may come to different conclusions, potentially supporting the null hypothesis. In addition, doing experiments in related multicellular model systems that the authors have previously worked in would substantially improve generalizability.

Reviewer #2 (Public Review):

This work attempts to connect the diet of a mother to the physiology and feeding behaviors of multiple generations of her offspring. Using genetic and molecular biology approaches in the fruit fly model, the authors argue that this Lamarckian inheritance is mediated by germline-inherited chromatin and is regulated by the general activity of a histone methylase. However, many of the measured effects are small and variable, the statistical tests to prove their significance are missing or poorly described, and some experiments are inadequately described and lack important controls.

1) The authors claim that the diet of a mother can influence the physiology of her progeny for several generations. However, the observed effects of maternal diet on later generations were small and variable for most assays (see Fig1C, S1.1A, B, D). Additionally, the effect size between F0 HSD to ND was often larger than the effect size between the progeny of F0 parents and ND. To put it another way, if the authors were to compare the F1, F2, etc. to the F0 HSD flies, they would conclude that the majority of the response to diet is not maternally transmitted, and is directly controlled by the diet of the individual being measured.<br /> 2) The authors chose to study PER, which had the largest average effect sizes between conditions. However, PER was highly variable in the averaged data, with some individuals showing large effects and others having no effects. A better characterization of transgenerational PER may increase the robustness of this assay and confidence in its results. For example, the authors could measure PER in lineages derived from individual flies to determine when transgenerational effects on PER decline or disappear. This form of data collection could help to explain the high variation in the averaged data presented in the paper.<br /> 3) What do the error bars represent on any figure? There are many examples where the data is highly variable and lies completely outside of the error bars. What is the statistical test for significance that is carried out in each figure? The brief comment about statistics in the methods section is inadequate. The authors should also supply the raw data used to generate the figures so that readers can perform their own statistical tests.<br /> 4) The model that global H3K27me3 is regulated by ancestral diet is unconvincing without further experimental validation and explanation. Points 4-10 address specific issues. The authors performed ChIP on cycle 11 embryos. This stage is extremely short (11 min) and contains roughly 10 times less chromatin than embryos only 30 minutes older. These features make it very difficult to collect large numbers of precisely staged embryos without significant contamination. It is also debatable whether early cell cycles (including and preceding cycle 11) are slow enough to deposit and propagate histone marks in the presence of new histone incorporation. See the opposing arguments in Zenk et al 2017 and Li et al 2014. The authors could perform ChIP on older embryos to avoid this controversy. Surely any maternally inherited information will also be present in cycle 14 or 15 embryos if it is to influence the development or physiology of the brain. The observed differences in global H3K27me3 levels in F1 vs ND flies could be explained by slightly different aged embryo collections or technical variations in the ChIP protocol. The authors could strengthen their conclusion by performing more ChIP replicates. Alternatively, the authors could use orthogonal approaches like antibody staining or western blots to measure global H3K27me3 levels in precisely staged embryos.<br /> 5) The authors measure PRC2 subunit mRNA levels in adult fly heads to attempt to explain the observed differences in inherited H3K27me3 levels in fly embryos. The authors should examine PRC2 components in germ cells and early embryos to understand how germ cells and early embryos generate H3K27me3 patterns.<br /> 6) The RNAi experiment targeting PRC2 components in embryos is uninterpretable without appropriate controls and an explanation of the genotypes used in the experimental paradigm. Are the authors crossing nosNGT mothers to UAS-RNAi fathers and assaying the progeny? What is the genotype of the F1 flies and how does it compare to the genotype of the ND flies? The authors should also note that the Gal4 drivers they use are not necessarily restricted to the ovary, and could directly affect other tissues controlling PER like neurons and muscle. Additionally, the authors should supply the appropriate controls to verify that their experimental paradigm has the intended effect. PRC2 proteins are presumably loaded into embryos and would be immune to zygotic-expressed RNAi. The authors could validate when PRC2 RNAi is effective by staining embryos for H3K27me3.<br /> 7) Although the authors do not note this, nosNGT>RNAi affects the PER of ND flies (compare Gal4>RNAi to just RNAi or just Gal4 in ND columns in Fig3A-D). This could be due to RNAi expression in neurons or muscles or some other indirect effect. Regardless of the mechanism, this result makes it difficult to interpret how RNAi treatments affect the transgenerational inheritance of PER if there is an equivalently strong non-transgenerational effect.<br /> 8) The matalpha gal4 experiment is inadequately explained in the text or methods. Are the authors expressing RNAi in the ovaries of the F0 flies that are fed an HSD? Does the ovary influence their PER somehow? Similar to point 8, there appears to be a non-transgenerational component to the RNAi phenotype that clouds the interpretation of the transgenerational effect (compare F0 in S3.1A-C).<br /> 9) For the EED inhibitor experiments (both PER and calcium imaging), it is unclear whether the authors fed the mothers or their adult progeny the EED inhibitor. If adult progeny were fed, what tissues were affected? The authors should stain various tissues with an H3K27me3 antibody to verify the effectiveness of their inhibitor. Finally, the effect of the EED inhibitor on calcium imaging was not convincing because the variation was so large.<br /> 10) In all of the PRC2 RNAi and inhibitor experiments, are there any other phenotypes that would suggest that the treatments are working? There are many published PRC2 loss-of-function phenotypes (molecular and developmental) in different tissues. The authors could assure the reader that their treatments are working as expected by doing these controls.<br /> 11) The authors propose that a transgenerationally inherited state of the caudal gene is responsible for the transgenerationally inherited PER. However, the experiments investigating the methylation state and expression level of caudal are unconvincing. Cad mRNA abundance varied immensely in the ND RNAseq samples. When the authors compared cad levels across generations, the effect size was small. A single outlier in the ND sample in both the RNAseq and the RTPCR experiments appears to drive up its mean and effect size. The H3K27me3 ChIP on cad is very similar in the F1 and ND samples and the acetylation peak on its promoter appears unchanged. The authors could vastly improve the caudal experiments in this paper by simply using cad antibodies to stain the relevant tissues that contribute to PER. For example, the authors could stain GR5a neurons for cad expression in different generations that inherit (or don't inherit) maternal PER to more accurately determine if cad levels are indeed transgenerationally regulated. The authors could also perform more ChIP experiments at a less variable stage to convincingly correlate epigenetic marks on cad with its expression level.

Reviewer #2 (Public Review):

This work describes the development of a new structure-based learning approach to predict transcription binding specificity and its application in the modeling of regulatory complexes in cis-regulatory modules. The development of accurate computer tools to model protein-DNA complexes and to predict DNA binding specificity is a very relevant research topic with significant impact in many areas.

This article highlights the importance of transcriptional regulatory elements in gene expression regulation and the challenges in understanding their mechanisms. Traditional definitions of activating regulatory elements, such as promoters and enhancers, are becoming unclear, suggesting an updated model based on DNA accessibility and enhancer/promoter potential. Experimental techniques can assess the sequence preferences of transcription factors (TFs) for binding sites. Recent models propose a cooperative model in which regulatory elements work together to increase the local concentrations of TFs, RNA polymerase II, and other co-factors. Co-operative binding can be mediated through protein-protein or DNA interactions. The authors developed a structure-based learning approach to predict TF binding features and model the regulatory complex(es) in cis-regulatory modules, integrating experimental knowledge of structures of TF-DNA complexes and high-throughput TF-DNA interactions. They developed a server to characterize and model the binding specificity of a TF sequence or its structure, which was applied to the examples of interferon-β enhanceosome and the complex of factors SOX11/SOX2 and OCT4 with the nucleosome. The models highlight the co-operativity of TFs and suggest a potential role for nucleosome opening.

The results presented by the authors have a large variability in performance upon the different TF families tested. Therefore, it would be ideal if the performance/accuracy of the method is tested in some simple predictions and validated with prospective experimental data before applying it to model difficult scenarios such as those described here: SOX11/SOX2/OCT4 and nucleosome or interferon beta and enhanceosome. This will give more support to the models generated and thus the validity of the conclusions and hypothesis derived from them.

Reviewer #2 (Public Review):

Toker et al. use a frequency-resolved analysis of cortico-thalamic and thalamo-cortical information transfer to determine at which combinations of frequencies a frequency-specific transfer of information exists, and how this transfer is modulated by anesthesia, spike-and-wave seizures, and psychedelic states. They find that anesthesia and seizures lower the transfer of information at a specific combination of frequencies (sending: 1.5-13Hz, receiving 50-100Hz), whereas psychedelic states induced by 5-MeO-DMT increase. The reductions were observed for both directions whereas significant increases were only observed from cortex to thalamus.

Neural mean-field modeling shows that these empirical observations may be linked to a deviation of neural dynamics from the critical point between ordered and chaotic dynamics.

The manuscript tackles an important question using innovative methods. Yet, the analysis of spectrally resolved information transfer at present suffers from an unfortunate choice of analysis parameters (especially a history length of 1, and a low number of surrogate data), that need to be changed to fully install trust in the presented results. The statistical analysis seems to suffer from so-called 'double-dipping', but there are several possible ways to fix this issue.

Reviewer #2 (Public Review):

Davies et al combine TurboID with conditional mutagenesis to reveal how a perturbing event alters the accessibility of a sub-cellular proteome to proximity biotinylation. The approach builds on established techniques for antibody-mediated enrichment of biotinylated peptides (rather than purification of whole biotinylated proteins by avidin) to enable mapping of the specific lysines that are biotinylated by TurboID and how access to these sites changes between conditions. The insights gained have a range of potential implications touching on protein trafficking/localization, complex dynamics and membrane topology. The authors apply this strategy to study trafficking of the key P. falciparum adhesin PfEMP1 to the infected erythrocyte surface. This group has previously shown that the exported parasite kinase FIKK4.1 is important for this process but the specific mechanism is unknown. In the first part of the present study, the authors develop PerTurboID and analyze the altered biotinylation patterns upon FIKK4.1 deletion in parasite lines bearing TurboID tags on PTP4 or KAHRP, two proteins required for this pathway and likely direct substrates of FIKK4.1. Numerous changes in site-specific biotinylation are quantitatively assessed on hundreds of proteins and possible implications for these changes are discussed, including topology of parasite integral membrane proteins exported into the RBC compartment as well as how the conformation of the RhopH complex might be altered upon RBC membrane integration. In a final set of experiments, the authors show that among 18 exported FIKK kinases, FIKK4.1 is uniquely important to PfEMP1 surface display but not to the distinct RIFIN class of parasite proteins that are also trafficked to the RBC surface. On the whole, the data are compelling and provide an important new approach that advances the proximity labeling toolkit.

While the resolution of PerTurboID captures the site-specific changes in biotinylation abundance and position that occur upon loss of FIKK4.1, a limitation of the study is that these observations do not necessarily clarify the model for how FIKK4.1 is controlling the PfEMP1 trafficking pathway. The authors convincingly show that FIKK4.1 uniquely supports PfEMP1 surface presentation and cytoadhesion. However, this is not connected to the PerTurboID data in a way that provides a mechanism for how this is achieved by FIKK4.1 activity and in my opinion doesn't deliver on the title claim to "reveal the impact of kinase deletion on cytoadhesion". Certainly the changes in biotinylation suggest a range of interesting possibilities related to the accessibility and topology of proteins within and beyond the PfEMP1 trafficking pathway; however, it is hard to interpret the relationship of these changes to the process in view. For instance, deletion of FIKK4.1 increases biotinylation of several Maurer's clefts proteins in both the PTP4- and KAHRP-TurboID experiments but why this is or whether it is significant for PfEMP1 transport is unclear.

Reviewer #2 (Public Review):

This study follows up on a previous study by the group (Sibille et al Nature Communications 2022) in which high density Neuropixel probes were inserted tangentially through the superficial layers of the superior colliculus (SC) to record the activity of retinocollicular axons and postsynaptic collicular neurons in anesthetized mice. By correlating spike patterns, connected pairs could be identified which allowed the authors to demonstrate that functionally similar retinal axon-SC neuron pairs were strongly connected.

In the current study, the authors use similar techniques in vGAT-ChR2 mice and add a fiber optic to identify light-activated GABAergic and non-light-activated nonGABAergic neurons. Using their previously verified techniques to identify connected pairs, within regions of optogenetic activation they identified 214 connected pairs of retinal axons and nonGABAergic neurons and 91 pairs of connected retinal axons and GABAergic neurons. The main conclusion is that retinal activity contributed more to the activity of postsynaptic nonGABAergic SC neurons than to the activity of postsynaptic GABAergic SC neurons.

The study is very well done. The figures are well laid out and clearly establish the conclusions. My main comments are related to the comparison to other circuits and further questions that might be addressed in the SC.

It is stated several times that the superior colliculus and the visual cortex are the two major brain areas for visual processing and these areas are compared throughout the manuscript. However, since both the dorsal lateral geniculate nucleus (dLGN) and SC include similar synaptic motifs, including triadic arrangements of retinal boutons with GABAergic and nonGABAergic neurons, it might be more relevant to compare and contrast retinal convergence and other features in these structures.

The GABAergic and nonGABAergic neurons showed a wide range of firing rates. It might be interesting to sort the cells by firing rates to see if they exhibit different properties. For example, since the SC contains both GABAergic interneurons and projection neurons it would be interesting to examine whether GABAergic neurons with higher firing rates exhibit narrower spikes, similar to cortical fast spiking interneurons. Similarly, it might be of interest to sort the neurons by their receptive field sizes since this is associated with different SC neuron types.

The recording techniques allowed for the identification of the distance between connected retinocollicular fibers and postsynaptic neurons. It might also be interesting to compare the properties of connected pairs recorded at dorsal versus ventral locations since neurons with different genetic identities and response properties are located in different dorsal/ventral locations (e.g. Liu et al. Neuron 2023). Also, regarding the strength of connections, previous electron microscopy studies have shown that the retinocollicular terminals differ in density and size in the dorsal/ventral dimension (e.g Carter et al JCN 1991).

Was optogenetic activation of GABAergic neurons ever paired with visual activation? It would be interesting to examine the receptive fields of the nonGABAergic neurons before and after activation of the GABAergic neurons (as in Gale and Murphy J Neurosci 2016).

Reviewer #2 (Public Review):

The authors compare their single-cell data of the self-forming brain-eye centroids with the published single-cell data from human fetal retinas and brain/optic organoids. This analysis further supports the similarity of their centroids with the human fetal retinal cell clusters, including the detection of the VSX2+/PAX2+ cells. The new findings further support the presented centroids' applicability for future studies on human RGC development and axon guidance mechanisms.

Reviewer #2 (Public Review):

The authors wanted to address the differential processing of GSDME by caspase 3 and 7, finding that while in humans GSDME is only processed by CASP3, Takifugu GSDME, and other mammalian can be processed by CASP3 and 7. This is due to a change in a residue in the human CAPS7 active site that abrogates GSDME cleavage. This phenomenon is present in humans and other primates, but not in other mammals such as cats or rodents. This study sheds light on the evolutionary changes inside CASP7, using sequences from different species. Although the study is somehow interesting and elegantly provides strong evidence of this observation, it lacks the physiological relevance of this finding, i.e. on human side, mouse side, and fish what are the consequences of CASP3/7 vs CASP3 cleavage of GSDME.

Fish also present a duplication of GSDME gene and Takifugu present GSDMEa and GSDMEb. It is not clear in the whole study if when referring to TrGSDME is the a or b. This should be stated in the text and discussed in the differential function of both GSDME in fish physiology (i.e. PMIDs: 34252476, 32111733 or 36685536).

Reviewer #2 (Public Review):

In this study, Abele et al. present evidence to suggest that two different forms of regulated cell death, pyroptosis and apoptosis, are not equivalent in their ability to clear infection with recombinant Salmonella strains engineered to express the pro-pyroptotic NLRC4 agonist, FliC ("FliC-ON"), or the pro-apoptotic protein, BID ("BID-ON"). In general, individual experiments are well-controlled, and most conclusions are justified. However, the cohesion between different types of experiments could be strengthened and the overall impact and significance of the study could be articulated better.

Reviewer #2 (Public Review):

This work started with transcriptomic profiling of ductal cells to identify the upregulation of calcineurin in the zebrafish after beta-cell ablation. By suppressing calcineurin with its chemical inhibitor cyclosporin A and expressing a constitutively active form of calcineurin ubiquitously or specifically in ductal cells, the authors found that inhibited calcineurin activity promoted beta-cell regeneration transiently while ectopic calcineurin activity hindered beta-cell regeneration in the pancreatic tail. They also showed similar effects in the basal state but only when it was within a particular permissive window of Notch activity. To further investigate the roles of calcineurin in the ductal cells, the authors demonstrated that calcineurin inhibition additionally induced the proliferation of the ductal cells in the regenerative context or under a limited level of Notch activity. Interestingly, the enhanced proliferation was followed by a depletion of ductal cells, suggesting that calcineurin inhibition would exhaust the ductal cells. Based on the data, the authors proposed a very attractive and intriguing model of the role of calcineurin in maintaining the balance of the progenitor proliferation and the endocrine differentiation. However, the conclusions of this paper are only partially supported by the data as some evidence from the data remains suggestive.

1. In the transcriptomic profiling, genes differentially regulated in the ablated adults could be solely due to the chemical effects of metronidazole instead of the beta-cell ablation. A control group without ins:NTR-mCherry but treated with metronidazole is necessary to exclude the side effects of metronidazole.

2. Although it has been shown that the pancreatic duct is a major source of the secondary islets in the pancreatic tail in previous studies, there is no direct evidence showing the cyclosporin A-induced cells share the source in this manuscript. Without any proper lineage tracing work, the origin of those cyclosporin A-induced cells cannot be concluded.

3. It is interesting to see an increase of beta cells in the primary islet after cyclosporin A treatment (Supplemental Fig 2B). However, it remains unclear if their formation shares the same mechanism with the newly formed beta cells in the pancreatic tail.

4. The conclusion of the effect of cyclosporin A on the endocrine progenitors (Line 175) is not convincing because the data cannot distinguish the endocrine progenitors from the insulin-expressing cells. Indeed, Figure 2E shows that neurod1+ cells are fewer than ins+ cells (Figure 2D) in the pancreatic tail at 10 dpt, suggesting that all or at least the majority of neurod1+ cells are already ins+.

5. Figure 5D shows a significant loss of nkx6.1+ cells in the combined treatment group but there is no direct evidence showing this was a result of differentiation as the authors suggested. This cell loss also outnumbered the increase in ins+ cells (Figure 4D). The cell fates of these lost cells are still undetermined, and the authors did not demonstrate if apoptosis could be a reason of the cell loss.

Reviewer #2 (Public Review):

Novelty: The concept that capillary stalls occur in the ischemic penumbra is not new. However, there are several interesting findings in the current study.<br /> 1- Flow reversal, 2- the effect of flow disturbances on oxygenation, and 3- capillary pericytes do not affect the hemodynamics in the penumbra.<br /> However, more in-depth analysis is needed and the underlying mechanism of flow reversal and the link between flow reversal and pericytes is unclear.

Strengths:<br /> 1. The study employs a combination of techniques including Laser speckle imaging, two photon microscopy and biophysical modelling to specifically examine hemodynamic and metabolic changes in the penumbra following experimental stroke.<br /> 2. The importance of following microvascular flow changes during hours after stroke.<br /> 3. The authors used a rat model of stroke and confirmed previous work that has been performed in mice about capillary stalls and flow disturbance in the ischemic penumbra.

Weaknesses:<br /> 1- The reliance on laser speckle to define the ischemic core and penumbra is not convincing.<br /> 2- The mechanisms behind microvascular flow disturbance are poorly defined.<br /> 3- The inability to measure capillary flow simultaneously in the regions of interest: e.g, Bessel beam imaging or volumetric imaging.<br /> 4- Lack of baseline measurements.

Reviewer #2 (Public Review):

In the manuscript by Salmani et al., the authors explore the transcriptomic characterization of dopamine neurons in order to explore which neurons are particularly vulnerable to 6-OHDA-induced toxicity. To do this they perform single nucleus RNA sequencing of a large number of cells in the mouse midbrain in control animals and those exposed to 6-OHDA. This manuscript provides a detailed atlas of the transcriptome of various types of ventral midbrain cells - though the focus here is on dopaminergic cells, the data can be mined by other groups interested in other cell types as well. The results in terms of cell type classification are largely consistent with previous studies, though a more nuanced picture of cellular subtypes is portrayed here, a unique advantage of the large dataset obtained. The major advance here is exploring the transcriptional profile in the ventral midbrain of animals treated with 6-OHDA, highlighting potential candidate genes that may influence vulnerability. This approach could be generalizable to investigate how various experiences and insults alter unique cell subtypes in the midbrain, providing valuable information about how these stimuli impact DA cell biology and which cells may be the most strongly affected.

Overall, the manuscript is relatively heavy on characterization and comparatively light on functional interpretation of findings. This limits the impact of the proposed work. It also isn't clear what the vulnerability factors may be in the neurons that die. Beyond the characterization of which neurons die - what is the reason that these neurons are susceptible to lesion? Also, the interpretation of these findings is going to be limited by the fact that 6-OHDA is an injectable, and the effects depend on the accuracy of injection targeting and the equal access of the toxin to access all cell populations. Though the site of injection (MFB) should hit most/all of the forebrain-projecting DA cells, the injection sites for each animal were not characterized (and since the cells from animals were pooled, the effects of injection targeting on the group data would be hard to determine in any case).

I am also not clear why the authors don't explore more about what the genes/pathways are that differentiate these conditions and why some cells are particularly vulnerable or resilient. For example, one could run GO analyses, weighted gene co-expression network analysis, or any one of a number of analysis packages to highlight which genes/pathways may give rise to vulnerability or resilience. Since the manuscript is focused on identifying cells and gene expression profiles that define vulnerability and resilience, there is much more that could have been done with this based on the data that the authors collected.

Another limitation of this study as presented is the missed opportunity to integrate it with the rich literature on midbrain dopamine (and non-dopamine) neuron subtypes. Many subtypes have been explored, with divergent functions, and can usually be distinguished by either their projection site, neurotransmitter identity, or both. Unfortunately, the projection site does not seem to track particularly well with transcriptomic identities, aside from a few genes such as DAT or the DRD2 receptor. However, this could have been more thoroughly explored in this manuscript, either by introducing AAVretro barcodes through injection into downstream brain sites, or through existing evidence within their sequencing dataset. There are likely clear interpretations from some of that literature, some of which may be more exciting than others. For example, the authors note that vGluT2-expressing cells were part of the resilient territory. This might be because this is expressed in medially-located DA cells and not laterally-located ones, which tends to track which cells die and which don't.

It is not immediately clear why the authors used a relaxed gate for mCherry fluorescence in Figure 1. This makes it difficult to definitively isolate dopaminergic neurons - or at least, neurons with a DAT-Cre expression history. While the expression of TH/DAT should be able to give a fairly reliable identification of these cells, the reason for this decision is not made clear in the text.

Reviewer #2 (Public Review):

In their study, Zaman et al. demonstrate that deletion of either the receptor tyrosine kinase Kit from cerebellar interneurons or the kit ligand (KL) from Purkinje cells reduces the inhibition of Purkinje cells. They delete Kit or KL at different developmental time points, illustrating that Kit-KL interactions are not only required for developmental synapse formation but also for synapse maintenance in adult animals. The study is interesting as it highlights a molecular mechanism for the formation of inhibitory synapses in Purkinje cells.

The tools generated, such as the floxed Kit mouse line and the virus for Kit overexpression, may have broader applications in neuroscience and beyond.

However, to enhance the publication's impact and strengthen its hypotheses, conclusions, and scientific rigor, it would be beneficial to include additional experimental details, data analyses (particularly regarding the quantification of electrophysiology data), as well as methodological and textual clarifications.

One general weakness is that Kit expression is not limited to molecular layer interneurons but also extends to the Purkinje layer and Golgi interneurons. Although this expression may not conflict with the reported results, as Purkinje layer interneurons form few or no synapses onto Purkinje cells, it should be highlighted in the text (introduction and/or discussion).

In summary, the data support the hypothesis that the interaction between Kit and KL between cerebellar Molecular Layer Interneurons and Purkinje Cells plays a crucial role in promoting the formation and maintenance of inhibitory synapses onto PCs. This study provides valuable insights that could inform future investigations on how this mechanism contributes to the dynamic regulation of Purkinje cell inhibition across development and its impact on mouse behavior.

Reviewer #2 (Public Review):

This paper takes a novel look at the protein economy of primary human and mouse T-cells - in both resting and activated state. Their findings in primary human T-cells are that:

1. A large fraction of ribosomes are stalled in resting cultured primary human lymphocytes, and these stalled ribosomes are likely to be monosomes.<br /> 2. Elongation occurs at similar rates for HeLa cells and lymphocytes, with the active ribosomes in resting lymphocytes translating at a similar rate as fully activated lymphocytes.

They then turn their attention to mouse OT-1 lymphocytes, looking at translation rates both in vitro and in vivo. Day 1 resting T-cells also show stalling - which curiously wasn't seen on freshly purified cells - I didn't understand these differences.

In vivo, they show that it is possible to monitor accurate translation and measure rates. Perhaps most interestingly they note a paradoxically high ratio of cellular protein to ribosomes insufficient to support their rapid in vivo division, suggesting that the activated lymphocyte proteome in vivo may be generated in an unusual manner.

This was an interesting and provocative paper. Lots of interesting techniques and throwing down challenges to the community - it manages to address a number of important issues without necessarily providing answers.

Reviewer #2 (Public Review):

The authors introduce the notions of "variant vulnerability" and "drug applicability" as metrics quantifying the sensitivity of a given target variant across a panel of drugs and the effectiveness of a drug across variants, respectively. Given a data set comprising a measure of drug effect (such as growth rate suppression) for pairs of variants and drugs, the vulnerability of a variant is obtained by averaging this measure across drugs, whereas the applicability of a drug is obtained by averaging the measure across variants.

The authors apply the methodology to a data set that was published by Mira et al. in 2015. The data consist of growth rate measurements for a combinatorially complete set of 16 genetic variants of the antibiotic resistance enzyme beta-lactamase across 10 drugs and drug combinations at 3 different drug concentrations, comprising a total of 30 different environmental conditions. For reasons that did not become clear to me, the present authors select only 7 out of 30 environments for their analysis. In particular, for each chosen drug or drug combination, they choose the data set corresponding to the highest drug concentration. As a consequence, they cannot assess to what extent their metrics depend on drug concentration. This is a major concern since Mira et al. concluded in their study that the differences between growth rate landscapes measured at different concentrations were comparable to the differences between drugs. If the new metrics display a significant dependence on drug concentration, this would considerably limit their usefulness.

As a consequence of the small number of variant-drug combinations that are used, the conclusions that the authors draw from their analysis are mostly tentative with weak statistical support. For example, the authors argue that drug combinations tend to have higher drug applicability than single drugs, because a drug combination ranks highest in their panel of 7. However, the effect profile of the single drug cefprozil is almost indistinguishable from that of the top-ranking combination, and the second drug combination in the data set ranks only 5th out of 7.

To assess the environment-dependent epistasis among the genetic mutations comprising the variants under study, the authors decompose the data of Mira et al. into epistatic interactions of different orders. This part of the analysis is incomplete in two ways. First, in their study, Mira et al. pointed out that a fairly large fraction of the fitness differences between variants that they measured were not statistically significant, which means that the resulting fitness landscapes have large statistical uncertainties. These uncertainties should be reflected in the results of the interaction analysis in Figure 4 of the present manuscript. Second, the interpretation of the coefficients obtained from the epistatic decomposition depends strongly on the formalism that is being used (in the jargon of the field, either a Fourier or a Taylor analysis can be applied to fitness landscape data). The authors need to specify which formalism they have employed and phrase their interpretations accordingly.

Reviewer #2 (Public Review):

Multiple sclerosis is an inflammatory and demyelinating disease of the central nervous system where immune cells play an important role in disease pathobiology. Increased incidence of disease in individuals carrying certain HLA class-II genes plus studies in animal models suggests that HLA-DRB1*15 restricted CD4 T cells might be responsible for disease initiation, and other immune cells such as B cells, CD8 T cells, monocytes/macrophages, and dendritic cells (DC) also contribute to disease pathology. However, a direct role of human immune cells in disease is lacking to a lag between immune activation and the first sign of clinical disease. Therefore, there is an emphasis on understanding whether immune cells from HLA-DR15+ MS patients differ from HLA-DR15+ healthy controls in their phenotype and pro-inflammatory capacity. To overcome this, authors have used severely immunodeficient B2m-NOG mice that lack B, T cells, and NK cells and have defective innate immune responses and engrafted PBMCs from 3 human donors (HLA-DR15+ MS and HI donors, HLA-DR13+ MS donor) in these B2m-NOG mice to determine whether they can induce CNS inflammation and demyelination like MS.<br /> The study's strength is the use of PBMCs from HLADRB1-typed MS subjects and healthy control, the use of NOG mice, the characterization of immune subsets (revealing some interesting observations), CNS pathology etc. The major weaknesses are i) lack of sufficient sample size (n=1 in each group) to make any conclusion, ii) lack of phenotype in mice, iii) no disease phenotype even in humanized mice immunized for disease using standard disease induction protocol employed in an animal model of MS, and iv) mechanistic data on why CD8 T cells are more enriched than CD4+ T cells. The last point is very important as postmortem human MS patients' brain tissue had been shown to have more CD8+ T cells than CD4+ T cells.

Thus, this work is an important step in the right direction as previous humanized studies have not used HLA-DRB1 typed PBMCs however the weaknesses as highlighted above make the findings incremental to the field.

Reviewer #2 (Public Review):

Place cells fire sequentially during hippocampal theta oscillations, forming a spatial representation of behavioral experiences in a temporally-compressed manner. The firing sequences during theta cycles are widely considered as essential assemblies for learning, memory, and planning. Many theoretical studies have investigated the mechanism of hippocampal theta firing sequences; however, they are either entirely extrinsic or intrinsic. In other words, they attribute the theta sequences to external sensorimotor drives or focus exclusively on the inherent firing patterns facilitated by the recurrent network architectures. Both types of theories are inadequate for explaining the complexity of the phenomena, particularly considering the observations in a previous paper by the authors: theta sequences independent of animal movement trajectories may occur simultaneously with sensorimotor inputs (Yiu et al., 2022).

In this manuscript, the authors concentrate on the CA3 area of the hippocampus and develop a model that accounts for both mechanisms. Specifically, the model generates extrinsic sequences through the short-term facilitation of CA3 cell activities, and intrinsic sequences via recurrent projections from the dentate gyrus. The model demonstrates how the phase precession of place cells in theta sequences is modulated by running direction and the recurrent DG-CA3 network architecture. To evaluate the extent to which firing sequences are induced by sensorimotor inputs and recurrent network architecture, the authors use the Pearson correlation coefficient to measure the "intrinsicity" and "extrinsicity" of spike pairs in their simulations.

I find this research topic to be both important and interesting, and I appreciate the clarity of the paper. The idea of combining intrinsic and extrinsic mechanisms for theta sequences is novel, and the model effectively incorporates two crucial phenomena: phase precession and directionality of theta sequences. I particularly commend the authors' efforts to integrate previous theories into their model and conduct a systematic comparison. This is exactly what our community needs: not only the development of new models, but also understanding the critical relationships between different models.

Reviewer #2 (Public Review):

The manuscript by Elfstrom et al describes the impact of implementing self-sampling as the primary screening test in Sweden to address decreases in coverage following the COVID pandemic. The authors have a very rich dataset including all records of invitations to screen and screening results in the Stockholm area. A limitation is that there is no individual record linkage to allow investigation of the profile of the individuals who chose to screen using the self-sample.

The conclusions are generally well supported by the authors with the following exceptions:

1) There was not enough evidence presented in the manuscript to conclude that "The most likely explanation for the large increase in population coverage seen is that the sending of self-sampling kits resulted in improved attendance in particular among previously non-attending women."

2) The authors state there is no evidence that delays in screening have impacted cervical cancer rates however they present no data to this effect in the manuscript.

Reviewer #2 (Public Review):

In this article, Moses and Harel present genetic knock-out and partial rescue of the phenotypes of neuropeptides gh1 and fshb, and tshb in a short-lived vertebrate African turquoise killifish Nothobranchius furzeri. Neuropeptides are among the key regulators of growth, reproduction, and metabolism. Understanding their mechanisms of action has important implications for vertebrate physiology.

The authors first characterize the loss of function phenotypes of gh1, fshb, and tshb in killifish, followed by attempts to rescue the loss of function phenotypes through ectopic expression of two of the neuropeptides. The primary strength and innovation of this work are partially rescuing the phenotypes by muscular injection of plasmids followed by electroporation, including a doxycycline-inducible system for tunable expression control. The techniques for tunable expression control and rescue of knock-out phenotypes have not been established for killifish and will be useful to expand the technical repertoire of this emerging model organism. Once established, these techniques can be extended to other categories of genes to rapidly evaluate their function and the impact of their loss or gain of function on killifish and other fish models.

However, the phenotypes discussed need further characterization, many technical details are unclear, and it seems that appropriate controls are missing for some of the experiments. The rescued phenotypes also need more validation.

Reviewer #2 (Public Review):

This article focuses on drug resistance acquired by Plasmodium falciparum malaria parasites that have been pressured with different inhibitors of the essential enzyme DHODH (dihydroorotate dehydrogenase). The study focuses on collateral sensitivity between DSM265, which has been evaluated in a human clinical trial and found to select for resistance via the point mutation C276Y (C276F and G181S were also implicated; PMID 29909069), and the GSK compound TMCDC-125334, against which a panel of DHODH mutant parasites (including C276Y) were found to have increased sensitivity. The authors herein explore this case of "collateral sensitivity" by examining whether these two inhibitors, when used simultaneously, might preclude the selection of resistant parasites. The answer, in this case, is no; collateral sensitivity did not prevent parasites from acquiring a novel mutation (V532A) that mediated resistance to both. Culture competition assays provide evidence that this mutant retains normal fitness. The authors conclude that for this target the idea of combining these inhibitors is not a viable therapeutic strategy. The authors also illustrate how TMCDC-125334 can select for resistance via a separate mutation (I263S) or amplification of a chromosomal segment containing dhodh. They also present modeling data to examine binding poses and how mutations could impact drug binding, which is allosteric to the enzyme's substrates (orotate and FMN). The data are thorough and provide convincing evidence that in this case collateral sensitization by distinct chemotypes does not translate into a viable strategy to inhibit DHODH in a way that can preclude mutations that confer cross-resistance.

Reviewer #2 (Public Review):

In their recent manuscript, Broca-Brisson et al. deliver a multidisciplinary approach to investigate creatine transporter deficiency (CTD) using human-derived brain organoids. The authors have provided a compelling CTD human brain organoid model using induced pluripotent stem cells (iPSCs) derived from individuals with CTD. This model shows distinct differences in creatine uptake between organoids originating from CTD patients and their healthy counterparts. Furthermore, the researchers effectively restored creatine uptake by reintroducing the wild-type CRT in the iPSCs.

The team used advanced molecular biology techniques and sophisticated mass spectrometry to identify changes in protein regulation within these CTD brain organoids. They propose an intriguing theory linking reduced creatine uptake to abnormalities in the GSK3β kinase pathway and mitochondrial function, which might underlie intellectual disability seen in CTD patients.<br /> This study is well-structured and easy to follow, with clear and concise explanations of the experiments. The authors present an important idea: a dysfunction in just one protein transporter (CRT) can cause significant biochemical changes in the brain. Their findings are well-presented and backed by high-quality figures and comprehensive data analysis.

There are only minor suggestions for improvement in this manuscript. The authors strongly link creatine uptake, the GSK3β pathway, and intellectual disability. Enhancing this claim with data on phosphorylation differences between organoids derived from healthy individuals and those from CTD patients could solidify this foundation and facilitate a more holistic understanding of the disease. In addition, the in vitro model based on organoids might be closer than other experimental setups; however, proving that those differences are also present in vivo would greatly benefit the story.

There is also some uncertainty around the rescue experiment using the exogenous SLC6A8 gene. Could the difference in creatine uptake between the rescue iPSCs and the healthy control be due to CRT overexpression? Higher levels of the transporter may explain the elevated levels of intracellular creatine. Thus, a comparison using Western blotting experiments could be a valuable addition to evaluating the expression levels of this protein.

Overall, this study provides valuable insights into CTD and potential therapeutic targets. It enriches our understanding of CTD and opens up new avenues for future research in this field.

Reviewer #2 (Public Review):

The authors used cutting-edge bio-telemetry technology to decipher the roles of wind speed and wave height on the take-off of albatrosses from the water surface. They revealed that each of these factors contributes to take-off in a unique way with interesting interactions of the two factors. The authors achieved their objectives and their results support their conclusions. This work will set new standards in integrating information about bird movement and environmental conditions experienced by the bird in a comprehensive, integrative and hypothesis-driven framework. The approach of the authors is highly advanced, providing heuristic insights for many additional systems where organisms are influenced by, and respond to small-scale environmental conditions.

Reviewer #2 (Public Review):

The manuscript by Seah and Saranathan investigates the cell-based growth mechanism of so called honeycomb-structures in the upper lamina of papilionid wing scales by investigating a number of different species. The authors chose Parides eurimedes as a focus species with the developmental pathway of five other papilionid as a comparative backup. Through state-of-the-art microscopy images of different developmental steps, the author find that the intricate f-actin filaments reorganise, support cuticular discs that template the air holes that form the honeycomb lattice. The manuscript is well written and easy to follow, yet based on a somewhat limited sample size for their focus species, limiting attempts to suppress expression and alter structure shape.

The fact that the authors find a novel reorganisation mechanism is exciting and warrants further research, e.g. into the formation of other microscale features or smaller scale structures (e.g. the mentioned gyroid networks).<br /> The authors place their results in the discussion in the light of current literature (although the references could be expanded further to include the breadth of the field). However, the mechanistic explanation completely ignores the mechanical properties of the membranes as an origin of some of the observed phenomena (see McDougal's work for example) and places the occurence of some features into Turing patterns and Ostwald ripening, which I find somewhat unlikely and I suggest that the authors discover this aspects further in the discussion.

I have little concerns regarding the experimental approach beyond the somewhat limited sample size. One thing the authors should more clearly mention are the pupation periods for all investigated species as only the periods for two species are named.

Reviewer #2 (Public Review):

In this study, Sekulovski and colleagues report refinements to an in vitro model of human amnion formation. Working with 3D cultures and BMP4 to induce differentiation, the authors chart the time course of amnion induction in human pluripotent stem cells in their system using immunofluorescence and RNA-seq. They carry out validation through comparison of their data to existing embryo datasets, and through immunostaining of post-implantation marmoset embryos. Functional experiments show that the transcription factor TFAP2C drives the amnion differentiation program once it has been initiated.

There is currently great interest in the development of in vitro models of human embryonic development. While it is known that the amnion plays an important structural supporting role for the embryo, its other functions, such as morphogen production and differentiation potential, are not fully understood. Since a number of aspects of amnion development are specific to primates, models of amniogenesis will be valuable for the study of human development. Advantages of this model include its efficiency and the purity of the cell populations produced, a significant degree of synchrony in the differentiation process, benchmarking with single-cell data and immunocytochemistry from primate embryos, and identification of key markers of specific phases of differentiation. Weaknesses are the absence of other embryonic tissues in the model, and overinterpretation of certain findings, in particular relating bulk RNA-seq results to scRNA-seq data from published analyses of primate embryos and results from limited (though high quality) embryo immunostainings.

Reviewer #2 (Public Review):

The authors of the manuscript have developed and used cloning-free method. It is not entirely novel (rather it is based on previously described ISA method) but it is clearly efficient and useful complementation to the already existing methods. One of strong points of the approach use by authors is that it is very versatile, i.e. can be used in combination with already existing methods and tools. I find it important as many laboratories have already established their favorite methods to manipulate SARS-CoV-2 genome and are probably unwilling to change their approach entirely. Though authors highlight the benefits of their method these are probably not absolute - other methods may be as efficient or as fast. Still, I find myself thinking that for certain purposes I would like to complement my current approach with elements from authors CLEVER method.

The work does not contain much novel biological data - which is expected for a paper dedicated to development of new method (or for improving the existing one). It may be kind of shortcoming as it is commonly expected that authors who have developed new methods apply it for discovery of something novel. The work stops on step of rescue the viruses and confirming their biological properties. This part is done very well and represents a strength of the study. The properties of rescued viruses were also studied using NSG methods that revealed high accuracy of the used method, which is very important as the method relies on use of PCR that is known to generate random mistakes and therefore not always method of choice.

What I found missing is a real head-to-head comparison of the developed system with an existing alternatives, preferably some PCR-free standard methods such as use of BAC clones. There are a lot of comparisons but they are not direct, just data from different studies has been compared. Authors could also be more opened to discuss limitations of the method. One of these seems to be rather low rescue efficiency - 1 rescue event per 11,000 transfected cells. This is much lower compared to infectious plasmid (about 1 event per 100 cells or so) and infectious RNAs (often 1 event per 10 cells, for smaller genomes most of transfected cells become infected). This makes the CLEVER method poorly suitable for generation of large infectious virus libraries and excludes its usage for studies of mutant viruses that harbor strongly attenuating mutations. Many of such mutations may reduce virus genome infectivity by 3-4 orders of magnitude; with current efficiencies the use of CLEVER approach may result in false conclusions (mutant viruses will be classified as non-viable while in reality they are just strongly attenuated).

Reviewer #2 (Public Review):

In this work, the authors describe engineering of sgRNAs that render Cas9 DNA binding controllable by a second RNA trigger. The authors introduce several iterations of their engineered sgRNAs, as well as a computational pipeline to identify designs for user-specified RNA triggers which offers a helpful alternative to purely rational design. Also included is an investigation of the fate of the engineered sgRNAs when introduced into cells, and the use of this information to inform installation of modified nucleotides to improve engineered sgRNA stability. Engineered sgRNAs are demonstrated to be activated by trigger RNAs in both cultured mammalian cells and zebrafish.

The conclusions made by the authors in this work are predominantly supported by the data provided. However, some claims are not consistent with the data shown and some of the figures would benefit from revision or further clarification.

Strengths:<br /> - The sgRNA engineering in this paper is performed and presented in a systematic and logical fashion. Inclusion of a computational method to predict iSBH-sgRNAs adds to the strength of the engineering.<br /> - Investigation into the cellular fate of the engineered sgRNAs and the use of this information to guide inclusion of chemically modified nucleotides is also a strength.<br /> - Demonstration of activity in both cultured mammalian cells and in zebrafish embryos increases the impact and utility of the technology reported in this work.

Weaknesses:<br /> - While the methods here represent an important step forward in advancing the technology, they still fall short of the dynamic range and selectivity likely required for robust activation by endogenous RNA.<br /> - While the iSBH-sgRNAs where the RNA trigger overlaps with the spacer appear to function robustly, the modular iSBH-sgRNAs seem to perform quite a bit less well. The authors state that modular iSBH-sgRNAs show better activity without increasing background when the SAM system is added, but this is not supported by the data shown in Figure 3D, where in 3 out of 4 cases CRISPR activation in the absence of the RNA trigger is substantially increased.<br /> - There is very little discussion of how the performance of the technology reported in this work compares to previous iterations of RNA-triggered CRISPR systems, of which there are many examples.

Reviewer #2 (Public Review):

The manuscript by Berrocal et al. asks if shared bursting kinetics, as observed for various developmental genes in animals, hint towards a shared molecular mechanism or result from natural selection favoring such a strategy. Transcription happens in bursts. While transcriptional output can be modulated by altering various properties of bursting, certain strategies are observed more widely.  As the authors noted, recent experimental studies have found that even-skipped enhancers control transcriptional output by changing burst frequency and amplitude while burst duration remains largely constant. The authors compared the kinetics of transcriptional bursting between endogenous and ectopic gene expression patterns. It is argued that since enhancers act under different regulatory inputs in ectopically expressed genes, adaptation would lead to diverse bursting strategies as compared to endogenous gene expression patterns. To achieve this goal, the authors generated ectopic even-skipped transcription patterns in fruit fly embryos. The key finding is that bursting strategies are similar in endogenous and ectopic even-skipped expression. According to the authors, the findings favor the presence of a unified molecular mechanism shaping even-skipped bursting strategies.  This is an important piece of work. Everything has been carried out in a systematic fashion. However, the key argument of the paper is not entirely convincing.

Reviewer #2 (Public Review):

Strengths include:

1) Given the variability in responses from ChatGPT, the author pooled two scores for each review and demonstrated significant correlation between these two iterations. He confirmed also reasonable scoring by manipulating reviews. Finally, he compared a small subset (7 papers) to human scorers and again demonstrated correlation with sentiment and politeness.

2) The figures are consistently well presented and informative. Figure 2C nicely plots the scores with example reviews. The supplementary data are also thoughtful and include combination of first/last author genders. It is interesting that first author female last author male has the lowest score.

3) A series of detailed analysis including breaking down reviews by subfield (interesting to see the wide range of reviewer sentiment/politeness scores in computational papers), institution, and author's name and inferred gender using Genderize. The author suggests that peer review to blind the reviewers to authors' gender may be helpful to mitigating the impoliteness seen.

Weaknesses include:

1) This study does not utilize any of the wide range of Natural Language Processing (NLP) sentiment analysis tools. While the author did have a small subset reviewed by human scorers, the paper would be strengthened by examining all the reviews systematically using some of the freely available tools (for example, many resources are available through Hugging Face [https://huggingface.co/blog/sentiment-analysis-python ]). These methods have been used in previous examinations of review text analysis (Luo et al. 2022. Quantitative Science Studies 2:1271-1295). Why use ChatGPT rather than these older validated methods? How does ChatGPT compare to these established methods? See also: colab.research.google.com/drive/1ZzEe1lqsZIwhiSv1IkMZdOtjPTSTlKwB?usp=sharing

2) The author's claim in the last paragraph that his study is proof of concept for NLP to analyze peer review fails to take into account the array of literature already done in this domain. The statement in the introduction that past reports (only three citations) have been limited to small dataset sizes is untrue (Ghosal et al. 2022. PLoS One 17:e0259238 contains over 1000 peer review documents, including sentiment analysis) and reflects a lack of review on the topic before examining this question.

3) The author acknowledges the limitation that only papers under neuroscience were evaluated. Why not scale this method up to other fields within Nature Communications? Cross-field analysis of the features of interest would examine if these biases are present in other domains.

Reviewer #2 (Public Review):

In this article, Bracey et al. provide insights into the factors contributing to the distinct arrangement observed in sub-membrane microtubules (MTs) within mouse β-cells of the pancreas. Specifically, they propose that in clonal mouse pancreatic β-cells (MIN6), the motor protein KIF5B plays a role in sliding existing MTs towards the cell periphery and aligning them with each other along the plasma membrane. Furthermore, similar to other physiological features of β-cells, this process of MTs sliding is enhanced by a high glucose stimulus. Because a precise alignment of MTs beneath the cell membrane in β-cells is crucial for the regulated secretion of pancreatic enzymes and hormones, KIF5B assumes a significant role in pancreatic activity, both in healthy conditions and during diseases.

The authors provide evidence in support of their model by demonstrating that the levels of KIF5B mRNA in MIN6 cells are higher compared to other known KIFs. They further show that when KIF5B is genetically silenced using two different shRNAs, the MT sliding becomes less efficient. Additionally, silencing of KIF5A in the same cells leads to a general reorganization of MTs throughout the cell. Specifically, while control cells exhibit a convoluted and non-radial arrangement of MTs near the cell membrane, KIF5B-depleted cells display a sparse and less dense sub-membrane array of MTs. Based on these findings, the Authors conclude that the loss of KIF5B strongly affects the localization of MTs to the periphery of the cell. Using a dominant-negative approach, the authors also demonstrate that KIF5B facilitates the sliding of MTs by binding to cargo MTs through the kinesin-1 tail binding domain. Additionally, they present evidence suggesting that KIF5B-mediated MT sliding is dependent on glucose, similar to the activity levels of kinesin-1, which increase in the presence of glucose. Notably, when the glucose concentrations in the culturing media of MIN6 cells are reduced from 20 mM to 5 mM, a significant decrease in MT sliding is observed.

Strengths: This study unveils a previously unexplained mechanism that regulates the specific rearrangement of MTs beneath the cell membrane in pancreatic β-cells. The findings of this research have implications and are of significant interest because the precise regulation of the MT array at the secretion zone plays a critical role in controlling pancreatic function in both healthy and diseased states. In general, the author's conclusions are substantiated by the provided data, and the study demonstrates the utilization of state-of-the-art methodologies including quantification techniques, and elegant dominant-negative experiments.

Weaknesses: A few relatively minor issues are present and related to data interpretation and the conclusions drawn in the study. Namely, some inconsistencies between what appears to be the overall and sub-membrane MT array in scramble vs. KIF5B-depleted cells, the lack of details about the sub-cellular localization of KIF5B in these cells and the physiological significance of the effect of glucose levels in beta-cells of the pancreas.

Reviewer #2 (Public Review):

In this manuscript, Yao et al. present a series of experiments aiming at generating a cellular atlas of the human hippocampus across aging, and how it may be affected by injury, in particular, stroke. Although the aim of the study is interesting and relevant for a larger audience, due to the ongoing controversy around the existence of adult hippocampal neurogenesis in humans, a number or technical weaknesses result in poor support for many of the conclusions made from the results of these experiments.

In particular, a recent meta-analysis of five previous studies applying similar techniques to human samples has identified different aspects of sample size as main determinants of the statistical power needed to make significant conclusions. Some of these aspects are the number of nuclei sequenced and subject stratification. These two aspects are of concern in Yao's study. First, the number of sequenced nuclei is lower than the calculated number of nuclei required for detecting rare cell types. However, Yao et al. report succeeding in detecting rare populations, including several types of neural stem cells in different proliferation states, which have been demonstrated to be extremely scarce by previous studies. It would be very interesting to read how the authors interpret these differences. Secondly, the number of donors included in some of the groups is extremely low (n=1) and the miscellaneous information provided about the donors is practically inexistent. As individual factors such as chronic conditions, medication, lifestyle parameters, etc... are considered determinant for the variability of adult hippocampal neurogenesis levels across individuals, this represents a series limitation of the current study. Overall, several technical weaknesses severely limit the relevance of this study and the ability of the authors to achieve their experimental aims.

Reviewer #2 (Public Review):

This proof-of-principle study lays important groundwork for future studies. Murphy et al. expressed ChrimsonR and GCaMP6s in retinal ganglion cells of a living macaque. They recorded calcium responses and stimulated individual cells, optically. Neurons targeted for stimulation were activated strongly whereas neighboring neurons were not.

The ability to record from neuronal populations while simultaneously stimulating a subset in a controlled way is a high priority for systems neuroscience, and this has been particularly challenging in primates. This study marks an important milestone in the journey towards this goal.

The ability to detect stimulation of single RGCs was presumably due to the smallness of the light spot and the sparsity of transduction. Can the authors comment on the importance of the latter factor for their results? Is it possible that the stimulation protocol activated neurons nearby the targeted neuron that did not express GCaMP? Is it possible that off-target neurons near the targeted neuron expressed GCaMP, and were activated, but too weakly to produce a detectable GCaMP signal? In general, simply knowing that off-target signals were undetectable is not enough; knowing something about the threshold for the detection of off-target signals under the conditions of this experiment is critical.

Minor comments:<br /> Did the lights used to stimulate and record from the retina excite RGCs via the normal light-sensing pathway? Were any such responses recorded? What was their magnitude?

The data presented attest to a lack of crosstalk between targeted and neighboring cells. It is therefore surprising that lines 69-72 are dedicated to methods for "reducing the crosstalk problem". More information should be provided regarding the magnitude of this problem under the current protocol/instrumentation and the techniques that were used to circumvent it to obtain the data presented.

Optical crosstalk could be spatial or spectral. Laying out this distinction plainly could help the reader understand the issues quickly. The Methods indicate that cells were chosen on the basis that they were > 20 µm from their nearest (well-labeled) neighbor to mitigate optical crosstalk, but the following sentence is about spectral overlap.

Figure 2 legend: "...even the nearby cell somas do not show significantly elevated response (p >> 0.05, unpaired t-test) than other cells at more distant locations." This sentence does not indicate how some cells were classified as "nearby" whereas others were classified as being "at more distant locations". Perhaps a linear regression would be more appropriate than an unpaired t-test here.

Line 56: "These recordings were... acquired earlier in the session where no stimulus was present." More information should be provided regarding the conditions under which this baseline was obtained. I assume that the ChrimsonR-activating light was off and the 488 nm-GCaMP excitation light was on, but this was not stated explicitly. Were any other lights on (e.g. room lights or cone-imaging lights)? If there was no spatial component to the baseline measurement, "where" should be "when".

Please add a scalebar to Figure 1a to facilitate comparison with Figure 2.

Lines 165-173: Was the 488 nm light static or 10 Hz-modulated? The text indicates that GCaMP was excited with a 488 nm light and data were acquired using a scanning light ophthalmoscope, but line 198 says that "the 488 nm imaging light provides a static stimulus".

A potential application of this technology is for the study of visually guided behavior in awake macaques. This is an exciting prospect. With that in mind, a useful contribution of this report would be a frank discussion of the hurdles that remain for such application (in addition to eye movements, which are already discussed).

Reviewer #2 (Public Review):

This study investigates how genes in the Gr28 family of gustatory receptors function in the taste system of Drosophila larvae. Gr28 genes are intriguing because they have been implicated in taste as well as other functions, such as sensing temperature and ultraviolet light. This study makes several new findings. First, the authors show that four Gr28 genes are expressed in putative taste neurons, and these neurons can be largely divided into subsets that express Gr28a versus Gr28bc. The authors then demonstrate that these two neuronal subsets drive opposing behaviors (attraction versus avoidance) when activated. The avoidance-promoting neurons respond to bitter compounds and are required for bitter avoidance, and Gr28bc and Gr28ba were specifically implicated in bitter detection in these cells. Together, these findings provide insight into the complexity of taste receptor expression and function in Drosophila, even within a single receptor subfamily.

The conclusions are well-supported by the experimental data. Strengths of the paper include the use of precise genetic tools, thorough analyses of expression patterns, carefully validated behavioral assays, and well-controlled functional imaging experiments. The role of Gr28bc neurons is more thoroughly explored than that of Gr28a neurons. However, a previous study from the same lab (Mishra et al., 2018) showed that Gr28a neurons detect RNA and ribose, which are attractive to larvae. Presumably, this is the attractive response that is being recapitulated upon artificial activation of Gr28a neurons.

I only have one technical concern: In Figure 2B, the authors do not show confirmation that using Gr66a-lexA driving lexAop-Gal80 eliminates Gal4-driven gene expression in the desired cells (cells co-expressing Gr66a and Gr28a). This is important for interpreting the behavioral experiment in order to demonstrate that the Gr28a cells mediating attraction are distinct from Gr66a/Gr28bc cells.

Reviewer #2 (Public Review):

Leeds et al. investigated the role of mechanical coupling in coordinating the growth kinetics of microtubules in kinetochore-fibers (k-fibers). The authors developed a dual optical-trap system to explore how constant load redistributed between a pair of microtubules depending on their growth state coordinates their growth.

• The main finding of the paper is that the duration and frequency of pausing events during individual microtubule growth are decreased when tension is applied at their tips via kinetochore particles coupled to optically trapped beads. However, the study does not offer any insight into the possible mechanism behind this dependency. For example, it is not clear whether this is a specific property of the kinetochore particles that were used in this experiment, whether it could be attributed to specific proteins in these particles, or if this could potentially be an inherent property of the microtubules themselves.

• The authors simulate the coordination between two microtubules and show that by using the parameters of pausing and variability in growth rates both measured experimentally they can explain coordination between two microtubules measured in their experiments. This is a convincing result, but k-fibers typically have many more microtubules, and it seems important to understand how the ability to coordinate growth by this mechanism scales with the number of microtubules. It is not obvious whether this mechanism could explain the coordination of more than two microtubules.

• The range of stiffnesses chosen to simulate the microtubule coupling allows linkers to stretch hundreds of nanometers linearly. However, most proteins including those at kinetochore must have finite size and therefore should behave more like worm-like chains rather than linear springs. This means they may appear soft for small elongations, but the force would increase rapidly once the length gets close to the contour length. How this more realistic description of mechanics might affect the conclusions of the work is not clear.

• The novel dual-bead assay is interesting. However, it only provides virtual coupling between two otherwise independently growing microtubules. Since the growth of one affects the growth of the other only via software, it is unclear whether the same insight can be gained from the single-bead setup, for example, by moving the bead at a constant speed and monitoring how microtubule growth adjusts to the fixed speed. The advantages of the double-bead setup could have been demonstrated better.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the goal of the authors is to understand the process of mature sprout formation from mini-sprouts to develop new blood vessels during angiogenesis. For this, they use their earlier experimental setup of engineered blood vessels in combination with a modified spatio-temporal model for Notch signalling. The authors first study the role of VEGF on Tip (Delta-rich) and Stalk (Notch-rich) patterning. The Tip cells are further examined for their space-time dynamics as Mini-sprouts and mature Sprouts. The Notch signalling model is later supplemented with a phenomenological _random uniform model_ for Sprout selection as a plausible mechanism for Sprout formation from Mini-Sprouts. Finally, the authors look into the role of fibronectin in the Sprout formation process. Overall, the authors propose that VEGF interacts with Notch signalling in blood vessels to generate spatially disordered and co-localized Tip cells. VEGF and fibronectin then provide external cues to dynamically modulate mature Sprout formation from Mini-Sprouts that could control the location and density of developing blood vessels with a process that is consistent with a Turing-like mechanism.

Strengths and Weaknesses:<br /> In this manuscript, work motivation, problem definition, experimental procedures, analysis techniques, mathematical methods (including the parameters), and findings are all presented quite clearly. Moreover, the authors carefully indicate whenever they make any assumptions and do not mix unproven hypothesis with deduced or known facts. The experimental techniques and most of the mathematical methods used in this paper are borrowed from the earlier works of the corresponding authors and thus are not completely novel. However, the use of these ideas to provide a simple elucidation of the role of VEGF and fibronectin in Sprout formation, in an otherwise complex system, is very interesting and useful. Some of the data analysis methods presented in the paper - (i) quantification of Tip spatial patterns (Fig. 3) and (ii) Sprout temporal dynamics using Sankey diagram (Fig. 4) - seem quite novel to me in the context of Notch signalling literature. Similarly, the authors also provide a new mechanism (VEGF) to obtain disordered Delta-Notch patterning without explicitly including _noise_ in the system (Fig. 2 and Fig. S1). The authors also systematically quantify the statistics of spacing between the Sprouts and show that the Sprouts have a tendency to be away from each other, something that they could also partially recapitulate by additionally including a novel _random uniform model_ for Sprout selection (Fig. 5). Although the association between fibronectin and angiogenesis is known in the literature, in this manuscript, the authors could clearly demonstrate that fibronectin is present in high and low levels, respectively, around Sprouts and Mini-sprouts (Fig. 6). A combination of these findings could then motivate the authors to hypothesize, as mentioned above, a Turing-like mechanism for Sprout formation, something that I find interesting.

Although I find the relative simplicity of the experimental system and theoretical model and the clear findings they generate appealing, some aspects raise a few questions. The authors experimentally find 20 +- 0.08 percent of Tip cells in the model blood-vessels that is consistent with the salt-and-pepper pattern seen in Notch signalling model (~25 %). However, it is not clear to me if the reverse is true, i.e., 25% of Tip cells automatically imply a salt-and-pepper pattern - the authors do not seem to provide direct experimental evidence. Furthermore, the authors use their Notch signalling model on a regular hexagonal lattice, but there is a large variability in the cell sizes (Fig. 3) in the experimental system. Since it is observed in the literature that signalling depends on the contact area between the neighbouring cells, it is not clear how that would affect the findings presented in this paper. Similarly, since some of the cells are quite small compared to the others, I worry about how appropriate it is to express the distance between the Tip cells in terms of _cell numbers_ (Fig. 3). Regarding Sprout classification, as per Table 1, a bridge of two cells is formed as per early-stage-I mechanism for Sprout. On the other hand, the entire data interpretation of experiments seems to be based on early Stage II and matured stage in that same table (also Figs. 3 and 4) in which only one Tip cell seems to be counted per mature Sprout. However, if some Sprouts are formed via early stage-I mechanism, a projection in 2D for analysis would give a count of __two__ adjacent Tip cells, but corresponding to a __single__ Sprout. It could be possible that the presence of such two-cell Sprouts affects the statistics of inter-Sprout distances (Fig. 5). Finally, I find the proposed mechanism of Sprout formation dynamics to be somewhat unsatisfactory. Other than the experimental evidence regarding the spacing of Sprouts and the fibronectin levels around Sprouts and Mini-sprouts (Figs. 4 and 5), there is very little evidence to support the hypothesis about a Turing-like mechanism for Sprouting. Moreover, it seems to me that Turing patterns can appear in a wide variety of settings and could be applied to the current problem in an abstract manner without making any meaningful connections with the system variables. Also, from a modeling point of view, cell migration and mechanics, are expected to take a major part in Sprout formation, while cell division and inclusion would most likely influence Tip-Stalk cell formation. However, it seems that in the present work, these effects are coarse-grained into Notch signalling parameters and the Sprout selection model, thus making any experimental connection quite vague.

Overall Assessment

I feel that the authors, on the whole, do achieve their main goals. Although I have a few concerns that I have raised above, overall, I find the work presented in this manuscript to be a solid addition to the broad field of collective cell dynamics. The authors use well established experimental and mathematical methods while adding a few novel analysis techniques and modeling ideas to provide a compelling, albeit incomplete, picture of Sprout formation during angiogenesis. While the direct application of this work in the context of angiogenesis is obvious, the broad set of ideas and techniques (discussed above) in this work would also be useful to researchers who work on Notch signalling in morphogenesis, collective cell migration, and epithelial-mesenchymal-transition.

Reviewer #2 (Public Review):

It is well known that human and simian immunodeficiency viruses (HIV and SIV, respectively) evolved numerous mechanisms to compromise effective immune responses but the underlying mechanisms remain incompletely understood. Here, Yamamoto and Matano examined the humoral immune response in a large number of rhesus macaques infected with the difficult-to-neutralize SIVmac239 strain. They identified a subgroup of animals that showed significant neutralizing Ab responses. Sequence analyses revealed that in most of these animals (7/9) but only a minority in the control group (2/19) SIVmac variants containing a CD8+ T-cell escape mutation of G63E/R in the viral Nef gene emerged. They further show that this change attenuates the ability of Nef to stimulate PI3K/Akt/mTORC2 signaling. The authors propose that this induction of SIVmac239 nAb induction is reciprocal to antibody dysregulation caused by a previously identified human PI3K gain-of-function (Ref). Altogether, the results suggest that PI3K signaling plays a key role in B-cell maturation and generation of effective nAb responses.

Strengths of the study are that the authors analyzed a large number of SIVmac-infected macaques to unravel the biological significance of the known effect of the interaction of Nef with PI3K/Akt/mTORC2 signaling. This is interesting and may provide a novel means to improve humoral immune responses to HIV. Weaknesses are that only G63E and not G63R that also emerged in most animals was examined in most functional assays. Some effects of the G63E mutation seem modest and comparison to a grossly nef-defective SIVmac construct would be desirable to better assess to impact of the mutation of Nef-mediated stimulation of PI3K. While the impact of this Nef mutations on PI3K and the association with improved nAb responses is largely convincing, the results on the potential impact of soluble Nef on neighboring B cells is much less clear. SIVmac239 infects and manipulates helper CD4 T cells and these are essential for the activation and differentiation of B cells into antibody-producing plasma cells and effective humoral immune responses. Without additional functional evidence that Nef indeed specifically targets and manipulated B cells these results and conclusions should be made with much greater caution. Finally, the presentation of the results and conclusions is partly very convoluted and difficult to comprehend. Editing to improve clarity is highly recommended.

Reviewer #2 (Public Review):

Summary<br /> The authors seek to characterize the role of splicing factor SRSF1 during spermatogenesis. Using a conditional deletion of Srsf1 in germ cells, they find that SRSF1 is required for male fertility. Via immunostaining and RNA-seq analysis of the Srsf1 conditional knockout (cKO) testes, combined with SRSF1 CLIP-seq and IP-MS data from the testis, they ultimately conclude that Srsf1 is required for spermatogonial stem cell (SSC) homing and self-renewal due to alternative splicing of Tial1.

Strengths<br /> The overall methods and results are robust. The histological analysis of the Srsf1 cKO traces the origins of the fertility defect to the postnatal testis, and the authors have generated interesting datasets characterizing SRSF1's RNA targets and interacting proteins specifically in the testis.

Ultimately, the authors have shown that SRSF1's effects on alternative splicing are required to establish spermatogenesis. In the absence of Srsf1, the postnatal gonocytes/nascent spermatogonia do not properly relocate from the seminiferous tubules' lumen to basement membrane, and consequently, never initiate spermatogenesis. I believe this relocation event is what the authors are referring to as "SSC homing".

Weaknesses<br /> I do not think there is enough evidence to support two major conclusions. First, the authors conclude that SRSF1 is required for "SSC self-renewal." Given the defect in nascent spermatogonial development, it is not evident to me that SSCs actually form in the Srsf1 cKO. Second, the authors conclude that SRSF1 controls alternative splicing of Tial1 to "implement SSC homing and self-renewal." I'm unsure as to the basis for TIAL1's role in "SSC homing and self-renewal," particularly as the only reference provided about Tial1's effects on the germ line shows that germ cells are completely lost during embryonic gestation. As a result, it's ultimately unclear to me how SRSF1 mechanistically regulates the relocation of gonocytes/nascent spermatogonia to the basement membrane.

Alternative splicing is quite pronounced in the testis relative to other tissues. The authors have presented interesting work that shows that alternative splicing is required in the testicular germ line for the establishment of spermatogenesis.

Reviewer #2 (Public Review):

Relative simplicity and genetic accessibility of the fly brain make it a premier model system for studying the function of genes linked to various diseases in humans. Here, Pan et al. show that human UBA5, whose mutations cause developmental and epileptic encephalopathy, can functionally replace the fly homolog Uba5. The authors then systematically express in flies the different versions of the gene carrying clinically relevant SNPs and perform extensive phenotypic characterization such as survival rate, developmental timing, lifespan, locomotor and seizure activity, as well as in vitro biochemical characterization (stability, ATP binding, UFM-1 activation) of the corresponding recombinant proteins. The biochemical effects are well predicted by (or at least consistent with) the location of affected amino acids in the previously described Uba5 protein structure. Most strikingly, the severity of biochemical defects appears to closely track the severity of phenotypic defects observed in vivo in flies. While the paper does not provide many novel insights into the function of Uba5, it convincingly establishes the fly nervous system as a powerful model for future mechanistic studies.

One potential limitation is the design of the expression system in this work. Even though the authors state that "human cDNA is expressed under the control of the endogenous Uba5 enhancer and promoter", it is in fact the Gal4 gene that is expressed from the endogenous locus, meaning that the cDNA expression level would inevitably be amplified in comparison. The fact that different effects were observed when some experiments were performed at different temperatures (18 vs. 25) is also consistent with this. While I do not think this caveat weakens the conclusions of this paper, it may impact the interpretation of future experiments that use these tools, and thus should be clearly discussed in the paper. Especially considering the authors argue that most disease variants of UBA5 are partial loss-of-functions, the amplification effect could potentially mask the phenotypes of milder hypomorphic alleles. If the authors could also show that the T2A-Gal4 expression pattern in the brain matches well with that of endogenous RNA or protein (e.g. using HCR-FISH or antibody), it would help to alleviate this concern.

Reviewer #2 (Public Review):

This study provides an unbiased characterization of the cardiac proteome in the setting of intermittent fasting. The findings constitute a resource of quantitative proteomic data that sheds light on changes in cardiac function due to diet and that may be used in the future by other investigators. There are a number of key missing details that limit interpretation or present opportunities to strengthen the study. For example, the authors find that apolipoproteins are altered with fasting but it is not clear whether this is a contribution of myocardial tissue changes or systemic effects spilling into blood in cardiac tissues. Some statements in the text like "Approximately one-third of the differentially expressed proteins in IF groups compared to AL were enzymes with catalytic activity involved in energy homeostasis pathways" do not appear to be supported by data. It is not clear how the list of Kinases were generated for Figure 1B. Changes in chromatin or gene expression are not measured so the conclusion that EOD led to 'epigenetic changes' relative to IF16 is not well supported. There are also a number of areas where the text is vague. For example, it is not clear what is meant by 'trend shift' when discussing EOD results and Figure 3 generally could use additional information to better understands the figures. An interesting finding is that the IF16 groups showed cardiac hypertrophy (SFig 11b). This is potentially a novel finding and the text should elaborate more on this phenomenon.

Reviewer #2 (Public Review):

In their manuscript entitled "Transcriptional immune suppression and upregulation of double stranded DNA damage and repair repertoires in ecDNA-containing tumors" Lin et al. describe an important study on the transcriptional programs associated with the presence of extrachromosomal DNA in a cohort of 870 cancers of different origin. The authors find that compared to cancers lacking such amplifications, ecDNA+ cancers express higher levels of DNA damage repair-associated genes, but lower levels of immune-related gene programs.

This work is very timely and its findings have the potential to be very impactful, as the transcriptional context differences between ecDNA+ and ecDNA- cancers are currently largely unknown. The observation that immune programs are downregulated in ecDNA+ cancers may initiate new preclinical and translational studies that impact the way ecDNA+ cancers are treated in the future. Thus, this study has important theoretical implications that have the potential to substantially advance our understanding of ecDNA+ cancers.

Strengths<br /> The authors provide compelling evidence for their conclusions based on large patient datasets. The methods they used and analyses are rigorous.

Weaknesses<br /> The biological interpretation of the data remains observational. The direct implication of these genes in ecDNA(+) tumors is not tested experimentally.

Reviewer #2 (Public Review):

Here, Chitraju et al have studied the phenotype of mice with an adipocyte-specific deletion of the diglycerol acyltransferases DGAT1 and DGAT2, the two enzymes catalyzing the last step in triglyceride biosynthesis. These mice display reduced WAT TG stores but contrary to their expectations, the TG loss in WAT is not complete and the mice are resistant to a high-fat diet intervention and display a metabolically healthier profile compared to control littermates. The mechanisms underlying this are not entirely clear, but the double knockout (DKO) animals have increased EE and a lower RQ suggesting that enhanced FA oxidation and WAT "browning" may be involved. Moreover, both adiponectin and leptin are expressed in WAT and are detectable in circulation. The authors propose that "the capacity to store energy in adipocytes is somehow sensed and triggers thermogenesis in adipose tissue. This phenotype likely requires an intact adipocyte endocrine system...." Overall, I find this to be an interesting notion.

Reviewer #2 (Public Review):

Acknowledging practical difficulties in teasing out the principles behind animal locomotion from the body's functions and survival needs, the authors embark on a computational experiment to replay the "tape of life." Specifically, the chief objective of the study was to explore the necessity of symmetry and modularity for better-directed locomotion on the ground.

Towards this important goal, the authors put forward a comprehensive computational study using physics-based simulations of 3D voxel-based robots. Such a simulation environment allows one to capture salient dynamics behind locomotion, including interactions with the environment. The authors undertake simulations for three different gravitational environments, water, Earth, and Mars. The work has several methodological strengths, with respect to the ingenuity of the approach and the elegance of the analysis; I was particularly intrigued by the use of graph theory in the context of modularity. Results point to a complex, rich role of modularity and symmetry in locomotion, modulated by the gravitational environment.

The association between "locomotion ability" and average speed is, in my view, tenuous, whereby locomotion is a complex phenomenon that should be assessed across a range of intertwined dynamic metrics that include, for example, stability with respect to external perturbations and energy efficiency. I also am not fully convinced of i) the adequacy of the spatial resolution, whereby I failed to see a compelling argument regarding the completeness of 64 voxels; ii) the realism of the oscillatory patterns, whereby all the voxels are set to oscillate at the same, constant, frequency of 2Hz; and iii) the accuracy of simulations in water where added mass effects seem to be neglected. Overall, dynamics and control aspects could be improved in both the methods and the interpretation of the results. Finally, I believe that a stronger connection between the hypotheses of the study and the literature (in animal or robot locomotion) would help frame the narrative better. I would be particularly curious to see some tie with human bipedal locomotion.

The work bears important implications in the study of locomotion, shedding light on the role of modularity and symmetry, beyond what one could gather from mere observations. Not only do I expect these new insights to stimulate further research in the area of locomotion, but also I envision other communities embracing a similar computational approach to address related questions in life sciences and robotics.

Reviewer #2 (Public Review):

In this study, Tomasi et al identify a series of tRNA modifying enzymes from Mtb, show their function in the relevant tRNA modifications and by using at least one deleted strain for MnmA, they show the relevance of tRNA modification in intra-host survival and postulate their potential role in pathogenesis.

Conceptually it is a wonderful study, given that tRNA modifications are so fundamental to all life forms, showing their role in Mtb growth in the host is significant. However, the authors have not thoroughly analyzed the phenotype. The growth defect aspect or impact on pathogenesis needs to be adequately addressed.

- The authors show that ΔmnmA grows equally well in the in vitro cultures as the WT. However, they show attenuated growth in the macrophages. Is it because Glu1_TTC and Gln1-TTG tRNAs are not the preferred tRNAs for incorporation of Glu and Gln, respectively? And for some reason, they get preferred over the alternate tRNAs during infection? What dictates this selectivity?

- As such the growth defect shown in macrophages would be more convincing if the authors also show the phenotype of complementation with WT mnmA.

An important consideration here is the universal nature of these modifications across the life forms. Any strategy to utilize these enzymes as the potential therapeutic candidate would have to factor in this important aspect.

Reviewer #2 (Public Review):

Summary:<br /> Mouse models are widely used to determine key molecular mechanisms of atherosclerosis, the underlying pathology that leads to coronary artery disease. The authors use various systems biology approaches, namely co-expression and Bayesian Network analysis, as well as key driver analysis, to identify co-regulated genes and pathways involved in human and mouse atherosclerosis in artery and liver tissues. They identify species-specific and tissue-specific pathways enriched for the genetic association signals obtained in genome-wide association studies of human and mouse cohorts.

Strengths:<br /> The manuscript is well executed with appropriate analysis methods. It also provides a compelling series of results regarding mouse and human atherosclerosis.

Weaknesses:<br /> The manuscript has several weaknesses that should be acknowledged in the discussion. First, there are numerous models of mouse atherosclerosis; however, the HMDP atherosclerosis study uses only one model of mouse atherosclerosis, namely hyperlipidemic mice, due to the transgenic expression of human apolipoprotein E-Leiden (APOE-Leiden) and human cholesteryl ester transfer protein (CETP). Therefore, when drawing general conclusions about mouse pathways not being identified in humans, caution is warranted. Other models of mouse atherosclerosis may be able to capture different aspects of human atherosclerosis. Second, the mouse aorta tissue is atherosclerotic, whereas the atherosclerosis status of the GTEX aorta tissues is not known. Therefore, it is possible that some of the human or mouse-specific gene modules/pathways may be due to the difference in the disease status of the tissues from which the gene expression is obtained. Third, it is unclear how the sex of the mice (all female in the HMDP atherosclerosis study and all male in the baseline HMDP study) and the sex of the human donors affected the results. Did the authors regress out the influence of sex on gene expression in the human data before performing the co-expression and preservation studies? If not, this should be acknowledged. Fourth, some of the results are unexpected, and these should be discussed. For example, the authors identify that the leukocyte transendothelial migration pathway and PDGF signaling pathway are human-specific in their vascular tissue analysis. These pathways have been extensively described in mouse studies. Why do the authors think they identified these pathways as human-specific? Similarly, the authors identified gluconeogenesis and branched-chain amino acid catabolism as human and mouse-shared modules in the vascular tissue. Is there evidence of the involvement of these pathways in atherosclerosis in vascular cells?

Overall, acknowledging these drawbacks and adding points to the discussion will strengthen the manuscript.

Reviewer #2 (Public Review):

The manuscript by Nishikawa et al. addresses time-dependent changes in the electron transfer energetics in the photosynthetic reaction center from Blastochloris viridis, whose time-dependent structural changes upon light illumination were recently demonstrated by time-resolved serial femtosecond crystallography (SFX) using X-ray free-electron laser (XFEL) (Dods et al., Nature, 2021). Based on the redox potential Em values of bacteriopheophytin in the electron transfer active branch (BL) by solving the linear Poisson-Boltzmann equation, the authors found that Em(HL) values in the charge-separated 5-ps structure obtained by XFEL are not clearly changed, suggesting that the P+HL- state is not stabilized owing to protein reorganization. Furthermore, chlorin ring deformation upon HL- formation, which was expected from their QM/MM calculation, is not recognized in the 5-ps XFEL structure. Then the authors concluded that the structural changes in the XFEL structures are not related to the actual time course of charge separation. They argued that their calculated changes in Em and chlorin ring deformations using the XEFL structures may reflect the experimental errors rather than the real structural changes; they mentioned this problem is due to the fact that the XFEL structures were obtained at not high resolutions (mostly at 2.8 Å). I consider that their systematic calculations may suggest a useful theoretical interpretation of the XFEL study. However, the present manuscript insists as a whole negatively that the experimental errors may hamper to provide the actual structural changes relevant to the electron transfer events.

Reviewer #2 (Public Review):

This manuscript explores the interplay between Legionella Dot/Icm effectors that modulate ubiquitination of the host GTPase Rab10. Rab10 undergoes phosphoribosyl-ubiquitination (PR-Ub) by the SidE family of effectors which is required for its recruitment to the Legionella containing vacuole (LCV). Through a series of elegant experiments using effector gene knockouts, co-transfection studies and careful biochemistry, Kubori et al further demonstrate that:

1. The SidC family member SdcB contributes to the polyubiquitination (poly-Ub) of Rab10 and its retention at the LCV membrane.<br /> 2. The transglutaminase effector, MavC acts as an inhibitor of SdcB by crosslinking ubiquitin at Gln41 to lysine residues in SdcB.

Some further comments and questions are provided below.

1. From the data in Figure 1, it appears that the PR-Ub of Rab10 precedes and in fact is a prerequisite for poly-Ub of Rab10. The authors imply this but there's no explicit statement but isn't this the case?<br /> 2. The complex interplay of Legionella effectors and their meta-effectors targeting a single host protein (as shown previously for Rab1) suggests the timing and duration of Rab10 activity on the LCV is tightly regulated. How does the association of Rab10 with the LCV early during infection and then its loss from the LCV at later time points impact LCV biogenesis or stability? This could be clearer in the manuscript and the summary figure does not illustrate this aspect.<br /> 3. How do the activities of the SidE and SidC effectors influence the amount of active Rab10 on the LCV (not just its localisation and ubiquitination)<br /> 4. What is the fate of PR-Ub and then poly-Ub Rab10? How does poly-Ub of Rab10 result in its persistence at the LCV membrane rather than its degradation by the proteosome?<br /> 5. Mutation of Lys518, the amino acid in SdcB identified by mass spec as modified by MavC, did not abrogate SdcB Ub-crosslinking, which leaves open the question of how MavC does inhibit SdcB. Is there any evidence of MavC mediated modification to the active site of SdcB?<br /> 6. I found it difficult to understand the role of the ubiquitin glycine residues and the transglutaminase activity of MavC on the inhibition of SdcB function. Is structural modelling using Alphafold for example helpful to explain this?<br /> 7. Are the lys mutants of SdbB still active in poly-Ub of Rab10?

Reviewer #2 (Public Review):

Cho and Hetzer provide evidence that nuclear pore complexes (NPCs) are "trimmed" by caspases as a key element of muscle (and other) differentiation programs. Overall, the data are of high quality and are well presented. There is an interesting mechanism demonstrated whereby nuclear and cytosolically-oriented nups are specifically degraded from the NPC (fragments are sometimes associated with the NPCs), which leads to a specific inhibition of nuclear export. A highlight is a quantitative proteomic analysis of nuclear fractions that nicely demonstrates the change in the nuclear proteome upon NPC trimming, which includes elevated levels of many NES-containing factors. An important control is that these nuclear proteome changes don't occur when caspases are inhibited. These data are valuable although they fall short in demonstrating that NPC trimming is actually required for the execution of the differentiation program. It is recognized, however, that editing several nup genes at several sites to prevent caspase recognition would be extremely challenging and unfeasible, thus ultimately this does not detract from the significance of the findings. Indeed, there is a new broadly impactful concept being introduced - that caspases need not be destructive but they can be productively utilized to contribute to cell fate decisions.

Reviewer #2 (Public Review):

While the question of 'are AlphaFold-predicted structures useful for drug design' has largely seen comparisons of AF versus experimental protein structures, this paper takes a less explored (but perhaps more practically important) angle of 'are AlphaFold-predicted structures any better than the previous generation of homology modeling tools' to the protein-ligand (rigid) docking problem. The conclusions of this work will be of largest interest to the audience less familiar with the precision required for successful rigid docking, while the expert crowd might find them obvious, yet a good summary of results previously shown in the literature. Further work, understanding the structural objectives/metrics that should be placed on future AlphaFold-like models for better pose prediction performance, would greatly expand the practicality of the observations made here.

The main conclusion of the paper, that structural accuracy (expressed as RMSD) of the protein model is not a good predictor of the accuracy the model will show in rigid docking protein-ligand pose prediction, is a good reminder of the well-appreciated need for high-quality side chain placements in docking. The expected phenomenon of AlphaFold predicting 'more apo-like structures' is often discussed in the field, and readers should be cautious about drawing conclusions from the rigid (rather than flexible, as in some previous works) docking done here.

The authors have very clearly communicated that the use of AlphaFold-generated structures in traditional docking might not be a good idea, and motivated that the time of a molecular designer might be better spent preparing a high-quality homology model. The visual presentation of the conclusions is very clear but might leave the reader wanting a more in-depth discussion of which structural elements of the AF models lead to bad docking outcomes. For example, Fig. 3 presents an example of a very accurate AlphaFold prediction leading to the ligand being docked completely outside of the binding pocket. Close inspection of the Figure suggests a clash of the ligand with the slightly displaced tryptophan residue in the AF model that might be to blame, as can be confirmed by comparison of the model and PDB structure by the reader themselves but has not been discussed by the authors. Only a few examples of the systems used are shown even visually, leaving the reader unable to study more interesting cases in depth without re-doing the work themselves.

The authors acknowledged that several recent studies exist in this space. They point out two advancements made in their work, worthy of further review. Similarly, it's important to evaluate the novelty of this work's claims vs previously available results, and the diversity of information made available to the reader.

"First, we use structural models generated without any use of known structures of the target protein. For machine learning methods, this requires ensuring that no structure of the target protein was used to train the method." This is done by limiting the scope of the work to GPCRs whose structures became available only after the training date of AlphaFold (April 30, 2018), as well as not using templates available after that date during prediction. The use of a time limit seems less preferable than the approach taken in Ref. 1 of discarding templates above a sequence identity cutoff. On the other hand, the 'ablation test' performed in Ref. 2 showed no loss in accuracy when no templates were used at all. Authors should discuss in more detail whether these modeling choices could change anything in their conclusions and why they made their choices compared to those in previous work.

"Second, we perform a systematic comparison that takes into account the variation between experimentally determined structures of the same protein when bound to different ligands." Cross-docking is indeed a more appropriate comparison than self-docking (as done in previous works), and the observation that the accuracy of AF models is similar to that between different holo structures of the same protein is interesting. Previous literature on cross-docking should however be discussed, and the well-known conclusions from it that small variations in side-chain positions, in otherwise highly similar structures, can lead to large changes in docked poses. It is important to realize that AlphaFold models are 'just another structure' - if previous literature is sufficient to show the sensitivity of rigid docking, doing it again on AF structures does not add to our understanding. Further, Ref. 3 might have already addressed the question of correlation between binding site RMSD and docking pose prediction accuracy - see e.g. Supplementary Figure 3 there (also Figure S15 in Ref. 2).

Further, the authors should discuss the commonly brought up problem of AlphaFold generating 'more apo-like structures' - are the models used here actually 'holo-like' because of the low RMSDs? (what RMSD differences are to be expected between apo and holo structures of these systems?) How are the volumes of the pockets affected? The position on this problem taken by previous works is worth mentioning - "much higher rmsd values are found when using the AF2 models (...), which reflect the difficulties in performing docking into apo-like structures" in Ref. 1 and "computational model structures were predicted without consideration of binding ligands and resulted in apo structures" in Ref. 2.

Because of this 'apo problem', Ref. 2 assumed that rigid docking (as done here) would not succeed and used flexible docking where "two sidechains at the binding site were set to be flexible". In fact, the reader of this new paper will be left to wonder if it is not simply presenting a subset of the results already seen in Ref. 2, where "the success ratios dropped significantly for them because misoriented sidechains prevented a ligand from docking (Figure S14)". While this conclusion is not made as clear in Ref. 2 as it is here, a comparison of Figures 4 and S14 there will lead the reader to the same conclusion, and more -- that flexible docking meaningfully improves the performance of AF models, and more so than homology models.

Finally, certain data analyses present in previous works but not here should be necessary to make this work more informative to the readers:<br /> a) Consideration of multiple top poses, e.g., in Ref. 2, Figures 4 and S14 mentioned before, comparison of success rates in top 1 and top 3 docked poses add much context.<br /> b) Notes on the structural features preventing successful docking, see e.g., in Ref. 1, Table 2 or in Ref. 4, Tables 2 and 4.

This work has the potential to become an important piece of the puzzle, if deeper insights into the reasons for AF model failures are drawn by the authors. These could include a discussion of the problematic structural elements (clashes of side chain with ligands, missing interactions/waters, etc.), potential solutions with some preliminary data (flexible docking, softening interactions, etc.), or proposals for metrics better than RMSD to score the soundness of pockets generated by AF for docking.

References:<br /> 1. Díaz-Rovira, A. M., Martín, H., Beuming, T., Díaz, L., Guallar, V., & Ray, S. S. (2023). Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures. Journal of Chemical Information and Modeling, 63(6), 1668-1674. https://doi.org/10.1021/acs.jcim.2c01270<br /> 2. Heo, L., & Feig, M. (2022). Multi-state modeling of G-protein coupled receptors at experimental accuracy. Proteins: Structure, Function, and Bioinformatics, 90(11), 1873-1885. https://doi.org/10.1002/prot.26382<br /> 3. Beuming, T., & Sherman, W. (2012). Current assessment of docking into GPCR crystal structures and homology models: Successes, challenges, and guidelines. Journal of Chemical Information and Modeling, 52(12), 3263-3277. https://doi.org/10.1021/ci300411b<br /> 4. Scardino, V., Di Filippo, J. I., & Cavasotto, C. (2022). How good are AlphaFold models for docking-based virtual screening? [Preprint]. Chemistry. https://doi.org/10.26434/chemrxiv-2022-sgj8c

Reviewer #2 (Public Review):

In this manuscript, the authors attempt to examine how the temporal expression of the lin-4 microRNA is transcriptionally regulated. However, the experimental support for some claims is incomplete. The authors repeatedly use the ju1121(G247R) mutation of myrf-1, but more information is required to evaluate their claim that this mutation "abolishes its DNA binding capability but also negatively interferes with its close paralogue MYRF-2". Additionally, in the lin-4 scarlet endogenous transcriptional reporter, the lin-4 sequence is removed. Since lin-4 has been reported to autoregulate, it seems possible that the removal of lin-4 coding sequence could influence reporter expression. Further, concrete evidence for direct lin-4 regulation by MYRF-1 is lacking, as the approaches used are indirect and not standard in the field. Overall, while the aims of the work are mostly achieved, data regarding the direct regulation of lin-4 by MYRF-1 and placing the work into the context of previous related reports is lacking. Because of its very specific focus, this paper reports useful findings on how a single transcription factor family might control the expression of a microRNA.

Reviewer #2 (Public Review):

Summary of major findings:<br /> The manuscript "The Plasmodium falciparum artemisinin resistance-associated protein Kelch 13 is required for formation of normal cytostomes" authored by Tutor et al. provides evidence that Kelch13 is necessary for the formation and maintenance of cytostomes. The group provides compelling evidence using multiple state-of-the-art microscopy imaging techniques to demonstrate that when Kelch13 is mislocalized to the nucleus, cytostomes are decreased, cytostome morphology is aberrant, and there are decreased levels of heme within the parasite.

Impact of the study:<br /> Mutations in Kelch13 have been associated with artemisinin resistance. The biological function of Kelch13 has been a question of great interest. Kelch13 was shown to associate with proteins in the endocytic machinery although not with clathrin. It was previously shown that Kelch13 mutants have decreased levels of hemoglobin digestion-derived peptides, decreased Kelch13 protein (although levels are not decreased at all asexual stages), and decreased heme. Here, the authors show that when Kelch13 is mislocalized, there are decreased numbers of properly-formed cytostomes that lead to decreased heme within parasites. Although not formally demonstrated, it is thus possible that there is decreased subsequent heme-mediated activation of artemisinin, which would explain the connection between Kelch13 and artemisinin resistance.

Reviewer #2 (Public Review):

The authors attempt to address a long-standing controversy in the study of the neural correlates of visual awareness, namely whether neurons in prefrontal cortex are necessarily involved in conscious perception. Several leading theories of consciousness propose a necessary role for (at least some sub-regions of) PFC in basic perceptual awareness (e.g., global neuronal workspace theory, higher order theories), while several other leading theories posit that much of the previously reported PFC contributions to perceptual awareness may have been confounded by task-based cognition that co-varied between the aware and unaware reports (e.g., recurrent processing theory, integrated information theory). By employing intracranial EEG in human patients and a threshold detection task on low-contrast visual stimuli, the authors assessed the timing and location of neural populations in PFC that are differentially activated by stimuli that are consciously perceived vs. not perceived. Overall, the reported results support the view that certain regions of PFC do contribute to visual awareness, but at time-points earlier than traditionally predicted by GNWT and HOTs.

Major strengths of this paper include the straightforward visual threshold detection task including the careful calibration of the stimuli and the separate set of healthy control subjects used for validation of the behavioral and eye tracking results, the high quality of the neural data in six epilepsy patients, the clear patterns of differential high gamma activity and temporal generalization of decoding for seen versus unseen stimuli, and the authors' interpretation of these results within the larger research literature on this topic. This study appears to have been carefully conducted, the data were analyzed appropriately, and the overall conclusions seem warranted given the main patterns of results.

Weaknesses include the saccadic reaction time results and the potential flaws in the design of the reporting task. This is not a "no report" paradigm, rather, it's a paradigm aimed at balancing the post-perceptual cognitive and motor requirements between the seen and unseen trials. On each trial, subjects/patients either perceived the stimulus or not, and had to briefly maintain this "yes/no" judgment until a fixation cross changed color, and the color change indicated how to respond (saccade to the left or right). Differences in saccadic RTs (measured from the time of the fixation color change to moving the eyes to the left or right response square) were evident between the seen and unseen trials (faster for seen). If the authors' design achieved what they claim on page 3, "the report behaviors were matched between the two awareness states ", then shouldn't we expect no differences in saccadic RTs between the aware and unaware conditions? The fact that there were such differences may indicate differences in post-perceptual cognition during the time between the stimulus and the response cue. Alternatively, the RT difference could reflect task-strategies used by subjects/patients to remember the response mapping rules between the perception and the color cue (e.g., if the YES+GREEN=RIGHT and YES+RED=LEFT rules were held in memory, while the NO mappings were inferred secondarily rather than being actively held in memory). This saccadic RT result should be better explained in the context of the goals of this particular reporting-task.

Nevertheless, the current results do help advance our understanding of the contribution of PFC to visual awareness. These results, when situated within the larger context of the rapidly developing literature on this topic (using "no report" paradigms), e.g., the recent studies by Vishne et al. (2023) Cell Reports and the Cogitate consortium (2023) bioRxiv, provide converging evidence that some sub-regions of PFC contribute to visual awareness, but at latencies earlier than originally predicted by proponents of, especially, global neuronal workspace theory.

Reviewer #2 (Public Review):

The manuscript by Ramesh et al builds upon prior studies from the Sigrist group to examine synergistic interactions between the Spinophilin (Spn) and Syd-1 synaptic proteins and their role in regulating presynaptic homeostatic plasticity at Drosophila larval NMJs and adult olfactory memory in the Mushroom Body (MB). The authors show synergistic interactions between the two proteins in these processes, where late PHP and long-term memory are abolished in Spn mutants, but restored upon reduction of Syd-1 function in the mutants. The authors go on to show that Spn appears to act in PHP by regulating a late stage in AZ remodeling and longer-term increases in the readily releasable SV pool by controlling actin polymerization/dynamics through the Mical protein. Although key aspects of the overall bigger picture have been published before (Mical's role in PHP, antagonism between Spn and Syd-1 in AZ development, AZ remodeling in MB-dependent memory), the current paper ties together many of these observations into a bigger picture of how PHP plasticity at the NMJ is established and provides support for a role for PHP-required proteins in promoting long-term memory in the adult MB through effects on AZ structure and AZ protein content/amount. The study also provides new links to the role of Spn in regulating local synaptic actin dynamics and how this alters the readily releasable pool and SV release. Some points of note are provided below.

1. I'm a bit confused about the time course experiments the authors describe that seem to be contradictory in Figures 1 and 2. The authors indicate control animals transiently increase BRP AZ levels during PHP at 10 mins, but by 30 minutes this increase is gone, even though PHP remains. As such, the data in these early figures suggests increases in BRP AZ levels may support an early aspect of the PHP effect (though I note this appears controversial, as other data indicate blocking the rapid AZ remodeling by several manipulations such as Arl8 transport disruption, permits early PHP, but disrupts late PHP). In contrast, the authors show that Spn mutants do not display AZ BRP increase at 10 mins, and still show early PHP, but lack late PHP. I assume the early PHP does not require AZ remodeling or an increase in the RRP at this early time point?

2. In relation to point 1 above, the time course seems different in MB neurons, where the AZ remodeling (noted by increases in AZ BRP) seems to take 2-3 hours. Do the authors have any ideas into why the time course of PHP AZ remodeling at larval NMJs can occur in 10 minutes, but MB neuron remodeling seems to take hours?

3. Could the lack of rapid BRP accumulation during early PHP in Spn mutants be secondary to the larger # of AZs in those mutants and a known rate-limiting amount of BRP available that might not be enough to go to the extra AZs?

4. There isn't any validation of the Spn co-IP results shown in Figure 3 through other assays, and a lot of proteins are being pulled down. I can't see some of these being real (mitochondrial translation proteins? - how could Spn gain access to the inside of the mitochondria since it's a cytosolic protein?). As such, I don't know how to value that huge group of pull-down interactions without further validation, making it difficult to sort out how relevant these really are. The genetic validation of similar phenotypes in the Mical mutant, together with rescues, supports that interaction. Not sure about the rest of that list.

5. Are the authors worried about the fact that the Actin-GFP line they use to look at synaptic actin dynamics is driven by a GAL4, and the 2nd top hit of their Spn IP pull downs are translation regulators? Could the changes in actin-GFP they see between control and Spn mutants have anything to do with a different translation of the exogenous UAS-actin-GFP? Would have been helpful to do an endogenous stain for actin levels with an anti-actin antibody so no transcription/translation issues of a transgene would be at play. This would be easy to do for the quantification of total actin levels at the synapse.

6. Are Mical levels normalized in the Spn, Syd1 double mutants, given PHP is recovered?

Reviewer #2 (Public Review):

In this manuscript, Dumeaux et al. assess the heterogeneous cellular response of the fungal pathogen Candida albicans to antifungal agents, using single-cell RNA sequencing. The researchers develop and optimized single-cell transcriptomics platform for C. albicans, and exploit this technique to monitor the cellular response to treatment with three distinct antifungal agents. Through this analysis, they identify two distinct subpopulations of cells that undergo differential transcriptomic responses to antifungal treatment: one involving upregulation of translation and respiration, and the other involving stress responses. This work monitors how different and prolonged antifungal exposure alters and shifts fungal cell populations between these responses. This is an innovative study that exploits novel single-cell transcriptomic techniques to address a very interesting question regarding the heterogeneous nature of the fungal response to antifungal drug treatment. This work optimizes a protocol for single-cell RNA sequencing, which is a significant contribution to the fungal research community and will bolster future research efforts in this area. The identification of two distinct subpopulations of fungal cells with differential responses to antifungal treatment is an exciting and novel finding. While there are aspects of this manuscript that are of significant interest, there are also limitations to this work. The research is framed as a method to study antifungal drug tolerance, but it is not clear how it does so, based on the methods. This work also compares very different populations of cells (rapidly growing untreated cells compared with cells grown in antifungal for several days), making it difficult to assess the role of antifungal treatment specifically in this analysis. This manuscript is also written with a great deal of highly technical language that makes it difficult to dissect the major findings and outcomes from the study.

Reviewer #2 (Public Review):

This study highlights a connection between the power spectra of fMRI signals and the temporal dynamics of the hemodynamic response function (HRF). Using visual stimulation experiments and resting-state scans, the spectral features of resting-state fMRI signals in V1 and LGN are found to have a significant relationship to the relative timing of HRF responses during the task.

Overall, I found this to be an interesting and clearly written study, with high-quality data. The connection between BOLD signal spectra and vascular responses is not discussed in much of the resting-state fMRI literature, and represents an important message and consideration. While the connection between the amplitude of resting-state BOLD fluctuations and the amplitude of task HRFs has been investigated in the past, I am not aware of prior work that had considered the timing aspect. The present comparison between resting-state spectra and breath-holding task responses also provides useful data about the hemodynamic information carried by these two conditions.

The present experiments were conducted at 7T with high temporal resolution and focused on a visual experiment. The generalization of the findings to other task conditions, brain regions, and acquisition parameters would be a valuable future step. Understanding the translation to other datasets would be a practical consideration for researchers who are considering applying this method. Regarding the evaluation of the classification models, it currently appears possible that the train/test sets might contain closely spaced and thus correlated voxels. Accounting for this effect could help to better support the conclusions of this analysis. More discussion about the neural or vascular basis of the slow- versus fast-HRF voxels could also bring further insights to the work (for instance, the location of the fast and slow V1 voxels with respect to functional boundaries and vascular anatomy).

Reviewer #2 (Public Review):

This study shows that rab12 has a role in the phosphorylation of rab10 by LRRK2. Many publications have previously focused on the phosphorylation targets of LRRK2 and the significance of many remains unclear, but the study of LRRK2 activation has mostly focused on the role of disease-associated mutations (in LRRK2 and VPS35) and rab29. The work is performed entirely in an alveolar lung cell line, limiting relevance for the nervous system. Nonetheless, the authors take advantage of this simplified system to explore the mechanism by which rab12 activates LRRK2. In general, the work is performed very carefully with appropriate controls, excluding trivial explanations for the results, but there are several serious problems with the experiments and in particular the interpretation.

First, the authors note that rab29 appears to have a smaller or no effect when knocked down in these cells. However, the quantitation (Fig1-S1A) shows a much less significant knockdown of rab29 than rab12, so it would be important to repeat this with better knockdown or preferably a KO (by CRISPR) before making this conclusion. And the relationship to rab29 is important, so if a better KD or KO shows an effect, it would be important to assess by knocking down rab12 in the rab29 KO background.

Secondly, the knockdown of rab12 generally has a strong effect on the phosphorylation of the LRRK2 substrate rab10 but I could not find an experiment that shows whether rab12 has any effect on the residual phosphorylation of rab10 in the LRRK2 KO. There is not much phosphorylation left in the absence of LRRK2 but maybe this depends on rab12 just as much as in cells with LRRK2 and rab12 is operating independently of LRRK2, either through a different kinase or simply by making rab10 more available for phosphorylation. The epistasis experiment is crucial to address this possibility. To establish the connection to LRRK2, it would also help to compare the effect of rab12 KD on the phosphorylation of selected rabs that do or do not depend on LRRK2.

A strength of the work is the demonstration of p-rab10 recruitment to lysosomes by biochemistry and imaging. The demonstration that LRRK2 is required for this by biochemistry (Fig 4A) is very important but it would also be good to determine whether the requirement for LRRK2 extends to imaging. In support of a causal relationship, the authors also state that lysosomal accumulation of rab12 precedes LRRK2 but the data do not show this. Imaging with and without LRRK2 would provide more compelling evidence for a causative role.

The authors also touch base with PD mutations, showing that loss of rab12 reduces the phosphorylation of rab10. However, it is interesting that loss of rab12 has the same effect with R1441G LRRK2 and D620N VPS35 as it does in controls. This suggests that the effect of rab12 does not depend on the extent of LRRK2 activation. It is also surprising that R1441G LRRK2 does not increase p-rab10 phosphorylation (Fig 2G) as suggested in the literature and stated in the text.

Most important, the final figure suggests that PD-associated mutations in LRRK2 and VPS35 occlude the effect of lysosomal disruption on lysosomal recruitment of LRRK2 (Fig 4D) but do not impair the phosphorylation of rab10 also triggered by lysosomal disruption (4A-C). Phosphorylation of this target thus appears to be regulated independently of LRRK2 recruitment to the lysosome, suggesting another level of control (perhaps of kinase activity rather than localization) that has not been considered.

Reviewer #2 (Public Review):

The present manuscript by the Claire Wyart group analyses the behaviour of Reissner's fibre (RF) when it is cut, as well as the behaviour of cells touching RF when contact is interrupted. They show that RF is under tension that is higher in the rostral than in the caudal spinal cord. One of the proposed mechanisms is a caudally oriented movement of the cilia of ependymal radial glials cells (ERG) that is inherent rather than caused by the contact with RF. Kolmer Agduhr neurons that are also CSF contacting (CSF-cN), alter their activity when contact is lost through laser ablation of RF.

This is an interesting paper - RF has long been proposed to be a source of signalling molecules in the development and physiological function of neural cells in the spinal cord. Cilia are the main centre of signalling activity in ciliated cells (e.g. for sonic hedgehog signalling) and the fact that ERG cilia are in direct contact with RF is intriguing. Presumably, signalling molecules could be directly transferred from RF to ERG at the contact points.

Functionally, CSF-cN are augmenting spinal cord intrinsic sensory feedback on body curvature. This had been shown in vitro/ex vivo, but not clearly evaluated in the living animal. The data shown here demonstrate a possible mechanism for how the feedback can be mediated through contact with RF. This is of fundamental interest to understand the functioning of a locomotor network that is under evolutionary pressure to function early, since fish hatch at 3 days post fertilisation.

Interestingly, the authors propose (and discuss against the relevant literature) that the presence of RF in the central canal can influence the flow of the CSF, which should be investigated in further work.

Overall, the results are clearly presented, and methods are thoroughly given, including some indication on the reduction of bias (by blinding movies before analysis). The authors also clearly state the limitations of their work, mostly derived from optical limitation (size of the RF in the larval fish, and speed of the recording in the laser-equipped microscope). This doesn't affect the fundamental statements.

Reviewer #2 (Public Review):

This well-written manuscript provides a technical tour-de-force to provide a novel mechanism for sustaining CaMKII autophosphorylation through an interholoenzyme reaction mechanism the authors term inter-holoenzyme phosphorylation (IHP). The authors use molecular engineering to create designer molecules that permit detailed testing of the proposed interholoenzyme reaction mechanism. By catalytically inactivating one population of enzymes, they show using standard assays that the inactive enzyme can be phosphorylated by active holoenzymes. They go on to show that in cells, the inactive enzyme is phosphorylated only in the presence of co-expressed active CaMKII and that this does not appear to be due to active and inactive subunits mixing within the same holoenzyme. The authors suggest reasons for why previous experiments failed to expose IHP and in some experiments provide evidence that reproduces and then extends earlier studies. Some noted differences from earlier experiments are the reaction temperature, the time course of the reactions, and that significantly higher concentrations of the inactive (substrate) kinase in the present study amplify the IHP. These are plausible reasons for earlier studies not finding significant evidence for IHP and the presented data is well-controlled and of high quality.

The authors then take on the idea of subunit exchange employing multiple strategies. Using genetic expansion, they engineer an unnatural amino acid into the hub domain of the kinase (residue 384). In the presence of the photoactivatable crosslinker BZF and UV illumination, a ladder of subunits was generated indicating intraholoenzyme crosslinks were established. Using this cross-linked enzyme, presumably incapable of subunit exchange, the authors show significant phosphorylation of the kinase-dead mutant. This further supports that IHP is the cause of phosphorylation and not subunit exchange. Extending these experiments, they could not find evidence when CaMKIIF394BZF was mixed with the kinase-dead mutant and exposed to UV light, that there was evidence of the kinase-dead subunits exchanged into CaMKIIF394 (active) enzymes.

With an entirely different approach, the authors use isotopic labeling of different pools of wt CaMKII (N14 or N15) followed by bifunctional cross-linking and mass spec to assess potential intra- and inter-holoenzyme contacts. Several interesting findings came of these studies detailed in Figure 4, mapped in detail in Figure 5, and extensively documented in supplementary tables. Critically, numerous cross-links were found between different domains of the enzyme (catalytic, regulatory, hub) that are themselves a nice database of proximity measurements, but critical to the hypothesis, no heterotypic cross-links were found in the hub domains at any activated state or time point of incubation. This data supports two findings, that catalytic domains come into close proximity between holoenzymes when activated, supporting the potential for IHP, but that no subunit exchange occurs.

The authors then pursue the approach used originally to provide evidence of subunit mixing, single molecule-based fluorescence imaging. Using pools of CaMKII labeled with spectrally separable dyes, the authors reproduce the earlier findings (Stratton et al, 2016) showing that under activating conditions, but not basal conditions, colocalized spots were detected. Numerous controls were done that confirm the need for full activation (Ca2+/CaM + Mg2+/ATP) to visualize co-localized CaMKII holoenzymes. Extending these studies, the authors mix holoenzymes, fully activate them, and after sufficient time for subunit exchange (if it occurs), the reactions were quenched, and then samples were analyzed. The result was that no evidence of dual-colored holoenzymes was present; if subunits had mixed between holoenzymes, dual-colored spots should have been evident after quenching the reactions. This was not the case. Further, experiments repeated with pools of differentially labeled kinase dead enzymes produced no colocalization, as predicted, if activation of the catalytic domains is necessary to establish IHP.

Finally, the authors employ mass photometry to investigate the potential for interholoenzyme interactions. At basal conditions, only a mass peak consistent with CaMKII dodecamers was evident. Upon activation, a small fraction of dimeric complexes was evident (with Ca2+/CaM bound) but the majority of the peak was a dodecamer with 12 associated CaM molecules, and importantly, a significant fraction of a mass population was found consistent with a pair of holoenzymes with associated CaM. As an aside, the holoenzyme population appeared to be modestly destabilized as evidence of a minor fraction of dimers appeared as the authors diluted the enzyme, but the pools of holoenzyme and pairs of holoenzymes (with CaM) remained the dominant species when activated under all three enzyme concentrations assessed. Supporting the importance of activation for interactions between holoenzymes, the catalytically dead kinase even under activating conditions, shows no evidence of dimers of holoenzymes.

Each of the approaches is well-controlled, the data is of uniformly high quality, and the authors' interpretations are generally well-supported.

Reviewer #2 (Public Review):

The article by Joechner et al is a reanalysis of a large cohort data-set on sleep oscillation development. By combining an analysis with fixed frequencies derived from adults with adaptive frequency ranges, they highlight that initially spindle oscillations are slower and it takes until mid adolescence for spindles to be more adult like. Further, those spindles that already have adult-like frequency ranges also show the other properties known from adults. These results are intriguing and the analysis is well-done and thorough. I only have minor comments on how the article could be improved.

Some additional analysis that would complement the current findings: in Fig 1 it would be good to include the adult-like slow frontal spindles for comparison (similar as the inclusion for the centro parietal ones). Further, providing distributions could let the readers have some valuable insight into the events. Could the authors combine all events and show 3D scatter plots with frequency X amplitude X duration of each spindle event? And then either color code the events from different age groups or have them in separate plots. Additionally, the frequency cut-off for adult-events could be added to the plot. This would likely show nicely how the events shift in their properties over age and thus slowly reach adult-like characteristics.

On page 2. Line 17 the authors state that spindles align ripples. While this is the case, the interaction between these oscillations are more complex. Ripples will also occur before the spindle and the ripples before spindles have been shown to be causally related to memory consolidation. Please cite Maingret et al Nat Neurosci 2016. Further, the authors should also discuss other rodent work for example Garcia et al Frontiers 2022, which also investigates the development of spindles.

Reviewer #2 (Public Review):

This study investigates the associations and causal relationship between second primary cancers and the initial diagnosis of a primary cancer, utilizing a large-scale database. The study's unique contribution lies in its combination of pan-cancer analysis and the incorporation of Mendelian randomization, which adds novelty and enhances the value of the research.

Furthermore, the findings of this study have the potential to provide valuable insights into important clinical considerations, such as patients' prognosis, treatment decisions, and survivorship care.

Reviewer #2 (Public Review):

In this Manuscript, Huang et al generated engineered MSC (eMSC) to produce mutant b-GALH363A, and when stimulated with a pro-drug (MGP) they can release NO. These cells were tested in vivo in a mouse model of AKI. When MGP is systemically administrated in AKI mice, it can induce eMSC to release NO in a precise and spatiotemporal manner, possibly enhancing the therapeutic efficacy of these stem cells.

The authors have conducted a very interesting study. The results are likely of interest to the renal scientific community, especially in the context of acute kidney injury.

Weaknesses are present. Methods (animals, groups, time points, cell lines, bulk RNA-seq, etc.) are not clearly described and details are missing. Legends are not clear, and some Figures do not clearly represent the results discussed.

Reviewer #2 (Public Review):

In this study, Pinatel et al. address the role of interneuron myelination in the hippocampus using a 4.1B protein mouse knockout model. They show that deficiency in 4.1B significantly reduces myelin in CA1 stratum radiatum, specifically myelin along axons of parvalbumin and somatostatin hippocampal interneurons. In addition, there are striking defects in the distribution of ion channels along myelinated axons, with misplacement of Na channel clusters along the nodes of Ranvier and the heminodes, and a pronounced decrease in potassium channels (Kv1) at juxtaparanodes. The axon initial segments of SST are also shorter. Because the majority of myelinated axons in the stratum radiatum of the hippocampus belong to PV and SST interneurons such profound changes in myelination are expected to affect interneuronal function. Interestingly, the authors show that PV basket cells' properties appear largely unaffected, while there are substantial changes in stratum oriens O-LM cells. Inhibitory inputs to pyramidal neurons are also changed. Behaviorally, the 4.1B KO mice exhibit deficits in spatial working memory, supporting the role of interneuronal myelination in hippocampal function. This study provides important insights into the role of myelination for the function of inhibitory interneurons, as well as in the mechanisms of axonal node development and ion channel clustering, and thus will be of interest to a broad audience of circuit and cellular neuroscientists. However, the claims of the specificity of the reported changes in myelination need to be better supported by evidence.

Strengths:<br /> The authors combine a wide array of genetic, immunolabeling, optical, electrophysiological, and behavioral tools to address a still unresolved complex problem of the role of myelination of locally projecting inhibitory interneurons in the hippocampus. They convincingly show that changing myelination and ion channel distribution along nodes and heminodes significantly impairs the function of at least some interneuron types in the hippocampus and that this is accompanied by behavioral deficits in spatial memory.

Regarding the organization of myelinated axons, the lack of 4.1B causes striking changes at the nodes of Ranvier that are convincingly and beautifully presented in the Figures. While the reduction in Kv1 in 4.1B KO mice has been previously reported, the mislocalization of sodium channels at the nodes and heminodes had only been observed in developing but not adult spinal cords. This difference in the dependence of the sodium channel distribution on 4.1B in adult hippocampus vs spinal cord may hold important clues for the varying role of myelin along axons of different neuronal types.

The manuscript is very well written, the discussion is comprehensive, and provides detailed background and analysis of the current findings and their implications.

Weaknesses:<br /> Because of the wide diversity of interneuron types in the hippocampus, and also the presence of myelinated axons from other neuron types as well, including pyramidal neurons, it is very difficult to disentangle the effects of the observed changes in the 4.1 B KO mouse model. While the authors have been careful to explore different possibilities, some of the claims of the specificity of the reported changes in myelination are not completely founded. For example, there is no compelling evidence that the myelination of axons other than the local interneurons is unchanged. The evidence strongly supports the claims of changes in interneuronal myelination, but it leaves open the question of whether 4.1B lack affects the myelination of hippocampal pyramidal neurons or of long-range projections.

To be able to better interpret the changes in the 4.1B KO mice, knowledge of the distribution of 4.1B in the hippocampus of control mice will be very helpful. The authors state that 4.1B is observed in PV neurons but not in pyramidal neurons, however, the evidence is not convincing. Thus, the lack of immunolabeling at the pyramidal neuron cell bodies does not indicate that 4.1B is missing at the axonal level. The analysis also leaves out the question of whether 4.1 B is seen in the axons of somatostatin neurons.

Reviewer #2 (Public Review):

In this manuscript, the authors characterize activity-dependent transcriptional and epigenetic changes at two different time points (1h and 4hrs) after neuronal activation using rat striatal primary cultures. They show that while at 1h post-stimulation mostly a selective set of IEGs are up-regulated, at 4hrs a wider set of genes, identified as late-response genes (LRGs), are upregulated, with distinct functional signatures. By using ATAC-seq, the authors show how chromatin accessibility is mostly spared at 1h post-stimulation, while a prominent set of differentially accessible regions (DARs) could be identified at 4hrs post-stimulation, enriched in motifs for TFs upregulated at their initial time-point. These chromatin changes appear to be dependent on the earlier translation of proteins, as they are avoided when neuronal cultures are pre-treated with the protein synthesis inhibitor Anisomycin. Afterwards, the authors characterized a set of regulatory regions of a particular LRG, Pdyn, associated with neuropsychiatric disorders, by using CRISRPR to activate or inactivate an enhancer that increases its accessibility at 4hrs post-stimulation, showing that the expression of Pdyn is highly dependent on this regulatory region both, at basal level and for its proper activity-dependent stimulation and that it is enriched in motifs for IEGs upregulated at 1h post-stimulation. Using publicly available data from human GABAergic and glutamatergic neurons similarly stimulated with KCl, the authors show that this enhancer is conserved in humans and that it is mostly modified in GABAergic neurons in response to neuronal stimulation, but not in glutamatergic neurons. Finally, the authors suggest that the regulatory role of the Pdyn enhancer they focus on it might be cell-type specific, as single-nuclei ATAC-seq data generated in rat Nucleus Accumbens (NAc) shows that its coaccessibility score together with Pdyn promoter is more prominent in Drd1- and Grm8-MSNs.

Among the major strengths of the article, there is the generation of neuronal RNA-seq and ATAC-seq data in a model system, rat striatal neuronal cells, that hasn't been so broadly characterized as other more common ones such as mouse hippocampal neuronal cells and the functional characterization of an enhancer of the Pdyn gene that might be of interest for translational applications in which alterations of this gene might be occurring in neurological disorders.

On the other hand, the manuscript presents several weaknesses to consider. First of all, at a conceptual level, most of the findings related to the induction of particular transcriptional programs upon neuronal activation the changes in chromatin state, and the need for protein translation for proper induction of LRGs have been broadly characterized previously in the literature (Tyssowski et al., Neuron, 2018; Ibarra et al., Mol. Syst. Biol., 2022; and also reviewed by Yap and Greenberg, Neuron, 2018). In addition, it is not so obvious why to focus on Pdyn gene regulatory regions among the thousands of genes upregulated and with modified chromatin landscape after neuronal activation. The authors highlight three particular traits of this gene as the reason to choose it, but those traits are probably shared by most of the genes that are part of the LRGs set.

At the methodological level, some attention should be put into the timings chosen for generating the data. The authors claim that these time points (1h and 4hrs) identify the first (i.e IEGs) and second (i.e LRGs) waves of transcription. However, at 4hrs the highest over-expressed genes are still IEGs, as shown in the volcano plots of Figure 1B and 1C, showing a high overlap with up-regulated genes found at 1h (Figure 1D). This might suggest that the 4hrs time point is somewhere in between the first and second wave of transcription, probably missing some of the still-to-be-induced LRGs of the latest one.

Finally, while only prosed as a suggestion, the assumption that from the data generated in this article, we can envision a mechanism by which AP-1 family of transcription factors interacts with the SWI/SNF chromatin remodeling complex is going too far, as no evidence is provided implicated SWI/SNF in the data presented in the manuscript.

Reviewer #2 (Public Review):

The authors phototag DA and GABA neurons in the VTA in mice performing a t-maze task, and report choice-specific responses in the delay period of a memory-guided task, more so than in a variant task w/o a memory component. Overall, I found the results convincing. While showing responses that are choice selective in DA neurons is not entirely novel (e.g. Morris et al NN 2006, Parker et al NN 2016), the fact that this feature is stronger when there is a memory requirement is an interesting and novel observation.

I found the plots in 3B misleading because it looks like the main result is the sequential firing of DA neurons during the Tmaze. However, many of the neurons aren't significant by their permutation test. Often people either only plot the neurons that are significant, or plot with cross-validation (ie sort by half of the trials, and plot the other half).

Relatedly, the cross-task comparisons of sequences (Fig, 4,5) are hampered by the fact that they sort in one task, then plot in the other, which will make the sequences look less robust even if they were equally strong. What happens if they swap which task's sequences they use to order the neurons? I do realize they also show statistical comparisons of modulated units across tasks, which is helpful.

Overall, the introduction was scholarly and did a good job covering a vast literature. But the explanation of t-maze data towards the end of the introduction was confusing. In Line 87, I would not say "in the same task" but "in a similar task" because there are many differences between the tasks in question. And not clear what is meant by "by averaging neuronal population activities, none of these computational schemes would have been revealed. " There was trial averaging, at least in Harvey et al. I thought the main result of that paper related to coding schemes was that neural activity was sequential, not persistent. I think it would help the paper to say that clearly. Also, I'm not aware it was shown that choice selectivity diminishes when the memory demand of the task is removed - please clarify if that is true in both referenced papers. If so, an interpretation of this present data could be found in Lee et al biorxiv 2022, which presents a computational model that implies that the heterogeneity in the VTA DA system is a reflection of the heterogeneity found in upstream regions (the state representation), based on the idea that different subsets of DA neurons calculate prediction errors with respect to different subsets of the state representation.

I am surprised only 28% of DA neurons responded to reward - the reward is not completely certain in this task. This seems lower than other papers in mice (even Pavlovian conditioning, when the reward is entirely certain). It would be helpful if the authors comment on how this number compares to other papers.

Reviewer #2 (Public Review):

This work presents a new, automated, deep learning-based segmentation pipeline for fetal cerebral MRI based on the anatomical definitions of the new fetal atlas of the Developing Human Connectome Project. The authors' new software pipeline demonstrated robust performance across different acquisition protocols and gestational age ranges, reducing the need for manual refinement. To provide ground truth data for training their deep learning network, the authors employed a semi-supervised approach, in which atlas labels were propagated to the datasets, and they were corrected manually.

This work stands out for its extensive training on a large number of datasets, it achieves precise anatomical definition through a refined brain tissue parcellation protocol, and it evaluates the segmentation results against growth curves, allowing for a comprehensive assessment of fetal brain development. Due to the fact that abnormal anatomy was largely unobserved by the segmentation network, it is highly likely, however, that the BOUNTI pipeline would lead to some incorrect segmentations in subjects with moderate to large ventriculomegaly, as well as in cases of malformations of the corpus callosum, brainstem or neural tube defects. Further work is required for BOUNTI to generalize its application to pathological brains, as the vast majority of fetal cerebral MRI cases in clinical practice involve such abnormalities rather than normal brain development. This step is crucial for facilitating the clinical translation of BOUNTI. The algorithm is publicly available and works without limitations on datasets acquired in other centers.

Reviewer #2 (Public Review):

Sequences of neural activity underlie most of our behavior. And as experience suggests we are (in most cases) able to flexibly change the speed for our learned behavior which essentially means that brains are able to change the speed at which the sequence is retrieved from the memory. The authors here propose a mechanism by which networks in the brain can learn a sequence of spike patterns and retrieve them at variable speed. At a conceptual level I think the authors have a very nice idea: use of symmetric and asymmetric learning rules to learn the sequences and then use different inputs to neurons with symmetric or asymmetric plasticity to control the retrieval speed. The authors have demonstrated the feasibility of the idea in a rather idealized network model. I think it is important that the idea is demonstrated in more biologically plausible settings (e.g. spiking neurons, a network with exc. and inh. neurons with ongoing activity).

Summary

In this manuscript authors have addressed the problem of learning and retrieval sequential activity in neuronal networks. In particular, they have focussed on the problem of how sequence retrieval speed can be controlled?<br /> They have considered a model with excitatory rate-based neurons. Authors show that when sequences are learned with both temporally symmetric and asymmetric Hebbian plasticity, by modulating the external inputs to the network the sequence retrieval speed can be modulated. With the two types of Hebbian plasticity in the network, sequence learning essentially means that the network has both feedforward and recurrent connections related to the sequence. By giving different amounts of input to the feed-forward and recurrent components of the sequence, authors are able to adjust the speed.

Strengths<br /> - Authors solve the problem of sequence retrieval speed control by learning the sequence in both feedforward and recurrent connectivity within a network. It is a very interesting idea for two main reasons: 1. It does not rely on delays or short-term dynamics in neurons/synapses 2. It does not require that the animal is presented with the same sequences multiple times at different speeds. Different inputs to the feedforward and recurrent populations are sufficient to alter the speed. However, the work leaves several issues unaddressed as explained below.

Weaknesses<br /> - The main weakness of the paper is that it is mostly driven by a motivation to find a computational solution to the problem of sequence retrieval speed. In most cases they have not provided any arguments about the biological plausibility of the solution they have proposed e.g.:

-- Is there any experimental evidence that some neurons in the network have symmetric Hebbian plasticity and some temporally asymmetric? In the references authors have cited some references to support this. But usually the switch between temporally symmetric and asymmetric rules is dependent on spike patterns used for pairing (e.g. bursts vs single spikes). In the context of this manuscript, it would mean that in the same pattern, some neurons burst and some don't and this is the same for all the patterns in the sequence. As far as I see here authors have assumed a binary pattern of activity which is the same for all neurons that participate in the pattern.

-- How would external inputs know that they are impinging on a symmetric or asymmetric neuron? Authors have proposed a mechanism to learn these inputs. But that makes the sequence learning problem a two stage problem -- first an animal has to learn the sequence and then it has to learn to modulate the speed of retrieval. It should be possible to find experimental evidence to support this?

-- Authors have only considered homogeneous DC input for sequence retrieval. This kind of input is highly unnatural. It would be more plausible if the authors considered fluctuating input which is different from each neuron.

-- All the work is demonstrated using a firing rate based model of only excitatory neurons. I think it is important that some of the key results are demonstrated in a network of both excitatory and inhibitory spiking neurons. As the authors very well know it is not always trivial to extend rate-based models to spiking neurons.

I think at a conceptual level authors have a very nice idea but it needs to be demonstrated in a more biologically plausible setting (and by that I do not mean biophysical neurons etc.).

Reviewer #1 (Public Review):

The manuscript by Kulkarni et al proposes a new cellular origin of ENS, which is increased with age and therefore may be associated with the gradual decline of gut function. The study is based on an initial observation that many enteric neurons do not seem to retain tdTomato expression in Wnt1Cre-R26-Tom mice, suggesting a loss of neurons that are replaced by a non-neural crest source. Further detection of reporter expression within the ENS of Tek and Mesp Cre-lines indicated a mesodermal origin of the new enteric neurons. Mesodermally derived neurons (MENS) were associated with Met, while neural crest derived neurons (NENS) expressed Ret. GDNF could decrease occurrence of MENS (defined as tdTomato-negative cells), while HGF had the opposite effect. Age-associated decline in gut transit was alleviated with GDNF treatment, while Ret heterozygote mutants had an increase of MENS. Overall, the study suggests that neural crest derived neurons are replaced by mesodermal-derived neurons that lead to an overall reduction in GI-physiology and that manipulation of the balance between the two types of neurons could have beneficial effects of age-associated gut malfunction. Generation of neurons from non-ectodermal sources would be a paradigm shift not only in the ENS, but in the Neuroscience field as a whole. The presence of mesenchymal marker genes in subsets of cells of the ENS in native gut tissue is convincing and the lack of retained fluorescent reporter expression in ENS from the many neural and Cre drivers used is indeed clear.

The current state of the manuscript is though not conceivable as it has unsound interpretation of data at many places, most importantly there is no firm connection between the MENs identified in tissue and the scRNA cluster annotated as MENs. "scRNA-seq-MENs" show very little expression of the bona fide neuron markers used to detect "tissue-MENs" including Elavl4 and the overall proportions of "scRNA-seq-MENs" in the tissue is very far from that of "tissue-MENs". Hence, the claims that "tissue-MENs" equals "scRNA-seq MENs" could be excluded or their interpretation discussed in an unbiased manner. Marker expression of "scRNA-seq MENs" are suggestive of mesothelial cell identities, not ENS cells. Even the annotation of scRNA-seq profiles denoted as neural-crest derived enteric neurons (NENs) is highly questionable as 25% of the cells display bona fide lympathic epithelial cell markers and no neuronal markers.

Reviewer #2 (Public Review):

The question the authors pose is very simple and yet very important. Does the fact that many genes compete for Pol II to be transcribed explain why so many trans-eQTL contribute to the heritability of complex traits? That is, if a gene uses up a proportion of Pol II, does that in turn affect the transcriptional output of other genes relevant or even irrelevant for the trait in a way that their effect will be captured in a genome-wide association study? If yes, then the large number of genetic effects associated with variation in complex traits can be explained but such trans-propagating has effects on the transcriptional output of many genes.

This is a very timely question given that we still don't understand how, mechanistically, so many genes can be involved in complex traits variation. Their approach to this question is very simple and it is framed in classic enzyme-substrate equations. The authors show that the trans-propagating effect is too small to explain the ~70% of heritability of complex traits that are associated with trans-effects. Their conclusion relies on the comparison of the order of magnitude of a) the quantifiable transcriptional effects due to Pol II competition, and b) the observed percentage of variance explained by trans effects (data coming from Liu et al 2019, from the same lab).

The results shown in this manuscript rule out that competition for limited resources in the cell (not restricted to Pol II, but applicable to any other cellular resource like ribosomes, etc) could explain the heritability of complex traits.

Reviewer #2 (Public Review):

The manuscript investigates the connections between the ubiquitin ligase protein deltex and the wingless pathway. Two different connections are proposed, one is the function of deltex to modulate the gradient of wingless diffusion and hence modulate the spatial pattern of wingless pathway targets, which regulate at different thresholds of wingless concentration. The second is a direct interaction between deltex and armadillo, a downstream component of the wingless pathway. Deltex is proposed to cause the degradation of armadillo resulting in suppression of wingless pathway activity. The results and conclusions of the manuscript are interesting and for the most part, novel, although previously published work linking Notch and deltex to wingless signal regulation, and endocytosis to wingless gradient formation could be more extensively discussed. However neither of the two parts of the manuscript seem in themselves sufficiently complete, and combining both parts together therefore seems to lack focus.

The main issue with the manuscript is that many of the conclusions are inferred from genetic interactions in vivo between loss of function mutants and overexpression. While providing useful in vivo physiological context, this type of approach struggles to be able to make definitive conclusions on whether an interaction is due to a direct or indirect mechanism, as the authors themselves conclude at the end of section 2.3. The problem is confounded by the fact that there is already documented much cross-talk between the Notch signaling pathway and wingless at the transcriptional level, and deltex is already a Notch modulator that can alter wingless mRNA expression (See Hori et al 2004). Deltex in addition to promoting a ligand-independent Notch signal can also induce expression of Notch ligand, allowing further non-autonomous Notch activation and subsequent cell autonomous cis-inhibition of the initial deltex-induced signal. The dynamics and outcomes of the Notch signal response to deltex in vivo are therefore already very complicated to interpret before even considering unraveling indirect (via Notch) and direct interactions with wingless, although the two possibilities are not mutually exclusive.

Reviewer #2 (Public Review):

Wang, He et al. shed insight into the molecular mechanisms of deep-sea chemosymbiosis at the single-cell level. They do so by producing a comprehensive cell atlas of the gill of Gigantidas platifrons, a chemosymbiotic mussel that dominates the deep-sea ecosystem. They uncover novel cell types and find that the gene expression of bacteriocytes, the symbiont-hosting cells, supports two hypotheses of host-symbiont interactions: the "farming" pathway, where symbionts are directly digested, and the "milking" pathway, where nutrients released by the symbionts are used by the host. They perform an in situ transplantation experiment in the deep sea and reveal transitional changes in gene expression that support a model where starvation stress induces bacteriocytes to "farm" their symbionts, while recovery leads to the restoration of the "farming" and "milking" pathways.

A major strength of this study includes the successful application of advanced single-nucleus techniques to a non-model, deep-sea organism that remains challenging to sample. I also applaud the authors for performing an in situ transplantation experiment in a deep-sea environment. From gene expression profiles, the authors deftly provide a rich functional description of G. platifrons cell types that is well-contextualized within the unique biology of chemosymbiosis. These findings offer significant insight into the molecular mechanisms of deep-sea host-symbiont ecology, and will serve as a valuable resource for future studies into the striking biology of G. platifrons.

The authors' conclusions are generally well-supported by their results. However, I recognize that the difficulty of obtaining deep-sea specimens may have impacted experimental design. In this area, I would appreciate more in-depth discussion of these impacts when interpreting the data.

Because cells from multiple individuals were combined before sequencing, the in situ transplantation experiment lacks clear biological replicates. This may potentially result in technical variation (ie. batch effects) confounding biological variation, directly impacting the interpretation of observed changes between the Fanmao, Reconstitution, and Starvation conditions. It is notable that Fanmao cells were much more sparsely sampled. It appears that fewer cells were sequenced, resulting in the Starvation and Reconstitution conditions having 2-3x more cells after doublet filtering. It is not clear whether this is due to a technical factor impacting sequencing or whether these numbers are the result of the unique biology of Fanmao cells. Furthermore, from Table S19 it appears that while 98% of Fanmao cells survived doublet filtering, only ~40% and ~70% survived for the Starvation and Reconstitution conditions respectively, suggesting some kind of distinction in quality or approach.

There is a pronounced divergence in the relative proportions of cells per cell type cluster in Fanmao compared to Reconstitution and Starvation (Fig. S11). This is potentially a very interesting finding, but it is difficult to know if these differences are the expected biological outcome of the experiment or the fact that Fanmao cells are much more sparsely sampled. The study also finds notable differences in gene expression between Fanmao and the other two conditions- a key finding is that bacteriocytes had the largest Fanmao-vs-starvation distance (Fig. 6B). But it is also notable that for every cell type, one or both comparisons against Fanmao produced greater distances than comparisons between Starvation and Reconstitution (Fig. 6B). Again, it is difficult to interpret whether Fanmao's distinctiveness from the other two conditions is underlain by fascinating biology or technical batch effects. Without biological replicates, it remains challenging to disentangle the two.

Reviewer #2 (Public Review):

This is an interesting paper from a reputable group in the field of islet physiology. The authors have provided the results from extensive studies, which will contribute to the knowledge of islet dysfunction and diabetes pathophysiology. The authors studied "the human orthologues of the correlated mouse proteins that are proximal to the glycemia-associated SNPs in human GWAS". This implies two assumptions - (1) human and mouse proteins do not differ in terms of islet physiology and calcium signaling; (2) the proteins proximal to the SNPs are the causal factors for functional differences, though the SNPs could affect protein/gene function distant from the SNPs.

Reviewer #2 (Public Review):

The authors aimed to investigate whether digital insoles are an appropriate alternative to laboratory assessment with force plates when attempting to identify the knee injury status. The methods are rigorous and appropriate in the context of this research area. The results are impressive, and the figures are exceptional. The findings of this study can have a great impact on the field, showing that digital insoles can be accurately used for clinical purposes. The authors successfully achieved their aims.

Reviewer #2 (Public Review):

The manuscript by Hayashi et al provides the characterization of a new mouse line that targets V2 neurons and demonstrates the locomotor consequences of manipulating the large V2 population. Prior work has examined the effects of silencing and/or ablation of the excitatory V2a and inhibitory V2b neuronal populations independently. Since the two populations are derived from the same V2 lineage but have opposite transmitter phenotypes, one may expect some common synaptic targets and/or similar or complementary functional roles that require excitatory/inhibitory balance. Overall, the value and importance of the study is that comparison of prior manipulations of the V2a and V2b populations (individually in prior studies) with the more global V2 manipulation (here) provides additional insights into spinal locomotor circuitry.

The authors successfully generate a new Hes2cre mouse line that targets the V2 population with high accuracy. The characterizations as far as the specificity and efficiency of the line are compelling. This line is then used to examine the locomotor effects of, first, synaptically silencing all Hes2 neurons throughout the neuroaxis beginning in early development and, then, ablating spinal Hes2 neurons in the adult. The phenotypes of both groups of mice are quite similar, with some small exceptions. The most obvious disturbance in both is the shortened steps, faster step cycle, and more steps required to travel the same distance. As the authors point out, much of the phenotype may be due to a disruption in balance. Interestingly, the hyperextension that is characteristic of V2b neuronal ablation is lost when the function of V2a neurons is compromised as well, suggesting antagonistic functions of these populations in intralimb coordination.

The experiments are rigorous and the data are clearly presented. The findings are interesting to consider in context with prior work. Some comparisons are difficult since gait is not considered and one of the major roles of spinal V2a neurons has been demonstrated to be speed/gait-dependent. The ipsilateral deficits are a major conclusion but some of the supporting data are not clearly derived (or there was an error in the figure?). The use of spinal restricted manipulation removes many of the potential confounds of the full Hes2 silencing. It is still, however, not possible to disentangle the local spinal circuit effects from altered proprioceptive input pathways or ascending information from the lumbar cord to the cervical regions or the brainstem. Although of value to inform future experiments, this impacts the strength of the conclusions that can be drawn.

Reviewer #2 (Public Review):

The chemoreceptor proteins expressed by olfactory sensory neurons differ in their selectivity such that glomeruli vary in the breadth of volatile chemicals to which they respond. Prior work assessing the relationship between tuning breadth and the demographics of principal neuron types that innervate a glomerulus demonstrated that narrowly tuned glomeruli are innervated more projection neurons (output neurons) and fewer local interneurons relative to more broadly tuned glomeruli. The present study used high-resolution electron microscopy to determine which synaptic relationships between principal cell types also vary with glomerulus tuning breadth using a narrowly tuned glomerulus (DA2) and a broadly tuned glomerulus (DL5). The strength of this study lies in the comprehensive, synapse-level resolution of the approach. Furthermore, the authors implement a very elegant approach of using a 2-photon microscope to score the upper and lower bounds of each glomerulus, thus defining the bounds of their restricted regions of interest. There were several interesting differences including greater axo-axonic afferent synapses and dendrodentric output neuron synapses in the narrowly tuned glomerulus, and greater synapses upon sensory afferents from multiglomerular neurons and output neuron autapses in the broadly tuned glomerulus.

The study is limited by a few factors. There was a technical need to group all local interneurons, centrifugal neurons, and multiglomerular projection neurons into one category ("multiglomerular neurons") which complicates any interpretations as even multiglomerular projection neurons are very diverse. Additionally, there were as many differences between the two narrowly tuned glomeruli as there were comparing the narrowly and broadly tuned glomeruli. Architecture differences may therefore not reflect differences in tuning breadth, but rather the ecological significance of the odors detected by cognate sensory afferents. Finally, some synaptic relationships are described as differing and others as being the same between glomeruli, but with only one sample from each glomerulus, it is difficult to determine when measures differ when there is no measure of inter-animal variability. If these caveats are kept in mind, this work reveals some very interesting potential differences in circuit architecture associated with glomerular tuning breadth.

This work establishes specific hypotheses about network function within the olfactory system that can be pursued using targeted physiological approaches. It also identifies key traits that can be explored using other high-resolution EM datasets and other glomeruli that vary in their tuning selectivity. Finally, the laser "branding" technique used in this study establishes a reduced-cost procedure for obtaining smaller EM datasets from targeted volumes of interest by leveraging the ability to transgenically label brain regions in Drosophila.

Reviewer #2 (Public Review):

Working memory is not error free. Behavioral reports of items held in working memory display several types of bias, including contraction bias and serial dependence. Recent work from Akrami and colleagues demonstrates that inactivating rodent PPC reduces both forms of bias, raising the possibility of a common cause.

In the present study, Boboeva, Pezzotta, Clopath, and Akrami introduce circuit and descriptive variants of a model in which the contents of working memory can be replaced by previously remembered items. This volatility manifests as contraction bias and serial dependence in simulated behavior, parsimoniously explaining both sources of bias. The authors validate their model by showing that it can recapitulate previously published and novel behavioral results in rodents and neurotypical and atypical humans.

Both the modeling and the experimental work is rigorous, providing compelling evidence that a model of working memory in which reports sometimes sample past experience can produce both contraction bias and serial dependence, and that this model is consistent with behavioral observations across rodents and humans in the parametric working memory (PWM) task.

Evidence for the model advanced by the authors, however, remains incomplete. The model makes several bold predictions about behavior and neural activity, untested here, that either conflict with previous findings or have yet to be reported but are necessary to appropriately constrain the model.

First, in the most general (descriptive) formulation of the Boboeva et al. model, on a fraction of trials items in working memory are replaced by items observed on previous trials. In delayed estimation paradigms, which allow a more direct behavioral readout of memory items on a trial-by-trial basis than the PWM task considered here, reports should therefore be locked to previous items on a fraction of trials rather than display a small but consistent bias towards previous items. However, the latter has been reported (e.g., in primate spatial working memory, Papadimitriou et al., J Neurophysiol 2014). The ready availability of delayed estimation datasets online (e.g., from Rademaker and colleagues, https://osf.io/jmkc9/) will facilitate in-depth investigation and reconciliation of this issue.

Second, the bulk of the modeling efforts presented here are devoted to a circuit-level description of how putative posterior parietal cortex (PPC) and working-memory (WM) related networks may interact to produce such volatility and biases in memory. This effort is extremely useful because it allows the model to be constrained by neural observations and manipulations in addition to behavior, and the authors begin this line of inquiry here (by showing that the circuit model can account for effects of optogenetic inactivation of rodent PPC). Further experiments, particularly electrophysiology in PPC and WM-related areas, will allow further validation of the circuit model. For example, the model makes the strong prediction that WM-related activity should display 'jumps' to states reflecting previously presented items on some trials. This hypothesis is readily testable using modern high-density recording techniques and single-trial analyses.

Finally, while there has been a refreshing movement away from an overreliance on p-values in recent years (e.g., Amrhein et al., PeerJ 2017), hypothesis testing, when used appropriately, provides the reader with useful information about the amount of variability in experimental datasets. While the excellent visualizations and apparently strong effect sizes in the paper mitigate the need for p-values to an extent, the paucity of statistical analysis does impede interpretation of a number of panels in the paper (e.g., the results for the negatively skewed distribution in 5D, the reliability of the attractive effects in 6a/b for 2- and 3- trials back).

Reviewer #2 (Public Review):

This work describes transcriptome profiling of dissected skin of zebrafish at post-embryonic stages, at a time when adult structures and patterns are forming. The authors have used the state-of-the-art combinatorial indexing RNA-seq approach to generate single cell (nucleus) resolution. The data appears robust and is coherent across the four different genotypes used by the authors.

The authors present the data in a logical and accessible manner, with appropriate reference to the anatomy. They include helpful images of the biology and schematics to illustrate their interpretations.

The datasets are then interrogated to define cell and signalling relationships between skin compartments in six diverse contexts. The hypotheses generated from the datasets are then tested experimentally. Overall, the experiments are appropriate and rigorously performed. They ask very interesting questions of interactions in the skin and identify novel and specific mechanisms. They validate these well.

The authors use their datasets to define lineage relationships in the dermal scales and also in the epidermis. They show that circumferential pre-scale forming cells are precursors of focal scale forming cells while there appeared a more discontinuous relationship between lineages in the epidermis.

The authors present transcriptome evidence for enamel deposition function in epidermal subdomains. This is convincingly confirmed with an ameloblastin in situ. They further demonstrate distinct expression of SCPP and collagen genes in the SFC regions.

The authors then demonstrate that Eda and TH signalling to the basal epidermal cells generates FGF and PDGF ligands to signal to surrounding mesenchyme, regulating SFC differentiation and dermal stratification respectively.

Finally, they exploit RNA-seq data performed in parallel in the bnc2 mutants to identify the hypodermal cells as critical regulators of pigment patterning and define the signalling systems used.

Whilst these six interactions in the skin are disparate, the stories are unified by use of the sci-RNA-seq data to define interactions. Overall, it's an assembly of work which identifies novel and interesting cell interactions and cross-talk mechanisms.

The paper provides robust evidence of cell interrelationships in the skin undergoing morphogenesis and will be a welcome dataset for the field.

Reviewer #2 (Public Review):

Raymond Laboy et.al explored how transcriptional Mondo/Max-like complex (MML-1/MXL-2) is regulated by glucose metabolic signals using germ-line removal longevity model. They believed that MML-1/MXL-2 integrated multiple longevity pathways through nutrient sensing and therefore screened the glucose metabolic enzymes that regulated MML-1 nuclear localization. Hexokinase 1 and 2 were identified as the most vigorous regulators, which function through mitochondrial beta-oxidation and the pentose phosphate pathway (PPP), respectively. MML-1 localized to mitochondria associated with lipid droplets (LD), and MML-1 nuclear localization was correlated with LD size and metabolism. Their findings are interesting and may help us to further explore the mechanisms in multiple longevity models, however, the study is not complete and the working model remains obscure. For example, the exact metabolites that account for the direct regulation of MML-1 were not identified, and more detailed studies of the related cellular processes are needed.

The identification of responsible metabolites is necessary since multiple pieces of evidence from the study suggests that lipid other than glucose metabolites may be more likely to be the direct regulator of MML-1 and HXK regulate MML-1 indirectly by affecting the lipid metabolism: 1) inhibiting the PPP is sufficient to rescue MML-1 function independent of G6P levels; 2) HXK-1 regulates MML-1 by increasing fatty acid beta-oxidation; 3) LD size correlates with MML-1 nuclear localization and LD metabolism can directly regulate MML-1. The identification of metabolites will be helpful for understanding the mechanism.

Beta-oxidation and the PPP are involved in the regulation of MML-1 by HXK-1 and HXK-2, respectively. But how these two pathways participate in the regulation is not clear. Is it the beta-oxidation rate or the intermediate metabolites that matters? As for the PPP, it provides substrates for nucleotide synthesis and also its product NADPH is essential for redox balance. Is one of the metabolites or the NADPH levels involved in MML-1 regulation? More studies are needed to provide answers to these concerns.

Reviewer #2 (Public Review):

Synthetic autotrophy of biotechnologically relevant microorganisms offers exciting chances for CO2 neutral or even CO2 negative production of goods. The authors' lab has recently published an engineered and evolved Escherichia coli strain that can grow on CO2 as its only carbon source. Lab evolution was necessary to achieve growth. Evolved strains displayed tens of mutations, of which likely not all are necessary for the desired phenotype.

In the present paper the authors identify the mutations that are necessary and sufficient to enable autotrophic growth of engineered E. coli. Three mutations were identified, and their phenotypic role in enhancing growth via the introduced Calvin-Benson-Bassham cycle were characterized. It was demonstrated that these mutations allow autotrophic growth of E. coli with the introduced CBB cycle without any further metabolic intervention. Autotrophic growth is demonstrated by 13C labelling with 13C CO2, measured in proteinogenic amino acids. In Figures 2B and S1, the labeling data are shown, with an interval of the "predicted range under 13CO2". Here, the authors should describe how this interval was derived.

The methodology is clearly described and appropriate.

The present results will allow other labs to engineer E. coli and other microorganisms further to assimilate CO2 efficiently into biomass and metabolic products. The importance is evident in the opportunity to employ such strain in CO2 based biotech processes for the production of food and feed protein or chemicals, to reduce atmospheric CO2 levels and the consumption of fossil resources.

Reviewer #2 (Public Review):

The authors apply a deep learning approach to predict fracture using forearm HR-pQCT data pooled from 3 longitudinal cohorts totaling 2666 postmenopausal women. The deep learning based 'Structural Fragility Score - AI' was compared to FRAX w/BMD and BMD alone in its ability to identify women who went on to fracture within the next 5 years. SFS-AI performed significantly better than FRAX w/BMD and BMD alone in all metrics except specificity. This work establishes that deep learning methods applied to HR-pQCT data have great potential for use in predicting (and therefore preventing) fractures.

The low specificity of SFS-AI compared to FRAX and BMD is not adequately acknowledged or addressed - will this lead to over diagnosis / unnecessary interventions and is that a problem?

The paper does not adequately address the relative role of bone vs soft tissue features in the determination of SFS-AI. It would be possible to feed the algorithm only the segmented bone volumes, and compare AUC, etc, of SFS-AI (bone) to that acquired using the entire bone + muscle volume. It's possible (likely?) that most of the predictive power will remain. If muscle is an important part of this algorithm, then mid-diaphyseal tibia scans will be an interesting next application - since that scan site is closer to the muscle belly compared to the distal radius site which contains very little muscle volume.

Reviewer #2 (Public Review):

In this study Weinberger et al. investigated cardiac macrophage subsets after ischemia/reperfusion (I/R) injury in mice. The authors studied a ∆FIRE mouse model (deletion of a regulatory element in the Csf1r locus), in which only tissue resident macrophages might be ablated. The authors showed a reduction of resident macrophages in ∆FIRE mice and characterized its macrophages populations via scRNAseq at baseline conditions and after I/R injury. 2 days after I/R protocol ∆FIRE mice showed an enhanced pro inflammatory phenotype in the RNAseq data and differential effects on echocardiographic function 6 and 30 days after I/R injury. Via flow cytometry and histology the authors confirmed existing evidence of increased bone marrow-derived macrophage infiltration to the heart, specifically to the ischemic myocardium. Macrophage population in ∆FIRE mice after I/R injury were only changed in the remote zone. Further RNAseq data on resident or recruited macrophages showed transcriptional differences between both cell types in terms of homeostasis-related genes and inflammation. Depleting all macrophage using a Csf1r inhibitor resulted in a reduced cardiac function and increased fibrosis.

Strengths<br /> 1. The authors utilized robust methodology encompassing state of the art immunological methods, different genetic mouse models and transcriptomics.<br /> 2. The topic of this work is important given the emerging role of tissue resident macrophages in cardiac homeostasis and disease.

Weaknesses:<br /> 1. Specificity of ∆FIRE mouse model for ablating resident macrophages.<br /> The study builds on the assumption that only resident macrophages are ablated in ∆FIRE mice, while bone marrow-derived macrophages are unaffected. While the effects of the ∆FIRE model is nicely shown for resident macrophages, the authors did not directly assess bone marrow-derived macrophages. Moreover, in the immunohistological images in Fig. 1D nearly all macrophages appear to be absent. It would be helpful to further address the question of whether recruited macrophages are influenced in ∆FIRE mice. Evaluation of YFP positive heart and blood cells in ∆FIRE mice crossed with Flt3CreRosa26eYFP mice could clarify whether bone marrow-derived cardiac macrophages are influenced in ∆FIRE mice. This would be even more relevant in the I/R model where recruitment of bone marrow-derived macrophages is increased. A more direct assessment of recruited macrophages in ∆FIRE mice could also help to discuss potential similarities or discrepancies to the study of Bajpai et al, Circ Res 2018 (https://doi.org/10.1161/CIRCRESAHA.118.314028), which showed distinct effects of resident versus recruited macrophages after myocardial infarction. Providing the quantification of flow cytometry data (fig. 1E-F) would be supportive.

2. Limited adverse cardiac remodeling in ∆FIRE mice after I/R.<br /> The authors suggested an adverse cardiac remodeling in ∆FIRE mice. However, the relevance of a <5% reduction in ejection fraction/stroke volume within an overall normal range in ∆FIRE mice is questionable. Moreover, 6 days after I/R injury ∆FIRE mice were protected from the impairment in ejection fraction and had a smaller viability defect. Based on the data few questions may arise: Why was ablation of resident macrophages beneficial at earlier time points? Are recruited macrophages affected in ∆FIRE mice (see above)? Overall, the manuscript could benefit if the claim of an adverse remodeling in ∆FIRE mice would be discussed more carefully.

3. Underlying mechanisms.<br /> The study did not functionally evaluated targets from transcriptomics to provide further mechanistic insights. It would be helpful if the authors discuss potential mechanisms of the differential effects of macrophages after ischemia in more detail.

Other:<br /> - It is unclear why the authors performed RNAseq experiments 2 days after I/R (fig. 5/6), while the proposed functional phenotype occurred later.<br /> - A sample size of 2 animals per group appears very limited for RNAseq in ∆FIRE mice (fig. 6).

Reviewer #2 (Public Review):

The present manuscript investigates the implication of locus coeruleus-noradrenaline system in the stress-induced transcriptional changes of dorsal and ventral hippocampus, combining pharmacological, chemogenetic, and optogenetic techniques. Authors have revealed that stress-induced release of noradrenaline from locus coeruleus plays a modulatory role in the expression of a large scale of genes in both ventral and dorsal hippocampus through activation of β-adrenoreceptors. Similar transcriptional responses were observed after optogenetic and chemogenetic stimulation of locus coeruleus. Among all the genes analysed, authors identified the most affected ones in response to locus coeruleus-noradrenaline stimulation as being Dio2, Ppp1r3c, Ppp1r3g, Sik1, and Nr4a1. By comparing their transcriptomic data with publicly available datasets, authors revealed that these genes were upregulated upon exposure to different stressors. Additionally, authors found that upregulation of Ppp1r3c, Ppp1r3g, and Dio2 genes following swim stress was sustained from 90 min up to 2-4 hours after stress and that it was predominantly restricted to hippocampal astrocytes, while Sik1 and Nr4a1 genes showed a broader cellular expression and a sharp rise and fall in expression, within 90 min of stress onset.

Overall, the paper is well written and provides a useful inventory of dorsal and ventral hippocampal gene expression upregulated by activation of LC-NA system, which can be used as starting point for more functional studies related to the effects of stress-induced physiological and pathological changes. However, I believe that the study would have benefited of a more comprehensive analyses of sex differences. Experiments in females were conducted only in one experiment and analyses restricted to the ventral hippocampus. Although, the experiments were overall sound and the results broadly support the conclusion made, I think some methodological choices should be better explained and rationalized. For instance, the study focuses on identifying transcriptional changes in the hippocampus induced by stress-mediated activation of the LC-NA system, however NA release following stress exposure and pharmacological or optogenetic manipulation was mostly measured in the cortex. Furthermore, behavioral changes following systemic pharmacologic or chemogenetic manipulation were observed in the open field task immediately after peripheral injections of yohimbine or CNO, respectively. Is this timing sufficient for both drugs to cross the blood brain barrier and to exert behavioral effects? Finally, the study shows that activation of noradrenergic hippocampus-projecting LC neurons is sufficient to regulate the expression of several hippocampal genes, although the necessity of these projection to induce the observed transcriptional effects has been tested to some extent through systemic blockade of beta-adrenoceptor, I believe the study would have benefited of more selective (optogenetic or chemogenetic) necessity experiments.

Reviewer #2 (Public Review):

The new work from Lemcke et al. suggests that the infection with Influenza A virus causes such flu symptoms as sleepiness and loss of appetite through the direct action on the responsible brain region, the hypothalamus. To test this idea, the authors performed single-nucleus RNA sequencing of the mouse hypothalamus in controlled experimental conditions (0, 3, 7, and 23 days after intranasal infection) and analyzed changes in the gene expression in the specific cell populations. The key results are promising and spurring future research. After revision, the analysis was considerably improved. Alternative approaches were used for testing. Specifically, during the revision: 1) The annotation of cell types was considerably improved; 2) The authors performed an additional analysis comparing case-control studies (Cacoa), where they could partly confirm their earlier findings.

Reviewer #2 (Public Review):

Hong and collaborators investigated variations in the amount of synaptic proteins in plasma extracellular vesicles (EV) in Parkinson's Disease (PD) patients on one-year follow-up. Their findings suggest that plasma EV synaptic proteins may be used as clinical biomarkers of PD progression.

It is a preliminary study using semi-quantitative analysis of synaptic proteins.

The authors have a cohort of PD patients with clinical examination and a know-how on EV purification. Regarding this latter part, they may improve their description of EV purification. EV may be broken into smaller size EV after freezing. Does it explain the relatively small size in their EV preparation? Do the authors refer to the MISEV guidelines for EV purity? Regarding synaptic protein quantification, the choice of western blotting may not be the best one. ELISA and other multiplex arrays are available. How the authors do justify their choice? Do the authors try to sort plasma EV by membrane-associated neuronal EV markers using either vesicle sorting or immunoprecipitation?

Many technical aspects may be improved. Such technical questions weakened the authors' conclusions.

The discussion is pretty long to justify the data. It may be shortened by adding some information in the introduction.

Reviewer #2 (Public Review):

This paper uses a novel maze design to explore mouse navigation behaviour in an automated analogue of the Barnes maze. Overall I find the work to be solid, with the cleverly designed maze/protocol to be its major strength - however there are some issues that I believe should be addressed and clarified.

1. Whilst I'm generally a fan of the experimental protocol, the design means that internal odor cues on the maze change from trial to trial, along with cues external to the maze such as the sounds and visual features of the recording room, ultimately making it hard for the mice to use a completely allocentric spatial 'place' strategy to navigate. I do not think there is a way to control for these conflicts between reference frames in the statistical modelling, but I do think these issues should be addressed in the discussion.

2. Somewhat related - I could not find how the internal maze cues are moved for each trial to demarcate the new goal (i.e. the luminous cues) ? This should be clarified in the methods.

3. It appears some data is being withheld from Figures 2&3? E.g. Days 3/4 from Fig 2b-f and Days 1-5 on for Fig 3. Similarly, Trials 2-7 are excluded from Fig 3. If this is the case, why? It should be clarified in the main text and Figure captions, preferably with equivalent plots presenting all the data in the supplement.

4. I strongly believe the data and code should be made freely available rather than "upon reasonable request".

Reviewer #2 (Public Review):

The voltage-gated potassium channel KCNQ1/KCNE1 (IKs) plays important physiological functions, for instance in the repolarization phase of the cardiac action potential. Loss-of-function of KCNQ1/KCNE1 is linked to disease. Hence, KCNQ1/KCNE1 is a highlighted pharmacological target and mechanistic insights into how channel modulators enhance the function of the channel is of great interest. The authors have through several previous studies provided mechanistic insights into how small-molecule activators like ML277 act on KCNQ1. However, less is known about the binding site and mechanism of action of other type of channel activators, which require KCNE1 for their effect. In this study, Chan and co-workers use molecular dynamics approaches, mutagenesis and electrophysiology to propose an overall similar binding site for the KCNQ1/KCNE1 activators mefenamic acid and DIDS, located at the extracellular interface of KCNQ1 and KCNE1. The authors propose an induced-fit model for the binding site, which critically engages residues in the N-terminus of KCNE1. Moreover, the authors discuss possible mechanisms of action of how drug binding to this site may enhance channel function.

The authors address an important question, of broad relevance to researchers in the field. The manuscript is well written and the text easy to follow. A strength of the work is the parallel use of experimental and simulation approaches, which enables both functional testing and mechanistic predictions and interpretations. For instance, the authors have experimentally assessed the putative relevance of a large set of residues based on simulation predictions. A minor limitation is that not all residues of putative importance for drug binding/effects can be reliable evaluated in experiments, which is, however, clearly discussed by the authors and a challenge shared by electrophysiologists in the field.

Reviewer #2 (Public Review):

Tejeda Muñoz et al. investigate the intersection of Wnt signaling, macropinocytosis, lysosomes, focal adhesions and membrane trafficking in embryogenesis and cancer. Following up on their previous papers, the authors present evidence that PMA enhances Wnt signaling and embryonic patterning through macropinocytosis. Proteins that are associated with the endo-lysosomal pathway and Wnt signaling are co-increased in colorectal cancer samples, consistent with their pro-tumorigenic action. The function of macropinocytosis is not well understood in most physiological contexts, and its role in Wnt signaling is intriguing. The authors use a wide range of models - Xenopus embryos, cancer cells in culture and in xenografts and patient samples to investigate several endolysosomal processes that appear to act upstream or downstream of Wnt. A downside of this broad approach is a lack of mechanistic depth. In particular, few experiments monitor macropinocytosis directly, and macropinocytosis manipulations have pleiotropic effects that are open alternative interpretations. Several experiments are confirmatory of previous findings; the manuscript could be improved by focusing on the novel relationship between PMA-induced macropinocytosis and better support these conclusions with additional experiments.

The authors use a range of inhibitors that suppress macropinosome formation (EIPA, Bafilomycin A1, Rac1 inhibition). However, these are not specific macropinocytosis inhibitors (EIPA blocks an Na+/H+ exchanger, which is highly toxic and perturbs cellular pH balance; Bafilomycin blocks the V-ATPase, which has essential functions in the Golgi, endosomes and lysosomes; Rac1 signals through multiple downstream pathways). A specific macropinocytosis inhibitor does not exist, and it is thus important to support key conclusions with dextran uptake experiments.

The title states that PMA increases Wnt signaling through macropinocytosis. However, the mechanistic relationship between PMA-induced macropinocytosis and Wnt signaling is not well supported. The authors refer to a classical paper that demonstrates macropinocytosis induction by PMA in macrophages (PMID: 2613767). Unlike most cell types, macrophages display growth factor-induced and constitutive macropinocytic pathways (PMID: 30967001). It would thus be important to demonstrate macropinocytosis induction by PMA experimentally in Xenopus embryos / cancer cells. Does treatment with EIPA / Bafilomycin / Rac1i decrease the dextran signal in embryos? In macrophages, the PKC inhibitor Calphostin C blocks macropinocytosis induction by PMA (PMID: 25688212). Does Calphostin C block macropinocytosis in embryos / cancer cells? Do the various combinations of Wnts / Wnt agonists and PMA have additive or synergistic effects on dextran uptake? If the authors want to conclude that PMA activates Wnt signaling, it would also be important to demonstrate the effect of PMA on Wnt target gene expression.

The experiments concerning macropinosome formation in Xenopus embryos are not very convincing. Macropinosomes are circular vesicles whose size in mammalian cells ranges from 0.2 - 10 µM (PMID: 18612320). The TMR-dextran signal in Fig. 1A does not obviously label structures that look like macropinosomes; rather the signal is diffusely localized throughout the dorsal compartment, which could be extracellular (or perhaps cytosolic). I have similar concerns for the cell culture experiments, where dextran uptake is only shown for SW480 spheroids in Fig. S2. It would be helpful to quantify size of the circular structures (is this consistent with macropinosomes?).

In Fig. 4I - J, the dramatic decrease in b-catenin and especially in Rac1 after overnight EIPA treatment is rather surprising. How do the authors explain these findings? Is there any evidence that macropinocytosis stabilizes Rac1? Could this be another effect of EIPA or general toxicity?

On a similar note, Fig. 6 K - L the FAK staining in control cells appears to localize to focal adhesions, but in PMA-treated cells is strongly localized throughout the cell. Do the authors have any thoughts on how PMA stabilizes FAK and where the kinase localizes under these conditions? Does PMA treatment increase FAK signaling activity?

The tumor stainings in Figure 5 are interesting but correlative. Pak1 functions in multiple cellular processes and Pak1 levels are not a direct marker for macropinocytosis. In the discussion, the authors discuss evidence that the V-ATPase translocates to the plasma membrane in cancer to drive extracellular acidification. To which extent does the Voa3 staining reflect lysosomal V-ATPase? Do the authors have controls for antibody specificity?

Reviewer #2 (Public Review):

The manuscript by Sun et al. applies the powerful technology of profiling viral DNA sequences in numerous anatomical sites in autopsy samples from participants who maintained their antiviral therapy up to the time of death. The sequencing is of high quality in using end-point dilution PCR to generate individual viral genomes. There is a thoughtful discussion, although there are points that we disagree with. This is an important data set that increases the scope of how the field thinks about the latent reservoir with a new look at the potential of a reservoir within the CNS.

1. The participants are very different in their exposure to HIV replication and disease progression. Participant 1 appears to have been on ART for most of the time after diagnosis of infection (16 years) and died with a high CD4 T cell count. The other two participants had only one year on ART and died with relatively low CD4 T cell counts (under 200). This could lead to differences in the nature of the reservoir. In this regard, the amount of DNA per million cells appears to be about 10-fold lower across the compartments sampled for participant 1. Also, one might expect fewer intact proviruses surviving after 16 years on ART compared to only 1 year on ART. The depth of sampling may be too limited and the number of participants too few to assess if these differences are features of these participants because of their different exposures to HIV replication. On the positive side, finding similarities across these big differences in participant profiles does reinforce the generalizability of the observations.

2. The following analysis will be limited by sampling depth but where possible it would be interesting to compare the ratio of intact to defective DNA. A sanctuary might allow greater persistence of cells with intact viral DNA even without viral replication (i.e. reduced immune surveillance). Detecting one or two intact proviruses in a tissue sample does not lend itself to a level of precision to address this question, but statistical tests could be applied to infer when there is sampling of 5 or more intact proviruses to determine if their frequency as a ratio of total DNA in different anatomical sites is similar or different. This would allow adjustment for the different amount of viral DNA in different compartments while addressing the question of the frequency of intact versus defective proviruses. One complication in this analysis is if there was clonal expansion of a cell with an intact genome which would represent a fortuitous over-representation intact genomes in that compartment.

3. The key point of this work is that the participants were on therapy up to the time of death ("enforcing" viral latency). The predominance of defective genomes is consistent with this assumption. Is there data from untreated infections to compare to as a signature of whether the viral DNA population was under selective pressure from therapy or not? Presumably untreated infections contain more intact DNA relative to total DNA. This would represent independent evidence that therapy was in place.

4. There are several points in Figure 5 to raise about V3 loop sequences. The analysis includes a large number of "undetermined" sequences that did not have a V3 loop sequence to evaluate. We would argue it is a fair assumption that the deleted proviruses have the same distribution of X4 and R5 sequences as the ones that have a V3 sequence to evaluate. In this view it would be possible to exclude the sequences for which there is no data and just look at the ratio of X4 and R5 in the different compartments, specifically does this ratio change in a statistically significant way in different compartments? The authors use "CCR5 and non-CCR5" as the two entry phenotypes. The evidence is pretty strong that the "other" coreceptor the virus routinely uses is CXCR4, and G2P is providing the FPR for X4 viruses. Perhaps the authors are trying to create some space for other coreceptors on microglia, but we are pretty sure what they are measuring is X4 viruses, especially in this late disease state of participant 2. Finally, we have previously observed that the G2P FPR score of <2 is a strong indicator of being X4, FPR scores between 2 and 10 have a 50% chance of being X4, and FPR scores above 10 are reliably R5 (PMID27226378). In addition, we observed that X4 viruses form distinct phylogenetic lineages. The authors might consider these features of X4 viruses in the evaluation of their sequences. Specifically, it would be helpful to incorporate the FPR scores of the reported X4 viruses.

5. We have puzzled over the many reports of different cell types in the CNS being infected. When we examined these cells types (both as primary cells and as iPSC-derived cells), all cells could be infected with a version of HIV that had the promiscuous VSV-G protein on the virus surface as a pseudotype. However, only macrophages and microglia could be infected using the HIV Env protein, and then only if it was the M-tropic version and not the T-tropic version (PMID35975998). RNAseq analysis was consistent with this biological readout in that only macrophages and microglia expressed CD4, neurons and astrocytes do not. From the virology point of view, astrocytes are no more infectable than neurons.

6. The brain gets exposed to virus from the earliest stages of infection but this is not synonymous with viral replication. Most of the time there is virus in the CSF but it is present at 1-10% of the level of viral load in the blood and phylogenetically it looks like the virus in the blood, most consistent with trafficking T cells, some of which are infected (PMID25811757). The fact that the virus in the blood is almost always T cell-tropic in needing a high density of CD4 for entry makes it unlikely that monocytes are infected (with their low density of CD4) and thus are not the source of virus found in the CNS. It seems much more likely that infected T cells are the "Trojan Horse" carrying virus into the CNS.

7. While all participants were taking antiretroviral therapy at the time of their death, they were not all suppressed when the tissues were collected. The authors are careful not to mention "suppressive ART" in the text, which is appreciated. However, the title should be changed to also reflect this fact.

Reviewer #2 (Public Review):

In this manuscript the authors established synapsin's E-domain as an essential functional binding partner that allows α-syn functionality. They show very elegantly that only synapsin isoforms that have an E-domain bind α-syn and allow the inhibition mediated by α-syn. Deletion of the C-terminus (α-syn 96-110) eliminated this interaction. Hence, synapsin E-domain binds to α-syn enabling the inhibitory effect of α-syn on synaptic transmission.

The paper will be improved significantly if additional experiments are added to expand and provide a more mechanistic understanding of the effect of α-syn and the intricate interplay between synapsin, α-syn, and the SV. For an enthusiastic reader, the manuscript as it looks now with only 3 figures, ends prematurely. Some of the experiments above or others could complement, expand and strengthen the current manuscript, moving it from a short communication describing the phenomenon to a coherent textbook topic. Nevertheless, this work provides new and exciting evidence for the regulation of neurotransmitter release and its regulation by synapsin and α-syn.

Reviewer #2 (Public Review):

In this manuscript, Mendana et al developed a multiplexing method - Targeted Genetically-Encoded Multiplexing or TaG-EM - by inserting a DNA barcode upstream of the polyadenylation site in a Gal4-inducible UAS-GFP construct. This Multiplexing method can be used for population-scale behavioral measurements or can potentially be used in single-cell sequencing experiments to pool flies from different populations. The authors created 20 distinctly barcoded fly lines. First, TaG-EM was used to measure phototaxis and oviposition behaviors. Then, TaG-EM was applied to the fly gut cell types to demonstrate its applications in single-cell RNA-seq for cell type annotation and cell origin retrieving.

This TaG-EM system can be useful for multiplexed behavioral studies from next-generation sequencing (NGS) of pooled samples and for Transcriptomic Studies. I don't have major concerns for the first application, but I think the scRNA-seq part has several major issues and needs to be further optimized.

Major concerns:<br /> 1. It seems the barcode detection rate is low according to Fig S9 and Fig 5F, J and N. Could the authors evaluate the detection rate? If the detection rate is too low, it can cause problems when it is used to decode cell types.<br /> 2. Unsuccessful amplification of TaG-EM barcodes: The authors attempted to amplify the TaG-EM barcodes in parallel to the gene expression library preparation but encountered difficulties, as the resulting sequencing reads were predominantly off-target. This unsuccessful amplification raises concerns about the reliability and feasibility of this amplification approach, which could affect the detection and analysis of the TaG-EM barcodes in future experiments.<br /> 3. For Fig 5, the singe-cell clusters are not annotated. It is not clear what cell types are corresponding to which clusters. So, it is difficult to evaluate the accuracy of the assignment of barcodes.<br /> 4. The scRNA-seq UMAP in Fig 5 is a bit strange to me. The fly gut epithelium contains only a few major cell types, including ISC, EB, EC, and EE. However, the authors showed 38 clusters in fig 5B. It is true that some cell types, like EE (Guo et al., 2019, Cell Reports), have sub-populations, but I don't expect they will form these many sub-types. There are many peripheral small clusters that are not shown in other gut scRNA-seq studies (Hung et al., 2020; Li et al., 2022 Fly Cell Atlas; Lu et al., 2023 Aging Fly Cell Atlas). I suggest the authors try different data-processing methods to validate their clustering result.<br /> 5. Different gut drivers, PMC-, PC-, EB-, EC-, and EE-GAL4, were used. The authors should carefully characterize these GAL4 expression in larval guts and validate sequencing data. For example, does the ratio of each cell type in Fig 5B reflect the in vivo cell type ratio? The authors used cell-type markers mostly based on the knowledge from adult guts, but there are significant morphological and cell ratio differences between larval and adult guts (e.g., Mathur...Ohlstein, 2010 Science).<br /> 6. Doublets are removed based on the co-expression of two barcodes in Fig 5A. However, there are also other possible doublets, for example, from the same barcode cells or when one cell doesn't have detectable barcode. Did the authors try other computational approaches to remove doublets, like DoubleFinder (McGinnis et al., 2019) and Scrublet (Wolock et al., 2019)?<br /> 7. Did the authors remove ambient RNA which is a common issue for scRNA-seq experiments?<br /> 8. Why does TaG-EM barcode #4, driven by EC-GAL4, not label other classes of enterocyte cells such as betaTry+ positive ECs (Figures 5D-E)? similarly, why does TaG-EM barcode #9, driven by EE-GAL4, not label all EEs? Again, it is difficult to evaluate this part without proper data processing and accurate cell type annotation.<br /> 9. For Figure 2, when the authors tested different combinations of groups with various numbers of barcodes. They found remarkable consistency for the even groups. Once the numbers start to increase to 64, barcode abundance becomes highly variable (range of 12-18% for both male and female). I think this would be problematic because the differences seen in two groups for example may be due to the barcode selection rather than an actual biologically meaningful difference.<br /> 10. Barcode #14 cannot be reliably detected in oviposition experiment. This suggests that the BC 14 fly line might have additional mutations in the attp2 chromosome arm that affects this behavior. Perhaps other barcode lines also have unknown mutations and would cause issues for other untested behaviors. One possible solution is to back-cross all 20 lines with the same genetic background wild-type flies for >7 generations to make all these lines to have the same (or very similar) genetic background. This strategy is common for aging and behavior assays.

• for: climate clock
• comment
  • We are already, in fact a highly interdependent species.
  • We are so specialized that if the precarious system were to fail,
    • few have the breadth of knowledge to survive, much less thrive on their own.
  • The key shift that is required is therefore not from a siloed to an interdependent one as it is from
    • an unhealthy and exploitative interdependence to
    • a healthy one based on holistic wellbeing

Reviewer #2 (Public Review):

The manuscript by Petitgas et al demonstrates that loss of function for the only enzyme responsible for the purine salvage pathway in fruit-flies reproduces the metabolic and neurologic phenotypes of human patients with Lesch-Nyhan disease (LND). LND is caused by mutations in the enzyme HGPRT, but this enzyme does not exist in fruit-flies, which instead only have Aprt for purine recycling. They demonstrate that mutants lacking the Aprt enzyme accumulate uric acid, which like in humans can be rescued by feeding flies allopurinol, and have decreased longevity, locomotion and sleep impairments and seizures, with striking resemblance to HGPRT loss of function in humans. They demonstrate that both loss of function throughout development or specifically in the adult ubiquitously or in all neurons, or dopaminergic neurons, mushroom body neurons or glia, can reproduce the phenotypes (although knock-down in glia does not affect sleep). They show that the phenotypes can be rescued by over-expressing a wild-type form of the Aprt gene in neurons. They identify a decrease in adenosine levels as the cause underlying these phenotypes, as adenosine is a neurotransmitter functioning via the purinergic adenosine receptor in neurons. In fact, feeding flies throughout development and in the adult with either adenosine or m6A could prevent seizures. They also demonstrate that loss of adenosine caused a secondary up-regulation of ENT nucleoside transporters and of dopamine levels, that could explain the phenotypes of decreased sleep and hyperactivity and night. Finally, they provide the remarkable finding that over-expression of the human mutant HGPRT gene but not its wild-type form in neurons impaired locomotion and induced seizures. This means that the human mutant enzyme does not simply lack enzymatic activity, but it is toxic to neurons in some gain-of-function form. Altogether, these are very important and fundamental findings that convincingly demonstrate the establishment of a Drosophila model for the scientific community to investigate LND, to carry out drug testing screens and find cures.

The experiments are conducted with great rigour, using appropriate and exhaustive controls, and on the whole the evidence does convincingly or compellingly support the claims. The exception is an instance when authors mention 'data not shown' and here data should either be provided, or claims removed: "feeding flies with adenosine or m6A did not rescue the SING phenotype of Aprt mutants (data not shown)". It is important to show these data (see below).

Sleep is used to refer to lack of movement of flies to cross a beam for more than 5 minutes. However, lack of movement does not necessarily mean the flies are asleep, as they could be un-motivated to move (which could reflect abnormal dopamine levels) or engaged in incessant grooming instead. These differences are important for future investigation into the neural circuits affect by LND.

The authors claim that based on BLAST genome searchers, there are no HPRTI (encoding HGPRT) homologues in Drosophila. However, such a claim would require instead structure-based searches that take into account structural conservation despite high sequence divergence, as this may not be detected by regular BLAST.

This work raises important questions that still need resolving. For example, the link between uric acid accumulation, reduced adenosine levels, increased dopamine and behavioural neurologic consequences remain unresolved. It is important that they show that restoring uric acid levels does not rescue locomotion nor seizure phenotypes, as this means that this is not the cause of the neurologic phenotypes. Instead, their data indicate adenosine deficiency is the cause. However, one weakness is that for the manipulations they test some behaviours but not all. The authors could attempt to improve the link between mechanism and behaviour by testing whether over-expression of Aprt in neurons or glia, throughout development or in the adult, and feeding with adenosine and m6A can rescue each of the behavioural phenotypes handled: lifespan, SING, sleep and seizures. The authors could also attempt to knock-down dopamine levels concomitantly with feeding with adenosine or m6A to see if this rescues the phenotypes of SING and sleep. Visualising the neural circuits that express the adenosine receptor could reveal why the deficit in adenosine can affect distinct behaviours differentially, and which neurologic phenotypes are primary and which secondary consequences of the mutations. This would allow them to carry out epistasis analysis by knocking-down AdoR in specific circuits, whilst at the same time feeding Aprt mutants with Adenosine.

The revelation that the mutant form of human HGPRT has toxic effects is very intriguing and important and it invites the community to investigate this further into the future.

To conclude, this is a fundamental piece of work that opens the opportunity for the broader scientific community to use Drosophila to investigate LND.

Reviewer #2 (Public Review):

In this manuscript Sangaram et al provide a systematic methodology and pipeline for benchmarking cell type deconvolution algorithms for spatial transcriptomic data analysis in a reproducible manner. They developed a tissue pattern simulator that starts from single-cell RNA-seq data to create silver standards and used spatial aggregation strategies from real in situ-based spatial technologies to obtain gold standards. By using several established metrics combined with different deconvolution challenges they systematically scored and ranked 11 deconvolution methods and assessed both functional and usability criteria. Altogether, they present a reusable and extendable platform and reach very similar conclusions to other deconvolution benchmarking paper, including that RCTD, SpatialDWLS and Cell2location typically provide the best results.

More specifically, the authors of this study sought to construct a methodology for benchmarking cell type deconvolution algorithms for spatial transcriptomic data analysis in a reproducible manner. The authors leveraged publicly available scRNA-seq, seqFISH, and STARMap datasets to create synthetic spatial datasets modeled after that of the Visium platform. It should be noted that the underlying experimental techniques of seqFISH and STARMap (in situ hybridization) do not parallel that of Visium (sequencing), which could bias simulated data. Furthermore, to generate the ground truth datasets cells and their corresponding count matrix are represented by simple centroids. Although this simplifies the analysis it might not necessarily accurately reflect Visium spots where cells could lie on a boundary and affect deconvolution results. On the other hand, the authors state that in silver standard datasets one half of the scRNA-seq data was used for simulation and the other half was used as a reference for the algorithms, but the method of splitting the data, i.e., at random or proportionally by cell type, was not specified. Supplying optimal reference data is important to achieve best performance, as the authors note in their conclusions.

The authors thoroughly and rigorously compare methods while addressing situational discrepancies in model performance, indicative of a strong analysis. The authors make a point to address both inter- and intra- dataset reference handling, which has a significant impact on performance. Major strengths of the simulation engine include the ability to downsample and recapitulate several cell and tissue organization patterns.

It's important to realize that deconvolution approaches are typically part of larger exploratory data analysis (EDA) efforts and require users to change parameters and input data multiple times. Furthermore, many users might not have access to more advanced computing infrastructure (e.g. GPU) and thus running time, computing needs, and scalability are probably key factors that researchers would like to consider when looking to deconvolve their datasets.

The authors achieve their aim to benchmark different deconvolution methods and the results from their thorough analysis support the conclusions that many methods are still outperformed by bulk deconvolution methods. This study further informs the need for cell type deconvolution algorithms that can handle both cell abundance and rarity throughout a given tissue sample.

The reproducibility of the methods described will have significant utility for researchers looking to develop cell type deconvolution algorithms, as this platform will allow simultaneous replication of the described analysis and comparison to new methods.

Reviewer #2 (Public Review):

This work follows in the footsteps of earlier work showing that BMI prediction accuracy can vary dramatically by context, even within a relatively ancestrally homogenous sample. This is an important observation that is worth the extension to different context variables and samples.

Much of the follow-up analyses are commendably trying to take us a step further-towards explaining the underlying observed trends of variable prediction accuracy for BMI. Some of these analyses, however, are somewhat confounded and hard to interpret.

For example, many of the covariates which the authors use to stratify the sample by may drive range restriction effects. Further, the covariates considered could be causally affected by genotype and causally affect BMI, with reverse causality effects; other covariates may be partially causally affected by both genotype and BMI, resulting in collider bias. Finally, population structure differences between quintiles of a covariate may drive variable levels of stratification. These can bias estimation and confounds interpretations, at least one of which intuitively seems like a concern for each of the context variables (e.g., the covariates SES, LDL, diet, age, smoking, and alcohol drinking).

The increased prediction accuracy observed with some of the age-dependent prediction models is notable. Despite the clear utility of this investigation, I am not aware of much existing work that shows such improvements for context-aware prediction models (compared to additive/main effect models). I would be curious to see if the predictive utility extends to held-out data from a data set distinct from the UKB, where the model was trained, or whether it replicates when predicting variation within families. Such analyses could strengthen the evidence for these models capturing direct causal effects, rather than other reasons for the associations existing in the UKB sample.

Reviewer #2 (Public Review):

In this study the authors aim to elucidate the role of RAPSYN in BCR-ABL-mediated leukemogenesis. RAPSYN is mainly known as a scaffolding protein for anchoring acetylcholine receptors (AChRs) to the cytoskeleton in muscle cells, facilitating AChR clustering through neddylation (Li et al., 2016). The authors demonstrate, through a broad and rigorous array of biochemical assays, that RAPSYN also plays a crucial role in the neddylation of BCR-ABL in leukemia cells. Their results indicate that this process shields BCR-ABL from ubiquitination and subsequent degradation, likely through a mechanism involving competition for binding with the BCR-ABL ubiquitin ligase c-CBL. In addition, the authors delve into the regulatory mechanisms underlying RAPSYN stability, demonstrating that it is enhanced through phosphorylation by SRC. This discovery further deepens our understanding of the complex dynamics of the molecular interactions that regulate BCR-ABL stability in leukemia.

To confirm the physiological significance of their findings, the authors effectively utilize cell viability assays and in vivo models. The integration of these approaches lends strength and validity to their conclusions.

The implications of the findings presented in this study are important, particularly in relation to our understanding of the pathogenesis and potential therapeutic strategies for Philadelphia chromosome-positive leukemias. By illuminating the role of RAPSYN in the regulation of BCR-ABL stability, this research potentially uncovers avenues for the development of targeted therapies, making a significant contribution to the field.

Most of the conclusions drawn in this paper are well supported by data, but some aspects of the data need to be clarified and extended:

1) The authors propose that targeting RAPSYN in Ph+ leukemia could have a high therapeutic index, suggesting that inhibition of RAPSYN may lead to cytotoxicity in Ph+ leukemia with high specificity and minimal side effects. To substantiate this assertion, the authors should investigate the impact on cell viability upon RAPSYN knockdown in non-Ph leukemic cell lines or HS-5 cells (similar to Figure 1C), despite their lower RAPSYN protein levels.

2) The authors intriguingly show that the protein levels of RAPSYN are significantly enriched in Ph+ patient samples and cell lines (Figure 1A,B), even though the mRNA levels remain unchanged (Supplementary Figure 1 A-C). This observation merits a clear explanation in the context of the presented results. The data in the manuscript does imply a feedforward loop mechanism (Figure 7), where BCR-ABL activates SRC, which subsequently stabilizes RAPSYN, which in turn helps protect BCR-ABL from c-CBL-mediated degradation. If this is the working hypothesis, it would be beneficial for the reader to see supporting evidence.

3) The authors present compelling evidence to suggest that RAPSYN may possess direct NEDD8-ligase activity on BCR-ABL. To strengthen this claim, it may be valuable to conduct further assays involving a ligase-deficient mutant, such as C366A, beyond its use in Figure 2J. Incorporating this mutant into the in vitro assay illustrated in Figure 2K, for instance, could offer substantial validation for the claim. In addition, showing whether the ligase-deficient mutant is capable of phenocopying the phosphorylation-mutant Y336F, as showcased in Figures 5E, F, and 6D, F, would be beneficial.

4) The observations presented in Figures 6 C-G require additional clarification. Notably, there are discrepancies in relative cell viability effects in K562 cells, and to some extent in MEG-01 cells, under conditions that are indicated as being either identical or highly similar. For instance, this inconsistency is observable when comparing the left panels of Figure 6C and 6D in the case of NC overexpression + shSRC#2, and the left panels of Figure 6E and 6G with NC overexpression or shNC, respectively. Listing potential causes of these discrepancies would strengthen the overall validity of the findings and their subsequent interpretation.

5) Throughout the manuscript, immunoblots which showcase immunoprecipitations of BCR-ABL or His-BCR-ABL depict poly-neddylation (e.g. Figures 2E-M, 3D-G, and 5A-E) and poly-ubiquitination (e.g. Figures 3D-G) patterns/smears where these patterns seem to extend below the molecular weight of BCR-ABL. To enhance clarity, it would be valuable for the authors to provide an explanation in the text or the figure legend for this observation. Is it reflective of potential degradation of BCR-ABL or is there another explanation behind it?

Reviewer #2 (Public Review):

This is a very nice study showing how partial loss of vestibular function leads to long term alterations in behavioural responses of mice. Specifically, the authors show that VOR involving both canal and otolith afferents are strongly attenuated following treatment and partially recover. The main result is that loss of VOR is partially "compensated" by increased OKR in treated animals. Finally, the authors show that treatment primarily affects type I hair cells as opposed to type II. Overall, these results have potentially important implications for our understanding of how the VOR Is generated using input from both type I and type II hair cells. As detailed below however, more controls as well as analyses are needed.

Major points:

The authors analyze both canal and otolith contributions to the VOR which is great. There is however an asymmetry in the way that the results are presented in Figure 1. Please correct this and show time series of fixations for control and at W6 and W12. Moreover, the authors are plotting table and eye position traces in Fig. 1B but, based on the methods, gains are computed based on velocity. So please show eye velocity traces instead. Also, what was the goodness of fit of the model to the trace at W6? If lower than 0.5 then I think that it is misleading to show such a trace since there does not seem to be a significant VOR. This is important to show that the loss is partial as opposed to total. It seems to me that the treatment was not effective at all for aVOR for at least some animals. What happens if these are not included in the analysis?

Figure 2A shows a parallel time course for gains of aVOR and OCR at the population level. Is this also seen at the individual level?

Figure 3: please show individual datapoints in all conditions.

Figure 4: The authors show both gain and phase for OKR. Why not show gain and phase for aVOR and OCR in Figure 1. I realize that phase is shown in sup Figures but it is important to show in main figures. The authors show a significant increase in phase lead for aVOR but no further mention is made of this in the discussion. Moreover, how are the authors dealing with the fact that, as gain gets smaller, the error on the phase will increase. Specifically, what happens when the grey datapoints are not included?

Discussion: As mentioned above, the authors should discuss the mechanisms and implications of the observed phase lead following treatment. Moreover, recent literature showing that VN neurons that make the primary contribution to the VOR (i.e., PVP neurons) tend to show more regular resting discharges than other classes (i.e., EH cells), and that such regularity is needed for the VOR should be discussed (Mackrous et al. 2020 eLife). Specifically, how are type I and type II hair cells related to discharge regularity by central neurons in VN?

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microscopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensitized by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells. This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensitizing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microscopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microscopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from static measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #2 (Public Review):

The manuscript describes a large scale study of 8 eye tracking tasks in a large cohort of 18 month old children. The dataset is impressive and allows a comparison across children in different tasks that assess social, endogenous, and exogenous attention tasks. As such, it provides a benchmark for future studies that examine eye movements within different cohorts of children and across development and offers exciting possibilities to correlate these measures with behavior, other measures of motor and neural development, and to compare these measures with children diagnosed with neurodevelopmental disorders.

It does seem like additional insights can be gained from the study that could potentially address important topics in development, attention, and eye movements. Which components of attention are similar and in what way? The distinction between social vs non social is interesting but not ground breaking (e.g., the preference of toddlers to attend to faces); maybe looking at specific sub-tasks and clusters of participants the study can reveal new insights about the differences and similarities across tasks. The manuscript describes the importance of characterizing profiles of attention and individual differences, what kind of profiles are found in the study? Are there different profiles among this large cohort?<br /> Moreover, to allow comparison across analysis methods, ages, and neurodevelopmental disorders, it is important that the full dataset will be available online (i.e., all eye tracking data not just the metrics) as well as the software to run tasks that should also be made available to encourage using the battery across different research communities.

Reviewer #2 (Public Review):

Sleep and memory are intertwined processes, with sleep-deprivation having a negative impact on long-term memory in many species. Recently, the authors showed that fruit flies form sleep-dependent long-term appetitive memory only when fed. They showed that this context-dependent memory trace maps to the anterior-posterior (ap) α'β' mushroom body neurons (MBNs) (Chouhan et al., (2021) Nature). However, the molecular cascades induced during training that promote sleep and memory have remained enigmatic.

Here the authors investigate this issue by combining cell-specific transcriptomics, genetic perturbations, and measurements of sleep and memory. They identify an array of genes altered in expression following appetitive training. These genes are mainly downregulated, and predominantly encode regulators of transcription and RNA biosynthesis. This is a conceptually attractive finding given that long-term memory requires de novo protein translation.

The authors then screen these genes for novel regulators of sleep and memory. They show that one of these genes (Polr1F) acts in ap α'β' MBNs to promote wakefulness, while another (Regnase-1) promotes sleep. They also identify a specific role for Regnase-1 in ap α'β' MBNs in regulating short- and long-term memory formation, and demonstrate that Pol1rF inhibits translation throughout the fly brain.

The analyses of molecular alterations in ap α'β' MBNs are interesting and impressive. However, caveats remain regarding the effect of Polr1F and Regnase-1 on sleep. There are significant differences in the impact of Polr1F knockdown on sleep between datasets, and from the data currently presented, it is unclear whether Polr1F and Regnase-1 might also play important developmental roles in ap α'β' MBNs that influence sleep. These caveats can be readily addressed by additional experiments that would enhance the robustness of the manuscript.

Reviewer #2 (Public Review):

This manuscript focuses on the clinical impact of subjective experience or treatment with transcranial magnetic stimulation and transcranial direct current stimulation studies with retrospective analyses of 4 datasets. Subjective experience or treatment refers to the patient level thought of receiving active or sham treatments. The analyses suggest that subjective treatment effects are an important and under appreciated factor in randomized controlled trials. The authors present compelling evidence that has significance in the context of other modalities of treatment, treatment for other diseases, and plans for future randomized controlled trials. Other strengths included a rigorous approach and analyses. Some aspects of the manuscript are underdeveloped and the findings are over interpreted. Thank you for your efforts and the opportunity to review your work.

Reviewer #2 (Public Review):

The authors applied two visual working memory tasks, a memory-guided localization (MGL), examining short-term memory of the location of an item over a brief interval, and an N-back task, examining orientation of a centrally presented item, in order to test working memory performance in patients with multiple sclerosis (including a subgroup with relapsing-remitting and one with secondary progressive MS), compared with healthy control subjects. The authors used an approach in testing and statistically modelling visual working memory paradigm previously developed by Paul Bays, Masud Husain and colleagues. Such continuous measure approaches make it possible to quantify the precision, or resolution, of working memory, as opposed to measuring working memory using discretised, all-or-none measures.

The authors of the present study found that both MS subgroups performed worse than controls on the N-back task and that only the secondary progressive MS subgroup was significantly impaired on the MGL task. The underlying sources of error including incorrect association of an object's identity with its location or serial order, were also examined.

The application of more precise psychophysiological methods to test visual working memory in multiple sclerosis should be applauded. It has the potential to lead to more sensitive and specific tests which could potentially be used as useful outcome measures in clinical trials of disease modifying drugs, for example.

However, there are some significant limitations which severely affect the scientific validity and interpretability of the study:

a) There is a striking lack of key clinical information. The inclusion and exclusion criteria are unclear and a recruitment flowchart has not been provided. Therefore it is unclear what proportion of MS patients were ineligible due to, for example, visual impairment. Basic clinical data such as EDSS scores, disease duration, treatment history, and performance on standard cognitive testing were not provided. Basic clinical and demographic data for each subgroup were not provided in a clear format. This severely limits the interpretability of the study and its significance for this clinical population. For example, might it be that the SPMS patients performed worse on the MGL task because they were more cognitively impaired than RRMS patients? That question might be easily answered, but the answer is unclear based on the data provided.

b) The study is completely agnostic to the underlying pathophysiology. There is no neuroimaging available, therefore it is unclear how the specific working memory impairments observed might relate to lesioned underlying brain networks which are crucial for specific aspects of working memory. This severely limits the scientific impact of the results. This limitation is acknowledged by the authors, but the authors did not put forward any hypotheses on how their results may be underpinned by the underlying disease processes.

c) The present study does not compare the continuous-report testing with a discrete measure task so it is unclear if the former is more sensitive, or more feasible in this patient group, although this may not have been the purpose of the study.

Reviewer #2 (Public Review):

The authors present an image-analysis pipeline for mother-machine data, i.e., for time-lapses of single bacterial cells growing for many generations in one-dimensional microfluidic channels. The pipeline is available as a plugin of the python-based image-analysis platform Napari. The tool comes with two different previously published methods to segment cells (classical image transformation and thresholding as well as UNet-based analysis), which compare qualitatively and quantitatively well with the results of widely accessible tools developed by others (BACNET, DelTA, Omnipose). The tool comes with a graphical user interface and example scripts, which should make it valuable for other mother-machine users, even if this has not been demonstrated yet.

The authors also add a practical overview of how to prepare and conduct mother-machine experiments, citing their previous work and giving more advice on how to load cells using centrifugation. However, the latter part lacks detailed instructions.

Finally, the authors emphasize that machine-learning methods for image segmentation reproduce average quantities of training datasets, such as the length at birth or division. Therefore, differences in training can propagate to difference in measured average quantities. This result is not surprising and is normally considered a desired property of any machine-learning algorithm as also commented on below.

Points for improvement:<br /> Different datasets: The authors demonstrate the use of their method for bacteria growing in different growth conditions in their own microscope. However, they don't provide details on whether they had to adjust image-analysis parameters for each dataset. Similarly, they say that their method also works for other organisms including yeast and C. elegans (as part of the Results section) but they don't show evidence nor do they write whether the method needs to be tuned/trained for those datasets. Finally, they don't demonstrate that their method works on data from other labs, which might be different due to differences in setup or imaging conditions.

Bias due to training sets:<br /> The bias in ML-methods based on training datasets is not surprising but arguably a desired property of those methods. Similarly, threshold-based classical segmentation methods are biased by the choice of threshold values and other segmentation parameters. A point that would have profited from discussion in this regard: How to make image segmentation unbiased, that is, how to deliver physical cell boundaries? This can be done by image simulations and/or by comparison with alternative methods such as fluorescence microscopy.

The authors stress the user-friendliness of their method in comparison to others. For example, they write: 'Unfortunately, many of these tools present a steep learning curve for most biologists, as they require familiarity with command line tools, programming, and image analysis methods.' I suggest to instead emphasize that many of the tools published in recent years are designed to be very use friendly. And as will all methods, MM3 also comes at a prize, which is to install Napari followed by the installation of MM3, which, according to their own instructions, is not easy either.

Reviewer #2 (Public Review):

This manuscript investigates the mechanism behind the accumulation of phytosphingosine (PHS) and its role in triggering vacuole fission. The study proposes that membrane contact sites (MCSs) are involved in two steps of this process. First, tricalbin-tethered MCSs between the endoplasmic reticulum (ER) and the plasma membrane (PM) or Golgi modulate the intracellular amount of PHS. Second, the accumulated PHS induces vacuole fission, most likely via the nuclear-vacuolar junction (NVJ). The authors suggest that MCSs regulate vacuole morphology through sphingolipid metabolism.<br /> While some of the results in the manuscript are interesting the overall logic is hard to follow. In my assessment of the manuscript, my primary concern lies in its broad conclusions which, in my opinion, exceed the available data and raise doubts. Here are some instances where this comes into play for this manuscript:

2.) Major points for revision

1.) The rationale to start investigating a vacuolar fission phenotype in the beginning is very weak. It is basically based on a negative genetic interaction with NVJ1. Based on this vacuolar fragmentation is quantified. The binning for the quantifications is already problematic as, in my experience, WT cells often harbor one to three vacuoles. How are quantifications looking when 1-3 vacuoles are counted as "normal" and more than 3 vacuoles as "fragmented"? The observed changes seem to be relatively small and the various combinations of TCB mutants do not yield a clear picture.<br /> 2.) The analysis of the structural requirements of the Tcb3 protein is interesting but does not seem to add any additional value to this study. While it was used to quantify the mild vacuolar fragmentation phenotype it does not reoccur in any following analysis. Is the tcb3Δ sufficient to yield the lipid phenotype that is later proposed to cause the vacuolar fragmentation phenotype?<br /> 3.) The quantified lipid data also has several problems. i) The quantified effects are very small. The relative change in lipid levels does not allow any conclusion regarding the phenotypes. What is the change in absolute PHS in the cell. This would be important to know for judging the proposed effects. ii) It seems as if the lipid data is contradictory to the previous study from the lab regarding the role of tricalbins in ceramide transfer. Previously it was shown that ceramides remain unchanged and IPC levels were reduced. This was the rationale for proposing the tricalbins as ceramide transfer proteins between the ER and the mid-Golgi. What could be an explanation for this discrepancy? Does the measurement of PHS after labelling the cells with DHS just reflect differences in the activity of the Sur2 hydroxylase or does it reflect different steady state levels.<br /> 4.) Determining the vacuole fragmentation phenotype of a lag1Δlac1Δ double mutant does not allow the conclusion that elevated PHS levels are responsible for the observed phenotype. This just shows that lag1Δlac1Δ cells have fragmented vacuoles. Can the observed phenotype be rescued by treating the cells with myriocin? What is the growth rate of a LAG1 LAC1 double deletion as this strain has been previously reported to be very sick. Similarly, what is the growth phenotype of the various LCB3 LCB4 and LCB5 deletions and its combinations.<br /> 5.) The model in Figure 3 E proposes that treatment with PHS accumulates PHS in the endoplasmic reticulum. How do the authors know where exogenously added PHS ends up in the cell? It would also be important to determine the steady state levels of sphingolipids after treatment with PHS. Or in other words, how much PHS is taken up by the cells when 40 µM PHS is added?<br /> 6.) Previous studies have observed that myriocin treatment itself results in vacuolar fragmentation (e.g. Hepowit et al. biorXivs 2022, Fröhlich et al. eLife 2015). Why does both, depletion and accumulation of PHS lead to vacuolar fragmentation?<br /> 7.) The experiments regarding the NVJ genes are not conclusive. While the authors mention that a NVJ1/2/3 MDM1 mutant was shown to result in a complete loss of the NVJ the observed effects cannot be simply correlated. It is also not clear why PHS would be transported towards the vacuole. In the cited study (Girik et al.) the authors show PHS transport from the vacuole towards the ER. Here the authors claim that PHS is transported via the NVJ towards the vacuole. Also, the origin of the rationale of this study is the negative genetic interaction of tcb1/2/3Δ with nvj1. This interaction appears to result in a strong growth defect according to the Developmental Cell paper. What are the phenotypes of the mutants used here? Does the additional deletion of NVJ genes or MDM1 results in stronger growth phenotypes?<br /> 8.) As a consequence of the above points, several results are over-interpreted in the discussion. Most important, it is not clear that indeed the accumulation of PHS causes the observed phenotypes.

Reviewer #2 (Public Review):

It is well known that DMRT proteins and more specifically, DMRT1 plays a key role in sex determination processes of many species. While DMRT1 has been shown to be critical for the sex determination of fish, birds, and reptiles, it seems less crucial at the sex determination stages of the mice. It is important though for adult sex maintenance in mice.

Unlike its minor role in mouse sex determination, it seems that variants in DMRT1 in humans cause 46, XY DSD and sex reversal.

The paper by Dujardin et al., is a beautiful study that provides an answer to this long-lasting discrepancy of the difference between the two common mammal species: human and mouse. It is a really nice example of how working with other mammal species, like the rabbit, could serve as a nice model for understanding mammalian sex determination.

In this study the researchers first described the expression patterns of DMRT1 in the rabbit XY and XX gonads throughout the window of sex determination.

They then used CRISPR/Cas9 to generate DMRT1 KO rabbits and analysed the phenotype in XY and XX rabbits. They show that XY rabbits present with complete XY male-to-female sex reversal, very similar to what was observed in human 46, XY DSD patients (but not the mice model). They further show that in the XY sex-reversed gonads, germ cells fail to enter meiosis. They next analysed XX gonads and while there is no major effect on sex determination (as expected), the germ cells in these ovaries fail to enter meiosis, highlighting the critical role that DMRT1 has in germ cells.

I think it is really important that we start to embrace other mammal models that are not the mouse as we find many instances that the mouse is not the optimal system for understanding human sex determination. The study is well explained and presented. The data is clear, and the paper is fluent to read.

Reviewer #2 (Public Review):

Lin et al attempt to examine the role of lncRNAs in human evolution in this manuscript. They apply a suite of population genetics and functional genomics analyses that leverage existing data sets and public tools, some of which were previously built by the authors, who clearly have experience with lncRNA binding prediction. However, I worry that there is a lack of suitable methods and/or relevant controls at many points and that the interpretation is too quick to infer selection. While I don't doubt that lnc RNAs contribute to the evolution of modern humans, and certainly agree that this is a question worth asking, I think this paper would benefit from a more rigorous approach to tackling it.

At this point, my suggestions are mostly focused on tightening and strengthening the methods; it is hard for me to predict the consequence of these changes on the results or their interpretation, but as a general rule I also encourage the authors to not over-interpret their conclusions in terms of what phenotype was selected for when as they do at certain points (eg glucose metabolism).

I note some specific points that I think would benefit from more rigorous approaches, and suggest possible ways forward for these.

1. Much of this work is focused on comparing DNA binding domains in human-unique long-noncoding RNAs and DNA binding sites across the promoters of genes in the human genome, and I think the authors can afford to be a bit more methodical/selective in their processing and filtering steps here. The article begins by searching for orthologues of human lncRNAs to arrive at a set of 66 human-specific lncRNAs, which are then characterised further through the rest of the manuscript. Line 99 describes a binding affinity metric used to separate strong DBS from weak DBS; the methods (line 432) describe this as being the product of the DBS or lncRNA length times the average Identity of the underlying TTSs. This multiplication, in fact, undoes the standardising value of averaging and introduces a clear relationship between the length of a region being tested and its overall score, which in turn is likely to bias all downstream inference, since a long lncRNA with poor average affinity can end up with a higher score than a short one with higher average affinity, and it's not quite clear to me what the biological interpretation of that should be. Why was this metric defined in this way?

2. There is also a strong assumption that identified sites will always be bound (line 100), which I disagree is well-supported by additional evidence (lines 109-125). The authors show that predicted NEAT1 and MALAT1 DBS overlap experimentally validated sites for NEAT1, MALAT1, and MEG3, but this is not done systematically, or genome-wide, so it's hard to know if the examples shown are representative, or a best-case scenario.

It's also not quite clear how overlapping promoters or TSS are treated - are these collapsed into a single instance when calculating genome-wide significance? If, eg, a gene has five isoforms, and these differ in the 3' UTR but their promoter region contains a DBS, is this counted five times, or one? Since the interaction between the lncRNA and the DBS happens at the DNA level, it seems like not correcting for this uneven distribution of transcripts is likely to skew results, especially when testing against genome-wide distributions, eg in the results presented in sections 5 and 6. I do not think that comparing genes and transcripts putatively bound by the 40 HS lncRNAs to a random draw of 10,000 lncRNA/gene pairs drawn from the remaining ~13500 lncRNAs that are not HS is a fair comparison. Rather, it would be better to do many draws of 40 non-HS lncRNAs and determine an empirical null distribution that way, if possible actively controlling for the overall number of transcripts (also see the following point).

3. Thresholds for statistical testing are not consistent, or always well justified. For instance, in line 142 GO testing is performed on the top 2000 genes (according to different rankings), but there's no description of the background regions used as controls anywhere, or of why 2000 genes were chosen as a good number to test? Why not 1000, or 500? Are the results overall robust to these (and other) thresholds? Then line 190 the threshold for downstream testing is now the top 20% of genes, etc. I am not opposed to different thresholds in principle, but they should be justified.

Likewise, comparing Tajima's D values near promoters to genome-wide values is unfair, because promoters are known to be under strong evolutionary constraints relative to background regions; as such it is not surprising that the results of this comparison are significant. A fairer comparison would attempt to better match controls (eg to promoters without HS lncRNA DBS, which I realise may be nearly impossible), or generate empirical p-values via permutation or simulation.

4. There are huge differences in the comparisons between the Vindija and Altai Neanderthal genomes that to me suggest some sort of technical bias or the such is at play here. e.g. line 190 reports 1256 genes to have a high distance between the Altai Neanderthal and modern humans, but only 134 Vindija genes reach the same cutoff of 0.034. The temporal separation between the two specimens does not seem sufficient to explain this difference, nor the difference between the Altai Denisovan and Neanderthal results (2514 genes for Denisovan), which makes me wonder if it is a technical artefact relating to the quality of the genome builds? It would be worth checking.

5. Inferring evolution: There are some points of the manuscript where the authors are quick to infer positive selection. I would caution that GTEx contains a lot of different brain tissues, thus finding a brain eQTL is a lot easier than finding a liver eQTL, just because there are more opportunities for it. Likewise, claims in the text and in Tables 1 and 2 about the evolutionary pressures underlying specific genes should be more carefully stated. The same is true when the authors observe high Fst between groups (line 515), which is only one possible cause of high Fst - population differentiation and drift are just as capable of giving rise to it, especially at small sample sizes.

Reviewer #2 (Public Review):

In this manuscript, the authors have screened the ReFRAME library and identified candidate small molecules that can activate YAP. The found that SM04690, an inhibitor of the WNT signaling pathway, could efficiently activate YAP through CLK2 kinase which has been shown to phosphorylate SR proteins to alter gene alternative splicing. They further demonstrated that SM04690 mediated alternative splicing of AMOTL2 and rendered it unlocalized on the membrane. Alternatively spliced AMOTL2 prevented YAP from anchoring to the cell membrane which results in decreased YAP phosphorylation and activated YAP. Previous findings showed that WNT signaling more or less activate YAP. The authors revealed that an inhibitor of WNT siganaling could activate YAP. Thus, these findings are potentially interesting and important. However, the present manuscript provided a lot of indirect data and lacked key experiments.

Major points:<br /> 1. In Figure S3, since inhibition of CLK2 resulted in extensive changes in alternative splicing, why did the authors choose AMOTL2? How to exclude other factors such as EEF1A1 and HSPA5, do they affect YAP activation? Angiomotin-related AMOTL1 and AMOTL2 were identified as negative regulators of YAP and TAZ by preventing their nuclear translocation. It has been reported that high cell density promoted assembly of the Crumbs complex, which recruited AMOTL2 to tight junctions. Ubiquitination of AMOTL2 K347 and K408 served as a docking site for LATS2, which phosphorylated YAP to promote its cytoplasmic retention and degradation. How to determine that alternative splicing rather than ubiquitination of AMOTL2 affects YAP activity? Does AMOTL2 Δ5 affect the ubiquitination of AMOTL2? Does overexpression of AMOTL2 Δ5Δ9 cause YAP and puncta to co-localize?<br /> 2. The author proposed that AMOTL2 splicing isoform formed biomolecular condensates,.However, there was no relevant experimental data to support this conclusion. AMOTL2 is located not only on the cell membrane but also on the circulating endosome of the cell, and the puncta formed after AMOTL2 dissociation from the membrane is likely to be the localization of the circulating endosome. The author should co-stain AMOTL2 with markers of circulating endosomes, or conduct experiments to prove the liquidity of puncta to verify the phase separation of AMOTL2 splicing isoform.<br /> 3. The localization of YAP in cells is regulated by cell density, and YAP usually translocates to the nucleus at low cell density. In Figure 2E, the cell densities of DMSO and SM04690-treated groups are inconsistent. In Figure 4A, the magnification of t DMSO and SM04690-treated groups is inconsistent, and the SM04690-treated group seems to have a higher magnification.<br /> 4. There have been many reports that the WNT signaling pathway and the Hippo signaling pathway can crosstalk with each other. The authors should exclude the influence of the WNT signaling pathway by using SM04690.

Reviewer #2 (Public Review):

In their study, Podkowik et al. elucidate the protective role of the accessory gene regulator (agr) system in Staphylococcus aureus against hydrogen peroxide (H2O2) stress. Their findings demonstrate that agr safeguards the bacterium by controlling the accumulation of reactive oxygen species (ROS), independent of agr activation kinetics. This protection is facilitated through a regulatory interaction between RNAIII and Rot, impacting virulence factor production and metabolism, thereby influencing ROS levels. Notably, the study highlights the remarkable adaptive capabilities of S. aureus conferred by agr. The protective effects of agr extend beyond the peak of agr transcription at high cell density, persisting even during the early log-phase. This indicates the significance of agr-mediated protection throughout the infection process. The absence of agr has profound consequences, as observed by the upregulation of respiration and fermentation genes, leading to increased ROS generation and subsequent cellular demise. Interestingly, the study also reveals divergent effects of agr deficiency on susceptibility to hydrogen peroxide compared to ciprofloxacin. While agr deficiency heightens vulnerability to H2O2, it also upregulates the expression of bsaA, countering the endogenous ROS induced by ciprofloxacin. These findings underscore the complex and context-dependent nature of agr-mediated protection. Furthermore, in vivo investigations using murine models provide valuable insights into the importance of agr in promoting S. aureus fitness, particularly in the context of neutrophil-mediated clearance, with notable emphasis on the pulmonary milieu. Overall, this study significantly advances our understanding of agr-mediated protection in S. aureus and sheds light on the sophisticated adaptive mechanisms employed by the bacterium to fortify itself against oxidative stress encountered during infection.

The conclusions of this paper are mostly well supported by the data; however, certain aspects regarding the impact of agr loss on bacterial metabolic status require additional experimental clarification.

1) The RNA-seq analysis revealed that the Δagr strain exhibited increased expression of genes involved in respiration and fermentation, suggesting enhanced energy generation. However, metabolic modeling based on transcriptomic data indicated a decrease in tricarboxylic acid (TCA) cycle and lactate flux per unit of glucose uptake in the Δagr mutant. Additionally, intracellular ATP levels were significantly lower in the Δagr mutant compared to the wild-type strain, despite the carbon being directed into an acetate-generating, ATP-yielding carbon "overflow" pathway. Furthermore, growth analysis in nutrient-constrained medium demonstrated a decrease in the growth rate and yield of the Δagr mutant. Given that S. aureus actively utilizes the electron transport chain (ETC) to replenish NAD pools during aerobic growth on glucose, supporting glycolytic flux and pyruvate dehydrogenase complex (PDHC) activity while restricting TCA cycle activity through carbon catabolite repression (CCR), it is suggested that the authors analyze glucose consumption rates in conjunction with the determination of intracellular levels of pyruvate, AcCoA, and TCA cycle intermediates such as citrate and fumarate. These additional experiments will provide valuable insights into the metabolic fate of glucose and pyruvate and their subsequent impact on cellular respiration and fermentation in the Δagr mutant.

2) The authors highlighted the importance of redox balance in Δagr cells by emphasizing the tendency of these cells to prioritize NAD+-generating lactate production over generating additional ATP from acetate. However, the results regarding acetate and lactate production in Δagr cells during aerobic growth suggest that carbon is directed towards acetate generation rather than lactate.

3) The authors mentioned that respiration and fermentation typically reduce the NAD+/NADH ratios, and since these activities are elevated in Δagr strains (Figure 5F-G), they initially anticipated a lower NAD+/NADH ratio compared to wild-type agr cells. However, the increase in respiration and activation of fermentative pathways leads to a decrease in NADH levels, therefore resulting in an increase in the NAD+/NADH ratio.

Reviewer #2 (Public Review):

In this study the authors try to understand the interaction of a 110 kDa ß-glucosidase from the mollusk Aplysia kurodai, named akuBGL, with its substrate, laminarin, the main storage polysaccharide in brown algae. On the other hand, brown algae produce phlorotannin, a secondary metabolite that inhibits akuBGL. The authors study the interaction of phlorotannin with the protein EHEP, which protects akuBGL from phlorotannin by sequestering it in an insoluble complex.

The strongest aspect of this study is the outstanding crystallographic structures they obtained, including akuBGL (TNA soaked crystal) structure at 2.7 Å resolution, EHEP structure at 1.15 Å resolution, EHEP-TNA complex at 1.9 Å resolution, and phloroglucinol soaked EHEP structure at 1.4 Å resolution. EHEP structure is a new protein fold, constituting the major contribution of the study.

The drawback on EHEP structure is that protein purification, crystallization, phasing and initial model building were published somewhere else by the authors, so this structure is incremental research and not new.

Most of the conclusions are derived from the analysis of the crystallographic structures. Some of them are supported by other experimental data, but remain incomplete. The impossibility to obtain recombinant samples, implying that no mutants can be tested, makes it difficult to confirm some of the claims, especially about the substrate binding and the function of the two GH1Ds from akuBGL.

The authors hypothesize from their structure that the interaction of EHEP with phlorotannins might be pH dependent. Then they succeed to confirm their hypothesis, showing they can recover EHEP from precipitates at alkaline pH, and that the recovered EHEP can be reutilized.

A weakness in the model is raised by the fact that the stoichiometry of the complex EHEP:TNA is proposed to be 1:1, but in Figure 1 they show that 4 µM of EHEP protects akuBGL from 40 µM TNA, meaning EHEP sequesters more TNA than expected, this should be addressed in the manuscript.

The authors study the interaction of akuBGL with different ligands using docking. This technique is good for understanding the possible interaction between the two molecules but should not be used as evidence of binding affinity. This implies that the claims about the different binding affinities between laminarin and the inhibitors should be taken out of the preprint.

In the discussion section there is a mistake in the text that contradicts the results. It is written "EHEP-TNA could not dissolve in the buffer of pH > 8.0" but the result obtained is the opposite, the precipitate dissolved at alkaline pH.

Solving a new protein fold, as the authors report for EHEP, is relevant to the community because it contributes to the understanding of protein folding. The study is also relevant dew to the potential biotechnological application of the system in biofuel production. The understanding on how an enzyme as akuBGL can discriminate between substrates is important for the manipulation of such enzyme in terms of improving its activity or changing its specificity. The authors also provide with preliminary data that can be used by others to produce the proteins described or to design a strategy to recover EHEP from precipitates with phlorotannin at industrial scales.

In general methods are not carefully described, the section should be extended to improve the manuscript.

Reviewer #2 (Public Review):

In their manuscript, Kato et al investigate a key aspect of membrane protein quality control in plant photosynthesis. They study the turnover of plant photosystem II (PSII), a hetero-oligomeric membrane protein complex that undertakes the crucial light-driven water oxidation reaction in photosynthesis. The formidable water oxidation reaction makes PSII prone to photooxidative damage. PSII repair cycle is a protein repair pathway that replaces the photodamaged reaction center protein D1 with a new copy. The manuscript addresses an important question in PSII repair cycle - how is the damaged D1 protein recognized and selectively degraded by the membrane-bound ATP-dependent zinc metalloprotease FtsH in a processive manner? The authors show that oxidative post-translational modification (OPTM) of the D1 N-terminus is likely critical for the proper recognition and degradation of the damaged D1 by FtsH. Authors use a wide range of approaches and techniques to test their hypothesis that the singlet oxygen (1O2)-mediated oxidation of tryptophan 14 (W14) residue of D1 to N-formylkynurenine (NFK) facilitates the selective degradation of damaged D1. Overall, the authors propose an interesting new hypothesis for D1 degradation and their hypothesis is supported by most of the experimental data provided. The study certainly addresses an elusive aspect of PSII turnover and the data provided go some way in explaining the light-induced D1 turnover. However, some of the data are correlative and do not provide mechanistic insight. A rigorous demonstration of OPTM as a marker for D1 degradation is yet to be made in my opinion. Some strengths and weaknesses of the study are summarized below:

Strengths:

1. In support of their hypothesis, the authors find that FtsH mutants of Arabidopsis have increased OPTM, especially the formation of NFK at multiple Trp residues of D1 including the W14; a site-directed mutation of W14 to phenylalanine (W14F), mimicking NFK, results in accelerated D1 degradation in Chlamydomonas; accelerated D1 degradation of W14F mutant is mitigated in an ftsH1 mutant background of Chlamydomonas; and that the W14F mutation augmented the interaction between FtsH and the D1 substrate.

2. Authors raise an intriguing possibility that the OPTM disrupts the hydrogen bonding between W14 residue of D1 and the serine 25 (S25) of PsbI. According to the authors, this leads to an increased fluctuation of the D1 N-terminal tail, and as a consequence, recognition and binding of the photodamaged D1 by the protease. This is an interesting hypothesis and the authors provide some molecular dynamics simulation data in support of this. If this hypothesis is further supported, it represents a significant advancement.

3. The interdisciplinary experimental approach is certainly a strength of the study. The authors have successfully combined mass spectrometric analysis with several biochemical assays and molecular dynamics simulation. These, together with the generation of transplastomic algal cell lines, have enabled a clear test of the role of Trp oxidation in selective D1 degradation.

4. Trp oxidative modification as a degradation signal has precedent in chloroplasts. The authors cite the case of 1O2 sensor protein EXECUTER 1 (EX1), whose degradation by FtsH2, the same protease that degrades D1, requires prior oxidation of a Trp residue. The earlier observation of an attenuated degradation of a truncated D1 protein lacking the N-terminal tail is also consistent with authors' suggestion of the importance of the D1 N-terminus recognition by FtsH. It is also noteworthy that in light of the current study, D1 phosphorylation is unlikely to be a marker for degradation as posited by earlier studies.

Weaknesses:

1. The study lacks some data that would have made the conclusions more rigorous and convincing. It is unclear why the level of Trp oxidation was not analyzed in the Chlamydomonas ftsH 1-1 mutant as done for the var 2 mutant. Increased oxidation of W14 OPTM in Chlamydomonas ftsH 1-1 is a key prediction of the hypothesis. It is also unclear to me what is the rationale for showing D1-FtsH interaction data only for the double mutant but not for the single mutant (W14F). Why is the FtsH pulldown of D2 not statistically significant (p value = {less than or equal to}0.1). Wouldn't one expect FtsH pulls down the RC47 complex containing D1, D2, and RC47. Probing the RC47 level would have been useful in settling this. A key proposition of the authors' is that the hydrogen bonding between D1 W14 and S25 of PsbI is disrupted by the oxidative modification of W14. Can this hypothesis be further tested by replacing the S25 of PsbI with Ala, for example?

2. Although most of the work described is in vivo analysis, which is desirable, some in vitro degradation assays would have strengthened the conclusions. An in vitro degradation assay using the recombinant FtsH and a synthetic peptide encompassing D1 N-terminus with and without OPTM will test the enhanced D1 degradation that the authors predict. This will also help to discern the possibility that whether CP43 detachment alone is sufficient for D1 degradation as suggested for cyanobacteria.