Reviewer #1 (Public Review):

The manuscript by Lian et al. presents a population graph deep learning model constructed using Transformer-generated imaging features and non-imaging clinical characteristics that were proven to be effective at predicting the survival of patients with early-stage NSCLC. This study demonstrates GNN-based model significantly outperforms the TNM model and ResNet-Graph model in predicting survival in all datasets. The paper is well-written, clear for a general audience, takes nice innovations in computer vision into the medical field, and presents a usable tool for survival analysis. The strengths and limitations of the approach are brought forth in the discussion.

Reviewer #1 (Public Review):

This publication shows a strong understanding and implementation of large-scale multiprotein MD simulations. It is the first application of MD simulations to full-length membrane-bound TSHR. The authors showed that the LR is intrinsically disordered, contrasting a previously published homology model. Some simulation results are supported by cryo-EM structures. Finally, it is significant that the inclusion of TSH in the binding site altered the dynamics of the LR region, supporting a hypothesis that the LR is involved in a signaling mechanism, though the authors acknowledge this result as preliminary.

Weaknesses:<br /> The methods section lacks sufficient detail, and arbitrary choices made in the simulation setup may have biased the results. The author's finding that the LR is disordered does not provide obvious mechanistic insights, and the simulations with the bound ligand are too preliminary to make solid conclusions. Although this manuscript is technically strong, the significance of the results is often unclear.

Reviewer #1 (Public Review):

Wang et al., developed a CRISPR/Cas 9 based protocol with the aim to accurately and quickly detect bacteria in ICU patients with severe pneumonia.

The development of such a tool is important as quick and reliable identification of pathogens is extremely important. This study is innovative and aims to address an important clinical problem. The authors de novo designed an algorithm to screen species-specific . Then they used the species specific DNA tags to identify 10 pathogens.

1) It is not very clear on which epidemiological data these pathogens were selected on. Moreover, the selected pathogens are only bacteria.

2) Page 9. It is not very clear on how the primers' specificity was evaluated.

3) Page 9. Were patients on antibiotics before getting into the trial?

4) Page 10 At which timepoint the patients received different treatment based on the results of the culture or SSBD? Was this consistent?

5) Page 11. The second sentence of 3.1 section in results is not clear.

6) How were patients allocated to groups? Randomised?

7) The table describing the patient cohort is in supplementary. This shall be in the main manuscript. It seems that the control and experimental groups were not balanced.

8. The exact protocol of the study needs to be in the supplementary.

9. Were any samples poly-microbial?

10. Which was the threshold level of fluorescence (Figure 3) which was considered important?

Reviewer #3 (Public Review):

In the manuscript, the authors provided the development of a sensitive and rapid diagnostic tool for detection of pathogenic bacteria in respiratory infections given the limitations of traditional cultures in the clinical settings. Rapid identification and treatment of bacterial infections can impact the prognosis in sepsis. This work highlights how a new rapid diagnostic tool may be beneficial in the treatment of patients with bacterial pneumonia given the time-consuming nature and low sensitivity of traditional culture methods.

Strengths:

The manuscript authors created a diagnostic tool using CRISPR-Cas12 with bacterial species-specific DNA-tags to 10 epidemic bacteria at their local intensive care unit (ICU). The appendix data provided detailed reports of the reaction conditions, sample preparations and reaction incubation time.

A 2-stage validation process was used. The initial validation stage compared the use of the novel diagnostic tool to traditional cultures from bronchoalveolar lavage samples from ICU patients. Once the accuracy of the diagnostic tool was evaluated, the second validation stage was pursued in the form of a randomized controlled trial at the ICU of the study. The second validation stage demonstrated that the proposed novel diagnostic tool had faster results and correlated with improved APACHE II scores and more effective antibiotic coverage rates in the experimental group.

The use of the novel diagnostic test highlighted limitations traditional culture modalities may have in identifying polymicrobial infections which were identified more frequently in the two validation stages

Weaknesses:

Although the study has many strengths, a potential weakness could lie in the unclear use of next-generation sequence (NGS) testing where samples were reported to be sent at random. However, similar to the novel diagnostic tool proposed in this manuscript, NGS testing has been noted to have high sensitivity and specificity and both had similar results in the manuscript.

Additionally, the novel diagnostic testing demonstrated increased detection of polymicrobial infection when compared to traditional cultures; however, clinical evaluation will remain important to help decipher potential "false positive" results or identification of non-pathogenic colonization.

Based on the author's proposed aims to develop a rapid and sensitive diagnostic tool for bacterial pathogens in pneumonia; the authors demonstrated a highly sensitive and specific test when compared to gold-standard testing. Random samples were assessed against NGS testing technology with similar reported results. The development of this rapid, sensitive diagnostic tool can have wide-spread clinical implications to guide management in patient care where earlier time to effective treatment can have important impacts on prognosis.

Reviewer #1 (Public Review):

In this paper, Gao et al report that Kiaa1024L/Minar2 causes hearing loss in mice and in zebrafish. The animal studies are well executed. Mechanistically, the authors claim that Kiaa1024L/Minar2 is responsible for the enrichment of an accessible pool of cholesterol in the hair bundle membrane. Increasing cholesterol levels rescues hair cell defects whereas decreasing cholesterol aggravates the problem.

Unfortunately, the mechanistic arm of this study doesn't go beyond this correlation. The characterization of cholesterol levels and pools is not rigorous and it is unclear why cholesterol matters for hearing.

Reviewer #3 (Public Review):

In the manuscript by Gao et al, the authors were trying to achieve an understanding of how Kiaa1024L/Minar2 is necessary for hearing in vertebrates. It is known that the Kiaa1024L/Minar2 mutation causes deafness in mice but not much beyond that is known.

Strengths:<br /> - In this manuscript, they were successful in making two zebrafish mutant zebrafish strains in the Kiaa1024L/Minar2 gene using Crispr/Cas9. The mutant(s) has defects in hearing (using the C-start assay and determining thresholds) and reduced hair cell numbers in the ear (phalloidin labeling to determine hair cell density in utricle and saccule) and the lateral line (including using the AM1-43 assay). From these data, they demonstrate that hair cells are defective in these mutants.

- The authors show that Lamp1-GFP labeled lysosomes change in size in the minar2fs139 mutant. In addition, they show that GFP-Minar2 localizes to lysosomal membranes in cultured cells (human and monkey).

- They performed primary amino acid sequence analysis on Minar2 and showed that it contained a putative CSD of caveolin, which is known to interact with cholesterol. They then show that when Minar2 is expressed in cells in culture, there is an increase in cholesterol detection in the region that contained Minar2, supporting the idea that cholesterol interacts with Minar2.

The experiments in figure 5 seem to show that lowering cholesterol levels using pharmacology exacerbates hair cell defects in a minar2 mutant.

Weaknesses:<br /> 1. The authors attempt to show localization (Fig 2 A and B) of Minar2 to the stereocilia and the apical region of hair cells using GFP-MINAR2 fusion protein expression in hair cells of transgenic animals. Although this is a typical way of demonstrating localization, it is usually used to validate location after a similar pattern has been shown using an antibody (usually in mice.) So, special precautions must be taken when interpreting this kind of transgenic data. According to the authors, GFP-MINAR2 localized to the stereocilia and the apical region of hair cells. This needs to be validated by some other means. I can also see the localization of the green signal at the basolateral area of the cells in Fig 2a. Moreover, it's important to note that other mislocalized fusion proteins localize to the apical region of hair cells.

2. Figure 2C and D. The defects in the hair bundles are plausible but not convincing. Electron microscopy should be used to validate. Also, are hair bundle defects seen in the neuromast? EM would be easier to do there.

3. Fig 1A do prim 1- and prim 2-derived neuromasts express minar2? Do anterior neuromasts express minar2?

4. It's my impression that the authors don't take into account that there is much more plasma membrane in the stereocilia than in the basolateral membrane. So, this statement, "These data suggested that there are high levels of accessible cholesterol located to the stereocilia membranes, while the accessible cholesterol levels are marked lower in the basolateral membranes in the hair cells" based on Figure 4 needs to be reconsidered. The authors need to show that the little reporter that is present in the basolateral membrane is not equal to the reporter present in a single sheet of the plasma membrane in a stereocilium. I can see basolateral labeling in the lateral line hair cells.

5. It's not clear if there is a paralog of the Kiaa1024L/Minar2 gene.

Reviewer #1 (Public Review):

The authors examined the impact of pre-gravid obesity in human mothers on the monocytes of newborns by collecting umbilical cord blood. Additionally, the authors also used a non-human primate (NHP) model of diet-induced obesity to isolate fetal macrophage and assess the impact of maternal obesity on fetal macrophage function.

The comprehensive analysis of the human umbilical cord blood monocytes by studying cytokine release, bulk RNA-seq and bulk ATAC-seq, single cell RNA-seq and single cell ATAC-seq, responses to pathogen stimulation as well as metabolic studies such as glucose uptake are major strength of the work. They present convincing evidence that the monocytes of offspring with obese mothers have epigenetic and transcriptomic profiles consistent with impaired immune responses, both during baseline conditions and upon stimulation.

However, it is not clear from the data how the epigenetic data and the transcriptomic data are related to each other. The implication that the epigenetic changes drive the downstream transcriptional differences is not clearly demonstrated. Furthermore, it is not clear which of the observed attenuations of monocyte transcriptional responses overlap with chromatin accessibility differences. Such an overlap would make a stronger case for the mechanistic link.

The increased phagocytosis of E.coli in umbilical cord monocytes of newborns with obese mothers appear counter-intuitive because it implies greater host defense capacity.

One of the most remarkable aspects of the manuscript is the analysis of the fetal macrophages in a non-human primate (NHP) model of diet induced obesity because of the challenge of studying fetal macrophages in humans. The cytokine assays nicely show that the fetal macrophages in the obesity model show impaired cytokine production, consistent with what was seen in the umbilical cord blood monocytes of human newborns. This is especially important because circulating monocytes or monocyte progenitors seed the fetal tissues and give rise to fetal macrophages, thus elegantly linking the human work on circulating umbilical cord blood monocytes to the tissue macrophages in the NHP model.

However, the NHP studies do not show any additional macrophage characterization beyond the cytokine assays. Flow cytometry analysis of the macrophage phenotype and functional assays would strengthen the conclusions regarding macrophage dysregulation.

Reviewer #3 (Public Review):

The manuscript by Sureshchandra et al is a very extensive analysis of monocyte function and their molecular landscape in cord bloods from lean and obese mothers. They aimed to analyze the effects of pre-pregnancy BMI on the functioning of the innate immune system in newborns in a very extensive way. The combination of functional and molecular analyses strengthens their observations and shows many different sides of monocyte activation. I think this approach needs to be praised and should be an inspiration to many others who study monocyte function. This allows for a broad view on the matter and also shows where potential targeting will be necessary in the future. Overall, the manuscript and particularly the methods section is very well written and extensive, making it easy to study how robust the data are.

Reviewer #1 (Public Review):

The authors endeavored to determine molecular pathways that could enhance the viability and function of MSCs. The authors identified the master anti-oxidant regulator NRF2 as a direct regulator of DKK1, a Wnt pathway inhibitor. Moreover, the authors demonstrate over expression of NRF2 and DKK1 ameliorates liver regeneration in a model of acute on chronic liver failure. The strengths of this study are their multi-tier approach utilizing molecular biology, genetic interventions and in vitro and vivo models. These findings have uncovered a novel signaling loop with the potential for enhancing MSC function in vivo.

Reviewer #1 (Public Review):

Bacterial carboxysomes are compartments that enable the efficient fixation of carbon dioxide in certain types of bacteria. A focus of the current work is on two protein components that provide spatial regulation over carboxysomes. The McdA system is an ATPase that drives the positioning of carboxysomes. The McdB system is essential for maintaining carboxysome homeostasis, although how this role is achieved is unclear. Previous studies, by the lead author's lab, showed that the McdB system is a driver of phase separation in vitro and in cells. They proposed a putative connection between McdB phase separation and carboxysome homeostasis. The central premise of the current work is as follows: In order to understand if and how phase separation of McdB impacts carboxysome homeostasis, it is important to know how the driving forces for phase separation are encoded in the sequence and architecture of McdB. This is the central focus of the current work. The picture that emerges is of a protein that forms hexamers, which appears to be a trimer of dimers. The domains that drive that the dimerziation and trimerization appear to be essential for driving phase separation under the conditions interrogated by the authors. The N-terminal disordered region regulates the driving forces for phase separation - referred to as the solubility of McdB by the authors. To converge upon the molecular dissections, the authors use a combination of computational and biophysical methods. The work highlights the connection between oligomerization via specific interactions and emergent phase behavior that presumably derives from the concentration (and solution condition) dependent networking transitions of oligomerized McdB molecules.

Having failed to obtain specific structural resolution for the full-length McdB as a monomer or oligomer, the authors leverage a combination of computational tools, the primary one being iTASSER. This, in conjunction with disorder predictors, is used to identify / predict the domain structure of McdB. The domain structure predictions are tested using a limited proteolysis approach and, for the most part, the predictions stand up to scrutiny affirming the PONDR predictions. SEC-MALS data are used to pin down the oligomerization states of McdB and the consensus that emerges, through the investigations that are targeted toward a series of deletion constructs, is the picture summarized above.

Is the characterization of the oligomerization landscape complete and likely perfect? Quite possibly, the answer is no. Deletion constructs pose numerous challenges because they delete interactions and inevitably impose a modularity to the interpretation of the totality of the data. Accordingly, we are led to believe that the N-terminal IDR plays no role whatsoever in the oligomerization. Close scrutiny, driven by the puzzling choice of nomenclature and the Lys to Gln titrations in the N-terminal IDR raise certain unresolved issues. First, the central dimerization domain is referred to as being Q-rich. This does not square with the compositional biases of this region. If anything is Q/L or just L-rich. This in fact makes more sense because the region does have the architecture of canonical Leu-zippers, which do often feature Gln residues. However, there is nothing about the sequence features that mandates the designation of being Q-rich nor are there any meaningful connections to proteins with Q-rich or polyQ tracts. This aspect of the analysis and discussion is a serious and erroneous distraction. Back to the middle region that drives dimerization, the missing piece of the puzzle is the orientation of the dimers. One presumes these are canonical, antiparallel dimers. However, this issue is not addressed even though it is directly relevant to the topic of how the trimer of dimers is assembled. If the trimer is such that all binding sites are fully satisfied (with the binding sites presumably being on the C-terminal pseudo-IDR), then the hexamer should be a network terminating structure, which it does not seem to be based on the data. Instead, we find that only the full-length protein can undergo phase separation (albeit at rather high concentrations) in the absence of crowder. We also find that the driving forces for phase separation are pH dependent, with pH values above 8.5 being sufficient to dissolve condensates. Substitution of Lys to Gln in the N-terminal IDR leads to a graded weakening of the driving forces for phase separation. The totality of these data suggest a more complex interplay of the regions than is being advocated by the authors. Almost certainly, there are complementary electrostatic interactions among the N-terminal IDR and C-terminal pseudo IDR that are important and responsible for the networking transition that drives phase separation, even if these interactions do not contribute to hexamer formation. The net charge per residue of the 18-residue N-terminal IDR is +0.22 and the NCPR of the remainder is ≈ -0.1. To understand how the N-terminal IDR is essential, in the context of the full-length protein, to enable phase separation (in the absence of crowder), it is imperative that a model be constructed for the topology of the hexamer. It is also likely that the oligomer does not have a fixed stoichiometry.

Therefore, the central weakness of the current work is that it is too preliminary. A set of interesting findings are emerging but by fixating on Lys to Gln titrations within the N-terminal IDR and referring to these titrations as impacting solubility, a premature modular and confused picture emerges from the narrative that leaves too many questions unanswered.

The work itself is very important given the growing interest in bacterial condensates. However, given that the focus is on understanding the molecular interactions that govern McdB phase behavior - a necessary pre-requisite in the authors minds for understanding if and how phase separation impacts carboxysome homeostasis - it becomes imperative that the model that emerges be reasonably robust and complete. At this juncture, the model raises far too many questions. The MoRF analysis is distraction away from the central focus.

The problem, as I see it, is that the authors have gone down the wrong road in terms of how they have interpreted the preliminary set of results. Further, the methods used do not have the resolution to answer all the questions that need to be answered. Another issue is that a lot of standard tropes are erected and they become a distraction. For example, it is simply not true that in a protein featuring folded domains and IDRs it almost always is the case that the IDR is the driver of phase transitions. This depends on the context, the sequence details of the IDRs, and whether the interactions that contribute to the driving forces for phase separation are localized within the IDR or distributed throughout the sequence. In McdB it appears to be the latter, and much of the nuance is lost through the use of specific types of deletion constructs.

Overall, the work represents a good beginning but the data do not permit a clear denouement that allows one to connect the molecular and mesoscales to fully describe McdB phase behavior. Significantly more work needs to be done for such a picture to emerge.

Reviewer #3 (Public Review):

Through a series of rigorous in vitro studies, the authors determined McdB's domain architecture, its oligomerization domains, the regions required for phase separation, and how to fine-tune its phase separation activity. The SEC-MALS study provides clear evidence that the α-helical domains of McdB form a trimer-of-dimers hexamer. Through analysis of a small library of domain deletions by microscopy and SDS-PAGE gels of soluble and pellet fractions, the authors conclude that the Q-rich domain of McdB drives phase separation while the N-terminal IDR modulates solubility. A nicely executed study in Figure 4 demonstrated that McdB phase separation is highly sensitive to pH and is influenced by basic residues in the N terminal IDR. The study demonstrates that net charge, as opposed to specific residues, is critical for phase separation at 100 micromolar. In addition, the experimental design included analysis of McdB constructs that lack fluorescent proteins or organic dyes that may influence phase separation. Therefore, the observed material properties have full dependence on the McdB sequence.

Studies of proteins often neglect short, disordered segments at the N- or C- terminus due to unclear models for their potential role. This study was interesting because it revealed a short IDR as a critical regulator of phase separation. This includes experiments that remove the IDR (Fig 2 & 3) and mutate the basic residues to show their importance towards McdB phase separation. In a nice set of SDS-PAGE experiments, the authors showed that as the net charge of the IDR decreased the construct became more soluble.

One challenge is in the experimental design when mutating residues is to assess their impact on phase separation. The author's avoided substitutions to alanine, as alanine substitutions have synthetically stimulated phase separation in other systems. The authors, therefore, have a good rationale for selecting potentially milder mutations of lysine/arginine to glutamine. A potential caveat of mutation to glutamine is that stretches of glutamines have been associated with amyloid/prion formation. So, the introductions of glutamines into the IDR may also have unexpected effects on material properties. Despite these caveats, the authors show mutation of six basic residues in the short IDR abolished phase separation at 100 mM.

Computational studies (Fig 7) also suggest that this short N-IDR region may play a role as a MORF upon potential binding to a second protein McdA. The formulation of this hypothesis is strengthened by the fact that for other ParA/MinD-family ATPases, the associated partner proteins have also been shown to interact with their cognate ATPase via positively charged and disordered N-termini. This aspect of understanding McdB's N-IDR as a MORF is at a very early stage. This study lacks experimental evidence for an N-IDR: McdA interaction and experimental data showing conformational change upon McdA binding. However, the computation study sets up the future to consider whether and how the phase separation activity of McdB is related to its structural dynamics and interactions with McdA.

In summary, this study provides a strong foundation for the contribution of domains to McdB's in vitro phase separation. This knowledge will inform and impact future studies on McdB regulating carboxysomes and how the related family of ParA/MinD-family ATPases and their cognate regulatory proteins. For example, it is unknown if and how McdB's phase separation is utilized in vivo for carboxysome regulation. However, the revealed roles of the Q-rich domain and N-IDR will provide valuable knowledge in developing future research. In addition, the systematic domain analysis of McdB can be combined with a similar analysis of a broad range of other biomolecular condensates in bacteria and eukaryotes to understand the design principles of phase separating proteins.

Reviewer #1 (Public Review):

Drosophila ovarian follicle cells have been utilized as a model system to study organogenesis and tumorigenesis of epithelia. Studies have found that lack of proper cell polarity causes invasive delamination of cells and formation of multilayered epithelia, reminiscent of Epithelial-Mesenchymal Transition (EMT). Using this system, the authors analyzed the single-cell transcriptome of follicle cells and show that distinct cell populations emerge shortly after induction of polarity loss. Authors identified dynamic activation of Keap1-Nrf2 pathway Finally, subpopulation classification and analysis of regulon activity identified that Keap1-Nrf2 pathway is responsible for epithelial multilayering caused by polarity loss.

Strengths: The authors characterized the single-cell transcriptome of follicle cell subpopulations after induction of polarity loss. Using temperature-inducible driver, they can induce the polarity loss in a short period of time, which enables detection of epithelial populations in various transition stages. Detected cell-heterogeneity could be caused intrinsically or by environmental cues within in vivo tissue. Therefore, it is likely well recapitulating tumorigenesis in vivo.

Weaknesses:<br /> 1) Authors should show cells corresponding to identified key cell clusters within the tissue by immunostaining, GFP-trap, or RNA FISH.<br /> 2) Images are low magnification and difficult to see individual cells.<br /> 3) Manuscript is written weighted toward the technical aspect and more biology behind this study has to be discussed.

Reviewer #3 (Public Review):

In this manuscript, the authors aim to identify the regulators of epithelial invasiveness upon Lethal giant larvae (Lgl), a basolateral polarity protein, knockdown in the follicular epithelium of the Drosophila ovaries, which can serve as a model system to investigate cellular plasticity when apical-basal polarity is lost. Knockdown (KD) of Lgl causes a multilayered epithelium and through extensive single cell RNA-seq analyses, the authors demonstrate that Lgl-KD triggers the appearance of groups of cells exhibiting tumor-associated molecular signatures and invasive behaviour. Overall, the manuscript is technically sound and the combination of computational and experimental approaches results in a thorough characterisation of the earliest steps of epithelial de-stabilisation upon the loss of apical-basal polarity. In my view, the aims set by the authors are met and the experimental data provided support the claims. Interpretations are balanced and the display items are presented logically and informatively for even non-experts. Together, this work will set the basis for further investigations using apical-basal destabilisation of the follicle epithelium as a model of epithelial tumorigenesis.

Reviewer #1 (Public Review):

The authors' results revolutionize our understanding of the mechanism of arrestin-mediated GPCR internalization. They identified previously unknown elements on the non-receptor-binding side of arrestins participating in the process. The findings are ground-breaking and very important to the large field of GPCR signaling.

Reviewer #3 (Public Review):

Barsi-Rhyne reports a novel mode of engagement of beta arrestins as endocytic adaptors and associates this novel mode together with the previously known canonical mode to the regulation of endocytosis and signaling by class A versus class B receptors. The manuscript is very well written, very good to read, almost flawless, extremely interesting, and highly relevant to the GPCR field with very well-crafted figures and fantastic microscopy.

Reviewer #3 (Public Review):

The authors use a previously developed technology, CRISPR activation screening, in which pooled sgRNAs are used to guide an RNA-associated regulatory complex (MS2-p65-HSF1 transcriptional activators) to promoter regions resulting in increased expression of a specific target gene. The authors screen two different pooled libraries TM1 (single pass) and TM2+ (multiple pass) with 20 different recombinant biotinylated soluble ligands and identified 22 novel interactions. These interactions were further characterized by SPR and cell-based binding experiments; however, several of the interactions are low affinity and were not characterized for any activity or function beyond the relatively weak biochemical binding. Therefore, while the data provide evidence of potential novel interactions, the biological relevance remains unclear.

Reviewer #1 (Public Review):

In this manuscript, Siepe et al. developed a high-throughput screen designed to identify novel protein-protein interactions in the extracellular human proteome. Their CRISPRa-based method induced the expression of transmembrane receptors such that they could be screened for binding to proteins of interest. Major strengths of this approach include the ability to screen multiple ligands in parallel, the ability to identify low-affinity interactions, and the availability of custom single- and multi-pass transmembrane protein libraries for selective target screening. A potential weakness is that low-affinity binders and non-specific interactions can be difficult to distinguish in certain cases, and these scenarios require more complex statistical analysis. The authors also note that the CRISPRa strategy cannot induce the expression of multi-subunit receptors that may be required for some ligands. The screen was tested against a curated set of ligand candidates and identified more than twenty novel interactions with intriguing biological implications. Both the method and newly discovered interactions will be of immediate scientific interest given the growing need to identify receptors for orphan ligands. Overall, this technology should function as a powerful new tool for ligand deorphanization in the extracellular space.

Reviewer #1 (Public Review):

Previous studies have linked several lifestyle-related factors, such as body mass index and smoking, alcohol use with accelerated biological aging measured using epigenetic clocks, however, most of them focused on single lifestyle factors based on cross-sectional data from older adults. The current study has a couple of major strengths: it has a decent sample size, lifestyle was measured longitudinally during puberty and adolescence, it looked at the effect of multiple lifestyle measures collectively, it looked at multiple epigenetic clocks, and due to the data from twins, it could examine the contribution of genetic and environmental influences to the outcomes. I have a couple of comments that are mainly aimed at improving the clarity of the methods (e.g. how was multiple testing correction done, how did the association model account for the clustering of twin data, how many samples were measured on 450k vs EPIC and were raw or pre-QC'd data supplied to the online epigenetic age calculator), and interpretation of findings (why were 2 measures of Dunedin PACE of aging used, how much are results driven by BMI versus the other lifestyle factors, and the discussion on shared genetic influences should be more nuanced; it includes both pleiotropic effects and causal effects among lifestyle and biological ageing).

Reviewer #1 (Public Review):

This manuscript reports a systematic study of the cortical propagation patterns of human beta bursts (~13-35Hz) generated around simple finger movements (index and middle finger button presses).

The authors deployed a sophisticated and original methodology to measure the anatomical and dynamical characteristics of the cortical propagation of these transient events. MEG data from another study (visual discrimination task) was repurposed for the present investigation. The data sample is small (8 participants). However, beta bursts were extracted over a +/- 2s time window about each button press, from single trials, yielding the detection and analysis of hundreds of such events of interest. The main finding consists of the demonstration that the cortical activity at the source of movement related beta bursts follows two main propagation patterns: one along an anteroposterior directions (predominantly originating from pre central motor regions), and the other along a medio-lateral (i.e., dorso lateral) direction (predominantly originating from post central sensory regions). Some differences are reported, post-hoc, in terms of amplitude/cortical spread/propagation velocity between pre and post-movement beta bursts.

Several control tests are conducted to ascertain the veracity of those findings, accounting for expected variations of signal-to-noise ration across participants and sessions, cortical mesh characteristics and signal leakage expected from MEG source imaging.

One major perceived weakness is the purely descriptive nature of the reported findings: no meaningful difference was found between bursts traveling along the two different principal modes of propagation, and importantly, no relation with behavior (response time) was found. The same stands for pre vs. post motor bursts, except for the expected finding that post-motor bursts are more frequent and tend to be of greater amplitude (yielding the observation of a so-called beta rebound, on average across trials).

Overall, and despite substantial methodological explorations and the description of two modes of propagation, the study falls short of advancing our understanding of the functional role of movement related beta bursts.

For these reasons, the expected impact of the study on the field may be limited. The data is also relatively limited (simple button presses), in terms of behavioral features that could be related to the neurophysiological observations. One missed opportunity to explain the functional role of the distinct propagation patterns reports would have been, for instance, to measure the cortical "destination" of their respective trajectories.

Reviewer #3 (Public Review):

Aside from one critical reservation, I thought this paper was excellent. The figures are clear, the manuscript is well-written, the scope of the study is well-defined (i.e. it characterizes traveling beta), and the authors were circumspect in all aspects of the work, with the authors' consideration of wave propagation along different cortical meshes being but one example in a generally deft and careful approach.

However, the inverse problem remains the inverse problem, and I believe there is one thorny issue to treat regarding the 3D geometry of the central sulcus as it pertains to synchronized beta events before I can accept the authors' conclusions. After this subtle issue is treated, I believe the work will be an important step forward and generally impactful on the community interested in human brain rhythms.

The authors were gracious enough to raise the issue of spatially synchronized events themselves in their discussion: Their argument, with which I mainly agree, is that the beamformer method essentially removes synchronous components from consideration, leaving the traveling component for analysis.

However, synchronization across the sulcus introduces a further bias into event detection by means of physical source-cancellation. I will here defer to Ahlfors et al (2010), who state that "Substantial cancellation occur also for locally extended patches of simulated [cortical] activity, when the patches extended to opposite walls of sulci and gyri."

With that in mind, let's look at Figure 1, where the authors seem to show a higher density of beta events relatively deep in the sulcus compared to the sulcal walls. This is certainly an interesting result if true! But even given only the occasional synchronization of mesoscale cortical neighborhoods, it appears that events in the sulcal walls will still be systematically undersampled and those deep in the sulcus oversampled here, by vice or virtue of cortical geometry as it pertains to the magnetic field.

This spatial sampling bias could impact nearly all aspects of the event propagation analysis that follows, and so I believe it must be considered in some detail before I can fully agree with the manuscript's conclusions.

Reviewer #1 (Public Review):

The transcriptome of the cells of the human meniscus have been studied in bulk or superficially via single cell methods. In this study, the authors profile the types of cells present in the normal/healthy human meniscus as well as samples from degenerative menisci using single cell RNA seq. Using pre-existing analysis packages for single cell RNA seq data, they infer the roll of the various cell type clusters that they have identified and posit which cells interact with which cells as part of the healthy meniscus and in disease. They have developed an on-line viewer to facilitate use of these data by other research groups.

Strengths: The data has been rigorously collected and appropriate quality control steps have been implemented to ensure the veracity of the data. The result is a robust data set. This is coupled with the on line viewer portal they have created, allowing the data to be available in the public domain. Further, having this tool is a huge resource as it means that the end user does not need to have advanced programing skills to be able to use it. Some of the RNA seq results have been validated via in situ and immunofluorescence. The authors have compared their results to data already published and discuss disagreements.

Weaknesses: Some of the conclusions are very over reaching. The function of clusters, the role of cells and the interactions between cells are all inferred results based on data analyses. These results gave not been experimentally validated.

Reviewer 3 (Public Review):

This is an interesting study that describes a single cell RNAseq analysis of human menisci. The study describes cell profiling of healthy and degenerated menisci divided into two zones, inner and outer meniscus.

Reviewer #1 (Public Review):

The authors provide insight into which regions of the ribozymes are involved in pairings including some tertiary interactions. Overall, the data support known structures and give insight into the roles of bases as pairs, catalytic residues, and extensions. The epistasis analysis is novel and gives deeper insight than previous mutational analyses of ribozymes. However, more can be extracted from this data. This study will impact the field by helping classify the roles of possible bases. There are also numerous technical issues that must be addressed. The authors should consider why short and long pairings show different epistasis and discuss the robustness of pairings from an evolutionary point of view. The effect of the primer binding site on ribozyme activity needs to be discussed.

Reviewer #3 (Public Review):

This article by Roberts, Hayden and colleagues expands on an interesting high-throughput experimental approach developed by Kobori and Yokobayashi (2016; Angew Chem) by determining the relative activity for every possible single and double mutant of five known self-cleaving ribozymes. While this approach is not in itself new, the fact that the authors analyze their data by looking at epistasis (non-additive effects between pairs of mutations) provides an additional opportunity for extracting meaningful structural information that is proposed to be similar to chemical or enzymatic probing experiments obtained on these self-cleaving ribozymes. In fact, this type of high throughput mutagenesis analysis might provide data closer to comparative sequence analysis and as such, might provide even more reliable structural information than structural probing experiments, especially when a relative activity can be properly assessed for the studied RNAs.

(1) Overall, the experiments have been carefully performed and the data seem to be highly reliable.<br /> (2) The strength of this article is that it demonstrates the generality of the approach initially developed by Kobori and Yokobayashi (2016; Angew Chem) by validating its usefulness in identifying most (if not all) the structural features of the studied ribozymes. The determination of positive and negative epistasis is very useful as it can facilitate the identification of base pairs covariations that are indicative of RNA structural elements.<br /> (3) At the present time, the authors have not really discussed how their data analysis compares to comparative sequence analysis. This aspect is important.<br /> (4) It is necessary to mention more clearly that this article builds on the method of Kobori and Yokobayashi (2016). Overall, with the exception of a few experimental details, the experimental method described herein is almost identical to the one of Kobori and Yokobayashi (2016) and this should be better emphasized.<br /> (5) Most importantly, this article provides an analysis of self-cleaving ribozymes for which the three-dimensional structures are known. Considering the scope of this article, instead of mostly focusing on the 2D structural aspect, it would be absolutely necessary to provide more 3D structural information.<br /> (6) When a self-modifying enzymatic activity is associated with the studied RNA, a relative activity could potentially be derived from high throughput sequencing. Could the authors expand on the generality and requirement of their high throughput approach for the study of RNA?

Reviewer #1 (Public Review):

In the current manuscript, Bolte et al., examine how a single TBI alters the heterogeneity of dorsal meningeal immune cell responses and whether age at the time of injury affects long-term transcriptional profiles of this immune compartment of the brain. Multiple complementary approaches were undertaken to achieve high resolution of meningeal transcriptional response(s) to TBI including single-cell sequencing and bulk tissue sequencing. Several innate and adaptive immune phenotypes were quantified at the protein level, demonstrating these disease-associated responses are not solely relegated to transcriptional responses. The majority of the methods and analyses are robust, which is a notable strength of the manuscript. In its current iteration, a weakness is a lack of integration between gene sets that define meningeal immune cell subsets in the single cell data (e.g. Macrophages, Tcells, Bcells, Fibroblasts, etc.) and quantifying these DEGs (up or down-regulated) to examine whether the transcripts are altered in the chronic TBI/aging bulk sequencing data. A more thorough integration of these two datasets and their discussion would significantly bolster the main premise of the manuscript related to the resolution of inflammatory responses to TBI in the young versus the aged condition, chronically.

Reviewer #1 (Public Review):

In this study, Menjivar et al. examine the specific role of the enzyme arginase 1 (Arg1), which is expressed in immunosuppressive macrophages and catabolizes arginine to ornithine, in pancreatic cancer. They use an elegant genetic approach that leverages a dual recombinase-based genetically engineered mouse model of pancreatic cancer, which efficiently deletes Arg1 and recovers extracellular arginine in cultured macrophages. Within the pancreas, macrophage Arg1 deletion increased T cell infiltration and fewer mice developed invasive pancreatic cancer. Interestingly, when tumors did develop, the authors observed that compensatory mechanisms of arginine depletion were induced, including Arg1 overexpression in epithelial cells identified as tuft cells or Arg2 overexpression in macrophages. To overcome these compensatory mechanisms, pharmacological targeting of arginase was tested and found to increase T cell infiltration and sensitize to immune checkpoint blockade, suggesting this is a promising approach for pancreatic cancer.

Strengths:

This is a very rigorous, well-designed study and the findings are broadly interesting for the metabolism, immunometabolism, and pancreatic cancer communities. The methods are comprehensive and the experimental details in the legends are complete.

Weaknesses:

The claim that Arg1 deletion in macrophages delayed the formation of invasive disease is not completely justified by the data presented. Only a small number of mice are analyzed, and no statistics are included. Moreover, the abstract does not comprehensively summarize the findings. Many findings, including compensatory upregulation of ARG1 in tuft cells and ARG2 in myeloid cells, are not mentioned, nor was the rationale for the pharmacological approach. Finally, the claim that their data demonstrate that Arg1 is more than simply a marker of macrophage function. While this is the first time this has been examined in pancreatic cancer, a general role for Arg1 and arginine metabolism by myeloid cells in immunosuppression has already been established by multiple studies, including those cited by the authors, in multiple tumor types. This is an overstatement of the findings.

Reviewer #3 (Public Review):

Menjivar et al. present an analysis of the role of immunosuppressive Arginase 1 in myeloid cells in pancreatic cancer. They show that depletion of Arg1 in macrophages leads to attenuation in progression from PanIN to PDAC and use single cell analysis to understand underlying changes in immune activation, including an increase in cytotoxic T cells. Interestingly, the authors observed what seems to be a compensatory upregulation in Arg1 in epithelial cells and used arginase1 inhibitor to assess the therapeutic potential of targeting Arg1 systemically. This study is overall well performed and generates novel mouse models to study immunosuppression in pancreatic cancer. While the notion that Arginase1 is immunosuppressive is not novel, the observation that Arg1 is upregulated in epithelial cells is interesting. There are several instances of overstating conclusions that, if addressed in the main text (not just relegated to the discussion section), could significantly strengthen the manuscript.

Reviewer #1 (Public Review):

The article by Solvi and colleagues aims to investigate what type and degree of information (either absolute, relative, or a weighted combination of both) is used by bumblebees when retrieving the value of an item. The authors reported recent evidence in humans and birds that suggest they seem to use a combination of absolute memories and remembering of subjective ranking, and an absence of relevant studies for other species, including invertebrates.

Thus, the authors conducted four different experiments to study what type of information is guiding the decision of bumblebees when facing different qualitative and quantitative comparisons.

In the first two experiments, the authors reported the use of relative ranking of stimuli instead of a memory of their absolute value. According to the authors, these results are confirmed by experiment three, where bees were presented with two equally-ranked choices which, in fact, were not treated as different by bees. In the last experiment, bumblebees showed a preference for the highest rank item.

Despite the presentation of well-designed experiments, the conclusions that bumblebees are using only memories of ordinal comparisons, thus showing a different strategy with respect to humans and birds, seems to not be fully supported by the results. The behaviour on the first two experiments, for instance, could be explained by a recency effect, where the higher item of the last comparison is better retrieved (the work of Giurfa on transitive inferences in bees was not mentioned, though is relevant here). Furthermore, in the last experiment, bumblebees could not have used an ordinal ranking; their choice for the higher-ranking item could be based on its higher absolute quantitative value in terms of sucrose solution.

The different behaviours and strategies used by bees here could be better explained by differences in the experimental task proposed, rather than supporting a general statement about the evolution of different strategies in comparison to other species.

Reviewer #3 (Public Review):

The central conclusion of this beautiful experimental study is that bumblebees prefer flowers on the basis of their remembered ranking in their context, but are insensitive to their absolute properties. Thus, let's say that there 4 flower types, ranked as follows in nectar concentration: A>B>C>D. However, when the bee learns about these flowers, it does in either of two 'contexts', populated as follows: A & B, or C & D. Thus, the bee experiences that B is the worse option in the context in which it is found, and C is the better one in its own context. If, at a later time, the bee has to make a novel choice, this time between B and C, its memory for ranking leads it to prefer C over B, while its (putative) memory for nectar concentration should favour B over C. The authors find, in a variety of different treatments, evidence for the influence for ranking, but they do not find any evidence for sensitivity to absolute properties (i.e., concentration).

One difficulty that permeates the argument is the ubiquitous difficulty in proving the null hypothesis as true: lack of significant evidence for a putative effect in one or a few experiments, does not mean reliable absence of the effect.

Another difficulty is that in my view memory for absolute properties was not given a full chance: bees were always trained in situations where both dimensions (concentration and ranking) were present. In such situations, they preferentially used ranking. However, to learn ranking between flower types in sequential encounters, they must remember the absolute properties, so that in each encounter they contrast the present flower with the memory for others. Say the bee encounters a type B flower. How does it store its ranking if it doesn't remember the properties of A at all? To take this objection into account and still maintain the claim, it is necessary to say that it remembers the properties of A when in the A & B context, but it erases it from memory when in the context B & C.

Neglecting memory for concentration may be an overshadowing effect. Overshadowing is known in learning studies, and it means that, when more than one cue is paired with an outcome, the most salient between them may reduce learning about the predicting power of the other. In this case, bees may remember and use concentration when trained in contexts where there is only flower type, so that there is no chance of using ranking, and then offered choices between pairs of them. In this case, the bees would not have access to ranking, so that there would be a stronger opportunity for absolute memory to manifest itself.

In experiment 4, during training, they could move between two zones representing the 'contexts', each with 2 flower types, and they were then given choices between the 4 types, rather than just binary choices as previously. In this case, the bees did prefer the top-quality flower type (type A), which is consistent with memory for absolute concentration and with ranking, because A offered the highest concentration of the 4-type context. Why this happened is not clear, but it indicates that the context of choice may be crucial. It is known from other studies that the number of options at the time of choice can be very influential. For instance, in one study, it was shown that starlings appeared to be risk prone when offered a binary choice and risk averse when offered a trinary choice, even if the choices were all intermingled in the same sessions. In any case, this experiment raises doubts as to the claimed insensitivity to memory for nectar concentration. Another possibility is that the separation between contexts in this experiment (a partially avoidable wall) was not extreme as in the previous ones, so that the bees could now establish a ranking among the 4 types because they were all encountered intermingled to an extent.

There is one potential mechanism that may also be discussed. It is known from other species, that state at the time of learning influences subjective value of alternatives. To explain this effect I will exemplify the problem with a non-eusocial consumer. Say that food sources B and C are of equal caloric value. Say, further, that B is encountered when the subject is less food deprived than when it encounters C. Then the hedonic (conditioning) power of B will be lower, because it causes a smaller improvement in fitness (this was Daniel Bernoulli's argument regarding the concept of utility). In animal studies this effect is called State-Dependent Valuation Learning (SDVL). Since in the present experiments the context A & B was richer than the context C & D, the bees would have been in a consequently more favourable state (maybe carrying bigger sugar loads), so that each encounter with B would cause a smaller improvement than each encounter with C. This effect is totally different from remembering the ranking of flower types. The two alternative explanations for preference of C over B (ranking and SDVL) can, fortunately, be confronted because it is possible to change the state of the bees by a common 3rd source that could be used to equate or manipulate the average richness of the contexts.

All the reasons mentioned above should make it clear that this reviewer finds the study of very great interest and much merit, but considers that the conclusion for exclusive impact of ranking on preference should be tempered, or at least defended more strongly against these doubts.

Reviewer #1 (Public Review):

Li et al. use biochemical binding analysis combined with deletions/mutations to demonstrate that the bottom helix of the Rph3A C2B domain directly interacts with the first 10 residues (N-peptide region) on SNAP25, and this interaction is amplified by the intramolecular interaction of the C2B domain with RAB-binding domain. They establish the functional relevance of this interaction using live-cell imaging of dense-core vesicle exocytosis in neuroendocrine PC12 cells and in vitro SNARE assembly assay. They propose that the Rph3A binding to SNAP25 pre-structures the protein to efficiently assemble with Syntaxin and VAMP2, and thus, promoting the vesicle docking and priming process. This is a systematic analysis that clarifies the role of Rph3A in regulated exocytosis and provides novel insight into the underlying molecular mechanisms.

Reviewer #3 (Public Review):

In this ms Li et al. examine the molecular interaction of Rabphilin 3A with the SNARE complex protein SNAP25 and its potential impact in SNARE complex assembly and dense core vesicle fusion.

Overall the literature of rabphilin as a major rab3/27effector on synaptic function has been quite enigmatic. After its cloning and initial biochemical analysis, rather little new has been found about rabphilin, in particular since loss of function analysis has shown rather little synaptic phenotypes (Schluter 1999, Deak 2006), arguing against that rabphilin plays a crucial role in synaptic function.

While the interaction of rabphilin to SNAP25 via its bottom part of the C2 domain has been already described biochemically and structurally in the Deak et al. 2006, and others, the authors make significant efforts to further map the interactions between SNAP25 and rabphilin and indeed identified additional binding motifs in the first 10 amino acids of SNAP25 that appear critical for the rabphilin interaction.

Using KD-rescue experiments for SNAP25, in TIRF based imaging analysis of labeled dense core vesicles showed that the N-terminus of SN25 is absolutely essential for SV membrane proximity and release. Similar, somewhat weaker phenotypes were observed when binding deficient rabphilin mutants were overexpressed in PC12 cells coexpressing WT rabphilin. The loss of function phenotypes in the SN25 and rabphilin interaction mutants made the authors to claim that rabphilin-SN25 interactions are critical for docking and exocytosis. The role of these interaction sites were subsequently tested in SNARE assembly assays, which were largely supportive of rabphilin accelerating SNARE assembly in a SN25 -terminal dependent way.

Regarding the impact of this work, the transition of synaptic vesicles to form fusion competent trans-SNARE complex is very critical in our understanding of regulated vesicle exocytosis, and the authors put forward an attractive model forward in which rabphilin aids in catalyzing the SNARE complex assembly by controlling SNAP25 a-helicalicity of the SNARE motif. This would provide here a similar regulatory mechanism as put forward for the other two SNARE proteins via their interactions with Munc18 and intersection, respectively.

While discovery of the novel interaction site of rabphilin with the N-Terminus of SNAP25 is interesting, I have issues with the functional experiments. The key reliance of the paper is whether it provides convincing data on the functional role of the interactions, given the history of loss of function phenotypes for Rabphilin. First, the authors use PC12 cells and dense core vesicle docking and fusion assays. Primary neurons, where rabphilin function has been tested before, has unfortunately not been utilized, reducing the impact of docking and fusion phenotype.

In particular the loss of function phenotype in figure 3 of the n-terminally deleted SNAP25 in docking and fusion is profound, and at a similar level than the complete loss of the SNARE protein itself. This is of concern as this is in stark contrast to the phenotype of rabphilin loss in mammalian neurons where the phenotype of SNAP25 loss is very severe while rabphilin loss has almost no effect on secretion. This would argue that the N-terminal of SNAPP25 has other critical functions besides interacting with rabphilin. In addition, it could argue that the n-Terminal SNAP25 deletion mutant may be made in the cell (as indicated from the western blot) but may not be properly trafficked to the site of release.

Reviewer #3 (Public Review):

The manuscript by Le T.D.V. et al used in vitro cell culture and inhibitors for cellular signaling molecules and found that GLP-1 receptor activation stimulated the phosphorylation of Raptor, which was PKA-mediated and Akt-independent. The authors reported the physiological function of this GLP-1R-PKA-Raptor in liraglutide stimulated weight loss. This timely study has high significance in the field of metabolic research for the following reasons.

(1) The authors' findings are significant in the field of obesity research. GLP-1 receptor (GLP-1R) is a successful target for diabetes (and weight loss) therapeutics. However, the mechanisms of action for the weight-loss effect of GLP-1 agonists are not fully understood. Therefore, mechanistic studies to elucidate the signaling pathways of GLP-1 receptors pertaining to weight loss at the cellular level are timely.

(2) G protein-coupled receptors (GPCRs) induces various signaling activities, which could be cellular and tissue specific. As these are an important protein family for drug targeting, understanding the basic biology of these receptors is of interest to a broad readership.

(3) The authors have made important discoveries that Exendin-4 stimulated mTORC1 signaling was essential for the anorectic effect induced by Exendin-4. The study reported in this current manuscript provides more details of brain GLP-1R signaling pathways and is innovative.

Overall, the authors have presented sufficient background in a clear and logically organized structure, clearly stated the key question to be addressed, used the appropriate methodology, produced significant and innovative main findings, took potential caveats into consideration, and made a justified conclusion.

The manuscript can be further strengthened with more clarification on the following points.

1. In Figure 1 panels B and C, please provide the quantification for pCREB/CREB. In Figure 1 panel D, please provide the quantification for pAkt/Akt.

2. The western blots to assess the signaling activities revealed the phosphorylation status of the key signaling molecules at a single time point. Whether the overall signaling dynamics have been affected is unclear.

3. Figure 3 panels A and B demonstrated the remarkable importance of the Ser791 Raptor. However, this PKA-resistant mutant did not completely abolish the weight loss effect of Liraglutide. The authors pointed out the importance of AMPK in mTORC1 signaling. Other pathways that may complement GLP-1R-PKA-Raptor signaling can be further discussed.

4. Food intake was decreased on day 2 in Figure 3D but became comparable between WT and S791A Raptor groups on the following days. Could this be due to some compensatory mechanisms?

Reviewer #1 (Public Review):

This is an interesting manuscript that uses cell culture models to demonstrate the activation of mTORC1 by GPCR (GLP1R) PKA signaling and then goes on to use a PKA-insensitive mutant raptor-expressing mouse like to imply the importance of this PKA-dependent mTORC1 signaling for GLP1R agonist-dependent weight loss.

There are some important weaknesses in the manuscript as it currently stands, however:<br /> 1. There is no information on the mouse model, other than statements about the model expressing the mutant raptor in all cells and tissues. It is impossible to evaluate the results of this manuscript without some information on the genetics of the model, and some data showing the expression of the mutant, however.

2. The in vivo (mouse data) doesn't show the specificity of the weight-loss effects of GPCR-PKA signaling.

3. The cell culture data appear not to permit the direct comparison of results among conditions - is there no attenuation of Insulin-mediated pS6 by KT and no attenuation of Lira-mediated pS6 by MK? Relatedly, why does MK block FSK-mediated pS6?

Reviewer #1 (Public Review):

This work focuses on the mechanisms that underlie a previous observation by the authors that the type VI secretion system (T6SS) of a Pseudomonas chlororaphis (Pchl) strain can induce sporulation in Bacillus subtilis (Bsub). The authors bioinformatically characterize the T6SS system in Pchl and identify all the core components of the T6SS, as well as 8 putative effectors and their domain structures. They then show that the Pchl T6SS, and in particular its effector Tse1, is necessary to induce sporulation in Bsub. They demonstrate that Tse1 has peptidoglycan hydrolase activity and causes cell wall and cell membrane defects in Bsub. Finally, the authors also study the signaling pathway in Bsub that leads to the induction of sporulation, and their data suggest that cell wall damage may lead to the degradation of the anti-sigma factor RsiW, leading to activation of the extracellular sigma factor σW that causes increased levels of ppGpp. Sensing of high ppGpp levels by the kinases KinA and KinB may lead to phosphorylation of Spo0F, and induction of the sporulation cascade.

The findings add to the field's understanding of how competitive bacterial interactions work mechanistically and provide a detailed example of how bacteria may antagonize their neighbors, how this antagonism may be sensed, and the resulting defensive measures initiated.

While several of the conclusions of this paper are supported by the data, additional controls would bolster some aspects of the data, and some of the final interpretations are not substantiated by the current data.

- The Bsub signaling pathway that is proposed is intricate and extensive as shown in Fig 5A. However, the data supporting that is very sparse:<br /> a) The authors show no data showing that the proteases PrsW and/or RasP, or the extracellular sigma factor σW are necessary, or that the cleavage of RsiW is needed, for induction of sporulation - this could presumably be tested using mutants of those genes.<br /> b) Similarly, they don't demonstrate that the levels of ppGpp increase in the cell upon exposure to Pchl.<br /> c) There is some data showing that kinA and kinB mutants don't induce sporulation (Fig supplement 7A), but that is lacking the 'no attacker' control that would demonstrate an induction.<br /> d) There is some data showing that RsiW may be cleaved (Fig 5C, D), but that data would benefit from a positive control showing that the lack of YFP foci is seen in a condition where RsiW is known to be cleaved, as well as from a time-course showing that the foci are present prior to the addition of Tse1, and then disappear. As it is shown now, it is possible that the addition of Tse1 just blocks the production of RsiW or its insertion into the membrane (especially given the membrane damage seen). Further, there is no data that the disappearance of the YFP loci requires the proteases PrsW and /or RasP - such data would also support the idea that the disappearance is due to cleavage of RsiW.<br /> - The entire manuscript suggests that T6SS is solely responsible for the induction of sporulation. While T6SS does appear to play a major part in explaining the sporulation induction seen, in the absence of 'no attacker' controls for Fig. 2A, it is impossible to see this. From the data shown in Fig. 2C, and figure supplement 2A, the 'no attacker' sporulation rate seems to be ~20%, while the rate is ~40% with Pchl strains lacking T6SS, suggesting that an additional factor may be playing a role.

Reviewer #3 (Public Review):

The authors identify tse1, a gene located in the type 6 secretion system (T6SS) locus of the bacterium Pseudomonas chlororaphis, as necessary and sufficient for induction of Bacillus subtilis sporulation. The authors demonstrate that Tse1 is a hydrolase that targets peptidoglycan in the bacterial cell wall, triggering activation of the regulatory sigma factor sigma-w. The sporulation-inducing effects of sigma-w are dependent on the downstream presence of the sensor histidine kinases KinA and KinB. Overall, this is a well-structured paper that uses a combination of methods including bacterial genetics, HPCL, microscopy, and immunohistochemistry to elucidate the mechanism of action of Tse1 against B. subtilis peptidoglycan. There are some concerns regarding a few experimental controls that were not included/discussed and (in a few figures) the visual representation of the data could be improved. The structure of the manuscript and experiments is such that key questions are addressed in a logical flow that demonstrates the mechanisms described by the authors.

To begin, we have concerns regarding the sporulation assays and their results. The data should be presented as "Percent sporulation" or "Sporulation (%)" - not as a "sporulation rate": there is no kinetic element to any of these measurements, so no rate is being measured (be careful of this in the text as well, for instance near lines 204). More importantly, there is no data provided to indicate that changes in percent spores are not instead just the death of non-sporulated cells. For example, imagine that within a population of B. subtilis cells, 85% of the cells are vegetative and 15% are spores. If, upon exposure to tse1, a large proportion of the vegetative cells are killed (say, 80% of them), this could lead to an apparent increase in sporulation: from 15% for the untreated population to ~50% of the treated, but the difference would be entirely due to a change in the vegetative population, not due to a change in sporulation. The authors need to clearly describe how they conducted their sporulation assays (currently there is no information about this in the methods) as well as provide the raw data of the counts of vegetative cells for their assays to eliminate this concern.

A related concern is regarding the analysis of the kinases and the effects of their deletions on the impact of Tse1. Previous literature shows that the basal levels of sporulation in a B. subtilis kinA or a kinB mutant are severely defective relative to a wild-type strain; these mutants sporulate poorly on their own. Therefore, the data presented on Lines 394+ and the associated Supplemental Figure regarding the sporulation defects of these two mutants are not compelling for showing that these kinases are required for this effector to act. It is likely that simply missing these kinases would severely impact the ability of these strains to sporulate at all, irrespective of the presence of Tse1, and no discussion of this confounding concern is discussed.

Another concern is regarding the statistical tests used in Figure 2. For statistical tests in A, B, and D, it should be stated whether a post-test was used to correct for multiple comparisons, and, if so, which post-test was used. For C, we suggest the inclusion of a mock control in addition to the two conditions already included (i.e., an extraction from an E. coli strain expressing the empty vector) to provide a stronger control comparison.

An additional concern regarding controls is that there is an absence of loading controls for the immunoblot assays. In Figure 5D and all immunoblot assays, there is no mention of a loading control, which is a critical control that should be included.

Some of the visualizations could be improved to help the reader understand and appropriately interpret the data presented. For instance, in Figures 3 and 4 the scale bars are different across each of the Figure's imaging panels. These should be scaled consistently for better comparison. Additionally, the red false colorization makes the printed images difficult to see. Black-and-white would be easier to see and would not subtract from the images.

An additional weakness of the paper is that the RNA-seq data is not fully investigated, and there is an absence of methods included regarding the RNA-seq differential abundance analysis (it is mentioned on L379-380 but no information is provided in the methods). As stated by the authors, 58% of differentially regulated genes belonged to the w regulon, but the other 42% of genes are not discussed, and will hopefully be a target of future investigations.

Another methodological concern in this paper is the limited details provided for the calculation of the permeabilization rate (Figure 4, L359, L662-664). It is not clear how, or if, cell density was controlled for in these experiments.

Finally, one weakness of the paper is the broad conclusions that they draw. The authors claim that the mechanism of sporulation activation is conserved across Bacilli when the authors only test one B. subtilis and one B. cereus strain. They further argue (lines 469+) that Tse1 requires a PAAR repeat for its targeting, but do not provide direct evidence for this possibility.

Reviewer #1 (Public Review):

The authors have determined the structure of OmcZ cytochrome nanowires of Geobacter sulfurreducens by Cryo-EM.

OmcZ represents the third cytochrome nanowire of Geobacter to be structurally resolved. The structure reveals an octaheme cytochrome which oligomerizes to form an extended filament which scaffolds a continuous chain of hemes which serves to support long-range electron transport to terminal electron acceptors.

Previously identified nanowires which have been structurally resolved consisted of oligomers of OmcS and OmcE which, although lacking significant sequence identity, shared a common heme arrangement along the filament/nanowire.

OmcZ differs structurally from OmcS and OmcE, possessing a notably different heme chain configuration. OmcZ also differs from OmcS/OmcE in the nature of the interactions at the interface between subunits. Whilst in OmcS/OmcE a terminal heme is ligated by a histidine from the adjacent subunit of the wire, in OmcZ the terminal heme is ligated by a histidine within the same subunit, highlighting yet another difference between OmcZ and OmcS/E.

Based upon these observations, the authors suggest that OmcS and OmcE evolved from a common ancestor and that OmcZ evolved independently of OmcS/E. This is significant as it not only reveals the diversity of cytochrome nanowires which support long range electron transfer in Geobacter but also demonstrates that this mechanism of EET has potentially evolved multiple times and is likely to be exploited by other environmental microbes which utilize extracellular electron transport to support respiration.

Manuscript Strengths:<br /> The manuscript presents a solid detailed structural analysis of OmcZ providing new insight into the diverse range of electron transfer pathways utilized by Geobacter. By comparing OmcZ with other cytochrome nanowires of Geobacter (OmcS/OmcE) and with other electron transfer proteins such as the MtrABC complex, additional insight is gained into potential electron transfer properties of this cytochrome nanowire.

Manuscript Weaknesses:<br /> The manuscript compares previous characterisations of OmcZ filaments by X-ray scattering/IR nanospectroscopy prepared at pH 2 and pH 7 which indicated a higher percentage of alpha-helices and beta-sheets than what was observed by Cryo-EM from filaments prepared at pH 10.5 (this study). Due to the differences observed, it is suggested these previously utilized techniques are unreliable. Although there is a substantial difference in the proportion of beta-sheet that is observed/indicated between different methods, without a direct comparison available at the same pH it is perhaps not possible to attribute differences to the techniques alone.

Manuscript Impact:<br /> Through this work, the authors have made a significant contribution to the knowledge surrounding the electron transfer processes of Geobacter. Based on the structure obtained, they have sought to rationalise observed phenotypes associated with the different cytochrome nanowires and intriguingly propose how OmcZ may allow for more conductive biofilms through the formation of meshes of OmcZ filaments capable of exchanging electrons at solvent exposed hemes.

This manuscript will be of interest to scientists working across a range of disciplines including environmental microbiologists studying microbially driven redox processes in the subsurface, biochemists studying electron transfer proteins/pathways and in particular those working on extracellular electron transfer, and biotechnologists seeking to exploit bacterial electron transfer processes for biotechnological applications.

Reviewer #1 (Public Review):

This is an interesting paper, which has used cutting-edge approaches (DMS and ML) to probe an important phenomenon in protein function, namely allostery. The paper managed to acquire a large volume of data and to use this data efficiently to train ML models, which are then used to probe the question of why are some regions "allosteric" hot spots. The results are interesting and novel and suggest that despite structural homology, hotspot regions can differ among relatively close relatives, nevertheless, there are common mechanisms underpinning the allosteric mechanisms, likely linked to the conformational sampling of the proteins.

Strengths - To me, the strengths of the paper are predominantly in the experimental work, there's a huge amount of data generated through mutagenesis, screening, and DMS. This is likely to constitute a valuable dataset for future work. The experimental data allows mapping of the hotspots and much of the paper would be the same in terms of analysis without the ML, I think the experimental work with structural and sequence analysis would probably constitute a complete and impactful study alone, such is the quality. The ML obviously adds another layer of insight into the project. What is shown is that training on one homolog can allow the prediction of hotspots on related homologs. To some degree, this is as expected given these proteins share a common fold and function, yet the fact it is possible (albeit imperfect) despite quite a low sequence identity is notable.

Weaknesses - it is hard to describe this as a weakness, but the ML is obviously not perfect in the predictions, yet is still interesting. I don't have any major suggestions for revisions or changes - it is what it is and I think serves as a nice benchmark for follow-up studies with new methods and approaches. I think this reiterates the importance that the raw data is made available so that it can be used to benchmark alternative approaches and help advance the field. Scientifically, I think what is perhaps missing, and I don't want this to be misconstrued as a request for additional work, is a deeper analysis of the structural and dynamic molecular basis for the observations. In some ways, the ML is used to replace this and I think it doesn't do as good a job. It is clear for example that there are common mechanisms underpinning the allostery between these proteins, but they are left hanging to some degree. It should be possible to work out what these are with further biophysical analysis. To me, it is clear what we see here is likely some conservation in the dynamics of these proteins across the superfamily, and the allosteric mechanism involves modulation of the conformational sampling - which can happen through mutations/binding at different regions. Actually testing that hypothesis experimentally/computationally would be nice (rather than relying on inference from ML).

Achievement of aims: I think the aims are achieved, with the caveat as mentioned above, that the molecular basis for the observations is not really investigated or tested. The results support many of the conclusions, but without biophysical analysis, there is unavoidably some speculation in the discussion (which is reasonable and fine).

Impact: I think this will be impactful. I am sure others will love to get their hands on the data to run their own ML studies on, and the conclusions are interesting and impactful (seeing "deep" shared allostery across a fold). I think it is consistent with our understanding that protein folds have deep shared conformational tendencies, and that conformational sampling is at the core of much of what we term allostery.

Reviewer #3 (Public Review):

Leander et al used deep mutational scanning to assess the effect of nearly all possible point mutations on four homologous bacterial allosteric transcription factors (aTFs). In particular, they identified mutations that abrogated the transcription factor response to a small molecule effector. The authors go on to use machine learning to determine which physicochemical properties distinguish mutations with allostery-eliminating effects from those without an effect. They report that mutations that eliminate the allosteric response to small molecules are quite variable across homologs and that global features are more predictive of which mutations will break allostery relative to local properties. Overall, the experimental strategy is well-chosen, and a comprehensive comparison of mutational sensitivity across allosteric homologs is highly important to understand how conserved (or not) the implementation of allostery is across homologs. Moreover, the idea to use machine learning to assess which features are most predictive of "allosteric hotspots" is very nice, and provides some insight into what physical properties distinguish mutations that influence allostery. The authors include some interesting results on transfer learning (evaluating whether models trained on one protein predict allostery in another), and the use of alternate sequence representations (e.g. UniRep) in their machine learning analyses. However - at least in the manuscript's present form - the paper suffers from key conceptual difficulties and a lack of rigor in data analysis that substantially limits one's confidence in the authors' interpretations. More specifically:

1) A key conceptual challenge shaping the interpretation of this work lies in the definition of allostery, and allosteric hotspot. The authors define allosteric mutations as those that abrogate the response of a given aTF to a small molecule effector (inducer). Thus, the results focus on mutations that are "allosterically dead". However, this assay would seem to miss other types of allosteric mutations: for example, mutations that enhance the allosteric response to ligand would not be captured, and neither would mutations that more subtly tune the dynamic range between uninduced ("off) and induced ("on") states (without wholesale breaking the observed allostery). Prior work has even indicated the presence of TetR mutations that reverse the activity of the effector, causing it to act as a co-repressor rather than an inducer (Scholz et al (2004) PMID: 15255892). Because the work focuses only on allosterically dead mutations, it is unclear how the outcome of the experiments would change if a broader (and in our view more complete) definition of allostery were considered.

2) The experimental determination of which mutations impacted allostery is given only a limited description in the methods, but if we understand what was done, the analysis seems to neglect both (1) important caveats due to assay specifics and (2) more general limitations of deep mutational scanning data. In particular:<br /> a. The separation in fluorescence between the uninduced and induced states (the assay dynamic range, or fold induction) varies substantially amongst the four aTF homologs. Most concerningly, the fluorescence distributions for the uninduced and induced populations of the RolR single mutant library overlap almost completely (Figure 1, supplement 1), making it unclear if the authors can truly detect meaningful variation in regulation for this homolog.<br /> b. The methods state that "variants with at least 5 reads in both the presence and absence of ligand in at least two replicates were identified as dead". However, the use of a single threshold (5 reads) to define allosterically dead mutations across all mutations in all four homologs overlooks several important factors:<br /> i. Depending on the starting number of reads for a given mutation in the population (which may differ in orders of magnitude), the observation of 5 reads in the gated non-fluorescent region might be highly significant, or not significant at all. Often this is handled by considering a relative enrichment (say in the induced vs uninduced population) rather than a flat threshold across all variants.<br /> ii. Depending on the noise in the data (as captured in the nucleotide-specific q-scores) and the number of nucleotides changed relative to the WT (anywhere between 1-3 for a given amino acid mutation) one might have more or less chance of observing five reads for a given mutation simply due to sequencing noise.<br /> iii. Depending on the shape and separation of the induced (fluorescent) and uninduced (non-fluorescent) population distributions, one might have more or less chance of observing five reads by chance in the gated non-fluorescent region. The current single threshold does not account for variation in the dynamic range of the assay across homologs.<br /> c. The authors provide a brief written description of the "weighted score" used to define allosteric hotspots (see y-axis for figure 1B), but without an equation, it is not clear what was calculated. Nonetheless, understanding this weighted score seems central to their definition of allosteric hotspots<br /> d. The authors do not provide some of the standard "controls" often used to assess deep mutational scanning data. For example, one might expect that synonymous mutations are not categorized as allosterically dead using their methods (because they should still respond to ligand) and that most nonsense mutations are also not allosterically dead (because they should no longer repress GFP under either condition). In general, it is not clear how the authors validated the assay/confirmed that it is giving the expected results.<br /> 3) In several places, the manuscript lacks important statistical analyses needed to firmly establish the authors' claims<br /> a. The authors performed three replicates of the experiment, but reproducibility across replicates and noise in the assay is not presented/discussed<br /> b. In the analysis of long-range interactions, the authors assert that "hotspot interactions are more likely to be long-range than those of non-hotspots", but this was not accompanied by a statistical test (Figure 2 - figure supplement 1)

4) Data availability and analysis transparency need improvement. The raw fastq reads do not seem to be publicly available, nor did we see access to the code used to perform the analysis. If the code is not provided, the description of the analysis in the methods section needs to be more detailed for reproducibility.

Overall, these concerns with fundamental aspects of the data analysis make it challenging to assess the reproducibility of the results, the fidelity of the assay (in reporting allosterically dead mutations), and the extent to which the data robustly support the authors' claims.

Reviewer #1 (Public Review):

Pašukonis et al. sought to differentiate the explanatory power of two major hypotheses for sex differences in navigational ability: the adaptive specialization hypothesis, which links home range size and navigational ability, and the androgen spillover hypothesis, which links testosterone in males to navigational ability. To examine these alternative hypotheses, the authors quantify home range size, testosterone levels, and successful homing following translocation using three species of poison frog. Of particular interest, the authors were able to contrast species that vary in which sex has the larger home range, potentially disambiguating the relationship with androgens versus home range size, a feature that is lacking in many prior studies of sex differences in spatial ability. [While the authors cite one notable exception (Guigueno et al., 2014 on spatial ability in female cowbirds), they did not give this prior study as much weight as they probably should have.]

In many ways, this present study is a tour-de-force of field biology. Particular strengths include:

1) The combination of field-based observations with experimental intervention. Using intensive monitoring of individuals in the rainforest, the experimenters were able to delineate the size of home ranges, the maximum extent of movement, as well as specific behaviors (e.g., mating, parental transport of tadpoles) associated with different movement distances. This is particularly astonishing when extended to three different species.

2) The use of a natural navigational task. To assess navigational ability, the authors translocated individuals from their home ranges and determined the accuracy of, and success in, homing. While translocation is not exactly a natural experience (except for the rare occurrence, e.g., during an unusual flood), homing certainly is. Therefore, the author's assay tests wild animals in a real-world navigation problem. While the need for studying "cognition in nature" is widely recognized, it is often difficult to achieve.

3) The inclusion of multiple species that, while closely related, vary in sex roles. The authors include two species in which the male is predicted to have larger home ranges and one in which the female is predicted to do so. The potential strength of this feature is that it allows the authors to contrast the explanatory power of the adaptive specialization hypothesis - which would predict the sex with the larger home range will be more accurate and successful in homing - with the androgen spillover hypothesis - which would predict males (with their higher androgen levels) to be more accurate and successful in homing, regardless of home range size.

While the study offers a thorough and complex view of space-use and navigation in poison frogs, the study is held back by some weaknesses:

1) The comparison of accurate/successful homing across species is hampered by the application of discrete displacement distances that are not scaled to the species' natural movements. The three study species, chosen for their differences in reproductive sex roles, also differ considerably in their natural range of movements. Exploratory movements, whether near or far, give individuals the necessary experiences that familiarize them with areas so that later they can successfully/accurately return home from those areas. As a consequence, displacing O. sylvatica by 50 meters - a distance that may well be outside the range of prior experience - is unlikely to have the same significance as displacing A. femoralis by 50 meters - a species that regularly move tens of meters in a day. Species differences in accuracy/success in homing may simply reflect differences in experience, but not differences in spatial ability.

2) The authors' main conclusion is that their results contradict the adaptive specialization hypothesis for sex differences, but their results are more complex. Oophaga sylvatica is the one study species that provides the best test of this hypothesis, as the females have larger home range sizes and lower androgens. Yet, their results with O. sylvatica, in which males and females perform similarly in homing (i.e., there is a high p-value for the effect of sex), invite us to suspend judgement as to whether the sexes differ, rather than contradicting the adaptive specialization hypothesis. Not supporting one hypothesis does not necessarily lend strong support to the alternative hypothesis. Combined with the potential methodological shortcoming of using displacement distances that are not scaled to movement distances in O. sylvatica, caution is warranted.

The relationship between androgens and exploratory behaviors is an important addition to our understanding of the complexity of sex differences in spatial ability and these results do indeed provide indirect support for the androgen spillover hypothesis. Yet, more work needs to be done to disambiguate these two hypotheses in this group. Further, the authors may want to consider that both hypotheses are simultaneously at play, contributing to different features of navigation in the two sexes, and/or that the different species won't necessarily follow the same rules.

Reviewer #1 (Public Review):

Here the authors aim to unravel the missing link between heme receptors and heme uptake into the cell and heme utilization. Previously, these authors uncovered the hemophore CSA2 and heme receptor RBT5 as the first steps in heme acquisition, but how heme is actually taken up by the cell and utilized as an Fe source was unknown. These authors identified the ferric reductase-like proteins Frp1 and Frp2 as having major roles in heme acquisition and utilization of heme as a sole Fe source. These are the first studies to demonstrate a role for members of the ferric reductase-like family in heme uptake and utilization. Although the exact mechanisms by which Frp1 and Frp2 affect the heme pathway are still unknown, these studies will inspire many new directions into microbial heme utilization at the host-pathogen interface. The paper is well written for a diverse audience, the experiments are comprehensive and the results are consistent with the conclusions.

Reviewer #1 (Public Review):

Thyrring et al. provide a nice experiment testing the role of ocean acidification on the survival of two bivalve species. This novel work is fundamental in setting a more mechanistic understanding of the impacts of climate change on ocean species survival, and secondarily on their re-distribution across the globe. To me, the strength of the paper relies on the experimental setup, and on being honest about the limitations of metabolomics, fatty acids, and amino acids in explaining these results.

Reviewer #3 (Public Review):

The authors assess response to ocean acidification with three populations of mussels encompassing two species: Mytilus trossulus from the intertidal and subtidal and M. galloprovincialis from a subtidal aquaculture farm. All three species received an ambient of low pH treatment prior to a freezing treatment. The authors find species differences in freeze tolerance in mussels, with intertidal M. galloprovincialis showing the least freeze tolerance. The authors go a step further and do a comprehensive assessment of the metabolic capacity and molecular components with analyses of amino acids, fatty acids, and osmolytes and anaerobic byproducts.

The authors hypothesized metabolic changes due to OA and cold temperatures, yet they demonstrated a significant amount of stasis with high similarity among species at the molecular level. The fatty acids in the intertidal M trossulus, the most freeze tolerant, did not change. Further, there is little explanation of molecular/metabolic changes that could explain their results. Because of this somewhat unexpected lack of signal of these stressors, I would like to see an enhanced explanation of animal homeostasis. The authors mention previous results relating to heat stress, and I thought it would be beneficial to discuss how the lack of a molecular response to freezing is related to the strong responses seen in heat stress.

The idea that species in fluctuating environments (here, the intertidal) might respond differently to those in constant environments (here, the subtidal) has been explored in multiple systems. These general concepts could be elaborated on more in the paper to increase the connection to other studies.

Reviewer #1 (Public Review):

In this collaborative and comparative modeling paper, three groups of investigators with well-validated mathematical models of the natural history of cervical cancer explored the potential impact of disruptions in screening services such as those associated with COVID-19 on cancer incidence. Given known disparities in access to regular screening in the United States, the authors were particularly interested in identifying heterogeneity of effects - would externally imposed restrictions on screening have a disproportionate effect on women already at increased risk because of access issues such as prolonged intervals between screening, or reliance on less sensitive screening tests?

Strengths:

--The authors used three existing, well-validated cervical cancer natural history to compare results. This comparative approach, used by these authors as well as other collaborators within NCI's Cancer Intervention and Surveillance Modeling Network (CISNET), improves confidence in the overall validity and robustness of the results, given qualitatively similar findings across models that differ in terms of structure and underlying assumptions.<br /> --The models have previously been used in the context of US screening policy.<br /> --The models used birth cohorts as well as screening frequency, which accounts for age-period-cohort effects on both risk of HPV and cervical cancer as well as competing risks such as other causes of mortality and hysterectomy.<br /> --Cervical cancer screening both detects pre-malignant lesions and allows prevention of cervical cancer, leading to decreased incidence, and, for those lesions which have progressed to invasive cancer, detects asymptomatic lesions, leading to decreased morbidity and improved survival. The use of "symptomatically detected cancers" as the primary outcome of interest is appropriate.<br /> --The qualitative results are consistent with previous modeling results in the context of screening program design--the effects of a short-term delay in screening are greatest for women with a longer time since the most recent screen, or for women screened with less sensitive (cytology) compared to more sensitive (HPV) modalities. These findings were true for both short- and long-term impacts.<br /> --The policy recommendation to prioritize outreach and appointment availability for catch-up when restrictions are lifted to women who do not have up-to-date screening according to guidelines is supported by the findings.

Limitations:

--The limitations are, for the most part, those inherent in any modeling exercise and are well described and discussed by the authors.<br /> --As the authors note, the models do not explicitly incorporate disparate impacts by race/ethnicity or other social determinants of health, and thus cannot explicitly highlight disparities within specific groups.<br /> --Potential effects on cervical cancer mortality are not captured. Given the high survival of stage I cervical cancer and, in most cases, the relatively slow progression of disease, it seems plausible that even an increase in symptomatically diagnosed disease will not have a detectable effect on mortality if there is not a shift in stage distribution; however, given that treatment of invasive cervical cancer has much greater risk of short- and long-term morbidity compared to treatment of preinvasive lesions, there is likely to be an impact on quality of life if not survival.<br /> --Related, if the factors affecting underscreening are ALSO associated with delays in care once symptoms develop, there is a potential for disparate effects on morbidity and mortality as well.

These results should prove useful to policy makers, clinicians, and patients, both in helping identifying women for prioritizing access to screening services when availability is constrained or restored, and for reassuring those women who do have up-to-date screening that delays are unlikely to significantly affect their risk of developing cervical cancer.

Reviewer #1 (Public Review):

This paper introduces a detailed computational model for synaptic plasticity, that is innovative in a number of ways. First, it includes the stochastic character of many of the biophysical processes. Second, it introduces a new way to readout the plasticity cascade. Third, it fits a number of experiments that previous models could not fit. It is a complicated model and presents a step forward towards a realistic model of synaptic plasticity. The readout mechanism is artificial but does the job well.

Reviewer #3 (Public Review):

This manuscript presents and analyzes a novel calcium-dependent model of synaptic plasticity combining both presynaptic and postsynaptic mechanisms, with the goal of reproducing a very broad set of available experimental studies of the induction of long-term potentiation (LTP) vs. long-term depression (LTD) in a single excitatory mammalian synapse in the hippocampus. The stated objective is to develop a model that is more comprehensive than the often-used simplified phenomenological models, but at the same time to avoid biochemical modeling of the complex molecular pathways involved in LTP and LTD, retaining only its most critical elements. The key part of this approach is the proposed "geometric readout" principle, which allows to predict the induction of LTP vs. LTD by examining the concentration time course of the two enzymes known to be critical for this process, namely (1) the Ca2+/calmodulin-bound calcineurin phosphatase (CaN), and (2) the Ca2+/calmodulin-bound protein kinase (CaMKII). This "geometric readout" approach bypasses the modeling of downstream pathways, implicitly assuming that no further biochemical information is required to determine whether LTP or LTD (or no synaptic change) will arise from a given stimulation protocol. Therefore, it is assumed that the modeling of downstream biochemical targets of CaN and CaMKII can be avoided without sacrificing the predictive power of the model. Finally, the authors propose a simplified phenomenological Markov chain model to show that such "geometric readout" can be implemented mechanistically and dynamically, at least in principle.

Importantly, the presented model has fully stochastic elements, including stochastic gating of all channels, stochastic neurotransmitter release and stochastic implementation of all biochemical reactions, which allows to address the important question of the effect of intrinsic and external noise on the induction of LTP and LTD, which is studied in detail in this manuscript.

Mathematically, this modeling approach resembles a continuous stochastic version of the "liquid computing" / "reservoir computing" approach: in this case the "hidden layer", or the reservoir, consists of the CaMKII and CaM concentration variables. In this approach, the parameters determining the dynamics of these intermediate ("hidden") variables are kept fixed (here, they are constrained by known biophysical studies), while the "readout" parameters are being trained to predict a target set of experimental observations.

Strengths:

1) This modeling effort is very ambitious in trying to match an extremely broad array of experimental studies of LTP/LTD induction, including the effect of several different pre- and post-synaptic spike sequence protocols, the effect of stimulation frequency, the sensitivity to extracellular Ca2+ and Mg2+ concentrations and temperature, the dependence of LTP/LTD induction on developmental state and age, and its noise dependence. The model is shown to match this large set of data quite well, in most cases.

2) The choice for stochastic implementation of all parts of the model allows to fully explore the effects of intrinsic and extrinsic noise on the induction of LTP/LTD. This is very important and commendable, since regular noise-less spike firing induction protocols are not very realistic, and not every relevant physiologically.

3) The modeling of the main players in the biochemical pathways involved in LTP/LTD, namely CaMKII and CaN, aims at sufficient biological realism, and as noted above, is fully stochastic, while other elements in the process are modeled phenomenologically to simplify the model and reveal more clearly the main mechanism underlying the LTP/LTD decision switch.

4) There are several experimentally verifiable predictions that are proposed based on an in-depth analysis of the model behavior.

Weaknesses:

1) The stated explicit goal of this work is the construction of a model with an intermediate level of detail, as compared to simplified "one-dimensional" calcium-based phenomenological models on the one hand, and comprehensive biochemical pathway models on the other hand. However, the presented model comes across as extremely detailed nonetheless. Moreover, some of these details appear to be avoidable and not critical to this work. For instance, the treatment of presynaptic neurotransmitter release is both overly detailed and not sufficiently realistic: namely, the extracellular Ca2+ concentration directly affects vesicle release probability but has no effect on the presynaptic calcium concentration. I believe that the number of parameters and the complexity in the presynaptic model could be reduced without affecting the key features and findings of this work.

2) The main hypotheses and assumptions underlying this work need to be stated more explicitly, to clarify the main conclusions and goals of this modeling work. For instance, following much prior work, the presented model assumes that a compartment-based (not spatially-resolved) model of calcium-triggered processes is sufficient to reproduce all known properties of LTP and LTD induction and that neither spatially-resolved elements nor calcium-independent processes are required to predict the observed synaptic change. This could be stated more explicitly. It could also be clarified that the principal assumption underlying the proposed "geometric readout" mechanisms is that all information determining the induction of LTP vs. LTP is contained in the time-dependent spine-averaged Ca2+/calmodulin-bound CaN and CaMKII concentrations, and that no extra elements are required. Further, since both CaN and CaMKII concentrations are uniquely determined by the time course of postsynaptic Ca2+ concentration, the model implicitly assumes that the LTP/LTD induction depends solely on spine-averaged Ca2+ concentration time course, as in many prior simplified models. This should be stated explicitly to clarify the nature of the presented model.

3) In the Discussion, the authors appear to be very careful in framing their work as a conceptual new approach in modeling STD/STP, rather than a final definitive model: for instance, they explicitly discuss the possibility of extending the "geometric readout" approach to more than two time-dependent variables, and comment on the potential non-uniqueness of key model parameters. However, this makes it hard to judge whether the presented concrete predictions on LTP/LTD induction are simply intended as illustrations of the presented approach, or whether the authors strongly expect these predictions to hold. The level of confidence in the concrete model predictions should be clarified in the Discussion. If this confidence level is low, that would call into question the very goal of such a modeling approach.

4) The authors presented a simplified mechanistic dynamical Markov chain process to prove that the "geometric readout" step is implementable as a dynamical process, at least in principle. However, a more realistic biochemical implementation of the proposed "region indicator" variables may be complex and not guaranteed to be robust to noise. While the authors acknowledge and touch upon some of these issues in their discussion, it is important that the authors will prove in future work that the "geometric readout" is implementable as a biochemical reaction network. Barring such implementation, one must be extra careful when claiming advantages of this approach as compared to modeling work that attempts to reconstruct the entire biochemical pathways of LTP/LTD induction.

Reviewer #1 (Public Review):

The paper by Snoeck et al. addresses the evolution of the recognition of inceptin, a peptide from insect saliva, by plant immune receptor INR, a member of LRR-type receptor-like protein family. As a first step, the authors surveyed how broad inceptin recognition is among legumes and found that it likely emerged in the common ancestor of Phaseolid legumes. By considering available genomic information and supplementing it with several de novo sequenced species, the authors were able to show that all extant inceptin receptor sequences form a single phylogenetic clade, supporting a single origin for INR evolution, an event that was followed by several independent losses. The authors also describe a closely related INR-like clade that lacks inceptin recognition. By considering chimeras between INR and INR-like receptors, the authors map specificity to C1 (leucine-rich repeat) and C2 (insertion domain) regions of the protein. By testing inferred ancestral INR sequences they limit the number of amino acid residues responsible for the original ability to recognize inceptin to just a few residues.

The approach is well reasoned, the two complementary functional assays - ROS time course and ethylene accumulation time point - are qualitatively concordant, and the controls - expression level in heterologous assay - appropriate. Phylogenetic conclusions are likewise well supported. The authors have also done well to make the data on newly sequenced organisms available through NCBI.

There are two aspects of the study that could be improved. One is following up on the genomic events leading to independent INR loss events. Were there deletions, transposon insertions, point mutations leading to early stop codons, etc.? The other missing part is a structural interpretation of mutations leading to inceptin recognition. While I agree with the authors that an experimental structure of INR/peptide/co-receptor would be ideal, an AlphaFold or RoseTTaFold model of the N3/N4/N14 series might highlight where the key changes occurred leading to inceptin recognition. It could also hint at the N3 function, for example, was N3 already a likely foreign peptide receptor?

Reviewer #1 (Public Review):

Ryu V et al. performed a series of elegant studies to reveal a brain atlas for glycoprotein hormone receptors (i.e. TSHRs, LHCGRs, FSHRs) using combined coordinated methods and techniques including the RNAscope to detect mRNA at the single-transcript level. They find that these receptors and genes are differentially distributed in many brain regions, nuclei, and sub-nuclei. Generally, this is a timely and important study to reveal previously unknown but important central distributions of genes encoding anterior pituitary hormone receptors, providing a key resource for scientists to study the roles played by central anterior pituitary hormone receptor signaling in physiological and pathological conditions.

The experiments were designed and performed properly. The data were analyzed and interpreted accurately and presented logically in the manuscript. The conclusions of this paper were well supported by the data.

Reviewer #3 (Public Review):

Accumulating evidence supports the expression of anterior pituitary glycoprotein hormone family of receptors, namely FSHR, TSHR, and luteinizing hormone/human chorionic gonadotropin receptor (LHCGR), in various brain regions, and their function in regulating peripheral actions. However, the link between the stimulation of these receptors in the brain and the regulation of peripheral physiological processes remains poorly understood. Using RNAscope, a cutting-edge technology that detects single RNA transcripts, the authors created a comprehensive neuroanatomical atlas of glycoprotein hormone receptors in the mouse brain. Overall, these are a very comprehensive and well-done set of studies that offer new insights into the distributed brain network of anterior pituitary hormone receptors. The atlas provides an important resource for scientists to explore the link between the stimulation or inactivation of these receptors on somatic function.

Reviewer #1 (Public Review):

This manuscript analyzes COVID-19 associated mortality in the pre-Omicron and Omicron eras to assess whether there is evidence of lower mortality associated with the Omicron variant in a large population spanning multiple countries. They used population-level data on variant frequency to infer the time periods when Omicron emerged in different countries. While there are weaknesses associated with this assumption which are well discussed by the authors, they provide a validation analysis with individual-level data from a smaller subsample suggesting that the categorization of pre-Omicron and Omicron periods is able to correctly discriminate between patients infected with different variants in the vast majority of cases. We can therefore have high confidence that the patients in the analysis are in most cases correctly identified as being likely to be infected with Omicron. The advantage of using the population-level definition is of course to allow using much larger sample sizes to determine the mortality risk associated with different variants.

Many of the tables presented suggest that the clinical characteristics of patients differed substantially in the pre-Omicron and Omicron periods, so that it is necessary to adjust for many of these characteristics (age, vaccination status, comorbidities) in order to compare mortality rates. The analysis also adjusts for country-level effects by including a random effect in the model, so that the odds ratios can be interpreted as being the average country-level effect on mortality of Omicron emergence. The results strongly suggest that after adjusting for country-level changes in clinical characteristics of patients, the risk of mortality was lower for patients hospitalized with COVID-19 during the Omicron era than previously.

There are reasons to be cautious about interpreting the results as being entirely due to differences in variant virulence, which I think are well discussed by the authors, including potential residual confounding, and potential increases in incidental infections in patients hospitalized for non-COVID-19 reasons, which would lead to a lower mortality rate in the Omicron era independently of changes in variant virulence. However, the consistency of the results with other sources of data suggests there is good reason to believe in my opinion that at least some of the observed differences in mortality risk can be attributed to lower virulence of Omicron.

While the analysis includes data from multiple countries, the vast majority of observations came from two countries (UK and South Africa); the study, therefore, has limited power to assess if there are differences across countries.

Reviewer #3 (Public Review):

The authors combine outcomes data from patients hospitalised with COVID-19 across 30 countries to investigate differences in likelihood of death from the Omicron variant vs pre-Omicron variants. Data are from the ISARC COVID-19 database; variant status is inferred from country-specific GISAID data. The principal finding is a 36% reduced risk of 14-day death in the Omicron period (OR 0.64 (0.59 - 0.69)) compared with the pre-Omicron period, after multiple adjustment.

The strengths of this paper are the large N and large number of participating countries from different regions, and also the careful and thorough analytical approaches. The main findings are stress-tested through a range of sensitivity analyses using different variant-dominance thresholds and statistical approaches and found to be robust. The figures are clear, well-chosen and easily interpretable.

The principal weaknesses, as acknowledged in the discussion, are the imbalance in the data sources (96.6% of the observations came from GBR or SA), and the lack of fidelity of data on vaccination (vaccination status is limited to a binary 'one or more vaccinations received Y/N' variable). This latter means that conclusions about the innate severity of Omicron vs pre-Omicron variants cannot be drawn.

Nonetheless the findings represent a useful contribution to the literature on the severity of COVID-19 variants, and the approach establishes a template for rapid international collaboration, using GISAID data to infer variant status, that will be useful for formulating policy in response to new variants in the future.

Reviewer #1 (Public Review):

This is a beautiful paper, which blends strong theoretical results (very well organised in the supplementary material) with intuitive descriptions of the results. The novelty of the theoretical developments in their own right is perhaps eclipsed by similar recent theoretical work in deep learning around the neural tangent kernel, but it is nevertheless great to see these ideas shed light on neural phenomena -- and this paper does this very well. We found that the study is given just the right scope: two learning tasks of increasing difficulty, both simple enough to enable mathematical analysis yet close enough to the type of tasks used in neuroscience as to enable meaningful comparisons to neural data. It is rare enough to be mentioned: the figure are beautiful and we found them of very high illustratory value (e.g. Figs 3 and 7, in particular, allowed us to understand the main results in a matter of seconds). We haven't found any issue in the analysis and the paper is in great shape already.

Reviewer #1 (Public Review):

Primordial germ cells are formed in the posterior pole of developing Drosophila embryo via taking up of maternally supplied germline determinants (a.k.a., germ plasm). PGC formation occurs approximately at the stage of 10th nuclear division cycle, located between minor and major ZGA waves which take place in somatic nuclei. Zelda and CLAMP are two key factors essential for global zygotic genome activation in soma. Since Zelda mutant retain apparently intact PGCs, Zelda has been thought to be dispensable for PGC formation. However, in this study, the authors identified slight loss of PGC number in both mutants lacking Zelda and CLAMP, which led authors propose a model in which somatic ZGA factors influence PGC specification.

The authors show that maternal or zygotic RNAi against Zelda or CLAMP caused abnormally broader distribution of germ plasm and resulted in an abnormal positioning of PGCs slightly away from posterior poles. The authors suggest that germline determinants are not efficiently captured by the cellularizing PGCs. As a result, the number of specified PGCs was slightly fewer. The Authors further show abnormal segregation of centrosomes accompanied with (and may be a cause of) an abnormal germ plasm trafficking. Moreover, authors show an aberrant pattern of gene expression both in soma and PGC, such as reduction of dpp transcript in posterior region, reduction of tll in posterior, and increased slam and sxl-pe in nascent PGCs when their global transcript is normally silent, suggesting that the germline-soma distinction is compromised in these mutants.

Strengths:

Historically, PGC specification in Drosophila has been believed to occur mainly by preformation-based mechanism. However, the authors focus on extrinsic regulations, particularly, function of centrosomes and cytoskeletons in proper transport of germ plasm components. This is a certainly important aspect to understand similarity and differences of PGC specification mechanism across species. The same group has demonstrated several mutant conditions causing aberrant extrinsic regulation of PGC specification in the past, and thus they are uniquely suited to pursue this line. The authors monitor germ plasm localization and gene expression by smFISH, which enables quantitative analyses. Detection of nascent transcript also reports zygotic transcription in a highly quantitative manner.

Weaknesses:

Overall the manuscript is descriptive and does not clearly provide functional interpretations of observed phenotypes. Specifically, the authors need to consider and discuss potential mechanism of this process.

Reviewer #1 (Public Review):

This study is a follow-up to the previous work by the authors in establishing a surprising role for the presynaptic adhesion molecules, neurexin (Nrxn) variants containing the SS4+ splice site, in differentially controlling postsynaptic NMDA and AMPA receptors by forming links through a shared system of extracellular cerebellins (Cbln) and postsynaptic GluD1. Here the authors show at CA1 to subiculum synapses, that the role for Clbn2 in mediating the effects of Nrxn1-SS4+ and Nrxn3-SS4+ in enhancing NMDAR and suppressing AMPAR, respectively, is redundant with that of Clbn1. Moreover, Clbns do not appear to play a role in synapse formation. Dai and colleagues extend their previous work also by highlighting the common function for Nrxn-Clbn signaling system across different synapses albeit with subtle differences and point to a lack of a role for Nrxn-Clbn signaling in morphological synapse development. Overall the data are solid, while the key findings are mostly incremental, and the basis for the selectivity in the observed differential regulation of AMPARs and NMDARs via the same trans-synaptic link through Clbns at various types of synapses remain to be clarified. Importantly, the authors make a definitive conclusion concerning the lack of a role for Nrxn-Cbln signaling complexes in synapse formation during development. Nevertheless, this is a contentious issue, and as such, the conclusions could be more compellingly supported with further experiments.

Reviewer #3 (Public Review):

In this study, Dai and colleagues used genetic models combined to electrophysiological recordings and behavior as well as immunostaining and immunoblotting to investigate the role of trans-synaptic complexes involving presynaptic neurexins and cerebellins in shaping the function of central synapses. The study extends previous findings from the same authors as well as other groups showing an important role of these complexes in regulating the function of central synapses. Here, the authors sought to achieve two main objectives: (1) investigating whether their previous findings obtained at mature CA1-> subiculum synapses (Aoto et al., 2013; Dai et al., Neuron 2019; Dai et al., Nature 2021) extend to different synapse subtypes in the subiculum as well as to other central synapses including cortical and cerebellar synapses and (2) investigating whether Nrx-Cbln-GluD trans-synaptic complexes play a role in synapse formation as previously proposed by other groups.

Overall, the study provides interesting and solid electrophysiological data showing that different Nrxns and Cblns assemble trans-synaptic complexes that differently regulate AMPAR and NMDA-mediated synaptic transmission across distinct synaptic circuits (most likely through binding to postsynaptic GluD receptors).

However, the study has several important weaknesses:

(1) The novelty of the findings appears limited. Indeed, previous studies from the same authors with similar experimental paradigms and readouts already demonstrated the role of Nrxn-Cbln-GluD complexes in regulating AMPARs versus NMDARs in mature neurons (Aoto et al., Cell 2013; Dai et al., Neuron 2019; Dai et al., Nature 2021). Moreover, the absence of role of Cblns and GluD receptors in synapse formation was already suggested in previous studies from the same authors (Seigneur and Sudhof, J Neurosci 2018; Seigneur et al., PNAS 2018; Dai et al., Nature 2021).

(2) The conclusion made by the authors that the Nrxn-Cbln-GluD trans-synaptic complexes do not play a role in synapse formation/development is not sufficiently supported by their data, while previous studies suggest the opposite. Actually, this conclusion is essentially based on the two following measurements taken as a 'proxy' for synapse density: (1) 'the average vGluT1 intensity calculated from the entire area of subiculum' and (2) the 'synaptic proteins levels' assessed by immunoblotting. None of these measurements (only performed in the subiculum) allow to precisely assess synapse density on the neurons of interest. While the average vGluT1 intensity over large fields of view does not directly reflect the density of synapses and does not take into account the postsynaptic compartment, the immunoblotting data only reflects the overall expression of synaptic proteins without discriminating between intracellular, surface and synaptic pools and between cell types. In the subiculum from Cbln1+2 KO mice, the authors performed mEPSCs recordings and found an increase in frequency. However, this increase may reflect the unsilencing and/or potentiation of AMPAR-EPSCs above the detection threshold, irrespectively of the actual synapse number. Finally, the decrease in NMDAR-EPSCs is not discussed by the authors while it could actually reflect a decrease in synapse number.

(3) The authors do not provide sufficient data in order to interpret the increase in AMPAR-EPSCs and decrease in NMDAR-EPSCs amplitudes. Are the changes in AMPARs and NMDARs occurring at pre-existing synapses or do they result from alterations in the number of physical synapses and/or active synapses (see point#2)? In particular, the increase in AMPAR/NMDAR ratio accompanied by the increase in mEPSCs frequency might be well explained by the unsilencing of some synapses and/or by the fact that the available pool of AMPARs is distributed over a smaller number of synapses, resulting in higher quantal size. These effects could explain the blockade of LTP, i.e., through an occlusion mechanism.

(4) The authors did not demonstrate (or did not cite relevant studies) that the deletion of Cbln1 and/or Cbln2 does not affect the expression of the remaining Cblns isoforms (Cbln2 and/or Cbln4) or Nrxns1/3 and GluD1/2. This verification is important to preclude the emergence of any compensatory effect.

Reviewer #3 (Public Review):

This manuscript details a methodological approach for the characterisation of ligands based on nuclear receptor conformational ensembles. Using ancestral steroid receptor AncSR2 and atomistic MD simulations, the authors generated ensembles of the WT and mutants of the conserved Methionine residue at position 75. The mutation, as well as the ligands (3-ketosteroid hormones and estradiol), shifted the populations into distinct conformational clusters. These clusters were then well correlated to ligand activation, making use of the cell-based luciferase assay. Next, the binding affinities of the ligands to the WT, M75L, and M75I were probed by fluorescence polarization assay to understand the extraordinary activation of M75L by estradiol (inactive ligand). The decreased binding affinity of M25L for the ligands was further investigated using differential hydrogen-deuterium exchange (HDX). The deprotection pattern observed for the M25L mutant compared to WT and decreased binding affinity of the ligands for this mutant led to the conclusion that this specific mutation shifts the ensemble conformation to a ligand-bound state.

This approach can be useful for the prediction of ligand responses, understanding underlying mechanisms, and their detailed characterisation based on the population shifts of the nuclear receptor conformational ensembles. It is commendable that the results obtained from computational techniques are well supported by a range of biochemical and biophysical techniques. Logical correlation is established between the results and light is shed on the underlying molecular mechanism through in-depth discussion. The control of the mutants based on secondary structure, melting temperature, and purity through SDS-PAGE is appreciable. The techniques are well chosen and appropriate to reach the conclusions.

Reviewer #1 (Public Review):

In this study, the authors compare computational MD simulations with functional activity data to determine if ligand activity can be predicted from simulations. As a test case, the authors use the ligand-binding domain (LBD) of an ancestral steroid receptor (AncSR2) that they and others have previously studied, providing a well-characterized system for their analyses. The studies include wild-type (WT) AncSR2 as well as four mutant proteins where a single methionine residue that contacts the steroid hormone within the pocket (Met75) was mutated (to Ala, Phe, Ile, or Leu). Computational analyses are performed to assess the stability of the complexes and determine whether the conformational ensembles generated show similarities or differences between the WT vs. mutant forms, or apo vs. ligand-bound forms (aromatic vs. 3-keto non-aromatic A-ring, EST/estrogen vs. progesterone/PROG). Simulations included conventional and accelerated methods. Clustering analysis of the accelerated simulations revealed some similarities and differences, which the authors then compare to luciferase reporter assay data (Gal4-fusion + WT vs. mutant LBDs) for the mutants where they performed dose-response experiments (up to 1 µM ligand added). One of the mutants studied did not show any activity (M75I); however, M75I and M75L both showed increased basal transcriptional activity (constitutively active) vs. WT without an exogenously added ligand. The authors developed a fluorescent ligand binding assay and showed the M75I mutant does not bind ligands (at least up to 1 µM added ligand). Next, hydrogen/deuterium exchange mass spectrometry data are provided to inform how the M75L mutant is constitutively active. The HDX results indicate that several regions display higher deuterium uptake in the M75L mutant and PROG binding has a larger destabilizing effect on WT vs. M75L. Finally, some structural snapshots from the MD simulations are shown (Fig 6A-C) that the authors claim to explain the altered transcriptional response of the M75 mutants vs. WT.

This study may be one of the first to attempt to make qualitative correlations between computational simulations of ligand-bound/free nuclear receptor LBDs and functional outcome. One could see a future where many different ligands are docked and a more quantitative, streamlined pipeline is used to predict functional outcome-this study takes the important first step in trying to determine if there are simulation-function correlations.

Peer review report

Title: Patients’ satisfaction and quality of clinical laboratory services provision at public health facilities in northeast Ethiopia

version: 1

Reviewer: I wish for this review to remain anonymous. While certainly imperfect, I believe that well- conducted reviews anonymous are preferable to signed reviews and free of the bias that may affect reviewers in a relatively small field.

General assessment

The authors report on an ambitious study that sought to rigorously assess the level of patient satisfaction with a representative sample of laboratory service facilities in the Amhara region of Ethiopia. The relevance of this topic is clearly explained and the role of patient satisfaction in the assessment and life cycle of laboratory services is likely underappreciated – particularly in low-resource settings. The manuscript is reasonably well written but would benefit from some English-language copyediting, as well as editing for length as the manuscript contains several redundant passages.

Overall, the assessment of customer satisfaction as a metric of lab quality is potentially important, and not easily captured by accreditation processes such as SLIPTA or ISO. As such this is a valuable endeavour that will stimulate the field in my view.

In the present study, the authors directly address the fact that patients experience may not reflect the quality or safety of a diagnostic laboratory. They did so by conducting their own measures of laboratory quality assessment, with the aim of establishing whether patient satisfaction is associated with such measures.

Given that this aspects is in my view the core of the study, it is important that the methods used for the quality assessments be better explained and expanded. It is laudable that the authors undertook what appears to be an external quality assurance audit of malaria and TB slides examined in the last 3 months. It is important to understand exactly who performed this examination and what their qualifications were. Moreover, other details on the methods are important such as whether the slides were re-stained at the time of the audit.

Similarly, the section on facility assessment (line 177) suggests that the investigators performed a full SLIPTA audit on participating centres. This would require a huge amount of work from both auditors and the facilities in order to be a valid account. This should be described in much more detail. I was surprised to find few references detailing Ethiopian laboratory implementation or strengthening experiences (of which there are a few instructive published examples).

Finally, the finding that satisfaction is most strongly associated with objective measures of quality – such as use of fresh gloves (pre-analytical quality), EQA results of microscopy (analytical quality) and TAT (post-analytical quality) is interesting and supports the idea that quality is not a compartmental issue, but rather a local culture that permeates all laboratory activities. This is a finding that deserves to be highlighted, even if it is unclear that patient satisfaction should be used as a surrogate for more direct measures of lab quality. The emphasis on the lack association with the SLIPTA score is overstated in my view because there wasn’t sufficient variation in these scores – i.e. they were all rather poor - to yield an association.

Decision

Verified with reservations: The content is scientifically sound but has shortcomings that could be improved by further studies and/or minor revisions.

Reviewer #1 (Public Review):

This paper considers decision-making problems when information and/or reward changes over time. It shows that the policy - the decision boundary that tells subjects when to make a decision - can have a very complicated shape; much more complicated than is typically considered. The authors use well-established techniques in reinforcement learning, but apply them in regimes where they are not normally used. Possibly the most important aspect of the paper is that it presents the relevant techniques in a reasonably accessible manner (and with a little work it could become very accessible). The paper also shows, in one non-trivial decision-making task, that normative models outperform heuristic ones by a large margin.

Reviewer #3 (Public Review):

The goal of Barendregt et al. is to extend the normative model of decision thresholds to changing environments. The immediate precursors of this work are Drugowitsch et al (2012) and Malhotra et al (2018), both of which derive optimal decision boundaries using dynamic programming. However, both those papers assumed a stationary environment. Barendregt et al. relax this assumption and show that non-stationary environments predict some very strange decision boundaries - decision boundaries can be non-monotonic or infinite, depending on the change in the environment. They consider two types of changes: change in reward and change in signal-to-noise ratio. Decision boundaries for a change in reward are particularly intriguing. To show empirical support for their theory, Barendregt et al. compare decision boundaries derived from their task with the Urgency Gating Model (UGM) and show their model shows a better fit to the data, at least under some conditions.

Here are my thoughts on the paper:

1. The theory of the paper is elegantly developed and clearly presented. While I can't be certain that there are no errors in the theory or simulation, the results presented based on this theory make intuitive sense.

2. The authors have developed the theory diligently and explored different predictions. They not only present some example thresholds for a few selected conditions but explore the space of possible types of thresholds (Figure 2C & 3C). They go further and explore the benefits of adopting this theory over UGM and constant thresholds (Figure 3) and they also show some evidence that participant behaviour is more in line with their model than UGM in a previous study (the "Tokens task").

3a. As much as I appreciate the authors' efforts (and the elegance of the theory) it seems to me that the notion of 'changing environments' explored by authors is quite limited. The decision thresholds are derived from a world in which an observer makes a (large) sequence of decisions and every decision has the exact same form of change. For example, in one of the reward-change tasks, the reward switches from low to high during every trial. In other words, the environment changes repeatedly in every trial (and in the exact same manner). There may be some circumstances in the natural world where such a setup is justified - the authors identify one where change is a function of the time of the day. But in many circumstances, the environment changes at an entirely different timescale - over the course of a sequence of trials. For example, a forging animal may make a sequence of decisions in a scarce environment, followed by another sequence of decisions in a plentiful environment. That is the statistics of the environment change over several trials. As far as I can see, the assumptions made by the authors mean that the results of the model cannot be applied to changes that occur at this timescale.

3b. One particular area where the integrate-to-threshold models have been particularly successful is perceptual decision-making. For example, in motion perception (Shadlen & Newsome, 1996) or brightness perception (Ratcliff, 2003). This is where we have evidence of something like an integration signal in the cortex. However, these decisions are typically really fast, occurring at sub-second intervals. Another area is lexical decision tasks (e.g. Wagenmakers et al, 2008), where mean reaction times are <1s, frequently a lot faster. It is difficult to imagine that the model developed by the authors has much bearing on these types of decisions - firstly because it is unlikely that the reward structure in natural environments fluctuates at these timescales and secondly because participants are unlikely to pick up on such changes over the course of a small sequence of trials.

3c. This does not mean that the model developed by Barendregt et al. is of no value. There will be situations (like the Tokens task) where the model will be the correct normative model. But these limitations are important to clarify for researchers in the field.

4. The weakest part of the paper is its empirical support. The authors apply their model to the Tokens task. First of all, this is by no means the modal task used to study decision-making. The model developed by the authors simply does not apply to most perceptual decision-making tasks (see 3b above). So the ideal case would have been to design a task based on predictions of the model. For example, there is a clear prediction about RTs in Figure 4D, but this has never been tested. (My own view is that this prediction will only bear out under some scenarios - e.g. when decision-making is slow - but not during others). There are also some highly unusual boundaries predicted by the model - e.g. Figure 2i, 2ii, 2iv. I really doubt if participants ever adopt a boundary like this. The authors could have tested this, but haven't. I don't want to ask the authors to design and run these studies at this stage (it seems like a lot of work) but, at the very least, it would be good if the authors discussed whether they predict these highly idiosyncratic boundaries to bear out in empirical data. For example, an "infinite" threshold (Figure 2i, 2ii) means that participants never make a decision in this interval, even if they receive highly informative cues during this interval. Or do the authors believe that participants adopt some heuristic boundaries that approximate these normative boundaries? Currently, the authors seem to be arguing against heuristic models. Or perhaps they have a different heuristic model in mind? It would be good to know.

5. One neat aspect of the paper is showing that there are some participants who show non-monotonic boundaries in the Tokens task. This task was specifically designed to justify the UGM. But the authors show that their model fits some participants better than UGM itself. To the best of my knowledge, this is the first demonstration of the fact that participants can show non-monotonic decision boundaries.

7. Some of the write-ups need to make better contact with existing literature on boundary shapes. Here are some studies that come to mind:<br /> 7a. Some early models to predict dynamic decision boundaries were proposed by Busemeyer & Rapoport (1988) and Rapoport & Burkheimer (1971) in the context of a deferred decision-making task.<br /> 7b. One of the earliest models to use dynamic programming to predict non-constant decision boundaries was Frazier & Yu (2007). Indeed some boundaries predicted by the authors (e.g. Fig 2v) are very similar to boundaries predicted by this model. In fact, the switch from high to low reward used to propose boundaries in Fig 2v can be seen as a "softer" version of the deadline task in Frazier & Yu (2007).<br /> 7c. Another early observation that time-varying boundaries can account for empirical data was made by Ditterich (2006). Seems highly relevant to the authors' predictions, but is not cited.<br /> 7d. The authors seem to imply that their results are the first results showing non-monotonic thresholds. This is not true. See, for example, Malhotra et al. (2018). What is novel here is the specific shape of these non-monotonic boundaries.

8. One of the more realistic scenarios is presented in Fig 2-Figure supplement 3, where reward doesn't switch at a fixed time, but uses a Markov process. But the authors do not provide enough details of the task or the results. Is m_R = R_H / R_L? Is it the dark line that corresponds to m_R=\inf (as indicated by legend) or the dotted line (as indicated by caption)? For what value of drift are these thresholds derived?

9. Figure 4F: It is not clear to me why UGM in 0 noise condition have RTs aligned to the time reward increases from R1 to R2. Surely, this model does not take RR into account to compute the thresholds, does it? In fact, looking at Figure 4B, Supplement 1, the thresholds are always highest at t=0. Perhaps the authors can clarify.

Reviewer #1 (Public Review):

Fibrotic change is a widespread biological phenomenon associated with both normal development and abnormal responses, often in response to pathological circumstances. In the heart, it is associated with both pump failure and arrhythmic change. This present study presents an intriguing murine genetic platform in which such processes are reduced. This used diphtheria toxin A (DTA) on a PDGFRa-CreERT2/+ mouse line. The authors report a reduction in ventricular, atrial and septal fibroblast density. However, this was surprisingly associated with relatively normal cardiac function with relatively normal histology and heart to body weight ratio, cardiomyocyte cross-sectional area, and ejection fractions, left ventricular (LV) chamber size, systolic and diastolic blood pressure, despite reduced collagen VI but not laminin and collagen IV levels. There were only minimal extracellular matrix proteomic changes. Furthermore, left anterior descending artery ligation left relatively moderated mortalities, unaltered changes in cardiac mass, measures of left-heart failure and LV chamber size, with actually better ejection fractions in fibroblast-ablated mice. Furthermore there was a reduced pathological compromise of cardiac function following profibrotic angiotensin II/phenylephrine challenge. Fibroblast ablation here did not affect cardiac mass or lung weight, sparing diastolic and slightly reducing systolic LV chamber size. Yet WT and fibroblast-ablated mice respectively showed slight decreases and fully recovered LV ejection fractions. These findings suggests the value of this platform for studies of the effect of fibrosis following normal or pathological change.

Reviewer #3 (Public Review):

In this manuscript, Kuwabara and colleagues use genetic ablation to reduce the number of fibroblasts resident to the heart. At baseline, the authors observe that fibroblast numbers stay proportionally low after ablation, but with very minimal effects to the structure or composition of the extracellular matrix. Fibroblast ablation prior to myocardial infarction is shown to be beneficial to cardiac function without affecting relative abundance of scar tissue, whereas in an Ang/PE model of fibrosis collagen deposition is impaired and systolic function is preserved.

Reviewer #1 (Public Review):

The main result of the paper is a statistical dependence between the evolved size control strategy and the structure of the cell cycle, in that size control that manifests early (later) in the cell cycle tends to give adder- (weakly sizer-) like strategies. Notably, even when the final evolved network shows weak adder or weak sizer-like behaviour, they find strong sizer-like control in the evolutionary transient. Finally, they constrain the evolutionary algorithm to sense cell size only through stochastic fluctuations of protein concentrations and uncover a strategy that exhibits hallmarks of self-organised criticality.

The questions studied by the authors are both interesting and timely, and their results are intriguing and well documented. On the whole, the conclusions are convincingly argued, and the authors do an excellent job of extracting qualitative features from their evolved networks. However, the manuscript is a little difficult to read, with the figures being crowded and difficult to parse. In addition, while there is a lot of detail in some places (as in the description of one particular feedback control strategy), other results are less fleshed out (such as statistical summaries of the different simulations). The manuscript would benefit from a sharper presentation of the results.

A particularly interesting question addressed in the paper is why adders are more commonly found when sizers are believed to be better at controlling cell size. Here, the authors' simulations give two answers: first, that sizers tend to appear when cell size control is exerted later in the cycle (as in S. pombe). Second, that even when adders eventually evolve, the evolutionary transient passes through a strong sizer strategy. As the adder-vs-sizer question is repeatedly raised, it would strengthen the paper to have a longer and sharper discussion on (a) why early cell size control favours adders, and (b) why sizers appear as transients when fluctuations in cell size are large?

The final part of the paper, which describes a strategy based on sensing size through concentration fluctuations, is very interesting but brief, which is understandable given the quantity of results presented earlier in the paper. Nonetheless, it provides an excellent example of the power of the authors' approach.

Overall, the results in this paper are a compelling addition to the recent interest in cell size control.

Reviewer #3 (Public Review):

In this paper, Proux-Giraldeaux et al. develop evolutionary simulations to study how size control can emerge. In the first part of the paper, the authors initiate cell cycle simulations with a simple network that does not allow cell size sensing and ask what molecular networks can lead to size control after evolution. Results show that a wide range of network types allows size control, some of which are comparable to experimentally identified networks such as the dilution inhibitor model in budding yeast. In the second part of the paper, the authors use their framework to ask how the structure of the cell cycle, including the duration of G1 vs. S/G2/M and the form of size control in each of these phases (i.e. 'sizer' or 'adder'), affects the overall size control. While this is a very important question and the authors bring comprehensive and interesting answers, it is less clear that framing the findings in the context of evolution is meaningful. Indeed, the solutions for how the combination of strength of size control, noise levels, and respective duration of the phases can be found analytically/with simulations that are not 'evolving' the cell cycle structure. Additionally, the finding that a sizer in G1 can lead to an overall adder if it is followed by a timer in S/G2/M is only true if a significant amount of noise is added during the timer phase. At present, this finding is discussed as a result of 'evolution' which is confusing and the dependency of this conclusion on the level of noise during S/G2 does not appear very clearly.

With more cautiously formulated conclusions and a better discussion of already established theoretical and experimental work, this paper will become more accessible to experimentalists and will be a very valuable contribution to the field of cell size control.

Major suggestions:

1) Fig 4-5. While the use of the evolution simulation seems interesting to identify which underlying network(s) can result in size control, the use of the same framework to compare the result of sizer+timer vs. timer+sizer is less easy to interpret. Previous analytical/simulation approaches have explored how noise & duration of the timer phase can alter the 'sizer' or 'adder' signature (see doi.org/10.1016/j.celrep.2020.107992, doi.org/10.3389/fcell.2017.00092, for example) and what evolutionary simulations add to this question is unclear.<br /> - What is the authors' interpretation of why the optimization of Pareto vs. number of divisions yield different size control results (Fig. 4A)? Is it possible that these different fitness parameters allow for the evolution of different levels of noise/duration of the timer phase?<br /> - In the conclusion: 'G1 control is more conducive to the evolution of adders, while G2 control is more conducive to sizers', do the authors really believe that this is an evolutionary acquired trait, or are their observations instead the natural consequence of having a noise-adding phase (timer + multiplicative noise) after a phase with size control?<br /> - A perfect sizer in G1, followed by a timer (with exponential growth) in S/G2/M would simply give an overall 'noisy sizer' (i.e. the slope of final volume vs. initial volume would still be 0 but with some variability around the slope). Only beyond a certain level of noise added in S/G2/M, would the sizer signature be lost. Would it be possible for the authors to perform simulations with different levels of noise (on the timer in S/G2) to help understand this conclusion better? This conclusion could be one of the most valuable to experimentalists studying different organisms.

2) Some aspects of the mathematical formalism were unclear:<br /> - Working with the hypothesis that growth is exponential and at a constant rate is reasonable. However, the description of the scenario where growth modulation contributes to size homeostasis is incorrect. E.g. the statement 'cells further from the optimum size grow slower' is not accurate. If size control occurs via growth regulation, what is expected is a negative correlation between size and growth rate (big cells grow slow, small cells grow fast).<br /> - 'the quantity I is produced with a rate proportional to volume, degraded at a constant rate, diluted by cell growth': why is I diluted? Concentration should be constant if I increases at the same rate as volume. 'the quantity of I does not initially depend in any way on the volume'. Does the quantity of I not increase with volume (since concentration is constant)?

3) Fig. 2, The rescaling of the variables to tau and Veq was difficult to understand. Fig. 2A: If T_S/G2/M is at ~0.5 of the doubling time tau, how relevant is it to look at the behaviour of T_(Vc) for values of T_(Vc)/tau above 0.5 (and beyond 1)? Fig 2B: for which value of T(Vc) is the prediction made?

4) Discussion:<br /> - Including a discussion of previous theoretical work that explored the consequences of varying the relative duration of the timer and sizer phases would be valuable.<br /> - A reason commonly evoked to explain why cells might show sizer vs. adder behaviour is the role of the growth mode: S. pombe is a sizer but is thought to grow linearly, E. coli behaves like a sizer when it grows slower than usual (see Walden et al. 2015). It would be helpful to mention this when discussing S. pombe and remind the reader that the findings of this paper are limited to exponential growth mode.<br /> - The paper seems to be focusing on the noise of the size control mechanism (i.e. probability of transitioning through G1/S based on levels if I) but does not address the question of other sources of noise (i.e. asymmetry at division). What do the authors think about the role of such sources of noise as selective pressure on size control mechanisms evolution?

Sparkes, M. (2021, November 19). Wikipedia tests AI for spotting contradictory claims in articles. New Scientist. https://institutions.newscientist.com/article/2298169-wikipedia-tests-ai-for-spotting-contradictory-claims-in-articles/?utm_campaign=echobox&utm_medium=social&utm_source=Twitter&utm_term=Autofeed

McKee, M., Altmann, D., Costello, A., Friston, K., Haque, Z., Khunti, K., Michie, S., Oni, T., Pagel, C., Pillay, D., Reicher, S., Salisbury, H., Scally, G., Yates, K., Bauld, L., Bear, L., Drury, J., Parker, M., Phoenix, A., … West, R. (2022). Open science communication: The first year of the UK’s Independent Scientific Advisory Group for Emergencies. Health Policy. https://doi.org/10.1016/j.healthpol.2022.01.006

https://forum.zettelkasten.de/discussion/1348/phd-student-reporting-what-its-like-to-use-a-zettelkasten-for-the-first-time-with-my-classes/#Comment_16315

Reviewer #1 (Public Review):

The cohesin ring model postulates that DNA entry and exit must occur through one of the ring's three interfaces thus leading to entrapment. The authors previously tested this model in vitro by engineering disulfide crosslinkers into the different interfaces. Here the authors further test this model by generating cohesin complexes in which the different interfaces can be covalently closed. Using these variants, the authors show that entrapment of DNA can occur through the hinge and SMC3/SCC1 interfaces. Removal of SCC2 and/or SCC3 shows that these regulatory proteins contribute to DNA entrapment through these interfaces, respectively. Sealing of the hinge interface does not prevent entrapment indicating that transport occurs through the passage between the SMC1 and SMC3 ATPase heads. Their data are consistent with the model that DNA entrapment through the SMC and kleisin compartments can lead to initial entrapment. Opening of the hinge may be required for the establishment of cohesion while an opening of the SMC3/SCC1 interface may be required for release. Overall, this information advances our understanding of the molecular basis of DNA entrapment in the cohesin complex.

Reviewer #1 (Public Review):

Mackevicius et al image CA activity in nucleus HVC of isolated singing zebra finches before and after tutor exposure. HVC is well known for its sequential activity during singing - and isolate song is known for its abnormal variability, raising two possibilities. Tutor exposure and subsequent practice may or may not be necessary for chain foundation. Because birdsong is a learned behavior but also subject to innate predispositions, the current manuscript provides a really important test of how nature vs nurture affects the development of song - at the mechanistic level. The authors discover HVC chains do exist, but they are unusually uncoupled from vocal output. More, the more immature chain formation is at the time of tutor exposure, the more copying there is. This finding that the existing HVC chain could become time-locked to new acoustic elements is an important verification of the long assumed, but never explicitly tested, idea that plasticity in the HVC-RA pathway drives phonological change during natural development. These results are really important for the songbird field - as they mechanistically link the timing of tutor exposure to HVC chain maturity to imitation quality. These results also will be useful for the general community of biologists interested in how innate predispositions for animal behavior can express at the level of signals and circuits.

Reviewer #3 (Public Review):

This paper addresses whether the sequences of neural activity that are believed to underlie song production in songbirds emerge as a result of experience-dependent tutoring or rather preceded tutored song production. The primary approach relies on calcium imaging in HVC in untutored zebra finches. The key results include the detection of neural sequences in untutored birds, and that after late tutoring the sequences associated with the tutored song can be partially attributed to pre-existing sequences. This is a short paper that addresses an important question and seems to provide significant support for the notion that neural sequences in HVC emerge independent of tutored song, and that rather than being created by tutoring, learning exploits the presence of pre-existing sequences for song generation. The results of the paper rely in large part on the extraction of neural sequences in an unsupervised fashion, while the method used does require some assumptions (such as sequence length) the conclusions seem well supported by the data.

Reviewer #1 (Public Review):

It is a strength of the current manuscript that it provides a near-complete picture of how the metamorphosis of a higher brain centre comes about at the cellular level. The visualization of the data and analyses is a weakness.

I do not see any point where the conclusions of the authors need to be doubted, in particular as speculations are expressly defined as such whenever they are presented.

The fact that molecular or genetic analyses of how the described metamorphic processes are organized are not presented should, I think, not compromise enthusiasm about what is provided at the cellular level.

Reviewer #3 (Public Review):

Truman et al. investigated the contribution and remodeling of individual larval neurons that provide input and output to the Drosophila mushroom body through metamorphosis. Hereto, they used a collection of split-GAL4 lines targeting specific larval mushroom body input and output neurons, in combination with a conditional flip-switch and imaging, to follow the fates of these cells.

Interestingly, most of these larval neurons survive metamorphosis and persist in the adult brain and only a small percentage of neurons die. The authors also elegantly show that a substantial number of neurons actually trans-differentiate and exert a different role in the larval brain, compared to their final adult functionality (similar to their role in hemimetabolous insects). This process is relatively understudied in neuroscience and of great interest.

Using the ventral nerve cord as a proxy, the authors claim that the larval state of the neuron would be their derived state, while their adult identity is ancestral. While the authors did not show this directly for the mushroom body neurons under study, it is a very compelling hypothesis. However, writing the manuscript from this perspective and not from the perspective of the neuron (which first goes through a larval state, metamorphosis, and finally adult state), results in confusing language and I would suggest the authors adjust the manuscript to the 'lifeline' of the neuron.

In general, this manuscript does not explain how the larval brain has evolved as the title suggests but instead describes how the larval brain is remodeled during metamorphosis. It thus generates perspectives on the evolution of metamorphosis, rather than the larval state. Additionally, this manuscript would benefit from major rearrangements in both text and figures for the story to be better comprehended.

The introduction is very focused on the temporal patterning of the insect nervous system, while none of the data collected incorporate this temporal code. Temporal patterning comes back in the discussion but is purely speculative.

Furthermore, the second part of the introduction describes one strategy for remodeling and why that strategy is not likely but does not present an alternative hypothesis. The first section of the results might serve as a better introduction to the paper instead, as it places the results of the paper better and concludes with the main findings. The accompanying Figure 1 would also benefit from a schematic overview of the larval and adult mushroom bodies as presented in Fig. 2A (left).

In the second results section, the authors show the post-metamorphic fates of mushroom body input and output neurons and introduce the concept of trans-differentiation. Readers might benefit from a short explanation of this process. I also encourage the authors to revisit this part of the text since it gives the impression that the neurons themselves undergo active migration (instead of axon remodeling).

The discussion starts with a very comprehensive overview of the different strategies that neurons could use during metamorphosis (here too, re-writing the text from the neurons' perspective would increase the reflection of what actually happens to them).

The discussion covers multiple topics concerning trans-differentiation, metamorphosis, memory, and evolution and is often disconnected from the results. It could be significantly shortened to discuss the results of the paper and place them in current literature. Generally, the figures supporting the discussion are hard to comprehend and often do not reflect what the text is saying they are showing.

Reviewer #1 (Public Review):

In this manuscript, the authors use time-lapse microscopy in growing intestinal organdies and computational modelling to demonstrate a paradigm for the control of a pool of proliferative cells. They find strong correlations in the proliferative behaviour of sister cells. They propose a compartmentalised model, where cells in one compartment all have a high propensity to produce two proliferating daughter cells while cells in the other department produce daughter cells who both cease to proliferate.

The work establishes a previously suggested paradigm for the control of fluctuations in a pool of proliferating cells. This paradigm might be relevant for tissues other than the intestine such that this work will be of relevance to the general field of stem cell biology. I found this work to be a nice combination of modelling and the conclusions overall convincing. The authors could improve upon the precision in their wording and the discussion of the scope of their modelling results.

Reviewer #3 (Public Review):

The manuscript by Huelsz-Prince et al. studies the fate of intestinal crypt cells in organoids and, to some extent, in vivo, through a combination of live cell tracking (in organoids), static in vivo lineage tracing, and mathematical modelling. They find through live imaging that the vast majority of divisions in the crypt are symmetric with respect to the proliferative potential of daughter cells (something that has previously been shown indirectly). Furthermore, they show that fate outcomes depend on the distance of the mother cell from Paneth cells, but not on the position of daughter cells relative to the latter, and the fluctuations of numbers of proliferating cells are much less than would be expected from a naive cell fate model. They suggest a two-compartment model where one compartment represents the niche with a high propensity for divisions with two proliferating daughter cells and another compartment with a high propensity of divisions with two non-proliferating daughter cells, which is consistent with the data and the observed small fluctuations.

The work is very interesting and solid and establishes its main claims through a variety of measurements supported by mathematical modelling. The methodology is strong, using cutting-edge imaging, statistical and image analysis, and mathematical modelling. The methods firmly establish that cell divisions in the crypt are predominantly symmetric and that the propensity towards proliferating divisions increases with the proximity of the mother cell (but not of the daughter cells) to Paneth cells, a mechanism that maintains homeostatic control. Their theoretical finding that such a mechanism minimises fluctuations in cell numbers is nice but has already been reported in the authors' previous work (Kok et al. bioRxiv 2022). My only concern is that while their two-compartment model is consistent with the data, other models cannot be excluded. Most models with symmetric divisions and contact inhibition, or niche crowding control (negative feedback), where cells are expelled from a crowded niche via a differentiation rate that increases with cell numbers, would lead to similar results. The presented model can rightly be seen as a simplified paradigmatic representative of such model types, and it is a valid approach to use a simplified model to demonstrate qualitative features of this mechanism but to describe the real mechanism one should not take the two-compartment aspect too literally. Instead, the direct measurements presented in this work, showing that the propensity towards divisions with non-proliferating daughters increases with the distance of mother cells from Paneth cells, show that a model where the proliferative potential decreases continuously rather than abruptly is probably better suited to describe that mechanism.

Apart from that, the findings are very solid and certainly of high interest to any developmental biologist working on adult stem cell fate. While here the authors only establish this mechanism for intestinal cells, it can be reasonably suggested that a similar mechanism of homeostatic control is also present in other tissues, as the prevalence of symmetric divisions has been shown for many mammalian tissues.

Reviewer #1 (Public Review):

In this study, Apiz-Saab et al. build up prior work by the Muir lab, which examined the metabolite composition in the tumor microenvironment and found that some metabolites like arginine are present in very different levels from that in our standard culture media. In this study, the authors have formulated a custom media based on the composition of the tumor interstitial fluid (TIF media or TIFM) and found that pancreatic cancer cells cultured in this media have a metabolic state more like tumors in vivo. This is primarily driven by very low levels of arginine, which induces arginine biosynthesis is the cancer cells to cope with this nutrient limited state. Using genetic and pharmacological approaches, the authors demonstrate that arginase expression within tumor-infiltrating myeloid cells drives tumor microenvironmental arginine depletion in vivo.

Strengths:

This is a very rigorous, well-designed study and the findings are broadly interesting for the metabolism, immunometabolism, and pancreatic cancer communities. The methods are comprehensive and the experimental details in the legends are complete. The discussion is particularly well developed and does an excellent job of putting the findings in the context of the field.

Weaknesses:

The claim that arginine biosynthesis is an adaptation to myeloid arginine depletion could be further supported in vivo.

Reviewer #1 (Public Review):

Gupta et al. investigate a new molecular mechanism whereby the ETS transcription factor, ETV1, is upregulated in prostate cancer. Through a series of experiments in prostate epithelial and prostate cancer cell lines, including gene knockdown, knockout and reconstitution, they demonstrated that the concomitant loss of ERF and CIC enhance malignant phenotypes such as cell viability, invasiveness and migratory capacity. Their in vitro results were supported by in vivo subcutaneous tumour xenograft assays in immunodeficient mice. Additional analyses of publicly available data and multiple in-house assays indicated that ERF and CIC target ETV1, acting as transcriptional repressors and modulating ETV1-mediated transcriptional pathways. Finally, the authors show that ETV1 chemical and genetic inhibition moderately decrease cell viability and significantly decrease invasiveness in ERF and CIC deficient prostate cancer cells.

A major strength of this paper is the range and number of analyses performed to test their hypothesis that CIC and ERF cooperate to suppress ETS target genes in prostate cancer. The authors combine both publicly available and in-house data to answer their research questions, which are logically set out in the results section. However, there are also limitations specific to these data that slightly diminish the quality of the paper and make interpretation of their results difficult for the reader.

The premise of the molecular work is based on data from the cBioPortal but it is difficult to fully grasp the results presented due to study and assay numbers being omitted and figures being hard to interpret. The significance (or lack thereof) is also not specified in the text for a number of the subsequent cell line analyses and could be made clearer, especially when the authors are describing a trend rather than significant results. A key analysis method, single-sample Gene Set Enrichment Analysis, used to answer a question central to the paper's conclusions (whether ERF and CIC regulate ETV1 transcription), is poorly explained and presented in the methods and results sections. Furthermore, the methods section does not align with the results section, there is a missing methodology (e.g., how was the PNT2 gene expression data generated?), there are instances of figures being misnumbered and/or insufficiently described/labelled, and missing supplementary data. Finally, while the authors present what appears to be very clinically relevant data showing sensitivity to ETV1 inhibition was enhanced in cells with both ERF and CIC loss, they only present experiments in a single prostate cancer cell line. Given the potential clinical relevance of these data, further in vitro and in vivo assays in the other available cell lines would have provided further evidence for their conclusions, especially given the higher metastatic potential of one of these (PC-3 cells).

Despite the limitations described above, the interpretation and overall conclusions the authors draw from their analyses are generally sound. The study represents an advance in our understanding of how ETS family transcription factors are dysregulated in prostate cancer and suggests a new sub-class of prostate cancer patients based on somatic tumour alterations. Significantly, these patients could one day benefit from targeted ETV1 inhibitors, which are currently being assessed in clinical trials for other cancers.

Reviewer #3 (Public Review):

This study highlights the functional consequences of combined genomic losses of CIC and ERF which results in the activation of ETV1, in the absence of the canonical fusion event involving TMPRSS2 in a subset of prostate cancer. ETV1 is an oncogenic driver of cell growth and metastatic behaviour in many cancer types including prostate cancer. The experiments performed provided tantalizing evidence on the biological and functional consequences of combined losses of CIC and ERF and appeared to support the findings of the mined publicly available cancer genomic datasets.

The manuscript could be improved by providing evidence of proteomic interactions between CIC and ERF proteins in the form of immune-precipitation and Western protein blots. The authors had provided predominantly genomic, transcriptomic, and functional data. In most parts, the manuscript is logical and thorough and leveraged available genomic data. This is followed by genomic-functional experimentations. Given the postulate of co-operativity between CIC and ERF, it would be logical to investigate their potential proteomic interactions.

Reviewer #1 (Public Review):

The current study by Sakabe et al identifies an adrenergic signaling mechanism controlling cardiac regenerative capacity in mice. Using pharmacological and genetic loss-of-function studies, the authors demonstrate that inhibition of beta adrenergic signaling prolongs the cardiac regenerative window in neonatal mice. The study mechanistically connects several signaling pathways that are known to control cardiomyocyte proliferation including adrenergic signaling, G-proteins and the Hippo/Yap pathway. The results are potentially clinically significant given the widespread use of beta blockers in heart failure management.

Strengths:<br /> This is an impressive body of work that addresses an important and largely unresolved question in the field regarding signaling mechanisms controlling cardiac regeneration in the postnatal period in mammals. Through pharmacological and conditional genetic loss-of-function studies the authors provide several lines of evidence implicating the beta adrenergic signaling and the Hippo/Yap pathway in cardiomyocyte proliferation. The conditional genetic loss-of-function studies are a particular strength of the manuscript and provide strong support for the Gas/Yap-dependent nature of the cardiomyocyte proliferative response to beta adrenergic blockade.

Weaknesses:<br /> Although the study clearly implicates beta adrenergic signaling in the developmental regulation of cardiomyocyte proliferative potential, it is unclear whether the protective effects observed following myocardial infarction are due to cardiac regeneration or alternative mechanisms (e.g. immunomodulation, inhibition of cell death, angiogenesis, reduced contractile loading, improved coronary flow, etc). Induction of cardiomyocyte proliferation following administration of metoprolol in neonatal mice is fairly modest (~0.3% pH3-positive cardiomyocytes) and it seems unlikely that such a small number of proliferating cardiomyocytes could mediate such marked effects on cardiac function and fibrosis post-MI. In the absence of definitive data demonstrating that improvements in cardiac function are due to induction of cardiomyocyte proliferation (and by inference cardiac regeneration), such conclusions should be tempered. In addition, it is unclear why beta blocker studies were not conducted in adult mice (rather than P7/P14 mice) to determine whether inhibition of this pathway is sufficient to induce adult cardiomyocyte cell cycle re-entry and regeneration post-MI.

Reviewer #3 (Public Review):

The authors study mammalian heart regeneration and study the connection between Yap and β-adrenergic receptor (β-AR) blockade. Interestingly, metoprolol robustly enhanced cardiomyocyte proliferation and promoted cardiac regeneration post myocardial infarction, resulting in reduced scar formation and improved cardiac function. The conclusion was also supported by genetic deletion of Gnas. CMs had an immature cell state with enhanced activity of Hippo-effector YAP. They also find that increased YAP activity is modulated by RhoA.

Overall, the data are supportive of the conclusions and this may provide new insight into treating heart disease. The final mechanisms connecting Hippo signaling to Rho activity remain incompletely defined.

Reviewer #1 (Public Review):

This study reveals for the first time the involvement of Prdx5 in the differentiation, of osteoclast and osteoblast, which are key cellular components of bone remodelling. This novel function of Prdx5 was shown to be mediated by specific binding and regulating the function of hnRNPK, which acts as a transcription factor in bone remodelling. Moreover, Prdx5 was shown to be colocalized with hnRNPK during osteoblast differentiation. In support of this findings, the authors also reported that mice deficient for Prdx5 expression (Prdx6KO) showed osteoporosis-like phenotypes, which was associated with reduced osteoblast and increased osteoclast differentiation.

It is important to emphasise that the osteoporotic phenotypes were only demonstrated in males, but not in female mice. The observed phenotypes were not hormone-dependent, as no significant differences in examined bone parameters were observed between wild type and Prdx5KO female mice in an ovariectomy-induced osteoporosis model. However, women over 50 have a four times higher rate of osteoporosis compared with men, and the role of testosterone in the development of osteoporosis in Prdx5KO mice should be investigated. It is known that the osteoporosis is increased in men with low level of testosterone.

It is misleading for authors to state throughout the manuscript that osteoporotic phenotypes are observed in Prdx5KO mice, while it is only observed in male mice.

Reviewer #1 (Public Review):

In this manuscript, Williams et al. present a mainly bioinformatic analysis of HERV-K(HML-2) related retroviruses in Old World Monkey species. HML-2 viruses represent the youngest clade of human retroviruses and have also been shown in other primates. This manuscript is focused on identifying and characterizing HML-2-like proviruses in rhesus macaques. A majority of the identified proviruses (106/145) contain sequences that the authors determine were derived from ancient recombination with a HERV-K(HML-8)-related virus (likely after the OWM/great ape split). They further identify similar sequences in white-cheeked gibbons, which they conclude suggest interspecies transmission. Several of the viruses have deletions that disrupt the Rec-Response-Element (RcRE), which interacts with the viral Rec protein.This has previously been shown to be essential to overcome the host cell restrictions to nucleo-cytoplasmic export of unspliced/partially spliced mRNA. Export of this kind of RNA is essential for viral structural protein expression and genome packaging. The authors suggest that sequences derived from HML-8 contain a Constitutive Transport Element (CTE) that replaced the Rec-RcRE export system in the recombinant proviruses.

Strengths:<br /> This manuscript presents a comprehensive bioinformatic analysis of HML-2 like HERV-K proviruses present in rhesus macaques. Several of the authors are well-recognized experts in this kind of analysis and have published extensively in this area. They present convincing evidence (based on analysis of age, intact Open Reading Frames (ORFs), as well as insertional polymorphism) for recent and maybe even ongoing infectious activity of these viruses in rhesus macaques. The identification of the ancient recombination with HML-8 sequences is interesting and allows the authors to reasonably suggest that this event happened after the split of old world monkeys and great apes. The analysis led to the discovery of LTR deletions in recombinant proviruses. These were verified using PCR on genomic DNA and since the deletions included parts of the RcRE, this would indeed suggest that Rec/RcRE function is absent. This was also supported by the lack of function in conjunction with Rec, using a previously described reporter system, where GFP expression suggests the export of unspliced mRNA. However, when sequences from the 3' LTR (derived from HML-8) in the recombinant proviruses were tested in this reporter, GFP protein expression was observed in the absence of Rec. Based on this, the authors suggest that these sequences contain a Constitutive Transport Element (CTE). This is a potentially very interesting finding.

Weaknesses:

With the exception of the PCR analysis and the reporter assays, the manuscript does not contain any experiments or attempts to analyze current expression from any of the identified proviruses. No long-read RNASeq or other RNA analysis on cytoplasmic RNA was performed, nor any experiments to show that proteins are indeed expressed. The findings of a potential CTE are interesting, but the sequences that were appended to the reporter construct are much longer than previously identified CTEs. No data were presented to indicate whether this sequence show similarity to previously identified CTEs and no experiments to show whether this sequence functionally interacts with Nxf1, the protein shown to interact with previously identified bona fide CTEs. Also, since nucleo-cytoplasmic export was not directly analyzed, it remains possible that the sequences that were inserted into the reporter contained splice sites that would allow the RNA to be spliced "downstream" of the GFP gene, allowing the export of a "spliced" GFP mRNA.

Reviewer #1 (Public Review):

In their manuscript, the authors compared the CHC quality in around 50 different, including distantly related fruit fly species that occupy diverse habitats. Based on correlative data (produced by appropriate statistical analyses), they hypothesise that methyl-branched alkanes (mbCHC) might be decisive in resistance against desiccation in these species. They confirm previously published results that rather than the amount of CHC their quality plays an important role in desiccation resistance. Moreover, their results indicate that desiccation resistance increases with longer mbCHCs. In mbCHC coating experiments, the authors tentatively support their hypothesis.

Overall, the data are impressive, and their statistical analyses (Pearson's method, etc) are performed accurately yielding convincing results. These results, however, are not as strong as the statement in the abstract that longer mbCHCs are a "key determinant" of desiccation resistance. Admittedly, the authors' wording is very and appropriately careful about this issue.

One major weakness of this work is the coating experiments. The authors simply incubate Drosophila melanogaster flies with mbCHC and observe that they become more resistant to drought. Along with this "adding" experiment, "replacement" experiments should be carried out, ie add the mbCHC to flies without CHCs. Moreover, control experiments with CHCs with the same chain lengths but without branched methyl groups should be added. This point is crucial as the authors tempt with it to render their correlative data causative.

A second point that needs more attention regarding the discrepancies between males and females (e.g Fig. 3 & 5). These data are not discussed.

Reviewer #3 (Public Review):

This study utilizes 46 species of Drosophila and 4 closely related species to try and determine the relative role of specific hydrocarbons on desiccation resistance. The use of many species of Drosophila that have variations in hydrocarbon profiles and variations in natural desiccation resistances allowed the researchers to draw conclusions about the relative role of specific hydrocarbons contributing to preventing water loss through the cuticle. By using a statistical package they were able to conclude that methyl-branched hydrocarbons are the most important in those species that were more desiccation resistant. This is not surprising since a previous study has shown that 2 methyl-branched hydrocarbons have the highest melting temperatures. In addition, it seems that desiccation resistance also involves other factors since some species that had lower desiccation rates had similar amounts of methyl branched hydrocarbons. It is also difficult to extrapolate to other insects that have a variety of lipids on their cuticular surface. Probably most insects will have hydrocarbons but some have a variety of other lipids on the cuticular surface that will contribute to preventing desiccation. The use of Drosophila species in this study is fortuitous because apparently only hydrocarbons are found on the cuticular surface.

Reviewer #1 (Public Review):

According to the space-time wiring hypothesis proposed by (Kim, Greene et al. 2014), the BC-off SAC circuit mimics the structure of a Reichardt detector; BCs closer to SAC soma have slower dynamics (they can be more sustained, have a delay in activation or slower rise time), while BCs further away are more transient. Later studies confirmed the connectivity and expanded the model on SACs (Ding, Smith et al. 2016, Greene, Kim et al. 2016). However, physiological studies that used somatic recordings to assess the BC properties at different dendritic distances were inconclusive (Stincic, Smith et al. 2016, Fransen and Borghuis 2017). Here, the authors used iGluSnFR, a glutamate sensor to measure the signals impinging on SAC dendrites. Their experimental findings align with the space-time wiring hypothesis, revealing sustained responses closer to SAC soma (mediated by prolonged release from type 7 BCs, and only slightly affected by amacrine cells), which according to their simulated SAC should produce a substantial increase in direction selectivity (DS).

I find the work to be clear and well presented. However, I do have some reservations with the findings:

Main points:<br /> 1. Very low number of cells examined in the key experiment presented in the first figure. The authors used a viral approach to express flex- iGluSnFR in SACs in Chat-Cre mice. Sometimes (apparently twice) the construct was expressed in individual SACs - this is a very underpowered experiment! The low number of successes precludes adequately judging the validity of the findings.<br /> 2. The model doesn't represent key known properties of BC-SACs and the interactions within SAC dendrites. First, the authors decided to construct a ball and stick model that doesn't consider the dendritic morphology of the starburst cell. A stimulus moving over a SAC is expected to engage multiple dendrites with complex spatiotemporal patterns that are expected to have a substantial effect on the voltages recorded on the investigated dendrite (Koren, Grove et al. 2017). For example, the dendrites in the orthogonal orientation will be activated at about the same time as the proximal dendrites; how such strong input will affect dendritic integration is unclear but should be taken into account in the model. Second, the authors assume a similar peak BC drive between proximal and distal inputs. However, a recent study found an enhanced glutamate release from proximal BCs, mediated by cholinergic SAC drive ((Hellmer, Hall et al. 2021); not cited). How different release amplitude would affect the conclusions of the model?<br /> 3. Another reason for including an accurate dendritic morphology is in the differences in the number of BCs that target a cell. Because SAC dendrites cover the entire receptive field area, type 7 BCs, which occupy the proximal third of the dendrites (Ding, Smith et al. 2016, Greene, Kim et al. 2016), are expected to cover only 11% of the area covered by SAC dendrites (1/3 x 1/3 = 1/9) and correspondingly mediate just 11% of the BC drive. A non-bifurcating model presented here would dramatically overrepresent their contribution to SAC responses.<br /> 4. (Fransen and Borghuis 2017) found that off-SACs have a more pronounced distinction in the time to peak than on-SACs. I found it surprising that given the large body of work demonstrating the effectivity of the viral approach in expressing iGluSnFR in off BC (Borghuis, Marvin et al. 2013, Franke, Berens et al. 2017, Szatko, Korympidou et al. 2020, Gaynes, Budoff et al. 2021, Strauss, Korympidou et al. 2021), that the authors did not compare between on and off SAC populations.<br /> 5. Recent work (Gaynes, Budoff et al. 2021) suggests that BCs' responses to motion and to static flashes have distinct dynamics. However, the current manuscript tests responses to flashed stationary stimuli experimentally, and then combines them in a simulation modeling a moving stimulus. This potential limitation of the study should at least be discussed.

Reviewer #3 (Public Review):

In the study "Spatiotemporal properties of glutamate input support direction selectivity in the dendrites of retinal starburst amacrine cells", Srivastava, deRosenroll, and colleagues study the role of excitatory inputs in generating direction selectivity in the mouse retina. Computational and anatomical studies have suggested that the "space-time-wiring" model contributes to direction-selective responses in the mammalian retina. This model relies on temporally distinct excitatory inputs that are offset in space, thereby yielding stronger responses for motion in one versus the other direction. Conceptually, this is similar to the Reichardt detector of motion detection proposed many decades ago. So far, however, there is little functional evidence for the implementation of the space-time-wiring model.

Here, Srivastava, deRosenroll and colleagues use local glutamate imaging in the ex-vivo mouse retina combined with biophysical modeling to test whether temporally distinct and spatially offset excitatory inputs might generate direction-selective responses in starburst amacrine cells (SACs). Consistent with the space-time-wiring model, they find that glutamatergic inputs at proximal SAC dendrites are more sustained than inputs at distal dendrites. This finding was consistent across different sizes of stationary, flashed stimuli. They further linked the sustained input component to the genetically identified type 7 bipolar cell and showed that the difference in temporal responses across proximal and distal inputs was independent of inhibition, but rather relied on excitatory interactions. By estimating vesicle release rates and building a simple biophysical model, the authors suggest that next to already established mechanisms like asymmetric inhibition, excitatory inputs with distinct kinetics contribute to direction-selective responses in SACs for slow and relatively large stimuli.

In general, this study is well-written, the data is clearly presented and the conclusion that (i) the temporal kinetics of excitatory inputs varies along SAC dendrites and that (ii) this might then contribute to direction selectivity is supported by the data. The study addresses the important question of how excitation contributes to the generation of direction-selective responses. There have been several other studies published on this topic recently, and I believe that the results will be of great interest to the visual neuroscience community.

However, the authors should address the following concerns:<br /> - They should demonstrate that differences in response kinetics between proximal and distal dendrites are unrelated to differences in signal-to-noise ratio.<br /> - To demonstrate consistency across recordings/mice, the authors should indicate data points from different recordings (e.g. Fig. 2C).<br /> - The authors mention in the introduction that the space-time-wiring model is conceptually similar to other correlation-type motion detectors that have been experimentally verified in different species. It would be great to expand on the similarity and differences of the different mechanisms in the Discussion, especially focusing on Drosophila where experimental evidence at the synaptic level exists.<br /> - The authors use stationary spot stimuli of different sizes to characterize the response kinetics of excitatory inputs to SACs. I suggest the authors add an explanation for choosing only stationary stimuli for studying the role of excitatory inputs in direction selectivity/motion processing. In addition, the authors use simulated moving edges to stimulate the model bipolar cells. They should provide details about the size of the stimulus and the rationale behind using this size, given their previous results.<br /> - Using the biophysical model, the authors show that converting sustained bipolar cell inputs to transient ones reduces direction selectivity in SACs. I suggest the authors also do the opposite manipulation/flip the proximal and distal inputs or provide a rationale why they performed this specific manipulation.<br /> - In each figure, the authors should note whether traces show single trial responses or mean across how many trials. If the mean is presented (e.g. Suppl. Fig. 2a), the authors should include a measure of variability - either show single ROIs in addition and/or add an s.d. shading to the mean traces.

Reviewer #1 (Public Review):

The manuscript "Interplay between PML NBs and HIRA for H3.3 dynamics following type I interferon stimulus" by Kleijwegt and colleagues describes a study that's set out to explore the details of the PML-HIRA axis in H3.3 deposition at ISGs upon IFN-I stimulation. First, the authors establish that HIRA colocalized at PML NBs upon TNFa and TNFb treatment. This process is SUMO-dependent and facilitated by at least one of the identified SIM domains of HIRA. Next, the authors set out to determine whether interferon responsive genes (ISGs) are dependent on HIRA or PML. By knocking-down either HIRA or PML, only an effect on ISGs was observed when PML was knocked down. In fact, immune-FISH showed that PML NBs are in close proximity of ISGs upon TNFb treatment. To address the histone chaperone function of HIRA, the deposition of the replication-independent H3.3 on ISGs is tested. In specific, the enrichment of H3.3 across the ISG gene body. ChIP-seq data (Fig 5B) showed an enrichment around the TES, whereas qPCR (Fig 5A) showed less convincing enrichment (for details see below). When either HIRA or PML are knocked down, a mild loss of H3.3 enrichment was observed (Fig 5E). Interestingly, when HIRA is sequestered away from PML NBs by Sp100, an increased enrichment of H3.3 was observed. To understand the interplay between H3.3 deposition and HIRA's role in this process in the presence of PML NBs, H3.3 was overexpressed. Two population of cells were observed: low or high levels of H3.3. In the former, HIRA formed foci and the latter, HIRA did not form foci. Surprisingly, when HIRA is overexpressed, PML NBs form in the absence of TNFb. Finally, a two-sided model is proposed, where PML NBs is required for ISG transcription promoting H3.3 loading. The second side is that PML NBs function as a "storage center" for HIRA to regulate its availability.

Overall, it the model is intriguing, but the data presented seems insufficient to support the current claims.

Major concerns:<br /> - The suggested function of HIRA at the PML NBs as storage is interesting. Ideally, this would be tested by real-time single molecule tracking.

- The link between PML NBs containing HIRA and H3.3 deposition is very intriguing and indeed the ChIP-seq data shown in Figure 5B shows a clear increase in the H3.3 signal around the TES. This distribution is very intriguing as recent work (Fang et al 2018 Nat Comm) showed that H3.3 deposition across the gene body was diverse and dynamic. Ideally, the qPCR of some select ISGs would confirm the ChIP-seq data. Here a more complex picture emerges. Just as with the ChIP-seq, a modest decrease of H3.3 at the TSS was observed, but only in 2 of the 3 genes shown is H3.3 enriched at the TES and only in 1 gene (ISG54) is H3.3 enriched at the gene body. As qPCR is later used in the manuscript (Fig 5E and 5G), it is essential that the results of two different techniques give similar results. With regards to Fig 5E and 5G, it is unclear why certain gene regions are shown, but not others.<br /> Overall, the link between HIRA and PML in H3.3 loading is only mildly affected (Fig 5E and 5F). The conclusion that HIRA and PML are essential (Page 12, line 8) is not represented by the presented data. The authors propose that DAXX could play a role. Indeed, work on another H3 variant, CENP-A, showed that non-centromeric localization is dependent on both HIRA and DAXX (Nye et al 2018 PLoS ONE). It would be interesting to learn if a double knock-down of HIRA and DAXX can prevent the enrichment of H3.3 at TES of ISGs upon TNFb treatment.

- In Figure 6B, two versions of HIRA are overexpressed and the authors conclude that the number of PML NBs goes up. Earlier in the manuscript, the authors showed that PML NB formation upon IFNb exposure brings HIRA into the PML NBs via a SUMO-dependent mechanism. Is overexpression of HIRA and its accumulation in PML NBs also SUMO-dependent or SUMO-independent? Overexpressing the SIM mutants from Figure 3F would address this question. In addition, the link between the proposed HIRA being stored at PML NBs could be strengthened by overexpressing HIRA and see at both short and late time points whether H3.3 is enriched on ISG genes.

- BJ cells are known to senesce rather easily. Did the authors double-check what fraction of their cells were in senescence and whether this correlated with the high or low expression of ectopic H3.3?

- In Figure 6 - figure supplement D, it appears that the levels of HIRA go up upon TSA and IFNb treatment. Rather than relying on visual inspection, ideally, all Western blots should be quantified to confirm the assessment that protein levels are not affected by different experimental procedures.

Reviewer #1 (Public Review):

Neural circuits of the fruit fly mushroom body provide an interesting system to study molecular processes underlying learning and formation of memories since the input-output relationship of the circuit is quite well characterized and - importantly- genetic tools to manipulate specific circuit components are available. The current manuscript focuses on the role of different subunits of the nicotinergic Acetylcholine receptors.

The authors use a series of state-of-the-art techniques and several elegant - and partly innovative, explorative - approaches to address a specific set of neurons as models (the MBONs M4/M6), that are relevant for appetitive memories. The logic of the manuscript is overall well developed, and the experiments provided are widely in support of the model the authors propose. They first corroborate that synaptic transmission of M4/M6 is critical for appetitive memories and subsequently test different subunits of the nicotinergic Acetylcholine receptors using RNAi. Interestingly, only the alpha-5 subunit shows learning defects (acquisition or induction), while alpha-1,2, and 5 (and Dlg) show defects after 3 hours (what the authors call "memory expression"). This result indicated the differential requirement for acquisition versus that expression.

The subsequent and particularly elegant and intriguing set of experiments aims to recapitulate the processes while circumventing a direct synaptic transmission from the KCs. The authors therefore optogenetically activate DANs, while blocking KCs neurotransmitter release and measure neuronal activity in the MBONs. Only when ACh is locally applied an effect of synaptic facilitation can be observed. While this experiment is not particularly critical in the context of the current manuscript it provides a very different, complementary support for the proposed overall model.

In order to gain insight into the function of the alpha-5 and alpha-2 subunits, the authors next investigated the expression of these genes and report non-uniform patterns between the lobe systems, presumably by using GFP fusion reporters. A weakness in this section is that the technical details are not well described and thus the impact of these results remains a bit elusive. The results indicate that alpha-2 is somehow genetically "downstream" of alpha-5 and Dlg. While many parts of the manuscript are of great impact and clear, this notion - even though extremely interesting - may actually be one of the weakest parts, since no explanation for the phenomenon is provided. One would assume that dopamine signaling and coincidence detection may be involved. It is however true that the authors conceptually take this up to some degree in the discussion, particularly the point that alpha-2 may be a molecular central point to switch.

Using Calcium imaging in vivo the authors show that the physiological formation of a memory trace in M4/6 shows the expected dynamics in an alpha-2-dependent fashion and similarly that alpha-2 itself (by photobleaching) shows the expected expression/localization dynamics.

The final section is indeed an important extension and addresses the generality of the alpha-5 to alpha-2 transition by investigating familiarity rather than associative learning. The results provided are in line that this mechanism appears to be general, a point also taken up in the discussion. What I felt was especially refreshing in the discussion section is the global comparison of NMDA/AMPA as a concept and possibilities of how this task may be resolved in other systems using other transmitters, again maybe not at the molecular depth that may have added an explorative touch.

Reviewer #3 (Public Review):

In invertebrates, learning-dependent plasticity was reported to take place predominantly in presynaptic neurons. In Drosophila appetitive olfactory learning, cholinergic synapses between presynaptic Kenyon cells and postsynaptic MBONs undergo behaviourally relevant associative plasticity, and it was shown to reside largely in Kenyon cell output sites. This study provided several lines of evidence for postsynaptic plasticity in MBONs. The authors nicely showed the requirement of Kenyon cell output during training, strongly suggesting that behaviourally relevant associative plasticity also resides downstream of Kenyon cell output. This is further supported by impaired appetitive memory by downregulating nAChR subunits (a2, a5) and scaffold protein Dlg in specific MBONs. Live imaging experiments demonstrated that the learning-dependent depression in M4-MBON was reduced upon knocking down the a2 nAChR subunit. Using in-vivo FRAP experiments, the authors showed recovery rates of nAChR-a2::GFP were altered by the co-application of olfactory stimulation and DA. All these lines of evidence point to the significance of nAChR subunits in MBONs for postsynaptic plasticity.

On the technical side, this study achieved a very high standard, such as the measurement of low-expressed receptor mobility by in-vivo FRAP. The authors conducted a wide array of experiments for collecting data supporting postsynaptic mechanisms. The downside of this multitude is somewhat compromised coherence. To give an example, the authors duplicated many behaviour and imaging experiments in different MBONs for non-associative learning (Fig. 7 and 8), which is primarily out of the scope of this paper (cf. title).

Reviewer #1 (Public Review):

This paper describes an "ERAD-like" pathway for the turnover of the SUN2 protein. In this pathway, ubiquitylation of SUN2 in the nucleoplasm by the SCFbTRCP ubiquitin ligase leads to extraction of the membrane protein by p97 for delivery to the proteasome. This process involves phosphorylation of non-canonical degrons on SUN2 by CK2, which was identified using a genome-wide crispr screening approach. The CTDNEP1 phosphatase acts to reverse phosphorylation and stabilize SUN2. Non-degradable forms of SUN2 promote altered nuclear architecture and a delay in double-strand break repair. The conclusions are based on strong biochemical and cell biological data. The paper sets the stage for further analysis of how defects in SUN2 degradation alter additional nuclear processes.

Reviewer #3 (Public Review):

The manuscript by Krshnan et al. reports a cellular mechanism akin to the endoplasmic reticulum-associated degradation (ERAD) that degrades SUN2, a nuclear inner membrane protein. The authors previously identified the Asi ubiquitin ligase complex that mediates the degradation of inner nuclear membrane proteins in budding yeast. In this manuscript, they identified the SCF β TrCP, and SCF as another ligase that regulates the ubiquitination and degradation of SUN2 in mammalian cells. The key findings include the identification of a substrate recognition motif that appears to undergo casein kinase (CK) dependent phosphorylation. Mutagenesis studies show that mutants defective in phosphorylation are stabilized while a phosphor-mimetic mutant is more unstable. They further show that the degradation of SUN2 requires the AAA ATPase p97, which allows them to draw the analogy between SUN2 degradation and Vpu-induced degradation of CD4, which occurs on the ER membrane via the ERAD pathway. Lastly, they show that the stability of endogenous SUN2 is regulated by a phosphatase and that over-expression of a non-degradable SUN2 variant disrupts nuclear envelope morphology, cell cycle kinetics, and DNA repair efficiency. Overall, the study dissects another example of inner nuclear envelope protein turnover and the involvement of a pair of kinase and phosphatase in this regulation. The data are of extremely high quality and the manuscript is clearly written. That being said, the following questions should be addressed to improve the robustness of the conclusions and to avoid potential misinterpretation of the data.

1. Since SUN2 is normally incorporated into a SUN2-SYNE2-KASH2 LINC heterohexamer complex, the authors should be cautious with the use of over-expressed SUN2 in this study. Over-expressed SUN2 is expected to stay mostly as unassembled molecules and thus is likely degraded by a protein quality control mechanism that targets unassembled proteins. Consistent with this possibility, CK2 has been implicated in the regulated turnover of aggregation-prone proteins (Watabe, M. et al., JCS 2011). This mechanism would be potentially distinct from the one proposed for endogenous SUN2 degradation.<br /> 2. Certain conclusions appear to be an overstatement. This is particularly the case for the title, which implies that SUN2 is a protein that undergoes regulated turnover (under certain physiological conditions). Given that CK2 is a constitutive kinase and that the authors have not identified the conditions under which the activity of CTDNEP1 is regulated, it is premature to make such a conclusion.<br /> 3. Likewise, the demonstration of the impact of SUN2 accumulation on different cellular pathways mainly relies on the over-expression of a non-degradable SUN2 mutant. Whether similar defects could be seen when the degradation of endogenous SUN2 is blocked remains an open question.

Reviewer #1 (Public Review):

In this study, the authors use a powerful mix of histone mutations, biochemistry, and proteomics to show that histones H3 and H4 are imported into the nucleus as monomers by Imp5 where they are transferred to NASP and HAT1-RBBP7 respectively. This challenges the standing paradigm that H3 and H4 are imported into the nucleus as heterodimers.

Reviewer #3 (Public Review):

The goal of this paper is to describe how newly synthesized histones are imported into the nucleus.

Prior biochemical purifications suggest that H3-H4 dimers fold in the cytoplasm, are regulated by the sNASP histone chaperone, and translocate to the nucleus in association with the ASF1 histone chaperone and the importin-4 (Imp4) karyopherin. However, using an imaging-based approach, the authors previously showed that histones H3 and H4 can be imported into the nucleus as monomers.

Here, the authors show that new, cytoplasmic H3.1 and H4 monomers are bound by HSPA8 and importin-5 (Imp5). Imp5 then translocates monomeric histones into the nucleus and transfers H3.1 to sNASP. They further propose that the previously observed cytosolic H3-H4 dimers are not new histones but rather old nucleosomal histones that diffuse into the cytoplasm, which are then re-imported via Imp4. Therefore, folding of H3-H4 dimers exclusively occurs in the nucleus.

The authors certainly provide compelling evidence that monomeric histones are imported into the nucleus via Imp5. Constitutively monomeric histone mutants co-purified with Imp5 and the association was recapitulated in vitro. A wide range of exciting techniques is used to address how monomeric histones are handled in cells (i.e., biochemical, FRAP, imaging of cytoplasmic tethered and released histones, proximity-dependent protein labeling, etc). The aim of finding how monomeric histones are imported into the nucleus is certainly attained. More data could however support some of the conclusions regarding the association of histones to ASF1 and Imp4 and whether they truly exclusively represent evicted nucleosomal histones that diffused out of the nucleus.

Otherwise, the data shown here is certainly important for the field, as it provides an explanation of how monomeric histones are handled in the cytoplasm.

Reviewer #1 (Public Review):

Wang et al. suggested that the most prominent gene downregulated in postmenopausal osteoporosis patients is miR-27a. Data suggested that removing miR27a resulted in considerable bone loss due to disturbed remodeling. Their findings further imply that miR-27a is unnecessary for osteoblast differentiation and bone formation. This work presents good data to show that miR-27a is necessary for controlling bone resorption. The deletion of miR-27a in mice increases the number of osteoclast cells as well as key parameters for bone resorption. As a result, directing miR-27a to bone resorption surfaces using synthetic drugs such as bisphosphonates or osteoclast-targeted molecules such as acid octapeptides containing aspartic acid can be crucial therapeutics for future use and miRNAs can very well be the next wave of future therapeutics.

Reviewer #3 (Public Review):

The authors show miR-23a and miR-27a as an important regulator of bone homeostasis. They observed that miR 23a and miR27a regulates osteoclast function and loss of miR 23a and miR27a causes severe osteopenia conditions in mice without affecting osteoblast function. It has been already reported that miR27a regulates osteoclast function and inhibits osteoclast mediated bone resorption and F action formation (Guo L, et al). But the novelty of this manuscript is that single deletion of miR27a causes severe osteoporosis without affecting cortical bone. Reports suggest that p62 is an important regulator of osteoclastogenesis and deficiency of p62 impaired osteoclast differentiation. In paper, authors established a link between miR27a and p62 in osteoclast cells which could be a potential target for treatment of bone related disorders. Importantly, the mechanism of miR27a-p62 is not well explored in osteoclast cells.

Reviewer #1 (Public Review):

RPG was identified as a mutant with poor nodulation in 2008, but its activity was unclear. Interest in its role was rejuvenated by a phylogenetic approach where RPG was one of only three genes that have been consistently lost in non-nodulating species in the Fagales/Fabales/Cucurbitales/Rosales families. In this careful genetic and imaging-based analysis of the role of the RPG gene in nodulation, the authors use transient and stable genetic transformation in nodulating Medicago roots to clarify the subcellular localization of RPG and its relationship to infection thread maintenance along with two other better characterized symbiosis proteins VAPYRIN (VPY) and LUMPY INFECTION (LIN). Detailed phenotypic analysis of two RPG mutants indicates that RPG is required to sustain polarization of the membrane at the advancing tip of the IT, and without RPG, the plant microtubule cytoskeleton organization, nuclear position, and localization of VPY and the cell wall modifying enzyme NODULE PECTATE LYASE all differ from normal.

The model that emerges and that is well supported by the presented data is that RPG behaves as a scaffold to convert/customize the cellular machinery that organizes cytoskeleton and membrane into something that facilitates infection thread polarity. This work established a hierarchy of "infectosome" assembly using genetics and localization of components.

The strengths are the use of new imaging modalities to define the location and functional dependencies of complex components. This paper makes numerous significant advances in the understanding of infection thread maintenance and assigning a cellular activity for RPG. It leaves open still questions about how RPG is localized to discrete puncta and how it recruits other proteins to these still symbiosis-related structures.

This is an exciting addition to the literature detailing the mechanisms by which host plant cells make environments hospitable to symbionts, the strong correlation with the presence/absence of RPG and the ability/failure to support nodulation in a variety of plant species makes RPG an attractive candidate for engineering nodulation in crop plants that currently lack it.

Reviewer #1 (Public Review):

The objective of mobile phone (mHealth) interventions in African countries is to cost-effectively increase access to care and improve health. Due to resource constraints on the healthcare systems in many African countries, inaccessibility to healthcare is more noticeable in rural areas. While there is an increase in mHealth interventions in many African countries, it is salient to examine inequity in the distribution of smartphones that enable these interventions.

Investigators used the 2017-2018 Afrobarometer data from 33 countries to estimate the percentage of the population with a mobile phone (smartphone or otherwise). The analyses were conducted at different levels: (1) among all 33 countries; (2) at the country level; and (3) at the sub-national level (within each country).

The study is well designed, and the manuscript is clearly written. The findings are important from a policy and intervention perspective. This study shows that there are substantial inequities in smartphone ownership between and within African countries. These results have important implications for designing and rolling out mHealth interventions in African countries. This study shows that people who live in rural areas are less likely to own a smartphone and less likely to live close to a healthcare center. For mHealth intervention to work, individuals who are in high need of mHealth interventions would need to own mobile phones.

Reviewer #3 (Public Review):

The work is of general interest to audiences of public policy and public health. The data found some evidence that mobile health interventions may be affected by the type of mobile used but failed to substantiate the claim conclusively on how the lack of mobile ownership may hinder their rollout process. The claim about gender or geographic inequality must be elaborated in detail and many countries in developing countries are now connecting more users in rural areas through unconventional methods such as village phones instead of just mobile ownership.

Strengths:

The main strength of this paper is the usage of the cross-sectional data from the R7 Afrobarometer survey which is a newly available dataset and contains comprehensive data from more than 50 African countries. The usage of the Bayesian Logistic Regression (BLR) model produced some useful findings.

Weakness:

1) The authors have generalized a lot of things in a very simple manner. For example, they have assumed if participants have access to the internet means they own a smartphone and if they don't then they are basic phone users. It is possible a lot of smartphone owners do not have subscriptions to the internet due to the high cost of internet in African countries.

2) They have consistently talked about inequalities in gender, and rural-urban geographic regions based on the odds ratio derived from the BLR. A regression decomposition technique can quantify these differences more elaborately in detail.

3) They failed to explain why a lot of poor people own smartphones. This could be due to the usage of village phones (first implemented by Grameen Phone in Bangladesh). This has expanded in African countries as well where multiple users communicate through a community phone connecting more users in rural areas.

4) Basic phones may also be effective for mobile health interventions through voice-enabled systems and disseminating important messages to communities. (For e.g. there is extensive literature on how community-level messages, such as instructions on personal hygiene and usage of masks, were transmitted through basic phones during the beginning of covid19 in developing parts of Asia).

5) Further clarification of why lack of ownership of a mobile phone may propagate inequalities in health is needed beyond just simple associations. A latent factor may also cause these differences.

Reviewer #1 (Public Review):

In this study, the authors overexpress GFP-tagged NMN-D, a prokaryotic enzyme that consumes NMN, and find that it is strongly protective in several in vivo injury paradigms in flies. This is an important finding that clarifies previously published work, which found that an untagged NMN-D construct provided only weak axon protection (Hsu et al., 2021). The authors of the current manuscript argue convincingly that the previous result stemmed from the low stability of the untagged variant. Llobet Rossell et al. also use a very nice grooming assay for synaptic connectivity following axotomy to demonstrate that NMN-D overexpression maintains synaptic connectivity. Further pointing to NMN as a crucial regulator of dSARM activation, they show that increasing NMN levels by increasing NMN synthesis through mNAMPT overexpression accelerates injury-induced axon degeneration. They provide the support that NMN-D and mNAMPT overexpression are having the expected effect on NAD+ metabolic flux via LC-MS/MS. Finally, they provide evidence that a dSARM variant that cannot bind NMN does not rescue the dSARM LOF phenotype indicating that NMN exerts its effects through dSARM.

This study is carefully done and the evidence that NMN levels are a critical determinant of axon degeneration is strong. The combination of in vivo degeneration assays, metabolomics, and behavioral assays provides confidence in the results. For the most part, the conclusions of the study are well-supported by the results. The claim that the protection afforded by low NMN levels is as strong as the loss of SARM is justified. The authors repeatedly state that protection provided by low NMN is "even stronger" than that of essential mediators of axon degeneration, which doesn't really make sense given that all their data support the hypothesis that low NMN protects because it blocks dSARM activation. However, the primary weakness of this study is that the novelty of this work comes almost entirely from showing that the NMN/NAD+ ratio is determinative for axon degeneration in Drosophila; it has already been shown both in mammalian cultured neurons and in vivo.

Reviewer #3 (Public Review):

Llobet-Rosell et al. use Drosophila to decipher the relationships between factors in the Wallerian degeneration pathway and the metabolite NMN, an activator of the central pathway enzyme dSarm. NMN had previously been proposed to be a crucial regulator of Sarm, but there was a shortage of good in vivo evidence, especially in the crucial Drosophila system. The authors addressed this here by generating optimized fly lines, including a strongly expressing transgenic line for the NMN-consuming enzyme NMN-deamidase (NMNd). This variant conferred extremely strong protection against degeneration both in morphological and functional studies, thus confirming the key role of NMN as an activator of the degeneration pathway. They also confirm that NMNd alters NMN/NAD metabolism using mass spec of Drosophila heads, and then use Drosophila genetics to show that dSarm is the crucial NMN target. In a reverse experiment, the authors also use overexpression of murine NAMPT, an NMN-producing enzyme, to speed up degeneration. As in mammals, NMNd delays degeneration induced by loss of Nmnat.

A clear strength of the fly system is the degree of rescue conferred by the optimized NMN-D reagent which essentially establishes NMN as a crucial regulator in the pathway. The rigor of experimentation is also very high. Essentially all reagents are optimized, and most conclusions are backed by complementary analyses. The manuscript also nicely describes a metabolomic analysis of NAD biosynthetic pathways from fly heads.

Reviewer #1 (Public Review):

The monogamous vole provides unique opportunities to study the neural basis of pair bonding and this study exploits that opportunity in a novel way. Focusing on the nucleus accumbens, the authors conduct RNA-Seq to characterize the transcriptome in same-sex and opposite-sex pairs when bonded, when separated for a short time and when separated for a long time at which point the literature has in the past demonstrated the willingness to form a new bond. They determine that the transcriptome of pair bonding includes a preponderance of glial-associated gene changes and that it degrades with long-term separation. To the latter point, they then conduct a neuron enriching trap schema to find those genes subject to change specifically in neurons.

The strength of the report is the clever experimental design, the unusual animal model, and the comparisons of same-sex and opposite-sex pairs and long-term and short-term separations.

The weakness is that the behavioral changes observed are not what was expected based on prior work and are relatively modest, providing a disconnect between the outcome and the more dramatic transcriptional changes. A second weakness is the focus on the nucleus accumbens which is a brain region most closely associated with reward. While pair bonding may be rewarding, that component may be independent of the memory of a partner or the willingness to partner anew. Lastly, there is no clear connection between the identified transcriptome and either the formation or degradation of the pair bond.

Reviewer #3 (Public Review):

In this manuscript, the authors investigate the behavioral and brain transcriptional alterations associated with short- and long-term partner separation in the monogamous male prairie vole. Male prairie voles continue to show affiliative behavior after short- (2 days) and long-term (4-weeks) partner separation, with similar effects for same and opposite-sex pairs. However, the transcriptional signature in the nucleus accumbens exhibits marked alterations after long-term separation.

Strengths:<br /> 1) A key strength of this manuscript is its use of the monogamous prairie vole to investigate transcriptional alterations associated with pair bonding and subsequent pair separation. This sort of behavior cannot be investigated in typical rodent model systems (e.g., mice, rats), and the choice of using prairie voles allows for dissection of potential mechanisms of social bonding with relevance to partner loss in humans.<br /> 2) Investigation of behavioral measures and transcriptional alterations at both short- and long-term time points after pairing and separation is a strength of the manuscript. These time points were selected based on previous studies in laboratory and wild prairie voles related to the time it takes to form a pair bond and for the male prairie vole to leave the nest after the loss of the female pair. The datasets generated will be of great use to the scientific community.<br /> 3) The authors investigate the behavior and transcriptional profiles after same-sex as well as opposite-sex pairing. This is considered a thoughtful decision on the authors' part which allows them to tease apart the effects of same vs. opposite sex.<br /> 4) The use of numerous behavioral measures to assess both affiliative and aggressive behaviors is a strength of the approach.<br /> 5) The authors use many biostatistical approaches (e.g., RRHO, WGCNA, Enrichr) to probe the transcriptomics data. These approaches allow the authors to move beyond simply assessing transcriptional profiles separately, but to look for patterns that are similar or different across datasets.<br /> 6) The authors use rigorous statistical methods to assess behavioral measures.<br /> 7) The TRAP approach in prairie voles is novel and will provide a great resource to the research community.

Weaknesses:<br /> 1) The methods state that prairie voles were treated differently in the behavioral and transcriptomics studies. Specifically, for the separation in the behavioral studies, prairie voles were separated by sight, but not necessarily by the smell from partners (i.e., partners were kept ~1 foot apart). However, prairie voles in the transcriptomics studies were separated by both sight and smell (i.e., partners were sacrificed after separation). Thus, it is possible that the lack of degradation of pair bond-related behavior after long-term separation might be due to these prairie voles being able to smell their partners after separation. This is considered a moderate flaw in the design of the studies which limits the integration of results between behavior and transcriptomics. This might be why the authors do not see a strong behavioral degradation of pair bond-related behavior after long-term separation but do see a strong transcriptional signature.<br /> 2) While RRHO is helpful to assess overall patterns of transcriptional signatures across datasets, its utility for determining the exact transcripts is limited. This is because of how RRHO determines the overlapping transcripts for its Venn diagram feature (by taking the point where the p-value is most significant and taking the list to the outside corner of that quadrant).<br /> 3) TRAP expression was verified in only one animal. Thus, the approach has not been appropriately confirmed.

Reviewer #1 (Public Review):

In this paper the authors present variations in carbon oxidation state and hydration state in proteomes available in RefSeq. Then they use this information to predict community level proteomes, and their corresponding carbon oxidation states and hydration states, based on available 16S rRNA gene sequences from selected previously published datasets. When combining this with information about the environmental setting of the individual samples analyzed, the authors are able to demonstrate connections between redox conditions and proteomic carbon oxidation state and hydration state. Furthermore, they explore how individual taxonomic groups at different taxonomic levels contribute to forming these connections.

A weakness with the study is that the described environmental proteomes are inferred from 16S rRNA gene sequence data and not observed directly. However, there is good reason to believe that the conclusions drawn in the paper are valid.

The study sheds light on microbial adaptations on the genome level that so far have received relatively little attention. The paper is also interesting from an ecological perspective regarding the general question of how microbial communities are shaped by environmental settings.

Reviewer #1 (Public Review):

In this manuscript, Dodd et al. study the uptake and cytosolic release of tau fibrils. Based on a Crispr knock-out screen which identified that vacuolar H+ ATPase factors were required for tau fibril uptake, the authors focused on the role of endocytosis and endocytic acidification. Using genetic and pharmacological approaches, the authors made the surprising observation that endocytosis inhibition reduced tau fibril uptake but strongly increased fibril-induced tau seeding in the cells. Authors demonstrate that giant plasma membrane vesicles unable to undergo endocytosis still take up tau fibrils. Dodd and colleagues conclude that tau fibrils likely directly penetrate the lipid bilayer, which represents a novel entry pathway involved in tau seeding

The authors present some perplexing divergent effects of genetic or pharmacological inhibition of the endo-lysosomal system on tau fibril uptake and seeding. Surprisingly, inhibition of endo-lysosomal acidification inhibited fibril uptake but increased seeding in biosensor cells. Likewise, inhibition of Rab5a, a GTPase required for early endosome trafficking strongly impaired fibril uptake, but highly elevated tau seeding in biosensors. Low temperature during fibril incubation inhibited fibril uptake but increased seeding when cells were subsequently shifted to a normal growth temperature. Authors conclude that these results most likely show that endocytosis is not required for seeing tau. Instead, tau fibrils might directly translocate through the membrane to induce seeding.

Clearly, and rightfully stressed by the authors, the experiments demonstrate that the amount of internalized tau fibrils does not correlate with the tau seeding, arguing that minute (and potentially undetectable) amounts of tau seeds can very efficiently seed tau aggregation in biosensor cells. The massive amounts of labelled fibrils taken up by basically 100 % of cells might obscure the actual relevant tau seeds taken up by alternative routes that result in cytosolic tau seeding. This could apply to tau fibrils translocating through the membrane, as suggested by the authors, this could also apply to minute amounts of fibrils that still manage to enter the endo-lysosomal pathway even when this pathway is compromised pharmacologically or genetically. Any impairment of the endo-lysosomal system, however, likely also reduces the lysosomal degradative capacity. As a consequence, tau seeds in the endo-lysosomal system will have a higher propensity to escape to the cytosol to induce seeding. While the findings presented here are indeed very interesting and the Crispr screen is very elegant and informative, experiments are not sufficient to explain the cellular mechanisms involved.

Reviewer #3 (Public Review):

In this manuscript, Dodd et al. measure the internalization of exogenous fluorescently-labelled tau by cultured HEK cells and iPSC-derived neurons, as well as the aggregation of fluorescent fusion proteins of the repeat domain of tau with the P301S mutation (tau RD) expressed in these cells. They find that inhibition or reduction of V-ATPases and Rab5A reduces tau internalization and increases tau RD aggregation, as does culturing the cells at cold temperatures. The authors also find that exogenous fluorescently-labelled tau is internalized by HEK cell-derived GPMVs. All conditions are dependent on HSPGs, which presumably act as cell-surface attachment factors, similar to their role in the attachment of viruses to the cell surface. Based on the involvement of V-ATPases and Rab5A in endocytosis, the authors conclude that endocytosis of tau does not contribute to the aggregation of expressed tau. In addition, based on the lack of endocytosis in GPMVs, the authors conclude that tau can translocate across membranes and that this contributes to the aggregation of expressed tau.

The observation that conditions that decrease the overall internalization of exogenous tau can increase the aggregation of expressed tau suggests that multiple internalization routes exist, some of which are non-productive for the aggregation of expressed tau. This has important consequences for therapeutic strategies aiming to limit the internalization of tau. However, the conclusions that tau can translocate across membranes and that this contributes to the aggregation of expressed tau, whereas endocytosis of tau is non-productive for the aggregation of expressed tau, are not fully supported by the data.

Major comments:<br /> 1. There appear to be several alternative interpretations other than a reduction of endocytosis for the effects of perturbing V-ATPase and Rab5A function and culturing cells at cold temperatures. First, internalized tau was measured 4 h after the addition of exogenous tau to the cells. This seems like a long time for the study of endocytosis, which occurs in minutes. By 4 h, degradation of tau may have an effect on the amount of measurable internalized tau. This is important because, in addition to their roles in endocytosis, V-ATPases and Rab5A also have roles in protein degradation via the endolysosomal system. Similarly, culturing cells at cold temperatures for 4 h is expected to have many effects beyond the inhibition of endocytosis. In addition, the authors do not control for humidity and CO2 concentration, which could also affect their measurements. Perturbation of V-ATPases and Rab5A could also be exerting their effects by reducing the translocation of tau across endolysosomal membranes, instead of endocytosis. The authors found that the expression of dominant-negative dynamin increased the amount of internalized tau. Is this unexpected, given that dynamin is required for most forms of endocytosis and has been previously reported to be required for tau endocytosis (Wu et al. 2013. J. Biol. Chem. 288, 1856-1870; Falcon et al. 2018. J. Biol. Chem. 293, 2438-2451; Evans et al. 2018. Cell Rep. 22, 3612¬-3624)?

2. It is difficult to draw parallels between the experiments using cells and those using GPMVs. The authors use 25 nM tau for cell experiments, but 500 uM tau for GPMV experiments. This is a huge difference in concentration. The authors should carry out the GPMV experiments using the same concentration of tau as in the cell experiments. 500 uM is also a very high concentration and raises the question of if the GMPVs are completely sealed. GMPVs have recently been reported to be permeable to hydrophilic macromolecules (Skinkle et al. 2020. Biophys. J. 118, 1292-1300). Tau and the TAT peptide are more hydrophilic than the two negative controls used, transferrin and albumin.

3. It is not clear which molecular species of tau (monomers, oligomers, or fibrils) are being studied. The authors refer to tau fibrils, but the species of recombinant tau they are using are never characterised. Incubation of tau with heparin can be expected to result in a mixture of fibrils, oligomers, and monomers. Sonication may also change the distribution of tau species by liberating oligomers and monomers from fibrils. Similarly, key details about the immunoprecipitation are lacking, including neuropathological characterization of the human cases, the brain region, the amount of brain tissue, the lysis buffer, the epitope of the Tau B antibody, the amount of Dynabeads, and analysis of the immunoprecipitated sample to show what species of tau are present.

Reviewer #1 (Public Review):

The manuscript presents an interesting study that uses the previously generated yeast strains harboring human disease-specific mutations modelled in the yeast ortholog of SEC53 (V238M & F126L variants). These variants are either controlled by a strong heterologous promoter (pACT1) or a less-efficient native promoter. In either scenario, the strains manifest growth defects. The current study uses an experimental evolution strategy to evolve the strains to identify genetic suppressors of the slow growth phenotype. The authors identify several mutations in evolved strains and find a significant number of the suppressors in phosphoglucomutase 1, PGM1 (congenital disorders of glycosylation, CDG type I human ortholog). The synthetic setup replicates the compensatory mutations, but the growth rescue did not match the primary suppressors with several other mutations suggesting synergistic effects. Furthermore, reconstituted strains harboring LOF of PGM1 also showed the growth rescue, yet none of the evolved strains possessed a LOF of PGM1. The authors identify the PGM1 suppressors to be dominant. Finally, the protein activity assays reveal that the mutations in PGM1 reduce the protein activity rather than eliminate it. Overall, the assays show the power of yeast genetics for discovering the potential therapeutic targets in human diseases such as Congenital Disorders of Glycosylation 1.

1. While the authors identify the suppressors in known genetic interactors (GIs) of the yeast SEC53, it is worth testing if the compensatory mutations are rewiring the GIs, thereby explaining the lack of comparable compensations observed in reconstituted strains. If altered GIs explain the suppression, then while yeast serves as an excellent tool to perform these assays, the human context of the disease may require a different set of genetic suppressors and, therefore, a different target than the yeast PGM1 ortholog. Also, are Sec53 and Pgm1 proteins directly interacting in yeast and whether these mutations are on the interaction interface?

2. Based on the data obtained between pACT1 and pSEC53-driven expression of the SEC53 mutant alleles, the pattern of suppressors appears to be different. Authors report that the variants expressed from strong pACT1 promoters show more suppressors than those driven by native promoters. Is this a general trend in experimental evolution that slower-growing strains tend to show lesser suppressors? For example, on Page 6, line 154, "compensating for Sec53-F126L dimerization defects are rare or not easily accessible". The statement suggests that the authors did obtain suppressors that compensate for the dimerization defect. At the same time, while rare (also, are authors suggesting suppression of dimerization defect as in better dimerization?), the rate of obtaining suppressors seems to be linked to the severity of the fitness defects of the strains. The lack of suppressors may be a limitation of the evolution experiments. Indeed later in the manuscript, the authors noticed that while PGM1 suppressors obtained in V238M can also suppress F126L alleles, the suppression was not as efficient. Could it be that evolution experiments in slower-growing strains predominantly enrich suppressors in other pathways (i.e., not in the CDG orthologs) that restore the growth better and compete out the relatively weaker suppressors in PGM1? In fact, the authors report similar effects on Page 7, lines 204-210. These two paragraphs are contradictory and should be explained further.

3. Authors report that the LOF of PGM1 compensates for the SEC53 mutations. However, the evolution experiments did not capture any LOFs in PGM1. The fitness comparisons in evolution experiments are different as many different genotypes compete in a mix. Therefore, the fitness assays in a clonal population may not represent these differences well. To test this argument, authors can try to mimic the evolution experiments by mixing two genotypes to check competitive fitness, like the co-culture of pgm1 suppressor obtained via evolution experiments with pgm1Δ.

Reviewer #3 (Public Review):

Vignogna et al. used yeast genetics, experimental evolution and biochemistry to tackle human congenital disorders of glycosylation (CDG), a disease mostly caused by mutations in PMM2. They took advantage of the observation that the budding yeast gene SEC53 is almost identical to human PMM2, and used experimental evolution to find interactors of SEC53/PMM2. They found an overrepresentation of mutations in genes corresponding to other human CDG genes, including PGM1. Genetic and biochemical characterizations of the pgm1 mutations were carried out. This work is solid, although authors did not reveal why reduction of pgm1 activity could compensate for defects of a particular mutant allele of sec53.

Out of curiosity, if the authors were to simply focus on the preexisting mutations, would they have gotten the materials for most of the experiments in this article? In other words, how important is the experimental evolution?<br /> A strain table with full genotypes is needed.

Reviewer #1 (Public Review):

The goals of this paper were to provide structural and biochemical insight into the role that Fip1 plays as a protein interaction scaffold with other members of the cleavage and polyadenylation machinery. To that end, the authors solve two crystal structures as the featured basis for the study: FIp1 and CPSF30; Fip1 and Cst77. In both cases, subdomains of proteins were used for crystallography. These two structures are of high quality and are validated with rigorous and clear biochemistry. These points represent the technical strengths of the manuscript. One weakness of the study is the overall findings with those previously reported by the Tong laboratory. While the authors present some potentially newer information in terms of the number of PAP proteins capable of interacting with FIp1/CPSF30, there are experimental caveats that would need to be addressed first.

The novelty of the Fip1/CstF77 structure represents the strongest aspect of the paper as well as the observation that CstF77 can inhibit polyadenylation. The in vitro data support this conclusion but additional experiments would strengthen this overall model.

Reviewer #3 (Public Review):

The authors report the structure of CPSF30 bound to 2 molecules of FIP1, as well as the structure of FIP1 bound to CSTF77. Their data supports a model in which two molecules of FIP1, are present in the mPSF subcomplex of CPSF, although only one PAP may be bound to this complex. TheCstF77 binding to Fip1, which likely inhibits polyadenylation since it interferes with PAP binding, would occur as the complete complex assembles on the substrate, and result in the active cleavage complex not containing PAP or active in polyadenylation, only becoming active in polyadenylation after cleavage and loss of CstF from the complex.

Reviewer #1 (Public Review):

In this work, Maxime R. and co-authors intended to investigate the consequence of dystrophin absence/alteration in myoblasts, the effector cells of muscle growth and regeneration, and the early role of such cells in the pathogenesis of the disease. They carried out a transcriptomic analysis, comparing transcripts expressed by dystrophic myoblasts isolated from two murine models of DMD (Dmdmdx and Dmdmdx-βgeo) and control healthy mice. The expression of a large number of genes, comprising key regulator of myogenic differentiation (Myod1, Myog, Pax3 etc.) resulted affected in comparison to control in both mouse lines. Authors highlighted similar gene expression modifications also in a myoblast cell line previously established from the mdx mouse. To assess the outcomes from the gene ontology analysis, which pointed on the alteration of muscle system and regulation of muscle system processes, authors evaluated the proliferative, chemotactic and differentiative capacities of dystrophic myoblasts. Myoblasts presented increased proliferation, reduced chemotaxis and quite surprisingly, improved differentiating capacity, if considering the transcriptomic data. Finally, Maxime R. and co-authors carried out a transcriptomic analysis in myoblasts from DMD human subjects. Even though the profile of altered gene expression resulted similar and the GO studies seemed to focus on the same pathway categories, a significative divergence was observed particularly at the level of gene expression.

Authors link transcriptomic abnormalities and functional changes in proliferation, chemotaxis and differentiation of the dystrophic myoblasts with the alterations (probably epigenetic changes) occurring in satellite cells of dystrophic mice, consequent to the absence of the dystrophin protein. Such modifications in gene expression are supposed to be inherited by pathological myoblasts due to the division of the SC that is no longer asymmetric as occurring in healthy tissue.

Strengths<br /> Transcriptomic data from samples of different sources are solid and rigorous statistical analyses have been carried out.<br /> Transcriptomic and functional data from primary proliferating myoblasts of the two mouse models and from the myoblast cell line are similar. This is a convincing evidence that the transcriptomic alterations observed in primary myoblasts are not influenced by the exposure to the niche environment present in the dystrophic muscle, but that are cell autonomous.<br /> Authors adopted a 3D culture for the functional analysis concerning myoblasts differentiations, in this way better mimicking the process occurring in vivo.

Weaknesses<br /> The mdx mouse phenotype is mild in comparison to the severe symptoms and the rapid disease progression experimented by most of the human DMD subjects. Mdx mice is characterized by cycle of degeneration/regeneration initiating around the age of 6 weeks and continuing for several weeks. It was expected that authors discussed this point in detail, also considering that the animals used in this study were 8 weeks old. Furthermore, transcriptomic analysis of the human DMD myoblasts highlighted many differences as well as similarities when compared to mouse samples. Why do not focus more on this aspect?<br /> According to the authors, dystrophic abnormalities in myoblasts manifest irrespective of differences in genetic backgrounds and across species. The last one is a strong statement that should have been supported at least by functional data regarding chemotaxis proliferation and differentiation of human DMD myoblasts.<br /> In the discussion, the authors suggest two possible mechanisms as responsible for alterations in the behavior of the SC that ultimately affect the functionality of myoblasts, an RNA-mediated pathological process or an alteration in epigenetic regulation. They consider the latter mechanism more likely. This is based in particular on transcriptomic data showing the downregulation of important genes involved in histone modifications, normally linked to transcriptional activation. They also reported from the literature that HDAC inhibitors upregulate MyoD, a gene that is effectively downregulated in this study. Since the authors postulate that the epigenetic dysregulation of Myod1 expression is responsible for the pathological cascade of gene downregulation, ultimately leading to the pathological phenotype, it would have been interesting to evaluate the impact of HDACi on this pathways or the overexpression of enzymes responsible for H3K4 methylation as Smid1 (downregulated in this study).

Reviewer #1 (Public Review):

The study presents the observation of the coexistence of slowly moving broad waves of high F-actin concentration and rapidly propagating planar F-actin pulses in giant Dictyostelium discoideum cells and focuses on their theoretical analysis on the basis of a 3 component activator-inhibitor reaction-diffusion model with globally conserved actin. The authors conclude that the pattern coexistence is generic in a system with mass conservation close to a primary codimension-2 T-point bifurcation. The observation of the wave patterns in Dictyostelium discoideum is interesting and adds new phenomena to intracellular self-organization and patterns. The theoretical analysis is very careful and provides deep insight into the pattern mechanism in terms of non-linear dynamics. A very interesting and careful study. Well written, excellent figures.

While the observation of the wave patterns in Dictyostelium discoideum is interesting, the focus is on the theoretical analysis - which indeed is excellent. Unfortunately, the authors did not use their experimental abilities to verify predictions of their theoretical analysis, and thus in the end the conclusions lean heavily to the theoretical side. There are little biological conclusions from the theoretical analysis. In particular, a clear discussion on whether the observations are restricted to the artificial giant cells or may also have meaning for normal-sized cells is missing.

Reviewer #1 (Public Review):

In this study, the authors use T1-weighted structural MRI and FDG-PET data from an open access cohort to estimate individual-level structural and metabolic covariance networks across the Alzheimer-continuum using well-established sites of Alzheimer's pathology as seeds, detecting clear differences between diagnostic groups. They proceed to show that the evolution of these networks along the disease continuum is associated with memory performance in a nonlinear manner, with different trajectories. The results provide insight into structural and metabolic covariance network contributions to memory performance throughout the disease course, which adds to the current knowledge about potential network-based biomarkers and might have relevance to evaluating these markers in a clinical setting.

Strengths:<br /> - The analyses were performed on a well-characterised cohort with an adequate sample size that contributes to the robustness of the results.<br /> - Taking into account the non-linearity of the association between network-based descriptors and memory performance conforms better to current hypothetical models of biomarker dynamics in Alzheimer's disease.

Weaknesses:<br /> - It is encouraging that the results were replicated in a validation dataset, however, based on the manuscript it seems it is not independent from the main analysed data. Since the individual network estimation step relies on back-projection from a group-level salience map, the generalisability of the results might be better assessed by keeping the validation dataset independent.<br /> - Along this line, the employed method estimates individual network scores that quantify the relative contribution of a certain participant to the group-level salience map. This could potentially sensitise the method to the relative imbalance of group sizes across diagnoses and/or A/T categories, or to outliers.

Reviewer #1 (Public Review):

In this well-written manuscript by Barber and colleagues from UCL in the UK, the authors seek to identify a new predictive biomarker for patients with recurrent/metastatic head and neck cancer who are treated with chemotherapy. The manuscript is clearly written. This is an impressive body of correlative research performed in the context of samples collected from patients enrolled on a phase II trial, with samples collected and analyzed for immune monitoring. There are several novel assays employed beyond the standard immune monitoring. The question is of moderate clinical significance. There are a number of critical statistical limitations.

The question is of moderate clinical significance to the field. It is correct that we have only modest predictive biomarkers for chemotherapy response in R/M HNSCC. For this study to be significant, one would want to see a marked improvement over current biomarkers, in a robust and generalizable population. Unfortunately, this study falls short in these respects. First, the authors do not adequately discuss the prior literature. Even a fairly crude and old-fashioned blood-based biomarker such as neutrophil:lymphocyte ratio has quite good predictive and prognostic capability in R/M HNSCC. It is not clear to me that there is a compelling need to do better -- given that existing predictive biomarkers based on clinical nomograms or NLR are actually used in practice.

To establish that this fairly labor-intensive and expensive assay would add value, a comparison to other existing biomarkers is necessary. It is not clear qualitatively that the biomarker presented here is an improvement beyond what is currently available. This comparison could easily be performed.

A large number (31 of 87) patients were not included due to lack of biomaterials. No analyses have been performed to examine the characteristics of these patients. It is unlikely that the collection of biomaterials has no correlation with disease characteristics, prognostic features, outcomes, or the analytes in this study. This exclusion -- akin to unequal censoring in clinical trials -- is likely to significant impact results. Given that the population enrolled in a phase II trial, and that sub-population of patients who survive long enough and are feeling well enough to submit to large volume blood draws on trial, would not necessarily represent the real world population of R/M HNSCC patients, a broader population is needed to justify conclusions about this assay having robust predictive value.

It is unclear why OS as a hard endpoint was not analyzed here. No explanation is provided, other than OS was not available, a statement that is difficult to understand, given that PFS was available, and overall survival is a component of PFS.

There is no validation set for the biomarker. The biomarker was trained and cross-validated using Bayesian techniques to reduce overfitting. This is a valid approach for training and cross-validation, but for the biomarker to be testable and interpretable, it requires assessment in an independent dataset. There is no statistical technique that I am aware of that generates informative biomarkers without an independent validation dataset, and the use of these techniques to minimize overfitting does not circumvent this limitation, if one's goal is to develop a clinically useful biomarker. The 2 articles cited to justify this approach are not germane to the question -- one is an article describing the FRET-FLIM technique, and the other article describes the effectiveness of this approach to minimize overfitting.

In the end, the degree of predictive value, as assessed by C-index and the spread in the PFS curves, is modest, and not clearly an improvement beyond currently available biomarkers. Given that this dataset is the training dataset -- with no validation dataset -- in a population that is unlikely to be representative of the R/M population, it is not clear that this expensive and labor-intensive immune monitoring approach has much to offer.

Reviewer #3 (Public Review):

The authors explored the net patterns of selection in cancers as measured from tumor:normal exome and whole genome sequencing data. They found that by stratifying tumors on total mutation load, tumors with a low mutation burden exhibited net diversifying selection on previously identified oncogenic driver genes and net purifying selection on non-driver genes. Somewhat counter-intuitively both of these patterns decayed with increasing total mutation burden to the point where for tumors with the highest mutation burden, no net selection signals were identifiable. These findings were replicated using two dN/dS based approaches (with distinct means of defining the null expectation) and also using structural rearrangements as an orthogonal approach. The findings seem well demonstrated.

The proposed explanation for these observations is that of Hill-Roberson interference, where the (almost) perfect linkage disequilibrium of the whole genome in a clonally expanding population of cells provides little opportunity to separate mutations of opposing fitness effects leading to the accumulation of deleterious mutations without opportunity for their removal by selection. An important implication of this conclusion is that tumors, particularly those with a high mutation load, carry a high burden of deleterious mutations.

The modelling of clonal evolution demonstrates that Hill-Robertson like processes can in principal explain the decay of selection signals wither a high mutation burden, though this modelling by the authors own admission has lax parameter constraints and are gross simplifications of reality. As a proof of principal this modelling seems sufficient, and the estimated fitness effects appropriately qualified as "highly provisional".

The authors present the up-regulation of heat-shock/chaperone/protein-degradation pathways as a plausible mechanism through which cancers could manage the accumulation of many deleterious mutations and provide correlative evidence for increased expression of such genes in tumors with higher mutation burdens (Fig 2G). By considering only one such scenario the authors are perhaps placing too much emphasis on that one mechanistic hypothesis for (amino acid changing) mutational tolerance. Other plausible mechanisms include suppression of epitope presentation (adaptive immune evasion), replication stress etc.

Understanding that tumors carry substantial deleterious mutation loads and some prelimiary quantitative estimates of that will be of broad interest to the cancer genomics and also wider fields. The preprint is already being cited and found to be useful. The work also raises an important question - what are the main mechanisms employed to tolerate that deleterious mutation load, if there are predominant mechanisms such as the proposed protein-misfolding response, they become interesting targets for therapeutic suppression in a broad spectrum of cancers.

Reviewer #1 (Public Review):

Tilk et al investigate why cancer genomes show weak negative selection. They set out to differentiate between two scenarios whether selective pressures are relaxed during the course of tumor progression or whether selection is inefficient because of evolution without recombination thus genome-wide linkage resulting in interference among mutations referred to as Hill-Robertson interference. They calculate dN/dS for driver and passenger mutations in 50 cancer types with different levels of genome-wide mutational burden and show that passenger mutations exhibit negative selection and driver mutations show positive selection in low mutational burden tumors. The strength of selection is weakened in tumors with increasing mutational burden. The findings that the selection for passenger mutations is weak in high mutational burden tumors is novel and has not been shown before. Authors show this to be true for somatic copy number aberrations containing drivers versus passengers. Clonal mutations showed stronger selection than sub-clonal mutations. The accumulation of deleterious passenger mutations is buffered by upregulation of expression of genes encoding chaperones and the proteasome. Authors conclude that Hill-Robertson Interference can largely explain the weakened selection in drivers and passengers, which is also supported by their evolutionary model and they predict that cancer cells obtain fitness advantage from drivers by 130% and fitness cost from passengers of 40% conferring on cancer cells a net fitness advantage of 90%. This is an elegant study and the manuscript is well-written and logical. However, some aspects of the analyses require clarification.

1. Figure panels should be called out sequentially. For example, Fig. 2G is called out before Fig. 2D. This happens throughout the text, including main and supplementary figures, and should be corrected.<br /> 2. Fig. 2G shows that mean gene expression of genes encoding chaperones and the proteasome increases with increasing mutational burden. What about protein abundance? Is this in agreement with gene expression?<br /> 3. Fig. 2 mentions error bars in the figure legend, but no panel displays error bars. This is also true for Fig. S13 and other figures. Authors should display the error bars to which they are referring to make their analysis more convincing.<br /> 4. Pg. 9 line 295 describes results of the analysis across genes belonging to different GO terms. However, Fig. S13 only shows 3 categories: chromosome segregation, transcription and translation. How were these categories chosen? What about other categories? Such cherry picking doesn't convincingly support the conclusions that no specific GO functions are enriched. Also, translational regulation shows higher dN/dS in low mutation tumors suggesting that there is positive selection for passengers in this category. Authors should discuss in their manuscript why this is the case.<br /> 5. Fig. S15 shows the attenuation in selection of CNAs across cancer subtypes and broad cancer groups. However, HNSC and kidney cancer appear to be the exceptions. Authors should provide an explanation for these observations in the main text.<br /> 6. Generally, copy number variations are considered to be > 50 bp. Is there a rationale as to why authors chose 100 kb to be their cut-off in Fig. 2C? If the size of CNA is an important parameter, then authors should explain why that is.<br /> 7. Non-allelic recombination and non-homologous recombination mechanisms involving replication accidents that lead to chromosome breakage occur with some frequency in somatic cells. How does the frequency of these events impact the selection efficiency in cancer as it relates to drivers and passengers? Can this also be incorporated in their evolutionary model?<br /> 8. Authors mentioned that haploinsufficiency was not used in the model. What about loss of heterozygosity which is extensive in cancer genomes? Can this parameter be included in the evolutionary model and how would it impact the results?

Reviewer #1 (Public Review):

Tarasov and colleagues provide data that extensively phenotypes TGAC8 mice, which exhibit a cAMP-mediated increase in cardiac workload prior to developing heart failure. The authors confirm data from prior studies, showing increased cardiac output mediated by changes in heart rate with similar ejection fraction. Interestingly, canonical markers of LV hypertrophy did not differ from wildtype mice at the time period studied. The LV demonstrated proliferation of small cardiomyocytes and a network of interstitial non-cardiac myocytes. Transcriptomic and proteomic analyses of bulk LV tissue in TGAC8 mice compared to wildtype found pathways involved in immune responses, ROS scavenging, proliferation, and apoptosis to be activated in TGAC8 mice. Similarly, metabolic profiles shifted from fatty acid oxidation to glycolysis.

The study is overall well-planned and the amount of data presented by the authors is impressive. The work nicely incorporates animal-level physiology (echocardiography data), tests for known canonical markers of hypertrophy, and then delves into an unbiased analysis of the transcriptome and proteome of LV tissue in bulk. The techniques and analyses in the study are adequately executed and within the realm of expertise of the Lakatta laboratory. This study is a necessary and crucial first step to extensively phenotype this mouse line and generate hypotheses for further work.

Reviewer #3 (Public Review):

Tarasov et al have undertaken a very extensive series of studies in a transgenic mouse model (cardiomyocyte-specific overexpression of adenylyl cyclase type 8) that apparently resists the chronic stress of excessive cAMP signaling for around a year or so without overt heart failure. Based on the extensive analyses, including RNAseq and proteomic screening, the authors have hunted for potential "adaptive" or "protective" pathways. There is a wealth of information in this study and the experiments appear to have been carefully performed from a technical viewpoint. Many interesting pathways are identified and there is plenty of information where additional experiments could be designed.

General comments<br /> 1. Ultimately, this is a descriptive and hypothesis-generating study rather than providing directly proven mechanistic insights. T<br /> -Given several prior studies reporting a detrimental effect of chronically increased cAMP signaling, what is it that is different in this model? Is it something specific about AC8? Is it to do with when (in life) the stress commences?<br /> - Is the information herein relevant to stress adaptation in general or is it just something interesting in this specific mouse model?

2. None of the pathways that are apparently activated were directly perturbed so their mechanistic role requires further study.

Specific<br /> 1. The strain of the mice and their sex needs to be stated as well as the exact age at which the various assays were performed.<br /> 2. The hearts of the Tg mice have more cardiomyocytes but which are smaller. Since there is no observed increase in proliferation of cardiomyocytes, how (or when) did this increase in cell number occur?<br /> 3. While the mice do not show an increased mortality up to 12 months of age, HR/CO/EF are poor indices of contractile function. Data on end-systolic elastance or perhaps echo-based LV strain indices which will be relatively load-independent would be useful.<br /> 4. Quite a lot of conclusions are made relating to metabolism. However, this is entirely based on gene expression or protein levels. Given the substantial role of allosteric regulation in metabolic control, as well as the interconnectedness of metabolic pathways, ultimately any robust conclusions need to be based on an assessment of activity of key pathways.

Reviewer #1 (Public Review):

The manuscript by Park et al. uses CLEM, cryo-milling, and cryo-ET to visualize the major morphological transitions that occur in the bacteria Coxiella burnetii as it infects mammalian cells. These bacteria move from a small cell variant (SCV) to a large cell variant (LCV) that can replicate in the host lysosome. The authors focus on the C. burnetii T4SS which is similar in composition to the legionella Dot/Icm T4SS. They generate a homology model from the sub-tomographic 3D reconstruction of a little over 7,000 T4SSs using alpha-fold and structure of the Lp T4SS. This is a very descriptive analysis of gross cellular changes that occur in this bacteria during infection. While the authors suggest models for the developmental steps they think are occurring, it doesn't appear that they have fully mined their data for a more quantitative analysis of what is happening during the transition - let alone a mechanistic model for T4SS assembly and regulation.

Major Suggestions:

1. While it seems a reasonable hypothesis that the Dot/Icm T4SS assembly is regulated during the SCV-to-LCV transition it is unclear from the data shown if this is actually the case? Can the authors quantify the number and assembly state of the T4SSs during the transition from SCV to LCV (like they quantified the number of ribosomes)? They should also show sub-tomographic averages of the various structures making clear what parts of the complexes are assembled early and late during this transition? This type of detailed analysis is essential since it is the main point of the paper.<br /> 2. "The results indicate that 38.6% of the Dot/Icm machines identified from LCV images displayed an assembled inner membrane complex. By contrast, none of the bacteria in the transitional phase displayed a Dot/Icm-associated inner membrane complex, indicating that the complete core complex assembles upon transition to the LCV form (Fig. 5)."

Related to the previous comment - Figure 5 is a cartoon model - the authors should actually show this quantification and show structures.

3. A more detailed structural comparison between the Legionella and Coxiella burnetii T4SSs should be included as a major figure.

4. The number of ribosomes is quantified between SCV and LCV cells - however, it is unclear whether the argument is that in the SCV they are poised to be transcribed or that there is no change in transcriptional activity of the ribosomes during the transition.

5. The authors should further explain how folding of the inner membrane alone would allow for rapid expansion - shouldn't the outer membrane also have to rapidly expand? Do they see any sort of protein structures within the folded membrane that explains its topology?

6. Can the authors explain more why the LCV cells appear to be further away from the lysosome membrane? I would have predicted the bacteria would be closer so that proteins translocated through the T4SS would be able to get through the lysosome membrane and into the host cell cytoplasm - otherwise wouldn't these bacterial effector proteins simply be degraded in the acidified lysosome?

7. More information about the methods used needs to be included. How was phase shift correction done? How were different symmetry mismatches dealt with in the 3D reconstructions (in methods it simply states 13-fold symmetry was applied).

Reviewer #1 (Public Review):

This paper addresses an important question: whether the conduction velocity in white matter tracts is related to individual differences in memory performance. The authors use novel MRI techniques to estimate the "g-ratio" in vivo in humans - the ratio of the inner axon relative to the inner axon plus its outer myelin sheath. They find that autobiographical recall is positively related to the g-ratio in a specific white matter tract (the parahippocampal cingulum bundle) in a population of 217 healthy adults. This main finding is extended by showing that better memory is associated with larger inner axon diameters and lower neurite dispersion, which suggests more coherently organised neurites. The authors also argue that their results show that the magnetic resonance (MR) g-ratio can reveal novel insights into individual differences in cognition and how the human brain processes information.

The study is exploratory in nature and the analyses were not pre-registered. The technique has not been used before to associate cognitive performance with MR estimates of conduction velocity in candidate white matter tracts. It is therefore unknown how strong any associations are likely to be and what sort of sample size might be needed to observe them. Nevertheless, if the technique proves to be reliable, then it certainly offers a valuable new tool to understand individual differences in cognitive abilities. However, brain structure to behavior associations are notoriously variable across studies and have been argued to require very large sample sizes to obtain reproducible results.

The authors decided to analyse performance on a single task - the Autobiographical Memory Interview - and identified three candidate white matter tracts that connect the hippocampal region with other brain regions. While it is clear why these three tracts were chosen, it is less obvious why the authors chose to investigate associations with the Autobiographical Memory Interview and not other memory tests that were part of the battery of tests administered to the participants. It is reasonable to assume that something as general as the conduction velocity of a white matter tract would have an effect on memory ability across a range of tasks, so to single out one seems an unnecessarily narrow focus.

The results of the study are interesting and highlight a key role of the parahippocampal cingulum bundle in autobiographical memory recall. The results are corrected for multiple comparisons across the three fiber tracts of interest and the recall of "external details" provides a nice control compared to the "internal details" which are the measure of interest. The main findings are extended to show that it is likely to be an increase in axon diameter and an increase in neurite coherency that characterize those individuals with better autobiographical recall. Despite these positives, it remains unclear whether memory recall, in general, is better in people with higher g-ratios in this tract (as implied in the Abstract), or if this effect is specific to scores on the Autobiographical Memory Interview.

Reviewer #3 (Public Review):

The manuscript adds useful information about how structural properties of the brain are related to individual differences in autobiographical memory. A novel metric is used to assess features of white matter in tracts that are important for information exchange between the hippocampus and other brain regions. In one of these, the parahippocampal bundle, a relationship between the MR g-ratio and autobiographical memory recall is identified. This represents new and interesting information. The authors interpret the results in line with the theory that speed of signal transmission is important for cognitive function.

Reviewer #1 (Public Review):

Rasicci et al. have developed a FRET biosensor that is designed to light up when cardiac myosin folds. This structure is extremely important to understand, and its link to the super-relaxed (SRX) state has not been fully shown. Their study provides a comprehensive review of the literature and provides compelling data that the 15 heptad+leucine zipper+GFP construct does function well and that the DCM mutant E525K has a similar IVM velocity despite a reduced ATPase compared with HMM. They rely on the ionic strength-dependent changes in the rate of MantATP release to argue that the E525K mutation stabilizes the 'interacting heads motif' (IHM) state, which makes logical sense.

Strengths:

Well written and comprehensive.<br /> Utilizes the appropriate fluorescence-based sensor for measuring the folding of the myosin structure.<br /> Provides a detailed range of techniques to support the premise of the study

Weaknesses:

Over-interpretation of the outcomes from this study means that the IHM and SRX are the same. Similar studies, e.g. Anderson 2018 and Chu 2021 support the opposite view that IHM and SRX are not necessarily the same, Anderson (and Rohde 2018) point out that S1 has some element of a reduced ATPase, this clearly cannot be due to folding of the molecule. Also, mavacamten was used in these studies to show that even S1 is inhibited suggesting that SRX and IHM are not connected. This is not to say that with enough supporting evidence that these observations cannot be over-ridden, it is just not clear that there is enough in this study to support this conclusion.

I felt that the authors passed over the recent Chu 2021 paper too quickly, the Thomas group used a FRET sensor as well and provides a direct comparison as a technique, but with opposite conclusions. They also have supporting data in Rohde 2018 that their constructs were less ionic strength sensitive. It would be useful to understand what the authors think about this.

This section is likely to be important for end-users because initial expectations might be that these terms would inherently include such information.

Reviewer #1 (Public Review):

In their manuscript, Krug et al describe a CRISPR/Cas9 knock-out strategy for the creation of a pigment-less killifish (Nothobranchius furzeri) they term "klara". They target and inactivate three genes in parallel (mitfa, ltk, csf1ra). They employ the generated mutant offspring for studying mating preference as well as additional genome editing (knock-out of slc452) or the HDR-mediated knock-in to generate an inducible model for aging (inducible NTR mediated cell death by tagging the ORF of cdkn1a).

The authors present a valuable resource, a large bouquet of different, well-designed and described controlled experiments. The authors demonstrate the versatility of the established tool that may be of immediate use for the Noto community. Given the efficiency of the triple inactivation, the de novo inactivation may be more time and cost-efficient compared to the traditional sharing of the mutant animals.

When creating transgenic lines via HDR-mediated integration of donor sequences, the authors use the advanced protection of the donor construct by the addition of a 5'biotin. They validate integration by PCR genotyping and sequencing. However, these "proper" PCR bands can derive from in vitro recombination during the PCR (Won and Dawid, PlosOne2017), if a large number of cycles are used during PCR amplification. While PCR is often misleading, Southern Blot analysis delivers robust and unambiguous results. Here a single-copy integration is not relevant to the message of the manuscript.

Reviewer #3 (Public Review):

Krug et al. used emerging model species in biomedical research, Nothobranchius furzeri, to construct a triple mutant line that lacks all three major pigments found in fish (melanophores, iridophores, xanthophores). It demonstrates clearly that multiple genes can be inactivated simultaneously in this species, and that a new line can be a source of additional genetic manipulations. This is because their condition, vigour, and fecundity are standard compared to the wild type, which is convincingly demonstrated.

The introduction is appropriate and results generally correctly report what has been achieved, which is then adequately addressed in the discussion. Methods, as far as I can estimate, are sufficient to replicate the work.

The only substantial point I raise relates to the sexual selection (mate choice) part of the work. While it has no major effect on the overall conclusion, I think their interpretation needs to be reconsidered.

When reporting the results of mate choice experiment (L219ff), the authors state that males of wild and Klara type preferred wild-type females, because 75% of laid eggs belonged to wild-type females. However, another possibility is that Klara females had reduced fecundity, and the lower share of eggs had nothing to do with mate choice. In the same way, "90% of eggs were fertilized by wild-type males" (L223) is used to conclude that they were preferred by females (active mate choice). However, male success in N. furzeri is largely driven by male dominance (and not female mate choice) and it is more likely (and more precise to state) that wild-type males were more successful in male-male competition for access to females (and fertilize their eggs). This is especially so because wild-type males were larger (L. 322) and body size plays a major role in establishing dominance between N. furzeri males. This is then also pertaining to interpretation in discussion (L 318).

While I think this needs to be corrected to avoid misinterpretation, it has a minor impact on the overall high standard of the work or on general interpretation.

Reviewer #1 (Public Review):

This pre-registration study by Kerrén et al. examined the hypothesis that the brain resolves competition between overlapping memories based on phase separation of hippocampus theta oscillations. By applying a time-resolved decoding analysis, they demonstrate that the reactivations of target and competitor memories are locked to the varied phase of theta-band oscillation after repeated recalls. Moreover, subjects with larger phase separation show less memory interference. The study provides new evidence supporting the phase-coding neural mechanism to alleviate memory interference of multiple items.

Overall, this is a very interesting report testing an influential oscillatory-phase-based hypothesis in the memory field and would bring broad impacts to other fields, such as perception, attention, and decision making, given that lessening inference of distractor to target is a fundamental challenge.

Meanwhile, several aspects of the results need more evidence to strengthen the conclusion. The major weakness is the lack of significant decoding for the target and competitor by themselves, although I understand that the main hypothesis focuses on their different phase-locking relationship. Meanwhile, less significant decoding performance, I believe, is very crucial to verify the LDA analysis and data quality. Similarly, the original decoding performance time course did not show a clear out-of-phase pattern as revealed in the phase analysis. Overall, the authors need more results to confirm that the phase separation results are based on genuine reactivations and out-of-phase relationships.

Reviewer #3 (Public Review):

This manuscript uses MEG data acquired from human participants to examine whether representations of competing memories are associated with different phases of the theta rhythm in the human hippocampus. In brief, the authors use a proactive interference task in which subjects are asked to associate a word with two competing images and then subsequently recall the most recent image. Using pattern classifiers on the MEG data, the authors are able to decode reactivated content of the target and competitor memories and find that these patterns appear locked to different phases of the hippocampal rhythm. They also show that those subjects with worse memory performance had fewer differences in the phases to which target and competitor memories are locked. Together, the data provide support for a computational model of competing memories which suggests that oscillatory inhibition can be leveraged to strengthen or weaken inhibition of target and competitor memories (oscillating interference resolution model). One of the main strengths of the manuscript is that this is a pre-registered study, and so the specific hypotheses tested here have previously been reported. The current manuscript does not deviate too significantly from the pre-registered hypotheses and plan and reports the results of those proposed analyses. As such, this manuscript, therefore, presents a valuable addition to the literature, since it reports the results of a clearly established set of hypotheses testing a very specific question regarding memory interference.

Reviewer #1 (Public Review):

In this manuscript, the authors use C. elegans as a model system to show that calcium-dependent exocytosis of synaptic vesicles is differentially coupled to two different types of calcium channels. The authors take advantage of the fact that each major calcium channel family is represented by only a single gene in C. elegans, with CaV1 corresponding to L-type, CaV2 to P/Q-type, and CaV3 to T-type calcium channels, respectively Moreover, C. elegans contains only a single ryanodine-receptor channel that is responsible for releasing calcium from intracellular stores. While it is well established that CaV2 (as in other species) is mainly responsible for exocytotic transmitter release, the role of the other channels is not clear. Here the authors use smart genetic approaches involving tissue-specific deletion of individual channels and combinations of double mutants to document that CaV1 activity is responsible for the exocytosis of a distinct class of synaptic vesicles that is farther away from the active zone, couples to a distinct short form of Unc13, and that cooperates with RyR, with the release-relevant calcium release probably mainly being derived from intracellular stores.

With the caveat that this reviewer is not an expert in C.elegans, I consider this data-rich manuscript excellent, adding important information to the role of N-type calcium channels in transmission at the neuromuscular junction in C. elegans. As far as I can judge, the data are of high quality, and even the rather tricky spatial resolution of the adjacent release sites and the selective association of RyR with CaV1 and the short form of UNC13 using superresolution fluorescence microscopy is convincing. The manuscript is well written, and the data are succinctly discussed. As discussed by the authors it remains unclear whether such a mechanism also occurs in mammalian synapses, e.g. synapses in which exocytosis is also triggered by graded potential changes rather than by action potentials.

Reviewer #3 (Public Review):

In this manuscript, Jorgensen and colleagues elegantly used cutting-edge technologies to understand how different Ca entries lead to two different types of presynaptic release. They demonstrated that at the worm neuromuscular junctions two different classes of voltage-gated calcium channels, CaV2 and CaV1, mediate the release of distinct pools of synaptic vesicles. CaV2 channels are concentrated in densely packed clusters near the molecularly and EM-defined active zone structures. This type of release is dependent on synaptic vesicle priming protein UNC-13L. By contrast, they found that CaV1 channels are dispersed in synaptic varicosity and are coupled to internal calcium stores via the ryanodine receptor. CaV1 and ryanodine receptors mediate the fusion of vesicles docked broadly in synaptic varicosity and are colocalized with the vesicle priming protein UNC-13S.

The authors were able to direct their hypotheses because they have established powerful experimental methods such as rapid freezing EM coupled with neuronal stimulation. They used genetic null mutants for most of their experiments. They created endogenously labeled proteins to test the localization of proteins in live preparations. They used a combination and electrophysiological and behavioral assays. Since they worked with a system that has a small number of synaptic connections, they can reliably study the same set of synapses. The rigor of these experiments is extremely high.

The comprehensive approaches and the clear-cut results made this manuscript easily the top two or three papers I have read in the last couple of years of any journals.

Reviewer #1 (Public Review):

This work describes a new method, Proteinfer, which uses dilated neural networks to predict protein function, using EC terms and GO terms. The software is fast and the server-side performance is fast and reliable. The method is very clearly described. However, it is hard to judge the accuracy of this method based on the current manuscript, and some more work is needed to do so.

I would like to address the following statement by the authors: (p3, left column): "We focus on Swiss Prot to ensure that our models learn from human-curated labels, rather than labels generated by electronic annotation".

There is a subtle but important point to be made here: while SwissProt (SP) entries are human-curated, they might still have their function annotated ("labeled") electronically only. The SP entry comprises the sequence, source organism, paper(s) (if any), annotations, cross-references, etc. A validated entry does not mean that the annotation was necessarily validated manually: but rather that there is a paper backing the veracity of the sequence itself, and that it is not an automatic generation from a genome project.<br /> Example: 009L_FRG3G is a reviewed entry, and has four function annotations, all generated by BLAST, with an IEA (inferred by electronic annotation) evidence code. Most GO annotations in SwissProt are generated that way: a reviewed Swissprot entry, unlike what the authors imply, does not guarantee that the function annotation was made by non-electronic means. If the authors would like to use non-electronic annotations for functional labels, they should use those that are annotated with the GO experimental evidence codes (or, at the very least, not exclusively annotated with IEA). Therefore, most of the annotations in the authors' gold standard protein annotations are simply generated by BLAST and not reviewed by a person. Essentially the authors are comparing predictions with predictions, or at least not taking care not to do so. This is an important point that the authors need to address since there is no apparent gold standard they are using.

The above statement is relevant to GO. But since EC is mapped 1:1 to GO molecular function ontology (as a subset, there are many terms in GO MFO that are not enzymes of course), the authors can easily apply this to EC-based entries as well.

This may explain why, in Figure S8(b), BLAST retains such a high and even plateau of the precision-recall curve: BLAST hits are used throughout as gold-standard, and therefore BLAST performs so well. This is in contrast, say to CAFA assessments which use as a gold standard only those proteins which have experimental GO evidence codes, and therefore BLAST performs much poorer upon assessment.

Pooling GO DAGs together: It is unclear how the authors generate performance data over GO as a whole. GO is really 3 disjoint DAGs (molecular function ontology or MFO, Biological Process or BPO, Cellular component or CCO). Any assessment of performance should be over each DAG separately, to make biological sense. Pooling together the three GO DAGs which describe completely different aspects of the function is not informative. Interestingly enough, in the browser applications, the GO DAG results are distinctly separated into the respective DAGs.

Figure 3 and lack of baseline methods: the text refers to Figures 3A and 3B, but I could only see one figure with no panels. Is there an error here? It is not possible at this point to talk about the results in this figure as described. It looks like Figure 3A is missing, with Fmax scores. In any case, Figure 3(b?) has precision-recall curves showing the performance of predictions is the highest on Isomerases and lowest in hydrolases. It is hard to tell the Fmax values, but they seem reasonably high. However, there is no comparison with a baseline method such as BLAST or Naive, and those should be inserted. It is important to compare Proteinfer with these baseline methods to answer the following questions: (1) Does Proteinfer perform better than the go-to method of choice for most biologists? (2) does it perform better than what is expected given the frequency of these terms in the dataset? For an explanation of the Naive method which answers the latter question, see: (https://www.nature.com/articles/nmeth.2340)

Reviewer #3 (Public Review):

In this work, the authors employ a deep convolutional neural network approach to map protein sequence to function. The rationales are that (i) once trained, the neural network would offer fast predictions for new sequences, facilitating exploration and discovery without the need for extensive computational resources, (ii) that the embedding of protein sequences in a fixed-dimensional space would allow potential analyses and interpretation of sequence-function relationships across proteins, and (iii) predicting protein function in a way that is different from alignment-based approaches could lead to new insights or superior performance, at least in certain regimes, thereby complementing existing approaches. I believe the authors demonstrate i and iii convincingly, whereas ii was left open-ended.

A strength of the work is showing that the trained CNNs perform generally on par with existing alignment based-methods such as BLASTp, with a precision-recall tradeoff that differs from BLASTp. Because the method is more precise at lower recall values, whereas BLASTp has higher recall at lower precision values, it is indeed a good complement to BLASTp, as demonstrated by the top performance of the ensemble approach containing both methods.

Another strength of the work is its emphasis on usability and interpretability, as demonstrated in the graphical interface, use of class activation mapping for sub-sequence attribution, and the analysis of hierarchical functional clustering when projecting the high-dimensional embedding into UMAP projections.

However, a main weakness is the premise that this approach is new. For example, the authors claim that existing deep learning "models cannot infer functional annotation for full-length protein sequences." However, as the proposed method is a straightforward deep neural network implementation, there have been other very similar approaches published for protein function prediction. For example, Cai, Wang, and Deng, Frontiers in Bioengineering and Biotechnology (2020),<br /> the latter also being a CNN approach. As such, it is difficult to assess how this approach differs from or builds on previous work.

A second weakness is that it was not clear what new insights the UMAP projections of the sequence embedding could offer. For example, the authors mention that "a generalized mapping between sequence space and the space of protein functions...is useful for tasks other than those for which the models were trained." However, such tasks were not explicitly explained. The hierarchical clustering of enzymatic proteins shown in Fig. 5 and the clustering of non-enzymatic proteins in Fig. 6 are consistent with the expectation of separability in the high-dimensional embedding space that would be necessary for good CNN performance (although the sub-groups are sometimes not well-separated. For example, only the second level and leaf level are well-separated in the enzyme classification UMAP hierarchy). Therefore, the value-added of the UMAP representation should be something like using these plots to gain insight into a family or sub-family of enzymes.

The clear presentation, ease of use, and computationally accessible downstream analytics of this work make it of broad utility to the field.

Reviewer #1 (Public Review):

In the present study, Ibáñez-Solé1 et al evaluate transcriptional noise across aging and tissues in several publicly available mouse and human datasets.  Initially, the authors compare 4 generalized approaches to quantify transcriptional noise across cell types and later implement a new approach which uses iterative clustering to assess cellular noise.  Based on implementation of this approach (scallop), the authors survey noise across seven sc-seq datasets relevant for aging.  Here, the authors conclude that enhanced transcriptional noise is not a hallmark of aging, rather changes in cell identity and abundances, namely immune and endothelial cells.  The development of new tools to quantify transcriptional noise from sc-seq data presents appeal, as these datasets are increasing exponentially.  Further, the conclusion that increased transcriptional noise is not a defined aspect of aging is clearly an important contribution; however, given the provocative nature of this claim, more comprehensive and systematic analyses should be performed.  In particular, the robustness and appeal of scallop is still not sufficiently demonstrated and given the complexity  (multiple tissues, species and diverse relative age ranges) of datasets analyzed, a more thorough comparison should be performed.  I list a few thoughts below:

Initially, the authors develop Decibel, which centralizes noise quantification methods. The authors provide schematics shown in Fig 1, and compare noise estimates with aging in Fig 2 - Supplement 2.   Since the authors emphasize the necessary use of scallop as a "better" pipeline, more systematic comparisons to the other methods should be made side-by-side.  For example, scallop noise estimates (Fig 2) compared to other euclidean distance-based measures (Fig 2 supplement 2) looks fairly similar.  Are downstream observations (ex lung immune composition changes more than noise) supported from these methods as well?  If so, this would strengthen the overall conclusion on noise with age, but if not, it would be relevant to understand why.

Similarly, the 'validation of scallop seems mostly based on the ability to localize noisy vs stable cells in Fig 1 supplement 1 and relative robustness within dataset to input parameters (Fig 1 supplement 2).  A more systematic analysis should be performed to robustly establish this method.  For example, noise cell clustering comparisons across the 7 datasets used.  In addition, the Levy et all 2020 implemented a pathway-based approach to validate.  Specifically, surrogate genes were derived from GCL value where KEGG preservation was used as an output.  Similar additional types of analyses should be performed in scallop 

The conclusion that immune and endothelial cell transcriptional shifts associate more with age than noise are quite compelling, but seem entirely restricted to the mouse and human lung datasets.  It would be interesting to know if pan-tissues these same cell types enrich age-related effects or whether this phenomenon is localized

Related to these, there does not seem to be a specific rationale for why these datasets (the seven used in total or the lung for deep-dive), were selected.  Clearly, many mouse and human sc-RNA-seq datasets exist with large variations in age so expanding the datasets analyzed and/or providing sufficient rationale as to why these ones are appearing for noise analyses would be helpful. For example, querying "aging" across sc-seq datasets in Single cell portal yields 79 available datasets: https://singlecell.broadinstitute.org/single_cell?type=study&page=1&terms=aging&facets=organism_age%3A0%7C103%7Cyears

The analysis that noise is indistinguishable from cell fate shifts is compelling, but again relies on one specific example where alternative surfactant genes are used as markers.  The same question arises if this observation holds up to other cell types within other organs.  For example the human cell atlas contains over dozens of tissue with large variations in age (https://www.science.org/doi/10.1126/science.abl4290).

Reviewer #3 (Public Review):

In this manuscript, Ibáñez-Solé et al aim to clarify the answer to a very basic and important question that has gained a lot of attention in the past ~5 years due to fast-increasing pace of research in the aging field and development/optimization of single-cell gene expression quantification techniques: how does noise in gene expression change during the course of cellular/tissue aging? As the authors clearly describe, there have been multiple datasets available in the literature but one could not say the same for the number of available analysis pipelines, especially a pipeline that quantifies membership of single cells to their assigned cell type cluster. To address these needs, Ibáñez-Solé et al developed: 1. a toolkit (named Decibel) to implement the common methods for the quantification of age-related noise in scRNAseq data; and 2. a method (named Scallop) for obtaining membership information for single-cells regarding their assigned cell-type cluster. Their analyses showed that previously-published aging datasets had large variability between tissues and datasets, and importantly the author's results show that noise-increase in aging could not be claimed as a universal phenotype (as previously suggested by various studies).

Comments:

1. In two relevant papers (doi.org/10.1038/s41467-017-00752-9 and doi.org/10.1016/j.isci.2018.08.011), previous work had already shown what haploid/diploid genetic backgrounds could show in terms of intercellular/intracellular noise. Due to the direct nature of age/noise quantification in these papers, one cannot blame any computational pipeline-related issues for the "unconventional" results. The authors should cite and sufficiently discuss the noise-related results of these papers in their Discussion section. These two papers collectively show how the specific gene, its protein half-life and ploidy can lead to similar/different noise outcomes.

2. While the authors correctly put a lot of emphasis on studying the same cell type or tissue for a faithful interpretation of noise-related results, they ignore another important factor: tracking the same cell over time instead of calculating noise from single-cell populations at supposedly-different age points. Obviously, scRNAseq cannot analyze the same cell twice, but inability to assess noise-in-aging in the same cell over time is still an important concern. Noise could/does affect the generation durations and therefore neighboring cells in the same cluster may not have experienced the same amount of mitotic aging, for example. Also, perhaps a cell has already entered senescence at early age in the same tissue. This caveat should be properly discussed.

3. Another weakness of this study is that the authors did not show the source/cause of decreasing/stable/increasing noise during aging. Understanding the source of loss of cell type identity is also important but this manuscript was about noise in aging, so it would have been nice if there could be some attempts to explain why noise is having this/that trend in differentially aged cell types in specific tissues.

4. In the discussion section, the authors say that "Most importantly, Scallop measures transcriptional noise by membership to cell type-specific clusters which is a re-definition of the original formulation of noise by Raser and O'Shea." It is not clear what the authors refer to by "the original formulation of noise by Raser and O'Shea". Intrinsic/extrinsic noise formulations?? Please be more specific.

Reviewer #1 (Public Review):

Xian et al. systematically evaluated age and sex-dependent differences in paw skin and sciatic nerves (SCN) tissues of naïve mice, utilizing DIA-PASEF, a highly sensitive and reproducible proteomics approach. The authors demonstrated that the deep proteome profiling enabled a discovery of significant differences between male and female mice and adolescent and adult mice such as homeostasis and epidermal signaling in skin and, myelination and neuronal development in SCN that are known to be relevant to the pathophysiology. The authors claim the need for the appropriate age and sex matching in the experiment design and suggest the work as a unique systems biology proteome resource in mouse disease model. As I understand this is the first attempt to molecularly characterize the impact of mouse age and sex that would help warrant the reproducibility of the preclinical research.

Reviewer #3 (Public Review):

The paper emphasizes the importance of testing males and females in parallel when designing mice experiments as well as being consistent with age. In agreement with this, significant differences were observed between mice of different sexes and of varying ages. It also offers many insights into how DIA-PASEF workflows can improve performance in proteomics.

I would suggest to the authors they explain how experiments could be designed in a small scale in case there are time and financial constraints so that both female and male mice can be used simultaneously. It would also be beneficial to read over any challenges associated with the DIA-PASEF analysis. Enrich the discussion with performance comparison between DIA-PASEF and DDA-PASEF for mice proteomics data male versus female.<br /> Were there any unique proteins only found by DIA-PASEF?

Reviewer #1 (Public Review):

This manuscript reports the results of studies on the effects of an ActRIIB-Fc ligand trap inhibitor of myostatin on muscle contractures that develop when brachial plexus nerve roots are severed at 6 after birth. One component of this pathological response seems to be a failure to add sarcomeres as the skeleton grows resulting in short muscles. The authors use a carefully performed set of animal studies to test the effects of the ligand trap on denervation-induced limitations in range of motion in young mice. They also investigate several biochemical mechanisms that might contribute to contractures and be modified by the ligand trap. Finally, the test for gender discordance in the protective effect of a proteasome inhibitor against contractures. The major finding of these studies is that the ligand trap improved the range of motion at the elbow and shoulder in female mice but not in males. The major caveat to interpreting the data is that group sizes are relatively small such that the study may have been underpowered to detect smaller effects on a range of motion and biochemical endpoints.

Reviewer #3 (Public Review):

This timely manuscript describes the sex dimorphisms in neonatal development as it applies to muscle injury and denervation. More and more studies are identifying sex differences in skeletal muscle function and dysfunction. This is one more study to point out differences. A missing piece to the field and this study are the mechanistic links between skeletal muscle function/dysfunction and sex differences. This paper starts to point to a mechanism highlighting the non-canonical AKT pathway. This is a very well-written manuscript with a clear experimental plan and workflow. I have no major concerns.

My biggest question is the molecular mechanism linking sex differences and skeletal muscle function and dysfunction. However, this is perhaps a follow-up study to the already complete study the authors present.

Joint Public Review:

Tiedemann et al. evaluated the neural response to insulin before and after a 3-month caloric restriction diet compared to an active waiting group in 50 elderly persons overweight and with obesity. Peripheral insulin sensitivity and central insulin sensitivity of the mesolimbic reward circuitry predicted weight loss success and improvement after caloric restriction. This is the first longitudinal study showing an improvement in central insulin sensitivity in humans.

Strengths<br /> The major strength of the paper is the longitudinal study design in persons at high risk to develop type 2 diabetes with multiple fMRI visits, with a state-of-the-art fMRI design. Moreover, the study uses a placebo-controlled approach to evaluate insulin sensitivity in the brain. Another strength is the fact the control group is an active waiting group, which means that they received dietary counselling after the study and additionally benefited from the study by receiving training courses on stress management.

Weakness<br /> A weakness of the study is the lack of metabolic assessments of the participants. Only fasting blood samples were available. No oral glucose tolerance test or hyperinsulinemic-euglycemic clamps were acquired to quantify peripheral or whole-body insulin resistance. Whether peripheral insulin sensitivity and central insulin sensitivity are, in fact, independently involved in the success or treatment outcome of intervention still needs to be validated in future studies.<br /> Another weakness is the food cue picture set implemented in the study. The different categories of pictures were not carefully matched for psychological and physical variables (such as the complexity of a picture). This could potentially influence neural food cue reactivity.<br /> The study evaluated the regional BOLD response of the reward circuitry showing that insulin signaling in the VTA and nucleus accumbens play an important role in successful weight loss. The role of insulin signaling on projections (or connectivity) of the mesolimbic reward circuitry was not evaluated in the current study.

Reviewer #1 (Public Review):

This manuscript discusses evolutionary patterns of manipulation of others' allocation of investment in individual reproduction relative to group productivity. Three traits are considered: this investment, manipulation of others' investment, and resistance to this investment. The main result of the manuscript is that the joint evolution of these traits can lead to the maintenance of diversity through, as documented here, cyclic (or noisier) dynamics. Although there are some analytical results, this main conclusion is instead supported by individual-based simulations, which seem correctly performed (but for clonal populations, as emphasized below).

There could be material for a good paper here but the organization of the manuscript makes it difficult to fully evaluate. The narrative is highly condensed, with the drawbacks that this often entails in terms of accurately conveying the results of a study, as illustrated here by the following issue.

The population is apparently assumed to be clonal (more than just "haploid"), meaning that there is no recombination between the loci controlling the three traits. In the one case where this assumption is relaxed (quite artificially), the cyclic dynamics disappear (section 4.4 of the appendix). This is crucial information that cannot be appreciated in the main text.

The paragraph at line 368 offers a simple explanation for the joint dynamics of traits. However, this explanation would hold identically for a sexual population and a clonal population, whereas these two cases seem to have completely different dynamics. Thus, there is something essential to explain these differences, that is missing from the given explanation.

This is especially important because the finding that the joint evolution of several traits can lead to some form of diversity maintenance is not surprising. As the discussion acknowledges (but the introduction seems to downplay), it is also well understood that manipulation and counter-adaptations to it can occur in many contexts and lead to the maintenance of diversity. For this reason, similar results in the present case are not surprising, and the main outcome of the study should be to provide a deeper understanding of the forces leading to the different outcomes in the current models.

I do not see clearly what distinguishes "manipulative cheating" from other forms of manipulations that have been previously discussed in the literature (e.g, as cited lines 461). Couldn't this be clarified by some kind of mathematical criterion?

Reviewer #3 (Public Review):

The paper uses a mixture of game-theoretical models and individual-based simulations to study the coevolution of manipulation and resistance to manipulation in social interactions. This is a very impressive piece of theoretical research that will likely open new directions for both theoretical and empirical work.

Reviewer #1 (Public Review):

The study uses public behavioral and fMRI data to study the range adaptation properties of the orbitofrontal cortex (OFC) during risky choice that requires integrating potential gains and losses. The authors demonstrate how spill-over effects from the range of gains to the sensitivity to losses, cannot be explained by simple efficient coding accounts. The authors construct an artificial neural network (ANN) and show that Hebbian plasticity between attribute-specific and integration units can account for the context-dependent effect in behavior and fMRI data.

This is an interesting study that discusses a potential mechanism for context effects often seen in decision-making. A major concern is that the manuscript focuses on Hebbian plasticity as the key mechanism, whereas the results show that the choice of activation functions (sigmoidal vs. gaussian) has a comparable contribution to explaining behavior but is not discussed. In addition, the performance of even the best model is not very convincing for extreme ranges of expected value. There are additional moderate and minor concerns with result presentation and interpretation.

Reviewer #3 (Public Review):

The authors investigate range adaptation in the orbitofrontal cortex by taking advantage of an existing data set on willingness to gamble where two different groups experienced a wider or a narrower range of gains but the same range of losses. They find that sensitivity not only to gains but also to losses changes as a function of the gain range, such that for the part that was common to the two groups, people in the wide range group were less willing to gamble than people in the narrow range group. Moreover, a two-layer artificial network with Hebbian plasticity explains the behavioral effects of ranges and multivariate neural representations of value in the orbitofrontal cortex. The authors conclude that range adaptation occurs at the level of the integration layer rather than at the level of the attribute-specific input layer (where gains and losses are separate). The paper provides a welcome addition to the literature on how range adaptations may come about but would benefit from a couple of clarifications.

Major:<br /> 1) It appears like the Gaussian assumption may explain as much or even more of the variance as the plasticity assumption. However, the results do not really address this point. It would be good to provide some information about it for the behavioral findings, check whether the impression also holds for OFC and vmPFC activity, and discuss what the Gaussian assumption implies for the representation of value as such. After all, the monotonicity assumption pervades most previous research on value representation and seems to have been supported reasonably well so far (sometimes with the refinement that positive and negative coding monotonic signals/neurons may be intermixed). Relatedly, one may assume that the Gaussian assumption primarily holds for chosen value cells. But Figure 6 suggests that offer value units are more common in the model. Please explain.

2) The paper dismisses simplistic efficient coding scenarios that operate on neurons that transmit gain/loss information based on either finding common coding of gain and loss information but no difference between range groups or a difference between range groups but no common coding of gain and loss information. Did the authors also consider common coding of a) expected value, b) gains only, and differences between range groups in (a) and (b) signals, instead of looking at both gains and losses? Because the range manipulation primarily concerned gains rather than both gains and losses, there may be more power in looking at gains only. It may also be worth mentioning that at least for simple reward prediction error signals, a within-subject design, and regions other than the OFC, the simplistic analysis approach can find both effects (Kirschner et al., 2018, Brain). Of course, some of the mentioned or other differences may explain the difference in findings.

Reviewer #1 (Public Review):

Lymphatic drainage of CNS antigens has received lots of attention recently due to the (re)discovery of dural meningeal lymphatics. The role of dural lymphatics in CNS antigen drainage has been shown to be important in several contexts (AD, glioma, etc.). Few studies have focused on their role in CNS infections so far. In their study, Kovacs et al. dissected the role of meningeal lymphatic drainage in T cell responses during chronic Toxoplasma gondii infection in mice. They first show that T.g. infection drives DC accumulation in the dura and CSF at 6 weeks post-infection, which matches with the replication peak of T.g. in the brain, and with T cell expansion/activation in the dCLN. This effect on T cells in the dCLN was abrogated upon surgical blockade of dural drainage. However, dural lymphatic blockade did not affect T.g. burden, nor CD4+ and CD8+ T cell number and IFNg production in the brain in the chronic phase. They finally show that during chronic infection, antigen-specific T cells are generated not only in the dCLN but also in the periphery (ILN), which could account for the presence of T cells in the brain after surgical blockade of the lymphatics.

This study is interesting and some aspects are clearly convincing, but some precisions have to be made. More importantly, with similar tools, key experiments (as explained in the last paragraph: icv infection or chronic peripheral parasite clearance, etc.) have to be generated to bring more impact to this study and could reveal a new role for dCLN T cells.

Reviewer #1 (Public Review):

In this manuscript, Soto-Feliciano et al. investigate the tumor suppressive role of MLL3 in hepatocellular carcinoma (HCC). The authors used a variety of techniques including hydrodynamic tail vain injection (HTVI), CRISPR deletion, and shRNA to disrupt MLL3 expression in mouse models. They clearly show that MLL3 acts as a tumor suppressor in the context of MYC-induced HCC. They show that MLL3 acts by activating the Cdkn2a locus. Genomic analysis showed that MLL3 binds to enhancers and promoters, and specifically interacts with the Cdkn2a promoter. When MLL3 was downregulated, Cdkn2a levels fell and this corresponded to changes in relevant histone marks targeted by MLL3. The authors were also able to show that reintroduced MLL3 expression in a dox inducible system could rescue CDKN2A locus expression, which in turn reduced colony formation and induced apoptosis. Human genomic correlation showed that MLL3 and Cdkn2a mutations are generally mutually exclusive. Overall, the conclusions of the manuscript are well supported by a logical series of experiments with good controls and orthogonal approaches. While it would be useful to examine another HCC model such a CTNNB1-driven model, the current paper is convincing in its conclusions.

Reviewer #3 (Public Review):

The enhancer chromatin-modifying enzyme MLL3 functions as a tumor suppressor in multiple human cancers, however, the mechanisms underlying its tumor suppressive function remain unclear. The manuscript of Soto-Feliciano et al. focused on Myc-driven liver cancer and aimed to address and fill the gap. The authors used an elegant genetic design and approach to manipulate the overexpression of the Myc oncogene and knockout of the Mll3 tumor suppressor gene in mouse liver cancer models. Their genetic mouse models showed that loss of Mll3 constrains Myc-driven liver tumorigenesis, with tumors having a slightly later onset compared to mice with Myc overexpression in conjunction with p53 inactivation. Because MLL3 is a major histone-modifying enzyme for enhancer-associated H3K4 monomethylation and is responsible for enhancer activation and the following target gene transcription, they performed ChIP-seq analysis to study the roles of Mll3 in Myc-driven mouse liver cancer. Interestingly, their ChIP-seq studies revealed that loss of Mll3 preferentially limits Mll3 enrichments at promoters and thereby attenuates promoter-associated H3K4 trimethylation and target gene transcription, whereas the unchanged Mll3 genomic binding between the two genotypes (Myc;sgp53 and Myc;sgMll3) is largely located within enhancer (intergenic) regions. They further demonstrated that the cdkn2a locus is a genomic and transcriptional target of Mll3 in Myc-driven mouse liver cancer. Supporting their findings, genomic inactivations of MLL3 and CDKN2A displays mutual exclusivity in human liver cancer and many other cancer types. Furthermore, they described a possible mechanism for MLL3's role in MYC-driven liver cancer that MLL3 mediates MYC-induced apoptosis in a CDKN2A-dependent manner by manipulating Myc overexpression, Mll3 function, and Cdkn2a regulation in their genetic mice models. This manuscript describes a potential function of MLL3 in the control of tumor suppressor gene expression via modulating their promoter chromatin landscapes. More importantly, loss of normal function of MLL3 or the downstream effector CDKN2A may impair MYC-induced apoptosis, and in turn, lead to MYC-induced tumorigenesis.

Overall, the manuscript is well written, organized, and focused on an interesting topic, and with data presented supports the authors' claims.

Reviewer #1 (Public Review):

Mikelov et al. investigated IgH repertoires of memory B cells, plasmablasts, and plasma cells from peripheral blood collected at three time-points over the course of a year. In order to obtain deep and unbiased repertoire sequences, authors adopted uniquely developed IgH repertoire profiling technology. Based on collected peripheral blood data, authors claim that; 1) A high degree of clonal persistence in individual memory B cell subsets with inter-individual convergence in memory and ASCs. 2) ASC clonotypes are transient over time and related to memory B cells. 3) Reactivation of persisting memory B cells with new rounds of affinity maturation during proliferation and differentiation into ASCs. 4) Both positive and negative selection contribute to persisting and reactivated lineages preserving the functionality and specificity of BCRs.

The present study provides useful technical application for the analysis of longitudinal B cell repertoires, and bioinformatics and statistical data analysis are impressive. Regarding point 1), clonal persistence of memory B cells is already well known. On the other hand, inter-individual convergence between memory B cells and plasma cells might not be shown in healthy individuals even though the biological significance of circulating plasma cells is questionable.

Regarding 2), temporal stability of plasma cell clonotypes has been demonstrated already in the bone marrow with serial biopsies over time (Wu et al. DOI: 10.1038/ncomms13838). The Association of clonotypes between memory and plasma cells in the blood of healthy donors might be new, however, again its biological significance is questionable.

Regarding 3) and 4), it is hard to generalize observations from the presented data because the analysis was based on just four donor cases with different health conditions, i.e. a combination of healthy and allergic. The cell number of plasmablasts and plasma cells isolated from peripheral blood is extremely low compared to memory B cells, and in fact, the vast majority of ASCs reside in the tissues such as lymphoid organs, bone marrow, and mucosal tissues rather than in circulating blood (Mandric et al. DOI: 10.1038/s41467-020-16857-7). As the most critical problem, direct pieces of evidence to claim points, 3) and 4) are missing.

Reviewer #1 (Public Review):

This study uses the mouse calyx of Held synapse as a model to explore the presynaptic role of rac1, a regulator of actin signaling in the brain. Many of the now-classical methods and theory pioneered by Neher and colleagues are brought to bear on this problem. Additionally, the authors were able to make a cell-specific knockout of rac1 by developing a novel viral construct to express cre in the globular bushy cells of the cochlear nucleus; by doing this in a rac1 floxed mouse, they were able to KO rac1 in these neurons starting at around P14. The authors found that KO of rac1 enhanced EPSC amplitude, vesicle release probability, quantal release rates, EPSC onset time and jitter during high-frequency activity, and fast recovery rates from depression. Because the calyx synapses are the largest and most reliable of central nerve terminals, all these various effects had no effect on suprathreshold transmission during 'in vivo-like' stimulus protocols. Moreover, there was no effect morphologically on the synapse. Through some unavoidably serpentine reasoning, the authors suggest that loss of rac1 affects the so-called molecular priming of vesicles, possibly due to a restructuring of actin barriers at the active zone. The experimental analysis is at a very high level, and the work is definitely an important contribution to the field of presynaptic physiology and biophysics. It will be important to test the effects of the KO on other synapses that are not such high-performers as the calyx, and this direction might reveal significant effects on information processing by altered rac1 expression.

Major points:<br /> 1. The measurement of onset delay was used to test whether rac1-/- affects positional priming. While there is a clear effect of the KO on the latency to EPSC onset, there is no singular interpretation one can take, due to the ambiguity of the 'onset delay'. Note that in the Results authors state Lines 201-203: "The time between presynaptic AP and EPSC onset (EPSC onset delay) is determined by the distance between SVs and VGCC which defines the time it takes for Ca2+ to bind to the Ca2+ sensor and trigger SV release (Fedchyshyn and Wang, 2007)." However, in Methods "The duration between stimulus and EPSC onset was defined as EPSC onset delay." Thus the 'onset' measured is not between presynaptic spike and EPSC but from axonal stimulus and EPSC. KO of rac might also affect spike generation, spike conduction, calcium channel function, etc. Indeed some additional options are offered in the Discussion. Since the change in onset is ~100usec at most, a number of small factors all could contribute here. Moreover, the authors conclude that the KO does NOT affect positional priming since they would have expected the onset to shorten, given the other enhancements observed in earlier sections.<br /> It seems to me that all the authors can really conclude is that the onset shifted and they do not know why. If onset is driven by multiple factors, and differentially affected in the KO, then all bets are off. Thus, data in this section might be removed, or at least the authors could further qualify their interpretations given this ambiguity.

2. If the idea is that the loss of Rac1 leads to a reduced actin barrier at the active zone, is there an ultrastructural way to visualize this, labeling for actin for example? Authors conclude that new techniques are needed, but perhaps this is 'just' an EM question.

3. Authors use 1 mM kynurenic acid in the bath to avoid postsynaptic receptor saturation. But since this is a competitive antagonist and since the KO shows a large increase in release, could saturation or desensitization have been enhanced in the KO? This would affect the interpretation of recovery rates in the KO, which are quite fast.