Reviewer #3 (Public Review):

The paper by Ivanov et. al. examines how the auditory system adapts in reverberant acoustic conditions. Using a linear dereverberation framework, the study tests whether the tuning properties of neurons change in a similar manner to what is predicted by a linear dereverberation filter. The study shows that dereverberation is achieved by an extension of the inhibitory regions of receptive fields in a frequency-dependent manner. Notably, this result is complemented by showing a change in the cortical responses to probe sounds presented in the context of different reverberant conditions. Together, the similarity of the computational predictions and experimental findings supports an adaptive cortical mechanism that can reduce the effect of reverberation and in turn, support noise robust auditory perception.

Reviewer #1 (Public Review): 

This study provides relatively convincing in vivo phenotype data in mice related to vertical sleeve gastrectomy (VSG) and provides some potential mechanistic insight. This study can potentially provide some therapeutic intervention strategies on combining VSG and immunotherapy in treating breast cancer. On the other hand, this paper also has some weaknesses especially related to the detailed molecular mechanism and characterization as described below: 

1. The major weakness lies on the detailed characterization on which inflammatory response factors that may mediate the phenotype of HFS VSG mice when compared to WM Sham mice. The data presented currently is mainly limited to RNA-Seq data, which lacks detailed characterization. 

2. The other significant weakness also is related to the descriptive nature on characterizing the effect of immune features in Fig.4 for these mice. What is the potential mechanism on regulating T cell signaling or Cytolysis in HFS VSG mice vs WM sham mice? This at least needs some preliminary exploration and characterization.

Reviewer #2 (Public Review): 

This is a study based on the clinical observation that bariatric surgery in patients appears to be beneficial to reduce breast cancer risk. In mice with diet-induced obesity, followed by vertical sleeve gastrectomy (VSG) or dietary weight loss, tumor graft growth and response to immune checkpoint blockade were investigated. Bariatric surgery was found to be not as effective as dietary interventions in suppressing tumor growth despite achieving a similar extent of weight and adiposity loss. Leptin-mediated signaling was ruled out as a potential mechanism that could account for that difference. Notably, tumors in mice that received VSG displayed elevated inflammation and expression of the immune checkpoint ligand, PD-L1. In addition, mice that received VSG had reduced tumor-infiltrating T lymphocytes and cytolysis suggesting an ineffective anti-tumor microenvironment. Anti-PD-L1 immunotherapy suppressed tumor progression after VSG but not in control obese mice. Genomic analysis of adipose tissue after bariatric surgery from both patients and mouse models revealed a conserved gene expression signature.

Reviewer #3 (Public Review): 

In this manuscript, the authors have investigated how weight loss by bariatric surgery or weight-matched dietary intervention impairs breast cancer growth. They have shown that post-bariatric surgery, the tumors show augmented inflammation and an immune checkpoint; PD-L1 expression, which suppresses the anti-tumor immune responses. In addition, anti-PD-L1 therapy in these mice has shown to be more effective at slowing tumor growth. The authors report interesting observations, and the findings are well supported by the data, however, the use of only one syngeneic model tampers the reviewer's enthusiasm. Overall, the study is clinically important and helps in stratifying obese breast cancer patients that may respond to anti-PD-L1 immune checkpoint inhibition.

Reviewer #1 (Public Review):

In this manuscript, authors found Halo tag become resistant to lysosome degradation upon ligand binding, using this unique property, they developed a highly sensitive assay to monitor the autophagy flux. Measuring autophagy flux is one of the most important assays for studying autophagy, there are a few widely used assays to monitor the autophagy flux, such as p62 degradation, and LC3 processing, however, each of them has its own limitation, which is well known in the field. In this regard, this assay provides a simple, straight forward and sensitive assay for measuring autophagy flux, which I personally think is very likely it will be widely used by the autophagy community. This is a well-controlled, rigorous study and the manuscript is clearly written.

Reviewer #2 (Public Review):

Yim et al have utilised the HaloTag system to generate tools and assays to measure autophagy flux. The assays are highly accessible and straight forward to conduct. The study does not have any major weaknesses, with all key conclusions strongly supported by clear data. A major strength of the study is the robustness of the assay and its ease of use across SDS-PAGE and imaging techniques that I expect will help with its uptake by the research community. The assay utilises the HaLo tag and its inherent stability within lysosomes once pulsed with a HaLo ligand. This enables analysis of autophagy flux over a set period of time. The approach is highly complementary to the recently published study by Rudinskiy et al (2022) MBoC, but also includes additional tools to measure different types of selective autophagy and bulk autophagy. The inclusion of limitations of their approach within the discussion will be very useful for researchers planning to use the assay in their work. Overall, this is an excellent study that has generated very valuable tools for the study of autophagy.

Reviewer #3 (Public Review):

Monitoring autophagy induction and flux in mammalian cells is challenging and depends largely on the mammalian ATG8 proteins (LC3 and GABARAP), typically tagged at the N-terminus with a small tag (HA, flag, myc) or a range of fluorescent tags. When autophagy is induced these ATG8 proteins get captured into autophagosomes and delivered to lysosomes for degradation. Monitoring flux by western blots relies on a molecular weight shift caused by lipidation, and quantification of loss of signal from degradation (analysis of initiation), or accumulation by the addition of inhibition of lysosomal inhibitors (analyses of flux). Fluorescent tags provide similar results but the measurements rely on counting degradation sensitive or resistant fluorescent signals. Image-based analysis is more challenging than western blot but both require significant optimization. In this manuscript these existing assays are modified by the use of a probe (Halo tag) again appended to the N-terminus of ATG8s which becomes resistant to lysosomal degradation after binding a ligand (TMR). The ligand can be pulsed-in to allow detection of acute induction of autophagy eliminating the background from basal accumulation. Generation of the Halo-TMR is then monitored by western blot or using an in gel-fluorescent assay. The authors present data which shows the adaptability of the system for imaging analysis, and for both quantitative analysis using western blot and imaging of selective autophagy or bulk, non-selective autophagy. The authors have developed a robust, useful alternative to existing assay and present the results in a careful, well described brief manuscript. These modifications are important for the field and for those who require quantitative results. The drawbacks are similar to existing assays and will usually require the generation of stable cell lines because over-expressed ATG8s can aggregate and confound the measurements.

Reviewer #1 (Public Review):

Engert et al. provides a nearly complete synaptic level description of the anatomy of gustatory receptor neurons (GRNs), reveals their connectivity, and shows that they segregate based on taste modality. They find that pre and post-synaptic sites are distributed along the axons of all GRNs, as well as that each GRN receives synaptic inputs from other GRNs. Using hierarchical clustering Engert et al. defined six GRN classes based on their distinct morphology and connectivity. Next, they matched the anatomy of the six classes to the anatomy of known GRN classes that detect different taste modalities. They reveal that GRNs of each class are highly connected with each other, as well as weaker connected between different classes. Therefore, the authors performed calcium and voltage imaging to elucidate the role of the low-level connectivity of different GRN classes with each other. Interestingly, no crosstalk between the different classes could be detected.

The data are appropriately controlled and analyzed and support the conclusions drawn in this paper.

Strength<br /> This manuscript provides the first nearly complete synaptic level description of gustatory receptor neurons. Given that the authors reconstructed GRNs in the FAFB electron microscopy dataset manually or using a combination of automated segmentation and manual tracing using Catmaid, the synapse numbers presented here are all verified and trustworthy.

The authors went to great lengths to test every possible synaptic connection between different GRN classes using calcium and voltage imaging. Unfortunately, no mechanism for the alteration of the transmission of gustatory signals between different GRN classes was uncovered yet, however, it shows that it is extremely important to test the functionality of synapses seen in EM datasets.

Weaknesses<br /> Given that manual tracing in Catmaid is very time-consuming it is difficult to complete the entire population of GRNs and be competitive with similar work being performed using other EM platforms with automated segmentation. Therefore, the authors could not determine how large the actual GRN population is and whether there might be additional unknown classes.

Reviewer #2 (Public Review):

This paper utilizes the whole brain EM volume of the adult Drosophila to elucidate the synaptic connectivity patterns of the gustatory sensory neurons in the brain. The reconstruction is focused on the gustatory sensory axons of the labial nerve, which houses sensory axons from the labellum, maxillary palp and the eye, as well as motor axons that innervate the proboscis. The labellar gustatory axons terminate in specific regions of the SEZ termed the anterior central sensory center (ACSC). 87 projections were reconstructed on the right side (representing 83-96% of the total estimated); 57 projections were done for the left side (54-63% total), attributed to registration error in the volume. Morphological and synaptic clustering led to the identification of 6 different regions/zones in the ACSC on which the axons terminate. Critically, and this is the really great part of this work, the authors were able to correlate the EM data with light microscopic data in terms of the identity of neurons reconstructed, thus enabling the use of published functional data already available in terms of different taste modalities. This revealed that extensive synaptic connections are found between neurons of the same modality. The functional analysis further showed that activation of neurons of one modality does not affect those of others.

Reviewer #1 (Public Review):

This manuscript presents novel results which suggest that networks of cortical regions show different patterns of structural connectivity with hippocampal subregions. The results build on prior work, but also provide a spatially precise characterization of whole-brain structural connectivity patterns along the anterior-posterior hippocampal gradient. The paper is well-motivated and well-written. The authors discuss their findings in the context of previous investigations in non-human primates, and draw a number of parallels between these bodies of evidence. However, there were also some interesting differences between the connectivity patterns uncovered in resting-state fMRI and those identified using the present approach. It would also be helpful to highlight the key methodological advances or differences compared to prior work to contextualize the present findings.

1. The connectivity patterns along the anterior-posterior hippocampal axis broadly follow an anterior-posterior cortical bias, such that posterior regions, e.g. the visual cortex, are preferentially connected to the hippocampal tail, and anterior regions, e.g. the temporal pole, are preferentially connected to the hippocampal head. The authors focus on the twenty regions with the highest connectivity profiles, which appears to capture the majority of all connections. However, some of the present structural connectivity patterns differ in interesting ways from previously described cortical networks reported in resting-state fMRI studies. Most notably, the medial PFC and orbitofrontal regions combined account for less than 1% of all connections in the present investigation (Table S1 & S2). This is an interesting contrast to functional investigations which tend to find that these regions cluster with the aHPC (e.g., Adnan et al. 2016 Brain Struct Func; Barnett et al. 2021 PLoS Biol; Robinson et al. 2016 NeuroImage). In contrast, the present DWI results suggesting preferential pHPC-medial parietal connectivity dovetail with those observed in fMRI studies. It seems important to discuss why these differences may arise: whether this is a differentiation between structural and functional networks, or whether this is due to a difference in methods.

2. While the analytic pipeline is described in sufficient detail in the Methods, it is somewhat unclear to a non-DWI expert what the major methodological advance is over prior approaches. The authors refer to a tailored processing pipeline and 'an advance in the ability to map the anatomical connectivity (p. 5), but it's not immediately clear what these entail. It would be useful to highlight the key methodological differences or advances in the Introduction to help with the interpretation of the similarities and differences with previous connectivity findings.

3. Related to the point above, it was a bit unclear to me how the present connections map onto canonical white matter tracts. In Fig., 4A, the tracts are shown for a single participant, but it would be helpful to map or quantify know how many of the connections for a given hippocampal subregion are associated with a given tract to provide a link to prior work or clarify the approach. A fairly large body of prior research on hippocampal white matter connectivity has focused on the fornix, but it's a little difficult to align these prior findings with the connectivity density results in the current paper.

4. Finally, on a more speculative note: based on the endpoint density maps, there seems to be a lot of overlap between the EDMs associated with different cortical regions (which makes sense given the subregion results). Does this effectively mean that the same endpoints may be equally connected with multiple different cortical regions? Part of the answer can be found in Fig. 3D showing the combined EDM for three different regions, but how spatially unique is each endpoint? This is likely not a feasible question to address analytically but it might be helpful to provide some more context for what these maps represent and how they might relate to differences across individuals.

Reviewer #1 (Public Review):

This study investigated potential links between sleep structure elements and Alzheimer's disease (AD) development in healthy individuals in the late midlife to capture early signs of cognitive decline. Full polysomnography sleep recording and EEG analysis showed that slow waves are classified into two types (slow and fast switchers) and spindles are preferentially coupled only to slow switcher slow waves. The authors revealed that among sleep parameters including sleep duration and SW density, only this spindle-slow switcher SW coupling showed a negative correlation with Aβ burden in mPFC. Furthermore, the follow-up memory test revealed that uncoupling of spindle and slow switcher SWs is predictive of a memory worsening over 2 years. Therefore, this study successfully identified an early marker of Aβ deposit in mPFC and cognitive decline, which may help earlier diagnosis of AD.

Reviewer #2 (Public Review):

Chylinski et al. investigate sleep EEG properties in a cohort of older individuals, to test how sleep microarchitecture is linked to amyloid burden and memory changes over time, which is important for understanding the evolution of neurodegenerative disease. They report that the temporal coupling of spindles to a specific slow wave type, which they term 'slow switchers', is correlated with A-beta and predictive of subsequent memory decline years later. Strengths of the study are the extensive sleep phenotyping, relatively large cohort, and the acquisition of a follow-up cognitive timepoint two years later. The effect sizes are small, which may be expected due to the nature of this scientific question. The analyses are interesting, but some additional analyses and reporting would be beneficial in the methods and results, particularly the analyses focused on differentiating SW types.

Main issues:

The EEG signal processing and analysis methods need additional details. A coincidence of slow wave peaks and spindles is defined as 'co-occurrence' - within what time window do the two events have to co-occur to be considered coincident?

In Fig. 1, the analysis does not control for the fact that slow switcher SWs will have a longer time period before the peak than spindles. Fig. 1b's result that more spindles occur in the same phase period could be partially explained by the fact that this phase simply takes a longer period of time for slow switcher SWs (i.e. greater chance of having a spindle if it takes 5x as much time to get from phase -1 to 0, as suggested in Fig. 1c). A control analysis is needed to account for this.

The green shading in Fig. 1c seems to suggest some phase-coupling for fast switchers too, so it would be appropriate to add a statistic for the statement "no such preferred coupling was detected for fast switcher SWs".

The precise implementation of the main statistical tests is a bit unclear in the Methods. When stated "slow wave spindle coupling" is an independent variable, what precisely is in the variable? Is it the phase of the coupling? Is it the proportion of SWs with a spindle for one individual?

Given the small effect size reported for slow switcher SWs, it seems a potential reason for not finding the same result in fast switcher SWs is that there are ~4 times fewer fast switcher SWs. Even if fast switcher SWs had the same size as the underlying effect, is this sample size sufficient to detect it? Is it possible that the difference in the slow wave types reflects the different number of events in each group? Since the analysis does not directly test for a difference between fast and slow (but rather detects a significant effect with slow SWs, and fails to detect it with a smaller number of fast SWs, which does not specifically test for a difference between the two), it seems there is still additional evidence needed if aiming to draw conclusions about these fast and slow SWs being different.

Reviewer #3 (Public Review):

Strengths:<br /> - EEG analyses are novel, extensive, and carefully done.<br /> - Inclusion of baseline amyloid PET is a strength.<br /> - There is great interest in the transition from normal cognition to cognitive impairment in the earliest stages of disease, and therefore this study population is quite relevant.

Weaknesses:<br /> - The abstract isn't clear regarding the number of participants supporting the principal conclusions. The conclusion RE amyloid was based on the stated n=100, while the one concerning cognitive decline was based only on a subset of n=66.<br /> - In the statistical methods, the authors' stated primary analyses were 1) coupling of spindles to slow switching slow waves and 2) coupling of spindles to fast switching slow waves, neither of which has anything specific to do with cognition or dementia. They adjusted these two analyses for 2 comparisons with a threshold of p=0.025. The remainder of the analyses are considered by the authors to be exploratory and therefore not to require adjustment for multiple comparisons. However, in the abstract, the stated goal of the study is to investigate "whether 22 the coupling of spindles and slow waves are associated with early amyloid-beta (Aβ) brain burden, a hallmark of AD neuropathology, and cognitive change over 2 years". This doesn't align with the stated primary analyses in the statistical methods. Moreover, it suggests at a minimum 2 primary outcomes (amyloid burden and cognitive change), and 2 predictors (spindle-slow-switch phase, and spindle-fast-switch phase) for 4 primary analyses that need to be corrected for, resulting in a p-value threshold of 0.05/4 = 0.0125. Neither of the study's primary conclusions (1. that earlier occurrence of spindles on slow-depolarization slow waves is associated with higher prefrontal Ab burden p=0.014 and 2. that earlier occurrence of spindles on slow-depolarization slow waves is associated with greater longitudinal memory decline p=0.032) meets this cutoff. This is even if we disregard the many other comparisons that were made (in the study, there are at least 3 outcomes of interest - baseline cognition, baseline amyloid, and change in cognition) and many EEG predictors examined. Indeed, if we consider all the analyses performed in this study (3 outcomes as above [amyloid, baseline cognition, change in cognition] x 7-8 different EEG measures = 24 comparisons) the 2 significant results at p<0.05 are not all that much more than would be expected by chance.<br /> - It is not 100% clear how the authors selected specifically phase angles between spindles and slow waves (rather than, for instance, percent coincidence, or dispersion of phase angle as a measure of the "tightness" of coupling) as their primary predictors. If these were looked at they would require even more extensive adjustment for multiple comparisons.<br /> - The authors conclude that their findings suggest that "altered coupling of sleep microstructure elements, key to its mnesic function, contributes to poorer brain and cognitive trajectories in ageing." In their discussion, they do acknowledge that this sort of causal inference is not possible based on the non-interventional nature of this study. Indeed, it is certainly plausible that differences in the phase relationship between spindles and slow waves, rather than being contributors to cognitive decline, may instead be markers of early AD-related brain changes, not picked up on by amyloid PET (e.g. amyloid oligomers, or non-amyloid processes) that are the proximate cause of 2-year cognitive decline.

Reviewer #1 (Public Review):

The authors shed light on the role that non-CSC exerts in promoting cancer progression, revealing that non-CSC secreted fibromodulin is crucial in mediating angiogenesis in glioma via integrin-dependent Notch signaling. The data volume is sufficient and the argumentation is rigorous enough to support the conclusions. The results are important for gaining insight into the less concerned non-CSC component in promoting cancer, and would potentially enrich the treatment strategy for glioma.

Reviewer #2 (Public Review):

Tumors such as glioblastoma contain several types of cells: cancerous and reactive non-cancerous cells, and among cancerous cells, cancer cells with tumorigenic properties so-called "stem" and pseudo-differentiated cancer cells.

Strengths: a multidisciplinary international cooperation gathering complementary expertises. An impressive quantity of experiments and presented data (28 supplementary figures with multiple panels!). First description of Fibromodulin as a secreted factor acting in a paracrine manner to activate an Integrin-dependent Notch signaling in endothelial cells. A detailed analysis of the molecular signaling triggered by integrin activation. Most of the results support this claim.

Weaknesses: Several formulations in the introduction are controversial. Several results should be more clearly explained and the precise methods used are difficult to find since they are dispersed between the text, the "methods section" and often lacking in the legend of the figure. More precisely the following points should be addressed:

1- The formulation "non-cancer stem cells" is confusing since these are cancer cells but without the functional characteristics of cancer stem cells and within the tumor exist non-cancer cells co-opted to the tumor, such cells being called "microenvironment" even if they are bona fide part of the tumor.<br /> 2- Lines 91 to 95 are particularly controversial and even erroneous since CD133- GSC have been reported by several authors and nestin is not a selective marker of CSC. This is most-likely due to referencing reviews of a single group that promoted this dichotomy that do not correspond to most of the reported results. Furthermore it is well known that GSC proliferation or DGC reprogrammation to GSC are favored by hypoxia, illustrated in vivo by the failure of anti-VEGF treatment to increase life expectancy.<br /> 3- As soon as 2012-2013 (thus before the referenced Suva et al 2014 paper), the group of Thierry Virolle demonstrated that stem cell-like properties of GSC fuel glioblastoma development by providing the different cell types that comprise the tumor. Reference to their work is surprisingly missing. Of note, after describing that the miR-302-367 cluster is strongly induced during stemness suppression, they showed that stable miR-302-367 cluster expression is sufficient to suppress the stemness signature, self-renewal, and cell infiltration within a host brain tissue, through inhibition of the CXCR4 pathway involving the SHH-GLI-NANOG network. Micro-RNA profiling studies to search for regulators of stem cell plasticity, allowed them to identified miR-18a* as a potential candidate and its expression correlated with the stemness state. MiR-18a* expression promotes clonal proliferation in vitro and tumorigenicity in vivo. Turchi L, Debruyne DN, Almairac F, Virolle V, Fareh M, Neirijnck Y, Burel-Vandenbos F, Paquis P, Junier MP, Van Obberghen-Schilling E, Chneiweiss H, Virolle T. Tumorigenic potential of miR-18A* in glioma initiating cells requires NOTCH-1 signaling. Stem Cells. 2013 Jul;31(7):1252-65. doi: 10.1002/stem.1373. PMID: 23533157 Fareh M, Turchi L, Virolle V, Debruyne D, Almairac F, de-la-Forest Divonne S, Paquis P, Preynat-Seauve O, Krause KH, Chneiweiss H, Virolle T. The miR 302-367 cluster drastically affects self-renewal and infiltration properties of glioma-initiating cells through CXCR4 repression and consequent disruption of the SHH-GLI-NANOG network. Cell Death Differ. 2012 Feb;19(2):232-44. doi: 10.1038/cdd.2011.89.<br /> 4- A main question arises from the use of multiple cellular models, some highly valuables such as MGG4, MGG6 and MGG8, that correspond to patient-derived cell line maintained in culture conditions known to preserve the phenotype and genotype encountered in real patient tumors, and other cell lines (LN229, U251, U87) known to be highly unrepresentative since grown for a long time in serum conditions. However, after the first set of experiments, only MGG8 is used in the rest of the paper, with no validation on MGG4 and MGG6 and one should wonder why.<br /> 5- Results presented in Fig2C and 2D are really strange and do not support the claim that "results indicate that FMOD secreted by DGCs is essential for the growth of tumors initiated by GSCs. FMOD induces angiogenesis of host-derived and tumor-derived endothelial cells" First, one should wonder about the subcutaneous model used since such xenograft do not raise a glioblastoma-like tumor but a mesenchymal-like highly undifferentiated tumor. Second, considering the development of the graft at day 5 and the growth curve of GSC alone, one should wonder why inhibiting expression of FMOD in DGC triggers a necrosis of the initial tumor and not a slower growth parallel to the one of GSC alone.<br /> 6- Line 445: "Cellular hierarchy is well established in GBM." This is an old view mimicking normal differentiation. Since GSC can pseudo-differentiate into DGC and DGC can be reprogrammed into GSC, no hierarchy exists, only cells with different properties and functions in tumor growth. GSC is not the origin of glioblastoma but the ultimate state of aggressiveness.<br /> 7- Lines 452-53 "GSCs are known to promote the establishment of a highly vascularized microenvironment by being in close physical contact with endothelial cells (Calabrese et al., 2007)." This in only partially true since many soluble factors have been described to support the dialog between endothelial cells and GSC: secreted proteins such as VEGF, HDGF, GDF15, and multiple types of microRNA.

Reviewer #3 (Public Review):

This article proposed a hypothesis that non-cancer stem cells secreted factor-FMOD could impressively promote angiogenesis to induce tumor growth in vivo. The finding uncovered a potential interesting and important protein therapeutic target from non-cancer stem cells but not from the glioma stem-like cells. Authors utilized diverse in vitro and in vivo methods to elucidate their hypothesis. The logic is smooth and clear and the results are solid. This article showed us that it might be worth also looking at non-cancer stem cells more in tumor growth.

Joint Public review:

Endonuclease G (EndoG) is best known for its role in caspase-independent degradation of nuclear DNA during apoptosis, when the protein is released from mitochondria upon oxidative stress. This manuscript reveals a role of EndoG in generating a 9 bp deletion that is commonly found in the mitochondrial genome. The authors combine bioinformatics and experimental analysis to identify mitochondrial genome sequences that have the potential to form G4 tetraplexes. They focus the further analysis on the intergenic region of cytochrome c oxidase II/tRNALys that contains the most common mitochondrial deletion, a 9 bp deletion involving a repeated DNA sequence. Furthermore, using BG4, a presumptive anti-G4 DNA antibody, the authors use immunofluorescence and ChIP to provide evidence for the occurrence of G4 tetraplexes in the mitochondrial genome. Using purified Endo G and mitochondrial extracts, the authors show preferential binding of EndoG to mitochondrial genome sequences with the potential to form G4DNA and induction of DNA breaks at such sites using mutant oligonucleotide substrates that are predicted to break the G4 potential as controls. Moreover, the authors reconstitute in vitro the reaction to generate the 9 bp deletion using oligonucleotides and mitochondrial extracts, leading to the model that EndoG-mediated cleavage at G4 tetraplex forming sequences in the mitochondrial genome generates breaks that are repaired by MMEJ generating rearrangements including the common 9 bp deletion. Finally, the authors show that under oxidative stress induced by menadione, EndoG is released into the mitochondrial matrix, possibly making it more available to act on the mitochondrial genome. The manuscript spans a significant number of experiments ranging from bioinformatic analysis, protein biochemistry, cell genetics, and immunofluorescence. The data generally support the conclusions with some significant exceptions. The authors often inaccurately describe the results mixing in their interpretation, and the manuscript is difficult to read. Major problems are the specificity of the BG4 antibody, the resolution of the microscopy, and the lack of a critical control of separate incubation of the substrates for the reconstitution experiment. Conceptually, it is unclear what the physiological roles of EndoG release into the mitochondrial matrix and the generation of mitochondrial genome arrangements might be.

Reviewer #1 (Public Review):

The authors of this manuscript analyzed the lung morphology of five osteichthyan vertebrate species (four that do not belong to amniotes or teleost fishes plus a salamander) and based on their phylogenetic relationship, they concluded that the lung first arose as an unpaired organ and later became bilaterally paired in the amniote lineage and secondarily lost in the teleost fish lineage. I found the authors' anatomical investigation of morphology enabled by sample collection covering the long-missing osteichthyan lineages appealing and sufficient to draw the conclusion.

Reviewer #2 (Public Review):

This is an interesting paper that gives novel insights into the evolution of lungs. The study is straightforward. It is well written and the data are clear. The authors have analysed lung development in a number of species of bony fish, which have been chosen as they occupy an informative position on the evolutionary tree. They use synchrotron X-ray microtomography and histology to probe lung development. They conclude that the primitive state of vertebrate lungs was as unpaired structures but with the evolution of land-dwelling vertebrates, truly paired lungs emerged. These are a defining feature of the tetrapods and are crucial for life on land. The authors present a scenario for how they believe lungs evolved which is believable and insightful.

Reviewer #3 (Public Review):

This paper presents significant new data on the anatomy and developmental biology and evolution of lungs in key basal, key osteichthyan fishes, and a primitive tetrapod. The paper provides a good background to the problem, but one main point that should be considered is missing (see below). The 3D CT data combined with thin-section images substantiate their findings well. The work holds important implications for understanding how paired lungs first evolved in fishes and tetrapods, which has been a major evolutionary conundrum up to now. The methods and data presented are sound, and the illustrations are clear and relevant to the development of the intellectual arguments presented in the discussion.

There is a controversial old description of paired lungs in the placoderm (basal gnathostome) Bothriolepis (Denison 1941; Arsenault et al 2004; Goujet 2011, Janvier et a.l 2007) which is completely contrary to the main conclusions of this paper. One cannot simply ignore it - the authors must confront the data and discuss it, or else there is standing evidence that the basal condition for all gnathostomes is a paired lung.

Reviewer #1 (Public Review):

The study by Giulieri and colleagues focuses on the detection of genetic loci that experience selection in S. aureus during the transition from colonization to infection. The authors assembled a large collection of S. aureus genomes from prior studies and systematically analyzed them for genetic variation and signatures of genome degradation. They found significant convergent evolution in genes linked to antibiotic response and pathogenesis. The result is a high-resolution picture of S. aureus adaptation during the transition from colonization to infection.

The major strength of the paper is the large scale of the analysis and the inclusion of additional variants besides SNPs, which are frequently ignored because they can be hard to reliably detect and study. One additional strength is the use of "multilayered" annotation (i.e. including intergenic variants) to increase signals of convergent evolution. One weakness of the study is a lack of a classification of the variants detected in convergent loci. For example, which genes do the authors think are acquiring gain-of-function versus loss-of-function mutations? One other weakness is a lack of functional studies exploring some of the more novel signals detected (such as hypothetical proteins with "no data on S. aureus").

In general, the results support the conclusions drawn by the authors, the likely impact of the work is quite high, and overall it is a useful example of how to perform systematic detection of pathogen loci under selection in vivo during infection.

Reviewer #2 (Public Review):

This work provides a comprehensive within-host evolution analysis of all publicly available Staphylococcus aureus genome. The authors combined variant and chromosome structural variants detecting, internal variant annotation, gene and operon enrichment analysis, mutation co-occurence analysis and network analysis of adaptation signatures, to compile a comprehensive catalogue of bacterial genetic variation arising during host infection. This strategy enabled the detection of convergent adaptation patterns at an unprecedented resolution. Through study, they found evidence of a distinctive evolutionary pattern within the infecting populations compared to colonising bacteria. In addition to reported agr-mediated adaptation, they identified non-canonical genome-wide significant loci including sucA-sucB and stp1. The prevalence of adaptive changes increased with infection extent, emphasising the clinical significance of these signatures. These findings provide a high-resolution picture of the molecular changes when S. aureus transitions from colonisation to severe infection and may inform the correlation of infection outcomes with adaptation signatures.

Reviewer #1 (Public Review):

In this manuscript, Dard et al. investigate hippocampal dynamics over the course of early postnatal development. They find evidence for an abrupt developmental transition in this neural activity at the end of the first postnatal week in rodents and postulate that it is related to the emergence of internal representations. This work is interesting as it explores the developmental expression of neural activity patterns and contributes to the understanding of how cognitive functions could emerge from the immature brain. Additional methodological and statistical analysis is necessary to support the suggested conclusions of this work.

Strengths:<br /> The authors employ in vivo imaging of the hippocampus in developing mice and merge these techniques with cell labeling to try to establish clues as to the mechanisms of the neural activity patterns they observe. This work provides information in a relatively understudied field and could help to provide developmental timelines and cellular mechanisms for the maturation of neural networks.

Weaknesses:<br /> 1) The experiments involve an invasive neurosurgical procedure used to perform hippocampal imaging, which removes the ipsilateral overlying somatosensory cortex, and it is not possible to evaluate from the data provided that this surgery does not disrupt network function, especially given the focus on movement-related activity patterns.<br /> 2) State-dependent parameters are not adequately described, controlled, and examined quantitatively to ensure that data from similar behavioral states is being used for analysis across ages. Network activity from wakefulness, REM/active sleep and NREM/quiet sleep should not be presumed to be indistinguishable.<br /> 3) Currently employed statistics are not rigorous, unified, or sensitive, and do not support all of the authors' claims. Data shown suggest potentially significant changes that have not been identified due to suboptimal statistical approach and/or underpowering.<br /> 4) The authors use an artificial neural network approach to infer cell classification (pyramidal cell vs. interneuron). From the data provided, it is not possible to adequately evaluate whether these 'inferred' interneurons represent the same population as conventionally labeled interneurons.<br /> 5) Functional GABAergic activity is not assessed across development (only at P9-10), limiting mechanistic conclusions that can be drawn.<br /> 6) The present analyses are almost exclusively focused on movement-related epochs, substantially limiting conclusions that can be drawn as to what neural dynamics are actually occurring during epochs that the authors propose comprise internal representations.

Overall:<br /> The authors aimed to demonstrate a shift in hippocampal neural activity from primarily responding to external stimuli (i.e. body movements) to manifesting internal network dynamics. They identify local GABAergic innervation as a likely candidate mechanism for this shift. While interesting, the current analytic methods used are insufficient to fully support the authors' claims.

Reviewer #2 (Public Review):

The study by Dard et al aims to uncover the post-natal emergence of mature network dynamics in the hippocampus, with a particular focus on how pyramidal cells and interneurons change their response to spontaneous limb movement. Several previous studies have investigated this topic using electrophysiology, but this study is the first to utilize 2-photon calcium imaging, enabling the recording of hundreds of individual neurons, and discrimination between pyramidal cell and interneuron activity. The aims of the study are of broad interest to all neuroscientists studying development (including neurodevelopmental disorders) and the basic science of network dynamics.

The main conclusions of the study are that (1) in early life, most pyramidal cell activity occurs in bursts synchronized to spontaneous movement, (2) by P12, pyramidal cell activity is largely desynchronized from spontaneous movement, and indeed movement triggers an inhibition in the pyramidal network (approximately 2-4sec following movement), (3) unlike pyramidal cells, interneuron activity remains positively modulated by movement, throughout the period P1-P12, (4) the changes in pyramidal cell activity are achieved by means of increases in perisomatic inhibition, between P8 and P10.

It should be noted that conclusion (1) and to some extent conclusion (2) have already been reported, by previous studies using electrophysiology (as clearly acknowledged by the authors).

A principal strength of this manuscript is the extremely high quality of the data that the authors are able to use in support of (1) and (2), with very large numbers of neurons being analyzed to clearly delineate the relationship between neural activity and movement. The finding that pyramidal cells become inhibited following movement is novel, I believe. Furthermore, this study offers the first description of the development of interneuron activity, in this experimental context.

The main weakness of the manuscript is that the authors cannot provide direct functional evidence for the conclusion (4). As shown by the analysis in support of conclusion (3), interneuron activity with respect to movement does not actually change during the developmental period being studied, making it prima facie unlikely that this is the cause of changes in pyramidal network responses to movement. To overcome this, the study describes the activity of GABA-ergic axon terminals in the pyramidal cell layer at P9-10, but it appears that due to technical problems this was not possible in younger animals. It, therefore, remains unknown if the functional inhibitory inputs to pyramidal cells are changing over the ages studied. The study does describe increases in the protein synaptotagmin-2, in the pyramidal cell layer, between P3 and P11, but in my opinion, this molecular evidence for increases in perisomatic inhibition does not match the (very high) standards of neuronal function/activity reported elsewhere in the manuscript.

Reviewer #3 (Public Review):

Dard and colleagues use both in vivo calcium imaging and computational modelling to explore the relationship between the early movement of CA1 hippocampal activity in neonatal mice.

The manuscript represents a significant technical advance in that the authors have pioneered the use of multiphoton imaging to record activity in the hippocampus of awake neonates. Overall the presentation of the data is convincing although I would recommend a number of tweaks to the figures and the inclusion of some raw data to better direct and inform non-expert readers. I also believe that the assessment of long-range inputs using pseudo-rabies virus should be present in the main body of the manuscript as opposed to supplemental material.

The computational modelling supports their idea but does not exclude other possibilities. Further, it is not clear to what extent the strengthening of local excitatory input onto the interneurons - the dominant route of recurrent input in the hippocampus, is important; something that the authors acknowledge in the discussion.

Overall, I believe the paper adds to our knowledge of the timeline of development and further identified the postnatal day (P)9-P10 window as important in emergent cortical processing. The fact that this is linked to an increase in GABAergic innervation has implications for our understanding of both normal and dysfunctional brain development.

Reviewer #1 (Public Review):

Sadeh and Clopath analyze two mouse datasets from the Allen Brain Atlas and show that sensory representations can have apparent representational drift that is entirely due to behavioral modulation. The analysis serves as a caution against over-interpreting shifts in the neural code. The analysis of data is coupled with careful modeling work that shows that the behavioral state reliably shifts sensory representations independently of stimulus modulation (rather than acting as a gain factor), and further show that it is reproducibly shifted when the behavioral state is adequately controlled for. The methods presented point towards a more careful consideration and measurement of behavioral states during sensory recordings, and a re-analysis of previous findings. The findings held up for both standard drifting grating stimuli as well as natural movies.

The fact that neurons may have different tuning depending on the behavioral state of the animal raises obvious questions about readout. The authors show that neurons with strong behavioral shifts should simply be ignored and that this can be achieved if the downstream decoder weights inputs with more stimulus information. While questions remain about why behavior shifts representations and how that could be more effectively utilized by downstream circuits, the results presented clearly show that sensory representations might not always be simply drifting over time, and will spark some careful analysis of past and future experimental results.

Reviewer #2 (Public Review):

Studies from recent years have shown that neuronal responses to the same stimuli or behavior can gradually change with time - a phenomenon known as representational drift. Other recent studies have shown that changes in behavior can also modulate neuronal responses to a given sensory stimulus. In this manuscript, Sadeh and Clopath analyzed publicly available data from the Allen Institute to examine the relationship between animal behavioral variability and changes in neuronal representations. The paper is timely and certainly has the potential to be of interest to neuroscientists working in different fields. However, there are currently several important issues with the analysis of the data and their interpretations that the authors should address. We believe that after these concerns are addressed, this study will be an important contribution to the field.

1. The manuscript raises a potential problem: while previous work suggested that the passage of time leads to gradual changes in neuronal responses, the causality structure is different: i.e., the passage of time leads to gradual changes in behavior, which in turn lead to gradual changes in neuronal responses. The authors conclude that "variable behavioral signal might be misinterpreted as representational drift". While this may be true, in its current form, the paper lacks critical analyses that would support such a claim. It is possible that both factors - time and behavior - have a unique contribution to changes in neuronal responses, or that only time elicits changes in neuronal responses (and behavior is just correlated with time). Thus, the authors should demonstrate that these changes cannot be explained solely by the passage of time and elucidate the unique contributions of behavior (and elapsed time) to changes in representations.

2. There are also several issues with the analysis of the data and the presentation of the results. The most concerning of which is that the data shows a non-linear (and non-monotonic) relationship between behavioral changes and representational similarity. In many of the presented cases, the data points fall into two or more discrete clusters. This can lead to the false impression that there is a monotonic relationship between the two variables, even though there is no (or even opposite) relationship within each cluster. This is a crucial point since the clusters of data points most likely represent different blocks that were separated in time (or separation between within-block and across-block comparisons).

3. The authors also suggest that using measures of coding stability such as 'population-vector correlations' may be problematic for quantifying representational drift because it could be influenced by changes in the neuronal activity rates, which may be unrelated to the stimulus. We agree that it is important to carefully dissociate between the effects of behavior on changes in neuronal activity that are stimulus-dependent or independent, but we feel that the criticism raised by the authors ignores the findings of multiple previous papers, which (1) did not purely attribute the observed changes to the sensory component, and (2) did dissociate between stimulus-dependent changes (in the cells' tuning) and off-context/stimulus-independent changes (in the cells' activity rates).

4. Another important issue relates to the interchangeable use of the terms 'representational drift' and 'representational similarity'. Representational similarity is a measure to identify changes in representations, and drift is one such change. This may confuse the reader and lead to the misconception that all changes in neuronal responses are representational drift.

Reviewer #3 (Public Review):

Although it is increasingly realized that cortical neural representations are inherently unstable, the meaning of such "drift" can be difficult or impossible to interpret without knowing how the representations are being read out and used by the nervous system (i.e. how it contributes to what the experimental animal is actually doing now or in the future). Previous studies of representational drift have either ignored or explicitly rejected the contribution of what the animal is doing, mostly due to a lack of high-dimensional behavioural data. Here the authors use perhaps the most extensive open-source and rigorous neural data available to take a more detailed look at how behaviour affects cortical neural representations as they change over repeated presentations of the same visual stimuli.

The authors apply a variety of analyses to the same two datasets, all of which convincingly point to behavioural measures having a large impact on changing neural representations. They also pit models against each other to address how behavioural and stimulus signals combine to influence representations, whether independently or through behaviour influencing the gain of stimuli. One analysis uses subsets of neurons to decode the stimulus, and the independent model correctly predicts the subset to use for better decoding. However, one caveat may be that the nervous system does not need to decode the stimulus from the cortex independently of behaviour; if necessary, this could be done elsewhere in the nervous system with a parallel stream of visual information.

Overall the authors' claims are well-supported and this study should lead to a re-assessment of the concept of "representational drift". Nonetheless, a weakness of all analyses presented here is that they are all based on data in head-fixed mice that were passively viewing visual stimuli, such that it is unclear what relevance the behaviour has. Furthermore, the behavioural measurements available in the open-source dataset (pupil movements and running speed) are still a very low dimensional representation of what the mice were actually doing (e.g. detailed kinematics of all body movements and autonomic outputs). Thus, although the authors here as well as other large-scale neural recording studies in the past decade or so make it clear that relatively basic measures of behaviour can dramatically affect cortical representations of the outside world, the extent to which any cortical coding might be considered purely sensory remains an important question. Moreover, it is possible that lower-dimensional signals are overly represented in visual areas, and that in other areas of the cortex (e.g. somatosensory for proprioception), the line between behaviour parameters and sensory processing is blurred.

Reviewer #1 (Public Review):

Huang et al. sought to study the cellular origin of Tuft cells and the molecular mechanisms that govern their specification in severe lung injury. First the authors show ectopic emergence of Tuft cells in airways and distal parenchyma following different injuries. The authors also used lineage tracing models and uncovered that p63-expressing cells and to some extent Scgb1a1-lineaged labeled cells contribute to tuft cells after injury. Further, the authors modulated multiple pathways and claim that Notch inhibition blocks tuft cells whereas Wnt inhibition enhances Tuft cell development in basal cell cultures. Finally, the authors used Trpm5 and Pou2f3 knock-out models to claim that tuft cells are indispensable for alveolar regeneration.

In summary, the findings described in this manuscript are somewhat preliminary. The claim that the cellular origin of Tuft cells in influenza infection was not determined is incorrect. Current data from pathway modulation is preliminary and this requires genetic modulation to support their claims.

Major comments:

1. The abstract sounds incomplete and does not cover all key aspects of this manuscript. Currently, it is mainly focusing on the cellular origin of Tuft cells and the role of Wnt and notch signaling. However, it completely omits the findings from Trpm5 and Pou2f3 knock-out mice. In fact, the title of the manuscript highlights the indispensable nature of tuft cells in alveolar regeneration.

2. In lines 93-94, the authors state that "It is also unknown what cells generate these tuft cells.....". This statement is incorrect. Rane et al., 2019 used the same p63-creER mouse line and demonstrated that all tuft cells that ectopically emerge following H1N1 infection originate from p63+ lineage labeled basal cells. Therefore, this claim is not new.

3. Lines 152-153 state that "21.0% +/- 2.0 % tuft cells within EBCs are labeled with tdT when examined at 30 dpi...". It is not clear what the authors meant here ("within EBC's")? And also, the same sentence states that "......suggesting that club cell-derived EBCs generate a portion of tuft cells....". In this experiment, the authors used club cell lineage tracing mouse lines. So, how do the authors know that the club cell lineage-derived tuft cells came through intermediate EBC population? Current data do not show evidence for this claim. Is it possible that club cells can directly generate tuft cells?

4. Based on the data from Fig-3A, the authors claim that treatment with C59 significantly enhances tuft cell development in ALI cultures. Porcupine is known to facilitate Wnt secretion. So, which cells are producing Wnt in these cultures? It is important to determine which cells are producing Wnt and also which Wnt? Further, based on DBZ treatments, it appears that active Notch signaling is necessary to induce Tuft cell fate in basal cells. Where are Notch ligands expressed in these tissues? Is Notch active only in a small subset of basal cells (and hence generate rate tuft cells)? This is one of the key findings in this manuscript. Therefore, it is important to determine the expression pattern of Wnt and Notch pathway components.

5. How do the authors explain different phenotypes observed in Trpm5 knockout and Pou2f3 mutants? Is it possible that Trpm5 knockout mice have a subset of tuft cells and that they might be something to do with the phenotypic discrepancy between two mutant models?

6. One of the key findings in this manuscript is that Wnt and Notch signaling play a role in Tuft cell specification. All current experiments are based on pharmacological modulation. These need to be substantiated using genetic gain loss of function models.

Reviewer #2 (Public Review):

In this manuscript, the authors describe the ectopic differentiation of tuft cells that were derived from lineage-tagged p63+ cells post influenza virus infection. These tuft cells do not appear to proliferate or give rise to other lineages. They then claim that Wnt inhibitors increase the number of tuft cells while inhibiting Notch signalling decreases the number of tuft cells within Krt5+ pods after infection in vitro and in vivo. The authors further show that genetic deletion of Trpm5 in p63+ cells post-infection results in an increase in AT2 and AT1 cells in p63 lineage-tagged cells compared to control. Lastly, they demonstrate that depletion of tuft cells caused by genetic deletion of Pou2f3 in p63+ cells has no effect on the expansion or resolution of Krt5+ pods after infection, implying that tuft cells play no functional role in this process.

Overall, in vivo and in vitro phenotypes of tuft cells and alveolar cells are clear, but the lack of detailed cellular characterization and molecular mechanisms underlying the cellular events limits the value of this study.

1. Origin of tuft cells: Although the authors showed the emergence of ectopic tuft cells derived from labelled p63+ cells after infection, it cannot be ruled out that pre-existing p63+Krt5- intrapulmonary progenitors, as previously reported, can also contribute to tuft cell expansion (Rane et al. 2019; by labelling p63+ cells prior to infection, they showed that the majority of ectopic tuft cells are derived from p63+ cells after viral infection). It would be more informative if the authors show the differentiation of tuft cells derived from p63+Krt5+ cells by tracing Krt5+ cells after infection, which will tell us whether ectopic tuft cells are differentiated from ectopic basal cells within Krt5+ pods induced by virus infection.

2. Mechanisms of tuft cell differentiation: The authors tried to determine which signalling pathways regulate the differentiation of tuft cells from p63+ cells following infection. Although Wnt/Notch inhibitors affected the number of tuft cells derived from p63+ labelled cells, it remains unclear whether these signals directly modulate differentiation fate. The authors claimed that Wnt inhibition promotes tuft cell differentiation from ectopic basal cells. However, in Fig 3B, Wnt inhibition appears to trigger the expansion of p63+Krt5+ pod cells, resulting in increased tuft cell differentiation rather than directly enhancing tuft cell differentiation. Further, in Fig 3D, Notch inhibition appears to reduce p63+Krt5+ pod cells, resulting in decreased tuft cell differentiation. Importantly, a previous study has reported that Notch signalling is critical for Krt5+ pod expansion following influenza infection (Vaughan et al. 2015; Xi et al. 2017). Notch inhibition reduced Krt5+ pod expansion and induced their differentiation into Sftpc+ AT2 cells. In order to address the direct effect of Wnt/Notch signalling in the differentiation process of tuft cells from EBCs, the authors should provide a more detailed characterization of cellular composition (Krt5+ basal cells, club cells, ciliated cells, AT2 and AT1 cells, etc.) and activity (proliferation) within the pods with/without inhibitors/activators.

3. Impact of Trpm5 deletion in p63+ cells: It is interesting that Trpm5 deletion promotes the expansion of AT2 and AT1 cells derived from labelled p63+ cells following infection. It would be informative to check whether Trpm5 regulates Hif1a and/or Notch activity which has been reported to induce AT2 differentiation from ectopic basal cells (Xi et al. 2017). Although the authors stated that there was no discernible reduction in the size of Krt5+ pods in mutant mice, it would be interesting to investigate the relationship between AT2/AT1 cell retaining pods and the severity of injury (e.g. large Krt5+ pods retain more/less AT2/AT1 cells compared to small pods. What about other cell types, such as club and goblet cells, in Trpm5 mutant pods? Again, it cannot be ruled out that pre-existing p63+Krt5- intrapulmonary progenitor cells can directly convert into AT2/AT1 cells upon Trpm5 deletion rather than p63+Krt5+ cells induced by infection.

4. Ectopic tuft cells in COVID-19 lungs: The previous study by the authors' group revealed the presence of ectopic tuft cells in COVID-19 patient samples (Melms et al. 2021). There appears to be no additional information in this manuscript.

5. Quantification information and method: Overall, the quantification method should be clarified throughout the manuscript. Further, in the method section, the authors stated that the production of various airway epithelial cell types was counted and quantified on at least 5 "random" fields of view. However, virus infection causes spatially heterogeneous injury, resulting in a difficult to measure "blind test". The authors should address how they dealt with this issue.

Reviewer #3 (Public Review):

In this manuscript Huang et al. study how the lung regenerates after severe injury due to viral infection. They focus on how tuft cells may affect regeneration of the lung by ectopic basal cells and come to the conclusion that they are not required. The manuscript is intriguing but also very puzzling. The authors claim they are specifically targeting ectopic basal progenitor cells and show that they can regenerate the alveolar epithelium in the lung following severe injury. However, it is not clear that the p63-CreERT2 line the authors are using only labels ectopic basal cells. The question is what is a basal cell? Is an ectopic basal progenitor cell only defined by Trp63 expression?

The accompanying manuscript by Barr et al. uses a Krt5-CreERT2 line to target ectopic basal cells and using that tool the authors do not see a signification contribution of ectopic basal cells towards alveolar epithelial regeneration. As such the claim that ectopic basal cell progenitors drive alveolar epithelial regeneration is not well-founded.

The title itself is also not very informative and is a bit misleading. That being said I think the manuscript is still very interesting and can likely easily be improved through a better validation of which cells the p63-CreERT2 tool is targeting.

I, therefore, suggest the following experiments.

1) Please analyze which cells p63-CreERT2 labels immediately after PR8 and tamoxifen treatment. Are all the tdTomato labeled cells also Krt5 and p63 positive or are some alveolar epithelial cells or other airway cell types also labeled?

2) Please also show if p63-CreERT2 labels any cells in the adult lung parenchyma in the absence of injury after tamoxifen treatment.

3) Please analyze if p63-CreERT2 labels any cells with tdTomato in the absence of injury or after PR8 infection but without tamoxifen treatment.

4) Please analyze when after PR8 infection do the first p63-CreERT2 labeled tdTomato positive alveolar epithelial cells appear.

5) A clonal analysis of p63-CreERT2 labeled cells using a confetti reporter might also help interpret the origin of p63-CreERT2 labeled cells.

6) Lastly could the authors compare the single-cell RNAseq transcription profile of p63-CREERT2 labeled cells immediately after PR8 and tamoxifen treatment and also at 60dpi. A pseudotime analysis and trajectory interference analysis could help elucidate the identity of p63-CreERT2 labeled cells that are actually not ectopic basal progenitor cells.

Reviewer #1 (Public Review):

The manuscript by Tatli et al. entitled "Nanoscale resolution of microbial fiber degradation in action" characterizes how the cellulosome-producing, anaerobic bacterium Clostridium thermocellum responds to the presence of crystalline cellulose substrate and its subsequent degradation in real-time. Using state-of-the-art cryo-electron structural methods (i.e., microscopy and tomography) in combination with biochemistry, molecular biology, imaging, and microbial genetics and physiology the authors assess the location, density, enzyme composition of the cellulosomal complexes on the bacterial surface, its interactions with the crystalline cellulose substrate, and the corresponding changes in these properties that result from decomposition the substrate over time.

Specifically, using cryo-electron-based methods and imaging the authors showed extracellular cellulosomal densities at resolutions not seen previously and were able to measure distances between the bacterial S-layer and the cellulosome layer as well of the thickness of the latter. Taking advantage of cryo-electron tomography methods and data processing, the authors present nano-scale images of cellulosome-crystalline cellulose interactions, where the cellulosomal machinery is seen to envelop the substrate and disrupt the well-order compact, packing of cellulose microfibrils. They also present the cryo-EM structure of Cel48S, the most abundant cellulosomal glycoside hydrolase, which had a similar fold to the catalytic module previously determined by X-ray crystallography but also the topology of the linker region tethering the catalytic module to its type-I dockerin module that had not been previously observed. Expression of Cel48S in C. thermocellum was dramatically increased upon exposure to the crystalline cellulose substrate for the first 10-15 hr after which there was a subsequent decrease to basal levels between hours 15-20, which was associated with substrate availability and increased presence of degradation bioproducts. Finally, the authors used cryo-electron microscopy to assess single-cell cellulosome distribution across the bacterial population and its substrate dependency. Rather than a distribution of cellulosome densities on the cell surface across the microbial population, two predominant phenotypes were observed - a high-density phenotype and a low-density phenotype that shifted from a 1:5 to 5:1 ratio upon exposure to cellulose. The authors associate these latter observations with division-of-labour and bet-hedging evolutionary strategy whereby a population invested significant energy to produce the cellulosomal machinery and are thus primed for a substrate-rich environment while the low-density population ensures continued cell growth and nimble response to changing environmental conditions.

Strengths:

The manuscript is well-written and represents an influential body of work that will have broad appeal, including the environmental microbe, carbohydrate/biomass degradation, microbial and biopolymer engineering communities. The experiments are well-designed comprising ingenious use of microbial genetics, various substrates, and recombinant protein constructs with the C. thermocellum system to address observations in cyro-electron, biochemical and microbiology studies. The experimental analysis and interpretation are first-rate. The methods are appropriate, diverse yet truly complementary, and state-of-the-art.

While much effort has been dedicated to the structural characterization of the cellulosome, it has largely involved a dissection approach involving recombinant proteins. As such, there remains a significant gap in knowledge of the in vivo cellulosome structure and its interaction with crystalline cellulose. Furthermore, little is currently known as to how cellulosome-producing bacteria respond to changing environments, including C. thermocellum, which serves as the model cellulosomal bacterium. The data provides unprecedented in situ views of the cellulosomal machinery on the bacterial cell surface, its interaction of cellulose, and the disruption of the latter's structural organization. The quantitative nature of the work, particularly those associated with revealing the dynamic yet quite specific phenotypic heterogeneity of cellulosome-producing C. thermocellum (a high-density and a low-density population), is innovative in its approach and novel. These findings present intrigues and previously unforeseen insights into the response of C. thermocellum to the cellulose substrate. The authors have done an excellent job of linking previous observations to one made here using them to establish a foundation from which they formulate their conclusions.

Weaknesses:

There are no major weaknesses that significantly detract from the novelty and impact of the study. Not so much a weakness as much as simply unfortunate is the lack of the type-I dockerin module in the cryo-EM structure of Cel48S. The authors correctly note the apparent inherent flexibility of the N-terminal region of the dockerin module and the low calcium concentrations used, which may contribute to its absence in the structure.

Reviewer #2 (Public Review):

The manuscript by Itzhak Mizrahi is an original study using state-of-the-art integrative structural biology at multiple scales, using cryo-EM, imaging, electron tomography, microbiology and genetics to capture anaerobic bacterial cellulosomes from C. thermocellum in action. The study depicts the presence of cellulosomes at the bacterial extracellular surface, the impact of cellulosomal action on micro-crystalline cellulose, and identifies the presence of large globular enzymes in interaction with the substrate. Major findings are the multi-scale description of a 65 nm thick "belt" of cellulosomal particles around a single bacterial cell when displaying a high density of cellulosomes, down to the identification of specific components, such as the catalytic enzyme Cel48S, within this region when interacting and degrading cellulose micro-fibrils. These single-cell data are put in perspective with physiological growth properties of native and genetically modified C. thermocellum, showing that the bacterial population is heterogeneous with respect to the presence of cellulosomal complexes. Two types of populations, one displaying high density and a second with a much lower density of cellulosomes, co-exist in a ratio that depends on the available monosaccharides in solution, leading the authors to speculate that a division-of-labor strategy takes place in the C. thermocellum population.

The conclusions drawn are clearly justified by the presented data, interpretations and even speculations are designed as such by the authors and are plausible in view of the obtained results. The strength of the paper is the clever application of methods that allow spanning scales by several orders of magnitude, and that allow connecting single-cell data to physiological observations in the bulk of cultured cells.

Reviewer #1 (Public Review):

Overall a very interesting paper. There have been calls for discovery of herbicides that are multi-site inhibitors as a predicted way to delay resistance evolution to those herbicides. Fungicides are known that are multi-site inhibitors and these are known to have lower risk for resistance evolution. The authors provide evidence that their novel inhibitors of lysine synthesis inhibit both the first enzyme (previously shown) and the second enzyme in the lysine synthesis pathway. Inhibition of the first enzyme was shown to be due to inhibition at an allosteric site, while the same compound is shown in this paper to be a competitive inhibitor of the second enzyme. This two-site inhibition explains the relatively higher in vivo activity of the compound compared to its in vitro activity on the first enzyme alone. The authors show that this two-site inhibitor of lysine synthesis has biological activity to reduce growth of the global weed Lolium rigidum. The modeling work to show the specific amino acids in the target binding site that are predicted to interact with the compound is really interesting and gives insights into how target site resistance could eventually evolve to this herbicide.

Reviewer #2 (Public Review):

The paper contains a continued investigation of previously described dihydrodipicolinate synthase (DHDPS) inhibitor (MBDTA-2) to determine if the MBDTA-2 could be activated in planta to a more potent inhibitor. The hypothesis of potential demethylation of the methoxy was solid and the authors clearly showed that the hydroxy analog has lower affinity for inhibition of both forms of Arabidopsis DHDPS. The authors show diligence looking at MBDTA-2 inhibition of other plant enzymes to explain the previously described in vivo data. Dose response curves for MBDTA-2 inhibition of the second enzyme in the lysine biosynthesis pathway, dihydrodipicolinate reductase (DHDPR), show that MBDTA-2 provides about 10-fold greater inhibition for both forms of Arabidopsis DHDPR than it did for DHDPS with IC50 values for inhibition of DHDPR in the single micromolar concentration. This rate of inhibition suggests more relevance for translation to whole plant growth inhibition.

It is unfortunate that the co-crystallization attempts with DHDPR and MBDTA-2 were not successful as the physical interaction would be very useful for additional analog synthesis and structure-activity relationship (SAR) evaluation. Use of binding models is common in rational drug design so it is understandable for the authors to pursue a binding model for MBDTA-2. It is difficult to assess the utility of the docking model for SAR development without a better understanding of how many docking conformation predictions the software provided and/or a measure of the docking score. The increase in IC50 values under saturated substrate and co-factor concentrations does help support that MBDTA-2 is a competitive inhibitor with respect to either the substrate and/or the co-factor. The measure of the apparent Km values for the substrate and co-factor with MBDTA-2 at the sub-saturated IC50 values (6.92 and 8.58 micromolar) would help better understand the potential interaction between MBDTA-2 and the substrate and co-factor at the binding site.

The translation of enzyme inhibition to whole organism inhibition is a common barrier in ration drug design. The use of the model dicot plant Arabidopsis (previous publication) and, the agronomically important monocot weed, Lolium rigidum to assess potential translation of inhibition to whole plant activity is key to understanding the potential of lysine biosynthesis inhibition to be a herbicidal target site. The authors utilized a unique method to assess the growth inhibition of Lolium with multiple applications directly to the Lolium seed. Interpretation of the whole plant data for such an application would be clearer with the inclusion of the application rate and whole plant data for the positive control, chlorosulfuron PESTANAL.

Novel herbicidal target site are desperately needed and this paper has identified new opportunities to investigate. A key discussion point in this paper is that a dual-target enzyme inhibitor as a commercial herbicide would be beneficial, especially for the potential prevention of target-site based resistance in weeds. As the authors state, this has been addressed through the use of mixtures of herbicide active ingredient with different modes of action (MoA) targeting the same weed. One of the largest challenges in developing novel MoA herbicides, other than identifying novel herbicidal MoA, is the translation of in vitro activity to whole plant control in field applications for a single-target herbicide. It would be interesting to get the authors' perspectives on opportunities to utilize the binding data for MBDTA-2 on DHDPS and the docking model data for MBDTA-2 on DHDPR to identify new analogs that could have increased affinity for both enzymes with the goal to increase the whole plant activity.

Reviewer #3 (Public Review):

The authors provide further information on the mode action of a lysine synthesis inhibitor (MBTA-2) that is a potential herbicide. The authors determine that MBDTA-2 is not a proherbicide for DHDPS, but do not show that it is not a proherbicide for DHDPR. This should be done, especially since the findings support a very unusual conclusion: inhibition of consecutive enzymes of the lysine synthesis pathway by the same compound through binding an allosteric site for one enzyme and as a competitive inhibitor of the other. Having two molecular targets in the same pathway could hinder evolution of target site-base herbicide resistance. The bioassay of activity of MBDTA-2 on Lolium rigidum was done in such a way that it is difficult to determine if the activity is sufficient to be considered a herbicide.

Reviewer #1 (Public Review):

In this work, the authors determine the structure of ATAD1, a AAA protein responsible for removal of mistargeted tail anchored (TA) proteins from the mitochondria. In prior work, this group determined the structure of the yeast ortholog Msp1 and found that aromatic residues in key pore-loops were important for engaging substrates. In the current manuscript, the cryo-EM structure of ATAD1 reveals large similarities with the yeast ortholog but elaborates some details about interactions between subunits and unresolved regions from the prior work. Most important is the presence of an extended helix (a11) nestled between the subunits that was not visible in the prior cryo-EM Msp1 structure but was present in a published crystal structure from another group (Wohlever, et al. 2017).

Based on similar structural motifs in related AAA proteins, the authors hypothesize that a11 is important for oligomerization and ATAD1 activity. Indeed SEC and activity assays suggest that ADAT1∆a11 assembles poorly, has reduced ATP hydrolysis activity, and fails to bind peptide substrates as readily. Using a novel in vivo mislocalization assay, the authors also show that there are defects with the function of this variant consistent with reduced activity and a failure to form oligomers.

Overall this work extends our understanding of a family of AAA proteins responsible for extracting TA proteins mistargeted to the mitochondria.

Reviewer #2 (Public Review):

The AAA+ proteins ATAD1/Msp1 extract mislocalized TA-proteins from the outer membrane of mitochondria allowing for substrate retargeting to the ER. Msp1/ATAD1 belong to the meiotic clade of AAA+ proteins also including katanin and spastin that sever microtubules. The authors previously determined the cryo EM structure of C. thermophilum Msp1, now they report on the structure of the human homolog ATAD1. ATAD1 hexamers were determined two distinct (open vs closed) structures. The main difference between these structures is the position of the seam (M6) subunit, which contacts the clockwise subunit in the closed state. This structure represents an intermediate state in the ATPase and threading cycle of the AAA+ protein.

The authors additionally report on unique structural features that may enable ATAD1 fulfilling its specific function in membrane extraction of TA-proteins. They show that ATAD1 harbors a particularly long C-terminal a-helix 11. This extension was not visible in the former Ct Msp1 structure. Notably, other meiotic family members harbor a shorter a11 but an additional a12. ATAD1 a11 contacts the counterclockwise subunit in the hexameric AAA ring, implicating a role in hexamer stabilization.

Finally, the authors established a new, microscopic assay to study ATAD1/Msp1 activity in vivo. This assay is based on the direct visualization of mistargeting of the TA-protein GFP-Gos28. By co-expressing the fluorescent reporter and ATAD1 mutants in ATAD1+/+ and ATAD1-/- cells, the authors can differentiate between dominant loss-of-function mutants (toxic in ATAD1+/+ cells) and recessive mutants. This assay proves to be very useful for analysis of ATAD1 activity and allowed documenting oligomerization defects of ATAD1 a11 deletions, which was confirmed in vitro by analysis of respective purified proteins.

Reviewer #3 (Public Review):

AAA protein are involved in a variety of cellular activity. They all share the same structural fold and still they are all incredibly specialised. This study works towards the direction of understanding the unique specialisation of the AAA protein ATAD1. While the general mechanism of substrate threading by AAA proteins is by now fairly well-elucidated, it remains to describe and understand the finer structural protein details that make each specific AAA perform unfolding (threading) of certain substrate rather than others. Additionally, regulation and stabilisation of each AAA is also finely regulated by specific subdomain.

This work is definitively strong in addressing these two points for ATAD1.<br /> The structural data are solid and the analysis of the pore loops residues and the role of a11 overall convincing.<br /> The cell fluorescence microscopy assay is a very good tool for checking in the cell the hypothesis risen by analysing of the structure. However, the assay is currently only based on the localisation of the Gos28 substrate, which leaves open the possibility that ATAD1 a11 mutants will have a different phenotype on different substrates.<br /> Overall the work is a solid follow up of the work on Msp1 and advances slowly but soundly the knowledge on ATAD1 and its mechanism in the rescue of mislocalised TA proteins.

Reviewer #1 (Public Review):

In this manuscript John Lovell and colleagues introduce GENESPACE. GENESPACE is a computational tool that filters (gene sequence based) ortholog annotations by considering the location in the genome to restrict orthologous relationships to syntenic regions. The syntenic regions can be selected according to the context of the study, for example to in- or exclude homeologous regions. In addition, GENESPACE uses its ortholog annotation for the definition of syntenic regions across the focal genomes so that broad-scale chromosomal events can be visualized in an evolutionary context. The manuscript then continues to show the application of GENESPACE in three different scenarios. The first analysis makes use of the broad-scale synteny annotation of GENESPACE to analyze the origin of vertebrate sex chromosomes. The second analysis explores synteny in grass genomes, and evaluates the possibility to find PAV in these genomes given three previously defined QTL regions where a single parental allele induced the phenotypic variation. The third application deals with the assignment of paralogs within grass genomes introduced by the ancient Rho WGD. Using GENESPACE's feature to ignore the first (best) hits (orthologs), it is possible to assign WGD-induced paralogs. GENESPACE seems to be highly useful in practice, and I do not know any other tool that would perform a similar task. I would envision the broad application of GENESPACE as it is agnostic to the species or species group as long as chromosome-level assemblies are available.

Reviewer #2 (Public Review):

The new tool GENESPACE implements a pipeline in R that combines two existing tools, OrthoFinder and MCScanX. OrthoFinder is a popular tool for finding certain groups of homologous genes within the sets of protein sequences of multiple species. It thereby constructs gene trees as well as a species tree in order to distinguish orthologs from paralogs and produces 'orthogroups'. OrthoFinder does not use the positions of the genes in the genome. The older MCScanX finds syntenic regions between multiple genomes. The GENESPACE pipeline calls OrthoFinder and MCScanX to identify orthogroups, using synteny to prevent that gene pairs are in an orthogroup that are not syntenically matched.

The R package is relatively easy to install and run, the provided example runs through smoothly and it is straightforward to apply it to another annotated set of related genomes. The riparian plots give a good overview over large scale rearrangements and look neat although they are generated automatically. The 'pangenome' table of orthologous genes provide copy number differences and can be used to start any downstream analysis for orthologous sets of genes, such as a search for positive selection or accelerated evolution.

The paper discusses several application cases of GENESPACE that are likely of great interest to the respective genomics communities. Unfortunately, though, it is not going into details when describing the algorithm. The method description that was given is not always clear.

The plausibility and a better performance than OrthoFinder and MCScanX on their respective tasks is shown on polyploid and relatively closely related cotton genomes. However, a more comprehensive benchmark, in particular on data where the synteny is less pronounced was not done. It is therefore not clear up to what degree of synteny GENESPACE is better than OrthoFinder at inferring orthogroups.

Reviewer #1 (Public Review):

Sadhukhan and Nandi study theoretically the variation of cell shapes in an epithelial layer. Specifically, they consider the aspect ratio of the cell surface area and the surface area distribution. The authors use an effective equilibrium theory, where they restrict themselves to a regime, where the cell areas make a negligible contribution to the monolayer energy, which only depends on the cell perimeters. The energy is governed by the target perimeter P_0 and the perimeter elastic constant \lambda_P. The authors compute the distributions for the aspect ratio and the area. Each distribution depends on a single parameter, respectively called \alpha and \mu. A priori, neither of the two distributions is universal, but if the average aspect ratio shows a certain relation to \alpha, then the distribution of the scaled aspect ratio is universal. The deviation between the dependence of the mean aspect ratio on \alpha required for universality and the actual relation are small, such that shape fluctuations are nearly universal. The authors' derivation puts earlier experimental findings on a solid ground and very importantly shows that the distribution is NOT a consequence of jamming.

The authors also find that the relation between the standard deviation and the mean aspect ratio is universal. They check their analytical results by simulations of epithelia in terms of a cellular Potts model and a vertex model for which they explicitly verify their assumptions. Due to the success of these approaches had in the past to describe salient features of epithelial tissues, these comparisons strongly support the relevance of the authors' calculations for real epithelia.

The results obtained by the authors clarify the origin of the very intriguing near universality of aspect ratio fluctuations found in epithelial monolayers and should be of interest to all researchers with an interest in tissue properties. Unfortunately, the presentation is not at par with the quality of the results the authors obtain. Most importantly they often make use of jargon that is hard to understand for readers without a formal training in physics.

Reviewer #2 (Public Review):

The manuscript of Sadhukhan and Nandi presents a theoretical study of the shape fluctuations in a confluent epithelial monolayer. The theory which is following the lines of what has been done for 2 dimensional foams is knowledge new and original and the derivation of the results looks sound. It leads to the surprising result that the fluctuations in shape are "almost" universal and the distribution of the rescaled area depends only on a single parameter. The results are obtained with a series of approximations that would need to be discussed more extensively.

Reviewer #1 (Public Review):

Dotov et al. took joint drumming as a model of human collective dynamics. They tested interpersonal synchronization across progressively larger groups composed of 1, 2, 4 and 8 individuals. They conducted several analyses, generally showing that the stability of group coordination increases with group numerosity. They also propose a model that nicely mirrors some of the results.

The manuscript is very clear and very well written. The introduction covers a lot of relevant literature, including animal models that are very relevant in this field but often ignored by human studies. The methods cover a wide range of distinct analyses, including modelling, giving a comprehensive overview of the data. There are a few small technical differences across the experiments conducted with small vs. large groups, but I think this is to some extent unavoidable (yet, future studies might attempt to improve this). Furthermore, the currently adopted model accounts well for behaviors where all individuals produce a similar output and therefore are "equally important". However, it might be interesting to test to what extent this can be generalized to situations where each individual produces a distinct sound (as in a small orchestra) and therefore might selectively adapt to (more clearly) distinguishable individuals. Similarly, it would be interesting to test to what extent the current results (and model) can be generalized to interactions that more strongly rely on predictive behavior (as there is not much to predict here given that all participants have to drum at a stable, non-changing tempo).

An important implication of this study is that some well-known behaviors typically studied in dyadic interaction might be less prominent when group numerosity increases. I am specifically referring to "speeding up" (also termed "joint rushing") and "tap-by-tap error correction" (Wolf et al., 2019 and Konvalinka et al., 2010, also cited in the manuscript, are two recent examples). I am not sure whether this depends on how the data is analyzed (e.g. averaging the behavior of multiple drummers), yet this might be an important take-home message.

I am confident that this study will have a significant impact on the field, bringing more researchers close to the study of large groups, and generally bridging the gap between human and animal studies of collective behavior.

Reviewer #2 (Public Review):

In this manuscript Dotov et al. study how individuals in a group adjust their rhythms and maintain synchrony while drumming. The authors recognize correctly that most investigation of rhythm interaction examines pairs (dyads) rather than larger groups despite the ubiquity of group situations and interactions in human as well as non-human animals. Their study is both empirical, using human drummers, and modeling, evaluating how well variations of the Kuramoto coupled-oscillator describe timing of grouped drummers. Based on temporal analyses of drumming in groups of different sizes, it is concluded that this coupled oscillator model provides a 'good fit' to the data and that each individual in a group responds to the collective stimulus generated by all neighbors, the 'mean field'.

I have concerns about 1) the overall analysis and testing in the study and about 2) specific aspects of the model and how it relates to human cognition. Because the study is largely empirical, it would be most critical for the authors to propose two - or more - alternative hypotheses for achieving and maintaining synchrony in a group. Ideally, these alternatives would have different predictions, which could be tested by appropriate analyses of drummer timing. For example, in non-human animals, where the problem of rhythm interaction in groups has been examined more thoroughly than in humans, many acoustic species organize their timing by attending largely to a few nearby neighbors and ignoring the rest. Such 'selective attention' is known to occur in species where dyads (and triads) keep time with a Kuramoto oscillator, but the overall timing of the group does not arise from individual responses to the mean field. Can this alternative be evaluated in the drumming data ?<br /> Would this alternative fit the drumming data as well as, or better than , the mean field, 'wisdom of the crowd' model ?

A second concern arises from relying on a hybrid, continuous - pulsed version of the Kuramoto coupled oscillator. If the human drummers in the test could only hear but not see their neighbors, this hybrid model would seem appropriate: Each drummer only receives sensory input at the exact moment when a neighbor's drumstick strikes the drum. But the drummers see as well as hear their neighbors, and they may be receiving a considerable amount of information on their neighbors' rhythms throughout the drum cycle. Can this potential problem be addressed? In general, more attention should be paid to the cognitive aspects of the experiment: What exactly do the individual drummers perceive, and how might they perceive the 'mean field' ?

Reviewer #3 (Public Review):

The contribution provides approaches to understanding group behaviour using drumming as a case of collective dynamics. The experimental design is interestingly complemented with the novel application of several methods established in different disciplines. The key strengths of the contribution seem to be concentrated in 1) the combination of theoretical and methodological elements brought from the application of methods from neurosciences and psychology and 2) the methodological diversity and creative debate brought to the study of musical performance, including here the object of study, which looks at group drumming as a cultural trait in many societies.

Even though the experimental design and object of study do not represent an original approach, the proposed procedures and the analytical approaches shed light on elements poorly addressed in music studies. The performers' relationships, feedbacks, differences between solo and ensemble performance and interpersonal organization convey novel ideas to the field and most probably new insights to the methodological part.<br /> It must be mentioned that the authors accepted the challenge of leaving the nauseatic no-frills dyadic tests and tapping experiments in the direction of more culturally comprehensive (and complex) setups. This represents a very important strength of the paper and greatly improves the communication with performers and music studies, which have been affected by the poor impact of predictable non-musical experimental tasks (that can easily generate statistical significant measurements). More specifically, the originality of the experiment-analysis approach provided a novel framework to observe how the axis from individual to collective unfolds in interaction patterns. In special, the emergence of mutual prediction in large groups is quite interesting, although similar results might be found elsewhere.

On another side, important issues regarding the literature review, experimental design and assumptions should be addressed.<br /> I miss an important part of the literature that reports similar experiments under the thematic framework of musical expressivity/expression, groove, microtiming and timing studies. From the participatory discrepancies proposed in 1980's Keil (1987) to the work of Benadon et al (2018), Guy Madison, colleagues and others, this literature presents formidable studies that could help understand how timing and interactions are structured and conceptualized in the music studies and by musicians and experts. (I declare that I have no recent collaborations with the authors I mentioned throughout the text and that I don't feel comfortable suggesting my own contributions to the field). This is important because there are important ontological concerns in applying methods from sciences to cultural performances. One ontological issue that different cultural phenomena differ from, for example, animal behaviour. For example, the authors consider timing and synchrony in a way that does not comply with cultural concepts: p.4 "Here we consider a musical task in which timing consistency and synchrony is crucial". A large part of the literature mentioned above and evidence found in ethnographic literature indicate that the ability to modulate timing and synchrony-asynchrony elements are part of explicit cultural processes of meaning formation (see, for example, Lucas, Glaura and Clayton, Martin and Leante, Laura (2011) 'Inter-group entrainment in Afro-Brazilian Congado ritual.', Empirical musicology review., 6 (2). pp. 75-102.). Without these idiosyncrasies, what you listen to can't be considered a musical task in context and lacks basic expressivity elements that represent musical meaning on different levels (see, for example, the Swanwick's work about layers/levels of musical discourse formation). Such plain ideas about the ontology of musical activities (e.g. that musical practice is oriented by precision or synchrony) generate superficial constructs such as precision priority, dance synchrony, imaginary internal oscillators, strict predictive motor planning that are not present in cultural reports, excepting some cultures of classical European music based on notation and shaped by industrial models. The lack of proper cultural framing of the drumming task might also have induced the authors to instruct the participants to minimize "temporal variability" (musical timing) and maintain the rate of the stimulus (musical tempo), even though these limiting tasks mostly take part of musical training in some societies (examples of social drumming in non-western societies barely represent isochronous tempo or timing in any linguistic or conceptual way). The authors should examine how this instruction impacts the validity of results that describe the variability since it was affected by imposed conditions and might have limited the observed behaviour. The reporting of the results in the graphs must also allow the diagnosis of the effect of timing in such small time frame windows of action.

Reviewer #1 (Public Review):

It is well established that the energy expenditure and metabolic rate of metazoan organisms scale inversely to body mass, based on the measurement of oxygen consumption and caloric intake. However, the underlying regulatory mechanisms for this observation are poorly defined. To investigate whether metabolic scaling is associated with reduced levels of transcription of metabolic genes in larger animals, the authors reviewed existing transcriptional datasets from liver tissues of five animals (mice, rats, monkeys, humans and cattle) with a 30,000-fold range in average adult body weights. They identified a number of metabolic genes in different pathways of central carbon metabolism whose expression inversely scaled with body size, a majority of which required oxygen, NAD/H or ATP/ADP. Metabolic flux studies on intact liver sections, as well as in live animals also revealed decreased liver metabolic fluxes in rats compared to mice. Interestingly, these differences were not observed in primary hepatocyte cultures, indicating that metabolic scaling is primarily regulated by cell-extrinsic factors and tissue context. These are interesting findings and highlight the importance of measuring metabolic processes in vivo. The measurement of cellular metabolic fluxes in different contexts (cultured, ex vivo tissue sections and live animals) is a major strength of this study. The lack of direct evidence that enzyme levels correlate with mRNA, and the absence of both transcriptional and enzyme activity measurements in cultured cells are potential weaknesses.

Reviewer #2 (Public Review):

Akingbesote et al. aim to determine the molecular basis of metabolic scaling - the phenomenon that metabolic rates scale inversely with (0.75) body mass. More specifically, they test the hypothesis that expression of genes involved in the regulation of oxygen consumption and substrate metabolism as well as respective fluxes provide a molecular basis for metabolic scaling across five species: mice, rats, monkeys, humans, and cattle. To this end, Akingbesote et al. use publicly available transcriptomics data and identify genes that show decreasing (normalized) expression with increasing mass of organisms. This descriptive analysis is followed by discussing a few relevant examples and (KEGG) pathway enrichment analysis. The authors then used their published PINTA approach with data from their experiments with mice and rats to provide estimates of selected cytosolic and mitochondrial fluxes in vitro, ex vivo, and in vivo; these estimates are then employed in determining if metabolic fluxes scale. The conclusion drawn from these analyses is that estimates of selected fluxes do not differ in vitro between plated hepatocytes of mice and rats, but that differences can be detected using metabolic flux analysis in vivo. As a result, in vivo flux profiling is more relevant to assessing metabolic scaling.

The conclusions are only in part supported by the data and clarifications are needed both with respect to the analysis of transcriptomics data as well as flux estimates:

1. In looking for scaling in gene expression, the authors rely on the assumption that mRNA expression correlates well with protein abundance (citing Schwanhäusser et al., 2011); however, transcripts explain about 40% of variance in protein abundance (this observation holds across multiple species). Hence, the identified patterns based on the transcript data may have little implications for protein abundance or flux.<br /> 2. While the procedure used to identify transcripts whose expression scale is clearly described, focusing the enrichment on KEGG pathways can only identify metabolic genes that scale. It would be informative and instructive to investigate if and to what extent genes involved in non-metabolic processes, that affect metabolic rates, also scale.<br /> 3. The result on flux ratios and absolute fluxes, based on the equations in Table S1, rely on certain assumptions (e.g. metabolic and isotopic steady state, among the others listed in PINTA); the current presentation does not ensure that all assumptions of PINTA are met in the present setting, so the estimates may be biased, leading to alternative explanations for the observed differences in vivo or the lack thereof in vitro.<br /> 4. The findings regarding the flux estimates seem to be fully determined by observed differences in gluconeogenesis (as demonstrated in Fig. 4). Usage of more involved approaches for metabolic flux analysis may provide wider-reaching conclusions beyond selected fluxes that appear fully coupled.

Reviewer #3 (Public Review):

This manuscript addresses a fundamental aspect of mammalian biology referred to as scaling, in which metabolic processes calibrate to the size of the organism. Longstanding observations related to scaling have been established based on rates of oxygen consumption. This manuscript extends these observations to gene expression and metabolic fluxes in order to discover the metabolic pathways that scale with body mass. The analyses are focused on the liver, which is the metabolic hub of the organism. Gene expression levels gleaned from available databases for organisms of varied sizes are analyzed and queried for scaling based on body mass. This analysis reveals that scaling is mainly a characteristic of metabolic genes. These data inform metabolic flux studies in cultured cells, liver slices and whole organisms. These studies demonstrate that scaling of metabolic fluxes occurs, but not out of the context of the whole organism or intact liver (in the form of liver slices). Scaling of metabolic fluxes is not observed in cultured hepatocytes. Overall, this is an interesting line of inquiry. The data are largely correlative in nature but add important texture to traditional characterization of oxygen consumption rates. The application of flux studies is a particular strength because these reflect the true metabolic processes. Enthusiasm was tempered by certain claims that extend beyond data (e.g., the title that suggests that metabolic scaling applies to tissues other than the liver, which was studied), as well as low numbers of biological replicates in some experiments, studies conducted in a single-gender and a writing style that includes excessive technical jargon.

Reviewer #1 (Public Review):

This manuscript reports on a very extensive molecular and cellular study of the effect of splicing factor Srsf10 on spermatogenesis in male mice. Using Srsf10 knockout mice, the investigators determined that loss of Srsf10 specifically inhibited spermatogonia differentiation (into spermatocytes) and entrance into meiosis (essential for fertility). The deletion of Srsf10, a factor already well characterized and known to be involved in alternative splicing of mRNA (i.e, post-transcriptional), was responsible for male infertility. It had been shown previously that Srsf10 controls alternative splicing by binding to exons as well as to splicing factors during mitosis. It is of interest that spermatogonia are produced, suggesting that loss of Srsf10 with its effects on alternative splicing may not affect early steps in spermatogenesis. The extensive analysis of alternative splicing was carried out in mouse testes and accounts primarily for the novelty of the research. This manuscript should be of interest to molecular, developmental, and reproductive biologists.

Reviewer #2 (Public Review):

SRSF10, also known as SRp38, is an atypical serine/arginine-rich splicing factor that regulates the generation of isoforms of messenger RNAs (mRNAs) from common precursor pre-mRNAs, so that cells can express protein variants in need. It has been shown that SRSF10 regulates alternative splicing via binding of exons and constitutive splicing factors in response to cellular stimuli during mitotic cell cycle progression. Liu et al. now utilize genetically modified mouse model that lacks SRSF10 specifically in male germ cells to show that SRSF10 is required for spermatogenesis at a very early stage and thus male fertility by regulating alternative splicing of hundreds of genes.

Spermatogenesis encompasses a series of consecutive events to produce male gametes, sperm, including mitosis, meiosis and post-meiotic cellular morphogenesis. Although alternative splicing has been known as an important step to regulate gene expression in spermatogenic cells, the underlying molecular mechanisms remain to be fully understood. Using a genetic approach, the authors created mice that carry alleles of Vasa-Cre and floxed Srsf10 (Srsf10Flox/Flox:Vasa-Cre). Since Vasa (known as Mvh in mouse, the mouse Vasa homolog) is specifically expressed in germ cells, its promoter would drive the expression of Cre recombinase specifically in germ cells and remove floxed DNA fragment, generating mutant Srsf10 gene (missing exon 3 in this case) in germ cells. To find out whether removal of functional Srsf10 gene would affect male germline development (females are not mentioned in this manuscript), the authors analyzed the fertility of male mice, testis development and spermatogenic cells using various cell biological approaches. Their results showed that Srsf10 mutants suffered severe defects in spermatogenesis and produced no spermatogenic cells beyond meiotic stage, leading to male infertility. The authors further showed, by analyzing more detailed spermatogenic steps, that mutant mice retained only earliest stage spermatogonia at decreased levels, comparing to the control counterparts that still express SRSF10.

Spermatogonial stem cells (SSCs) are the founder cells of spermatogenesis, which contain heterogenous cell populations probably due to the progressive proliferation and differentiation of self-renewing SSCs. To find out whether defected spermatogenesis of Srsf10 mutants was caused by defects in SSCs. The authors applied known marker proteins of SSCs to analyze their sub-populations in more details, using immunofluorescent staining, cell sorting and single cell RNA sequencing. The results showed that the proliferating population of SSCs expressing PLZF (PLZF+) were decreased in number, whereas the earlier stage of un-differentiated SSCs expressing GFRa1 were less affected. Consequently, meiotic spermatocytes were severely disrupted. Analysis of cell proliferation using nascent DNA labeling (Edu) supported the notion that deletion of SRSF10 impeded mitotic cell cycle of PLZF+ differentiating progenitors. This is further supported by the single cell RNA sequencing analyses. Characterization of cellular transcriptome at single cell level can not only identify changes of gene expression but also be used to classify cell types according to their similar expression patterns of typical marker genes. Bioinformatics analyses of single cell RNAseq data indeed showed that Srsf10 mutants contained a DSSC3 cell group that was not presented in the controls. In addition, they also showed that the ratios of USSC1 and USSC2 groups, two undifferentiated SSC sub-populations, are altered in mutants, comparing to the controls, supporting their cellular analyses in SSC sub-populations.

To further determine how SRSF10 affected gene expression in spermatogenic cells, especially for SSCs, the authors conducted both bulk RNA sequencing and Isoseq experiments using sorted SSCs (THY1+KIT-). They found that expression of hundreds of genes was differentially affected in Srsf10 mutant SSCs, especially for genes involved in cell cycle regulation, cellular iron ion homeostasis and spermatogenesis. The authors went on, using Isoseq data, to show that isoforms of many transcripts (mRNAs) were altered in SSCs lacking SRSF10, mainly due to exon skipping and altered first exon splicing events. Consistently, these affected genes are mostly involved in mitotic cell cycle progression and stress responses.

Overall, the authors presented convincing evidence on the defects of spermatogenesis and male sterility due to Srsf10 mutation. The RNA sequencing results support the role of SRSF10 in regulating alternative splicing of cell cycle regulators and post-translational modifiers that may impede mitotic cell cycle progression and stress responses. The RNA sequencing data also provided a rich source to further study the molecular mechanisms that underlie the SRSF10-mediated alternative splicing involved in the regulation of mouse SSCs.

However, some caveats can be seen in the manuscript that may undermine the significance of the study. For example, the authors concluded that the main causative event leading to male infertility in Srsf10 mutants is due to the defected expansion of differentiating progenitors, the progenies of un-differentiated SSCs, but not the formation of SSCs in neonatal mice. This should be further tested. Since the deletion of Srsf10 gene mediated by Mvh-Cre starts in embryonic stage of pro-spermatogonia, experiments designated to the proliferation status and population changes of pro-spermatogonia and un-differentiated SSCs should be carried out. In fact, single cell analyses and comparison of GFRa1+ cells suggested that SSCs may be altered at the beginning as well. Consequently, defects in the initiation of meiosis, as the authors concluded, may not be a causal but consequential effect due to the defective proliferation of progenitors. The manuscript also contains some in-consistency in the description of SSC sub-populations at various stages, in data presentation and interpretation, lack of sufficient introduction of research rationale, materials and methods used, as well as discussions of possibilities the current results indicate. These issues should be amenable using their current mouse models and experimental approaches. It will also be of interest to see if spermatogenic cells are maintained in adult or even aged mice in the absence of SRSF10.

Reviewer #3 (Public Review):

The maintenance of spermatogonia stem cells is essential for fertility and a model for stem cell homeostasis. In this study the authors investigate the role of the alternative splicing factor SRSF10 in spermatogenesis, following on the discovery that germ cell-specific knock out of SRSF10 in mice caused a loss of spermatogonia and fertility in males.

This study begins by crossing SRSF10 floxed mice to those expressing Cre in the male germline. This resulted in infertile male mice with significant defects in spermatogonia differentiation. To investigate the molecular defects associated with these developmental defects, the authors carried out transcriptomic analysis in full testes at several different developmental time points. RNA-Seq of this bulk tissue revealed differential gene expression in the SRSF10-depleted testes that is consistent with reduced expansion and proliferation of spermatogonia. Subsequent single-cell RNA-Seq also revealed a gene expression profile consistent with loss of spermatogonia, while cell cycle analysis demonstrated a reduction in cell division and increase in apoptosis in the SRSF10-depleted cells. Finally analysis of alternative splicing in SRSF10-depleted cells identified several hundred impacted splicing changes, consistent with previous studies implicating SRSF10 as a splicing regulatory protein. Notably, many of the confirmed changes in splicing occurred in genes with known activities in spermatogonia development.

Together these studies provide useful physiologic and descriptive data on the impact of SRSF10 in mouse fertility. Future studies will be needed to determine which of the gene expression changes observed in the SRSF10-depleted cells drive the differentiation defects and which are a consequence of stalled development and proliferation. Moreover, the molecular mechanism by which SRSF10 impacts key splicing or gene expression events, and how many of these are direct targets of SRSF10 remains unexplored.

Reviewer #1 (Public Review):

Lee and Chen investigate the representation of between-movie boundaries in the brain, with a particular focus on the spontaneous boundaries that occur as people shift between movie recalls. Are these sorts of recall boundaries represented the same as those that occur (a) between the visual presentation of different movies (between-movies boundaries at encoding) and/or (b) between events within a single movie (within-movie boundaries at encoding)-or are these recall boundaries different? The main findings were that between-movie boundaries were quite similar regardless of the task phase (encoding/retrieval), but dissimilar from within-movie boundaries.

This paper has many strengths, including the interesting research question and sophisticated analytic approach. The authors have done an excellent job presenting many important controls and, despite its complexity, the work is presented in a way that is clear and enjoyable to engage with. While a relatively brief paper and simple story, as the authors note in the discussion, there are many possible interpretations or underlying mechanisms that could be giving rise to this phenomenon-which is quite exciting. So, the paper may be a source of more questions than answers! I see this as a great feature. I think this work will inspire many new investigations into boundary representation and event segmentation.

I do have a few suggestions and questions for the authors to address the current weaknesses, as outlined below:

1. I am generally interested in better understanding how differences in the sensory experience (most notably, presentation of visual input in the movies versus its absence during the between-movie boundaries) across timepoints could be playing a role in these results. If I understand correctly, the between-movie boundaries will always contain a (mostly) blank screen (with simple white text at encoding) along and silence, for both encoding and recall phases. In contrast, the no-boundary periods as well as the within-movie boundaries would always contain visual input (movies). There are a few reasons why this is concerning to me. First, the boundary periods are relatively much more homogenous in terms of input/experience, and so it intuitively makes sense to me that the neural pattern would also therefore be quite similar across different boundary periods (even across phases). The primary comparisons as shown in Fig 2 are comparing these homogenous boundary experiences with highly variable within-movie experiences (either due to ongoing recall/speech, or movie viewing). It seems to follow that for this reason alone one should expect the boundary patterns to be more similar to one another, and I am not sure whether that is the sort of boundary processing that is of interest here. Second, as is evident in Figure 3A, the "middle" (no-boundary movie) patterns are a much more heterogeneous bunch, with some pairs showing positive and others showing negative correlations with one another-potentially reflecting variability in the input. Given this, it of course has to be the case that the average correlation of within-movie patterns is low (near zero; Figure 3C) but it also may follow that the boundary patterns are negatively correlated with the event offset (within-movie boundary) patterns. I appreciated the analysis related to the audio controls, but am not sure the authors were able to account for the visual differences.

2. I was not sure I fully understood the offset vs. onset yoking analysis-both how it was performed, and how the conclusions followed from the results. First, I was a bit confused about how the difference in delay duration between movies at encoding (6s) versus at recall (9.3s on average, but variable; see also comment #5) would play into this and whether those are meaningful time points to display on the Figure 3D charts that might help the reader interpret those findings. Second, the authors state that this analysis shows the boundary patterns were driven by offset (more than onset) responses, but I was not sure what aspect of the results led to that conclusion. Can the authors say more about the evidence supporting this conclusion? It looks to me like there are strong correlations that emerge both after offset and onset (i.e., just above and to the right of the origin there are numerous time points with positive [red] correlations). Perhaps it is because the red positive correlations start earlier, prior to the recall itself, when yoked to the onset, but I am not sure why this means it is related to offset and not some preparation for the onset of recall (see also comment #5). Also, is it interesting or meaningful that the patterns seem more compressed at recall than at encoding (i.e., the outlined red areas are skinnier than they are tall)?

3. I am not sure the reason for masking Figure 2B and C with the a>0 and c>0 maps. First of all, it seems as though in RSA the actual correlation being positive or negative is not terribly meaningful and can depend on preprocessing decisions, etc. In addition to that potential issue though I'm also just generally interested in more understanding the logic behind this decision. Can the authors explain that and include it in the main paper? Were there any regions that showed for example a<br /> 4. I am inferring (though could be incorrect) that some of the pattern similarity analyses would be directly comparing (i.e., correlating) patterns derived from the same scanning run. Can the authors confirm if this is the case? If so, it would be important to consider in the paper how temporal autocorrelation within scanning run may be impacting the results (for example, how does temporal distance between the different events vary (or not) across the different comparisons?). Ideally, the authors would be able to demonstrate that the same pattern of results can be found when limiting to cross-run comparisons only. Relatedly, it would be important to know whether the across-phase comparisons (e.g., in that there are more regions that show significant recall-recall similarity vs. encoding-recall in Figure 2B/C) might also be impacted by differences in whether the patterns were derived from the same run (whereas half of the comparisons could be same-run for recall-recall, none of the comparisons would be from the same run in encoding-recall, and so the overall correlation may be higher for recall-recall... or encoding-encoding).

5. The definition of the time periods of interest were a bit confusing to me. For example in the main analysis, the duration seemed arbitrary at 15 seconds, and I believe it always began at the offset of the preceding movie (shifted by 4.5s for hemodynamic lag). To clarify, at encoding, this means that it would always include the beginning of the "next" movie, but never the end of the preceding movie, is that correct? So the boundary between movie A and movie B (looking at Fig 1) would include some activation associated with the beginning of movie B viewing-is that correct? It seems a bit strange to me given the goals and framing of the study that movie B would be included here, given I thought most of the "action" would be happening with the movie A memory in this case. It seems as though this definition may also produce systematic differences between encoding and recall: for one, the delays between recalls are variable and longer (9.3s on average with an SD of 16.8s) than the fixed 6s title screen, so the contents going into the neural patterns at recall would be different (contain less recall time and more blank-screen time); but also during recall, it seems as though the participant would be bringing to mind memories of the upcoming movie B they are about to recall, while there is no way for participants to anticipate anything specific about the upcoming movie during encoding. Can the authors clarify these points in the paper?

Reviewer #2 (Public Review):

This experiment by Lee and Chen sought to examine internally-guided boundaries between events during recall, specifically recall of audiovisual short movies viewed a few minutes prior. They performed a set of well thought out pattern similarity analyses to determine if activity in the Default Mode Network (DMN) and more specifically Poster Medial Cortex (PMC) were related across encoding and retrieval of separate movies. Briefly, the authors found characteristic univariate activation patterns in the brain's 'Default Mode Network' (DMN) during event transitions, but extend prior work to show that these activations are present during internally-guided event transitions. Furthermore, fascinatingly, the authors report increases in pattern similarity at event offsets that persist across encoding and recall, and which were not present during the middle of events. This is taken as evidence of a general cognitive state that exists at event transitions, and exists beyond the level of a single event.

In our view, the authors' results support their interpretations. Not only are there internally generated boundaries that mark shifts between broader contexts, but these boundaries appear to be distinct from those that are found within continuous narratives. This is a very interesting dataset, cleverly analyzed, which points to interesting new directions for the fields of event cognition and event memory. Namely, the distinction between within-event and between-event boundaries adds new depth to the discussion of event boundaries more broadly, and the notion of a general common cognitive state during event transitions is a thought-provoking result that will certainly influence our group's thinking on this topic, and likely many others.

We truthfully do not have substantive criticisms of this manuscript. We think the study is well done, the analyses seem properly conducted, and the manuscript is generally written well and clearly. There are a few minor requests for clarification that we will note in our separate recommendation to the authors, but the only critique bordering on a 'major' concern is the very short 'Discussion' portion of the manuscript. While we recognize that this is a short-format submission, and while the authors did a fine job of trying to synthesize their results and situate them in the context of the field in 2 short paragraphs, I honestly think there should be more discussion of the findings and their implications. In sum, however, we think that this is a solid paper that we found very exciting.

Reviewer #3 (Public Review):

The aim of this paper is to investigate whether internally driven changes in mental context involve similar neural mechanisms as externally driven changes. In particular, the paper investigates whether there are consistent neural responses that align with the transition between stimuli when watching or recalling movies. The authors show that there is a consistent pattern of neural responses, particularly in precuneus and angular gyrus, that is evoked by the offset of a movie during movie watching and by the self-generated transitions between movies during movie-recall. Their results suggest that self-generated shifts in mental context involve similar neural processes as externally-generated shifts.

The paper is well written and the results are interesting. The analyses and methods are thoughtful and rigorous and provide an interesting new perspective on internally driven changes in mental context that (as far as I know) have not been investigated before in this way. It is clear that the authors spend a lot of time and effort on additional analyses to understand in detail what is going on and which factors might be driving the observed differences.

I do have a couple of concerns about the interpretation of the findings. In the abstract the authors state that the findings reflect: 'a cognitive state related to the flushing and reconfiguration of situation models'. If the between-movie activity patterns reflect the flushing/reconfiguration of the prior context, it is very surprising that there is a negative correlation to within-movie boundary patterns. The premise of event segmentation theory is that event boundaries (within a given context/movie) result in a reset of the event model, therefore also resulting in a 'flushing' of the prior context. I have three main concerns related to this point:

1. To what extent can the similarity between recall and encoding be driven by the (sudden lack of) external input that occurs at transitions between movies? The authors already investigated the role of auditory input, but of course there is also a sudden lack/reduction of visual input during the boundaries between movies at encoding. The authors state that: "Visual features (i.e., black screen) or pauses in speech cannot explain boundary-specific similarity between encoding and recall phases, because boundary and non-boundary periods were identical in terms of visual input during recall and speech generation during movie watching." I do not agree with this statement. When looking at the similarity between encoding and recall during boundaries, the characteristics of the input are more similar (absent visual and auditory input) than when looking at the similarity between encoding and recall in the middle of movies (present vs. absent visual and auditory input). This confound should be taken into account in the analyses.

2. Could the negative correlation between within-movie boundary patterns and between-movie boundary patterns be due to the long time-window that is averaged (15 seconds)? Both within and between-movie boundaries might result in a similar transient activity patterns, which persists longer for the between-movie boundaries possibly due to the 6 seconds of black/title screen at movie offset.

3. In addition to these two concerns, it is unclear to me at this point to what extent these findings can be related to a reset of context that might occur in a real-life setting or if it is specific to the current experimental setup. Do the authors think that subtle shifts in context (e.g. within a given movie/context) involve fundamentally different mechanisms as compared to more stark transitions that occur between contexts (e.g. between movies)?

Reviewer #1 (Public Review):

The work, mostly performed in yeast S. cerevisiae, shows that the knockout of DIP2 leads to accumulation in cells of some DAG subspecies (36:0 and 36:1), and also a deficit of similar TAG subspecies (something which mostly occurs, as they showed, in early to mid log growth phase). Accordingly, over-expression of DIP2 leads to the opposite outcome (lower DAG and higher TAG subspecies levels). ∆DIP2 cells showed increased ER stress and UPR, which can be counterbalanced by incubating cells with oleic acid. Moreover, the authors show that the absence of DIP2 causes vacuole fusion defects, which they ascribe to a localization of the protein in the vacuole and possibly to the fact that enhanced levels of DAG in the vacuole membrane can promote vacuole fusion. Although it is true that neither of these claims are fully supported by the experimental results, the data that the authors show serves as a starting point for future, more robust studies to test those claims. Finally, the authors show that the DBD1 domain is not necessary and that the two FLD domains are key for the observed lipid metabolism induced by DIP2 expression. Altogether this manuscript presents interesting new data on an uncharacterized protein that seems to be regulating the metabolism of relatively low abundant DAG/TAG subspecies in cells, and by doing so possibly control cell homeostasis.

Reviewer #2 (Public Review):

In this manuscript the authors study the role of the protein DIP2 which contains two fatty acyl-AMP ligase (FAAL)-like domains in lipid metabolism. They find that deletion of DIP2 in yeast, drosophila and mouse cells results in the increase of specific diacylglycerol species which is often coupled to a reduction in triglycerides. Overexpression studies in yeast corroborate this evidence. The DIP2 KO induced lipid deregulation correlates with a moderate induction of ER stress that can be rescued by promoting diacylglycerol conversion to triglycerides through the administration of oleic acid. The authors also show that DIP2 expression in yeast is maximal in the exponential growth phase where the effects of its KO on lipid metabolism are more sustained. The authors report that DIP2 in yeast is localized at the vacuole and at mitochondria and that manipulation of DIP2 levels impair the normal vacuolar response to osmotic stresses. They conclude by demonstrating that the (FAAL)-like domains of DIP2 are necessary and sufficient to sustain the function of DIP2 in regulating diacylglycerol levels and ER stress, with mutations in specific amino acids possibly required for the FAAL enzymatic activity rendering DIP2 inactive. While the manuscript is compelling in many respects and most of the conclusions drawn by the authors are well supported by their data, this paper does not contain a clear proof of the enzymatic activity of DIP2, nor a molecular explanation for the substrate specificity and mode of action of DIP2.

Reviewer #3 (Public Review):

This study examines a family of poorly defined enzymes that contain fatty acyl-AMP ligase like domains (FAALs). The study reveals that these DISCO-interacting protein 2 (DIP2) enzymes are required to maintain a specific pool of diacylglycerol (DAG) lipids containing primarily C36 acyl chain lengths in budding yeast. Using primarily yeast, the study shows that deletion of ScDIP2 significantly increases C36 DAG pools while leaving the more abundant C32 and C34 DAG pools generally unaltered. Triglyceride (TAG) is also reduced in this deletion. Conversely, ScDIP2 over-expression promotes C36 inclusion in TAG. The ScDIP2 KO yeast manifests ER stress that can be relieved by the addition of oleic acid, but not other fatty acids. In the last section of the study, ScDIP2 is proposed to localize to the vacuole and mitochondria, where it maintains a specific DAG pool to enable proper vacuole morphology and fusion, as well as proper osmoregulation of the vacuole.

This is a well executed study that begins to characterize a conserved and generally poorly understood family of enzymes. However, questions still remain about some of the conclusions of the study. There are two general issues with the study. The first is the specificity of the effect of loss of ScDIP2. The study beautifully shows that loss of ScDIP2 (or its over-expression) affects a specific sub-pool of DAG (mainly the C36 species). TAG levels are also somewhat lower. However, how ScDIP2 impacts other lipid precursors to DAG synthesis such as PA and lyso-PA is under-examined, and should be looked at as they can also affect ER stress. Whether the change in DAG/TAG is primarily driven by decreased synthesis versus increased lipolysis also required additional analysis.

The second issue relates to how ScDIP2 relates to the yeast vacuole. It is proposed that some of the ScDIP2 enzyme is vacuole localized, and influences vacuole morphology. The evidence presented here does not strongly support that model. From imaging at least, it appears that ScDIP2 is primarily mitochondria localized. It is therefore possible that it influences vacuole lipid composition and morphology distally from the mitochondria. Resolving ScDIP2's native subcellular localization would strengthen the manuscript.

Reviewer #1 (Public Review):

Mitra et al. extensively utilized the publicly available pan-cancer multi-omics datasets including CCLE, TCGA, RNAseq, and ChIPseq datasets from GEO, and conducted impressive computational analysis work to discover the potential regulatory functions of lncRNA at the pan-cancer level. The idea of using co-essential modules generated by Wainberg et al. 2021 is very interesting and was important to leverage the genome-wide set of functional modules to identify the new lncRNA functions. The overall statistical analyses are rigorous, and the evidence in this paper is logical and solid, especially given the additional RNAseq/ChIPseq data analysis. The validation experiments using cell lines were also appropriate. Overall, this is an excellent paper that combines both dry and wet lab experiments to systematically discover unknown functions of lncRNAs in cancer.

Reviewer #2 (Public Review):

Mitra and colleagues performed statistical analyses to evaluate associations between lncRNAs and mRNAs, using transcriptome data generated in tumor tissue samples in multiple cancer types from both CCLE and TCGA projects. They further integrated the association results into previously well-characterized co-essential pathways/modules (Wainberg et al., 2021), together with additional pathway/Hallmark genesets annotations, aiming to explore function potential for lncRNAs. Based on these analyses, they characterized 30 high-confidence pan-cancer proliferation/growth-regulating lncRNAs. Importantly, they provided in vitro functional evidence to verify potential tumor-suppressive roles of two prioritized lncRNAs (PSLR-1 and PSLR-2) in proliferation and growth in two lung adenocarcinoma cell models. Overall, this is a well-motivated and conducted study, especially given the large number of lncRNAs that currently have poor-characterized functions. The findings in this manuscript could advance the overall understanding of the roles of lncRNAs in cancer formation and progression.

Reviewer #1 (Public Review):

The work by Flores-Kim et al. reports the identification and characterisation of WhyD, an enzyme involved in the hydrolysis of wall teichoic acids (WTAs) in S. pneumoniae. They explore the role of WhyD in autolysis control and cell growth and division. This study reveals that WhyD controls the abundance and localization of WTAs, which in turn inhibits autolytic activities.

The methods used convincingly address the questions asked by the authors and overall, the data are robust and support the conclusions drawn, providing experimental evidence for a mechanism proposed a long time ago but that remained poorly characterised. This work provides a convincing model to explain how the enzymatic activity of WhyD contributes to control peptidoglycan hydrolysis in the context of actively growing and dividing S. pneumoniae cells. It also highlights that WhyD activity is not sufficient to prevent autolysis and cell death during late stationary phase, indicating that this process awaits further analysis.

Reviewer #2 (Public Review):

Authors identified the gene whyD, an essential factor for the bacterial human pathogen Streptococcus pneumoniae (Sp) survival. Surprisingly, WhyD importance is reversed in the absence of LytA, making cells' survival dependent on the simultaneous deletion of both genes. The authors investigated the relationship between these 2 proteins and the production and localization of peptidoglycan (PG), teichoic acids (WTAs), and lipoteichoic acids (LTAs) in the cell wall. Combining genetically engineered strains, in vivo tagging of proteins, chemical probes, and purification of recombinant proteins, authors concluded that more than regulating levels of WTA and LTA, WhyD acts as a topological factor, supporting the spatio-temporal synthesis and degradation of cell wall necessary for cell elongation.

Strengths:

Working with Streptococcus, as with any less-studied bacterial model compared to B. subtilis and E. coli, is challenging but increasingly important to understand human pathogens and their interactions with antibiotic drugs. This work represents a tour de force and joins a relatively small collection of state-of-the-art studies by combining genetics, biochemistry, and cell biology approach to solve a specific problem. The questions asked in each experiment are clear and the performed experiments were, in the majority, well designed with proper controls. The paper is well-written and accessible to the general scientific community.

One of the highlights of this study is the development of a good proxy for WTA localization - something not trivial - providing the field with endless possibilities to study the immediate and lasting effects of antibiotic resistance and other genetic pathways in this model. A second important development - not so obvious as the first one - is the successful use of purified hydrolases from one species (in this case, LytA from S. pneumoniae) in orthogonal organisms (here, B. subtilis). This is an elegant assay that can be useful to study the function of proteins from challenging model systems.

Weaknesses:

Although this is certainly a technically difficult goal, the paper does not show a direct interaction between WhyD (or its GlpQ sub-domain) with WTAs. While the effect of WhyD over WTA levels showed here is undeniable, and the proposed interaction is the simplest explanation, it's not possible to assert whether this is the case without a crosslink co-purification using an inactive mutant of WhyD.

Another aspect the paper could improve is the explanation of the labeled cell-wall analogs, very well established in the cell-wall field but likely obscure to other biologists. Especially on figures that nothing at all is said about the data (Figures 4 and 5). The microscopy data, despite evidently being well-performed, begs for better quantitation and visualization. For example, it's not clear whether there were replicates, the sample size (informing that at least 300 cells were used is not enough information to inform on sample size effects). Sub-panels where no signal is apparently detected (e.g. Figure 7 and supplements) should be clarified and the background should be displayed.

Reviewer #3 (Public Review):

In this study, the authors aim at identifying new factors regulating the activity of bacterial cell wall hydrolases using Streptococcus pneumoniae as a model. Based on previous Tn-Seq screens, they showed that the gene whyD becomes non essential in a ∆lytA genetic background and that this protein restrains the activity of LytA. To explain this observation, they provide in vivo and in vitro evidence showing that WhyD specifically hydrolyses wall teichoic acids. A series of experiments is then achieved to demonstrate that WhyD regulates the activity of the cell wall hydrolase LytA to prevent cell lysis and that of other cell wall hydrolases to allow the insertion of new peptidoglycan in the preexisting polymer, promoting thus cell elongation. Notably, it is shown that WhyD localizes at mid-cell and together with FDAA labelling and the use of an inactive form of LytA as a WTA localization probe, it is further shown that WhyD co-localizes with nascent peptidoglycan, while WTA are more abundant in the peripheral cell wall, during cell elongation. By contrast, WTA localization changes in cells nearing cell constriction, co-localizing with WhyD and nascent peptidoglycan. It is concluded that WhyD modulates the presence of wall teichoic acids in the PG layer in the course of the cell cycle, modulating in time and space their availability for choline-binding hydrolases in order to prevent aberrant cell lysis, proper cell elongation and final separation. The methodology used is logical, appropriate and well-executed. The data are clear and the interpretations are reasonable. This work represents a nice contribution to our understanding of the regulation of cell wall hydrolysis in bacterial growth and morphogenesis.

Reviewer #1 (Public Review):

1. In terms of the prior hypothesis here I think the authors justify a prior with respect to striatum and I think the most principled analysis of their hypothesis would be based on volumes of interest in striatum. Figure 1 does show difference in MTsat in striatum between neurotypicals and DLDs but the changes are all in the caudate I think- I cannot see anything in putamen. The authors actually describe changes in only one part of anterior caudate. The authors do describe a number of previous conflicting studies that examine caudate structural changes but that is not their hypothesis. The discussion goes into developmental changes affecting striatum at different times that might be relevant and would require a longitudinal study for a definitive study - as the authors acknowledge.<br /> 2. There is a lot of overlap between the caudate signal in the two groups - although the correlation of individual differences is reasonable. The caudate signal would not allow group classification.<br /> 3. Outside of the caudate they do show changes in left IFG and auditory cortex that are hypothesised. But there is a lot else going on - I was struck by occipital changes in figure 1 which are only mentioned once in the manuscript.<br /> 4. Should I be concerned by i) apparent signal changes in right anterior lateral ventricle from group comparison in figure 1 ii) signal change correlation in right anterior lateral ventricle in figure 4 (slice 22) and iii) signal change outside the pial surface of the occipital lobe in figure 1?

Reviewer #2 (Public Review):

This work demonstrates the value that multiparameter mapping imaging protocols can have in uncovering microstructural neural differences in populations with atypical development. Previous studies looking at differences in brain structure have typically used voxel based morphometry (VBM) approaches where differences in volumes can be hard to interpret due to complex tissue compositions. The imaging protocol outlined in this paper can specifically index different tissue properties e.g. myelin, giving a much more sensitive and interpretable measure of structural brain differences. This paper applies this methodology to a population of adolescents with developmental language disorder (DLD). Previous evidence of structural brain differences in DLD is very inconsistent and, indeed, using traditional VBM the authors do not find a difference between children with DLD and those with typical language development. However, they provide convincing evidence that despite no macrostructural differences, children with DLD show clear differences in levels of myelin in the dorsal striatum and in brain regions in the wider speech and language network. This can help to reconcile previous inconsistent findings and provide a useful springboard for both theoretical and empirical work uncovering the nature of the brain bases of language disorders.

Strengths:

The imaging protocol is robust and is explained very clearly by the authors. It has been used before in other populations so is an established method but has not been applied to populations of children with DLD before, yielding novel and very interesting results. The authors demonstrate that this is a methodology which could have great value in other populations that display atypical development, increasing the impact of these findings.

The sample size is large for research in this area which increases confidence in the results and the conclusions.

Rather than relying solely on group differences in brain microstructure to draw conclusions about neural bases of language development, the authors correlated brain microstructural measures with performance on standardised language tests, allowing stronger inferences to be drawn about the relationships between structure and function. This is often an important omission from developmental neuroimaging work. It gave increased confidence in the finding that alterations in striatal myelin are linked to language difficulties.

Weaknesses:

The authors rightly use the CATALISE definition of developmental language disorder, which differs from much of the previous literature by not requiring that children with language difficulties have nonverbal ability that is in the normal range. As can be common when using this definition of DLD, the group with DLD have significantly weaker nonverbal ability than the typically developing group. The authors show that brain microstructural differences correlate with language ability but they don't rule out a correlation with nonverbal or wider cognitive skills. Given the widespread differences in myelination across areas of the brain, including those that weren't predicted e.g. medial temporal lobe, it is plausible that perhaps some of the brain microstructural differences are not linked directly to language impairment but a broader constellation of difficulties. Some of the arguments in the paper would be strengthened if this interpretation could be ruled out.

The authors acknowledge in the limitations section that their data cannot speak to whether brain differences are a cause or consequence of language impairment. However, there are some implied assumptions throughout the discussion of the results that brain differences in myelination have functional consequences for language learning. A correlation between structure and function does not indicate this level of causality, particularly in an adolescent population - function could just as easily have had structural consequences or environmental differences could have influenced both structure and function. In my view, the speculations about functional consequences of myelin differences are not fully supported by the data collected.

The data suggest that there is much greater variability in left caudate nucleus MTsat values for the DLD group than the other two groups. The impact this may have on the results is not discussed in the interpretation and it is unclear whether this greater variability occurs throughout all of the key MPM measures for the DLD group.

Reviewer #3 (Public Review):

Developmental Language Disorder (DLD) is observed in children who struggle to learn and use oral language despite no obvious cause. It is extremely wide-spread affecting 7-10% of children, and extremely consequential as it persists throughout life and has downstream effects on reading, academic outcomes, and career success. A large number of prior studies have attempted to identify the structural neural differences that are associated with DLD. These have generally shown mixed results, but support a number of candidate regions including left hemisphere language areas (particularly the inferior frontal gyrus), and striatal regions that are possibly linked to learning. However, these studies have suffered from small sample sizes and conflicting results. Part of this may be their reliance on traditional voxel-based-morphometric techniques which estimate cortical thickness and gray matter density. The authors argue that these measures are biologically imprecise; gray matter can be thinner for example, due to synaptic pruning or increased mylenation.

The authors of this study offer a powerful new tool for understanding these differences. Multi-Parameter Mapping (MPM) is based on standard MRI techniques but offers several measures with much greater biological precision that can be tied specifically to myelination, a key marker of efficient neural transmission. The test a very large number of children (>150) with and without DLD using MPM and show strong evidence for fundamental biological differences in these children.

This study features a number of key strengths. First, at the level of neuro-imaging, the MPM technique is new in this population and offers fundamental insight that cannot be obtained by other measures. Indeed, the authors wisely use a traditional gray matter approach (voxel based morphometry) and find few if any differences between children with DLD and typical development. This offers a powerful proof of the sensitivity of this approach. Moreover, the authors analyze their data comprehensively, looking at two measures of myelin (MTsat and R1) and their convergence.

However, at the most important level, I think structural approaches (like MPM, diffusion weighted imaging and so forth) offer tremendous promise for dealing with this as they avoid the ambiguity associated with interpreting functional MRI. Are children showing reduced BOLD because they are less good at language processing? Or do the differences in brain function cause poorer language processing? Structural approaches - and MPM in particular - offer tremendous promise as they unambiguously assess the fundamental neuro-biology.

Beyond the neuro-imaging this study is also strong in their sample and the measurements of language. The sample size is very large and an order of magnitude larger than existing studies. It is well characterized, and the authors use a large set of well-motivated measures that capture the relevant dimensionality of language. Moreover, the authors treat language both as a clinical category and a continuous measure which is consistent with current thinking on the nature of DLD as potentially the low end of a continuous scale rather than a discrete disorder.

Finally, the discussion of this paper for the most part does a good job of fitting these neurobiological findings into our broader understanding of DLD. It does an excellent job of mapping the observed brain differences onto functional differences in the child. Importantly, in doing this it does a very good job of avoiding the common trope of assuming neural differences play a causal role in DLD (when in fact, reduced atypical development could cause neural differences).

Despite these strengths, I have a number of substantive concerns that if addressed will improve the overall impact of this paper.

First, as the authors are aware, trhere is a long running and active debate in DLD as to whether DLD is the tail end of continuous distribution of children or a unique disorder (Leonard, 1987, 1991; Tomblin, 2011; Tomblin & Zhang, 1999). The results here offer great promise for informing that debate. And in that vein the authors quite appropriately analyze their data in two ways: once using DLD as a categorical variable and once using continuous measures of language. However, they don't really attempt to wrestle with the differences between the model.

Second, I was a little surprised to see the authors highlight left IFG in the discussion to the degree they did. While there was clear evidence for reduced myelin there in the MTsat analysis, this did not hold up in R1 analysis, and even in the MTsat, IFG was clearly not the primary locus. Rather the areas of differences seemed to be centered at Pre- and Post-Central gyrus and extending ventrally (to IFG) and posteriorly from there. Given debate on the role of IFG in language specific processing in general (Diachek, Blank, Siegelman, Affourtit, & Fedorenko, 2020; Fedorenko, Duncan, & Kanwisher, 2013), it was not immediately clear to me why that area was important to highlight. For example, some of the posterior temporal areas (and motor areas) that were found were equally important for perceptual, lexical and phonological processing that are important for other theories of DLD.

The authors rightly point to their differences in the striatum as supporting theories of DLD centered around differences learning. However, as they discuss, there are also large differences throughout the brain in both perceptual, motor and language areas. These would seem to support theories of DLD centered around processing and representation. In particular, the differences in myelination likely are linked to differences in the efficiency of neural coding. This would seem to favor two theoretical views that might be worth mentioning - speed of processing (Miller, Kail, Leonard, & Tomblin, 2001), and approaches based on lexical processing (McMurray, Klein-Packard, & Tomblin, 2019; McMurray, Samelson, Lee, & Tomblin, 2010; Nation, 2014). I was surprised these were not mentioned, given the clear link to the timecourse of processing. Does then suggest that these theories might complement each other? It would be useful to see some more discussion of the implications of these findings for broader theories.

Reviewer #1 (Public Review):

In this work the authors study biofilm growth and wrinkling in a controlled microfluidic setup. They argue that previous work involving such growth on agar substrates are complicated by the nature of transport within the agar and that the present arrangement simplifies the approach. Growth-induced wrinkling is a commonly observed phenomenon, and it stands to reason that the biofilm problem may be amenable to the kinds of theoretical approaches already developed.

Using confocal imaging to study the folding and wrinkling of confined, growing biofilms, the authors find a number of very interesting results, including insights into the role of compressive forces and of adhesion with the substrate in the instability. Their imaging also reveals the generation of fluid-filled channels within the biofilm complex that contain motile bacteria.

Existing theory from many years ago on growth-induced buckling and delamination (from the metallurgical literature) is used to rationalize the experimental observations, particularly with regard to the role of adhesion.

Overall this work provides an impressive array of results that provide real insight into the basic problem of biofilm wrinkling. Yet, a quantitative connection between the experimental observations and theories of wrinkling is absent. Although there is a discussion of the prior theory, it is not presented in a manner that significantly enhances the discussion, as there are parameters undefined/unexplained (toughness, for example) and no attempt is made to connect the observed wavelength with that arising from theory.

Reviewer #2 (Public Review):

In this paper, the authors study the buckling instability and wrinkling of a Pseudomonas aeruginosa biofilm growing on a PDMS substrate inside a rectangular microfluidic channel in the presence of fluid flow. Overall, this paper addresses a topic of growing interest - the mechanical aspects of biofilm growth - and some of its results are clearly novel, namely: (i) the control of biofilm wrinkling and the subsequent formation of a network of channels by means of the flow of nutrients and the substrate surface properties, (ii) disentangling the main driving mechanisms for wrinkling and their interplay, and (iii) the presence of swimming bacteria inside channels formed within wrinkles, whose swimming speed remains unaffected by the external flow outside the biofilm. Nonetheless, the paper contains a few shortcomings, in particular regarding the underdeveloped connection to theory and a lack of detail regarding quantitative measurements, that need to be addressed.

Reviewer #1 (Public Review):

This manuscript presents predictions of COVID-19 cases, hospitalizations, and deaths over July-December 2021 in the US using data up to July 2021, and combining the predictions from nine different models. The predicted resurgence in cases during late summer has been largely borne out by the data that has come out since. The results have some important implications for public health policy, as discussed below.

Unsurprisingly, the models find that outcomes over the next six months are highly dependent on prior and future vaccination coverage by state, reinforcing the importance of increasing vaccination coverage. The authors also theorize some of the variation in model predictions may be due to non-pharmaceutical intervention adherence, however they do not explore this relationship in this paper. It would have been interesting to see if there was also a correlation between the predicted number of cases per state and non-pharmaceutical intervention adherence across states in July, but this information is possibly more difficult to acquire.<br /> The combination of predictions from different models is a strength, as it allows to assess future uncertainty related to different model structures and parameter assumptions. However, it is disappointing that this paper does not capitalize on this strength by also showing a comparison of individual model predictions, other than by showing the prediction intervals. The authors mentioned the models varied both in the magnitude of COVID-19 incidence, but also in the timing of the peak of infection. It would have been interesting to see a figure showing a cross-model comparison of predictions for a given scenario (the most suitable scenario given the data would have likely been the worst-case low vaccination-high variant transmissibility scenario). This would have allowed a better understanding of uncertainty related to different model assumptions, and would have allowed the reader to assess whether the median captures well the central tendency across models.

An interesting feature of this paper is the comparison of model predictions against prospective data which was not used to fit the model. This comparison shows that reported cases up to July 31 exceeded those projected in all scenarios. This discrepancy should not be interpreted as indicating the models' predictions are invalid, but rather as a healthy and important exercise in reassessing our assumptions regarding the spread of SARS-CoV-2. It suggests that current SARS-CoV-2 variants in the US might for example be more transmissible than what was assumed in the models, or that adherence to non-pharmaceutical interventions is lower than anticipated. It would have been useful if the authors could have further explored which models with which assumptions were most closely able to match the earlier surge in cases. Nonetheless, even though the models may have underestimated the speed at which the cases would start to increase in July, there is a very strong correlation between the observed and predicted number of cases by state during July. This suggests that though the absolute number of COVID-19 cases and deaths the models predict over the next 6 months could be be an underestimate, the models do capture the major drivers of epidemic surges and are able to predict with good accuracy which states are likely to be most impacted by the pandemic over the next few months.

Reviewer #2 (Public Review):

The paper is well written, and data and codes have been made publicly available.

The Delta wave in the US has been descending since early September. So it looks like two scenarios (i.e., high/low vaccination + high variant transmissibility) in Figure 1 predicted the timing of the decline quite well.

Reviewer #3 (Public Review):

This paper reports on rounds 6 and 7 of the COVID-19 scenario modelling hub projections for the US COVID-19 epidemic. The specific focus of this round is the impact of the more transmissible Delta variant during the second half of 2021, for a range of vaccine uptake and virus transmissibility assumptions.

The ensemble models show logical relative differences in the likely magnitude of impacts of COVID over the period of interest given the various paired assumptions. The uncertainty in these estimates is very high and observed data following the period of projection up the time of submission are tracking along the 95% prediction interval.

Looking at what's happened since, my sense is that the models assuming high transmissibility have captured the time course reasonably well but vastly underestimated magnitude. Given all of the very reasonably acknowledged uncertainties, it's not surprising that projections beyond a few weeks are rarely informative, but the conclusions regarding the suitability of this approach to inform response should be tempered. Forecasts beyond more than a few weeks are unlikely to be sufficiently valid for public health and clinical preparedness, which these findings clearly show.

The ensemble modelling exercise is very useful, but there's a lot of information lost in merging the outputs of models that make different critical assumptions about VE against infectiousness and age distribution of uptake, which were allowed to vary between models, with other assumptions. I would be very interested to hear more about the variation in outputs that went into the ensemble and which aggregate assumptions have resulted in trajectories that have been more accurately predictive of the observed epidemic. This process is important for validation and improvement of future rounds.

At the level of states there is clear heterogeneity in coverage and mandated or spontaneous behaviours that drive variation in outcomes. The models seem to capture those rankings well, even if the absolute values prove to be underestimates. It would be interesting to understand how heterogeneity below the level of states contributes to the overestimation of vaccine impact and whether this is also more marked in some states than others.

There's no mention of waning immunity but given the time at which the US rollout commenced, one anticipates declining effectiveness against transmission in this observation window for early uptake cohorts.

Reviewer #1 (Public Review):

In this manuscript from the Crump laboratory, the authors use zebrafish histological staining, lineage tracing, enhancer transgenes, and mutants to find evidence for the gill origin of vertebrate jaws hypothesis. The data are mostly strong and convincing. I have only a few comments on some of the data and some suggestions for strengthening the discussion.

• This study nicely integrates into the literature on the origins of the mandibular jaw including discussion of fossil evidence and work in chondrichthyans. However, there is no discussion of jawless fish and how the considerable literature from species such as lamprey relates to the findings presented here. Do agnathans develop a pseudobranch?

• Are there gill-like structures derived from the second arch in teleosts? The authors mention the hemibranch derived from the hyoid arch in cartilaginous fishes. Is there a similar structure in any teleost? If there are no arch 2 gill-like structures, how this fits into the gill origin of jaw hypothesis should be discussed.

• The first sentence of the discussion is not fully supported by the data. More species need to be included to make this claim. This statement would need to be tempered.

• I like that the authors mention the alternative hypothesis in their discussion that "the pseudobranch arose independently from the jaw by co-option of a gill filament developmental program".

• More care needs to be taken with the word homology and to make the distinction between serial homology vs historical or functional homology. This manuscript would be stronger if it were to include a definition of these terms (or at least serial homology because that's the argument here). To avoid confusion, the authors should also specifically state serial homology when that's what the authors mean, for example in the second sentence of the discussion.

Reviewer #2 (Public Review):

Thiruppathy and colleagues investigate pseudobranch development in a teleost fish, the zebrafish. The pseudobranch has long been hypothesized to be a vestigial mandibular arch gill. The classic model of jaw evolution in vertebrates posits that the gnathostome jaw evolved from modifications of an anterior gill-bearing segment. Here the authors were trying to test the hypotheses that (1) the pseudobranch is derived from the mandibular arch, and (2) that the pseudobranch is a segmental homolog of the gills.

One major strength of the methods and results is that they strongly support both hypotheses. The authors use cutting-edge fate mapping methods involving photoconversion of transgene-driven fluorophores as well as Cre-mediated lineage tracing to fate map mandibular arch mesenchyme and epithelium. By fate mapping both neural crest-derived mesenchyme (by photoconverting sox10: kikGR, as well as labeling crest with a sox10:Cre excision of a stop cassette) and pharyngeal endoderm (by photoconverting fgf10b:nEOS) in the mandibular segment, the authors test for mandibular origins of two different pseudobranch progenitors (mesenchyme and epithelium). Another strength is including the enhancer activity of three different enhancers as a character to assess the serial homology of the pseudobranch and gills. A third strength is using gene function to test for serial homology by assessing pseudobranch and gill development in gata3 mutants.

One weakness is that the resolution of the photoconverted kikGR cells within the pseudobranch is low and visualizing individual cells is difficult in Figure 1e. However, the resolution of labeled crest cells in Fig. S1A is higher, and convincingly shows labeled neural crest cells in the center of the forming pseudobranch. A second weakness is that the fgf10b:nEOS expression, although strongest in the endodermal pouches, appears to also be more lowly expressed in non-endodermal cells.

Overall, the authors have achieved their goals and their conclusions of a first arch origin of the pseudobranch and likely serial homology between the pseudobranch and gills are both well supported.

The likely impact of this work, together with the cited 2022 bioRxiv paper (in which Hirschberger and Gillis show convincingly that the pseudobranch in a chondrichthyan is derived in part from mandibular arch pharyngeal endoderm and expresses similar gene expression patterns as gills during development) is high. Together the papers argue strongly that the pseudobranch is a mandibular arch-derived segmental homolog of a gill. These are exactly the kinds of data needed from extant gnathostomes to critically test the classic theory of a gill arch origin of the gnathostome jaw. As chondrichthyans are derived relative to the gnathostome stem ancestor, one possibility could be that the condition in chondrichthyans is derived and does not reflect the state of the gnathostome ancestor. This paper, in adding teleosts, argues against that scenario, as the more parsimonious explanation is that derived teleosts and chondrichthyans inherited a mandibular arch serial homolog of a gill from their common ancestor. This paper will likely have an impact on the field of developmental biology in that the fate mapping methods used are generally applicable to a range of other lineage questions. But the likely biggest impact will be on the field of vertebrate evolutionary biology, as together these two papers will be landmark studies arguing for a gill arch origin of the jaw, and strong motivations for paleontologists to look harder for fossils that can be evaluated for having a gill in the mandibular segment.

Reviewer #1 (Public Review):

The authors use smFISH to study the activation dynamics of the ventrally-expressed T48 gene. They observe that different concentrations of Dorsal (in a gradient with peak levels in the ventral-most nuclei) lead to 1) different probabilities of "priming" and thus timing of activation, and 2) RNA polymerase loading. Together these define a dual mechanism of T48 mRNA accumulation in a graded manner. The experiments are elegant and the data well presented. The issue I have is that the idea of graded activation by Dorsal is not new. It is known that Dl and Twi regulate T48 (as hypothesized on lines 204 and 214), and that the T48 enhancer contains both TF binding sites, as this was published by the Lim lab in PNAS - Keller et al, 2020. This paper reported on the dynamics of transcriptional activation of T48, using live imaging to examine the effects of Dorsal (by removing binding sites) on transcriptional activation timing and transcriptional output (similar to priming and Pol II loading rate in this study). The Keller et al. paper concluded that T48 "exhibits a dynamic expression pattern, where nuclei along the ventral midline are the first to begin transcription, with more lateral nuclei becoming active as NC14 progresses." It further showed that "Dl site modulations change spatial boundaries of t48, mostly by affecting the timing of activation and bursting frequency rather than transcriptional amplitude or bursting duration." Thus, the Keller et al. paper came to basically the same conclusion about the effects of Dl on T48 transcription. Even the experiment utilizing constitutively-active Dl had a similar effect on T48 as the Lim experiment optimizing Dl binding sites in the T48 enhancer.

Reviewer #2 (Public Review):

The manuscript by Carmon et al investigates the mechanisms underlying the graded response to a morphogen gradient. Specifically they monitor the transcriptional response of two Dorsal targets, T48 and mist during mesoderm formation in Drosophila early embryos. For this, they employ single molecule FISH (smFISH) on wild type and ventralized embryos at various timing of mesoderm induction.<br /> The major finding of this manuscript is that the gradual response of T48/mist relies on two complementary mechanisms:<br /> 1) the priming of these genes by Dorsal<br /> 2) the loading rate of Pol II, also dependent on Dorsal nuclear levels.

The data are of high quality and represent solid quantitative information.

A weakness is that I find is the lack of functional analysis to validate some of the conclusions made from the quantitative analysis of the transcriptional response.<br /> For example, it would be exciting to assess the functional consequences of an intronless T48 gene by CRISPR gene editing. Phenotypes in terms of timing/coordination in the constriction of the actomyosin network would greatly improve the biological significance of the transcriptional findings of this manuscript.

The overall conclusions of this paper are interesting and supported by their data.

Specific comments:

1-The word 'priming' is used in various instances (text and title of figures) but might be an over-interpretation. The results show the timing of transcriptional activation in various domains. A gradual activation could be interpreted as a gradual priming by a TF, but this is an interpretation.<br /> In particular, while the pioneering action of Zelda has been demonstrated and provides its 'priming' capacity, a similar scenario has not yet been shown for Dorsal. Thus, priming should be discussed with respect to a coordinated action between Dorsal and Zelda. Alternatively, if the authors believe that it's an action solely based on Dorsal, then this should be demonstrated (e.g. RNAi Zelda) or at least discussed.

By the way, manipulation of Dorsal binding sites or Zelda binding site at T48 mesodermal enhancer has already been performed (albeit in a transgenic context). The authors should discuss and cite this important work: Keller et al 2021, DOI: 10.1073/pnas.1917040117 .

The differential response to the Dorsal gradient shown for twi (switch like) compared to T48 (gradual) is an interesting paradigm. It would have been interesting to discuss the potential mechanisms supporting this differential response. For example, the contribution of Dorsal binding sites types and arrangement (grammar) and that of pioneering activity of Zelda. Many papers determined the contribution of Zelda binding to fostering the response to Dorsal gradient (with static approaches Crocker et al, 2017; Foo et al, Sun et al., or with live imaging Yamada et al. 2019, Dufourt et al. 2018). It would be interesting to discuss these results.

2-It is well known that expression is extremely dynamic during the course of nc14. The authors usually show two images, and state that one is an earlier stage. I could not find how embryonic staging was performed, with the exception of the statement line 205 'monitoring embryos of different ages (as defined by the number of T48 TSs)'.<br /> A more quantitative assessment of embryonic timing by examining the level of membrane invagination for the embryo treated by smFISH and quantification would be preferable.<br /> Moreover, it would be exciting to compare the quantifications of %TS activation from smFISH with those already published, from MS2-tagged lines (Lim et al. 2017).

Reviewer #3 (Public Review):

This is an interesting study addressing an overlooked feature of morphogen gradient interpretation. By studying transcriptional activation of early zygotic genes during early Drosophila development the authors' findings suggest that morphogens, which are known to pattern tissues in distinct domain of gene expression, can also induce a graded transcriptional response within these domains. They speculate this might help orchestrating the spatiotemporal organization of downstream morphogenetic movements. While the results presented convincingly demonstrate graded activation of target genes, the underlying mechanisms remain unclear and additional information are needed to link the expression of these genes to the activity of Dorsal. Furthermore, whether the graded transcription of the genes analysed is translated in corresponding gradients of protein activity and the impact on morphogenesis remain to be investigated.

Reviewer #1 (Public Review):

Xiong and colleagues use an elegant combination of theory development, simulations, and empirical population genomics to interrogate a largely unexplored phenomenon in speciation/ hybridization genomics: the consequences and implications of admixture between species with differing substitution rates. The work presented in this well-written manuscript is thorough, thought provoking, and represents an important advancement for the field. However, there are a few instances where I feel the strength of the conclusions drawn is not fully supported.

The authors begin by presenting evidence based on whole genome sequencing that the two focal species, P. syfanius and P. maackii, are highly diverged despite ongoing hybridization. Though the discussion of remarkable mitochondrial sequence similarity is underdeveloped. I do not understand how such a pattern is not most likely the result of introgression from one species to the other given the relatively high FST across much of the nuclear genome coupled with the generally higher mitochondrial mutation rate in animals.

Next, they posit that barrier loci are likely to exist. To support this assertion, the authors use a combination of parental population genetic diversity and divergence comparisons and ancestry pattern analysis in hybrid populations. They show that there is a strong correlation between divergence across pure species and within species diversity across the autosomes. Then using four hybrid individuals they show that low ancestry randomness, as quantified estimates of between group and within group entropy, is associated with genomic region of reduced within group diversity and elevated between group divergence. The use of entropy estimates as a stand-in for admixture proportions and ancestry block analysis when sample size is severely limited is particularly clever. Though I must admit, I do not fully understand the derivations of the two entropy measures, it seems to me that relatedness might have a strong effect on the interpretability of between individual entropy estimates (Sb). With very small population sizes this may be a real issue. A brief discussion of potential caveats in using the new method developed here seems warranted given its potential usefulness to the population genomics field more broadly. One plausible but less likely alternative interpretation of these patterns is briefly discussed.

The authors then move on to evidence for divergent substitution rates. Analysis of both D3 and D4 statistics using several different outgroups and a series of progressively stringent FST thresholds shows that site patterns between the two species are highly asymmetrical with P. maackii lineage harboring more substitutions than P. syfanius. The authors offer two possible explanations for this finding and then test both hypotheses.

First, they use a comparative tree-based method to show that there is little phylogenetic evidence for lineage biased hybridization from outgroups into either of the focal lineages. Further, the range overlaps of the study species do not correspond with the inferred direction of allele sharing from the Dstat analysis. This is a good argument against contemporary gene flow between the outgroups and P. syfanius, but I am not convinced that ancient gene flow that could have occurred when, say, species distributions may have been different, can be ruled out using this analysis.

To test whether this asymmetry can be explained by a difference in substitution rate between the two species the authors show that observed D3 increases and D4 decreases with increasingly divergent outgroups as predicted by theory developed here. The authors take this as evidence supporting the divergent substitution rates. Though they claim only that existence such rate divergence is likely. The unfortunately limited samples sizes seem to preclude attaining more certainty than this. Interestingly, as a byproduct of using D4 as an extended measure of site pattern asymmetry the authors highlight one way in which the ABBA-BABA test can give false positives for introgression. This is an important contribution to the field.

Finally, the authors observe a monotonic relationship substitution rate ratio and relative genetic divergence across the genome which is in line with their theoretical predictions for differential substitution rates in the face of gene flow. From this they infer an 80% increase in substitution rate from P. syfanius to P. maackii. It is remarkable to be able to extract these substitution rates from genomic regions with the least gene flow. However the veracity of these estimates relies on the assumptions I have highlighted above and should be presented with appropriate caution.

Reviewer #2 (Public Review):

In their manuscript ("Admixture of evolutionary rates across a hybrid zone"), Xiong et al. use whole genome resequencing data to assess rates of genome evolution between two species of butterflies and determine whether putative barrier loci between the species are also those that evolve at asymmetric rates between them. This work presents a novel hypothesis and rigorously tests these ideas using a combination of empirical and theoretical work. I think the authors could more formally link loci that are evolving at highly asymmetric rates with those that are most likely to be barrier loci by evaluating the relationship between ancestry entropy and ratios of substitution rates between species. Additionally, clarifying the relationship between barrier loci and asymmetric evolution would be beneficial (i.e. are loci that we typically envision to be barrier loci, such as loci involved in reproductive isolation, evolving at asymmetric rates or do asymmetrically evolving loci represent a new type of barrier loci?).

Reviewer #1 (Public Review):

In this paper, the authors address the underlying processes that lead to cell fate specification in the early mouse embryo. They explore the role of laminin-integrin signaling in specifying the fate of the inside cells to become the Inner Cell Mass and the subsequent separation of the epiblast and the primitive endoderm. They provide a nice description of the expression patterns of components of the integrin signaling pathway in early development, which adds to previously published work. Then using a combination of gain of function and loss of function experiments, they provide some evidence for integrin signaling playing a role in both lineage segregation events. Gain of function experiments involved culturing inside cells from morulae in the presence of Matrigel as a source of laminins, while loss of function experiments involved the use of blocking antibodies to integrinb1 and genetic studies with Itgb1-/- and Lamc1-/- embryos. They show that culture in Matrigel blocked the ability of inside cells to repolarize and form trophectoderm (TE) and that this was mediated by the integrin receptor. However, loss of function of both integrin and laminin had no effect on the formation of ICM and TE in the intact embryo, diminishing the impact of these results. The clearer results came from examining the formation and organization of the primitive endoderm where loss of function of both integrin and laminin led to a failure of the primitive endoderm to organize into a single epithelial layer.

Overall the claim that integrin signaling plays an active role in inducing ICM specification is not strongly supported. Rather, the role of Matrigel/integrin activation can be interpreted as blocking repolarization and thus blocking TE specification. They do not present any evidence of active induction of inside fate. Second, as they admit in the discussion, the culture of inside cells in Matrigel is an artificial situation, which may provide a non-physiological level of ECM/integrin activation. They might make a stronger case by testing directly the role of exogenous Laminin, rather than the complex Matrigel. Exogenous Laminin has been shown to rescue some phenotypic defects in laminin-null EBs (Li et al. 2002 J Cell Biol.). Given that embryos mutant for integrin or laminins (not shown) do not show any defects in ICM/TE specification at the blastocyst stage, the importance of integrin signaling for ICM/TE cell fate specification cannot be considered as a predominant influence on early patterning.

However both integrin and laminin mutant embryos do show defects during the next lineage differentiation event separating Epiblast (EPI) and primitive endoderm (PrE). The primitive endoderm and epiblast are formed and specified but PrE fails to form an organized epithelial layer overlying the EPI, suggestive of a role for the ECM in this process. This observation is interesting but not novel- similar defects in laminin mutant embryos and embryoid bodies have been previously reported. There is no new experimental insight into the actual mechanism of action of this ECM/integrin interaction.

Although this study provides some new information, the underlying mechanisms of action are not sufficiently explored and the study will be primarily of interest to specialists in mouse development.

Reviewer #2 (Public Review):

This manuscript from the Hiragii Lab identifies two new roles for Integrins and Laminins in the preimplantation mouse embryo.

One of these roles is to influence trophectoderm-regenerating ability following removal of native trophectoderm by immunosurgery. In this context, the authors show that integrin/laminin can repress Hippo signaling in isolated inner cell masses.

The other role reported for Integrin/Laminin is to ensure that primitive endoderm cells form a monolayer after sorting out from epiblast cells within the inner cell mass. Unfortunately, however, there does not appear to be a role for integrin/laminin in regulating Hippo signaling or trophectoderm fate specification during normal development.

The evidence for the two roles for integrin/laminin comes from immunofluorescence (evaluation of integrin/laminin and cell fate markers), loss of function studies (blocking antibodies and null alleles), and gain of function studies (where Matrigel contains laminins, among other things...).

Ultimately, this paper might interest preimplantation devotees, but will be less interesting to the broader scientific community in its current form. This is because the most compelling observations revolve around immunosurgery/regeneration, where the biological relevance is uncertain, while the embryo development phenotypes are minor and their analysis relatively superficial. If the study had revealed the mechanism by which integrin/laminin "talk to" the Hippo signaling pathway, it might have been more impactful.

Reviewer #3 (Public Review):

Kim at al., proposed a model in which the extracellular matrix, a fibrous environment, serves as a niche for inner cell mass specification in the preimplantation mouse embryo. The presence of extracellular matrix components within the preimplantation embryo was demonstrated a few decades ago. However, our current knowledge on their role during early mammalian develpoment still remains limited to processes during peri- and postimplantation embryogenesis.

To simulate the in vivo environment, the authors removed the surrounding outer cells of the embryo by enzymatic digestion, called immunosurgery, and embedded the uncovered inner cell mass into Matrigel. Matrigel-embedded inner cell mass from embryos undergoing the first cell fate decision maintained their inner fate, shown by Sox2-positive immunostaining. In contrast, inner cell mass soaked in culture media (KSOM) developed into a proper blastocyst embryo with Sox2-positive inner and oblong CDX2-positive outer cells, and a blastocoel. The authors then underpinned their hypothesis by adding blocking antibodies against integrin beta1 and alpha6 into Matrigel or using embryos from integrin beta1-knockout mice. The manuscript takes then a sudden and contradictory twist. The authors proceed to perform similar experiments with inner cell mass isolated from blastocyst embryos to investigate the extracellular matrix-dependent processes on the second cell fate decision. At this developmental stage, the cells showed no altered cell fates but instead spatial mis-positioning. The authors finished with an interesting signalling mechanism but supported solely by co-expression analysis of Talin, Laminin gamma1 and integrin beta1 using immunostainings.

Overall, the strength of the paper is diminished by describing too many different aspects, but none of them in sufficient detail.

1) Sox2 has been extensively described to be involved in ICM specification. While the authors claim it as "the earliest marker of ICM specification", they then use it to visualise final internalised cells, several hours after the onset of internalisation. Therefore, the authors used a previously published Sox2-GFP mouse line. When checking the original paper on the generation of this Sox2-GFP line, it becomes clear that this is a reporter line in which GFP is under the regulatory elements of the Sox2 gene but not a Sox2-GFP fusion gene. Thus, measuring Sox2 expression levels as stated by the authors is not correct and it is questionable if the cytoplasmic GFP levels exactly reflect the physiological nuclear Sox2 expression levels.

2) The presence of extracellular matrix components, including integrins and laminins, have been shown in previous studies as cited by the authors in the introduction. Thus, the novelty of result chapter 2 is unclear.

3) It is an interesting approach to remove the outer cells from the morula embryos, unknown however if 16- or 32-cell stage embryos, by enzymatic digestion, allowing to study and manipulate the "naked" inner cell mass. The authors show one inner cell mass in which all cells maintained their inner cell fate. However, due to the lack of control experiments, the author's statement that "During the first lineage segregation, Matrigel is sufficient to drive ICM specification in an integrin-dependent manner,..." is not adequately supported. It is stated several times throughout the manuscript that the "extracellular matrix induces/drives ICM specification", contradictory to the subsequent chapter headed "integrin b1 is not required for initial ICM specification...".

4) The strength of the paper is a more comprehensible effect on the spatial organisation of epiblast and primitive endoderm cells in the more advanced blastocyst. A more in-depth analysis on this phenomenon, including the signalling mechanism, would have helped to substantiate the author's claims. The sorting of epiblast and primitive endoderm cells is position-independent as stated by the authors. Thus, the title of the manuscript is not in accordance with this finding.

5) A major drawback is the presentation of all imaging results in 2D. The preimplantation mouse embryo is a 3D object. If presented in 2D there is no reliable presentation of cell number of the embryos shown, no reliable quantification of expression intensities as it depends if cells are cut in the centre or at the edges and cropped outer cells can be mistaken by inner cells.

The study needs to be put into context with more recent embryo-related publications. Currently over 80% of all cited references in introduction and discussion are >10 years old. For instance, previous studies demonstrating the necessity of Sox2 in epiblast formation (Avilion et al., 2003) and Sox2-DNA dynamics rather than expression levels (Goolam et al., 2016; White et al., 2016) were not taken into consideration. Major advances in the molecular mechanisms of inner cell allocation have been achieved, including Yap signalling, E-cadherin, cytoskeleton-dependent tension, but neglected by the authors.

Reviewer #1 (Public Review):

A) The authors state that there is widespread introgression. While there are quite a number of deep introgressions in the phylogeny, it is unclear what proportion of the genome is involved. Presumably, to be significant in these tests it's not a tiny fraction of the genome in any one introgression. However, without that information, the paper doesn't really live up to the claim of pervasive introgression. Obviously how we use the word pervasive is somewhat subjective, but getting rough estimates of the proportion of the genome affected would be helpful to readers. It seems like the authors might be able to turn the asymmetry in the discordant trees into an estimate of the proportion.

B) The red arrows depicting the upper bounds on the timing of the introgression seem potentially quite confusing to the reader. Nearly all of them fall between sister clades, which is not consistent with the tests performed. The authors discuss this in the text and provide a different view in the supplement. I understand the desire to try and make an upper bound of the number of introgression events. However, some of the arrows on the phylogenies suggest histories and timings of introgression quite different from that seen in the pairwise matrices. For example, D eugracilis shared gene flow with the ancestor of the clade that included D biarmipes (if I'm reading clade 4 correctly in B) but the arrow on the phylogeny shows the introgression into the ancestor of the entire large clade that included D eugracilis and D melanogaster. That interpretation does not seem supported by the data and leads the reader to a quite different conclusion than the data suggests.

Given the focus of the paper on time tree and introgression, it seems a shame that there are no numbers put on how diverged these hybridized lineages were. Could lower bounds of the divergence be constructed using the minimum branch length that separates the two (confidence intervals could also be constructed for these numbers). In part, these numbers are interesting because of the relatively rich information on pre and postzygotic barriers and Coyne and Orr style analyses for Drosophila. Obviously, there are caveats that would have to accompany such an analysis/figure, but it would do a lot to enhance the biological interpretation of the paper. It may also serve to assure the reader that the introgression is between lineages of low enough divergence that hybridization is not implausible. Obviously, this analysis may be difficult for introgression events that are ard to place on the tree, so perhaps restricting the analysis to well-defined events may still be informative.

The authors state: "It is also possible that some patterns we observe reflect scenarios where introgressed segments have persisted along some lineages but been purged along with others. This phenomenon could also cause older gene flow between sister lineages, which should generally be undetectable according to the BLT and DCT methods, to instead appear as introgression between non-sister lineages that our methods can detect. "<br /> --Is this a likely explanation? If a reasonably large number of trees support the introgression genome-wide then by chance (either drift, hitchhiking, or selection of a random allele) it is unlikely that a signal would be lost in one lineage. Now selection could remove a particular ancestry along a lineage, but here specific we would need the introgression to occur into an ancestral population persist for a (perhaps long) time and then suddenly be selected out in only one of the daughter populations. That's not completely implausible but it doesn't seem a very general concern.

Reviewer #2 (Public Review):

Suvorov and colleagues present a well-supported genome-scale phylogeny for 149 Drosophila species based on thousands of single-copy-orthologs. They then use several approaches to estimate the extent of introgression across the phylogeny, and report that it is common both recently and deeper in the past.

The main strength of this paper is that it uses a scale of sequencing that allows an assessment of genus-wide trends with reasonably good power. It also presents two new analysis approaches, but these represent fairly minor modifications of existing techniques to suit multiple gene alignments, and unfortunately their reliability is not evaluated in this paper. Nevertheless, the main finding that introgression is common appears to be well supported. This finding echoes those of similar recent studies on taxa such as cichlid fishes and Heliconius butterflies. The different approaches used, and different levels of sampling in these different studies do not allow for quantitative comparisons, leaving us with the somewhat vague conclusion that introgression is 'common' in all of these taxa. Perhaps most critically, the present paper does not delve any deeper into the evolutionary impacts of introgression, nor the factors at the species or genomic level that might determine its frequency. Below I describe some areas of concern in more detail.

1. Extent of introgression

Perhaps equally as interesting as the frequency of introgression per species across the phylogeny is the proportion of the genome of each species that is affected. Without such estimates, the full extent of introgression is difficult to assess.

2. Sampling effects

Since this paper is attempting to make an (admittedly crude) estimate of the extent of introgression in the entire genus, some discussion is needed to address the possible consequences of the fact that only around 10% of species in the genus are represented. For example, if sampling is very even, perhaps most ancient events would be detectable, but more recent events may tend to be missed simply because the species involved are not sampled.

3. Ancestral structure

The reasoning provided for dismissing the possible effect of ancestral population structure is unconvincing. First, the authors argue that it "seems less likely" that non-sister taxa would have bred more frequently in the ancestral population. However, this is the entire basis of the problem: it might be unlikely, but it can happen. Eriksson and Manica (2012 https://doi.org/10.1073/pnas.1200567109) provided a very reasonable scenario in which colonisation of a new region can lead to this pattern.

Second, the authors argue that QuIBL "should not be impacted by ancestral structure because this method searches for evidence of a mixture of coalescence times: one older time consistent with ILS and one time that is more recent than the split in the true species tree and that therefore cannot be explained by ancestral structure." This argument needs clarification. My understanding is that the split in the "true species tree" would also be inflated if there was ancestral structure.

My view is that ancestral structure leading to discordance between gene trees and species trees is itself an interesting phenomenon. In some ways, it is not conceptually distinct from introgression occurring soon "after" speciation if we consider ancestral structure as the beginning of a continuous speciation process, so I don't think it would weaken the paper to accept this as a possible contributing process.

4. Discordant count test

The statistical analysis in the DCT accounts for multiple testing of many triplets for introgression, but there is no mention of the fact that these triplets are non-independent. It is not clear to me whether this makes the correction used more or less conservative.

If there are any cases where the internal branch is long and the number of ILS gene trees is very small or zero, use of a chi-squared test may not be appropriate.

5. Branch length test

The authors acknowledge that the BLT is "conceptually similar" to that of Hahn and Hibbins 2019 https://doi.org/10.1093/molbev/msz178, but to me it seems that the only material difference is the statistical procedure for testing for an significant difference between branch lengths.

An important consideration that appears to have been ignored is whether selection can impact the distribution of branch lengths, especially since many of the the BUSCO genes used here will be under strong selective constraint.

6. Intra-locus recombination

The paper needs to address the possible impact of intra-locus recombination on all of the introgression tests. For the DCT, I imagine that counts would be biased toward the species tree topology if the inferred trees span multiple distinct genealogies (see for example simulations by Martin and Van Belleghem 2017 https://doi.org/10.1534/genetics.116.194720 Figure S7). This might reduce test sensitivity.

Similarly, for the BLT, I would expect that true introgression would be more difficult to detect in the presence of recombination. It is possible that the block jackknife procedure of Hahn and Hibbins (2019, https://doi.org/10.1093/molbev/msz178) may be more suitable than the comparison of distributions of point estimates for genes used here.

Reviewer #3 (Public Review):

The authors compiled a collection of published and newly sequenced genomes to assemble the largest collection of Drosophila genomes to date. Using this dataset they extracted a set of single copy orthologs to use for phylogenomic analyses, with a focus on estimating a time-calibrated phylogeny and introgression.

This new dataset is a valuable resource that will serve the broader community of Drosophila researchers opening many new avenues for future phylogenomics research. The workflow of focusing on BUSCO genes for all comparative analyses is simple in a good way -- it is easy to understand how the data were collected and it should be easily reproducible -- which makes it easy to read past the genomics details and focus on the analyses of these data.

However, I feel this is an important aspect of the paper that should receive more details, perhaps in the supplement. I may have missed it, but I could not find statistics about this ortholog data set. On average, how long is each locus, how many variable sites are there, how many taxa are missing data for any given locus due to paralogy? Do the BUSCO genes include both introns and exons? It is also unclear from the description exactly how the BUSCO genes were extracted from genomes. Are they extracted from the final assembled genomes, or do you perform variant calling after identifying them to call heterozygous site? If heterozygosity is excluded, how might this impact metrics such as the branch length tests, especially among close relatives? It likely impacts node age estimates as well?

The authors use this dataset to infer phylogenetic relationships among taxa using both ML concatenation (IQtree) and a two-step MSC approach (Astral) which yielded quite similar topologies, and they examined the impact of filtering loci with treeshrink, which had minimal impact. This new topology represents a substantial step forward for understanding the relationships among major Drosophila clades.

One of the main results of this study is a new set of node age estimates on the tree. For this they estimated branch lengths in mcmctree from a concatenated matrix of 1000 loci in the presence of fossil calibrations. The fossil calibration scheme selected as the best option includes three fossils, one dating the divergence at the split from mosquitos (uniform 195-230Ma) and two ingroup calibrations (U(43,64) and U(15,43)). To me, the credible intervals on node ages seem incredibly narrow. The authors mention this as an improvement compared to earlier studies, but they also mention later that the total amount of sequence data does not greatly impact node dating. So I'm a bit confused why the node ages are expected to be more accurate here. It seems to me that time calibrations should be most accurate when the greatest number of fossils are available, and when very appropriate Bayesian priors on set on the analysis. The effect of sequence variation is then relatively small. But here there are very few fossils, one of which is hugely distant, and so I would not expect highly precise age estimates. So I guess my question to the authors is, what do you think is going on here? Perhaps further description in the supplement of how the mcmctree method implemented here differs from traditional node dating done in a program like BEAST would help to clarify.

Considering that this paper aims to infer the new best time calibrated tree for the Drosophila community, I think that the current description of fossil calibration schemes, which primarily refers to other publication names in the supplement, is insufficient. Which fossils are used in those studies, are you using those fossils as calibrations here, or are you implementing secondary calibrations based on their phylogenetic results? The reader should not have to read every one of those papers to understand the basis of the calibrations in this paper.

Fig.1 shows nodal age posterior probabilities. Are these 95% confidence intervals? The taxon labels are too small in this figure, both on the large tree and especially in the inset figure. The legend refers to fossil taxon names used for calibrations, but because it is still unclear to me where the fossils are placed on the tree. Are the calibrations indicated somewhere in the figure?

The authors demonstrate evidence of introgression by showing mostly overlapping evidence from two different types of tests. Together, these tests show that most major clades contain significant imbalanced discordance in gene tree counts or branch lengths. The taxon labels in Figure 2 are unfortunately quite unreadable, especially the matrix labels, which makes it difficult to interpret.

I do not see a reason for presenting new names and acronyms for the introgression tests used in this study. The "DCT" is described as being similar to a suite of existing tests which are also based on comparison of rooted-triplet gene tree frequencies. These methods have been presented in many frameworks (BUCKy, D-stat, f4, etc.) and the only difference here seems to be the precise method used to determine significance. Similarly "the BLT is conceptually similar to the D3 test" could be replaced by just saying we implemented the D3 test which we refer to here as a 'branch length test (BLT)' to clarify that you have not in fact created a new test (e.g., you say "The first method we developed was the discordant-count test...")

I am not very satisfied with the estimates of the "upper bounds" of introgression used here. It seems that there could possibly be many ways in which admixture edges could be drawn on the tree to explain the matrix of significant test results, and it is better to let formal network inference methods (e.g., SNAQ, Phylonet) infer these edges rather than guess at their placement. The current approach of "placing introgression events between pairs of branches for which most descendant extant taxa show evidence of introgression" leaves significant room for subjectivity.

The authors did implement phylonet, but not very exhaustively. Why only fit a single edge on the tree instead of multiple? The authors state "networks with more reticulation events would most likely exhibit a better fit to observed patterns of introgression but the biological interpretation of complex networks with multiple reticulations is more challenging". I don't think this type of result is any more complicated to understand than the current approach used by the authors of drawing edges manually. And it is much less subjective. The authors say that it is computationally intractable, and this may be true for clades above ~15 tips, but testing on smaller trees by subsampling 10-12 tips seems feasible. From my experience network inference using pseudo-likelihood methods in SNAQ or phylonet takes a few minutes to fit 1 edge, and a few hours to fit 2-3 edges.

Currently the two major results of the paper seem disjointed. The authors infer a time-calibrated tree, and they infer introgression events, but there is not much connection between the two. I applaud the authors on one hand for being cautious in interpreting their "upper bounds" of introgression to say too much about when they think introgression has occurred in the context of the time-calibrated tree. I think there is insufficient confidence in the introgression timing estimates to do that. But, what about the inverse relationships? Does this extent of introgression across the tree impact your confidence in the estimated timing of divergence events? One expectation would be that it is biasing all of the divergence times to appear younger. See my suggestions for addressing this.

Overall, this study presents an impressive new dataset and important new results that greatly impact our understanding of the evolutionary history of Drosophila. Although the estimates of node ages and introgression events may be imperfect, they are clearly a step forward. It is clear from these results that introgression has occurred throughout the history of Drosophila, and this study paves the way for further investigation of these patterns, as the authors propose in their conclusions.

Reviewer #1 (Public Review):

Hollon et al. characterized dopamine release in mice performing optogenetic intracranial self-stimulation (opto-ICSS). In this paradigm, a lever press resulted in an optogenetic stimulation of dopamine neurons in the substantia nigra pars compacta (SNc). The authors monitored dopamine concentrations in the dorsomedial striatum using fast-scan cyclic voltammetry (FSCV). The authors show that dopamine release evoked by opto-ICSS is reduced when stimulation comes as a result of the animal's action, relative to non-contingent stimulation. This reduction is sensitive to the action sequence preceding the rewarded action, arguing for a role of dopamine prediction errors in the hierarchical control of behavior. The authors conclude that "these findings demonstrate that nigrostriatal dopamine signals sequence-specific prediction errors in action-outcome associations".

Overall, the experiments (e.g. yoked stimulation control) are generally well-designed and the results are very interesting, if not completely novel. The authors cite two old papers (Garris et al., 1999; Kilpatrick et al., 2000) that used electrical ICSS combined with FSCV to report the same main finding as this paper: that evoked dopamine is dramatically reduced when it comes as the (predictable) result of an action. More recent studies have used optogenetic stimulation in combination with FSCV (Owesson-White et al., 2016; Rodeberg et al., 2016) or dopamine sensor (Covey and Cheer, 2019) in the nucleus accumbens (NAc). Another important study, which is not cited in the manuscript, is Kremer et al. (2020) which recorded from opto-tagged dopamine neurons in the ventral tegmental area (VTA) during a self-paced, operant task (although a cue was used in the middle of a trial) and demonstrated aspects of reward prediction error coding. Although there are some technical differences (e.g. the use of yoked stimulation delivery schedule), these previous studies overlap with some of the experiments and conclusions of the present study.

Considering these previous studies, the novelty of the present study lies in two points. First, dopamine concentrations were monitored in the dorsomedial striatum, where dopamine signals were less characterized than in the well-studied NAc. Second, more importantly, the authors demonstrate that the expectation-dependent suppression is sensitive to the relative position within a sequence (Fig. 4I-P). This finding, along with the lack of suppression after the first lever press (Fig. 4A-H), suggests "sequence-level hierarchical control over instrumental behavior." The manuscript is generally well-written, and the data are presented clearly. Although the manuscript contains various interesting observations, there are many claims that are not directly supported by the presented data. These concerns need to be addressed before publication.

1. The title states that "Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors". The authors make similar conclusions multiple times in the manuscript. However, the current experiment involves artificial stimulation. This is particularly problematic as at least some previous studies have indicated that not all dopamine neurons are activated by reward (e.g. Howe and Dombeck, 2016; da Silva et al., 2018). Although optogenetic stimulation provides some technical advantages, it is unclear whether the authors' findings apply to more natural conditions. In order to show that nigrostriatal dopamine signals sequence-specific action-outcome prediction errors, it is essential to examine dopamine signals using a natural reward. Without such a demonstration, the conclusion should be revised to reflect what are directly demonstrated by the presented data.

2. Another related issue is the artificial nature of this stimulation. For instance, Coddington and Dudman (2018) showed that reward-matched stimulation (in their setup, 1 mW, 150 ms duration, 10 ms pulses at 30 Hz) can have very different effects than artificially large stimulation. The stimulation parameters used here (5 mW, 1000 ms duration, 10 ms pulses at 50 Hz) are expected to drive dopamine neurons outside the physiological range. To address this issue, the authors should carefully discuss the magnitude and the time course of dopamine response evoked by the stimulation based on the data. Ideally, the dopamine dynamics should be compared directly between those evoked optogenetically and those evoked by a natural reward. If such a data does not exist, the authors could use carefully-calibrated estimates of dopamine concentration between these conditions collected in separate experiments. In such a case, how dopamine concentrations are calibrated needs to be reported.

3. Line 103: "Thus, in this entirely within-subject design, we recorded at the same striatal location with the same temporal sequence of stimulations across both phases of the session, delivered to the same site within the SNc using identical optogenetic stimulation parameters to directly depolarize these nigrostriatal dopamine neurons." Although this statement is true, it is not guaranteed that the animal's location or general motivational states are controlled across the control and experimental conditions. Accordingly, or in addition, sensory experiences (inputs) can be very different.

4. One of the novel aspects of this study is monitoring dopamine concentrations in the dorsomedial striatum. However, interpretation of FSCV signals might not be very straightforward if the region contains noradrenergic or serotonergic inputs. How do the authors control for that? Although the use of optogenetic stimulation would help address this issue, it is not entirely obvious whether the observed signals contains only dopamine signals. More justifications of signal specificity would be very helpful, especially for those who are not familiar with the technique (FSCV).

5. In Figure 1F, cyclic voltammograms show very different patterns between the self-stimulation and passive playback conditions. Can the authors be sure that the signals represent dopamine?

6. Line 181. "Furthermore, because the sensory feedback from pressing the Active and Inactive levers is rather similar to the animals, these results confirmed that the dopamine inhibition results from the expectations associated with specific self-initiated, goal-directed action but not simply a conditioned sensory cue". This statement appears to be a little overstatement. First, the visual inputs at Active and Inactive levers are not identical because of the different geometric locations in the operant chamber. This experiment alone does not allow the authors to make the above claim (the later experiment testing the effect of action sequence directly addresses this issue). Furthermore, this experiment does not demonstrate the necessity of "self-initiated" nor "goal-directed". The above statement gives the impression that these two factors are important. These discussions should be made more carefully throughout the manuscript.

7. Related to the above issue, a movement or sequence of movements would inevitably induce proprioceptive and mechanical sensations, and the sequence thereof. Furthermore, pressing the left and right lever would be accompanied by different sensory experiences (the visual scenes are different, and perhaps the animal's posture, and the resulting proprioceptive signals, may be different too). Therefore, strictly speaking, whether the suppression of dopamine response was induced by "action" or by sensory inputs cannot be separated by the presented data. It appears that the data strongly support the importance of sequence (or history) but does not distinguish whether it is the sequence of actions or sensory experiences, or a combination of them. Considering these issues, the distinction between "cue-reward prediction errors" and "action-outcome prediction errors" appears to be difficult, at least from the presented data. It would be important to discuss this point more carefully.

Reviewer #2 (Public Review):

The study examines dopamine transients when a mouse is approaching a lever to optogentically self-stimulate SNc neurons and how sequences influence the nigrostriatal dopamine response to the consequence of these actions.

The claim is that a goal directed action partially reduces the optogenetically evoked DA transients.

The study then characterises the temporal dynamics, modulation by reward omission and during action sequences, but fall short of elucidating the underlying mechanism (a postsynaptic effect on stimulation efficacy is hinted) or establishing causalities with elements of the behavior.

Reviewer #3 (Public Review):

Hollon and colleagues record dopamine signaling in the dorsal striatum using fast scan voltammetry while mice engage in optogenetic self stimulation of nigrostriatal dopamine neurons. They find that optically evoked dopamine release following goal directed self-stimulation is smaller, relative to unpredicted optical stimulation, indicating that goal directed actions can contain reward prediction errors to track expected nigrostriatal dopamine signaling, independent of additional sensory processing typical of natural reward learning. They also show that this expected-mediated inhibition occurs when mice engage in a two-step sequence of actions to obtain optogenetic stimulation. I have a few comments about these results in the context of other recently published data, and some questions about the analysis.

The major take home from this paper is that optically evoked dopamine signals in the dorsomedial striatum diminish when they are under an animal's control. This expectation-based inhibition of dopamine is a signature of natural reward learning and it's really interesting that the same phenomenon occurs when dopamine neurons are artificially activated with optogenetics. My primary thought is that it is not clear what new information these studies demonstrate beyond some recently published findings. Covey and Cheer 2019 demonstrate a similar effect in the nucleus accumbens - optogenetically evoked dopamine diminishes as it become expected during ICSS. Older FSCV papers also demonstrated this goal-directed inhibition effect for electrical self stimulation - which, while different from opto ICSS, I think in this case it's effectively the same.

Here we see that also applies to the dorsomedial striatum, which is an important thing to demonstrate, especially in light of lots of recent data showing heterogeneous dopamine circuit encoding and function across striatal sub region. But it's unclear from the paper if there was an expectation that this process would be different in dorsal striatum? At the level of the cell body, at least, reward prediction error signatures are similar across dopamine neurons in the VTA and SNC. However, given that nigrostriatal and mesostriatal dopamine terminals have different characteristics (for example, dorsal striatum dopamine terminals have higher levels of DAT), different input connectivity, etc, you might expect heterogeneity. In light of that recent literature it is perhaps surprising therefore that a similar expected-mediated effect on optically-evoked dopamine holds in the dorsal striatum, but the current manuscript does not give insight into why that is.

Reviewer #1 (Public Review):

Dixon and colleagues aim to fill a major gap in our understanding of the epidemiology of human disease caused by Taenia solium (taeniasis and cysticercosis), a major food-borne zoonotic cestode. They use a rather heterogeneous dataset comprising "age-prevalence" data to fit catalytic models and infer a key epidemiological parameter, the force of infection of taeniasis and cysticercosis.

The authors are to be commended for exploring the scarce information regarding the prevalence of Taenia antigens and antibodies in different endemic settings. It remains unclear to me why were the much more numerous studies relying on fecal egg detection for diagnosing taeniasis not included in their analysis. One reason might be that their main focus is on T. solium infection and classic parasitological diagnosis cannot distinguish between T. solium and the even more common human pathogen, T. saginata - but the most common coproantigen detection method used worldwide (Allan et al., 1990) also fails to reliably distinguish between these two species.

The first clear limitation of the primary datasets analyzed for addressing the prevalence of taeniasis is that they combine infections with different species of Taenia - T. solium, T. saginata, and perhaps T. asiatica.

Second, they combine diagnostic data based on coproantigen and antibody detection for modeling the force of infection of taeniasis. These are data of a completely different nature. Although the authors use reverse catalytic models to account for "infection loss", they are coping with different biologic processes classified under "infection loss" - the slow decline in antibody responses vs. the sudden clearance of coproantigens following treatment or spontaneous worm elimination. In areas of high endemicity, people may be often reinfected ("infection acquisition") but antibody seroconversion rates will grossly underestimate reinfection rates if many individuals remain seropositive at the time they are reinfected.

The "human cysticercosis" component of the study also relies on antigen and antibody detection. The diagnostic methods are assumed to be both species-specific (i.e., they distinguish between T. solium and T. saginata) and, even more critically, to be stage-specific (i.e., they distinguish between antibodies elicited by exposure to T. solium cysticerci and those elicited by adult worms). This appears to be the case of the classic EITB assay, but it remains unclear whether the diagnostic method (López et al.) used in the large, nationwide Colombian dataset is sufficiently species- and stage-specific.

Finally, the brief description of the source studies overlooks basic information. Were study participants randomly sampled in each study site? What about sampling units - individuals or households? Are study sites representative of the countries?

Given the potential limitations that are inherent to the datasets analyzed, it remains uncertain whether the authors can provide "global force-of-infection trends" derived from a small number of studies with different diagnostic approaches - although they can surely describe productive ways of interrogating available data, point to their limitations and suggest standardized study designs that might generate better data for future pooled analyses.

Reviewer #2 (Public Review):

In this work the authors study human taeniasis and cysticercosis based on previously published data from South America, Africa and Asia. They use parsimonious catalytic models incorporating sensitivity and specificity of the diagnostics to estimate rates of infection and infection loss.

Strengths:

The work is based on multiple datasets extracted from 16 epidemiological studies from 3 continents. The authors account for diagnostic performance uncertainty (beta distribution priors were used to capture literature estimates) and consider parsimonious models to describe antigen and antibody age-prevalence profile of human taeniasis and human cysticercosis. Model parameters were estimated with Bayesian methods assuming uniform or weakly informative prior distributions.

Weakness:

Although parsimonious models are always desirable, they sometimes lack mechanisms and only provide an overview of systems. The authors considered simple and reversible catalytic models to describe datasets from distinct setting. By doing so, site specific drivers, heterogeneity in exposure and susceptibility or age-related immunity mechanisms were disregarded.

Reviewer #3 (Public Review):

In this manuscript, the authors systematically review the literature on the epidemiology of Taenia solium in humans to identify possible patterns in transmission dynamics across different regions. The authors' review extends on the work of a prior systematic review (Coral-Almeida et al 2014). The calculation of region-specific force-of-infections (FoIs) is new to this manuscript and answers an important gap in the field. The authors also calculate region-specific FoIs across different departments of Colombia, demonstrating the usefulness of their method in a more real-world setting.

The authors clearly describe their goals and results, and their claims are supported by their data. While there are limitations to these results, the authors appropriately acknowledge these and their potential impacts in the manuscript.

Reviewer #1 (Public Review):

The paper presents a Bayesian model framework for estimating individual perceptual uncertainty from continuous tracking data, taking into account motor variability, action cost, and possible misestimation of the generative dynamics. While the contribution is mostly technical, the analyses are well done and clearly explained. The paper provides therefore a didactic resource for students wishing to implement similar models on continuous action data.

First off, the paper is lucidly written - which made it a very pleasant read, especially compared to many other modeling papers, and the authors are to be congratulated for this. As such, the paper provides a valuable resource for didactic purposes alone. While the employed methods are not necessarily individually novel, the assembly of various parts into a coherent framework appears nonetheless valuable. I have two major concerns, though:

1. My main comment regards the model comparison using WAIC (Figure 4E) or cross-validation (Figure S4a): If we translate these numbers into Bayes factors, they are extraordinarily high. I assume that the p(x_i|\theta_s) in equation 7 are calculated assuming that the motor noise on u_{i,t} is independent? This would assume that motor processes act i.i.d with a timeframe of 60ms, which is probably not a very realistic assumption- given that much of the motor variability (as stated by the authors) comes likely from a central (i.e. planning) origin. Would the delta-WAIC not be much smaller if motor noise was assumed to be correlated across time points? Would this assumption change the \sigma estimates?<br /> 2. While the results in Figure 4a are interesting, the deviation of the \sigma estimates from the standard psychophysical estimates for the most difficult condition remains unexplained. What are the limits of this method in estimating perceptual acuity near the perceptual threshold? Is there a problem that subjects just "give up" and the motor cost becomes overwhelming? Would this not invalidate the method for threshold detection?

Reviewer #2 (Public Review):

This manuscript develops and describes a framework for the analysis of data from so-called continuous psychophysics experiments, a relatively recent approach that leverages continuous behavioral tracking in response to dynamic stimuli (e.g. targets following a position random walk). Continuous psychophysics has the potential to dramatically improve the pace of data collection without sacrificing the ability to accurately estimate parameters of psychophysical interest. The manuscript applies ideas from optimal control theory to enrich the analysis of such data. They develop a nested set of data-analytic models: Model 1: the Kalman filter (KF), Model 2: the optimal actor (which is a special case of a linear quadratic regulator appropriate for linear dynamics and Gaussian variability), Model 3: the bounded actor w. behavioral costs, and Model 4: the bounded actor w. behavioral costs and subjective beliefs. Each successive model incorporates parameters that the previous model did not. Each parameter is of potential importance in any serious attempt to human model visuomotor behavior. They advertise that their methods improve the accuracy the inferred values of certain parameters relative to previous methods. And they advertise that their methods enable the estimation of certain parameters that previous analyses did not.

What were the parameters? In this context, the Kalman filter model has one free parameter: perceptual uncertainty of target position (\sigma). The optimal actor (Model 2) incorporates perceptual uncertainty of cursor position (\sigma_p) and motor variability (\sigma_m), in addition to perceptual uncertainty of target position (\sigma) that is included in the Kalman filter (Model 1). The bounded actor with behavioral costs (Model 3) incorporates a control cost parameter (c) that penalizes effort ('movement energy'). And the bounded actor with behavioral costs and subjective beliefs (Model 4) further incorporates the human observer possibly mistaken 'beliefs' about target dynamics (i.e. how the human's internal model of target motion differs from the true generative model. Model allows for the true target dynamics (position-random-walk with drift = \sigma_rw) to be mistakenly believed to be governed by a position-random-walk with drift = \sigma_s plus a velocity-random-walk with drift = \sigma_v).

The authors develop each of these models, show on simulated data that true model parameters can be accurately inferred, and then analyze previously collected data from three papers that helped to introduce the continuous psychophysics approach (Bonnen et al. 2015, 2017 & Knoll et al. 2018). They report that, of the considered models, the most sophisticated model (Model 4) provides the best accounting of previously collected data. This model more faithfully approximates the cross-correlograms relating target and human tracking velocities than the Kalman filter model, and is favored by the widely applicable information criterion (WAIC).

The manuscript makes clear and timely contributions. Methods that are capable of accurately estimating the parameters described above from continuous psychophysics experiments have obvious value to the community. The manuscript tackles a difficult problem and seems to have made important progress.<br /> Some topics of central importance were not discussed with sufficient detail to satisfy an interested reader, so I believe that additional discussion and/or analyses are required. But the work appears to be well-executed and poised to make a nice contribution to the field.

The manuscript, however, was an uneven read. Parts of it were very nicely written, and clearly explained the issues of interest. Other parts seemed organized around debatable logic, making inappropriate comparisons to--and misleading characterizations of--previous work. Other parts still were weakened by poor editing, typos, and grammatical mistakes.

Overall, it is a nice piece of work. But the authors should provide substantially more discussion so that readers will develop a better intuition and how and why the inference routines enable accurate estimation, and how the values of certain parameters trade off with one another. Most especially, the authors should be very careful to accurately describe and appropriately use the previous literature.

Reviewer #3 (Public Review):

This paper represents a powerful extension of previous work on continuous or "tracking-based" psychophysics, an approach introduced by Bonnen et al 2015. Bonnen et al showed that one can infer an observer's psychophysical sensitivity far more rapidly using a tracking task than with traditional binary or 2AFC psychophysical experiments. Bonnen at al modeled an observer performing a tracking task with a Kalman filter, which assumes that the observer produces an optimal estimate of the stimulus in each time bin by combining knowledge of the task statistics with noisy measurements of a moving sensory stimulus. However, the quantitative estimates of an observer's perceptual sensitivity using the Kalman filter method were an order of magnitude larger than the sensitivity estimates obtained using traditional psychophysics experiments. Here, Straub & Rothkopf show that this mismatch can be overcome by building a more accurate model of the psychophysical observer, incorporating realistic aspects of sensory-motor behavior that are missing from the Kalman filter model, namely sensory uncertainty about the observer's cursor or hand position, motor noise, movement costs, and possible mismatch between the true dynamic stimulus statistics and the observer's assumptions about those statistics. The paper can therefore be viewed as a natural extension of Bonnen et al and other work on continuous psychophysics. However, it is an extremely powerful and important extension, which allows for a far more accurate description of observer behavior during a tracking task, and for more precise connection between sensitivity estimates obtained from continuous and traditional psychophysics. The paper is thus a theoretical tour de force, and I expect it to have a major impact on the field.

Reviewer #1 (Public Review):

Previous work on dystonin has shown that loss of function mutations in dystonin cause hereditary sensory and autonomic neuropathy type 6. In affected patients, there is dysfunction of cardiac and skeletal muscle. In patients, the mutations disrupt both dystonin a and b. The goal of the current study was to determine whether mutations specific to dystonin b are responsible for the muscle phenotype. The identification of the distinct roles of dystonin a and b in muscle versus nerve represents an advance.

A strength of the paper is the analysis of both cardiac and skeletal muscle.

A concern is that while identification that disruption of the function of dystonin b disrupts muscle is an advance, it is unclear how important an advance it is. The paper would be more exciting if it was definitively shown that disruption of dystonin b causes myopathy in humans.

Reviewer #2 (Public Review):

In general, the study has several novel comments, the experimental design is appropriate and the manuscript is well written. While the manuscript contains a lot of data, still it is a bit descriptive. There are also some issues, which should be addressed.

1) In Figure 1E, the authors demonstrate a small but significant decrease in body weight of mutant mice. The difference is not so drastic. They also mentioned that some mice showed kyphosis. Please provide data on what percentage of mutant mice showed kyphosis. Please also provide individual hind limb muscle weight normalized with body weight.

2) There is a lot of variability in the age of the mice employed for this study. For example, in Figure 3, the authors mentioned 23 months old mice (Fig. 3a) and over 20 months old and over 18 months old mice. What was the exact age of the mice? Why three different age mice were used for the same set of experiments? The authors should also comment on whether the onset of myopathy in skeletal and cardiac muscle occurs at the same or different age in mutant mice.

3) Authors have studied protein aggregation only in the soleus muscle of mutant mice. Do the same types of aggregates also form in cardiomyocytes? They write that desmin aggregates were observed in cardiomyocytes of mutant mice. Please show those results in a supplemental figure.

4) In Figure 5, the authors suggest that mutant mice have mitochondrial abnormalities. However, this analysis is quite abstract and inconclusive. Immunohistochemical images show higher levels of CytoC and Tom20 whereas QRT-PCR demonstrates a significant decrease in mRNA levels of some of the mitochondria-related molecules. Authors should perform additional experiments to determine whether there is any difference in mitochondrial content between WT and mutant mice. In addition, they should perform some functional assays (i.e. OCR, seahorse experiment etc.) to measure mitochondria oxidative phosphorylation capacity is affected in mutant mice.

5) The morphology of the mitochondria in TEM images shows features that are commonly observed during oxidative damage. Is there any evidence of oxidative stress in skeletal and cardiac muscle of mutant mice?

Reviewer #3 (Public Review):

This manuscript by Yoshioka et al. provides an extensive analysis of cardiac and skeletal muscle in a mouse model of Dst-b mutation. The authors have generated the mutant mouse model to selectively mutate Dst-b isoform of Dystonin and show that such a mutation leads to cardiomyopathy and late-onset myofibrillar myopathy. This is a novel discovery which adds valuable information to the genetic basis and molecular mechanism of MFM mediated by Dst-b. However, the manuscript needs substantial revision and additional feasible experiments.

In Figure3A, the authors suggest that there are smaller myofibers in the mutated mice however they do not provide enough data to support that. Cross-sectional areas between the mutant and WT have to be counted and represented as bins. This can better show the presence of smaller myofibers and muscle degeneration in the mutant mice.

In Figure 3A-B, the authors show that mutant mice have significantly more myofibers with centrally located myonuclei indicating the constant degeneration and regeneration in the mutant mice. Another indicator of this is the number of activated muscle stem cells. Under homeostasis, authors can compare the number of quiescent muscle stem cells and activated muscle stem cells. If there is constant degeneration and regeneration in the mutant muscle, there will be more cycling muscle stem cells and that will further prove such phenotype in question. Alternatively, they can use EdU water and quantify the number of EdU+/Pax7+ cells between the mutant and WT.

In figure 2F, the authors show behavioral tests on the mutant mice of age 1 year. They do not show any significant difference in muscle strength. However, most of the myopathic phenotypes they observe are at 23 months of age, these behavioral tests can be repeated at that age to see if there is more muscle weakness in the mutant mice compared to the WT. Also, are these behavioral test readouts affected by the cardiomyopathy independent of skeletal muscle strength?

They show in Figure 3B that the number of CNF's are affected to a different extent in different muscles. These muscles have a different composition of myofibers, one consisting mostly of slow-type fibers while the other is mostly of fast-type. The question of whether Dst-b mutation effect of muscle fiber types is not clear. Is there a difference?

The cardiac myopathy phenotype that is clearly shown in figure 3 is shown in mice of 16 months of age whereas the skeletal muscle myopathy phenotype is shown in 23-month-old mice. The reason for the choice of the age of the mice should be discussed. Does the cardiac phenotype precede the skeletal muscle phenotype? Have they looked at the skeletal muscle phenotype at earlier ages? If so, that data should be provided as well and discussed.

The authors clearly show the formation of protein aggregates in the myofibers in the mutant mice. They further characterize the composition of these desmin aggregates by determining their co aggregates such as plectin and ab-Crystallin. Another component of the z-disk that has been shown to be involved in the aggregates in MFM is myotilin. The authors should also show the presence/ absence and co-aggregation of this protein with the desmin aggregates present in the mutant mice.

The authors show abnormal accumulation of mitochondria through cyt c and Tom20 staining. The increased Tom20 levels in the mutant are shown in figure 5A which is from mice that are 23-month-old. However, in figure 3-figure supplement 1a they also show elevated Tom20 staining in the mutant mice that are only 1-2 months old. However, no other phenotype is observed at this age except for the disrupted mitochondria according to the data provided. This needs to be discussed and addressed.

In Figure 5, the authors show changes in gene expression levels of genes involved in oxidative phosphorylation which supports the disrupted mitochondrial function. Additionally, ROS levels could be compared between the WT and mutant mice.

In Figure 5 authors show disrupted oxidative phosphorylation in the mutant soleus muscle. Is this also associated with the fiber-type switch? Since mouse soleus muscle is a mix of fast and slow fiber types, they can look at differences in gene expression of key marker genes for slow and fast myofibers.

In figure 2, the authors show that mutant mice increase their body weight at a normal pace until 13 weeks of age after which the mutant mice become lighter than their WT counterparts. Is this suggestive of loss of muscle mass? If so, the authors show the muscle atrophy phenotype in 23-month-old mice with cross-sections. Does this mean muscle atrophy starts at an earlier age at 16 months in these mice? Please provide details on the age of the mice for each experiment. In addition, in the text authors phrase that the mutant mice become leaner. Lean usually means a decrease in fat mass and an increase in muscle mass. Is this the case? If so, there is no data to support that and the phenotype in the mutant mice suggests there is muscle atrophy in these mice. Therefore, it would not be appropriate to suggest that these mice get lean. However, it is interesting that the bodyweight of the mutant mice gets significantly lighter after 13 weeks. EchoMRI analysis can be performed between these mice to see the total body composition to determine if there is a change in the different type of fat, lean or water composition.

Authors have performed RNA-Seq for the left ventricle from the mutant and the WT mice. Separate clustering of the WT and the mutant has to be shown at least through a PCA plot. Some IGV tracks to show the expression level changes in key genes between the mutant and WT should be shown as well. In addition, they could show how some of the genes involved in autophagy and protein degradation are affected since these are mainly the mechanism by which there is protein aggregation in MFM's.

Reviewer #1 (Public Review): 

In this manuscript, the authors conduct research that is intended to overcome a major challenge of human genetic association studies - the movement from locus to causal polymorphism and causal gene. This is particularly challenging for polymorphisms that affect non-coding regions that affect transcription or RNA stability. Here the authors integrate genome-wide association study (GWAS) phenotype and eQTL data with two independent approaches, TWAS (which integrates gene and GWAS data in a single analysis) and eQTL-GWAS loci co-localization. They use publicly available data from a large UK Biobank GWAS study on bone mineral density (BMD) and transcriptomic data from 49 tissues prepared as part of GTex. The authors use state of the art methods for this analysis, use creative filters to reduce their candidate gene list (e.g. a novel curated list of "known" bone genes, comparison to data on the impact of gene deletion on bone from mice) then conduct a follow-up study for one candidate gene in genetically modified mice. The value of this study is that it clearly shows the value of the integrative approaches but also presents a realistic picture of the strengths, weaknesses, and barriers that influence the quality of candidate gene identification from human GWAS, especially for a phenotype with no eQTL data like bone. The work represents the current state of the art. As such, this work will be a valuable resource for both researchers interested in bone biology as well as those more generally interested in candidate gene selection from human GWAS.

Reviewer #2 (Public Review): 

The present study used summary statistics from the largest GWAS for bone mineral density, the strongest predictor for osteoporosis, and prioritized genes for further studies using Transcriptome-wide association studies and eQTL data available from 49 GTEx tissues. This resulted in the prioritization of 521 genes, from which they chose the strongest signal (PPP6R3R) to carry on functional studies in mice knockouts. Mice mutants showed reduced trabecular bone and volumetric BMD, providing support as causal gene BMD. 

This study is important because it is one of the first studies to take such a complex bioinformatics approach for gene prioritization and to exemplify how multifaceted are the association found in BMD loci. The gene selected for functional evaluation in mice is located next to LRP5 a known gene to play a role in osteoporosis, therefore suggesting that multiple genes are casual within this locus. One limitation is that bone is not enriched in GTEx data.

Reviewer #3 (Public Review): 

This well-written manuscript includes a carefully performed bioinformatic analysis, accompanied by an analysis of a candidate gene prioritized for its potential role in bone mineral density (BMD). Authors clearly indicated that although GWAS successfully identified many variants associated with clinically relevant traits, such as BMD, gene-level discoveries are limited without biological context. Therefore, this study nicely illustrates the application of existing bioinformatic strategies to improve our understanding of the genetics of BMD, specifically by using TWAS and eQTL colocalization using GTEx data to identify potentially causal BMD genes. Supplemental files, including a list of 'known bone genes', provide a valuable resource for the field.

Reviewer #1 (Public Review): 

Glioblastoma is a complex disease and understanding its molecular complexity would be of great potential utility. Here the authors identify a subgroup of glioblastoma with a distinct methylation profile. They then perform transcriptomic analysis to support an astrocytic phenotype. However, it is unclear how this fits into existing paradigms and how this signature relates to known mutational profiles in glioblastoma.

Reviewer #2 (Public Review): 

Boot et al. investigate epigenetic DNA methylation changes in matching glioma-initiating cells and neural progenitors from glioblastoma patients. The authors find that a subset of glioblastomas shows a bias towards hypomethylation. This hypomethylation-bias subset of tumours also shows enrichment in an astrocytic lineage gene expression signature. A deeper analysis of differentially methylated regions in this subset of tumours revealed enrichment of binding sites of astrocyte-specific transcription factors in the differentially methylated regions. Functionally, xenografts of hypomethylation-bias tumour lines are more invasive, and the authors identified SPRX2 as a new candidate regulator of invasion and sphere formation. The authors further identify RARRES2 as a candidate molecule correlated with immune infiltration. 

The strengths of this manuscript are the in-depth comparison of methylation signatures between glioblastoma cells and induced progenitor cells that will provide a useful resource, as well as the demonstration that an astrocyte lineage-specific signature identifies a subset of glioblastomas and implies functional relevance of developmental pathways in these tumours. 

Overall, this is a very interesting study, but some weaknesses remain. These weaknesses include a reliance on in vitro experiments and bioinformatical correlations for functional validation of candidates. Furthermore, it is not quite clear where the cutoff value lies to separate bias vs. non-bias tumours - presumably glioblastomas will lie on a spectrum ranging from hyper- to hypomethylation. 

The presented data should be more comprehensive to fully justify the conclusions. 

Some of the claims made in the manuscript should be tempered: for example, whether astrocyte signature-enriched glioblastoma points to a shared ontogeny with astroglial progenitors is speculative. Increased macrophage infiltration into bias/enriched glioblastoma is only supported by correlation evidence from RNA-sequencing data.

Reviewer #3 (Public Review): 

The authors have used 10/11 GICs along with matched fibroblasts from dura that were reprogrammed to EPSC and then differentiated to iNSC and subsequently into iAPC and iOPC. On (most) of these lines the authors have done methylation and gene expression analysis. They find some GIC samples with reduced methylation levels (3 or 4, depending on the cutoff), and the remainder of the manuscript is based on finding what makes these cells differ from the others. Methylation analysis is done by comparing GICs with syngeneic iNSCs and screening for enrichment of TF binding motifs in various comparisons. DEG analysis compares a variety of different conditions.

Reviewer #1 (Public Review): 

Overall this is a well-written manuscript that dissects the complex molecular mechanisms by which INPP5E is targeted to the primary cilium. INPP5E is a well-studied ciliary phosphoinositide phosphatase that is mutated in several ciliopathies such as Joubert Syndrome. Despite intense investigation, the precise mechanisms by which INPP5E is targeted to the primary cilium were so far unclear. By combining cell-based analysis of various INPP5E mutant constructs with biochemical assays and structure prediction, the authors convincingly show that ciliary targeting of INPP5E requires its folded catalytic domain and is controlled by four ciliary localization signals, two of which were not described previously. The experiments are generally well carried out and the claims are justified by the data. The manuscript would be strengthened by a quantitative analysis of some of the key data. Specifically: 

1) For all IFM figures comparing cilia localization of various EGFP-INPP5E mutants, the authors should provide information about how many times experiments were repeated, and how many cells/cilia were analyzed in each case. In Figures 2d and 3c, the authors do provide a quantitative analysis of cilia localization of selected mutants, but the number of cells examined is not stated in the legend, nor in the Methods section. Furthermore, it is unclear whether relative expression levels and/or stability of the different INPP5E mutant constructs in RPE1 cells are similar. This is important to clarify as it could influence their cilia localization. 

2) Figure 5g and line 368: based on the IP in Figure 5g the authors conclude that co-IP of ARL13B with INPP5E is "somewhat reduced" with deltaCLS2 and deltaCLS3. However, judging from Figure 5g it also seems that the IP of the latter two is less efficient than that of the other INPP5E mutants, which could explain the apparently reduced co-IP of ARL13B with deltaCLS2 and deltaCLS3. It would be desirable if authors could somehow quantify the IP results, e.g. by measuring band intensities and quantifying ARL13B levels in each IP pellet relative to the amount of EGFP-INPP5E levels in the same IP pellets. The same applies to Figures 6a, 6d and 7a where the amount of EGFP-INPP5E IPed seems to vary quite a bit between mutants. In particular, the band intensities for the deltaCLS2 and deltaCLS3 mutants seem quite low in the EGFP blots of these figures compared to the other mutants. 

3) Figure 6e: the specific bands corresponding to WT(1-1460) and NT(WW/mut) CEP164-EGFP are not clearly apparent in this figure. The former seems largely to be absent in the EGFP blot (?) and the latter shows several bands. It would be important to clarify. this point

Reviewer #2 (Public Review): 

Cilleros-Rodriguez & Martin-Morales et al. address questions surrounding the recruitment of the phosphoinositide 5-phosphatase INPP5E to primary cilia. Utilizing structural analyses, site-directed mutagenesis, co-localization and binding studies, the authors show that ciliary targeting of INPP5E requires the folded INPP5E catalytic domain in addition to four ciliary localization signals (CLSs), two of which were newly identified here. They further examine the roles these INPP5E CLSs play in cilia localization and INPP5E/cilia protein interactions, revealing a level of co-operativity and redundancy among the four signals. The ciliary phosphoinositide phosphatase INPP5E hydrolyzes ciliary transition zone phosphoinositides and is mutated in ciliopathies such as Joubert syndrome (JBTS). The authors additionally show that some INPP5E gene mutations associated with JBTS prevent INPP5E ciliary recruitment. Collectively, these data reveal an interesting degree of complexity in targeting INPP5E to cilia that requires a high degree of regulation. 

Strengths: 

The authors use an extensive and systematic approach to identify and test a substantial number of INPP5E mutants to define novel CLSs and reveal important residues required for INPP5E ciliary localization. 

The authors use a methodical approach using INPP5E CLS deletion mutants to examine INPP5E interaction with ciliary protein binding effectors to delineate possible molecular mechanisms governing INPP5E ciliary localization. 

Together, these data reveal valuable insight into the complex regulation of INPP5E recruitment to cilia, which will be important for developmental and cell biologists. 

Weaknesses: 

The data presented are mostly sound with appropriate controls; however, further data acquisition and analysis are required to support the conclusions made by the authors. 

INPP5E plays an essential role in cilia dynamics, regulating processes that include phosphoinositide and protein composition, cilia-dependent signaling pathways, ciliogenesis and cilia stability. Functional examination of the INPP5E CLSs would provide stronger evidence of their biological significance and provide a conceptual advance in the cilia field essential to development and disease. This is particularly important considering that INPP5E mutations associated with JBTS have variable effects on INPP5E cilia localization. 

Examination of INPP5E recruitment to cilia by immunofluorescence requires expansion considering the publication of a recent article in the field (doi:10.3389/fcell.2021.634649) that explores possible preparation artifacts affecting INPP5E cilia localization. 

Extended mechanistic examination is also required to support the authors' conclusions that they have identified the molecular mechanisms governing INPP5E ciliary targeting by each CLS. Immunoprecipitation studies alone are insufficient to support these claims. Alterations of immunoprecipitation are not necessarily indicative of alterations of binding without validation of protein folding and direct binding studies. Additional functional studies would increase confidence that these ciliary protein interactions are important in this context. 

Key questions that would provide a conceptual advance in the cilia field have not been addressed in this manuscript and would provide further evidence to support claims regarding the biological significance of the INPP5E CLSs. The manuscript would be improved with additional functional studies, for example, examination of the role the CLSs play in cilia protein or phosphoinositide composition, cilia-dependent signaling pathways, cilia assembly or disassembly. 

Indirect immunofluorescence analysis in fixed cells has been used extensively here to demonstrate the identification and characterization of cilia localization signals in INPP5E. A recent publication in this field has reported possible artifactual INPP5E localization issues dependent upon fixation conditions (doi:10.3389/fcell.2021.634649). Could the authors assess their data in the context of this new report? Could the authors repeat a part of their principal localization experiments with alternative fixation conditions to address any possible artifactual issues with INPP5E ciliary localization? This would increase confidence in the co-localization studies. At a minimum, this needs to be addressed in the manuscript discussion. 

The claim that the authors have elucidated the mechanisms of INPP5E ciliary targeting requires further evidence that has not been addressed in the immunoprecipitation studies described within the manuscript. Are these protein interactions essential for INPP5E cilia localization? What are the functional consequences of these interactions? Are these direct or indirect interactions? The authors use the terminology "decreased binding" when actually referring to "decreased immunoprecipitation". This language needs to be clarified in the text as a reduction in co-immunoprecipitation could also be due to protein misfolding in the mutants. The authors need to validate that the mutant proteins are correctly folding and confirm immunoprecipitation data with direct binding studies before a claim of "decreased binding" can be substantiated. 

In conclusion, the questions asked in this manuscript could provide an impactful advance in the cilia field, but the data collected to date do not fully support the authors' conclusions.

Reviewer #3 (Public Review): 

This is an excellent manuscript that provides a lot of new and important information regarding INPP5E and the mechanisms of ciliary targeting. The authors show that ciliary targeting of INPP5E is not a simple 1-motif mechanism and identify 4 motifs (CLS1-4) located on the same side of the protein structure that are all required for different aspects of ciliary targeting through interactions with numerous different ciliary players. The combination of structural modeling with interaction studies, ciliary localization and phosphatase assays to assess the proper folding of the catalytic domain allows for a very thorough investigation of each of the four CLS motifs. This is combined with a large number of carefully designed mutations to probe the function of different CLS motifs to provide a very complete study. 

The work is well carried out and the manuscript is scholarly written with a high degree of clarity. I am sure it will have a high impact on the ciliary community.

Reviewer #1 (Public Review):

The authors have investigated MicroRNA-155 (miR155) that is overexpressed in various inflammatory diseases and cancer. Both these conditions present with bone resorption and osteolysis. Cancer/cancer metastasis-induced bone loss-mediated fracture is a serious clinical problem. In the studies performed by the authors miR155 showed a catabolic effect on osteogenesis and bone mass phenotype via interaction with the Sphingosine 1-phosphate receptor-1 (S1PR1) gene, suggesting inhibition of miR155 as a potential strategy for bone regeneration and bone defect healing.<br /> The study highlights the importance that miR155 inhibitors could be potential therapeutics to promote bone regeneration even in inflammatory conditions.<br /> The study delivers a partial mechanistic study. The study is simple and understandable, and the results are quite new.

Reviewer #2 (Public Review):

In this manuscript, Zheng et al. investigated the regulation of S1PR1 gene by MicroRNA-155 and determined the inhibitory role of this miRNA in osteogenic differentiation of bone marrow mesenchymal stem cells. They observed two opposite phenotypes, low bone mass in mice overexpressing miR-155 and high bone mass in miR-155 null mice. They showed miR-155 sponge (inhibitor of miR-155) increases S1PR1 protein levels. Furthermore, they found that miR-155 inhibits osteoblast differentiation markers alkaline phosphatase and Runx-2 in invitro and invivo experiments. Although several components of the miR-155 and S1PR1 cascade have been previously reported in other cellular systems, the connection with osteogenesis is novel and very interesting. However, several concerns should be addressed:

1. Are the similar effects observed in female mice? Authors did experiments in 8 week old male mice. Does this high bone mass phenotype in miR-155 KO or miR-155 Tg mice change over the period of time as mice age? It would be interesting in the bone field if these mice are resistant/prone to age related bone loss or inflamaging.

2. To check miRNA expression levels from serum, plasm, tissue or cells TaqMan-based pre-miRNA assay is a better detection method for its specificity and accuracy than SYBR green detection method. For small RNAs like miRNAs, U6 and 5S are the two commonly used normalizers for miRNA qRT-PCR. Please comment and revise.

3. What are the expression levels of miR-155 in bone tissue from miR-155 Tg and miR-155 KO mice? It is important to show miR-155 levels are reduced or knocked down in miR-155 KO bones and increased in miR-155 Tg bones. Authors should mention and explain these effects in the results and discussion.

4. What are the effects at cellular levels in miR-155 TG and miR-155 KO mice? Static histomorphometry data using Goldner's Trichome and TRAP staining will be important to study osteoblast and osteoclast numbers. Authors should mention and explain these effects in the results and discussion.

5. What are the effects on Dynamic histomorphometry in miR-155 TG and miR-155 KO mice? Calcein/Alizarin Red injections followed by histomorphentry will be important to study Bone formation and mineral apposition rates. Authors should mention and explain these effects in the results and discussion.

6. What are the effects on serum levels of bone formation and resorption markers in miR-155 TG and miR-155 KO mice? PINP or Osteoclacin and CTX-1 eliza histomorphentry will be important to study how these mice lose or gain bone. Authors should mention and explain these effects in the results and discussion.

7. Fig.5F : Authors observed no change or possibly reduction in Runx2 levels in miR-155 KO BMSCs. However, they observed increase in Runx2 levels in BMSCs treated with miR-155 sponge. The authors should mention and explain why they did not observe increased Runx2 levels in miR-155 KO BMSCs, in the result and discussion.

8. Fig 7A, Authors showed binding site of miR-155-5p in the 3'UTR of S1PR1 gene but did not mention whether it is conserved among different species like mouse, human or rat etc. Authors should comment on these points in the result and discussion.

Reviewer #3 (Public Review):

This manuscript will be of broad interest to orthopedists, tissue engineers, and particularly to those who are involved in developing osteoinductive scaffolds. Anti-miR155- could be used as an anabolic tool with the potential of improving bone density phenotypes, as well as being used directly as an additive to tissue engineering components. Having characterized the effects of silencing mir-155 on various pathways and stem cells, its use in a clinical setting may be possible, as said in the paper. The author might add some additional experiments to make it more robust when it comes to validating the finding and taking things forward for further clinical application.

As miR155 is a potent regulator of thousands of mRNA targets, the author could include some experiments looking at effects on a couple of different pathways.

Reviewer #1 (Public Review):

This is an interesting study by Lukasz et al as they attempt to link chronic neurotransmission and metabolic stress in sensory hair cells in the zebrafish lateral line system. Previous work has shown that neurotransmission is major driver of ATP production and ROS buildup in neurons and is hypothesized in this study to affect superimposed regulate oxidative stress in hair cells, such as those imposed by ototoxins such as aminoglycosides.

Strengths: the authors used 2 genetically modified zebrafish lines that disrupted presynaptic calcium influx, or the fusion of synaptic vesicles, respectively, and reported that hair cell survival was modestly enhanced after ototoxic damage which was accompanied by reduced mitochondrial activity based on live cell imaging. Pharmacologic experiments also nicely confirmed these findings. Based on comparative experiments, synaptic vesicle fusion contributed more significantly to metabolic stress.

Weakness. Interestingly, age of hair cells also displayed differential neurotransmission and sensitivity of ototoxicity, which, unfortunately, complicates the interpretation of the above findings which were not analyzed based on cell age.

Overall this is a well written manuscript and easy to follow, describing a topic that is highly significant, however, the data fall short of supporting all the conclusions made.

Reviewer #2 (Public Review):

In this manuscript Lukasz et al., investigate the role of neurotransmission in hair cell toxicity. They first show that chronic inhibition of both presynaptic calcium influx and exocytosis either using genetic mutants or long-term treatment with drugs leads to protection from multiple forms of aminoglycoside-induced hair cell death. They then go on to investigate potential mechanisms for this protection showing that while drug uptake and clearance are normal in these mutants there is reduced mitochondria activity and oxidation, which has previously been linked to reduced hair cell death. They also show that as hair cells age they become synaptically silent suggesting a potential homeostatic mechanism for avoiding the oxidation being caused by synaptic activity.

Strengths:

The researchers clearly show disrupting synaptic activity can protect hair cells from ototoxic insult. The use of both mutant lines and drugs to eliminate synaptic activity both strengthens their conclusions and in the case of the drugs allowed them to work out the timing of this effect showing that chronic but not acute synaptic activity blockage is important. Additionally, by using a mutant and drug that disrupted endocytosis while leaving presynaptic calcium and mitochondrial calcium undisrupted they were able to specifically highlight the role of the synaptic vesicle cycle and not just calcium influx.

Many things protect hair cells from ototoxic insult by affecting hair cell mechanotransduction activity and thus toxin uptake. The authors nicely address this confound by looking at mechanotransduction activity in their mutants, as well as drug uptake and clearance and show these look grossly normal.

The authors addressed the unexpected results of seeing more hair cell death in synaptically silent as compared to active hair cells in a single neuromast by investigating the age of the active vs inactive hair cells and come up with an additional finding that active hair cells tend to be younger. This along with the investigation of mitochondria activity and oxidation when synaptic activity is disrupted broadens the relevance of the paper behind just ototoxic hair cell death.

Weaknesses:

The majority of measures of mitochondria oxidation and activity use the fluorescence levels of a single colored dye. If for some reason these dyes were not able to get into the hair cells or mitochondria of the mutant animals this would confound their results. It is true that the authors don't see significant defects in Neomycin uptake in their mutants, but for the otofb mutant it seems a decrease may be emerging with time and the mitochondrial dyes used longer treatment periods. One thing supporting the validity of the dye data is that the TMRE data shows the same patterns as the MitoTimer data which is a genetically encoded indicator.

Reviewer #1 (Public Review):

This manuscript represents a potentially important study that attempts to elucidate the mechanisms through which the glycogen synthase kinase 3β (GSK3β) is involved in the progression of triple negative breast cancer (TNBC). Starting with an analysis of the TCGA and GEO databases the authors identify the serine/threonine phosphatase, PPP1R14C, as significantly upregulated in TNBC - because of this and the known ability of PPP1R14C to regulate the ability of PP1 to dephosphorylate GSK3β the focus of the manuscript is on the relationship of these molecules in TNBC. The significance of the role of PPP1R14C in TNBC was demonstrated by the negative correlation of PPP1R14C expression levels and the outcomes of TNBC patients; the higher the expression of PPP1R14C in TNBC, the poorer the outcome. These data certainly support the importance of investigating the mechanistic link between PPP1R14C, GSK3β and TNBC. Mechanistically, the authors demonstrate using gain-of- and loss-of-function approaches that PPP1R14C promotes and antagonizes breast cancer cell proliferation, mobility and anchorage-independent growth, respectively. Consistent with this, xenograft studies showed that increased PPP1R14C expression promoted whereas knockdown reduced tumor growth. These data are generally convincing on face value but unfortunately are limited in the robustness of the derived conclusions given the issues that surround interfering with the expression of regulatory PP1 subunits (see below). The authors show that PPP1R14C appears to specifically regulate GSK3β phosphorylation at Ser9 through modulating PP1's ability to dephosphorylate this site which subsequently controlled TRIM-23-mediated GSK3β stability. Finally, a claimed PP1 activator was used to provide further mechanistic insight into the actions of PP1 on GSK3β phosphorylation and stability. These results showed that the inhibitor and PP1 silencing led to reduced tumor volume. Collectively, this is an interesting manuscript, but it falls short in terms of strong mechanistic insight, largely due to limitations in the strength of evidence provided from many of the experiments.

The major confounding issue of this manuscript rests on the interpretation of the overexpression and silencing experiments of PPP1R14C. Due to the actions of PPP1R14C as a regulatory subunit of PP1, disrupting its expression either through upregulation or downregulation shifts the stoichiometry of binding with the multitude of other PP1-interacting proteins. Therefore, one cannot conclude that the effects on tumor formation, proliferation, migration, anchorage independent growth etc as a result of either overexperessing/silencing PPP1R14C is due solely to its actions rather than a secondary consequence of disrupted PP1 complex formation with its other interacting proteins.

In relation to the abovementioned point, it was very curious as to why either overexpressing or silencing PPP1R14C was so selective to the effects on GSK3β. The authors report a lack of effect on other PP1 substrates. The quality of the immunoblots of these other substrates (e.g. Myc) was insufficient.

To provide a mechanism for the effects of PPP1R14C in regulating GSK3β phosphorylation at Ser9 and subsequently ubiquitination by TRIM25, the authors perform a series of co-immunoprecipitation experiments. These experiments show that while TRIM25 does form a complex with PPP1R14C/GSK3β but they do not provide proof that the E3-ubiquitin ligase event is in fact mediated by TRIM25.

The pharmacological use of the C2 ceramide provides an appealing orthogonal, experimental manipulation, but it is hard to see evidence that this is a specific activator of PP1. In that regard it is even harder to understand how it appears to recapitulate the effects of PPP1R14C silencing.

Reviewer #2 (Public Review):

This manuscript aims to provide novel insights into the molecular mechanisms contributing to the progression of triple-negative breast cancer (TNBC), the most lethal type of breast cancer. It starts from the observation that PPP1R14C (also known as KEPI), an established inhibitor of protein phosphatase-1, is upregulated in TNBC. The authors subsequently show that PPP1R14C upregulation correlates with a poor prognosis in patients and tumor progression. They also provide data indicating that PPP1R14C enhances the phosphorylation of an inhibitory site (Ser9) of protein kinase GSK3beta and targets GSK3beta for proteolytic degradation via the E3 ubiquitin ligase TRIM25. Co-immunoprecipitation data suggest that PPP1R14C forms two ternary complexes, one with GSK3beta and PP1, and another one with GSK3beta and TRIM25, and it is suggested that these complexes mediate the observed effects of PPP1R14C overexpression on GSK3beta inhibition and degradation.

The topic is important and interesting but the provided data do not justify the major conclusions made. No compelling evidence is provided for a direct interaction between PPP1R14C and GSK3beta or TRIM25. Also, PPP1R14C is only an inhibitor of PP1 when phosphorylated on a specific residue but such phosphorylation is not demonstrated. PP1 knockdown or PPP1R14C overexpression experiments are not sufficiently conclusive because they affect many distinct PP1 holoenzymes. The manuscript also suffers from a lack of essential control- and validation experiments.

Reviewer #3 (Public Review):

The manuscript presents data that high expression of Protein Phosphatase 1 inhibitor in triple-negative breast cancer contributes to the poor outcome by downregulation of an important kinase, GSK3β. If substantiated, this would enhance our understanding of the pathophysiology of this important disease and might suggest new treatment options. Indeed, changes in PPP1R14C expression alter the behaviour of TNBC in cells and in mouse models, but the mechanistic links to GSK3 are not robustly established.

Fig 1-2 identified the PPP1R14C as upregulated in TNBC and with a significant correlation with worse outcome. Fig 3 and 4 show in vitro and in vivo effects of changes in PP1R14C consistent with increased proliferation, migration and metastasis in vivo. These studies look very solid and appear to identify a role for this phosphatase regulator in TNBC.

The weaker part of the manuscript is the mechanistic link to GSK3 regulation. Over-expression and knockdown of PPP1R14C have effects on GSK3β phosphorylation and downstream targets, but the direct connection is unclear and made challenging by a number of complex experimental issues.

The big questions -<br /> 1. Is GSK3 directly ubiquitylated by TRIM25 on K183? I don't think the data are strong here, for reasons elaborated on below.

2. Is GSK3 really the important target of PPP1R14C/PP1 complex? The biological data are correlative and the direct experiment, does GSK3β (S9A/K183R) rescue PPP1R14C over-expression, would need to be done. But since I suspect K183R is kinase-dead, this may fail.

3. The studies with C2 are confounded by the broad effects (including on PP2A) of treating cells with ceramide. Calling C2 a specific PP1 activator is I think unwarranted.

Specific comments:<br /> Why is there a band in Fig 5D lane 2, the Flag-PPP1R14C lane, in the absence of Flag-PPP1R14C?

Why in Fig 5E, F, G are there two bands in the pGSK3bS9 blot?<br /> The authors would need to show the total GSK3 coming down here too, and the total GSK3 present in Fig 5H as well.

I have trouble understanding the result in Fig 5H. According to this, global PP1 phosphatase activity increases 3 fold when PPP1R14C is knocked down. First, there is no method noted for this assay. How do we know this is specific to PP1? Second, PPP1R14C is only one of many PP1 interactors. How can its knockdown change cellular PP1 activity 3-fold? I note the knockout mouse for PPP1R14C had a 15% increase in thalamus PP1 activity (see fig 3, https://doi.org/10.1016/j.neuroscience.2009.10.007). This experiment needs much more in the way of controls.

Fig 6 evaluates the role of PPP1R14C in GSK3 protein stability. There is a fundamental weakness here - How do the authors know the ubiquitylated smear in the various Fig 6 assays is GSK3 versus a ubiquitylated protein that interacts with active GSK3? GSK3 phosphorylation directs many proteins (famously β-catenin and Myc) for ubiquitylation and degradation, so the co-IP of ubiquitylated proteins with GSK3 is to be expected if the IP stringency is not very very high. This is consistent with inactive pSER9 GSK3 not bringing down ubiquitylated proteins. An IP after for example boiling in SDS to break up large complexes would be needed to test if GSK3 itself, rather than associated substrates, is directly ubiquitylated.

Is TRIM25 specific for GSK3? It's identified by mass spectrometry. However, when I plug TRIM25 into the CRAPome database (https://reprint-apms.org) I find it comes down in 136/716 (19%) of all MS IP studies, making it a very common contaminant in IP. Thus the bar is high to show this is specific. Here the interaction is validated with over-expression of various truncation mutants.

Line 235: "K183 of GSK3β has been recognized as the ubiquitylation site". First, what is the reference for this statement? I found one paper (https://doi.org/10.1074/jbc.M116.771667 that claims this residue is important for FBXO17 K48 modification, not the K63 linkage associated with TRIM25). In the crystal structure of GSK3β, that K183 appears to coordinate the phosphates of ATP, so the effect of the K183R mutation may be to make the kinase inactive, which would confound their results. So an important experiment is, does K183R retain wildtype kinase activity? Or is it inactive, and so act like the phosphorylated S9 GSK3?

The reference for ceramide as a PP1 activator is not a primary reference, it is to a paper in the Journal of Endodontics, which uses it. It would be important to cite primary literature for this usage of C2. I note that many papers cite C2 ceramide as a PP2A activator. It is unclear what the rationale is for using it as a specific PP1 activator?

Reviewer #1 (Public Review):

Mitchell et al. investigated the gut morphogenesis in Drosophila. Light-sheet microscopy was used to obtain detailed 3D live images of the folding process. Authors developed a new computational framework called 'TubULAR', which enabled them to process 3D images to extract the detailed shape of the gut as well as to image endoderm cells on the surface of the gut. Using this novel framework, the authors demonstrated that endoderm cells change their aspect ratio during morphogenesis while maintaining their area approximately constant. Furthermore, the authors demonstrated that the endoderm and muscle cells move in unison during morphogenesis. By combining the results of the experiments with the knockout of hox genes, optogenetically controlled induction or inhibition of muscle contractions, and modulation of the calcium signaling pathway, the authors were able to uncover the molecular mechanism that regulates the folding of the gut. In particular, hox genes regulate calcium signaling, which induces muscle contractions that deform the tightly connected endoderm.

This is an excellent manuscript that significantly advanced our understanding of gut morphogenesis in Drosophila. Furthermore, the novel computational framework 'TubULAR' for the analysis of 3D images will be a great resource for the community.

Reviewer #1 (Public Review):

This article considers the application of maximum entropy models to the analysis of fMRI data from macaque monkeys under anesthesia using various drugs, and while awake. The authors binarize the raw fMRI data, and use the binarized data to infer networks that underlie maximum entropy models of the binarized data.

The authors argue that the fragmentation of networks, specifically the uncoupling of specific brain regions from others, underlies the transition into loss of consciousness<br /> under anesthesia.

The authors use concepts/ideas from statistical mechanics, specifically criticality and supercriticality to to describe the state of the brain during awake to anesthetized states.

Overall comments:

I found the paper well written, organized and motivated from the perspective of the need for network analyses of brain activity. As I do not have expertise in anesthesia, I will let the other referees comment on the aspects of the paper that deal with its implications on our understanding of anesthesia. My comments will focus on the methodological aspects of this work.

From a methodological perspective, the authors justify the binarization of the fMRI data from existing work, notably references 26 and 27. In my opinion, the strength of the paper lies its ability to show that the binarized representation, after some processing, can serve as a useful feature for classifying different states of the brain. I found this very interesting.

In my opinion, the paper's weakness lies in the fact that I find some of the methodological choices hard to justify. If I grant the authors the binarization of the data, I thought the choice of maximum entropy models, as opposed to other forms of point-process models, such as the ones shared below (other examples abound), requires further justification. Among other things, these models seem to assume independence of binary vectors across times, which I find hard to justify.

Reviewer #2 (Public Review):

Ponce-Alvarez et al. investigated the use of concepts from thermodynamics and statistical mechanics to uncover control parameters behind the transitions between brain states. The authors performed fMRI recordings in 5 monkeys using different anesthetics to transition the animals from awake to anesthetized state. Then the authors proceed to binarize the signals using an arbitrary z-scored based threshold, and apply a host of thermodynamic equations to the binarize data in order to identify system control parameters. The authors conclude that the coupling of the binarized activity from each individual ROI to the population activity is related to brain state transitions, just like temperature is related to transition from ice to water.

Major concerns: Statistical mechanics was developed to explain macroscopic behavior of well defined physical systems of atoms or molecules. The concepts were developed to explain how vibrational modes and collisions between atoms/molecules are related to temperature and pressure, and how they can be related to the ability of the system to perform a work. Within that framework, concepts such as entropy, criticality, control parameter, partition function, and Helmholtz free energy (to name some) have a clear physical meaning. I am not sure that the same is true for the brain, unless it was to be explicitly studied in terms of brain temperature. For example, hypothermia produces an EEG very similar to the one observed during deep general anesthesia. While the BOLD signal could be a proxy for brain temperature, the study provides no model to translate such signal to brain temperature. I don't deny that neuron activity is driven by thermodynamic processes coming mostly from the mitochondria. However, a lot of legwork has to be performed before such concepts can be applied to fMRI signals. This painful lesson was learned many years ago in meteorology, a field temperature and pressure measurements are explicit, but where still it is not possible to abstract microscopic changes to macroscopic variables, and computationally intensive finite element modeling must be performed instead. We currently do have models that take into account the explicit structure of the brain, and have the computational power needed to implement it.

Other concerns:<br /> 1) Is binarization necessary? the authors show that similar results could be achieved without binarizing, so the entire paper could have been written without it. Seems to me that binarization of continuous signals is a trick to use only when online or closed loop manipulations are needed.

2) The actual N of the study is 5, the number of biological units (monkeys). This is not a limitation per se, as many monkey studies only have two animals, but the scans within the same monkey should be considered as repeated measures within each subject, and the authors should use a mixed-effects model to analyze the results, rather than treating each scan as independent. Furthermore, in each graph the data coming from each monkey should be labeled differently. It is also unclear how many monkeys received each of the anesthetic treatments. This should be precisely described, and even if the design is unbalanced, it can be rigorously dealt with by using a mixed-effects model.

3) The authors report a correlation of 0.3 as being "high". Unlike information-theoretical quantities, statistics like correlation have the advantage of being bounded, in this case between -1 and 1. A correlation value of 0.3 is in itself rather modest, and indicative of a weak correlation in itself, independent of its statistical significance when compared to other measures. Given that the rest of the study relies in this metric, the rest of results are not convincing.

4) The authors correctly acknowledge that the parameters that control the state transitions are unknown, and state their purpose to find them. However, proper parameter selection it is not performed.

Reviewer #1 (Public Review):

Chintalapati et al. present DATES, a method that leverages ancestry covariance patterns across the genome of a single individual to infer the timing of admixture events. Authors perform simulations of demographic models that mimic key events in European prehistory, as well as the characteristics of ancient DNA (aDNA) datasets (e.g., pseudo-haploid calls and a large proportion of missing data). Based on these simulations, the authors assess the performance of DATES across a range of admixture proportions, timeframes, the proportion of missing data, and different numbers of populations as surrogate parental groups. One of the major strengths of this manuscript is that it presents a very robust method that is broadly useful in paleogenomic studies and that fills an important gap in the field (previous LD- and haplotype-based methods do not perform as well with aDNA datasets).

The method is applied to >1000 ancient human genomes (publicly available) to characterize major admixture events during the European Holocene. The manuscript addresses long-standing questions in the field, for instance, the genetic formation of Anatolian farmers and Steppe pastoralists, the chronology of the neolithization of Europe, and the spread of Steppe ancestry across Europe, among others. The authors do a very good job in synthesizing known and new observations into a comprehensive and exciting story - this is particularly impressive given the breadth of events that this work covers.

The authors' claims are fully justified by their results. The manuscript is very easy to read, and the results are presented in a very clear way.

Reviewer #2 (Public Review):

Chintalapati et al. present an updated and refined version of the software DATES, for estimating the number of generations since admixture in either a single diploid individual or a pool of admixed samples. They show via extensive simulations that DATES performs well under many different individual conditions; including small samples sizes, missing data or pseudo-haploid genotype calling. The authors then systematically applied DATES to a large dataset of published ancient human SNP capture data, to investigate the timings of admixture between populations in Holocene West Eurasia.

Through simulations, the authors convincingly demonstrate that DATES outperforms comparable methods, and produces accurate inference under many challenging circumstances. However, the strength of the empirical analyses is undermined by the lack of simulations which explicitly test the performance of DATES under the combination of conditions present in the empirical data. Many of the empirical analyses contain all of the problematic features that are only tested in isolation in the simulations-e.g., small sample sizes in both the target and reference populations, with non-contemporaneous sampling, variable data missingness, and pseudo-haploid genotypes. For example, in Figure 1b the authors show the effects of pseudo-haploid genotypes and data missingness for a target population of size n=10; however, this is substantially larger than the average target population used in the empirical analyses, and does not test the combined effects of small sample sizes and poor data quality in the reference populations. The authors also show that the performance of DATES is sensitive to low Fst between the admixing populations, but do not provide simulations calibrated to the levels of Fst between the empirical reference populations. These issues make it difficult to interpret the robustness of the empirical analyses, despite the large number of simulations presented.

Overall, the paper is well written, and the authors do a good job of presenting a complex series of results. The software DATES represents a significant improvement over comparable methods and has the potential to make substantial contributions to the study of admixture history in many species.

Reviewer #1 (Public Review):

In this study, the authors have analysed the contribution of brain-derived cell-free DNA in the blood of patients with psychosis based on the established methylation-based tissue deconvolution methodology. Using tissue- or cell-type methylation marker, they have demonstrated a higher level of brain-derived cell-free DNA in patients who experienced psychotic symptoms compared to healthy controls. The finding would serve as a proof of concept for the methylation analysis of cell-free DNA in psychosis.

Reviewer #2 (Public Review):

The authors correlate levels of brain cell specific methylation loci in cell free DNA isolated from plasma with the onset of psychotic symptoms in schizophrenia. The outline of the study is clear and concise and the methods build upon established techniques in a novel cohort of patients.

They have identified a collection of genomic loci that can be identified as deriving from the brain when unmethylated. Thus, they provide a method for easy and cheap detection of brain derived DNA in the circulation.

Whilst the cohort size studied is small it is in keeping with those previously published in similar studies. They have set out to provide 'proof of concept' regarding the use of biomarkers for diagnosis and prognostication in schizophrenia. Whilst they provide interesting data on the presence of brain derived DNA in the plasma of those with acute psychosis, significant further work is required to identify whether this concept is useful and reproducible enough to eventually become a biomarker for this disease.

Reviewer #1 (Public Review):

In this article, the authors introduce EncoreDNM (Enrichment correlation estimator for De Novo Mutations), a novel statistical framework that leverages exome-wide DNM counts, including genes that do not reach exome-wide statistical significance in single-disorder analysis, to estimate concordant DNM associations between disorders. EncoreDNM uses a generalized linear mixed-effects model to quantify the occurrence of DNMs while accounting for de novo mutability of each gene and technical inconsistencies between studies. They demonstrate the performance of EncoreDNM through extensive simulations and analyses of DNM data of nine disorders. Compared with existing methods, EncoreDNM is statistically more powerful while better controls the type-I error rates.

The authors provide a useful tool to leverage exome-wide DNM counts, including genes that do not reach exome-wide statistical significance in single-disorder analysis, to estimate concordant DNM associations between disorders. EncoreDNM uses a generalized linear mixed-effects model to quantify the occurrence of DNMs while accounting for de novo mutability of each gene and technical inconsistencies between studies.

The major strength of EncoreDNM is that as a rigorous statistical model, its type-I error rate is well controlled compared to mTADA. Therefore, anyone using this tool could confidently claim the findings with false-positive well controlled.

The results of this study largely support the authors' conclusion.

Reviewer #2 (Public Review):

The authors present the development and application of EncoreDNM, a computational tool to identify shared genetic architecture between various disorders based on de novo mutations (DNMs). The authors present compelling evidence that their tool is able to find shared genetic architecture between various disorders that have previously been shown to have a shared common genetic component, which lends credence to their results.

While I appreciate that EncoreDNM may improve researcher's ability to find such an enrichment and putatively identify genes correlated between various disorders, I found the comparison to previous approaches (particularly mTADA) lacking. Additionally, the description of the method itself is sparse and the reasoning behind the inclusion of various parameters in their model needs to be expanded upon for readers.

As such, I cannot determine if EncoreDNM represents a significant advance for the field. Clarification on these points would be needed to evaluate this article's relative contribution to the field.

Reviewer #1 (Public Review):

This paper presents a large number of significant findings that extend understanding of the molecular mechanism of transcriptional memory operating at the yeast INO1 gene, wherein an episode of derepression by inositol starvation sets in motion a series of events that poise the INO1 promoter, following its repression by re-supplying inositol (the memory phase), for more rapid derepression in a subsequent starvation (re-activation) episode. The Brickner lab has dissected this mechanism extensively in the past. Here they conduct an extensive program of well-executed experiments that lead to several new insights. (i) They confirm that memory leads to more rapid INO1 derepression during reactivation, but observing this requires anchor-away (AA) of the repressor Opi1, and they show that Nup100-dependent memory increases competitive cell fitness during reactivation conditions. (ii) They show that memory (as assayed by peripheral nuclear localization of INO1) does not require ongoing transcription by examining the rpb1-1 Pol II mutation and deletion of the INO1 TATA element. They show by ChIP experiments that deposition of H3K4me2 at the INO1 promoter during memory requires nuclear pore factor Nup100 and the memory-specific TF Sfl1, and that Sfl1 binding to the cis-acting MRS signal at INO1, required for memory, is dependent on Nup100, the Set3 subunit of the HDAC Set3C, and the Swr1 subunit of the SWR complex required for H2AZ deposition, that both Sfl1 and Set3 are required for H2AZ deposition at INO1, and that AA of COMPASS (required for H3K4 methylation) impairs Sfl1 binding at INO1. These findings support a positive feedback loop wherein Sfl1/Nup1 dependent association with the NPC leads to H3K4me2 by COMPASS, which supports H2AZ deposition in the INO1 promoter, and these chromatin modifications, in turn, reinforce Sfl1 occupancy during memory. They also suggest a role for a reader of H3K4me2, Set3, in memory in addition to writer, COMPASS. (iii) The TF Hms2, related to Sfli, is also required for poised Pol II and H3K4me2 during memory, but unlike Sfl1 binds to the promoter during conventional activation as well as during reactivation, and is required for Sfl1 binding during reactivation. Interestingly, Hms2 is even more important than Sfl1 for retention of INO1 at the nuclear periphery during memory. (iv) Using an analog-sensitive allele of the Ssn3 kinase of the Cdk8 complex, they show that Ssn3 kinase activity is crucial for rapid INO1 induction and increased cell fitness during reactivation, but not during conventional activation, acting to enhance poised Pol II binding but being dispensable for Set3-dependent H3K4me2 deposition. Interestingly, AA of TBP does not impair H3K4me2 during memory, unlike during conventional activation where it is co-transcriptional. (vi) The Leo1 subunit of Paf1C is identified as the only subunit of this complex that is specifically required for H3K4me2 deposition during memory but not conventional activation, and its deletion leads to reductions in poised PolII, INO1 mRNA induction kinetics, cell fitness, and nuclear periphery localization, during memory. (vii) Using an SFL1-AID allele for regulated depletion of Sfl1 during different stages of memory formation, they provide evidence that once memory has been established, H3K4me2 can persist for up to two hours in the absence of Sfl1, implying that Sfl1 is needed primarily to establish the memory state. Other experiments carried out using a URA3 gene with an MRS inserted in the promoter, which is sufficient for localization to the nuclear periphery and attendant H3K4me2 deposition but not for other aspects of transcriptional memory, they provide evidence that H3K4me2 can persist through about 4 mitoses following depletion of Sfl1, suggesting its epigenetic inheritance; and they demonstrate physical association of COMPASS and Set3, which they propose underlies the mechanism of epigenetic inheritance.

The experiments are very well conceived and executed and the data support the main conclusions of the paper. The findings are significant in providing evidence that a positive feedback loop exists between Sfl1-dependent interaction with the nuclear pore complex and H3K4me2 deposition in the INO1 promoter, which is essential for recruitment of poised Pol II, but which does not require Pol II or transcription initiation to occur. This distinguishes this activity of COMPASS from conventional H3K4 methylation, which is coupled to transcription, and the Pol II-independent mechanism is shown to specifically require Nup100, SET3C, and the Leo1 subunit of the Paf1 complex. It is also significant that this specialized H3K4me2 deposition can persist through multiple cell cycles, which might be enhanced by physical association between the writer of this mark (COMPASS) and a SET3C as a presumptive reader.

Some additional discussion will be required to explain why nuclear depletion of Opi1 is required to observe that memory stimulates the rate of INO1 derepression during reactivation, and a new experiment is likely needed to show that the persistence of H3K4me2 deposition following depletion of Sfl1 will be diminished in cells lacking Set3 to bolster the model that the proposed epigenetic inheritance of H3K4me2 requires Set3C as a reader of this histone mark.

Reviewer #2 (Public Review):

The manuscript by Sump et al. investigates multiple molecular aspects of epigenetic transcriptional memory of inositol exposure and characterizes a transcription-independent H3K4 di-methylation pathway that is maintained over cell divisions and that appears to underlie the memory phenomenon.

Initially, the authors show that transcriptional memory of INO1 gene confers a fitness advantage, which is dependent on a nuclear pore protein Nup100, and that inactivating RNAP II or the INO1 promoter still leads to memory-associated re-localization of the INO1 gene to the nuclear periphery. This Nup100-dependent re-localization occurred very rapidly upon inositol-driven activation, before significant transcription occurred, reinforcing the conclusion that memory mechanisms are separate from transcription. The authors also identify a positive feedback loop between the key players of this memory pathway, such that deposition of H3K4Me2 and histone variant H2AZ during memory depend on Nup100 and transcription factor Sfl1 yet binding of Sfl1 to the promoter during memory, in turn, depends on H3K4Me2 and Nup100. In addition to Sfl1, authors identify an additional transcription factor with a similar DNA-binding domain, Hms2, which is required for INO1 memory and for maintenance of H3K4Me2 and RNAP II during memory.

The authors construct an elegant system to conditionally inhibit Ssn3, the Cdk8 kinase that has been found to bind RNAP II PIC specifically during memory. Using this inhibition, they show that inhibiting Ssn3 leads to loss of poised RNAP II from INO1 promoter during memory, but not during activation. Importantly, they find that decreasing RNAP II at the promoter during memory or activation does not affect memory-associated H3K4Me2. These results illustrate that the memory H3K4Me2 pathway does not require RNAP II, and that this mark is the more upstream and primary mediator of memory. To further characterize the memory H3K4Me2 pathway, the authors examine the Paf1 complex and identify Leo1 as a critical factor. Loss of Leo1 leads to loss of H3K4Me2 specifically during memory, as well as to loss of RNAP II and peripheral localization, and to a slower rate of reactivation. Interestingly, the loss of peripheral localization is delayed, suggesting that Leo1 is important for maintaining memory but not for establishing it.

To further understand mitotic heritability of H3K4Me2, the authors use an ectopic insertion of the MRS element in a different location. After Sfl1 degradation, the MRS was found to retain H3K4Me2 for 4 generations, which is much longer than the presence of RNAP II-dependent H3K4 di-methylation or than what is expected of just passive dilution due to replication. These dynamics suggested that there may be an active mechanism of maintaining H3K4Me2 at memory-marked promoters.

Overall, this is a thorough and elegant analysis of transcriptional memory mechanisms, which introduces significant new insight into our understanding of what serves as the primary memory mark. Multiple factors have been implicated in memory in this and other systems, and this work highlights the importance of H3K4 di-methylation, as opposed to RNAP II poising, H2AZ incorporation, and perhaps most importantly, transcription itself. The existence of RNAP II-independent H3K4 di-methylation pathway is demonstrated convincingly and is an important finding for the field. I have a few suggestions for experiments that can make the mechanistic conclusions more convincing or that expand the mechanism a bit more.

Reviewer #3 (Public Review):

Sump et al. use elegant time dependent perturbation tools to dissect the molecular mechanisms of INO1 memory. Previous work has uncovered several components that are responsible for both gene activation and spatial repositioning of the INO1 locus to the nuclear periphery. Sequence dependent GRS and MRS sequences that are portable enable the relocalization, activation and the establishment of memory. The system is ideally suited for a mechanistic dissection of how transcriptional memory is both established and maintained. Using a series of anchor away and auxin inducible degradation approaches, the authors demonstrate convincingly that the INO1 system exhibits memory that enhances cell fitness in budding yeast under competitive growth conditions (Figure 1). The authors also show that H3K4me2 is present at the INO1 locus even under conditions where RNA polII is absent highlighting a parallel pathway that contributes to placing this activating mark independent of transcription. The maintenance of H3K4me2 given that it is transcription independent (as per the authors) must arise from an autonomous positive feedback loop that depends on COMPASS-SET3C read-write activity. The manuscript does not provide sufficient data to provide strong support for this claim. Remarkably the authors also highlight the existence of at least six transcription factors that regulate INO1 activation and memory. Whether the interplay between their binding, dissociation and persistence contributes to memory remains a distinct possibility.

Reviewer #1 (Public Review):

The authors set out to investigate the possible adaptation of a sensory system to live in caves, where visual cues are strongly reduced or absent. In this case, although some light sensitivity may remain in blind populations of Astyanax, image-forming capabilities are entirely absent. Therefore, animals that need to determine their position relative to obstacles, find prey or escape from potential predators, may need to rely more heavily on mechanosensation. The question here is whether the lateral-line system has been modified to serve this need, and how so.

The major strength of the work is a clever choice of Astyanax populations, (three blind that independently colonised caves at different times) and one sighted (surface dwelling). The experiments are clean and sufficient to draw conclusions.

A weakness of the methods and results is that the authors choose to concentrate on the posterior lateral line upon the conclusion that because the number of neuromasts does not vary between cave and surface populations, any adaptation must occur elsewhere. However, their rationale is weakened by the fact that the authors do not examine the number of mechanoreceptive hair cells (as a minimum) and their sensitivity to mechanical stimulation (ideally). I also did not quite like their use of "regression of the efferent system". In my opinion, this implies that the ancestral animals have a weak efferent system, which developed further in Astyanax (generally) and then regressed upon cave colonisation. I would have used another word that more precisely defines a weakening of activity.

The results support the conclusions of the paper, but I am not entirely convinced by their assertion that "elevated afferent activity underlies the increased responsiveness of cavefish to flow stimuli". First, what do they mean by "responsiveness of cavefish to flow stimuli"? Is it reduced limen, elevated frequency of responses (across trials in individuals or and within a population)? Also, I am not familiar with the cave system. But is there any actual eater flow in caves? Perhaps the fish do not actually detect flow, but pressure distributions to enable the avoidance of obstacles.

I found it curious that the authors did not discuss the evident reduction of swim bout length in cavefish, compared to surface fish (F2Aii). Also, this difference seems not to correlate with motoneuron spike bout frequency. Perhaps the authors can add a sentence or two to discuss these issues, or simply explain to me if I got it wrong.

The work is very likely to have a significant impact in the field, and increase the overall interest in Astyanax as a valuable system for neurobiology and evolution.

Reviewer #2 (Public Review):

Lunsford et al. reported a novel knowledge about the mechanism of sensitivity gaining in a sensory system, in which cavefish gained sensitivity likely due to attenuation of the afferent inhibition. This manuscript can potentially impact the neuroscience field by proposing a new mechanism for sensory gaining. This report also contains technical advancement in the cavefish system and excellent comparative data among surface and cave populations potentially impacting the evolutionary biology field.

The strength is that, by applying the fine neurophysiological technique, the authors first found that cavefish increased spontaneous activities in the afferent inputs from the mechanosensory lateral line to the brain. They also found that cavefish regress the inhibitory efferent signal from the brain to the mechanosensory lateral line, in which the efferent inhibits the afferent signal. This inhibition is typical in the fish system to mask the lateral line/flow sensing information during the tail-beating. Cavefish seemed to reduce the efferent signal; then the authors argue that cavefish gained the lateral line sensitivity more.

The weakness is that the authors did not show the relationship between sensitivity and spontaneous activities in the afferents. The signal/noise ratio (S/N ratio) is important where the animal can be aware of the actual signals from the environment out of the noisy self-generating spontaneous signals (but c.f. stochastic resonance). As far as I read, this study misses the comparison between the signal and the spontaneous spikes, or any other alternatives that support their conclusion: cavefish gained higher sensitivity.<br /> If the authors address this point, I believe this study has a strong scientific impact on neurophysiology and evolutionary biology to show how animals evolved higher sensing ability.

Reviewer #3 (Public Review):

Lunsford et al. investigated the neurophysiological activity of posterior lateral line afferent neurons between surface and cave populations of the Mexican tetra A. mexicanus. Cave populations of A. mexicanus show heightened lateral line sensitivity, which has been attributed in part to the increased number of neuromasts in the anterior lateral line. The authors of this study exploit a key developmental difference in larval cavefish: the density of posterior lateral line neuromasts is not significantly different between cave and surface populations. Using this key observation, the authors demonstrate several key findings: First, Pachon cavefish neuromast afferents exhibit significantly higher spontaneous activity. Second that Pachon cavefish do not decrease afferent activity during bouts of fictive swimming, as do surface and other 'sighted' fish like zebrafish. This is presumably due to the absence of a corollary discharge signal produced by motor activity: the authors demonstrate this is the case by ablating efferent neurons in the hindbrain of both cave and surface fish, showing that surface fish afferents no longer decrease their firing rate during swimming, whereas cavefish are unaffected by the ablation. Finally, leveraging a key strength of the cavefish model, the authors examine three populations that have independently colonized caves from surface populations, demonstrating that the same general effect as found in Pachon cavefish, with interesting variation in the Molino cave.

This is an exciting and important paper to those seeking to understand the evolution of sensory systems and their adaptation to different environments. The paper is exceptionally well written, with clear and beautiful figures. It applies widely used neurophysiological techniques in zebrafish to an increasingly important evolutionary model A. mexicanus. By exploiting key developmental and population-level differences, the authors demonstrate plausibly adaptive differences in neural circuits, not just those due to external morphology. These findings motivate a series of exciting hypotheses for future studies, including hypotheses about sensory function in other lineages of troglodytic fish.

Overall, the studies described in this manuscript are simple and elegant, however require familiarity with the neural circuitry of the lateral line to understand the experiments. This could be improved in the introductory materials and potentially through an explanation of the terms labeled in figure 3Ci and 3Cii. Second, while a major finding of the paper, little attention is paid to potential mechanisms to explain the increased spontaneous activity of neuromasts in multiple populations of cavefish. While it is clear that both CD inactivity and increased spontaneous activity will lead to increased lateral line sensitivity, some assessment of the relative importance of these two phenomena would be useful. Given that ablated surface fish survive according to the methods, it seems that experimenters have larval fish lacking CD, but with lower spontaneous afferent activity. Some comparison of spontaneous swimming activity between these manipulated groups could have provided some additional insight. These concerns are relatively minor to an otherwise excellent and exciting paper.

Reviewer #1 (Public Review):

This analysis of yeast secretory vesicles centers around the Rab GTPase Sec4. The authors previously imaged Sec4-labeled secretory vesicles, but not at a speed that would allow the processes of secretory vesicle biogenesis and exocytosis to be dissected. Various Rab4 interactors engage at different times. The data presented here indicate that Sec2, the GEF for Sec4, binds to the TGN, followed quickly by Sec4, followed by the type V myosin Myo2. The multisubunit exocyst tether is a Sec4 effector that is recruited during secretory vesicle transport. Next comes the Rho3 GTPase at the plasma membrane. Then the loss of Sec2 and Myo2 is followed by recruitment of the SNARE regulator Sro7, followed by recruitment of the SM protein Sec1 and the lipid-interacting protein Mso1, which together trigger the final SNARE-dependent fusion event.

In my opinion, this is an impressive study that takes the analysis of secretory vesicles to a new level. The authors describe rigorous quantitative microscopy that leads to a plausible mechanistic timeline of vesicle tethering and fusion. Among the insights described is evidence that the exocyst progressively associates with secretory vesicles during their transport, and that Sec3 is a stable component of the heterooctameric exocyst rather than a separable "landmark" subunit. Additional data imply that loss of Sec4 from the vesicle is needed to progress to fusion, that Sro7 and Sec1 associate with secretory vesicles only after tethering, and that Mso1 helps to concentrate Sec1 locally at sites of vesicle fusion. Overall, this approach gives a new perspective on the choreographed events that take place during the lifetime of a secretory vesicle.

As the authors note in the Discussion, when we consider the first stage in the lifetime of a secretory vesicle, the biogenesis mechanism "is still shrouded in mystery." I'm curious about whether the microscopy provided any information about when secretory vesicles leave the TGN. Do they leave throughout the lifetime of a TGN structure, or do they leave in a burst when a TGN structure disperses as marked by loss of Sec7? This information might take us a step closer to understanding how secretory vesicles are made.

I have no significant concerns with the data or the presentation.

Reviewer #2 (Public Review):

This manuscript presents a tour de force of imaging of budding yeast exocytosis factors and their arrival and disappearance from "fusion sites" at the plasma membrane. This work builds on previous data from their lab and others, with substantial technological advances that improve the temporal and spatial resolution of various factors, plus data for additional factors. The presented data fills detailed gaps in the field and may lead to (or support) new mechanistic hypotheses.

The authors use substantially improved microscopy techniques and new tagged constructs, to image their dynamics and co-localization with increased resolution compared to previous studies. These data provide an improved picture of how these factors engage temporally and spatially in live yeast cells during exocytosis.

Overall, these studies are of high importance in the field. However, the manuscript itself is difficult for a non-specialist reader to follow, very little introduction and explanations are given for most of the numerous components. The authors are encouraged to integrate their data together better with published biochemistry and structural work into more complete mechanisms for vesicle trafficking, tethering and fusion. The manuscript would be improved by a clearer model(s) of how these factors come together to carry out exocytosis.

Moreover, many conclusions (especially as they appear in the Results and Figures) are written as if they are well supported by the data (or others' data), when they are often speculative, or reasonable alternative explanations exist. The authors should be clear about which conclusions are well supported, and which are hypotheses. (e.g. Fig 6I, which is a terrific figure, but some of the "conclusions/statements" are speculations).

The mechanistic and experimental definitions for the start/end of "tethering" and "fusion" are not clearly stated in the main text, which leads to confusion when examining the arrival of different factors (and seems to lead to circular arguments about what is defining what). Are these definitions well supported by the previously published and current data? E.g. is the disappearance of GFP-Sec4 really equal to the fusion event? Without data showing membrane-merger or content delivery, this needs to be described as an assumption that is being made.

The Sro7 results and conclusions are complicated, and not always carefully supported, for several reasons: there is a functionally redundant paralog Sro77, and data shows Sro7 can bind to Sec4, Sec9 and Exo84 in exocyst (Brennwald, Novick and Guo labs). The authors should be clearer, as they seem to pick and choose which interactions they think are relevant for different observations.

The assumption that yeast Sec1 behaves similarly to other Sec1/Munc18 proteins for "templating" SNARE complex assembly, e.g. Vps33 in Baker et al, is unlikely, given the binding studies from a number of labs (Carr, McNew, Jantti). Furthermore, the evidence for Sec1 interaction with exocyst suggests that they may work together (Novick, Munson labs). Previous data from the Guo lab (Yue et al 2017) and new BioRxiv data from the Munson/Yoon labs suggest that exocyst may play key roles in SNARE complex assembly and fusion.

There is concern that the number of molecules of each of the factors measured is accurate, and how the authors really know that they are visualizing single vesicle events (especially with data showing that "hot-spots" may exist). For example, why is the number of molecules of exocyst is ~double or more than that previously observed (Picco et al; Ahmed et al with mammalian exocyst).

For puncta of exocyst subunits in the mother or moving towards the plasma membrane, what is the evidence that they are actually on vesicles? The clearest argument seems to be the velocity at which they move, but this could be due to the direct interaction of exocyst with the myosin (which is a tighter interaction in vitro than exocyst-Sec4 binding), rather than being on vesicles. Furthermore, do all the exocyst complexes in the cell show this behavior, or could these be newly synthesized/assembled complexes?

With regard to the exocyst octamer leaving at the time of "fusion," the authors should discuss Ahmed et al.'s finding of Sec3 leaving prematurely in mammalian cells, as well as data from the Toomre lab.

Reviewer #3 (Public Review):

Four decades after the seminal work of the Schekman's lab on the genetic identification of the core eukaryotic secretory machinery the molecular roles of the individual components have been largely characterized. Yet our understanding of how these components are organized to define processes is wanting, with notable controversies still hovering over at several levels of the secretory pathway, including the events that take place in the ER/Golgi interface, the transit across the Golgi, the biogenesis of secretory vesicles and the delivery, tethering and docking of these vesicles to the membrane. This manuscript mostly addresses the latest steps of this chain of events and makes some incursions into the biogenesis of vesicles at the TGN. It represents a serious and honest attempt to define the timeline of events that, driven by key components such as the Sec4 ras-in-brain (Rab) GTPase, its effectors myosin-5, Sro7 and the exocyst, its GEF, Sec2 and the prototypic Sec/Munc protein Sec1, a regulator of trans-SNARE complex formation, ultimately result in the tethering, docking and fusion of vesicles with the membrane of the polarized bud of the ascomycete yeast Saccharomyces cerevisiae. Tethering, as defined by light microscopy appears to be a robust process reproducibly lasting for five seconds, before fusion, as defined by the loss of vesicle components, takes place. Important evidence is provided that the exocyst is incorporated as an holo-complex to secretory vesicles. Overall, even though this work will likely suffer modifications and amendments as knowledge and technology progress, it will nevertheless become the reference blueprint around which any future work in the field will pivot.

This work represents a very substantial advance in the field of exocytosis. Besides reporting with unmatched time resolution the tethering of vesicles with the membrane, it describes a herculean effort to gain mechanistic understanding of the process by using a score of genetic perturbations and fluorescent reporters. I feel that evidence that Sec3 travels with the exocyst rather than contributing a milestone for exocyst landing will be disputed, but this referee finds it as convincing as appealing. Nearly as important is the timing of Sec1 action in the fusion step. However, it is the delineation of a timeline that will make this paper a reference in the field.

Understanding the technology for image acquisition is critical to appreciating the strengths of this MS (333 ms/Z-stack time point may be considered super-resolution - in the time dimension. Therefore, its description requires clarification in places. The experimental work is almost exclusively based on live microscopy using fluorescent proteins tagged by allelic replacement. The microscopy routine for single fluorophore analysis provides time series with a resolution of 3-5 fps that enables authors to resolve, using robust statistical tests, events separated by seconds. In this context, it is notable that dual-channel imaging appears to be made by sequential, not simultaneous, acquisition, which deserves a currently missing comment. Moreover, given the weight that image acquisition plays in this project, it might be described and justified better. The Materials and methods lack detail, for example, the laser lines & power used for excitation. This referee could not fully understand the routine of image acquisition, specifically, the continuous movement of the stage in the Z-axis as images are streamed (to the RAM or to the disk? the latter takes time, line 177); does it mean that Z-stepping is solely governed by the exposure time? The CCD camera penalizes pixel size (16 µm) at the expense of achieving outstanding quantum efficiency. The optical path includes a 100x objective and a 2x magnification lens to compensate for the large camera pixel size, thereby achieving 0.085 µm/pixel, but these lenses 'waste' part of the fluorescent signal. One wonders if the CMOS camera (6.5 µm pixel size) coupled with a 63x objective wouldn't be appropriate? A brief discussion on this choice would be helpful for readers.

There is an elephant in the room of in vivo microscopy that no one dares to comment on: reporter proteins are mutant versions carrying a heavy and potentially oligomerising rucksack - the fluorescent protein tag. The authors take the honest approach of acknowledging that some of the tagged proteins such as Sec4 are disfunctional and that certain reporters are incompatible with each other as they give rise to synthetic negative effects. In the end, they conclude that using diploids carrying the GFP-tagged allele in heterozygosis with the wt represents the most physiological approach to track proteins until less intrusive fluorescent tags are developed.

It is remarkable that Sec2 and Sec4 are recruited to membranes even before a vesicle is formed (Fig 6I). I find somewhat weak the evidence that RAB11s 'mark' the TGN, and disturbing the fact that RAB11 reaches the PM (does GFP tagging prevent GAP accession?). I should like to recommend strongly that the authors integrate into the introduction/discussion information on the late steps of exocytosis available for Aspergillus nidulans, another ascomycete that is particularly well suited for studying this process. Here RAB11 is not a late Golgi resident but is transiently (20 s) recruited to TGN cisternae in the late stages of their 120 s maturation cycle to drive the transition between Golgi and post-Golgi (Pantazopoulou MBoC, 2014). Recruitment of RAB11 to the TGN is preceded by the arrival of its TRAPPII GEF (Pinar, PNAS 2015; Pinar PLOS Gen 2019), a huge complex that is incorporated en bloc to the TGN (Pinar JoCS, 2020). Upon RAB11 acquisition RAB11 membranes engage molecular motors (Penalva, MBoC 2017) to undertake a several-micron journey that transports them to a vesicle supply center located underneath the apex (review, Pinar & Penalva, 2021). Here is where Sec4 is located, strongly indicating that there is a division of work between two Rabs each mediating one of the two stages between the TGN and the membrane (Pantazopoulou, 2014, MBoC).

Reviewer #1 (Public Review):

This article presents an assay to measure the viscoelastic properties of living cells based on their deformation and tank treading motion in a viscous flow. This experimental technique could indeed be easy to implement and thus be adapted in other labs. The analysis of the experiment, measuring G' and G' and the derived quantities, for instance, is not so straightforward but the authors publicly share their analysis tools. They validate their approach by comparing their results to similar measurements made by AFM on elastic PAAm beads and THP1 cells, the two techniques give very close values of the mechanical parameters. Once the robustness of the technique is established, they apply it to three case studies: the dose-response of cells to Latrunculin, the effect of cytochalasin on intermediate filament-deficient cells and the effect of the cell cycle on cell mechanics.

I have no criticism on the experiments and their analysis that are very convincing.

Reviewer #2 (Public Review):

In the manuscript, the cellular deformation that is due to the shear stress generated in a classical microfluidic channel is used to deform detached cells that are moving in the flow. A very elegant point of the paper is that the same cells are used in the provided software to determine the fluid flow, which is a key parameter of the method. This is particularly important, as an independent way to crosscheck the fluid flow with the expected values is important for the reliability of the method. Instead of complicated shape analysis that are required in other microfluidic methods, here the authors simply use the elongation of the cell and the orientation angle with respect to the fluid flow direction. The nice thing here is that a well-known theory from R. Roscoe can be successfully used to relate these quantities to the viscoelastic shear modulus. Thanks to the knowledge of the fluid flow profile, the mechanical properties can be related to the tank treading frequency of the cells, which in turn depends on the position in the channel, and the flow speed. Hence, after knowing the flow profile, which can be determined with a sufficiently fast camera, and the actual static cell shape, it is possible to obtain frequency dependent information. Assuming then that cells do have a statistically accessible mean viscoelastic property, the massive and quick data acquisition can be used to get the shear modulus over a large span of frequencies.

The very impressive strength of the paper is that it opens the door for basically any, non-specialized cell biology lab to perform measurements of the viscoelastic properties of typically used cell types in solution. This allows to include global mechanical properties in any future analysis and I am convinced that this method can become a main tool for a rapid viscoelastic characterization of cell types and cell treatment.

Although it is both elegant and versatile, there remain a couple of important questions open to be further studied before the method is as reliable as it is suggested by the authors. A main problem is that the model and the data simply don't really work together. This is most prominent in Figure 3a. This is explained by the authors as a result of non-linear stress stiffening. Surely this is a possible explanation, but the fact that the question is not fully answered in the paper makes the whole method seems not sufficiently backed. I agree that the test with the elastic beads are beautiful, but also here the results obtained with the microfluidic method and the AFM seem not to match sufficiently to simply use the proposed model in conjecture with a single powerlaw approach to fully translate the single frequency data into a frequency dependent plot. There are more and more hints that two powerlaw models are more reasonable to describe cell mechanics. If true this would abolish the approach to exploit only a single image to get the mechanical powerlaw exponent and the prefactor in a single image. Despite all the excitement about the method, I have the feeling that the used models are stretched to their extreme, and the fact that the only real crosscheck (figure 3a) does not work for the powerlaw exponent undermines this impression.

Reviewer #1 (Public Review):

In this study, Yan and colleagues performed a comprehensive analysis of information flow during an episodic memory task. Using the information transfer technique from Michael Cole's group, they showed that there were different patterns of information transfer during encoding and retrieval phases of the episodic memory task. They showed greater correlation between information transfer and task activation during the encoding phase, compared with the retrieval phase. Furthermore, information transfer intensities could be used to predict memory performance, above and beyond using simple functional connectivity or task activations. A mediation analysis showed that task activation indirectly affected memory performance via information transfer. Finally, information transfer was stronger for direct anatomical connections compared with no/weak anatomical connections (as measured by diffusion MRI).

Overall, I think this is an ambitious study, packed with an impressive amount of analyses. However, there are serious issues that need to be addressed, including the fact that there is a serious lack of methodological details, so it is hard to fully judge the paper. Furthermore, given that this is a memory study, an obvious weakness is the lack of inclusion of hippocampus in the analyses.

1) From what I can tell, the authors only utilized cortical regions in their analyses. Their HIPP system basically covered parahippocampal and entorhinal cortex, but does not include the hippocampus itself. This seems like a major weakness given this study is focused on episodic memory.

2) Sentences like "The activity flow mapping procedure unified both biophysical and computational mechanisms into a single information-theoretic framework" are overly strong and causal. How does activity flow incorporate biophysical mechanisms?

3) I have some conceptual concerns about information transfer. Basically, information transfer (ITE) is defined as the difference between the goodness of matched prediction (MatchAB) and goodness of mismatched prediction (MismatchAB). As such, ITE can be significant even if goodness of matched prediction is poor, as long as goodness of mismatched prediction is even worse.

4) It is still unclear to me whether the information transfer mapping was applied to the task contrast beta values? There was also no explanation of what task regressors were used? For example, did the authors simply model the each encoding and each retrieval trial as a "block"? And each encoding and retrieval trial had its own beta coefficient? In the analysis, were the betas from control trials utilized? For the retrieval trials, did the authors distinguish between correct and incorrect trials?

5) I don't understand how Figure 5 was obtained. My understanding is that information transfer intensity is a 360 x 360 matrix (Figure 2). How do we end up with a spatial map? What is being correlated for each region? The correlation is across subjects?

6) The associations between information transfer intensity and memory performance (Figure 6) are impressive. The strength of prediction (Figure 7) is also quite strong. I would suggest the authors also report the coefficient of determination R^2 (see https://doi.org/10.1016/j.neuroimage.2019.02.057).

7) The section "Prediction model establishment" is unclear. More specifically, it is unclear whether the prediction results were good because of "circular reasoning" with feature selection performed on the full set of subjects. This seems to be the case based on lines 816 to 819: "First, correlation analysis (Pearson correlation, Spearman correlation or robust regression) between each edge in the information transfer matrices and memory scores was performed across subjects. Second, a threshold was applied to the matrix that only retained edges that were significantly and positively correlated with behavior (p < 0.01)". The feature selection process should only be performed in the training set (n - 1 subjects).

8) In this dataset, did the bipolar group have worse memory performance than the control group? If not, I don't see the value of Figure 10. If yes, could differences in information transfer intensity explain the performance gap?

9) The UCLA dataset also contains participants with ADHD and schizophrenia/schizoaffective disorder. Did these participants also have worse memory performance? Why were these two other patient groups not considered?

10) More details need to be provided about the analysis in Figure 10 - Supplementary Figure 2. For example, were training and prediction performed for each trial? The input is also not clear. The authors wrote "The input was the averaged resting-state BOLD signal in each brain region" So the authors averaged the bold signal within each ROI, but did they use the first time point of the trial? Do they average BOLD signal across all timepoints within each trial? What was the cost function they minimize? What was the step size? Neural networks have many hyperparameters? How were they tuned? Hyperparameters should be tuned on a separate validation set, which is separate from the training and test set. How did the authors split their data into training, validation and test sets? In Figure 10 - Supplementary Figure 2, the authors drew the ROI signals like a time series. So did the authors feed in a time series to the neural network or was it a single input vector?

Reviewer #2 (Public Review):

In this paper, Yan and colleagues examined the relationship between resting-state functional connectivity and memory task-related activity. They used a recently developed method for measuring the flow of information across network nodes. This technique allows one to test the predictive relationship between two regions, weighted by their functional connectivity, and to link these predictions to differences between task conditions. The relation between resting-state functional connectivity and task-related activation differences is an important and timely issue. A few previous studies have investigated this question in the context of memory, although none to my knowledge using this technique. However, the manuscript does not clearly justify what new knowledge is gained through this technique, nor how these findings are specific to memory versus other kinds of cognitive tasks. Part of the issue is simply that the paper provides insufficient explanation of the technique- it takes for granted that the method reveals the 'cognitive information transfer between brain regions' without explaining what exactly this means or why this should be the case. Another issue is that the paper provides insufficient details about how the analyses are implemented. For instance, it is not clear which task conditions are being compared to support the information transfer analysis- but this is absolutely critical for evaluating the methods and interpreting the results. Finally, the results are not well integrated with the memory literature, limiting its impact on the field.

Reviewer #3 (Public Review):

The stated goal of the paper and analyses is to "provide a more intuitive understanding of the brain communication mechanism underlying episodic memory." This question is quite vague and, ultimately, I do not think the paper succeeds in providing a more intuitive understanding of how brain regions communicate during episodic memory. The paper presents some interesting methods, but I do not think the results leave the reader with a clear(er) intuition about how and why the various measures do or do not differ from alternative, existing approaches for measuring correlations/interactions/connectivity between brain regions.

The paper lacks an overarching question or theoretical framework. Instead, the paper has a stronger methods focus. The methods are sophisticated and potentially interesting, but the payoff of these methods is not established. What is the key new insight? What problem does this new method solve that other, existing methods cannot solve? These concerns are amplified by the fact that many of the results are actually entirely consistent with existing evidence (e.g., see the Discussion).

The paper is quite dense, with an abundance of different analyses and methods. The result is that none of the individual results are considered in sufficient detail or with a sufficient number of control analyses or comparisons that would help establish the true utility of the current method compared to existing methods.

The description of the main measure as "information transfer" is misleading. The term implies that the measure is reflecting some kind of information about the stimulus (or memory state) and that there is some directionality (in time) to the transfer. But neither of these is true. The "information" measure is simply related to the accuracy with which a distributed pattern of activity is predicted from one region/voxel to another. But, if two different regions (or voxels) have correlated activity, then it will be mathematically true that one region's activity 'predicts' the other region's activity. I am not saying that this is unimportant or uninteresting, but it is different than showing that some information (e.g., about the stimulus or task state) that is contained within one region is (re) expressed in a different region at a subsequent time point (which would be more consistent with information transfer).

Ultimately, the regions that were implicated in encoding and retrieval using the information transfer method are precisely the regions that would be expected based on univariate studies from the past 2 or 3 decades. I realize that the approach used here for identifying these regions is much more sophisticated, but is the method ultimately just picking up on univariate effects (in a more sensitive way)? For example, is it the case that a high percentage of successful "information transfer" at retrieval is BECAUSE that region tends to show univariate activation (or deactivation) during retrieval? Indeed, the paper specifically reports positive relationships between task evoked responses and information transfer. The question, then, is whether the measures are ultimately redundant-or to what degree are they redundant? It therefore seems essential to show that there is some fundamental, new insight afforded by the information transfer method compared to task-based univariate measures. Or, for some of the specific relationships between regions that differed during encoding vs. retrieval, the question would be whether the current method has a clear, demonstrable advantage relative to other connectivity approaches like beta-series correlations? The effects in Figure 4, for example, do appear to demonstrate some very robust differences between encoding and retrieval, but without a direct comparison against alternative methods, the potential impact of this approach is not clear.

Even for places where the information transfer is compared against other methods (e.g., task activation) it is not clear whether this is a fair comparison-are the number of features/predictors matched across these measures?

The data in Figure 10 are perhaps the most interesting because they highlight a dramatic difference between task-evoked measures and the information transfer measures. But, again, a deeper consideration of these data-and potential explanations-would be helpful.

The "brain state" analysis (line 422) is not well motivated and it was hard to understand exactly how this analysis was performed (or why). Likewise, the artificial neural network is considered in such superficial detail that it is hard to draw much of an inference from the simulation.

While there are some clear relationships between information transfer measures and memory performance (Fig 6), it would again be useful to compare these relationships using measures other than information transfer. For example, what about resting state FC? This was done for the analyses in Figure 7 - Supp 2, but not for the analyses in Fig 6. In fact, resting state FC was almost as good a predictor as the information transfer measure. The comparison against resting state FC is a particularly relevant comparison because several of the networks show fairly similar correlations regardless of whether the task was encoding or retrieval (see Fig 6). If correlations persist when using resting state measures, this would undermine any argument about these correlations reflecting the success of "information transfer" that specifically occurred during the memory task.

The Discussion is not well focused and is overly speculative. There is a lot of speculation about how the different network interact and what, exactly, these interactions reflect. But the current study really does not inform these ideas one way or another. For example, there is discussion of attention, recollection vs. familiarity, etc., but none of these things are tested in any way in the present study.

Reviewer #1 (Public Review):

In the manuscript, by Jiang et al. ("Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis") the authors showed how the investigation of co-mutations can shed some light on tumor progression and the different outcomes for several tumor types. While this manuscript contributes to the field providing results of approximately 30,000 subjects (and over 50 cancer types), they aim to cover a larger number of subjects and tumor types than previous works. The authors are using different sources of public data and a great amount of genetic data to find co-mutations and show their relevance to most cancer types. However, it was not very clear why they are using different sources that use different methodologies to find mutations.

Reviewer #2 (Public Review):

Jiang et al. provide a repertoire of co-mutations in genes from 3 large cancer genomics datasets. The authors propose that some of the highly frequent co-mutations in some tumor types may be due to the high mutation burden associated with POLE and MSI signatures. Furthermore, some of the most frequently co-mutated pairs involve large genes. In addition to showing disparities in co-mutations according to age, and race, they also identify co-mutations associated with survival.

Although the repertoire is extensive, there is no statistical test to determine whether a pair of co-mutations is significant compared to expected results from the mutation rate model. In Figure 1, Jiang et al. describe multiple known confounders, such as mutation rate and gene length, that have been integrated in multiple cancer driver discovery methods since 2013 (e.g. Lawrence et al., Nature, 2013, and Rheinbay et al. (PCAWG), Nature, 2020). With respect to the co-mutation analysis, the Maftools R package has a somatic interaction function that calculates Fisher's exact test to determine if co-mutations occur more than expected. The developers of DISCOVER, based on pan-cancer datasets, argued that most co-occurrence of mutations in genes is by chance (PMID: 27986087).

The association with survival is noteworthy, showing that TP53-KRAS is a predictor of survival for pancreatic tumors. However >95% of pancreatic cancer patients have KRAS mutations, making it unclear how the authors find a similar number for WT compared to mutated patients. In addition, the results from the TCGA UCEC and ICGC UCEC-US show an excellent prognosis of tumors with co-mutations MUC16-KIAA2022 and PTEN-PCDHB13, however, ~100% overall survival is typical for POLE hypermutated endometrial cancers. It might be that co-mutations of MUC16-KIAA2022 and PTEN-PCDHB13 are biomarkers of POLE tumors. It will be important to test this, demonstrating again the importance of statistical tests to determine if the frequencies of these co-mutations are significantly enriched.

Jiang et al. provide a repertoire of co-mutations in genes from 3 large cancer genomics datasets. The authors propose that some of the highly frequent co-mutations in some tumor types may be due to the high mutation burden associated with POLE and MSI signatures. Furthermore, some of the most frequently co-mutated pairs involve large genes. In addition to showing disparities in co-mutations according to age, and race, they also identify co-mutations associated with survival.

Although the repertoire is extensive, there is no statistical test to determine whether a pair of co-mutations is significant compared to expected results from the mutation rate model. In Figure 1, Jiang et al. describe multiple known confounders, such as mutation rate and gene length, that have been integrated in multiple cancer driver discovery methods since 2013 (e.g. Lawrence et al., Nature, 2013, and Rheinbay et al. (PCAWG), Nature, 2020). With respect to the co-mutation analysis, the Maftools R package has a somatic interaction function that calculates Fisher's exact test to determine if co-mutations occur more than expected. The developers of DISCOVER, based on pan-cancer datasets, argued that most co-occurrence of mutations in genes is by chance (PMID: 27986087).

The association with survival is noteworthy, showing that TP53-KRAS is a predictor of survival for pancreatic tumors. However >95% of pancreatic cancer patients have KRAS mutations, making it unclear how the authors find a similar number for WT compared to mutated patients. In addition, the results from the TCGA UCEC and ICGC UCEC-US show an excellent prognosis of tumors with co-mutations MUC16-KIAA2022 and PTEN-PCDHB13, however, ~100% overall survival is typical for POLE hypermutated endometrial cancers. It might be that co-mutations of MUC16-KIAA2022 and PTEN-PCDHB13 are biomarkers of POLE tumors. It will be important to test this, demonstrating again the importance of statistical tests to determine if the frequencies of these co-mutations are significantly enriched.

Reviewer #1 (Public Review):

To address this question, the authors combine fully resolved fluid mechanics numerical simulations of an odor plume with the framework of partially observed Markov decision process (POMDP) - a framework for devising the optimal decision-making policy that an autonomous agent should use in order to achieve its goal given that it only has partial access to information to guide its decisions. The main result is that while stopping to sniff in the air bears the cost of halting progression towards the source - animals tend to stop moving to sniff in the air - this is offset by the benefit of being able to detect odor packets at a larger distance from the source (odors travel a shorter distance near the ground). Interestingly, sniffing in the air takes place more often when the agent loses the plume and tends to coincide with periods when the agent casts crosswind (a known strategy used by animals to regain contact with the plume) while sniffing near the ground is preferred when the agents are within the plume.

In a second part of the paper, the authors concentrate on the search dynamics far from the source where ground cues tend to be absent. Combining analytical calculations and a simplified POMDP (for this part they ignore ground sniffing) they ask: 1) how wide should the agent cast? 2) how long should the agent spend casting before surging upwind? 3) where should the agent sniff during the casting phase? Here the main results are that surge length and cast width should equal the detection range x_thr and the prior width of the plume L_y respectively. They also find that the optimal time to spent casting obeys the marginal value theory, i.e. it is the time at which the marginal value of staying in a cast equals that of surging and exploring a new yet unexplored patch in the agent's belief of its own position relative to the source. These results provide a rationale for the observed alternation between ground and air sniffing, and for casting and how the timing between these events should be selected.

I find the question relevant, the quantitative analysis carefully reasoned, and the results compelling and of broad interest. The authors should address the following comments, which mostly center around clarifying the assumptions made regarding the agents' prior knowledge, and the need for better placing this study within the context of previous research, especially regarding memory requirements of the strategy and comparison with more reactive (memory-less) strategies. Finally, a broader discussion of the limitations of the current study (e.g. what happens if x_thr and y_thr change over time?) and of the next steps would strengthen the paper.

An assumption behind the entire study is that agents can hold in memory their belief, which in this case is their location relative to the expected location of the source. Over time this memory enables agents that start with a wide prior to refining their belief. This strong assumption makes the strategy discussed here quite different from other more reactive strategies proposed in the literature that do not require agents to build an internal map of the expected location of the source. While it is easy for a robot to maintain such a memory, how and to what extent animals do so using known mechanisms such as path integration and/or systems such as grid and place cells is less clear. A more explicit description of the key memory requirements of the strategy discussed here (once learned) and a discussion of how it might be implemented by animals, as well as a discussion of the differences in that aspect with other strategies proposed in the literature, including reactive strategies, would strengthen the paper and significantly broaden is significance.

Along the same lines, the study assumes that the agent stores an internal model of the statistics of the plume, e.g. x_thr and y_thr, L_y etc. The predictions made in 6e/f, for example, are likely only valid if the agent already knows the constraints of the plume it is searching for (i.e. x_thr and y_thr), which seems unlikely in most natural scenarios. Perhaps the authors could discuss some ways in which these might be inferred. The authors nicely show that an agent trained with the Poisson model navigates well even in the full time-dependent simulation. But what is missing is a discussion of how animals would get trained in the first place and what information they would need access to in order to do so. Perhaps examine how an agent trained in environment A performs in environment B as a function of how strong the statistical difference between environment A and B are. One could for example change the Poisson statistics between A and B.

Related to the previous point: the simulated plume is straight, i.e. there is no variation in the mean flow and therefore no random meandering of the plume. This means that once the walker hits the center of the plume, if it orients upwind, it is likely to reach the source because there is a continuous stream of odor on the ground it can follow, with just a few castings whenever it drifts slightly off the centerline. Is there a way for the authors to explore what would happen in the case of meandering plumes without having to run another massive simulation? Perhaps a simplified model of odor plume could be used or one could even just use the same simulated plume Poisson statistics but translate this solution perpendicular to the main flow at a slow oscillatory rate. Will the navigator now stop and sniff in the air more often? Will these sniffing events coincide with moments when the navigator loses the plume? Will agents be able to still use a constant x_thr and y_thr or would they have to learn their statics? Or will agents revert to a more memoryless or hybrid strategy?

How does the benefit of sniffing the ground vs the air change if odor molecules adsorb and de-adsorb on the surface, thus increasing the distance from the source where ground odor can be detected?

There is a difference in clarity between the first part of the paper and the second part that starts at line 232 with the section "Searching for airborne cues". I recommend the authors work on that second section to improve clarity. For example, the goal of that section is not immediately clear. The first paragraph talks about expanding on the intuition gained from the first part and "to address the search dynamics" but does not spell out what key question about search dynamics is to be addressed. This only becomes clear at line 260. Knowing where this is going would help readers understand the motivation behind the simplified model. Maybe lines 258-263 or something similar could be moved into the first paragraph of that section. Also related to the previous comments it would be helpful to clearly state what is assumed known by the agent and what is not. Is the agent assumed to have learned the values of x_thr, v and N in equation (2) before starting the search?

As we progress through that section, important details start to be omitted and making it more difficult to follow. For example, what is the definition of t_sniff (I am guessing it is given in line 313?)? What is meant by optimization depth (line 316)? What is meant by episode index, is this referring to N (line 322)? Can the authors provide intuition about why the optimized casting strategy expands over time rather than starting wide right away (line 315)?

Reviewer #2 (Public Review):

This manuscript describes how animals can more accurately find targets using olfactory cues by alternating sniffing close to the ground and sniffing up in the air. Near the ground, the air is less turbulent but contains signals of a smaller magnitude. High in the air, the signals propagate further but are more intermittent. The authors perform large-scale simulations of odor concentrations taking into account wind, turbulence, and the impact of the boundary layer near the ground. The authors then use these simulations results to find the optimal sequence of decisions about moving forward, sniffing near the ground, or up in the air. The decisions are made using partially observable Markov decision processes.

The simulations of odor distributions will provide a useful contribution to the field independent of the conclusions on optimal search strategies

Reviewer #1 (Public Review):

The authors used single cell RNA-seq to assess the heterogeneity of megakaryocytes, thereby identifying a distinct CXCR4 high subpopulation that was also enriched in inflammatory genes and other chemokines or cytokines. They sort CXCR4 high cells and are able to investigate specific functional properties of this megakaryocyte population. This work complements prior studies which have suggested immune modulatory roles for certain megakaryocyte subsets such as the work of Pariser and colleagues (JCI 2021) on the antigen presentation capacity of lung megakaryocytes or the work by Liu et al (Advanced Science 2021) on immune surveillance gene expression in megakaryocytes (MKs).

The strengths of the paper are:

1. Analysis of scRNA-seq to identify MK subsets with validation

2. The use of sorted CXCR4 cells to interrogate the specific in vitro functions of this immune modulatory subset (using CXCR4 low MKs as a comparison) such as phagocytosis assays

3. Elegant use of the PF4-Cre DTR model to ablate MKs while replenishing CXCR4 high cells as a means to assess functional effects of this subset in vivo which is a reasonable approach in the absence of a Cre that would specifically delete this subset.

Potential weaknesses are:

1. The unclear distinction between previously identified immune modulatory MK subsets such as the lung MKs which have antigen-processing capacity (Pariser et al) and the currently identified MK5 subset. The authors indicate that the MK5 subset has transcriptomic similarities to the previously described antigen-processing MK subset but this does not explain whether MK5 and/or CXCR4 high subset is indeed the primary antigen-processing subset in MKs. This is an important question because it would help address whether the immune modulatory roles are all concentrated in one MK subset or whether different MK subsets may play distinct roles in innate and adaptive immunity. For example, in Fig 3, there is a broad claim that MKs can modulate innate and adaptive immunity but it is not clear whether this claim is valid only for the specific MK5/CXCR4 subset.

2. It would be helpful to understand whether the CXCR4 status of MKs can change over time. Are the CXCR4 high cells generated in infection (Fig 5) generated by the conversion of CXCR4 low cells (or non MK5 cells)? Or do CXCR4 high / MK5 cells differentiate from MK progenitors directly?

Reviewer #2 (Public Review):

Wang J. et al. examines bone marrow megakaryocyte (MK) heterogeneity, and the role that a specific subpopulation plays in the mouse immune response to Listeria monocytogenes infection. Using single cell RNA-sequencing (scRNAseq) the authors identified a bone marrow MK subpopulation, characterized by high CXCR4 expression. This subset referred to as MK-derived immune-stimulating cell (MDIC) population has immune-stimulatory properties and supports the migration and activation of innate immune cells potentially via TNFα and IL-6 secretion.

In agreement with recent studies mapping in situ myelopoiesis which occurs near bone marrow sinusoidal vessels upon acute inflammatory stress with L. monocytogenes (Zhang J. et al Nature 2021), the authors observed a significant association of myeloid cells with perivascular CXCR4high MK but not with the more abundant CXCR4low MK subset. This study also revealed that MK in vivo deletion leads to a significant increase in the bacterial load in extramedullary hematopoietic organs accompanied by a reduction in the number of myeloid cells, although it is unclear if a similar MDIC population exists outside the bone marrow. Accordingly, it is unclear the effect of MK depletion in the context of L. monocytogenes infection in bone marrow myelopoiesis.

Notably, in a rescue experiment, MDIC infusion was able to partially rescue the bacterial clearance defect in MK depleted and infected mice, further confirming the important role of MDICs in regulating bacterial immune responses.<br /> Using Pf4-cre reporter mice the authors further evaluated the capacity of bone marrow MDIC to enter circulation and migrate into organs upon bacterial infection potentially in response to an increase in CXCL12 expression in extramedullary organs. Finally, in agreement with recent studies (Haas S. et al Cell Stem Cell 2015), Wang et al. discovered that upon inflammatory stress, emergency hematopoietic stem cell-derived megakaryopoiesis is activated to restore platelets lost upon inflammation-induced thrombocytopenia but also to regulate immune response to bacterial infection.

Overall, this study builds on recently published work regarding MK heterogeneity which technically is very challenging to investigate. Although it's suggested that MDIC greatly overlap with the recently described CD53+LSP1+ MK immune population (Sun S. et al Blood 2021), it is still unclear the extent to which these subsets overlap, accordingly, it's still unclear the relationship between bone marrow MDIC and previously described lung MK subsets, though to be enriched in immune function. Nevertheless, the authors performed a detailed characterization of bone marrow MDIC in homeostasis and in acute inflammatory stress, providing new evidence and mechanistic clues on the mechanisms by which MK subsets regulate immune function to bacterial infection.

While this manuscript has many strengths, some of the author's conclusions and claims require further technical support and discussion. In particular:

The potential mechanism via TNFα and IL-6 secretion is very interesting, however further data is necessary to support the author's claim. First, it's unclear if steady-state MDIC MK express TNFα and IL-6. If so, does this expression change upon infection? Second, mechanistically it would be important to evaluate or at least discuss how MDIC sense bacterial infection and respond by secreting TNFα and IL-6. Third, in Fig 2L and 2M it's missing a control for the effect of anti-TNFα and anti-IL-6 on phagocytes activity in the absence of MKs. Fourth, in Fig 2J and 2K it's unusual to evaluate TNFα and IL-6 levels by imaging.

The authors further explored the potential role of MKs in regulating adaptive immune function against bacterial infection, however these studies were very superficial and further studies are needed to substantiate this claim.

Overall, the study relies heavily on subjective imaging quantification. The identification of CXCR4high and low MK subsets does not seem entirely objective and it is prone to inaccuracies due to the technical difficulty of bone imaging. The usage of other surface marker(s) for the MDIC subset would significantly improve the study. Accordingly, many of the experiments should be accompanied and/or replaced by flow cytometry analyses such as the phagocytosis experiments in Fig 2; quantification of MKs in Fig 4 H, I and N.

Regarding MK-deletion experiments, studies from the Passegue lab have shown that this will cause persistent bone marrow myeloid granulocyte/macrophage progenitor (GMP) formation during 5FU stress, most likely due to the reduction in the levels of PF4 and TGFb1 and the effect on hematopoietic stem cells. What happens to bone marrow myelopoiesis upon MK-deletion and bacterial infection? The authors describe a significant reduction in the liver and spleen but it's unclear the effect on the bone marrow. It would be helpful to discuss this point.

Reviewer #3 (Public Review):

Overall this is an interesting study that adds significant knowledge to our understanding and characterization of Mks as immune cells. The identification of CXCR4hi Mks as immune regulatory cells is potentially important, particularly in the bacteria model used in this study.

At this stage, the authors have however made a number of conclusions not yet supported by the data. In particularly differentiating the role of Mks versus the platelets they produce is not clear, so many conclusions about MDIC in immune responses need to be better supported and differentiated from platelet functions.

Reviewer #1 (Public Review):

Overall the main conclusions of the papers are supported and justified by the author's data with one potential weakness on the gamma-power calculations.

Strengths<br /> 1. The authors adopted a novel cue-based detection behavior paradigm where animal detection of cue is dependent on the attention level. This task is essentially crucial to the author's claim that Scn8a heterozygotes have attention deficits and subsequent tests to unravel the mechanisms.<br /> 2. The authors then take advantage of these validated tasks and carried out a series of optogenetic 'gain and loss of function' experiments and simultaneous neural recordings to understand the mechanisms involved.<br /> 3. The authors rigorously examined specific reliance of PV neuron activity during attention related cue presentation, they used machine learning based algorithms to mine their data and their trained classifier can correctly identify the correct trial with 92% precision.<br /> 4. Very interestingly, optogenetic activation of PV neurons at gamma-frequency increased the animal's ability on the cue-based attention task, together with their earlier results that gamma-band power was reduced in SCN8A heterozygotes, and the well-documented involvement of PV neurons in the gamma-oscillations, this experiment supports the author's claim that PV cells activities are important cellular substrate for attention during the cue-presentation.

Weaknesses<br /> The main weakness has to do with the claim of reduced Gamma powers during AET (Fig 6). 1) The results described a 'rel. power' in each of the panels (Fig 6A-F), However, I could not find a description of how the 'rel. power' is calculated. In the methods section (Line 546 to 549), it was described as the theta or beta band was extracted. I don't know what extracted means, please clarify. 2) Additionally, only the high-gamma-band (i.e. 60-90Hz) showed significant reduction in the Int and Long cues, so maybe the conclusion should be more specific on the high-gamma power? 3) there are appears to be two sets of statistics for the power calculation (Fig 6 legend), and one P value for the long is larger than 0.05 (0.0597 ), I am confused with these p values and I hope the authors can clarify it for me. 4) The representative traces presented in panel B of Fig 6 appears to show that the amplitude of high -gamma, but not low-gamma was reduced during the cue presentation phase compared with the pre-cue phase, is this correct?

Reviewer #2 (Public Review):

In this manuscript, Ferguson and Huguenard use Scn8a+/- mice to investigate the mechanism underlying attentional deficits seen in absence epilepsy. They report that Scn8a+/- mice perform worse than controls when informational cues about the location of rewards are of intermediate duration. Interestingly, this attentional deficit seems unrelated to acute seizure activity because a) seizure activity in the S1 was lower during the task than in home cage and did not differ with performance and b) valproic acid reduced seizure activity but did not improve attentional performance. Stimulating mPFC GABA neurons throughout the cue in control mice also disrupts performance (across all cue lengths), indicating that this task depends on the mPFC. Moreover, long and intermediate cues increased calcium activity in PV interneurons on correct trials, and this feature was attenuated in Scn8a+/- mice. Similarly, mPFC gamma power (thought to be mediated by PV interneurons) was reduced during intermediate cues in the Scn8a+/- mice. Finally, optogenetic stimulation of PV neurons at 40 Hz improved the performance of Scn8a+/- mice to the intermediate cues.

Overall, I think this well-written manuscript makes a compelling argument that mPFC PV dysfunction contributes to attentional deficits in Scn8a+/- mice, which is of translational interest. While I think the overall contours of the argument make sense, some of the details of the results and analyses need clarification.

1. From the presented data, the link between cue-evoked PV activity and disrupted Scn8a+/- attentional performance is unclear. This lack of clarity may stem from the way data is presented. What one would have expected the data to look like is an increase in PV activity (either in time to peak, peak, or average response) in controls that correlates with performance and then an attenuation in that parameter in the Scn8a+/- mice in the condition where they show attenuated performance. Instead, a) PV activity seems to increase even in conditions with low attentional demands, suggesting that cue-evoked PV activity is not necessarily linked to attention. b) Animals are collapsed across genotype to reveal the parameter that correlates with performance (time to peak and peak, but not average). This analysis is potentially circular since the animals that perform poorly tend to be the Scn8a+/- mice. It would seem from the text that a similar concern impacts the machine learning/ROC analyses as well. C) Oddly, only one of these parameters that correlates with performance (peak) is reduced in Scn8a+/- mice. Moreover, it is also reduced at cue length with low attentional demands, yet has no behavioral impact. D) Instead, one of the parameters that does not correlate with performance (amplitude) is attenuated in Scn8a+/- mice at the intermediate cue. These 4 discrepancies weaken the argument that cue-evoked PV activity necessary for attention in control mice is disrupted in Scn8a+/- mice.<br /> 2. The authors show that high, but not low, gamma power is reduced in Scn8a+/- mice at the intermediate cue associated with poor performance. However, they then rescue with low gamma stimulation. Does this low gamma stimulation increase high gamma power?<br /> 3. Is it possible that seizure activity in S1 and PFC are not synchronized? The authors have ecog data from PFC. What happens to seizures during the task there?<br /> 4. Questions about the ChR2 stimulation: perhaps it is a typo, but the methods say that 0.5 mW power were used. This is well below the effective stimulation power for ChR2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995811/ Similarly, the authors use continuous stimulation for 5 seconds to disrupt mPFC activity. Is this stimulation effective for the entire period? ChR2 desensitizes with continuous stimulation. https://www.eneuro.org/content/7/1/ENEURO.0222-18.2019

Reviewer #1 (Public Review):

The authors examined changes in brain region size in several groups of weakly electric fishes (Mormyroidea), the Gymnotiformes and weakly electric catfishes (Synodontis spp.), which evolved electroreception independently of mormyroids.

The major strengths and weaknesses of the methods and results are as follows.

Major strengths are the careful examination of many weakly electric fish, which are an interesting group for examination of mosaic growth. The analyses are thoughtful and well done. The use of phylogenetically informed linear models is an additional strength. One concern is whether the significant decreases in other brain areas are real.

The authors achieved their aims, and in general the results support their conclusions, except for the above mentioned concern about whether there are significant decreases in other brain areas.

Reviewer #2 (Public Review):

Schumacher and Carlson present volumetric data on the brain and main brain areas in several linages of fish that have independently evolved electroreceptors and electrogenesis. The main question is if the evolution of this novel sensory system has led to similar changes in the brain. Previously, the same authors (Sukhum et al 2018) have shown an increase in the relative size of the cerebellum and hindbrain in mormyrid fishes, one group of electrogenic fish. Here they have collected data on South American weakly electric fishes (Gymnotiformes) and weakly electric catfishes (Synodontis spp.) as well as some outgroups. (22 additionally species). I think the question is very interesting, and the inclusion of electrogenic catfishes is particularly interesting as they are a largely understudied group. I do have some concerns about how the data has been analysed and presented.

1) A first conclusion is that gymnotiform and siluriform brains are not as enlarged as mormyrid brains, and that this suggests that an increase in brain size is not directly tied to an electrosensory system evolution. I think the story here is more complicated than that. From the data presented, it seems that mormyrids have a different body size-brain volume slope than other groups, but is unclear if this was tested in the PGLS model for brain vs body size, although mormirids show different slopes than other groups in the scaling of the cerebellum to brian volume. This difference in slope for body brain allometry has been confirmed by a manuscript published after the submission of this manuscript (Tsuboi 2021 BBE) with a large data set (~ 850 species, 21 of Osteoglossiformes). This steep slope close to one means that mormyrids with large body size have very large relative brain sizes but smaller mormyrids don't (this can be seen in figure 2). I think this needs to be addressed more carefully. First testing in the PGLS for body size vs brain size if mormyrids have a different slope and then in the discussion. Why mormyrids but not other electrogenic fish have evolved such a unique brain scaling?

2) I think the number of outgroups species used are too few and spread among several different linages of teleosts. I think this unfortunately tampers some of the conclusions. Particularly seems to leave unanswered the question if other electrogenic fish have brain larger than non electrosensory or electrogenic fish. A large data set of brain and body size data for teleost has been published (Tsuboi et al 2018; 2021). Adding this data should allow to test for changes in body-brain size relationships in the each electrogenic clades. The addition of the additional data should allow to accurately test for difference in relative brain size between and within electrogenic clades and make it possible to test when exactly in the phylogeny of teleost have grade shits in the body-brain allometry have happened.

3) Next, the authors use a principal component analysis and phylogenetic linear models to test how much of brain variation is explained by concerted evolution vs mosaic and where the mosaic change have happened. Here, despite the few non electrogenic/electrocereptive species, the differences are more clear. I do think that in the case of the linear models, the use brain volume as the independent variable is unnecessary. By regressing the total brain volume, the authors are regressing each structure partially against the same value, and not surprisingly, this generates tight linear correlations. Further, this makes grade shifts (i.e. changes in relative size) less apparent. I think only brain volume -the structure should be used and shown in all figures. This has been the standard in the field when testing for grade shifts.

4) Related to the previous point, the authors report significant decreases electrogenic clades in the size of the olfactory bulb, rest of the brain and optic tectum. I think this is and artifact that results from including the cerebellum and other enlarged areas (TS and hindbrain) in the dependent variable. Similarly, the authors state that they found no increase in the size of the telencephalon in electrogenic clades and that non-electric osteoglossiforms have a mosaic increase in telencephalon relative to non-electric otophysans. Again, I think this suffers from the same problem. Figure 4-figure supplement 2 actually provides some insight in this respect. When plotted against the rest of the brain, no apparent differences are found in the size of the optic tectum. In the case of the olfactory bulb only two of the out-group species seem to have larger OB than all other species. Regarding the telencephalon, when plotted against RoB, all osteoglossiform seem to have similar telencephalon size. These conclusions need to be carefully evaluated.

Reviewer #3 (Public Review):

The authors use micro-CT scanning and sophisticated statistical techniques to compare the sizes of various major brain regions across a sample of 32 fish species, including lineages that have independently evolved passive electroreception and, in a smaller subset, the ability to generate and sense weakly electric fields. They found that most of the variation in brain region sizes is linked to variation in total brain size, indicating concerted evolution. However, the analysis also reveals that the electrogenic lineages/species have selectively enlarged the cerebellum, the midbrain torus semicircularis, and the hindbrain. These findings are interesting and usefully extend the last author's prior work on a subset of these species.

A significant strength of the work is that it includes a relatively large number of species, makes a good attempt to understand how these species are related to one another (though the authors admit that the phylogeny is tentative), and that the analytical methods are quantitative and relatively sophisticated. It is also true that other researchers have long argued about the relative frequency and importance of concerted versus mosaic evolution. The present study is a valiant attempt to address this issue.

However, some key results must be viewed cautiously. Most important is that the dramatic increase in the cerebellum (and torus semicircularis and hindbrain), relative to the rest of the brain, must necessarily lead to some other brain regions appearing to have decreased in size. Therefore, their absolute size may well have stayed the same or even increased in evolution; it's just that the enlarged brain regions decrease the proportions of at least some other regions. The authors mentioned this caveat in their previous paper on mormyroids (Sukhum et al., 2018), but not in the present manuscript. As a result of the problem, it is difficult to interpret the documented variation in olfactory bulb, optic tectum, or telencephalon size; is that variation "real" or just artifacts of major changes in the size of other brain regions (mainly cerebellum, torus, and hindbrain). The best way to address this problem would have been to repeat the analysis using a "reference" brain region that is thought not to vary dramatically in size across the species of interest (e.g., "rest of brain"). However, I acknowledge that this approach also has limitations. Still, the problem should be addressed somehow.

One strength of the manuscript is that it provides information about y-intercepts and slopes. Many other studies simply note increases or decreases in average volume (before or after correcting for absolute brain size). I like knowing which changes in relative brain region size are grade shifts (changes in intercept) versus changes in slope. However, the authors don't really do anything with those results. What do they mean? Are there different kinds of evo-devo mechanisms that underlie the two types of changes (slope versus intercept)?

On a related note, do the major brain regions vary in allometric slope within a given lineage? The realization that such differences do exist (at least in mammals and cartilaginous fishes) contributed much to the excitement around the concept of concerted evolution, since it means that evolutionary changes in absolute brain size can lead to major shifts in brain region proportions, but the authors seemingly ignore this point.

Finally, I must confess that some of the study's findings didn't surprise me. It is well known among fish neurobiologists that mormyrids have a dramatically enlarged cerebellum and that all electrogenic gymnotoids and mormyroids have a very large torus semicircularis and dorsal/alar hindbrain. One didn't need the fancy analytical techniques to confirm this. To be fair, however, it had not been clear whether the cerebellum is enlarged in gymnotoid electric fish and their non-electrogenic relatives (the authors report that it is). Nor was it known that the weakly electric catfishes have a larger cerebellum (not so much for the torus) than their non-electric relatives. This is new information that raises interesting questions about how the electric catfishes are using their electrosensory system (I would have liked to see some discussion of this).

On balance, I appreciate that the authors have provided a large and useful data set , which they used to address an interesting set of questions about how brain evolution "works." I'm just disappointed that, for me, there are relatively few significant, novel insights. For example, the notion that "selection can impact structural brain composition to favor specific regions involved in novel behaviors" (last sentence of the abstract) is one that I've accepted for a long time. Maybe the conclusion can be made more interesting by focusing more explicitly on changes in the size of major brain regions versus smaller cell groups (where mosaic evolution is widely accepted).

Reviewer #1 (Public Review):

The authors succeeded in providing a well-done investigation on the psychological impacts of the COVID-19 pandemic. The major strength of this manuscript is the sound methodology and the huge sample size recruited for the investigations of study aims. Another strength of this manuscript is incorporating the role of social media in this issue. No major weakness is apparent in this investigation.

Reviewer #2 (Public Review):

Daimer et. al investigated cross-sectional associations using an online survey for assessing the association of 2 predictor factors (i.e., life concerns of COVID-19 and social relationships) with 3 mental health outcomes namely schizotypal trait, depression, and anxiety related symptoms. The authors also assessed for mediating factors such as sleep duration, alcohol consumption, drug use, social media exposure, etc. and finally explored any mediating effects of anxiety and depressive symptoms in the association between the predictor factors with schizotypal trait. The main take-away message of the analysis is the direct positive associations observed for COVID-19-related concerns, social adversity with the mental health outcomes and also to some extent via the mediating effects of excessive media use.

The conclusions of this paper are mostly well supported by statistical analysis; however, some biases need to emphasized as limitations.

1. Authors do not address the plausible reasons for their finding on the association of more exercise with higher levels of anxiety.

2. Though the authors have stated the uncertainty of the observed associations but do not fully discuss its reasons, e.g., the sampling bias (i.e., recruitment via social media will lead to disproportionately selecting participants with excessive media consumption leading to biased associations) or volunteer bias (i.e., if some age-group/one particular gender/particular educational category are more likely to participate than others) which are inherent to this type of study designs.

Reviewer #1 (Public Review):

The authors have 3D-modelled the whole squamation of one of the most basal gnathostomes as the basis for establishing the distribution of scale forms over the body, showing that the squamation was highly regionalized. Based on the structure of the scales they hypothesize that a bipartite structure of scales could be a plesiomorphic character of jawed vertebrates.

The high-resolution CT scanning results are very impressive, and well figured out and described. This investigation is the first to use this method to reproduce a whole squamation, and as such has produced information not previously possible based on visual examination of articulated fossils. I see no weaknesses in their methods, and their results support their major conclusions. It is unfortunate that thin sectioning of scales was not more informative, but this is a preservation, not a preparative issue. Whether the study supports one of the authors' conclusions - that the development of a scale middle layer is accompanied by the complexity of the crown sculpture - is contentious.

No doubt this 3D scanning method will be used in the future on other whole squamations of early vertebrates, testing the hypothesis that a highly regionalized squamation is a plesiomorphic gnathostome feature - or the other possibility, that the limits of previous investigations based on a visual assessment of variation in squamation over an animal could not provide the same wealth of detail, resulting in underassessment of regionality.

Reviewer #2 (Public Review):

Wang and Zhu investigated the squamation and scale morphology of Parayunnanolepis xitunensis using x-ray computed tomography. They present a thorough analysis of the squamation and individual histological scale structure across the body of an individual holotype specimen. Through this investigation, the authors provide evidence that yunnanolepidoid scales, which have a bipartite histological structure and a regionalized squamation pattern, might be plesiomorphic for jawed vertebrates. These data add to the ongoing investigation of the morphological variation of dermal scales throughout evolutionary time.

The conclusions of this paper are mostly well supported by data, but some aspects of data presentation and discussion could be extended.

1) The study provided three-dimensional data from the scales of one articulated individual specimen, which is an amazing contribution to the literature. However, there was no discussion about the size or maturity of the specimen. There is some mention in the literature of how dermal scales change throughout ontogeny. Although in this case it would be quite difficult to obtain multiple specimens of different sizes, there was no discussion about how the morphology of the scales might be different depending on the maturity of the specimen. Additionally, there was no discussion about developing scales. For example, there are a few scales presented in the manuscript that do not fit into any of the scale categories described. Perhaps these scales are regenerating or developing, depending on the age of the specimen.

2) The authors use data throughout the text based on measurements of individual scales from Parayunnanolepis xitunensis to provide some general quantitative comparisons across scales on the body. However, none of this data is provided or summarized in the manuscript. There is evidence from dermal denticles in modern sharks demonstrating the same morphological differences across the body. The inclusion of this data would provide quantitative comparative metrics that could be used to study changes in scale morphology across evolutionary time.

3) Although the figures document the various scale categories well, more detail should be included for the figures to stand alone. In many cases, the reader needs information from the text to understand the figure.

Reviewer #3 (Public Review):

Wang and Zhu use a combination of computed tomographic methods and thin sectioning to provide what I think is the most complete characterisation of the articulated squamation of a stem-group gnathostome yet published. They use computed tomography to characterise body scale morphologies across the body of the antiarch placoderm Parayunnanolepis xitunensis, identifying 13 regionalised morphotypes of scale. Thin sectioning of isolated scales from the same locality, assigned to Parayunnanolepis on the basis of these morphotypes, shows that they lack a spongy middle layer, a structural characteristic of antiarch's dermal head armour as well as the dermal skeletons of many other Palaeozoic vertebrates. Based on their inferred position for yunnanolepids, as early-branching members of the antiarchs, themselves the sister-group to all other mandibulate jawed vertebrates, they interpret this data to mean that this absence of a spongy middle layer and regionalised squamation are plesiomorphic both for antiarchs and for mandibulate stem-gnathostomes more generally.

The methods provide a detailed overview of the material the authors describe, and the authors' interpretation of that data is on the whole justified. The real strength of the paper is in identifying all of these morphotypes on an articulated fossil, which preserves the head skeleton, and sufficient phylogenetic information to be able to place confidently in the early vertebrate tree. I'm sure that this will ensure it becomes a key reference work for those interested in the dermal skeleton of Palaeozoic fishes.

The weaknesses mainly lie with the comparison of this data more broadly amongst Palaeozoic jawed vertebrates. There's a conspicuous absence of comparison to jawless stem-group gnathostomes other than osteostracans which would help inform plesiomorphic gnathostome states, despite there being published data on their body squamations. The manuscript would also benefit from considering alternative phylogenetic placements for antiarchs, and the effect that this would have on their conclusions.

The information they provide will doubtless be of great use to other workers interested in the anatomy of placoderm body squamations and in identifying isolated parayunnanolepid scales. As touched on above the fact that this data comes from an animal that is commonly incorporated into phylogenetic analyses means that this new data will be used by those scoring characters for these analyses and perhaps to help formulate new body squamation characters. This means that this new information will also indirectly feed into major areas of investigation in early jawed vertebrate palaeontology such as the evolution of jaws.

Reviewer #1 (Public Review):

This is a fascinating study that apparently began with an original observation (a Hif-1a splice variant heretofore unexamined in insect flight muscles) that sparked the sort of "can't miss" question that all scientists crave, where any outcome is interesting. In this case, what are two Hif-1a variants doing in a highly aerobic tissue in migratory locusts, a species that is both physiologically fascinating and a major agricultural pest? The authors undertook a well-designed and thorough experimental study that used a broad swath of methods to examine bioinformatic data, tissue- and age-specific gene and protein expression, downstream regulation of metabolic genes and metabolites, upstream regulation by PHD, redox regulation, and effects on speed and duration of locusts during prolonged flight. Numerous molecular manipulations were performed to make the study rigorous and results easy to interpret. Ultimately, by using this highly integrative approach, the study provides a compelling picture that the Hif-1a2 splice variant plays a previously undescribed function by regulating Dj-1, which is both an antioxidant and a regulator of other anti-oxidant genes, thereby limiting oxidative damage during prolonged aerobic activity and long migratory flights.

The study and its presentation have many strengths. These include the clear formation of a series of testable hypotheses and critical experiments, progressing from each set of experimental results to the next hypotheses and experiments, and an interesting and nuanced discussion of the results that is well framed in prior findings in other species (including birds and humans) that are similar or different in their physiology and behavior. Ultimately it is an interesting and thought-provoking paper, and a valuable contribution to knowledge in areas that encompass oxygen-related regulatory biology, insect physiology, and animal flight.

Something that is present in a supplementary figure but not discussed in the text is a taxonomic consideration of the presence of Hif-1a splice variants in other insects. Are these unique to locusts or Orthoptera, or are they general to all insects? There are, for example, four Hif-1a splice variants in Drosophila, so the authors should discuss what is known and unknown in this realm.

The most prominent unanswered question from a mechanistic standpoint is "what causes the Hif-1a2 variant to have unique upstream and downstream regulation?". Age and tissue specific expression of Hif-1a2 implies that the locust Hif-1a gene may have promoters that differently affect alternative splicing during development, and in an oxygen sensitive fashion in mature flight muscle. The paper states that lack of regulation of genes that inhibit mitochondria suggests that Hif-1a2 transcription factor activity is altered by absence of the C-TAD. Figure 6F is a compact summary of the functional differences, but a more complex supplementary figure showing a hypothesis that summarizes both the upstream and downstream regulatory details would help readers form a mechanistic understanding. The text could do this by elaborating a bit more on the ideas in lines 288-290.

In the conclusion, the authors should perhaps be more explicit about the hypothesis that Hif-1a2, which is expressed in normoxia and more so at low oxygen tension, provides continuously variable expression of anti-oxidant genes so that protection is in place before the damage occurs. This is different from the way Hif-1a1 is typically activated only at very low oxygen tension, which in a highly active tissue may provide protective effects too late to prevent oxidative damage. Thinking in this way may stimulate experiments across time courses and/or graded oxygen tension that provide additional insight and further refine thinking about canonical versus non-canonical function of Hif gene variants. Such a discussion may be a springboard for pondering why all species don't do this. Or is it possible that they do, and this study is only the first glimpse?

On a related note, the discussion may benefit by considering other findings regarding oxidative damage caused by flight in insects that differ in their flight physiology, behavior and life history. (https://academic.oup.com/biomedgerontology/article/61/2/136/542463; https://www.science.org/doi/abs/10.1126/science.aah4634; https://journals.biologists.com/jeb/article/221/6/jeb171009/246/Enzyme-polymorphism-oxygen-and-injury-a-lipidomic). Have these species independently evolved different mechanisms, or might this new discovery be part of a suite of mechanisms for oxygen-related physiological and protective mechanisms in insect flight muscles?

Reviewer #2 (Public Review):

I am impressed by the comprehensiveness of the work. A caveat is that I am not a molecular biologist and so my expertise in this area is lacking, particularly with regards to the lab-based methodology. My only primary concern (or confusion) with the paper is the link between Hif-1alpha2, which is shown to be induced by flying, and the upregulation of glucose oxidation facilitating prolonged flight. The comparative physiology greats, August Krogh and Torkel Weis-Fogh, showed many years ago that locust flight was only initially powered by carbohydrate catabolism, and that sustained locust flight is powered by lipid catabolism. I believe more recent work has confirmed this finding. Thus, I am unconvinced, that the upregulation of glucose oxidation is likely to facilitate prolonged flight. In my mind, I could imagine that it may rather help to augment the power output of initial flight, when locust metabolic requirements are greatest as they must power the requirements of ascent. Thereafter, I think locusts switch to lipid catabolism, and generally utilise thermal currents and tail winds to migrate. I could be convinced otherwise. Perhaps this just needs to be better clarified in the paper.

Reviewer #3 (Public Review):

The functions of hypoxia-inducible factor (Hif) in cellular adaptations to hypoxia have been widely investigated; however, the roles of Hif in aerobic conditions have drawn less attention. In this study, the authors identified the expression of Hif-1a2, a splice variant of Hif-1a, in locusts flight muscles and found that Hif-1a2 sustains prolonged flight behavior of flight muscles independent of oxygen concentrations. Hif-1a2 activates DJ-1, an oxidative stress sensor, to maintain the redox homeostasis in flight muscles. Long-term flight is generally energetically demanding and unfavorable to tissues and animals and these findings elucidate the mechanisms underlying how flight muscle cells alleviate oxidative damages through unconventional roles of Hif-1a2.

The authors have clearly demonstrated from the expression of Hif-1a variants in locusts to the direct target of Hif-1a2 in flight muscles with appropriate methods and experiments. It is well supported by a series of data that Hif-1a2 is specific to flight muscles and is required for prolonged flight. Moreover, the transcriptome analysis indicated differentially expressed genes in Hif-1a2 RNAi genetic background including glucose metabolism and DJ-1. Despite the Hif-1a2-dependent expression, genes involved in glucose metabolism are generally dispensable for long flight behaviors. However, loss of Hif-1a2 elevates the level of ROS identical to the phenotype led by DJ-1 knock-down. Further, the authors probed that Hif-1a2 directly binds to DJ-1 as a downstream for the long flight. Overall, experiments are well executed and analyzed, and the claims are sufficiently supported by the data.

Reviewer #1 (Public Review):

Mechanisms that control erythroid precursors' response to stress signaling to replenish erythrocytes have important implications in acute/chronic anemia. Dr. Hewitt and his group recently described that Samd14 is required for the regenerative capacity of the erythroid system in response to acute hemolytic anemia. However, the interacting proteins and the mechanisms of Samd14 function remain largely known. In this manuscript, the authors first identified the actin capping proteins (CP) as Samd14-interacting complexes through an atypical CP binding (CPB) domain in erythroid cells. Using shRNA-mediated depletion of CP proteins, genetic complementation assays, and biochemical studies of protein interaction domains, the authors provide evidence that Samd14-CP interaction is a determinant of erythroid precursor cell levels and functions by promoting SCF/c-Kit signaling. The authors further showed that the Samd14-CP interaction via CPB is not required for Epo signaling, indicating the stage- and signal-specific effects for Samd14-CP interaction in erythroid cell maturation.

Overall, this is a well-executed study describing a number of new findings related to the mechanistic and functional dissection of Samd14 in stress erythroid signaling. The detailed characterization of Samd14-CP interaction by IP-proteomics, genetic depletion experiments, and cellular and biochemical assays were well designed and executed, and the results support the main conclusions. A major strength of the current study is the rigorous dissection of the Samd14-CP interaction using orthogonal cellular, biochemical and genetic complementation approaches. This study establishes a previously unrecognized mechanism for Samd14-CP interaction and its functional role in regulating c-Kit signaling in the pathophysiology of erythroid regeneration in response to PHZ-induced acute hemolytic anemia. Thus, this work will be of broad interest to the study of erythropoiesis and cellular signaling.

Reviewer #2 (Public Review):

In this manuscript, Ray and colleagues follow up on their interesting observation that SAMD14 is required for the response to acute anemia. Using mass spectrometry and co-IP, the authors demonstrate that SAMD14 interacts with components of the alpha capping complex (CP), and then they demonstrate that this interaction is not dependent on the SAM domain. They also use shRNA to study the role of the CP protein Capzb in terminal erythroid maturation. Based on the known roles of SAMD14 and the CP in signal transduction they then investigate the role of SAMD14-CP interactions in kit receptor and EPO signaling, using a combination of flow cytometry and colony forming assays. The manuscript depicts an interesting new pathway that may be important for augmenting KIT and EPO signaling in a maturation stage-specific manner.

Strengths of this paper include using unbiased methods to identify SAMD14 interacting proteins, as well as the use of the genetic complementation to study the function of SAMD14 in stress erythroid progenitors.

The data in this paper is consistent with previous work suggesting an important role for SAMD14 in stress erythropoiesis, however some clarifications/extensions of the experiments are needed to more fully support the conclusions regarding the role of SAMD14-CP interactions in regulating erythroid maturation and augmenting kit and EPO signaling .

1) Additional data are needed to support the role of Capzb in erythropoiesis. The conclusion that shRNA knockdown of Capzb promotes erythroid maturation is based largely on CD71 levels (Fig 2B). As CD71 levels can be impacted by EPO signaling, cytospins would be helpful to support the role for Capzb in restraining erythroid maturation. Reporting on the viability and proliferation of the Capzb KD cells would also provide important insights into its role in erythroid maturation. A role for Capzb in the regulation of erythroid maturation could be further supported by overexpression studies.

2) Many of the conclusions in the manuscript are based on analyses of pERK or pAKT via flow cytometry in immunophenotypically defined populations of erythroblasts. While these assays are nicely done, functional assays of those specific immunophenotypic populations (colony forming ability, proliferation, etc) or alternate assessment of KIT/EPO signaling would help further support conclusions.

Reviewer #3 (Public Review):

The manuscript from Ray et al. extends their previous findings that Samd14 acts downstream of the Kit receptor to augment signaling during stress erythropoiesis. The authors demonstrate that the interaction between the F actin capping (CP) complex and Samd14 is required for increased activation of Erk and AKT signaling pathways downstream of the Kit receptor.

Strengths. The strengths of this paper include the identification of an unknown component of the Kit receptor signaling pathway, the CP complex. The authors demonstrate this interaction and map the interaction domain on Samd14 using a careful and comprehensive set of experiments. The authors show convincingly that blocking this interaction affects Kit receptor signaling and the development of BFU-E and CFU-E in the spleen.

Weaknesses. The identification of a new component of the Kit receptor signaling complex is an important result. However previous work showed that loss of Samd14 decreases Erk and Akt activity. This paper identifies the next step in this cascade but still shows us that Erk and Akt signaling are reduced. There are no data that address how Samd14 and the CP complex increase the activation of these pathways. The observation that knockdown of the CP complex leads to fewer BFU-E suggests that the role of the Samd14/CP complex may be earlier in stress erythropoiesis than the time points investigated. Further analysis of the role of this complex in stress erythropoiesis will need to be done.

Reviewer #1 (Public Review):

This study convincingly shows that Androglobin (Adgb) merits its name. The authors show that the absence of Adgb from mice is associated with infertility of the males and gross defects in late-stage spermatogenesis. By various rigorous analyses, including mRNA sequencing, immunoblotting, immunoprecipitation, and microscopy, they further show that Adgb influences the levels of several septins, most notably Sept 10, interacts with the protein, and contributes to its cleavage. This is a well-written and thorough paper that significantly advances our understanding of mammalian spermatogenesis.

Reviewer #2 (Public Review):

Keppner and colleagues report the physiological role of androglobin in spermatogenesis. The group has been working on androglobin for years. By generating KO mice, the authors identified an essential role of this chimeric globin in spermatogenesis and male fertility. Abundant data were presented, but it remains unclear to this reviewer how ablation of Adgb leads to the structural defects observed and which defects are primary and which ones are secondary. Although attempts were made to reveal the underlying mechanism, the authors failed to explain how the absence of ADGB causes disruptions and how ADGB-interacting septins are involved.

Reviewer #3 (Public Review):

The manuscript of Keppner et al is a novel preliminary characterization of a testes specific noncanonical globin protein called Androglobin. This work builds off of the authors prior findings over the last decade on the androglobin gene, generating the first Adgb knockout mouse model to explore its physiological functions. Herein they convincingly demonstrate that the loss of Adgb results a striking reproductive phenotype, where knockout male mice are infertile and azoospermic due to complete disruption of spermatid elongation and maturation. They attempt to explain how this phenotype may manifest in these mice and show that Adgb binds with a number of critical proteins for spermatogenesis including septins. However, the only mechanism they are able to implicate (primarily via in vitro over expression studies) is that Adgb may be capable of proteolytic cleavage of septins via a calmodulin dependent interaction. Though certainly an exciting theory, the reasoning and mechanisms behind these phenomena still are unexplored and warrant further investigation.

Reviewer #1 (Public Review):

In the submitted manuscript, Sorrells and colleagues have characterized the shift in behavioral state female mosquitoes show after exposure to CO2. The authors have generated a mosquito line where CsChrimson is specifically expressed in the Gr3 expressing CO2 sensing neurons. Activation of these neurons through a 5s pulse of red light induced increased walking and probing behavior, which lasted 14 minutes.

All in all, a very interesting and well-executed study. The topic is important, and the successful use of optogenetics in Aedes is nice to see! The text is easy to follow, the figures all acceptable. The schematic drawings of the setups are, however, not the prettiest...nor that easy to interpret.

The submitted manuscript was accompanied by extensive reviewer comments from a previous submission. The main concern of those reviewers mostly centered around the novelty and broader importance of the work, issues which I believe are of no relevance here, or at least not to the same extent. The minor-ish technical concerns raised by the referees were all, in my view, addressed satisfactorily by the authors, and I see no reason for further experimentation. One point is perhaps worth repeating, namely whether the small size of the behavioral chambers could have influenced the results. Perhaps? It would clearly be interesting to see how the mosquitoes would behave in a larger arena. But that would be for another study.

Reviewer #2 (Public Review):

In this manuscript the authors develop an optogenetic tool to deliver 'fictive' CO2 to mosquitoes. This allowed them to unravel how CO2 can trigger host seeking behaviours in female mosquitoes. They validate their tool by demonstrating that it induces blood feeding in females that is comparable feeding induced by gaseous CO2. They then use it to make 3 important observations about the 'activating' quality of CO2:

1. It induces a behavioural state that involves increased walking and probing and decreased flight. This state lasts for about 15 minutes and is specific to the activation of the CO2 sensory neurons: visual stimuli does not induce it and neither does activation of sweet-sensing neurons.<br /> 2. This behavioural state is modulated just as blood-feeding behaviour is: blood-fed females and males don't show this behaviour, and, most interestingly, fru null males (who are attracted to vertebrate hosts) do!<br /> 3. Host seeking involves multimodal sensory integration, and the authors use their tool to decipher aspects of this integration. They use heat as the second cue and address...<br /> 1. ...whether the order of the two cues is important. They suggest that it is.<br /> 2. ...how long the 'activation' ability of CO2 lasts. They suggest that heat needs to be presented about 4 minutes after CO2 for it to be integrated.<br /> 3. ...for how long the integrated, persistent state lasts. They suggest that it lasts for 15 minutes.

In summary, through this study, the authors have developed optogenetic tools in Aedes aegypti, which will be of use to a wider community, and have used it to advance our understanding of mosquito host-seeking behaviour. It will be of great value - both technologically and conceptually - to the community.

Reviewer #3 (Public Review):

In this work, the authors aimed to use new genetic tools to control the activity of olfactory neurons that sense carbon dioxide. These genetic tools specifically express Chrimson (a red-light activated channel rhodopsin) only in CO2-sensing neurons in the maxillary palp of the mosquito. Using this method, the authors could use red light to activate the CO2-sensing neurons as if these neurons had been stimulated by CO2. This 'fictive' CO2 activation allowed the authors to carefully and temporally control when these neurons would be activated in relation to other sensory cues such as heat or the presence of a blood-meal. CO2-sensing neurons could also now be activated in the absence of air flow. This simplifies the sensory stimuli presented to the mosquito so that behaviors induced by CO2 sensory neuron stimulation can be examined without the complicating factor of persistent mechanosensory stimulations. The behavioral experiments and new assays are clever and well designed, and the authors present robust evidence that fictive CO2-sensory neuron stimulation leads to a persistent host-seeking state in the female mosquito. This activated state lasts for many minutes and influences such behaviors as probing and blood-feeding. The genetic tools and data analyses methods introduced here will allow the authors and others in the field to make advances into investigating how activation of CO2 sensory neurons leads to potent changes in the nervous system of the mosquito. This work further pioneers the use of optogenetics to link neurons and behaviors in a mosquito system and paves the wave for similar studies in other non-model insects.

A weakness of the current work is the lack of direct neuronal activity measurements under the optogenetic stimulations. While the authors present strong evidence that their light stimulations can lead to behavior, it is not clear how these stimulations relate to activities induced by natural CO2 stimulations. These could be addressed by using their Gr3>Chrimson mosquitoes and performing single sensillum recordings from capitate peg sensilla (which house the CO2-sensing neurons), and examining how red light intensities change the activity of these neurons. This would ensure that the conditions used for fictive CO2 stimulations are a fair approximation of natural CO2 conditions. Alternatively, the authors could present evidence that their particular light stimulation parameters were chosen based on experimental behavioral experiments.

Reviewer #1 (Public Review):

Authors' claims and conclusions are mostly well-supported by the data. A strength of the manuscript is the novel proposal of a key role for GABAergic SST+ CeA neurons in the MP model. Furthermore, chemogenetic inactivation or pharmacological inhibition of CeA-SST+ neurons by pregabalin (PGB), a selective ligand for α2δ subunit of voltage-gated calcium channels, convincingly alleviate chronic MP.

A weakness of the manuscript is that the investigation of the role of the input from the parabrachial nucleus (PBN) to the CeA should be more thorough fully investigated. Furthermore. pregabalin (PGB), is widely used in the experiments submitted, but whether PGB affects the mechano-sensitivity and emotional behavior separately is not addressed.

Reviewer #2 (Public Review):

Previous studies, that investigated the involvement of the CeA and of its constituting neurons in central sensitization, have led to largely inconsistent results. The work described in this manuscript provides new support for a critical role of CeA SST+ neurons in the chronification of pain and of the comorbid affective behaviors. The reported shift in activity of PCK+ and SST+ neurons after the development of chronic muscle pain, that was opposite for these two neuron classes, lends additional molecular evidence for the involvement of these neurons in this process.

The ability of PGB to reduce pain and negative emotions in models of chronic pain is not new as well as its influence on excitatory postsynaptic currents in neurons of the CeA. However, the work by Lin and associates offers novel insights on how PGB regulates nociceptive transmission between PBN and CeA SST+ neurons. Moreover, using in vivo Ca2+ imaging in behaving mice, the influence of PGB on CeA SST+ neurons was examined, which revealed that although a majority of the recorded cells reduced their activity in the presence of PGB, other SST+ neurons were either excited or insensitive to PGB application. This led the authors to suggest the existence of several functionally different SST+ neurons in the CeA with complex intrinsic and extrinsic connectivity.<br /> This is a nice and well performed study that used a variety of state-of-the-art methodologies. Most of the data are well controlled and support the main conclusions. In my opinion, one shortcoming of this paper is the excessive emphasis on the effect of PGB in reducing the nociceptive and emotional manifestations of chronic muscle pain, as these have already been well studied in models of neuropathic pain. On the other hand, this study provides important new information about neuronal and synaptic dysfunctions in the CeA during priming and the development of chronic pain.

In conclusion, despite the findings described in this manuscript are in general sound, a few issues, in my opinion, need to be addressed to further strengthen both the science and its presentation.

Reviewer #3 (Public Review):

In this manuscript, the authors showed that in the chronic muscle pain (MP) model, which causes mechanical allodynia, mice also developed anxiety-like and depression-like behaviors. Slice recording data showed chronic MP increased excitatory synaptic transmission to CeA-SST and enhanced their excitability, while decreased excitatory transmission to CeA-PKCd neurons and reduced their excitability. Local CeA infusion of pregabalin (PGB) or chemogenetic inhibition of CeA-SST neurons during the priming phase can reverse the mechanical allodynia and affective phenotypes in the MP mice. Whereas increasing activity of CeA-PKCd neurons reduced mechanical hypersensitivity without changing the affective behaviors. Taken together, the authors concluded that CeA-SST as a key node for central sensitization in this model.

This study adds to the CeA-pain research field which has revealed opposite results for CeA-SST and CeA-PKCd neurons in pain versus anti-nociception by different groups. In this regard, it shows the role of different CeA neurons in another chronic pain model and will fuel further studies by the field to reconcile the different results.

Reviewer #1 (Public Review):

Abnormal vascular leakage is one of the hallmarks of vascular injury and inflammation, which are frequently accompanied by major diseases such as diabetes, sepsis, and wet-AMD. It is induced by the impairment of the endothelial cell (EC) barrier. The integrity of the EC barrier is accomplished primarily by proper arrangements of adherens junctions (AJs) and tight junctions (TJs), where transmembrane proteins form intercellular interactions to bridge adjacent EC membranes. Among these, claudin5 is known to be a key molecule in maintaining blood-brain-barrier (BBB), but its function in the peripheral tissues has been poorly understood. In this study, throughout the assembly of multiple single‐cell RNAseq datasets into a single integrated database, the authors found that claudin5 expression diminishes along the arteriovenous axis, which correlates with EC barrier integrity. They elegantly showed that the claudin5 deficiency enhances histamine‐induced leakage in organ- and vessel type‐specific, and size‐selective manners. They reasoned that these could be the result of alternative compositions of AJs and TJs, and this study will aid our ability to modify EC barrier stability in a targeted, organ‐specific manner. Overall, the data are analyzed thoroughly and the conclusions drawn are novel and appealing. Understanding molecular and cellular mechanisms for EC barrier integrity at homeostasis and its impairment at pathologic conditions are fundamental and central topics for understanding vascular biology and related cardiovascular diseases, making this study timely and important. It would be constructive if the authors provide more exquisite underlying molecular and cellular mechanisms to support their claims.

Reviewer #2 (Public Review):

Richards et al. investigate the function of the tight junction protein Claudin-5 (Cldn5) in the regulation of permeability of the mature vasculature, with a focus on tissues with continuous capillary cell-cell junctions. Richards et al. employ an inducible endothelial cell-specific deletion of Cldn5 in adult mice, which enabled challenging the mice using histamine. Previously, such studies were not possible due to neonatal lethality of the mice where Cldn5 was deleted constitutively leading to loss of the blood-brain barrier (BBB) integrity. Richards et al. show that Cldn5 has an organ-specific and vessel-type specific function in the regulation of size-selective vascular permeability in response to histamine (>2000 kDa), whereas basal permeability (to 4-70kDa substances) was not affected. In line with this, loss of Cldn5 did not cause severe disruption of the ultrastructure of the endothelial cell-cell junctions but made the junctions more permeable in response to challenge. Loss of Cldn5 in vivo increased the expression of VE-Cadherin and decreased zonula occludens‐1, proteins associated with adherens and tight junctions, respectively, which may have affected the observed vascular phenotype.

The authors have previously shown that vascular segments with little or no expression of Cldn5 are most permeable in the mouse ear dermis, where Cldn5 is most highly expressed in arterioles but decreased towards capillary and venous beds. Extending these studies to other tissues, including the back skin, trachea, skeletal muscle and the heart, Richards et al. found decreased Cldn5 expression along the arteriovenous axis, but not a similarly correlated permeability pattern, suggesting other mechanisms. In the ear dermis, Cldn5 deletion did not increase the permeability of arterioles but increased the number and extent of leakage sites in small and mid-sized vessels (up to 15 um) with low Clnd5 expression.

The conclusions by Richards et al. are mostly well supported by data. The results to some extent challenge the concept of the significance of Cldn5 in vascular integrity and indicate that the function of Cldn5 is highly organ and vessel-type specific. Compensatory mechanisms by other junction proteins can not be excluded. Compared to the known function of Cldn5, the permeability of the CNS vasculature to small substances was not investigated, and thus the potential differential sensitivity of the mature vasculature to the loss of Cldn5 is not considered.

Reviewer #3 (Public Review):

This manuscript is of broad interest to readers who study vascular integrity that controls exchanging molecules across the endothelial cell and parenchymal cells. It uncovers role for inter-endothelial cell-cell adhesion molecules expressed in manners-dependent on blood vessels in vascular barrier function. The analyses using gene and protein expression and using imaging techniques support the claims of this manuscript.

The authors first examined the expression of vessel-specific markers using scRNA-seq datasets and grouped them into 5 subsets: arterial, arterial/capillary, capillary, capillary/venous, and venous. Among the tight junction (TJ) molecules, they focused on claudin5, because its expression diminished along the arteriovenous axis. Vascular leakage outside of the central nervous system (CNS) including ear skin, back skin, skeletal muscle, trachea, and heart was tested in the mice conditionally depleted of cldn5 because they previously examined the correlation of Cldn5 expression with vessel diameter using Cldn(BAC)-GFP mice. Furthermore, basal vascular leakage and inflammatory cytokine (histamine)-induced leakage was quantitatively analyzed to investigate the relevance of Claudin5 expression to vascular integrity. Depletion of Claudin5 affected the leakage and resulted in variation of leakage that was dependent on vessel subtypes. Finally, they found no apparent junctional structure but noticed compensatory compositional changes of adhesion molecules in the Cldn5-deficient mice. Cladin5 expression inversely correlated with that of adherence junction (AJ) molecule, vascular endothelial cadherin (VE-Cadherin) and TJ molecule, Occludin, while its expression positively correlated with that of ZO-1.

The present data support the authors' claim that Claudin5-dependent barrier function varied in the organ vessels and demonstrate one example of organotypic vessel structure and function in vivo. To precisely confirm their claim, there are several points that should be explained. Several experiments that strengthen their conclusions are required for clarification of their claim.

Strength<br /> 1. Gene expression of Inter-endothelial junction molecules including AJ molecules (Cadherin family members), TJ molecules (Claudin family genes, JAM, Occludin, Nectin, etc), and intracellular molecules (Amot, Cingulin, Catenin) was thoroughly and quantitatively examined in endothelial cell subsets.<br /> 2. Spaciotemporal leakage of various sizes of molecules from endothelial subsets was clearly imaged to understand the relevance of Claudin5 expression to barrier function.<br /> 3. To analyze the role of Claudin5 in barrier function, the authors used both Claudin (BAC)-GFP transgenic mice and Cldn5 iEC knockout mice.<br /> These methods enabled them to demonstrate the importance of Claudin5 for organotypic barrier function.

Weakness<br /> 1. Although barrier function is carefully investigated from the view of adhesion molecules, the expression pattern of cytokine receptors might be also involved in cytokine-induced permeability and leakage. If cytokine receptors are not evenly expressed in vessel subsets, the permeability might vary in arteriole, capillary, and venule.<br /> 2. Gene expression is thoroughly investigated in the ear skin, back skin, trachea, skeletal muscle and heart. However, protein expression was not carefully examined. Claudin5 was still expressed even after the cldn5 gene was knocked out in endothelial cells. Therefore, immunohistochemistry of blood vessels should be carefully performed to confirm where the compensatory increase of gene expression is found.<br /> 3. Given the dominant expression of Claudin5 in arterioles over venules, leakage in the cldn5 KO mouse should be mostly found in arterioles. The authors did not show clear data about the leakage sites. Figure 4B vi and Figure 3C ii suggested the leakage in the capillaries and venules.<br /> 4. The interpretation of the results might need a more careful explanation.

Overall, the results support their claim. However, additional experiments would fortify their claim and make their claims more logical.

Reviewer #1 (Public Review):

McCoy et al. provided a dataset of urban trees across large US cities. This new dataset complements urban tree information on their spatial distribution, nativity status and healthy condition, compared to past work focusing on canopy cover and species richness. The authors provided example analyses and relevant tools to explore how this new dataset can provide new insights on spatial composition and nativity status in urban forests. They also suggested potential avenues to combine this new dataset with other data sources (e.g., citizen-scientist data, social and demographic data) to ignite new research and discoveries on urban biodiversity and ecosystem services and social science. The authors describe the data processing and structure clearly and offered good guidance about the usages of this dataset with several example analyses. The analyses were sound in general but would need some further clarifications.

Reviewer #2 (Public Review):

McCoy et al. has developed a new urban tree species database from existing city tree inventories. They designed procedures to collect and clean a large amount of data, i.e., more than five million trees from 63 US cities. They found that urban trees were significantly clustered by species in 93% of cities using the compiled data. They also showed that climate significantly shaped both nativity and tree diversity. Also, they identified the homogenization effect of the non-native species. The interest in patterns of urban biodiversity and its driving mechanism has been rising recently. This paper provides an important data source for addressing research questions on this topic. The finding presented by the authors exemplified its potential.

Strengths

Compared to the existing urban tree database, such as the one developed by Ossola et al.(Global Ecology and Biogeography 2020), the new database added information on spatial location, nativity statuses, and tree health conditions besides occurrences. The new information expands data usability and saves valuable time for researchers. The authors also make the tools available so others can use them to process their own data sets.

Because of the added information, various analyses of the diversity pattern of urban trees and the potential driving mechanism could be conducted. The authors found that individual species nonrandomly clustered urban trees. This finding corroborates the existing knowledge that some common species dominate urban trees. Nevertheless, the authors showed that the dominance was apparent in the spatial dimension. The preliminary finding that the native status of a tree had no apparent impact on tree health is interesting. It can potentially contribute to the debate on native vs. exotic in urban tree species selection, which the author mentioned in the paper.

Weakness

While the new database and the analysis based on it has strengths, some aspects of the concepts and data analysis need to be clarified and extended.

First, the authors need to define several critical concepts used in the paper, including city trees, urban forests, biodiversity, and species diversity. The authors used city trees and urban forests interchangeably throughout the paper. Nevertheless, a widely accepted definition of the urban forest is:"All woody and associated vegetation in and around dense human settlements." Konijnendijk et al. had a good discussion on the terminology used in urban forestry (Urban Forestry & Urban Greening, 2006). Similarly, biodiversity is different from species diversity. Effective species number is a diversity indicator. Therefore, it is challenging to accept conclusions being drawn on biodiversity in urban forests without clear definitions.

Second, the tree inventories varied significantly regarding the number of records (214~720,140). The variation can be due to the actual variation of tree abundance in studied cities or incomplete inventories. Biases can be introduced into the findings when comparing these inventories without adjusting the unequal sample sizes. The authors did not detail how they dealt with this issue when conducting the analysis.

Reviewer #3 (Public Review):

This paper's strength is in the utility of the assembled datasets and some interesting and creative proof of concept analyses. This is an amazing resource for comparative analysis. However the paper felt a little sparse in the conceptual and methodological underpinnings of the questions asked to demonstrate the utility of the analysis.

Specifically, I suggest:

A) More substance in the introduction (currently only two short paragraphs) and a clear statement of research questions.

B) Add data on the extent to which each dataset represents a complete sample of each city's trees. I know are complete inventories, but some consist of 720 trees and cannot be a complete sample. A column in the meta data indicating effort and if there were any bias in where sampling occurred if the dataset is not complete are needed for others to use this data appropriately. For example, we know tree cover/diversity increases with wealth (which the author rightly cites). Let's say in City X, trees were only inventoried in one wealthy neighborhood. They would not be a representative sample of the city and dataset users need to be aware of this before they draw incorrect conclusions about City X where the sample was biased compared to city Y where the inventory was complete, including a sampling of all affluent and poor areas. This is also needed to support the research questions throughout the paper.

C) The authors chose to use effective species counts as their alpha diversity metric of choice. They explain why: "effective species counts (a measure that allows comparison between cities of different sizes)" (Ln 109). While effective species number is an excellent metric with much better behavior and attributes in linear modeling, I believe it is still strongly dependent on both city area and the number of individual trees sampled and so the above statement and all of the comparisons that flow out of it in the manuscript are currently unsupported. Just as species richness needs to be rarified or extrapolated to be compared at an equivalent # of individuals or area to be accurate so too does EFN (effective species count). Fortunately there is an R package (iNext) based on Chao's method (citation below) that makes it very easy to create effective species accumulation curves for each city by tree individuals sampled.<br /> a. Chao, Anne, Nicholas J. Gotelli, T. C. Hsieh, Elizabeth L. Sander, K. H. Ma, Robert K. Colwell, and Aaron M. Ellison. 2014. "Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies." Ecological Monographs 84 (1): 45-67. https://doi.org/https://doi.org/10.1890/13-0133.1.<br /> b. The standardization (rarefaction/extrapolation) of EFN or richness for # individual trees sampled needs to be made for all analyses that make claims to compare diversity metrics across cities or between groups like urban and park areas (i.e. Fig 2a,b,c; Fig 3b; Fig 5a,b, S1a, S2a, S5, Table S2)<br /> c. If the authors have an argument for why diversity/area or diversity/sampling effort relationships do not apply for a particular question, then they should make that case instead.

D) The question posed by the Beta diversity analysis is fascinating (i.e. is it non-native species that are driving biotic homogenization across species. However, while frequency (which I assume is relative abundance but maybe it is incidence data- please define) is used to deal with different sample sizes consider whether it makes sense to include incomplete, or very small city datasets in the analysis even with frequency data. For example one city only has ~720 trees listed. If this is an incomplete dataset which seems likely, it will probably be much more differentiated (overlap less) from another city with small numbers simply due to incomplete sampling. Diversity analysis in cities always requires tradeoffs and cannot be identical to methods used in "natural" forested ecosystems, but I encourage the authors to explore this a bit. Perhaps a sensitivity analysis could help where incomplete or small sample sizes are dropped or datasets are resampled via random draw to equalize sizes? The latter would handle incomplete samples but would not deal with bias in which neighborhoods were sampled (see point B above).

E) Additional context/conceptual underpinning the clustering analysis would be great.<br /> a. The authors state in Line 390-395:"For city trees, which are often organized along grids or the underlying street layout of a city, this method can more meaningfully cluster trees than merely calculating the meters between trees and identifying nearest neighbors (which may be close as the crow flies but separated from each other by tall buildings)."- I very much agree with this sentiment and it is biologically meaningful for animal and plant dispersal, but as written it is unclear to me how the method described in the text "knows" that a tall building or elevation or some sort of feature exists to separate clusters rather than empty space or a ball field. Please clarify.<br /> b. Would you ever expect composition to be truly random either in a city or a natural forest given environmental conditions etc.? In some sense, the ones closest to random are the most surprising. Can you dive into one to give an example of what is going on in that city?<br /> c. It seems like there are two metrics here- the size of the cluster and then the observed/expected EFN per cluster. The latter is analyzed in this paper but is there any important information in the former? It seems like an interesting structural measurement of the city and possibly useful in its own right.<br /> d. Are there any target levels of randomness? Could the authors suggest how this might be determined moving forward with their datasets to illustrate this for foresters?

F) The statement that this dataset enables "the design of rich heterogenous ecosystems built around urban forests" (Ln 72) seems strange. To my mind this tool will enable a more nuanced evaluation of the urban forests that already exist and suggest ways to target future plantings for increased resilience to climate, pest resistance, biodiversity support etc. I don't understand what ecosystem you would build around and not in the urban forest. If this is what is meant please elaborate. For example, do you mean non-tree installations?

Reviewer #1 (Public Review):

This manuscript is filling an important gap in the literature which is the association between the excitation/inhibition unbalance found in animal models and findings in human neurophysiology. Thus, the idea of using computational models as a linkage between those two levels of analysis help to reconcile many of the previous results. In addition, it provides a reinforcement of the strong relationship between neurophysiological findings with MEG and protein imaging by PET. Therefore, I have found this work of great interest for the literature on the neurophysiology of dementia. I have some comments that are mainly trying to have a better understanding of the findings and aim to get potential associations with previous stages of the disease.

If the patients involved in the study are already with a diagnosis of dementia (MMSE <24; CDR 0.72), why they are still presenting hyperexcitability? Typically, amyloid hyperexcitability starts years before in earlier stages of the disease. The continuous hyper calcium neuronal intake should induce toxic effects leading to neuronal death and accelerating degeneration. Furthermore, in the AD stage, Tau is typically dominating inducing neuronal silence. If this process is true, why at the stage of dementia patients still show hyperexcitable activity instead of showing a more global reduced neuronal activity?

About the role of Aβ in the modulation of alpha and beta. As indicated in the manuscript alpha and beta bands tend to be enhanced in power due to the presence of Aβ. However, the final effect is a reduction of power in comparison with the control group leading to the idea that the Tau effect is stronger in these particular frequency bands. Because tau and Aβ distribution across the cortex is differential I wonder whether in regions with fewer tau deposits alpha and beta power increased. This could be a good validation/test for the model proposed in this study.

In some previous studies, increased excitatory activity, due to loss of inhibition, leads to effects in the gamma band. This was shown in both animal models (Palop and Mucke, 2016) and in humans (Rammp et al, 2020; Cuesta et al, 2022). Is there any reason for not finding effects in this frequency band?<br /> The readers, and I, could need an explanation of the association between slow waves and hyperexcitability. In data from human patients with brain damage and atrophy, a typical finding is delta to theta activity. Therefore, white and grey matter damage explains better the appearance of these rhythms. Again, in epilepsy, a seizure could lead to high-frequency oscillations and it is after a seizure when slow waves show up (when inhibition bit excitation). I perfectly understand that in the data presented in this work amyloid modulates this rhythm, but explanations such as amyloid induce neurodegeneration and consequently, slow waves, could not be ruled out. The explanations already indicated in the discussion section are perfectly fine and could inspire future work, but more traditional ones could be indicated as well. Honestly, I was expecting having Tau more associated with slow waves, as tau has been linked to brain atrophy. Is true that Tau is affecting the reduction of more rapid frequencies such as alpha and beta, but its association with neurodegeneration should not lead to the increase of slow waves?

Previous work has found a strong association between amyloid and alpha rhythm in the frontal regions. Here authors found this association with delta to theta in the same brain regions. However, delta is associated with local hyperexcitability and, in Nakamura et al (2018,) they associate alpha with the same phenomena. How this could be justified? I fully agree that findings in oscillatory activity, and its associations with pathological proteins, are stage-specific rather than disease-specific. However, why alpha and delta increases can be associated with the same neuronal mechanism (hyperexcitability) at different stages of the disease?

Reviewer #2 (Public Review):

Ranasinghe and co-authors explored the relationship between amyloid-beta and tau deposition and neural oscillatory behaviour in Alzheimer's disease (AD) by using a computational neural mass model that can generate neurophysiological power spectra comparable to EEG- or MEG-like, macroscopic brain activity assessments. The model parameters that represent neuronal excitation and inhibition were tuned to optimally resemble the empirical MEG data from AD patients in different relevant frequency bands, and subsequently, the different parameter changes in all 68 cortical neural masses, representing local neuronal excitation or inhibition, were compared with the local amyloid and tau deposition rates. This comparison was used to demonstrate the different, frequency-specific effects of these two proteins, to form an integrated, multimodal/-scale explanation of the molecular/neurophysiological AD disease mechanism.

The role of neurophysiology in AD pathophysiology is underestimated in the AD research community, as for many it appears to be a more 'downstream' aspect than protein deposition, inflammation or genetic predisposition. However, given the tight relation between cognition and brain activity, the clear involvement of neurophysiology at micro and macro levels, and reports that neuronal activity can influence structural pathology in AD, its central role is evident. It is very laudable that this author group aims to focus on the combination of neurophysiology and computational modelling to further explore how AD pathology actually leads to cognitive impairment. As multi-scale, simultaneous, longitudinal recordings in humans are too burdensome, computational modeling represents a very flexible and powerful new instrument to bridge different levels of detail and predict developments over time. However, the pitfall of using models is the endless options for designing the model, as they will ultimately affect the results and interpretation. However, by constraining the model with biologically plausible effects and parameters, and by validating it with empirical data, it can not only serve to unravel mechanistic principles of disease but also predict successful interventions. Currently, it is not known what model simplifications can be accepted, and which elements need more detail, and this probably also depends on the specific research question and hypotheses. The novel, well-described approach makes the present study a valuable addition to a research field that is under development.

The conclusions of this paper are mostly well supported by data, but some methodological aspects, as described below, limit the power of the study, or rather provide a valuable perspective on the proposed neurophysiological mechanisms, but not the only valid one.

Strengths:

• The group is a high-profile team, known for many influential publications.<br /> • The use of state-of-the-art techniques like tau-PET and source-space MEG combined with computational modeling may currently be the most powerful approach available for this purpose.<br /> • AD patient diagnosis is pathology-supported and conforms to NIAAA.<br /> • The modeling and empirical data are processed and analyzed rigorously; statistical analysis is sound.<br /> • The methods and result sections are well-written and presented in a logical order.

Weaknesses:

• The chosen AD patient cohort is relevant and well-defined, but broad: it includes both persons at the predementia and dementia levels of AD. Since brain changes during the AD disease course are gradual and variable, this heterogeneous group may have limited the observed changes and interpretation. Changes in brain activity are frequently reported to be non-linear, involving transient increases in activity in the early, predementia phase. Also, the effect of amyloid-beta may depend on deposition load (see for example Gaubert ea, Brain 2019). The group heterogeneity in the present study may have obscured distinct activity patterns in different phases of the disease.<br /> • As the authors state, PET is sensitive to aggregates of proteins. However, soluble oligomers in early phases are toxic as well but cannot be assessed with the current approach. This may have led to a misinterpretation of local toxic effects which is hard to quantify, limiting the power of the current approach. As the authors state, the neural gain parameter might be more sensitive to early, soluble protein toxicity, but how can this be supported?<br /> • Pathological deposition in subcortical regions and its effect on large-scale oscillatory behaviour is not considered in this study, while early subcortical (e.g. entorhinal) changes are a key feature of the disease. As the authors used source space MEG, involving subcortical structures is technically feasible (e.g. AAL atlas), and may have given a more accurate view.<br /> • The authors use a recently developed spectral graph neural mass model, which has several theoretical advantages over more complex, biophysically realistic models. However, there are also disadvantages. Since the model does not generate oscillatory output that can be assessed visually, it is unclear whether the parameter changes required to match the empirical MEG spectra are still within a range that would produce realistic oscillatory behaviour, that also visually resembles AD patient data. Also, since model parameters are less directly linked to neuronal properties as in for example a Jansen-Rit model, the meaning of parameter changes is more difficult to grasp. For example, it seems logical that increased time constants in the model lead to spectral slowing, but how would time-constant abnormalities translate to (inter)neuron dysfunction? Also, since no simulations are required, the contribution of coupled neural masses that influence each other's behaviour during a neurodegenerative process is not captured.<br /> • In the discussion section, the associations between a-beta and tau and neuronal hyper/hypoactivity are adequately compared to recent basic science literature, but since the authors state that the observed effects indicate an overall, net balance between underlying excitatory and inhibitory dysfunction, it is not clear how the model could help to further determine the exact link between -for example- a-beta-induced glutamate toxicity and neuronal behaviour. This less specific link makes it easier but also more ambiguous to explain the directions of the observed effects.

In conclusion, the aim to unravel the multiscale pathophysiological mechanism of AD is one of the current top priorities. Various groups are pursuing similar multimodal modelling approaches to explain AD pathophysiology, with different methodologies. Considering its strengths and weaknesses, this study can become a valuable contribution to the AD neurophysiology computational modeling field, and may also help to interest and unite researchers coming primarily from clinical backgrounds (PET, MEG, pharma). Further use of spectral graph models should be investigated, and replication of current results with a different type of model may strengthen the conclusions. In general, a comparison between the various neural mass models with their specific strengths and weaknesses for representing AD pathophysiology will help the field forward.

Reviewer #1 (Public Review):

Wang et al. construct the novel TLR2-4 receptor by fusing the extracellular domain of TLR2 and the transmembrane and intracellular domains of TLR4, which could specifically recognize S. aureus and activate the autophagy pathway. This study indicated that macrophages derived from TLR2-4 transgenic goats could produce a strong autophagy response for the clearance of S. aureus. Detailed transcriptomic analysis and biochemical experiments showed that the TLR2-4-mediated enhanced autophagy process was mainly completed through the TAK1/TBK1-JNK/ERK, TBK1-TFEB-OPTN and cAMP-PKA-NF-κB-ATGs signaling pathways. The improved autophagy level via TLR2-4 could effectively improve resistance to S. aureus infection in dairy ruminants, which might provide new insight into further improving animal health and welfare, reducing the use of antibiotics, and ensuring human food safety.

The work of this paper is original and logical. However, only the macrophages derived from one TLR2-4 transgenic goat have been used for the mechanism study, the lack of trait determination in TLR2-4 transgenic goat dramatically reduced the value of this work in molecular breeding.

Reviewer #2 (Public Review):

Wang et al. investigated the mechanism of eliminating S. aureus in macrophages by combining TLR2 to recognize S. aureus and TLR4 to activate autophagy to regulate the autophagy pathway. They found that TLR2-4 could enhance autophagy-dependent elimination of S. aureus by activating JNK/ERK signaling and increase the expression of autophagy-related genes (ATGs) by cAMP signaling. They show that TLR2-4 can also activate the TBK1-TFEB-OPTN signaling to regulate S. aureus-induced autophagy. Interestingly, the data also indicate that the PI3K-AKT-FoxO1 signaling was barely involved in regulating S. aureus-induced autophagy in TLR2-4 macrophages. S. aureus is a major pathogen causing mastitis in dairy milch animals including goats. These data add in an interesting way to the ongoing discussion on whether S. aureus could be extremely eliminated in goats.

Reviewer #3 (Public Review):

To improve the resistance against S. aureus infection, Wang et al. generated TLR2-4 domain-fusion transgenic goat using CRISPR/Cas9 gene-editing tool. They find that TLR2-4 could recognize S. aureus and enhance the elimination of S. aureus by promoting the autophagy pathway in macrophages. Evidence of the autophagy-dependent clearance of S. aureus is shown meticulously using several methods. The conversion of LC3-I to LC3-II is up-regulated in TLR2-4 macrophages with S. aureus treatment and more autophagosomes are observed in TLR2-4 macrophages than that in WT macrophages after being stimulated by S. aureus under transmission electron microscopy and laser scanning confocal microscopy. TLR2-4 activates the TBK1-TFEB-OPTN signaling to enhance S. aureus-induced autophagy and triggers the TAK1/TBK1-JNK/ERK signaling to promote the autophagy level to effectively eliminate S. aureus in macrophages. Interestingly, they show that JNK and ERK signaling decreased the expression of the autophagy-related genes ATG5 and ATG12. By performing ELISA and qPCR analysis of TLR2-4 and WT macrophages with S. aureus infection, the authors find that cAMP-PKA-NF-kB signaling enhanced the expression of ATG5 and ATG12 to enhance autophagy levels in TLR2-4 macrophages.

Reviewer #1 (Public Review):

Caveney et al. set out to use the technique of cryo-electron microscopy to deduce how the cytokine interleukine-27 (IL-27) interacts with its heterodimeric receptor composed of gp130 and IL-27Rα. Here they find that the quaternary structure of the human IL-27/gp130/IL27Rα complex contains three interface sites that are characteristic of other gp130 family cytokines. This work indicates that the interface between EBI3 and IL-27p28, the two subunits that make up the heterodimeric cytokine IL-27, is highly hydrophobic in nature, providing an indication of how these subunits pair for efficient secretion. A conserved tryptophan residue was identified in the IL-27p28 subunit that is also found in IL-6 and viral IL-6 that facilitates its interaction with domain 1 of gp130. Importantly, the results indicate that the interaction of IL-27 with its receptor closely resembles the interaction of IL-6 with its receptor rather than the other related heterodimeric cytokines IL-12 and IL-23. Whereas IL-27p28 lies centrally allowing it to interact with all receptor components, like IL-6, each subunit of IL-12 and IL-23 engages a separate subunit of the receptor. Moreover, the overall architecture of the IL-27-IL-27R complex also helps to explain previous findings that indicated IL-27p28 can block IL-6 and why soluble IL-27p28 cannot induce signaling through the receptor in the absence of the EBI3 subunit.

Limitations associated with the study include an inability to purify a soluble receptor complex composed of human IL-27, IL-27Rα domains 1-2, and gp130 domains 1-3 due to dissociation during size-exclusion chromatography that the authors based on the low affinity of gp130 for binding IL-27. To deal with this purification issue, the addition of a flexible linker of 20 amino acids was required to connect IL-27p28 to gp130 to facilitate purification of the cytokine-receptor complex. While this linker creates an artificial connection between these proteins, the authors conclude that it does not restrict the binding of IL-27p28 to gp130. Furthermore, while the authors highlight the amino acids important for the interaction between the IL-27-IL-27R subunits and suggest that they are plausible sites to target to antagonize this interaction the study does not set forth to test this idea here. Moving forward, it will be of interest to see if monoclonal antibodies can be identified or designed to block these key interactions between the cytokine and receptor subunits.

Overall, the conclusions of this paper are supported by the data, and the data provide valuable information for the design and testing of therapeutics to block or agonize the interaction of this important anti-inflammatory cytokine with its cognate receptor.

Reviewer #2 (Public Review):

The cytokine IL-27 is structurally interesting because its receptor-like subunit EBI3 can also form other heterodimeric cytokines, namely IL-35, in which EBI3 binds to p35 (the cytokine-like subunit of IL-12). In the cytokine IL-39, EBI3 binds to p19 (the cytokine-like subunit of IL-23). Moreover, the four-helical protein p28 has been described to interact with the IL-6R. In this case, p28 assembles a receptor complex consisting of a homodimer of gp130, which predominantly signals via STAT3 and therefore elicits intracellular responses different from IL-27. It would be highly desirable that the structure of the complex of cytokine IL-27 in complex with the two receptor subunits gp130 and WSX-1 would explain the structural flexibility of p28 and EBI3. The resolution of the presented structure, which has been obtained by cryogenic-electron microscopy, is 3.47 Å, which might not be high enough to yield such a detailed molecular explanation.

Reviewer #3 (Public Review):

Cytokines are small proteins that play key roles in inflammation and immune homeostasis. In order to elicit proper signaling, cytokines must interact with a broad repertoire of receptors in complex ways, often requiring several stabilizing binding partners. This work reveals a structure of the four-part cytokine/receptor complex, composed of the heterodimeric cytokine, interleukin 27, bound to soluble fragments of two different receptors, IL-27Ra and gp130 thus providing insight into signaling mechanisms and a blueprint for targeted therapeutic intervention

Strengths:

The expression and purification of active membrane protein receptors can be an extremely difficult process. Furthermore, the reconstitution of stable multi-component complexes can be especially difficult. The authors employ a clever strategy to increase the stability of IL-27/IL-27Ra and gp130 by tethering gp130 and p28 via a flexible linker.

A thorough comparative analysis of existing cytokine/receptor structures is done, which clearly lays out the key differences and similarities between IL-27/IL-27Ra/gp130 and other complexes categorizing it with IL-6 instead of IL-12 which helps reconcile existing signaling data.

The map and model were provided, which is appreciated. The map supports the reported resolution of 3.5Å and modeled density is consistent.

Figures are clear, consistent, and well-thought-out.

Weaknesses:

Other than the structure, no new comparison structures or additional biochemistry or cell biology experiments were done to support the more speculative/future directions discussion, however, a comparative analysis was done with existing structures (see above).

The aim was to determine the molecular details of IL-27 engaged with its receptors and the conclusions are a straightforward comparative analysis of this structure with other cytokine/receptor structures.

This is a clever strategy for obtaining a multi-part soluble monodisperse cytokine/receptor complex suitable for structural analysis which can be applied to other similar complexes, or this complex engaged with potential therapeutics. The molecular details of the structure will aid the design of function-altering mutations which can be used to reveal more specifics about the signaling mechanisms, as well as guide the rational design of potential therapeutics.

Reviewer #1 (Public Review):

The work aims to develop a new data analysis pipeline that allows them to identify individual membrane proteins from native membranes using analysis of AFM force-extension curves. They introduce a method to isolate patches of cell membrane for AFM force spectroscopy analysis. To validate their approach, they first demonstrate the ability to measure unfolding traces of known membrane proteins transfected into cells. They develop a custom data analysis pipeline that clusters repeated patterns in the unfolding traces. My understanding is that the data analysis procedure is not completely automated, but may require some user intervention to select useable regions of the curves. The data processing is therefore likely a combination of manual and automated steps. Following the demonstration to cluster the curves, they use mass spectrometry databases along with protein structure databases to make Bayesian predictions about which membrane proteins are most likely observed. This provides a new bioanalytical technique for membranes.

Reviewer #2 (Public Review):

The authors present experimental and computational advances towards the development of their approach, as well as very nice experiments validating their technique. The discussion of the different artifacts, and features of their method that address these artifacts, is very nice. The discussion of the method's limitations in the discussion section is also nice.<br /> However, I do not feel that this manuscript has taken the critical step of clearly illustrating the benefits that this approach brings to the broader scientific community. The authors should look through their dataset for useful insights to better illustrate the utility of their technique. Again, I think the technique is very interesting and important but additional analyses that clearly illustrate the utility of the technique to scientific research will be necessary.

Reviewer #3 (Public Review):

Gavanetto et al. propose an interesting method to identify membrane proteins based on the analysis of single-molecule AFM (smAFM) force-extension traces obtained from native plasma membranes. In the proposed pipeline, the authors use smAFM to non-specifically probe isolated plasma membranes by recording a large number (millions) of force-extension traces. While, as expected, most of them lack any binding or represent spurious events, the authors use an unsupervised clustering algorithm to identify groups of force-extension curves with a similar mechanical pattern, suggesting that each cluster corresponds to a unique protein species that can be fingerprinted by its specific force-extension pattern. By implementing a Bayesian framework, the authors contrast the identified groups with proteomics databases, which provide the most likely proteins that correspond to the identified force-extension clusters. A set of control experiments complements the manuscript to validate the proposed methodology, such as the application of their pipeline using purified samples or overexpressing a specific protein species to enrich its population.

The primary strength of the manuscript is its originality, as it proposes a novel application of smAFM as a protein-detection method that can be applied in native samples. This methodology combines ingredients from conventional mass spectrometry and cryoEM; the contour length released upon extending a protein is a direct measure of its sequence extension (related to its mass), but the force pattern contains insightful information about the protein's structure. In this sense, the authors' proposal is very smart. However, the relationship between protein structure and mechanics is far from straightforward, and here perhaps lies one of the main limitations of the proposed method. This is particularly true for the case of membrane proteins, where we cannot talk about protein unfolding in its classical sense but rather about pullout events which is likely what each peak corresponds to (indeed, the authors speak throughout the paper about unfolding events, which I believe is not the correct term). Unlike most proteins-which unfold along a unique pathway, especially at high forces-the removal of membrane proteins is typically rather heterogeneous, with multiple parallel pathways. In this sense, the authors are likely to identify only those proteins with a unique or dominant pathway and miss potential proteins that exhibit heterogeneity. A second limitation recognized by the authors is the low yield of their method since only a meagre percentage of traces (~2%) correspond to identifiable protein patterns. This implies that only very abundant proteins can be identified, severely limiting the proposed method's practical applicability.

Overall, I believe that recognizing its natural limitations; the present work is a remarkable contribution to protein science due to the originality of the approach and the careful control experiments that validate the method.

Reviewer #1 (Public Review):

In their paper, Kroell and Rolfs use a set of sophisticated psychophysical experiments in visually-intact observers, to show that visual processing at the fovea within the 250ms or so before saccading to a peripheral target containing orientation information, is influenced by orientation signals at the target. Their approach straddles the boundary between enforcing fixation throughout stimulus presentation (a standard in the field) and leaving it totally unconstrained. As such, they move the field of saccade pre-processing towards active vision in order to answer key questions about whether the fovea predicts features at the gaze target, over what time frame, with what precision, and over what spatial extent around the foveal center. The results support the notion that there is feature-selective enhancement centered on the center of gaze, rather than on the predictively remapped location of the target. The results further show that this enhancement extends about 3 deg radially from the foveal center and that it starts ~ 200ms or so before saccade onset. They also show that this enhancement is reinforced if the target remains present throughout the saccade. The hypothesized implications of these findings are that they could enable continuity of perception trans-saccadically and potentially, improve post-saccadic gaze correction.

Strengths:<br /> The findings appear solid and backed up by converging evidence from several experimental manipulations. These included several approaches to overcome current methodological constraints to the critical examination of foveal processing while being careful not to interfere with saccade planning and performance. The authors examined the spatial frequency characteristics of the foveal enhancement relative, hit rates and false alarm rates for detecting a foveal probe that was congruent or incongruent in terms of orientation to the peripheral saccade target embedded in flickering, dynamic noise (i/f )images. While hit rates are relatively easy to interpret, the authors also reconstructed key features of the background noise to interpret false alarms as reflecting foveal enhancement that could be correlated with target orientation signals. The study also - in an extensive Supplementary Materials section - uses appropriate statistical analyses and controls for multiple factors impacting experimental/stimulus design and analysis. The approach, as well as the level of care towards experimental details provided in this manuscript, should prove welcome and useful for any other investigators interested in the questions posed.

Weaknesses:<br /> I find no major weaknesses in the experiments, analyses or interpretations. The conclusions of the paper appear well supported by the data. My main suggestion would be to see a clearer discussion of the implications of the present findings for truly naturalistic, visually-guided performance and action. Please consider the implication of the phenomena and behaviors reported here when what is located at the gaze center (while peripheral targets are present), is not a noisy, relatively feature-poor, low-saliency background, but another high-saliency target, likely crowded by other nearby targets. As such, a key question that emerges and should be addressed in the Discussion at least is whether the fovea's role described in the present experiments is restricted to visual scenarios used here, or whether they generalize to the rather different visual environments of everyday life.

Reviewer #2 (Public Review):

Human and primates move their eyes with rapid saccades to reposition the high-resolution region of the retina, the fovea, over objects of interest. Thus, each saccade involves moving the fovea from a pre-saccadic location to a saccade target. Although it has been long known that saccades profoundly alter visual processing at the time of saccade, scientists simply do not know how the brain combines information across saccades to support our normal perceptual experience. This paper addresses a piece of that puzzle by examining how eye movements affect processing at the fovea before it moves. Using a dynamic noise background and a dual psychophysical task, the authors probe both the performance and selectivity of visual processing for orientation at the fovea in the few hundred milliseconds preceding a saccade. They find that hit rates and false alarm rates are dynamically and automatically modulated by the saccade planning. By taking advantage of the specific sequence of noise shown on each trial, they demonstrate that the tuning of foveal processing is affected by the orientation of the saccade target suggesting foveal specific feedback.

A major strength of the paper is the experimental design. The use of dynamic filtered noise to probe perceptual processing is a clever way of measuring the dynamics of selectivity at the fovea during saccade preparation. The use of a dual-task allows the authors to evaluate the tuning of foveal processing as well and how it depends on the peripheral target orientation. They show compellingly that the orientation of the saccade target (the future location of the fovea) affects processing at the fovea before it moves.

There are two weaknesses with the paper in its current form. The first is that the key claim of foveal "enhancement" relies on the tuning of the false alarms. A more standard measure of enhancement would be to look at the sensitivity, or d-prime, of the performance on the task. In this study, hits and false alarms increase together, which is traditionally interpreted as a criterion shift and not an enhancement. However, because of the external noise, false alarms are driven by real signals. The authors are aware of this and argue that the fact that the false alarms are tuned indicates enhancement. But it is unclear to me that a criterion shift wouldn't also explain this tuning and the change in the noise images. For example, in a task with 4 alternative choices (Present/Congruent, Present/Incongruent, Absent/Congruent, Absent/Incongruent), shifting the criterion towards the congruent target would increase hits and false alarms for that target and still result in a tuned template (because that template is presumably what drove the decision variable that the adjusted criterion operates on). I believe this weakness could be addressed with a computational model that shows that a criterion shift on the output of a tuned template cannot produce the pattern of hits and false alarms.

The second weakness is that the author's claim that feedback is spatially selective to the fovea is confounded by the fact that acuity and contrast sensitivity are higher in the fovea. Therefore, the subject's performance would already be spatially tuned. Even the very central degree, the foveola, is inhomogeneous. Thus, finding spatially-tuned sensitivity to the probes may simply indicate global feature gain on top of already spatially tuned processing in the fovea. Another possible explanation that is consistent with the "no enhancement" interpretation is that the fovea has increased. This is consistent with the observation that the congruency effects were aligned to the center of gaze and not the saccade endpoint. It looks from the Gaussian fits that a single gain parameter would explain the difference in the shape of the congruent and incongruent hit rates, but I could not figure out if this was explicitly tested from the existing methods. Additional experiments without prepared saccades would be an easy way to address this issue. Is the hit rate tuned when there is no saccade preparation? If so, it seems likely that the spatial selectivity is not tuned feedback, but inhomogeneous feedforward processing.

This paper is important because it compellingly demonstrates that visual processing in the fovea anticipates what is coming once the eyes move. The exact form of the modulation remains unclear and the authors could do more to support their interpretations. However, understanding this type of active and predictive processing is a part of the puzzle of how sensory systems work in concert with motor behavior to serve the goals of the organism.

Reviewer #3 (Public Review):

This manuscript examines one important and at the same time little investigated question in vision science: what happens to the processing of the foveal input right before the onset of a saccade. This is clearly something of relevance as humans perform saccades about 3 times every second. Whereas what happens to visual perception in the visual periphery at the saccade goal is well characterized, little is known about what happens at the very center of gaze, which represents the future retinal location where the saccade target will be viewed at high resolution upon landing. To address this problem the authors implemented an elegant experiment in which they probed foveal vision at different times before the onset of the saccade by using a target, with the same or different orientation with respect to the stimulus at the saccade goal, embedded in dynamic noise. The authors show that foveal processing of the saccade target is initiated before saccade execution resulting in the visual system being more sensitive to foveal stimuli which features match with those of the stimuli at the saccades goal. According to the authors, this process enables a smooth transition of visual perception before and after the saccade. The experiment is well designed and the results are solid, overall I think this work represents a valuable contribution to the field and its results have important implications. My comments below:

1. The change in the overall performance between the baseline condition and when the probe is presented after the saccade target is large, but I wonder if there are other unrelated factors that contribute to this difference, for example, simply presenting the probe after vs before the onset of a peripheral stimulus, or the fact that in the baseline the probe is presented right after a fixation marker, but in the other condition there was a longer time interval between the presentation of the marker and the probe transient. The authors should discuss how these confounding factors have been accounted for.

2. Somewhat related to point 3, the authors conclude that the effects reported here are the result of saccade preparation/execution, however, a control condition in which the saccade is not performed is missing. This leaves me wondering whether the effect is only present during saccade preparation or if it may also be present to some extent or to its full extent when covert attention is engaged, i.e when subjects perform the same task without making a saccade.

3. Differently from other tasks addressing pre-saccadic perception in the literature here subjects do not have to discriminate the peripheral stimulus at the saccade goal, and most processing resources are presumably focused at the foveal location. Could this have influenced the results reported here?

4. The spatial profile of the enhancement is very interesting and it clearly shows that the enhancement is limited to a central region. To which extent this profile is influenced by the fact that the probe was presented at larger eccentricities and therefore was less visible at 4.5 deg than it was at 0 deg? According to the caption, when the probe was presented more eccentrically the performance was raised to a foveal level by adaptively increasing probe transparency. This is not clear, was this done separately based on performance at baseline? Does this mean that the contrast of the stimulus was different for the points at +- 3 dva but the performance was comparable at baseline? Please explain.

5. The enhancement is significant within a region of 6.4 dva around the center of gaze. This is a rather large region, especially considering that it extends also in the direction opposite to the saccade. I was expecting the enhancement to be more confined to the central foveal region. Was the effect shown in Figure 2D influenced by the fact that saccades in this task were characterized by a large undershoot (Fig 1 D)? Did the effect change if only saccades landing closer to the target were included in the analysis? There may not be enough data for resolving the time course, but maybe there are differences in the size of the main effect.

6. Is the size of the enhanced region around the center of gaze related to the precision of saccades? Presumably, if saccades are less precise a larger enhanced area may be more beneficial.

Reviewer #1 (Public Review):

This manuscript by Venturini & colleagues identifies EBV deletions as a potential biomarker for CAEBV. The authors identify the same large deletion in the blood but not saliva of CAEBV patients. No mechanism linking deletion with clonality is identified so the utility of the study would be purely based on using these deletions as biomarkers. To this end, loss of large deletions is shown overtime in 3 treated patients (2 with PBSCT & one with Rituxan). The sequencing work and statistical comparisons are rigorous.

While these findings are certainly of interest and raise an interesting hypothetical way to track treatment of CAEBV, the generalizability is limited by small sample size and lack of clarity in presentation. The study raises hypotheses for subsequent mechanistic work in animal models and could serve as a pilot for future formal, hypothesis-driven biomarker studies.

Reviewer #2 (Public Review):

EBV infections are widespread among humans and have been associated with a variety of diseases, including CAEBV. The pathogenesis of this rare syndrome is mysterious but presumably is a result of unusual host and/or viral factors. This paper explores whether variation among EBV strains might somehow play a role in CAEBV. The main conclusions the author propose - that unique genomic deletions are found in EBV strains in CAEBV patients and are lost after recovery - is intriguing, but not convincingly supported by the data.

Strengths of the study: The authors did a large amount of sequencing and many analyses of the EBV genomic sequence data in this heterogenous group of samples. Among the interesting findings is that salivary samples (which were available for CAEBV patients only) were considerably more diverse than most other blood and tissues EBV sequences. They also found considerable variation in the sequences among patients as well as changes over time within an individual. For example, they detected many point mutations and large and small deletions. One large region of the genome was deleted in all CAEBV samples, so a specific association with CAEBV is possible, but the same region was deleted In one other non-CAEBV sample. At a minimum, these data will be provide a useful compilation of EBV genomic variation that will aid future studies of possible causal links to differing EBV-associated diseases. The authors appropriately only allude to 'associations." Many viruses generate defective progeny, so quite possibly some or many of the variants detected in these samples are not able to replicate. Nonetheless, they might be useful marker of disease status.

Weaknesses of the study.<br /> Although the authors sequenced EBV from many samples of blood, saliva and tissues, the complexity and variety of these patients limits the power of even this large effort to generate firm conclusions. The three well-studied CAEBV patients all had very complex medical histories, including treatments such as rituximab (all 3) and hematopoietic stem cell transplantation (2 of 3). The comparison groups were quite diverse as well. Thus, unless a specific common variation is a major contributor to CAEBV, these data set are unlikely to have the power to reveal any specific genotypic association with EBV diseases.

The potentially very interesting conclusion that deletions disappear over time and with resolution of CAEBV seems to be quite an over-simplification of the results. EBV in patient 1 has multiple deletions at times 1-3 and 5, but not 4, 6 and 7. EBV in patient 2 is not very informative about any patterns. The statement on line 153 that the deletions in patient 2 "were stable overtime but then lost ..." is particularly unconvincing since it is based on just two time points. It is not clear how frequent of these deletions in all these patients are in the samples at these times points. The comparison to changes over time in non-CAEBV samples (i.e. IM and PTLD) is hard to evaluate without more information - for example how far apart in time these samples were collected. The conclusion that deletions disappear following treatment such as stem cell transplantation is inconsistent with the results from Patient 3, whose 1st post-transplant sample still had the deletions (although it may be that this time point was relatively early after the transplant and so the EBV genome might still reflect the pre-transplant viruses).

As is common in these kinds of studies, a considerable amount of filtering of the primary data is necessary to limit the data to a reliable set. However, these steps limit the strength of the conclusions. For example, discarding sequences with "less than 90% genome coverage" (line 71) could eliminate some potentially important large deletions. Perhaps there were not any samples that had high read depth but low genomic coverage, which would somewhat mitigate this concern. What is the rationale for considering deletion <30 bp to be artifacts (Figure 2 - Supplementary 4)? Figure 3- Supplementary 1 shows only one sample for each patients - is this a fair representation of the whole set?

Reviewer #3 (Public Review):

This study confirms previous data on the detection of deletions in the Epstein-Barr virus (EBV) genome associated with chronic active infection (CAEBV). The rare persistence of unresolved EBV replication in CAEBV is associated with significant morbidity and increased risk of developing lymphoproliferative disease. This study further implicates deleted forms of the EBV genome in contributing to this process and indicates that these deleted virus genomes appear to resolve upon treatment. Novel data shows that specific large deletions are found in the blood but not the saliva of CAEBV patients and that deletions are also found in the blood of individuals with the acute manifestation of EBV infection, infectious mononucleosis. While implicating deleted forms of EBV in the aetiology of CAEBV, there is no direct casual evidence linking these phenomena. Given the small size, it is difficult to make firm conclusions. Deleted forms of EBV are also found in patients in some patients with either post-transplant lymphoproliferative disease (PTLD) or Hodgkin's lymphoma (HL). The relevance of these forms of EBV to the pathogenesis of these conditions remains unknown.

Reviewer #1 (Public Review):

This is a study that is aimed at understanding the binding mechanism of D-serine to the two different binding lobes of the NMDA receptor. D-serine is a known agonist and binder of the GluN1 ligand-binding domain, but its interaction with the GluN2A is unknown. Using long time-scale conventional molecular dynamics simulations, the researchers observe that D-serine interacts and associates readily with both binding domains, often via protein surface pathways referred to as a guided-diffusion mechanism. As observed previously, free-energy calculations show that D-serine stabilizes the closure of both binding domains. Finally, analysis of the effect of glycans shows that these modifications play a role in further stabilizing the closed state of the ligand-binding domains.

Amongst this broad and careful analysis, the major finding from this work is that D-serine surprisingly associates with GluN2A, which has been known to bind glutamate to enable activation of the channel. Since the binding of D-serine to GluN2A had not been observed previously, they proposed that D-serine acts as an inhibitor for glutamate at high concentrations. This hypothesis was investigated and supported by electrophysiological experiments, yielding a novel result that presents new interpretations for the field. However, the guided-diffusion mechanism still remains hypothetical and is unclear as to whether this is in fact a driving force, or requirement, for the binding. Specifically, the following questions warrant further investigation:

1. Specific or non-specific association? It is possible that non-specific association events of ligands to the protein could be an intrinsic artifact of the MD simulations. To investigate this, it would be informative to compare the current results with a negative control simulation where the ligand was replaced with a similar amino acid or molecule that has been verified as a non-binder for NMDAR.