Reviewer #1 (Public Review):

The authors employed population receptive field (pRF) mapping to characterize responses to visual stimuli in early visual cortical areas V1-V3 and to compare the similarity of pRF properties in pairs of monozygotic versus dizygotic twins. They find closer correspondence of the anatomical location and spatial extent of the visual areas, pRF locations (polar angle and eccentricity) in the retinotopic cortical maps of visual space, and spatial selectivity of responses (pRFs size) in monozygotic twins, relative to dizygotic twins, indicating heritability of these structural and functional properties of early visual cortex.

The pRF mapping procedures used in this study are appropriate and standard in the field, and the statistical analysis and data presentation are thorough and rigorous. Given the many previous demonstrations of heritability in multiple aspects of visual perception and physiological responses to visual stimuli, it would be very surprising if any of the properties studied by the authors did not exhibit some amount of heritability. This paper therefore adds to the list of known heritable properties of the visual system but does not contribute theoretical or conceptual advances or challenge any existing frameworks.

The fact that pRF eccentricity was more correlated and showed less heritability than pRF polar angle is interesting but was not interpreted or followed up in any meaningful way. Overall, the analyses are basic (% overlap of retinotopic maps and the three main pRF parameters) and descriptive.

Reviewer #1 (Public Review):

Using well-designed surveys, the authors collected mosquito samples and human data along with environmental variables to estimate parasite prevalence (PR) and the entomological inoculation rate (EIR) in three regions of Malawi. They developed advanced geostatistical models to estimate PR and EIR and illustrated the spatial-temporal variation. The online interactivity web-based application showing the spatial-temporal pattern of PR and EIR as well as hot spots in map is particularly useful for visual understandings. These estimations then allow to unveil the time-lagged relationship between PR and EIR. Their data and research approach add very useful information for improving vector-born disease control strategies. Certainly, the data and findings are very useful for malaria control in Malawi.

Their conclusion seems largely supported by their statistical models and data. However, some outstanding research questions remain. In addition, some statistical issues need to be justified and clarified.

1) While the spatial-temporal pattern of PR and EIR is illustrated, what are the mechanisms underlying those spatial-temporal variation? Specifically, I think environmental factors and spatial distribution of human population certainly play important roles. Indeed, environmental factors were included in their geostatistical models to estimate PR and EIR. However, the authors made no attempt to provide explanation and discussion for these results (results shown as tables in their appendix).

2) Furthermore, if environmental factors are left out of focus, what is the additional value of using modelled PfSR and PfEIR for evaluation instead of empirical (observed) PfSR and PfEIR? What is the scientific motivation and justification of using modelled PfSR and PfEIR instead of empirical ones to make the spatial-temporal map and further statistical analyses and then to draw their conclusion on the relationship between modelled PfSR and PfEIR? Statistically, if the same environmental variable is used to fit PfSR and PfEIR, then there is potential spurious correlation (statistical artifact) between the modelled PfSR and PfEIR. The authors need to demonstrated this is NOT the case in their results and analyses.

3) With A, B, C three regions separated by the national park in the middle (large spatial missing data), is the assumption of isotropic Gaussian process reasonable in their geostatistical model? Sites between A and B have very large distances, but there is no observation data in between. Alternatively, the authors can model the three regions separately?

4) For hotspot detection, it is unclear whether the hotspots are decided: (1) when the point estimates of PfEIR and PfPR exceed the threshold; or, (2) when the lower 95% confidence bound of the estimates exceed the threshold? If it is the case (1), please justify. Statistically, case (2) is more appropriate. The uncertainty associated with estimates needs to be carefully addressed throughout the manuscript. In any case, please elaborate how the exceedance probability is obtained. My similar concern also appears in other analyses, for example the confidence interval shown in Figure 4.

Reviewer #1 (Public Review):

This work described a novel approach, host-associated microbe PCR (hamPCR), to both quantify microbial load compared to the host and describe interkingdom microbial community composition with the same amplicon library preparation. The authors used the host single (low-copy) genes as PCR targets to set the host reference for microbial amplicons. To handle the problem that in many cases, the host DNA is excessive compared to the microbiome DNA, the authors adjusted the host-to-microbe amplicon ratio before sequencing. To prove the concept, hamPCR was tested with the synthetic communities, was compared to the shotgun metagenomics results, was applied in the biological systems involving the interkingdom microbial communities (oomycetes and bacteria), or diverse hosts, or crop hosts with large genomes. Substantial data from diverse biological systems confirmed the hamPCR approach is accurate, versatile, easy-to-setup, low-in-cost, improving the sample capacity and revealing the invisible phenomena using regular microbial amplicon sequencing approaches.

Since the amplification of host genes would be the key step for this hamPCR approach, the authors might also include more strategy discussions about the selection of single (low copy) genes for a specific host and the primer design for the host genes to guarantee the hamPCR usage in the biological systems other than those mentioned in the manuscript.

Reviewer #1:

The question is interesting, and the paradigm in principle well suited to answer it. Unfortunately, a number of shortcomings hinder a clear interpretation of the results. I think that the paper, notably the EEG analyses, need to be revised substantially, which might affect the results. Therefore I will just list the main points which need to be addressed and not go in more detail.

The behavioral effect of adaptation on duration perception appears very unspecific, namely it occurs in all but the spatially neutral condition. The authors conclude that the inversely directed motion did not have an effect because it did not survive the Bonferroni correction, yet they report a p-value of 0.02 and Cohen's d of 0.58, suggesting a medium effect. In order to prove the absence of an effect, I suggest to report Bayes factors, and only interpret the effect as absent if the Bayes factor is conclusive towards the H0.

In my view, if there was an effect of inversely directed motion, this poses a question as to the successful demonstration of specific adaptation effects in the behavior, which needs to be taken into account in the interpretation.

The EEG analyses and displayed results show some important shortcomings, which hinder a clear interpretation at this stage. Just to list a few main points:

-As apparent from Figures 3-5, the time-frequency plots show a lot of stripes and pixels, when one would expect rather smooth transitions over frequency and time. This suggests that the parameters for the time-frequency transformation might not be appropriate.

-The analyses compare time windows that differ in many respects, for instance the 15 s long adaptation phase versus short-lived stimulus-evoked activity at reference onset. Interpreting these differences as specific to the duration distortion effects does not seem justified, due to the diverging inputs presented during those time windows.

-Important aspects of the paradigm are not taken into account in the EEG analyses, for instance the fact that participants perform a saccade between the offset of adaptation and the onset of the reference. The saccade-related signatures in the EEG have to be accounted or controlled for, especially for effects occurring after adaptation offset.

-Some of the effects (for instance the decoding analysis, or the linear mixed models testing for additive but not interactive effects) show differences in EEG activity related to visual processing of the stimuli, but might not specifically relate to the duration distortions. In my view, more trivial differences in processing the visual inputs should be accounted for (see also the point above), and clearly separated from specific timing effects.

Reviewer #1 (Public Review):

In this project, the authors set out to create an easy to use piece of software with the following properties: The software should be capable of creating immersive (closed loop) virtual environments across display hardware and display geometries. The software should permit easy distribution of formal experiment descriptions with minimal changes required to adapt a particular experimental workflow to the hardware present in any given lab while maintaining world-coordinates and physical properties (e.g. luminance levels and refresh rates) of visual stimuli. The software should provide equal or superior performance for generating complex visual cues and/or immersive visual environments in comparison with existing options. The software should be automatically integrated with many other potential data streams produced by 2-photon imaging, electrophysiology, behavioral measurements, markerless pose estimation processing, behavioral sensors, etc.

To accomplish these goals, the authors created two major software libraries. The first is a package for the Bonsai visual programming language called "Bonsai.Shaders" that brings traditionally low-level, imperative OpenGL programming into Bonsai's reactive framework. This library allows shader programs running on the GPU to seamlessly interact, using drag and drop visual programming, with the multitude of other processing and IO elements already present in numerous Bonsai packages. The creation of this library alone is quite a feat given the complexities of mapping the procedural, imperative, and stateful design of OpenGL libraries to Bonsai's event driven, reactive architecture. However, this library is not mentioned in the manuscript despite its power for tasks far beyond the creation of visual stimuli (e.g. GPU-based coprocessing) and, unlike BonVision itself, is largely undocumented. I don't think that this library should take center stage in this manuscript, but I do think its use in the creation of BonVision as well as some documentation on its operators would be very useful for understanding BonVision itself.

Following the creation of Bonsai.Shaders, the authors used it to create BonVision which is an abstraction on top of the Shaders library that allows plug and play creation of visual stimuli and immersive visual environments that react to input from the outside world. Impressively, this library was implemented almost entirely using the Bonsai visual programming language itself, showcasing its power as a domain-specific language. However, this fact was not mentioned in the manuscript and I feel it is a worthwhile point to make. The design of BonVision, combined with the functional nature of Bonsai, enforces hard boundaries between the experimental design of visual stimuli and (1) the behavioral input hardware used to drive them, (2) the dimensionality of the stimuli (i.e. 2D textures via 3D objects), (3) the specific geometry of 3D displays (e.g. dual monitors, versus spherical projection, versus head mounted stereo vision hardware), and (4) automated hardware calibration routines. Because of these boundaries, experiments designed using BonVision become easy to share across labs even if they have very different experimental setups. Since Bonsai has integrated and standardized mechanisms for sharing entire workflows (via copy paste of XML descriptions or upload of workflows to publicly accessible Nuget package servers), this feature is immediately usable by labs in the real world.

After creating these pieces of software, the authors benchmarked them against other widely used alternatives. IonVisoin met or exceeded frame rate and rendering latency performance measures when compared to other single purpose libraries. BonVision is able to do this while maintaining its generality by taking advantage of advanced JIT compilation features provided by the .NET runtime and using bindings to low-level graphics libraries that were written with performance in mind. The authors go on to show the real-world utility of BonVision's performance by mapping the visual receptive fields of LFP in mouse superior colliculus and spiking in V1. The fact that they were able to obtain receptive fields indicates that visual stimuli had sufficient temporal precision. However, I do not follow the logic as to why this is because the receptive fields seem to have been created using post-hoc aligned stimulus-ephys data, that was created by measuring the physical onset times of each frame using a photodiode (line 389). Wouldn't this preclude any need for accurate stimulus timing presentation?

Finally the authors use BonVision to perform one human psychophysical and several animal VR experiments to prove the functionality of the package in real-world scenarios. This includes an object size discrimination task with humans that relies on non-local cues to determine the efficacy of the cube map projection approach to 3D spaces (Fig 5D). Although the results seem reasonable to me (a non-expert in this domain), I feel it would be useful for the authors to compare this psychophysical discrimination curve to other comparable results. The animal experiments prove the utility of BonVision for common rodent VR tasks.

In summary, the professionalism of the code base, the functional nature of Bonsai workflows, the removal of overhead via advanced JIT compilation techniques, the abstraction of shader programming to high-level drag and drop workflows, integration with a multitude of input and output hardware, integrated and standardized calibration routines, and integrated package management and workflow sharing capabilities make Bonsai/BonVision serious competitors to widely-used, closed-source visual programming tools for experiment control such as LabView and Simulink. BonVision showcases the power of the Bonsai language and package management ecosystem while providing superior design to alternatives in terms of ease of integration with data sources and facilitation of sharing standardized experiments. The authors exceeded the apparent aims of the project and I believe BonVision will become a widely used tool that has major benefits for improving experiment reproducibility across laboratories.

Reviewer #1 (Public Review):

In this study, Boroumand et al investigate abundance and metabolic phenotype of Ly6Chi and Ly6Clo monocytes in the bone marrow (BM) following feeding a HFD for 3, 8 and 18 weeks compared with a control diet. The authors suggest that upon accumulation of white adipocytes in the BM (8 weeks of feeding), monocytes are skewed towards the Ly6Chi subset, which have been shown to give rise to many macrophage subsets in obese tissues. The authors further demonstrate metabolic changes in Ly6Clo monocytes which may contribute towards this phenotype. Finally, through a series of in vitro and ex vivo cultures, the authors suggest that the increase in Ly6Chi monocytes is due to conversion of Ly6Clo monocytes into Ly6Chi monocytes as a result of the increased prevalence of white adipocytes in the bone marrow.

Overall the findings of this work are interesting to the field and in the future it will be interesting to determine how these changes in the bone marrow relate to the different subsets of recruited macrophages present in obese tissues. For example, whether these monocytes preferentially generate CD9+Trem2+ Lipid associated macrophages recently described in obese adipose tissue (Jaitin et al, Cell, 2019) or if they are equally capable of generating monocyte-derived tissue resident macrophages in obese tissues.

The main strength of this paper is in the identification of the changes in the monocyte subsets abundance early after feeding a HFD and in uncovering the metabolic changes in and between these two monocyte subsets in obese mice. One concern regarding the data as a whole is that, while the authors have nicely indicated the number of samples/mice in each figure, there is no mention of how many times each experiment was performed. Including this would greatly aid in an understanding of the reproducibility of the results. Additionally, the inclusion of the different gating strategies used particularly for the first figures would be advantageous to fully appreciate the findings being presented. This is particularly relevant for the identification of Ly6Chi and Ly6Clo BM monocytes.

The conclusions made regarding the role of white adipocytes in skewing the monocyte subsets and particularly regarding the conversion of Ly6Clo monocytes to Ly6Chi are however less convincing. The authors use a culture strategy where they grow BM monocytes in vitro for 5 days. They then culture these 'monocytes' for a further 18 hours with conditioned media from BM adipocytes from control or HFD fed mice. They show that culture with 8 & 18 week conditioned media results in the increased abundance of Ly6Chi monocytes. The authors later claim this is not through proliferation of the existing Ly6Chi monocytes but conversion from Ly6Clo monocytes. However, the alternate explanation could be that there are some progenitors remaining in these cultures that can give rise to Ly6Chi monocytes following exposure to the conditioned media. To further validate these claims, it would be beneficial to sort Ly6Chi monocytes and culture them with the conditioned media to demonstrate the numbers do not increase. Moreover, it is important to demonstrate that there are no progenitors left in these cultures when the conditioned media is added. Indeed, later in the manuscript, when Ly6Clo monocytes are sorted and cultured with media from EWAT or BAT, it would be important to confirm that the sorted cells are a pure population of Ly6Clo monocytes with no contamination from progenitors that are also Ly6Clo that could give rise to Ly6Chi monocytes without going through the Ly6Clo monocyte stage.

In a similar vein, the authors suggest no conversion of Ly6Chi monocytes to Ly6Clo monocytes, but that Ly6Clo monocytes would convert into Ly6Chi monocytes (fig. 7). As this is a rather controversial claim, additional data in support of this conclusion would be beneficial. For example, after 18 hours of culture it is possible that if the authors are sorting Ly6Chi monocytes on the basis of Ly6Chi expression, that the antibody staining may be maintained for 18 hours. Similarly, after culture, it is possible that the cells are less healthy and hence non-specific binding should also be ruled out. Alternatively, qPCR for gene expression associated with Ly6Chi and Ly6Clo monocytes could be utilised to further substantiate the claims. For example, Spn expression for Ly6Clo monocytes, Ly6c2 expression for Ly6Chi monocytes.

Thus overall, this manuscript nicely demonstrates changes in the BM monocyte subsets and their metabolism, however some additional controls are required to further validate the claim that Ly6Chi monocytes are increased due to Ly6Clo monocyte conversion to Ly6Chi monocytes.

Reviewer #1 (Public Review):

In this manuscript, Holt and colleagues investigate how the mechanoreceptor PIEZO1 mediates keratinocyte cell migration and re-epithelialization during wound healing. The authors utilized epidermal-specific Piezo1 knockout mice (Piezo1cKO) and epidermal-specific Piezo1 gain of function mice (Piezo1GoF) to investigate the contribution of keratinocyte Piezo1 to wound healing in vivo. Piezo1cKO mice exhibited faster wound closure, whereas Piezo1GoF mice exhibited slower wound closure compared to controls, suggesting that the presence of epidermal Piezo1 affects the speed of wound healing. To determine if these effects observed in vivo were due to changes in keratinocyte re-epithelization, the authors utilized an in vitro model of wound healing by inducing scratches to mimic "wounds" in keratinocyte monolayers. Similar to the in vivo findings, Piezo1cKO keratinocytes exhibited enhanced wound closure compared to controls. In a separate line of experiments, the authors found that enrichment of Piezo1 at the wound edge induces localized cellular retraction that slows keratinocyte re-epithelization and wound closure. Overall, major strengths are that the topic is of significant interest, Piezo channels and their function is of broad topical interest, and the manuscript is well written. Wound healing is a major health concern and understanding the mechanisms underlying how wounds heal could generate improved therapeutics for faster healing. The key weaknesses are that there are missing controls and missing cohorts (Piezo1GoF or Piezo1cKO) in several of the experimental data sets, and there is a concern about the wide variation in controls for some experiments.

Reviewer #1 (Public Review):

Slavetinsky et al., describe the development of monoclonal antibodies targeting the S. aureus MprF lipid flippase, which is responsible for membrane incorporation of the phospholipid lysyl-phosphatidylglycerol (LysPG). Incorporation renders the cell more positively charged and has been associated with increased virulence and resistance of MRSA to antibiotics and host antimicrobial peptides. MprF is a bifunctional protein; the N-terminal region translocates lipids (flippase), and the C-terminal region synthesizes LysPG. Overall, this is an interesting approach with significant potential.

Strengths:

Several epitopes on MprF (three outer loops) were targeted through the synthesis of peptides, which provided a number of antibodies that inhibit the flippase function. The authors identified one specific antibody (M-C7.1) that was shown to target a loop whose previous location was debatable; thus, these finding indicate the loop can be accessible from the outside of the cell. Antibody binding sensitized MRSA to host peptides and antibiotics (e.g., daptomycin). The antibody was shown to inhibit flippase function and also decreased bacterial survival in phagocytes. Overall, the antibody could be used as an anti-virulence agent, diminishing the severity of S. aureus-associated disease. The emergence of antibiotic resistance and difficult to treat S. aureus infections requires orthogonal therapeutic approaches; as such, the findings of this study could have significant impact.

Weaknesses:

A major emphasis of the study is that the antibody sensitizes S. aureus to host defenses. This reviewer would like to see dose-responses/titrations of the antibody vs the different CAMPs, using standard susceptibility testing methodology. In addition, during the preliminary ELISAs, have the authors established whether the mprF mutant has lower surface adhesion to maxisorp immuno plates? This would be an important control. When studying M-C7.1 mechanism of action, it is unclear why the data is being normalized to L-1 and why unbound cytochrome C is being quantified. It could be more intuitive to assess bound cytochrome C; can the raw data be included rather than normalized data? A control with delta-mprF alone would also be useful for these experiments. When assessing survival in phagocytes, Figure 5 would benefit from a delta-mprF control to compare M-C7.1 efficacy. This figure also requires statistical analysis. Overall, the conclusions of the study could be further strengthened from additional pre-clinical assessment of the antibody.

Reviewer #1 (Public Review):

This manuscript from Eric Snyder's laboratory details cell lineage states that are controlled by NKX2-1 and oncogenic MAPK signaling in BRAFV600E-driven lung cancers. The work builds on previous works from Snyder's group that showed NKX2-1 suppresses a latent gastric differentiation program in KRASG12D-driven lung cancers. Switching the model from KRAS to BRAF, now the Snyder laboratory demonstrates multiple similarities between the oncogenic drivers and details key differences that have significant impact on our understanding of lung cancer etiology and possibly treatment. The depth of data analysis and breadth of methodology used represent a real tour de force in cancer modeling. The insights highlight the complex interplay between mitogenic signaling and developmentally-related pathways during cancer progression. The insights gleaned from the study have some potential in influence treatment strategies. As such, this study will appeal to a broad audience. The stated conclusions from the work are entirely sound and wholly supported by the data presented.

The authors demonstrate that: Simultaneous activation of BRAFV600E expression and deletion of NKX2-1 suppresses the efficiency of tumor initiation (tumor number goes down). In contrast, genetic deletion of NKX2-1 after tumors have established does not impact tumor maintenance but instead is compatible with tumor progression. Modeling the effects of MAPK pathway inhibition (BRAFi+MEKi), the authors demonstrate that BRAF/p53 (BP) tumors enter a state of quiescence. However, BP tumors with NKX2-1 deletion (BPN) fail to enter the quiescent state. Mechanistically, this is due to activation of a WNT-dependent activation of CyclinD2 that acts with CDK4/6 to suppress RB. Further treatment with CDK4/6 inhibitors can drive cells into quiescence but does not lead to durable tumor growth inhibition as tumors rebound after treatment cessation. Consistent with their previous work in KRAS-driven lung cancers, deletion of NKX2-1 reveals a latent gastric cell differentiation program driven by relocalization of FOX factors toward gastric specific genes. Interestingly, MAPKi in BPN tumors further drives these cells toward a chief-like or tuft-like cell state that is also due to WNT-dependent signaling, and FOXA1/2-dependent effects at specific genes normally restricted to tuft and chief cells.

Reviewer #1 (Public Review):

The data in the paper are mostly convincing, but might be somewhat over-interpreted: statistical analysis of the Tables is required. Yes, long slender bloodstream forms can definitely differentiate to pro cyclic forms and infect Tsetse. However, they take longer to differentiate than stumpy forms do, and even though morphologically stumpy forms are not an obligatory intermediate, expression of at least one stumpy-form mRNA (and presumably, others in the pathway) is definitely required. This should be stated in the Abstract. The conclusion that there is no cell-cycle arrest at all is not really supported by the data.

Joint Public Review:

This is an elegant study that delves into germline initiation and ovule development at a resolution not previously reported. The topic is of general significance for developmental biologists, and particularly interesting for groups studying the basis for germline development. Using a multitude of assays, starting from 3D segmentation analysis, progressing to modelling, reporter line analysis and mutant characterization, the authors document cellular components of ovule primordium growth and uncover new aspects of spore mother cell (SMC) emergence, in which ovule geometry appears to play a relevant role. The authors concluded that anisotropic growth is one of important factors to drive overall development of ovules, especially in Phase I, and that the L1 dome and the basal domain, but not the SMC and neighboring L2 companion cells, are consecutive sites of cell proliferation, thus contributing to morphological changes of ovules in Phases I and II. In terms of novelty, this work identified growth principles conducive to ovule primordium growth, added a layer of complexity to the nucellar epidermis towards SMC specification, and provided a new concept of SMC development: SMC fate emergence and SMC singleness resolution, where cell geometry plays a very active role

The katanin mutant is an interesting choice since it has been reported previously to impact cell growth. As expected, in katanin mutants, the primordium became enlarged in size and was more isotropic (lower height/width ratio) in shape. A reduced anisotropy also induced aberrant enlargement of SMC companion L2 cells in katanin mutant ovules. From PCNA and CYCB1.1 expression patterns, which are S- and M-phase markers, respectively, the authors found that the SMC precursor and its companion cells showed a highly frequent S-phase pattern. Taken together with infrequent divisions, the SMC and its neighbors have properties distinct to other ovular cells in longer S-phase duration. In addition, SMC singleness was suggested to be determined partly by Katanin-dependent anisotropic condition.

The claims made through the work are well documented and supported. In terms of experimental clarity and composition, the authors describe very well how the samples were obtained/how they were named, the statistical analysis appears robust and well described, and several of the markers analyzed provide a comprehensive landscape of what is occurring in the ectopic cells.

Reviewer #1 (Public Review):

The neuroendocrine system of the maggot has been mapped in parts at both the light and electron microscopic levels in earlier studies. In this manuscript, Hückesfeld et al map the entire endocrine system all the way from its sensory input neurons to the interneurons and secretory neurons and the glands. This is invaluable for many reasons, including because information about external stimuli are likely integrated at the level of interneurons.

The authors use this connectome to model how and to what extent each sensory modality might influence the different neurosecretory cells. They use the CO2 sensing pathway to functionally validate their model in vivo using CaMPARI. Through this they validate a circuitry where CO2 sensing neurons in the trachea influence 4 types of neurosecretory cells via 4 interneuron pathways. Interestingly, they find that the CO2 sensory information is not necessarily what dominates the sensory input onto some these neurons.

Reviewer #1 (Public Review):

In this manuscript Rao et al. describe an interesting relationship between KSR1 and the translation regulation of EPSTI1 (a regulator of EMT). They identified this relationship by polysome RNAseq of CRC cells in the context of KSR1 knockdown (KD) which they confirm by polysome QPCR. They then go on to show that KSR KD and add back influences EPSTI1 expression at the protein but not mRNA level and impacts cell viability, anchorage-independent growth, and possibly cell migration. They focus on the cell migration phenotype and show that it is associated with changes in EMT-related genes including E-cad and N-cad. Interestingly, add back of EPSTI1 can reverse the phenotype elicited by KSR1 deletion. Overall, this story is interesting and translation regulation by KSR1 has not been described previously. However, Rao et al. do not provide a mechanism for how KSR1 regulates the translation of EPSTI1, and it is unclear whether this occurs through eIF4E, as the authors suggest.

Reviewer #1 (Public Review):

The paper is investigating the mechanism of lineage switch in lung cancer. In about 10-15% of lung cancers treated with inhibitors of oncogenic receptors such as EGFR or KRAS, cancer cells emerge over time with newly acquired features of neuroendocrine differentiation. The authors proposed that it is a direct result of inhibition of MAPK pathway signaling so that reduced MAPK activity activates previously silent genes regulating neural crest differentiation. While this theory is of interest, the experiments presented herein are construed on the opposite sequence by way of introducing activated MAPK via oncogenic KRAS or EGFR to 3 neuroendocrine cell lines resulting in lower expression of neuronal transcription factors. The authors propose MAPK-activated ETS family TFs are responsible for the repression of NE lineage.

Several principal issues presented by the authors raise some concerns:

1) Despite presenting some evidence to the effect of suppression of NE transcription factors by overactivating MAPK signaling, the conversion of adenocarcinoma to NE (the opposite transition) is not being addressed in the paper. Therefore, it is rather illogical to investigate the process of transition that is not taking place in the real world.

2) The authors do not consider a possibility of multi-clonality of human cancers and clonal competition as a mechanism leading to acquired resistance and the emergence of NE clones that are not suppressible by the inhibitors of MAPK pathway (e.g. EGFR inhibitors, or KRAS/RAF/MEK inhibitors). Starting the experiments with clonal populations of long-term cultured cell lines may be an insurmountable difficulty to switch these cells between the epithelial and NE phenotypes which proved to be frustratingly non-productive in the hands of the authors. Taken out of context of tumor microenvironment, these phenotypic transitions may be co-regulated by a combination of cell-intrinsic and extrinsic factors.

3) Despite zeroing in on ETVs downstream of ERK1/2, the paper does not go as far as showing the direct effect of these TFs as repressors of NE differentiation (ASCL1, BRN2, NEUROD1 etc.).

4) The line of evidence that Dox-activated MAPK signaling via massive over expression of KRAS or EGFR induces dramatic increase in marks of transcriptionally active chromatin (such H3K27ac and others) is to be expected in this entirely artificial system. Indeed, the addition of doxycycline results in massive burst of proliferation and overexpression of ETV1 and ETV4, the canonical MAPK targets. Again, this switch appears unrelated with the opposite of epithelial-to-NE de-differentiation.

Reviewer #1 (Public Review):

SARM1 is an enzyme that is present in neurons and degrade NAD+. Previous studies have shown that disrupting SARM1 inhibits axon degeneration and thus it could be a target for treating neurodegenerative diseases. NAD+ is also an important metabolite that is required for many biological pathways. Thus, SARM1 activity must be carefully regulated. Recent studies have provided structural and biochemical insights about how SARM1 activity is auto-inhibited in basal states. The manuscript by Dr. Thompson and coworkers provide a nice new model regarding how SARM1 could be potentially activated. They provide strong in vitro data to support that phase transition, promoted by PEG molecules and citrate, could dramatically increase the activity of SARM1 TIR domain (which is the catalytic domain) in vitro. The authors also showed that in the worm, C. elegans, citrate promotes SARM1 puncta formation and axon degeneration, which is consistent with the in vitro data. They also generated multiple mutants of SARM1 TIR domain and showed many of the mutants have decreased phase transition and decreased activity in vitro. One of mutant, G601P, also showed decreased puncta formation when expressed in HEK 293T cells as SARM1 SAM-TIR domains E462A mutant (a catalytic mutant so that expression will not cause toxicity) fused with GFP.

The manuscript has many strengths, including the strong and very careful in vitro characterization of the purified SARM1 TIR domain, which provide a lot of useful information regarding the kinetic parameters, substrate specificity, and inhibition profiles. The worm data with citrate is consistent with the in vitro data, which is also a strength.

The impact of the finding lies in two aspects. First, it provides a new understanding about how SARM1 activity might be regulated in vivo by phase transition. This is especially true given most studies so far focuses on how it is inhibited at basal conditions. It also adds another example to the list of enzymes that are regulated by phase separation. Second, the finding that PEG and citrate strongly activate SARM1 in vitro also provides a much improved assay for the development of small molecule modulators of SARM1 for potential therapeutic applications.

There are two minor weaknesses associated with the studies of the manuscript. One is that all the in vitro studies used just the TIR domain of SARM1, not the full length SARM1. Another minor weakness is associated with the data in Figure 5. Most of the mutants have dramatically lower catalytic activities (>100-fold), but the precipitate formation is only modestly affected (2-fold). Although this does not affect the overall conclusion of the manuscript, it prevents the mutants from being more useful for mechanistic dissection.

Reviewer #1:

In this paper, the authors proposed a new approach by mounting a PDMS microwells of specific sizes on agar surface to confine swarming and planktonic SM3 cell, they found swarming bacteria exhibit a "single-swirl" motion pattern and concentrated planktonic bacteria exhibit"multi-swirls" motion pattern in the diameter range of 31-90 μm. The phase diagram shows that in smaller wells concentrated planktonic SM3 forms a single vortex and in larger wells swarming SM3 also breaks into mesoscale vortices.

After that, they conducted systematic experiments to explore parameters defining the divergence of motion patterns in confinement including cell density, cell length, cell speed and surfactant. They concluded that the single swirl pattern depends on cohesive cell-cell interaction mediated by biochemical factors removable through matrix dilution.

This paper gives a new method to discern swarmers from Planktonic Bacteria and carefully studies the factors that influence the formation of bacterial vortices under restriction. However, major revisions are required to improve the quality of this paper.

Major questions and comments:

1) When the authors put the PDMS chip mounting on the edge of the swarming colony, the PDMS chip is completely attached to agar or suspended in a bacterial solution. The distance between PDMS chip and agar surface should be quantified. It is better to have a schematic diagram of the experimental device.

2) Is the bacteria still expanding outward after a PDMS chip was mounted on agar surface? The effect of PDMS chips on the expansion of bacteria on the agar surface needs to be discussed.

3) "Diluted swarming SM3 show unique dynamic clustering patterns". In the diluted bacteria experiment, the authors found that the diluted swarming bacteria can form bacterial rafts and the concentrated planktonic SM3 disperse uniformly and move randomly. Hence, when bacteria expand and gradually fill up new empty microwells, is there a process of transition from raft to single vortex state?

4) In the experiment of altering the conditions of swarming SM3, the authors diluted the swarming cells in Lysogenic Broth (LB) by 20-fold, re-concentrated the cells by centrifugation and removed extra LB to recover the initial cell density. After these operations, they found the previous single swirl turned to multiple swirls and got a conclusion that matrix dilution can affect single swirl patterns. The authors think centrifugation may wash away some surrounding matrix or polymers on the surface of bacteria. Therefore, the steps of centrifugation need to be presented and the effect of centrifugation on the physiological behavior of bacteria should be discussed.

5) This article covers the PDMS chip directly on the agar surface and finds that swarm and planktonic bacteria have different spatial correlation scales in the restricted microwells. The authors have done a lot of experiments to prove the difference between clusters and planktonic bacteria and explain the reason for the single vortex. However, the conclusion is not clear. Therefore, the authors should focus more on the analysis of this new experimental phenomenon, such as critical length and vortex phase diagram, rather than just describing the experiments they did.

6) The authors mentioned the critical length for swarming SM3 is ~ 49 μm, whereas, for concentrated planktonic SM3, it is ~ 17 μm. Does this quoted data match what you get from their experimental method? I do not see any follow-up discussion and evidence.

7) As shown in Figure 1 and Movie_S1_mp4, the direction of the single vortex motion of bacteria is clockwise. However, the article simply ignores that the single vortexes of bacteria all present the same direction, and there is no analysis and reasonable explanation on the vortex direction. As shown in Movie_S5_mp4 on the numerical simulations of circularly confined SM3, simulated bacteria vortex counterclockwise in completely opposite directions. The influence of the microwell boundary on the direction of the vortex should be clearly explained at the level of bacterial movement and preferentially with theoretical simulation.

8) Swarming and concentrated planktonic Bacillus subtilis 3610 show the same motion pattern across different confinement sizes. However, the authors did not give definitive conclusions and evidence. As shown in Figure S1, bacillus subtilis 3610 show completely different cluster behavior. Therefore, the discussion of 3601WT may cause readers' confusion on the article. It may be better to put it in the supporting material.

Minor questions and comments

9) Figure 1C, 1D, 6A, 6B may be more convenient to have a scale bar.

Reviewer #1 (Public Review):

The authors have succeeded in their attempt to develop and characterize a rigorous preclinical model of prenatal methadone exposure secondary to pre-pregnancy prescription opioid use. The model is a technical advance in terms of the opioid exposure being consistent throughout pregnancy and the outcome measures of methadone impact are rigorous. Many aspects neurodevelopment and key physiological processes are assessed and key knowledge is provided about the effects of prenatal methadone exposure on physical development, sensorimotor behavior and neuronal properties.

Major strengths include the thoroughness and rigor of analyses and the multiple body systems study. In addition, scientific questions are approached using physical, biochemical and behavioral assessments to fully characterize the effects of prenatal methadone exposure.

The strengths of this paper outweighs the weaknesses. Weakness are minor and include an incomplete assessment or discussion of whether withdrawal in the postnatal period may explain the pathophysiology described and changes in circuitry. Similarly, white matter analyses are not included MRI assessments confining the results to gray matter brain regions.

Reviewer #1:

In this manuscript, the authors look at the influence of root stock genotype on a single scion genotype in Vitis. This includes a lovely highly replicated design including differential water availability. While the experimental design is very elegant, I'm less sure that using general PCs or ML is the best approach to grab the signal of interest.

Is there evidence that the top 20 PCs of the metabolome or the top 100 PCs are an end point of gaining new information about the system. For example, if the top 20 PCs are all different descriptions of the water availability, then PC 21 might start to grab more information about the root-scion relationship. For example in this dataset, PC2-10 were largely about temporal block (line 314-316). In large genomic datasets like this, they have an immense amount of variation such that r2 is not a meaningful way to capture what is in a PC. I can understand the desire to minimize the statistical analysis but if the goal is to fully interrogate the dataset, the authors should provide an empirical reason for stopping at pre-ordained PCs. Or possibly better would be to grab the lsmeans for the main factors in the model to exclude factors of blocking and then run the PCs as that is the underlying interest in the experiment.

The focus on PCs or using ML on the full dataset also hinders the ability to get at the underlying root/scion and water availability connection. Given that phenology and blocking are the main sources of variance, using these approaches rather than a direct GLM or PC on lsmeans/BLUPs weakens the authors ability to use the power in their experimental design. PC and ML can only capture the largest components of variance while GLMS that account for these larger sources of variance can begin to dive into the underlying questions. There is a possibility that the authors did attempt these directed GLMS with no luck but that was not stated.

I think the use of PCs is maybe my biggest hindrance on the manuscript as the section on lines 409-430 which is the capstone of the paper but ends up being correlations of faceless PCs. Unfortunately this leaves the reader with the idea that phenology is simply too strong to obtain any information about the root/scion connection or the water availability connection.

Reviewer #1 (Public Review):

The authors find that plin2 transcript is induced in intestine of 6 dpf zebrafish larvae following a single feeding, while plin3 transcript is expressed in the fasted and fed states in the intestine. They use TALENS to knock-in EGFP and TagRFPt into the plin2 and plin3 loci, with the encoded gene products being the fusion proteins EGFP-plin2 and Plin3-TagRFPt. The EGFP-plin2 protein shows greater induction of fluorescence following a meal. The overall aim of these initial expression characterizations and development of lipid droplet reporter knock-ins is to be able to monitor the life cycle of these organelles in a living whole organism.

Higher resolution photomicrographs of lipid droplets with these knock-in lines concurrently stained with the the fluorescent lipid dyes BODIPY 558/568 C12 and BODIPY FL-C12 are presented with a time series following feeding in intestine; additional cell types beyond enterocytes (i.e., hepatocytes, adipocytes, and cells surrounding lateral line structures) are presented.

The authors have provided a technical advance to the field of lipid droplet biology. With the tractable revisions set out below, their tools set the stage for chemical and genetic screens for factors and compounds that modulate the normal life cycle of these dynamic organelles.

Reviewer #1 (Public Review):

In initial experiments, low levels of IL-33 were detected in Toxaplasma-infected mice. How do these levels compare with normal physiological levels? It would help the reader to understand the relative levels to expect.

The authors identify that most IL-33 is produced by stromal cells rather than hematopoietic cells. The frequency of tdTomato parasites appear to be much less than for the distribution of IL-33 producing cells. Does the parasite expression reflect 100% of parasites or are the number of IL-33-producing stromal cells stimulated in the infection much larger than the identifiable parasite number? That is, is the activation of the stromal cells a direct effect of the Toxaplasma infection or does it depend on intermediates to amplify the effect?

Although the data presented are interesting and the authors identify that both stromal cells and hematopoietic cells contribute to the protective effect of IL-33, it is somewhat confusing amongst the hematopoietic cells, which cells are really driving the response amongst those categorized as 'innate'. Within the hematopoietic compartment, a number of associations are delineated but the causal connections are less clear. The provision of exogenous cytokines indeed have the effect they show in their results, but it remains unclear to this reviewer, whether these effects directly act on the hematopoietic cells, or stromal cells which alone are not sufficient to contain the infection and thus develop a higher pathogen load confounding their contributions.

This work would be strengthened significantly by delineating more clearly the contributions of each compartment. Currently, the correlations are modelled on the responses in the omentum and it would be useful to understand if this reflects the broader response.

This work would benefit from a schematic to indicate how the authors believe the different cells are connected and which are the real drivers/where connections have been demonstrated in driving the immune response.

Reviewer #1 (Public Review):

1) I found the initial description of the overall structure confusing. At first the authors say the complex is a tetramer, which is not what was seen by the Conti lab and then follow that with a confusing discussion leading to the conclusion that the dimer with a rigid subunit and a flexible one is the functional unit. It rather feels like they arrive at this conclusion because that's what Conti's lab saw, rather than any other reason. Since the human complex is a tetramer, perhaps the tetrameric complex observed here is one possible form and that possibility should be considered more carefully. Please state whether there is any similarity in the arrangements between the human tetramer and the tetramer observed here. I found the figure 2 supp 1C was not easy to follow. Coloring each of the four protomers differently would make things clearer.

2) The authors previously determined the structure of yra1C domain bound to sub2 and several labs have shown this interaction activates Sub2 atpase activity. Are those interaction observed previously between Yra1 and Sub2 compatible with this new structure? If so, perhaps the authors could provide a model showing how Yra1 fits into this larger complex. Also, could Yra1 C domain and Gbp2 bind simultaneously to a single THO-Sub2 protomer or would one protomer bind Yra1 and perhaps another bind Gbp2? This is worth considering because this would strengthen the concept that TREX acts as a general platform engaging with multiple export factors to drive recruitment of multiple Mex67 molecules and eventual export of the Mex67:mRNP complex. In the human system, the SR proteins and Alyref have an overlapping binding site on Nxf1, suggesting they may not act together to recruit a single Nxf1, but rather they recruit different Nxf1 molecules perhaps to the same mRNP via a single multimeric THO platform.

Reviewer #1 (Public Review):

Overall this is a well-done study, but some additional controls and experiments are required, as discussed below. The authors have done a considerable amount of work, resulting in quite a lot of negative data, and so should be commended for persistence to eventually identify the link between neutrophils with IL-18, though type I IFN signaling.

Major Comments:

• A major conclusion of this manuscript is prolonged type I IFN production following vaginal HSV-2 infection, but the data presented herein did not actually demonstrate this. At 2 days post infection, IFN beta was higher (although not significantly) in HSV-2 infection, but much higher in HSV-1 infection compared to uninfected controls. At 5 days post infection the authors show mRNA data, but not protein data. If the authors are relying on prolonged type I IFN production, then they should demonstrate increased IFN beta during HSV-2 infection at multiple days after infection including 5dpi and 7dpi.

• Does the CNS viral load or kinetics of viral entry into the CNS differ in mice depleted of neutrophils, IFNAR cKO mice, or mice treated with anti- IL-18? Do neutrophils and/or IL-18 participate at all in neuronal protection from infection?

• In Figure 3 the authors show that neutrophil "infection" clusters 2 and 5 express high levels of ISGs. Only 4 of these ISGs are shown in the accompanying figures. Please list which ISGs were increased in neutrophils after both HSV-2 and HSV-1 infection, perhaps in a table. Were there any ISGs specifically higher after HSV-2 infection alone, any after HSV-1 infection alone?

• The authors claim that HSV-1 infection recruits non-pathogenic neutrophils compared to the pathogenic neutrophils recruited during HSV-2 infection. Can the authors please discuss if these differences in inflammation or transcriptional differences between the neutrophils in these two different infections could be due to differences in host response to these two viruses rather than differences in inflammation? Please elaborate on why HSV-1 used as opposed to a less inflammatory strain of HSV-2. Furthermore, does HSV-1 infection induce vaginal IL-18 production in a neutrophil-dependent fashion as well?

Reviewer #1 (Public Review):

In this paper, the authors investigated the role of the N-terminal acetyltransferase Naa10 in mouse development. In addition, they identified a new paralog, Naa12, and demonstrated that it has a redundant role with Naa10 in controlling mouse embryonic development. The results are very clear and should be of interests to those working on development and N-terminal acetylation.

I have several comments for the authors to consider:

1) It is important to show that N-terminal acetylation is lost in the double knockouts. Only with that, the authors can conclude that they have identified the "the complete machinery for the process of amino-terminal acetylation of proteins in mouse development."

2) Naa12 is new, so if not done yet, the sequence needs to be deposited into Genbank.

3) The presentation needs to be polished.

i) The title "Naa12 rescues embryonic lethality in Naa10-Deficient 1 Mice in the amino-terminal acetylation pathway" is misleading. When I saw the title, I got the impression that Naa10-dficient 1 mice show embryonic lethality. I would suggest to change it to indicate that Naa10 and Naa12 have redundant roles in embryonic development. Also, "Naa10-Deficient 1 Mice" needs to be changed to "Naa10-deficient mice."

ii) In the impact statement "Mice doubly deficient for Naa10 and Naa12 display embryonic lethality...", the word "doubly " is unnecessary.

iii) Too many acronyms, which make the reading a bit difficult. The terms NTA and Nt-acetylation could be avoided. iv) At the end of page 9, please cite the sequence alignment in Fig. S6

v) On page 12, "Naa12 may rescue loss of Naa10 in mice" could be more assertive.

vi) Overall, I feel that the authors could polish the manuscript so that the salient points could be conveyed more easily to readers.

Reviewer #1 (Public Review):

This is a highly interesting manuscript by Gonzalez-Calvo et al., describing the involvement of the CCP domain containing protein SUSD4 in the degradation of GluA4 receptors at cerebellar synapses. The novelty of this work lies in the specificity of this degradation pathway. In comparison, synaptic proteins involved in AMPA receptor endocytosis, such as GRIP1 and PICK1, play a role in multiple trafficking processes. In addition, CCP domain proteins play a role in synaptic pruning, which is closely related to LTD. We will return later to this point.

The paper will certainly enrich the field and further our understanding of cellular plasticity in the cerebellum. These are exciting findings that should be published. I have three relatively minor comments:

1) Figure 2E: it is surprising that the potentiation shown in WT mice is not longer lasting. Under the experimental conditions used here, plasticity seems to be biased towards depression. In the methods, the authors state that they use 2mM Calcium and 1mM Magnesium in their external saline. A recent study (Titley et al., J. Physiol. 597, 2019) has demonstrated that under realistic conditions (incl. an ion milieu of 1.2 mM Calcium and 1mM Magnesium), LTP results under most conditions - even those involving climbing fiber co-stimulation - while LTD only results from prolonged complex spike firing. Optimally, the authors would establish a real LTP control in their WT mice (using conditions as described in Titley et al or similar) and test for changes in the mutants. As LTP is not the focus of this paper and this might be out of the scope of this work, it should be acceptable to leave it as it is, but this caveat should at least be discussed.

2) Figure 3: The climbing fiber physiology is described in detail, but what is missing is a characterization of potential changes in the complex spike waveform, recorded in current-clamp mode. This should certainly be provided. This is important as it has been shown that changes in the complex spike waveform affect the probability for LTD induction (Mathy et al., Neuron 62, 2009). The CF-EPSC is a rather indirect measure.

3) Is synaptic pruning at parallel fiber synapses impaired in the SUSD4 mutants? The LTD deficit is quite obvious. In the light of the role of autophagy in pruning, and the molecular similarity between LTD and pruning, it would be of interest to see whether activity-dependent pruning at these synapses is altered. This aspect is somewhat addressed by the VGLuT1 measures shown in Figure 2, but should be discussed in more depth.

Reviewer #1 (Public Review):

Galbraith et al., using systems immunology approach document in a very detailed manner, provide the textbook example of innate and adaptive immune responses over time following an infection. Here, their clinical assessment is linked to SARS-CoV2 infection. While novelty aspects are not immense, this study is nonetheless well executed, detailed and thorough.

The authors perform association studies and propose that simple seroconversion test should be considered in determining the clinical treatment. While some would argue that is already practiced and perhaps expected, the authors have done an excellent job at detailed immune analyses which they coupled with statistically sound associations. Thus these findings are important to document, and should be considered as experimental ex vivo evidence of what clinical practice may have implicitly already considered.

Reviewer #1 (Public Review):

In this manuscript the authors present a new and improved open-source option for a home cage pellet dispensing device that carries with it the ability to offer continuous monitoring of feeding behavior as well home-cage operant testing. This device solves many issues in the way individuals typically go about studying animal feeding behavior including but not limited to testing at only certain times of the day for limited amounts of time and food restriction in a manner that optimizes cost, functionality, scalability, and customizability over traditional or commercial options. Of note, besides offering the ability to capture massive amounts of home cage feeding and operant data directly in the vivarium of animal housing facilities, a major strength of this approach is that the authors demonstrate that the same amount of learning that would typically require 16 days (one-hour testing sessions) can be accomplished overnight (and with interesting circadian effects on decision-making that are often overlooked). The authors demonstrate useability of this device across institutions in other labs and integration with optogenetics (as well as citing recent studies integrating the device with recording systems).

Reviewer #1 (Public Review):

The authors use smFRET and cross linking to constrain relative orientations of CC1-CC3 helices in STIM1 resting and active conformations. The data are excellent and especially because structures of full length STIM1 are currently lacking they paint an important picture of the structural basis for STIM1 activation. The number of smFRET pairs examined in the inactive state is fairly large and paints a good picture of the relative orientations of helices. In contrast, only a few pairs of sites were examined in activated STIM1 which paint a clear picture of CC1a1 dissociation from CC3, but the remaining postulated conformational changes during activation are inferred primarily from cross linking, and it would have been nice to probe those with smFRET as well. Nonetheless, the data yet provide very useful constraints on STIM1 conformational rearrangements that will be of great value to further structure-function studies.

Joint Public Review:

The manuscript by Tachinawa et al. presents a new method (named RhIP), to study incorporation of recombinant epitope-tagged histone dimers into permeabilized cell nuclei. Using RhIP, the authors demonstrate that both H3-H4 and H2A-H2B and their variants are incorporated in this setup. They proceed with investigating context-specific features of these events, providing evidence that ongoing replication and overall chromatin structure may influence histone dimer incorporation in RhIP. This argues for RhIP having the potential to reveal the mechanisms of chromatin assembly and disassembly genome-wide, and determine how cell cycle and chromatin structure influence these dynamics.

The system is capable of recapitulating major known chromatin assembly pathways and supports existing knowledge of histone dimer dynamics on chromatin. RhIP is also valuable in directly testing histone mutants or variants, as proven by authors.

H3.1 incorporation is shown to be exquisitely dependent on replication, demonstrating that replication itself, as well as replication-dependent chromatin assembly are successfully reconstituted with isolated nuclei, cytosolic extracts and recombinant histones.

The focus of the study is on the incorporation H2A variants, in particular H2A.Z. These data supports known notions about H2A.Z dynamics in chromatin, showing a preference for transcription start sites, and the dependence on the M6 region.

However, the major limitation of the current manuscript is that it remains unclear what properties are driving the observed RhIP effects. This is not fully elucidated and thus limits the ability of RhIP to enable the discovery of new mechanisms.

While replication-dependent mechanisms are well captured by RhIP, it is less clear if transcription and chromatin remodeling is functional in this system and thus if transcription-dependent nucleosome exchange processes are faithfully recapitulated. It is important to improve the comparison of RhIP with 'in vivo' (i.e. existing ChIP-seq datasets) localisation and explicitly develop hypotheses why in some cases the data matches the 'in vivo' situation and in others not. It would be helpful to improve the interpretation of the data to include all existing caveats to the assay setup.

Reviewer #1:

The definition of individuality and its neurogenetic basis is a fundamental problem in ethology and neuroscience. Individuals might fall into discrete groups of personality types; alternatively, individuals might be better described by a broader spectrum of independent traits. An unbiased and quantitative analysis of behavioural traits that make up an individual's personality is a prerequisite of investigating the neuronal and genetic basis of individuality. Given the technical challenges in systematically measuring many behavioural traits across sufficiently large and genetically defined populations and over long time-scales, these questions remain unanswered. This manuscript represents a tour-de-force trying to shed more light in these directions. Werkhoven and colleagues aim at characterizing structure in correlations among a large set of quantitative behavioural measures obtained from the model organism Drosophila melanogaster. The authors performed a large number of high throughput behavioural experiments that cover behavioural paradigms ranging from locomotion to perceptual decision-making. Data were acquired from an inbred, hence isogenic fly line, an outbred line, and various neuronal circuit manipulations. In addition, gene expression data were obtained from individuals. In this way, the authors were able to capture hundreds of behavioural metrics from hundreds of flies, while keeping their individual identities over the course of 13 days. They developed a computational analysis pipeline that quantifies the correlation matrix computed from these metrics. In a 2-step procedure, they condense this matrix into a "distilled" matrix, the entries of which contain all remaining behavioural covariates that were not a priori expected by the authors.

A central claim in this paper is that any structure in this distilled matrix should reveal the principal axes along which individuality should be described. Based on these measurements and analyses flies could not be categorized into discrete types. Moreover, behavioral covariates appear rather sparse and derive from a high-dimensional behavioral space. This would mean that each individual fly is better described by a large combinatorial set of parameters. The same qualitative finding was made between inbred and outbred flies, leading the authors to a conclusion that larger genetic diversity does not change the principal organization of behaviour. The authors perform a set of neuronal-circuit manipulations and claim in conclusion that specific neuronal activity patterns underlie structure in behavioural correlations. Some correlations between gene expression and behavioral metrics were discovered, for example gene expression of metabolic pathways can predict some variability found in the behaviour of flies. The behavioural pipeline is sophisticated and presents a great leap forward in enabling researchers to capture a large set of behavioural measures from a large fly population, keeping the identity of individuals. The work is also presenting an innovative and interesting analysis pipeline.

Although we applaud these ambitious experimental paradigms and computational techniques used, we have several major reservations about this work. Reading through the manuscript multiple times, one is left confused whether the major finding is that no structure whatsoever can be found in these data and to what extent the remaining sparse correlations are of biological / ethological relevance. Another major concern arises from the high level of trial-trial variability that is found in the data, which seems to preclude identification of persistent idiosyncrasies in the behavioural traits of individuals and impedes the reproducibility of the data matrices in two repetitions of the main experiment. We feel that most of the authors' conclusions and claims are confounded by these caveats.

1) Distinguishing persistent idiosyncrasies from trial-to-trial variability and reproducibility of decathlon data

A major challenge in measuring personality traits or individuality is to distinguish between persistent idiosyncrasies and trial-to-trial variation; the latter could result from inherent stochastic properties of behaviors, environmental or measurement noise. To identify an idiosyncratic behavioral trait in an animal one needs to show that individuals exhibit a distinct distribution in a behavioral metric that cannot be explained by trial-to-trial variability. Such a distinction cannot be made if a behavioral metric is measured just once or during a short period, but requires repeated measures over longer time-scales from a sufficiently large population of animals. Unfortunately, in this study many measures have been taken during just one 1-2hs episode per individual of a decathlon. For other measures that were taken repeatedly (circadian assays, unsupervised video acquisition) no efforts have been undertaken by the authors to make the above distinction. Hence, the authors' conclusion that there are no "types" of flies seems premature. In Figure S1 we are surprised to see how low most behavioral measures auto-correlate when recorded on two subsequent days; most auto-correlations further drop to meaningless values when compared over time-periods that correspond to the different epochs of a decathlon. This indicates that trial-to-trial variability dominates the data. In our view it makes little sense to ask whether two behavioral metrics are correlated or not, if their autocorrelations measured over the same time-scale are already extremely low. Moreover, Fig S5B shows that the two decathlons generate largely different data matrices (correlation ~0.25), raising concerns that the results are not reproducible. We wonder whether any structure in behavioral correlations was masked by various sources of noise in this study.

Related to above, there should be error bars and number of flies for the plots in Fig S1. This figure undermines the starting point of the paper claiming persistent idiosyncratic behaviors.

2) Given the concerns above, it is not surprising that the outbred fly line delivers another set of covariates which lack otherwise any further structure. If experiments with >100 inbred flies cannot deliver reproducible results, it cannot be expected that a similarly sized population of outbred flies would. Perhaps the needed population size must be orders of magnitudes larger in this case.

3) Figure 3. It is intriguing to observe how the relationship between switchiness and clumpiness is perturbed upon temperature shifts. But, it seems rather uncorrelated at the restrictive temperature in the Iso line, with a slightly positive value. However, the switchiness-clumpiness correlation is not reproducible in both perturbation types at permissive temperatures. Note, that at both temperatures the Shi and Trp datasets show no - or very low correlations: the Trp lines produce correlations from approx. -0.2 (permissive T) to 0.1 (restrictive T); the Shi lines 0, 0.1 respectively. Fig 3D is very misleading in showing the best fits to the combined datasets. We are not convinced that there is a robust sign-inversion in any of these correlation. The authors' major conclusion that " thermogenetic manipulation and specific neuronal activity patterns underlie the structure of behavioral variation" is not supported by these data. The effect of temperature in the control line, although interesting, is a major caveat for interpreting the results from the Shi and Trp results.

4) The authors measure a large set of low- and high-level behavioral metrics, e.g. walking speed and choices in Y-mazes respectively. A fundamental problem is that many of these metrics potentially have common underlying but trivial causes, e.g. covariation between speeds measured in various conditions is expected. Therefore, the authors condense their original correlation matrix (Fig 1E) into a distilled matrix (1G) by making such judgements. In the present form, it is impossible to evaluate how systematic or arbitrarily these choices were. In many cases, where the same measure was recorded repeatedly (e.g. circadian bout length) or across different conditions (e.g. mean speed) it is obvious, but for other cases it is not obvious at all for the non-expert: for example, why are circadian-bout-length and LED-Y-maze-choice-number lumped into one block of expected behavioral covariates? The current manuscript lacks detailed explanations how the authors systematically created the distilled matrix. Can the sparseness of the distilled matrix be a consequence of too generous pre-allocations? See also point (6). The bulk of the analysis in this paper is done on the "distilled matrices" which are produced by removing correlations within previously defined groups of behavioral metrics. This is said to cleanly reveal unexpected correlations, leading to a main result of the paper, the correlations between "Switchiness" and "Clumpiness". However, if the a priori categories were defined differently, then in the extreme case this correlation would have been completely removed. How sensitive is this correlation to the choice of categories, especially given that many of the Switchiness and Clumpiness metrics are from similar assays (Fig. S8)?

5) For the second pipeline that uses t-SNE and watershed (Fig. 2 and S3C), a previous publication from some of the authors [1] appears to show low repeatability of this analysis.Thus, the repeatability and noise levels of the pipeline must be investigated further. These were 3x 1h recordings per decathlon. Related to comments (1-2), the authors need to show that the differences across flies (Fig 2C,D) are not expected from the level of trial-to-trial variability. Perhaps more data from individual flies need to be recorded?

6) 1G: To our understanding, within-block entries to the distilled matrix should indicate zero correlations, because these are correlations between PCA-projections. But we see many nonzero entries. Given the information provided in the methods it is unclear why this is the case; this requires further explanation.

In any case, within-block correlations are expected to be at least very low. Hence, we expect the distilled matrix to be relatively sparse given how it was calculated. Of interest are then the across-block correlations, the authors should make this point more clear to the readers.

7) Some of the author's claims are related to the spectral dimensionality reduction technique described in Fig. S9. However, none of the real data shown in the main paper figures look qualitatively similar to the toy data. Indeed, the histograms from the main figures are on a log scale, and are thus not comparable to the toy data results. Although the technique might be well suited for certain classes of data, one interpretation of the main paper figures seems to be that no structure is revealed whatsoever. More work should be done to exclude this as a possible interpretation, at least by generating toy data that look like the real Datasets; also with respect to point (6) above.

8) Throughout the paper, the authors use the term "independence" for orthogonal / uncorrelated datasets. Correlation/uncorrelation - dependence/independence are not interchangeable terms. To my understanding PCA decomposes into independent variables only under certain circumstances (multivariate normal distributed data). Have the authors tested for independence?

[1] Todd, J.G., Kain, J.S. and de Bivort, B.L., 2017. Systematic exploration of unsupervised methods for mapping behavior. Physical biology, 14(1), p.015002.

Reviewer #1:

Major points:

1) On the conceptual level, the authors claim that low-intensity amplitude-modulated transcranial focused ultrasound stimulation (AM-tFUS) inhibits local inhibitory interneurons and excites excitatory neurons at high intensity. However, the problem I have with this is that these cell types are highly interconnected within the local circuits, and changing the activity of the inhibitory cell type should have the opposite effect on the excitatory cell type. This has been documented in many experiments (Babl et al., Cell Reports, 2019; Royer et al., Europ.Journ. of Neurosc., 2010), and it unclear why the authors did not see similar effects. Furthermore, it is particularly troubling that the authors observe sustained suppression (five minutes) of the inhibitory neurons yet fail to see any effects on the excitatory neurons (Fig. 3B,D). This conceptual problem raises questions about the experimental setup, which I address below.

2) The authors performed electrophysiological recordings while delivering AM-tFUS with different intensities. To claim the differential effects on the excitatory and inhibitory interneurons, the authors first need to isolate single units in their recordings. However, the authors fail to cluster single units, as documented in the methods section (line 338). There could be several reasons why the authors failed to complete this step. I suggest the following ways to remedy this problem: A. The authors should use a silicone probe with a higher density of recording sites (the distance between the individual sites can be as small as 25 um in some NeuroNexus probes) than the one used in the MS, or use a Neuropixels probe so that the clustering algorithms have a chance to isolate single units. Using NeuroNexus probes with 100 um separation between the recording sites makes it impossible for different channels to "see" the same neuron and severely limits the spike sorting algorithms that separate units based on their unique spatio-temporal waveforms. B. After clustering, the authors should use autoccorellograms to verify that the single units do not violate the refractory period (Hill et al., Journal of Neuroscience, 2011). This is particularly important in areas, such as the hippocampus, which has a high density of neurons, and care should be taken to avoid multiunit recordings. C. The authors should perform one long recording session that comprises all experimental manipulations-the delivery of AM-tFUS, the sham control, and the rest period-to trace how the same units change their firing rate as a function of the experimental manipulations. This would also be very helpful in understanding how the firing rate change in one class of neurons is accompanied by changes in another class. D. Although this might be tricky, the authors could try to perform electrophysiological recordings by lowering the electrode perpendicular to the brain surface. This would allow them to record excitatory neurons and inhibitory interneurons that are connected to each other within the local circuit. This type of recording, would give the authors a greater chance of observing how changes in the firing of the inhibitory cell type affects the activity of the excitatory cell type and vice versa. This type of recording would also be highly desirable for understanding changes in oscillations of the local field potential (LFP) (see below).

3) The authors should report the sites that they have recorded by labelling the electrode with fluorescent dye or performing lesions at the recording sites.

4) When analyzing the effect of AM-tFUS on theta frequency oscillations, the authors should perform current source density (CSD) analysis to verify that the observed effects are local and do not originate from distant sources by volume conduction (Buzsaki et al., Nat. Rev. Neurosc. 2016). Performing electrophysiological recordings perpendicular to the brain surface, as I recommend in 2D, would be necessary for this. The CSD analysis would identify the location in the hippocampus where the change in theta power occurs.

5) The authors argue that temperature changes of 0.2 degrees were not sufficient to alter the firing rate of the neurons. However, the paper to which they refer (Darrow et al., Brain Stim, 2019) shows, in Fig. 7, that heating up brain tissue with a laser even at 0.2C can induce changes in somatosensory evoked LFPs. The authors should perform control experiments that are analogous to those in the cited paper to manipulate the temperature while recording the neurons in order to verify that the observed effects are not due to the changes in temperature.

Minor points:

1) The authors should not use label cells in Fig. 3 as they cannot claim that they recorded single units.

2) In Fig. 5C, Fig. S3B,C, and Fig. S4B,C, the authors should show the full scale of the values. Furthermore, the outliers in these plots (not seen in the figures) may drive the general trends, and removing them should be considered.

3) During AM-tFUS at intermediate power intensity (Fig. 4D,G), the authors observe a very dramatic change in LFP power in the 1-3 Hz frequency range. Although there is no clear underlying change in the firing of neurons at this intensity (Fig. 3E,F,G,H), the authors could speculate on what is happening in this case.

4) Fig. 5B shows a clear reduction of power in the theta frequency range after AM-tFUS in the dentate gyrus as well as in CA1 and CA3. This effect is also seen in Fig. 4G and Fig. S1,2. Although this effect does not reach the level of statistical significance, the authors should report the p-values.

5) Although the suppression of firing rates for a five-minute period after low-intensity AM-tFUS application is interesting, I am not sure if such prolonged after-stimulation effects have ever been documented using other modes of neuromodulation. Therefore, the authors should discuss this effect in line with previous work.

Reviewer #1 (Public Review):

In this paper, Judd et al performed intersectional viral-mediated genetics to resolve a projection map from Ntsr1-positive and inhibitory neurons in the anterior interposed nucleus. They show that, in contrast of what is currently thought, inhibitory neurons that project to the inferior olive in fact bifurcate to multiple brainstem and midbrain areas. This is a thorough and timely paper, with valuable information for cerebellar scientists with implications that will be of interest to the general neuroscience audience. As a direct consequence of the vast amount of information, this paper summarizes a lot of data using acronyms and summary schematics, which makes it at times difficult to follow the core story. A bigger concern is that the main conclusion arguing that inhibitory neurons make widespread extra-cerebellar projections relies on the assumption that the Cre-lines used in the study are able to specifically/exclusively mark to those inhibitory neurons – these details were not fully worked out in this study.

Reviewer #1 (Public Review):

The general thesis of the work, provided by the authors, is the demonstration that latrophilins 2 and 3 function as classical GPCRs at the synapse and that this activity is necessary for synapse formation at a specific synapse within the hippocampus. The topic is interesting and important for several reasons. First, the knowledge of GPCRs at synaptic connections is focused largely on neurotransmitter receptors in the literature – metabotropic GluR and AChR as well as neuromodulatory neurotransmitter receptors (NPY, Seratonin etc). The mechanism demonstrated in this work concerns the function of a GPCR receptor system that could confer specificity to synapse formation.

The effect sizes that are documented throughout this work are large, giving this reviewer confidence that the effects are robust and will be reproducible and, more importantly, are indeed a biological mechanism related to synapses.

The other major strength of the work is that the studies in neuronal cell culture are recapitulated in vivo providing additional confidence in the validity and importance of the work. Indeed, the concept of specificity requires this type of in vivo work as the identity of synapses in culture systems can not be readily determined.

A further strength is the rational and implementation of three mutant receptors that are used to dissect the signaling modalities of these receptors, validated for their effects on the protein and then used as rescue constructs in synaptogenesis assays.

Reviewer #1:

This is an interesting study of the effects of intracellularly-applied amyloid beta (Ab) in primary hippocampal cultures of embryonic rats or in area CA1 of hippocampal slices or anesthetized rats that are less than 35 days old (therefore prepubertal). In vivo, whole cell recordings were made of CA1 neurons which is difficult and therefore a strength. Both synthetic Ab and human-derived Ab were applied by adding them to the internal solution of a patch electrode. Several interesting effects were documented, such as increased evoked and miniature EPSCs (mEPSCs) as well as some effects on IPSCs and neuronal properties. A major question is whether these effects were pharmacological or physiological.

An intriguing finding was that the increased EPSCs was reduced by inhibiting a PKC-mediated effect of nitric oxide (NO). Furthermore, the effect of intracellular Ab on the recorded cell had effects on neighboring cells. Whether those were due to diffusion of NO, synaptic inputs from the recorded cell on neighboring cells, or release of Ab from the recorded cell was not clear. The authors suggested this is 'functional spreading of hyperexcitabiliity' similar to the way prions are spread transynaptically (actually this has been suggested for Ab too; see work by Karen Duff or Brad Hyman's groups) although this seems premature because the work that has been done with prions and Ab involves spread over a long time and a long distance relative to the results of the present study. Still the results are interesting and could be relevant in some way to the development of the disease or hyperexcitability.

MAJOR CONCERNS

One major issue is whether the results are relevant to Alzheimer's disease (AD) or represent interesting pharmacological data about what Ab can potentially do in some of its forms in normal tissue. The cultures are from embryonic rats and it is not clear how well they can predict what occurs in aged humans with AD. This issue is not only a question related to the preparation of tissue but the use of Ab intracellularly. It is not clear that synthetic or human Ab that is prepared outside the animal and used to fill electrodes to dialyze a cell is similar to the Ab generated in a cell of a person with AD. Independent of the methods to determine whether it is oligomeric outside the cell, once dialyzed it is not clear how it may change and where it would go. In AD Ab has a particular location and precursor where it forms and how it travels to the external milieu. As a product of its precursor APP, several peptides are produced besides Ab and many labs think they are as important as Ab in the disease. Although a strength to use atomic force microscopy to attempt to verify the form of Ab being used, it is not clear what form was actually in the dialyzed cell and how that compared to the form in AD.

How this work relates to other studies that are similar is important. It seems that few other studies that have applied Ab are mentioned because few have studied it intracellularly. However, they are relevant because adding Ab has been shown to cause an increase in hippocampal neurons of excitatory activity at low concentration but at higher concentrations synaptic transmission is weakened. Many studies of mouse models of AD pathology suggest reduced synaptic transmission and plasticity, although many others show hyperexcitability, often without adding Ab at all.

PKC and NO do a lot of things throughout the brain and body. How do the effects the authors have identified relate to all these other effects. For example, if PKC is activated by another mechanism, would it occlude the effects of Ab? What are the changes in PKC and NO in AD?

ADDITIONAL CONCERNS

I am not sure of the validation of Ab using the anti amyloid or 6E10 antibodies. The western blot shows a large region that both antibodies detect and the 6E10 antibody shows an even greater band. It is not clear what the large range of bands that are shown imply except nonspecificity. The antigen that the antibodies recognize should be stated exactly.

Clarifying sample sizes throughout the study is needed.

Do the cultures include interneurons? Are the excitatory and inhibitory neurons interconnected? This information will help interpret the results.

The external solution for cultures contains 5.4 mM K+ which is quite high, and can induce hyperexcitability. Therefore it is important to be sure controls did not show hyperexcitability even after persistent recordings. Similarly, the use of 100uM AMPA and GABA seem very high. Justifying these high concentrations is important. They should lead to hyperexcitability and toxicity (AMPA) over time. Another point of concern is that the concentration of K+ for the slice work is 3 mM, much different than cultures. There are also differences in Mg2+ and Ca2+, making data hard to compare in the two preparations.

Line 295 mentions 2 min recording periods were used to acquire sufficient events. One wants to know if this was done throughout the paper and if so, how many events per 2 min was considered sufficient?

Terms related to intrinsic membrane properties and firing need to be explained much more because each lab has a slightly different method.

In the statistics part of the Methods, why is Welch's ANOVA (followed by Games-Howell) used when variance was unequal. Usually the test to determine inequality is provided, so it is clear it was done objectively and with a reasonable test. Then if the data are unequal there is often a choice for a non parametric test, which is common. Some groups transform the data such as taking the log of all data values. If this reduces the variance between groups, sufficient to pass the test to determine inequality, it leads to a parametric test like a one-way ANOVA followed by Tukey's posthoc test.

In the Results, Line 331 suggests that the authors think they know what a low concentration is for Ab. I don't think it is known in AD what is low and what is high. In other studies of Ab, low concentrations were picomolar (Puzzo et al., listed in the references). So it is not clear the term low is justified for 50 nM.

The bursts of activity are not quantified. What was defined as a burst? What was the burst frequency and did it change over the recording period?

In the section about mPSCs in culture, starting on Line 348, were these events EPSCs or IPSCs? It is important because in the section starting on Line 383 there were changes in IPSCs but the authors conclude a major role of EPSCs only. For example, Line 400 suggests that the effects of Ab were on AMPA receptor-mediated activity but it seems from the data there were also some effects on IPSCs.

Line 434. Provide evidence that the fluorescent probe accurately measures NO.

At the top of page 19 there is a section that needs to be moved earlier because it relates to the work in cultures. That earlier section needs to be reinterpreted given changes in membrane properties occurred. Also, if there is increased synaptic activity in cells dialyzed with Ab, TTX needs to be added to be sure of intrinsic properties. The increase in excitability the authors discuss could be due to the synaptic activity or changes in properties, or both and this needs clarification.

The last paragraph on page 20 is not useful because DRG neurons are so different from hippocampal neurons. One could have effects in DRG but not hippocampus, and vice-versa. The paragraph starting on Line 616 should be revised. It is not a series of compelling arguments in its present form. For example, saying that AMPAR are linked to epilepsy seems quite obvious, and does not mean that the work presented here is like epilepsy because AMPAR events increased in several assays. Increased AMPAR events also occur when there is a change in behavioral state, plasticity, etc.

In the conclusions, I don't think the data suggest a synaptic change in AMPAR alone. There are intrinsic changes and changes in GABAergic events. Many sites in the brain could have different effects but were not studied. It is not clear effects of NO were coordinated in the way they affected adjacent neurons to the recorded cell. NO simply could have diffused to an area around the recorded cell. I may have missed evidence to the contrary, but effects could have been mediated by axons of the recorded cell and not NO.

In Figure 1b, there is a representative example. Could the neurons be shown? Then one knows the relationship of the signal to the location of neurons.

Graphs should show points. This is one way to clarify sample size easily also.

MINOR POINTS

Line 169 mentions stable access resistance and one usually provides a number indicating how little it increased over time, such as 10-20%. Similarly the way synaptic events were discriminated by noise is not provided (line 291). Instead, a brief description is provided.

Line 292 mentions noise ~2 pA but it is much higher in the data shown in the figures.

Solvents of drugs are not listed at all, and controls that show no effect of vehicle need clarification in some cases.

On Line 371, Ab-mediated neurotransmission is used. I believe this needs to be modulated rather than mediated, or an explanation is needed.

On Line 381, how do the authors know that EPSCs are mediated primarily by AMPA receptors in this preparation?

On Line 393, what is the comparison of AMPA-mediated events to [where it is stated they are what is mostly changing]?

In all of the sections where drugs were applied, abbreviations need to be spelled out before the first use, concentrations need to be confirmed as specifically action on the intended receptor, and indirect effects on other cells need to be discussed if bath-applied.

The sentence starting on Line 417 is a repetition of a prior sentence on the previous page.

Line 433. Clarify what low concentrations mean here.

Line 444. mPSCs are referred to here. One needs to know what were the values for E and IPSCs.

In this section it is often stated that there is a decrease but actually the dialyzed cells are compared to controls so different language is needed.

Line 461. It is not clear that the hippocampus is the first site to be affected in AD. The entorhinal cortex is earlier in the studies of some, and in the mouse models it is usually the cortex that gets plaque first. In humans, the locus coeruleus may be earlier than the entorhinal cortex.

How the plots of current vs. spikes were done is important. If there were differences in membrane potential, that could affect the spike output. If there were differences in input resistance or threshold, that also could play a role. One can control for these potential confounds, so explanations are needed.

Line 472. Vm does not generate fluctuations in this case. Vm changes, and synaptic potentials get larger or smaller, add new components or lose them, etc.

Line 476. It is not clear why cells are firing at membrane potentials so hyperpolarized to threshold.

The streptavidin/calbindin labeling is good but the morphology of the cell is not like a pyramidal cell of area CA1 because there is a major branch of the dendrites at almost a right angle to the apical dendrites. The electrophysiology of this cell might be like an interneuron, and two of the figures show firing with a large afterhyperpolarization similar to an interneuron.

In Figure 3, what are EPSCs and what are spikes would be helpful to point out. The concentration, 500 nm, may never be reached in the brain of an individual with AD, or do the authors have evidence that concentration is relevant in vivo?

There are typos in figure headings, such as Contro instead of Control and in figure 4g, AMPAergic has the c below AMPAergi

Reviewer #1:

Huss et al. have developed a novel tool (ORACLE) for generating libraries of phage variants. They go on to apply this tool to study the residues important for T7 host specificity, providing a rich dataset for in-depth functional studies. They validate a subset of hits and use this information to engineer T7 variants that may be able to overcome bacterial resistance against a urinary tract infection associated strain, consistent with their in vitro results. Their approach provides both a valuable new tool and intriguing biological insights prompting future studies.

Major suggestions for improvement:

1) The writing could be much more concise.

2) Claims about generalizability should either be removed or supported by additional data. This study focused on a single phage gene and a single host bacterial species. As such, it is not clear if ORACLE will work well in other contexts.

Reviewer #1 (Public Review):

The authors present data suggesting that RNF43 affects WNT5a signaling through turnover of ROR1 and ROR2 receptors on the cell surface. The strengths of this work are the many overexpression, knockdown and mutant cell lines the authors use to delineate specific protein interactions and localizations. The authors have done a good job of analyzing the interaction of multiple proteins within the Wnt signaling pathways to determine how RNF43 affects expression of proteins associated with non-canonical Wnt signaling. The weakness of this study is that most of these protein interactions were performed in 293 cells and not in melanoma cell lines. One melanoma cell line was used to relate the protein interactions studied in 293 cells to signaling in melanoma. The authors present data that suggest RNF43 decreases invasion and proliferation of melanoma cells in vitro. Analyzing the role of RNF43 in invasion, proliferation and signaling in more than one melanoma cell line would strengthen the authors conclusions about the role of RNF43 in Wnt5A signaling in melanoma.

Reviewer #1 (Public Review):

The study aims to determine the mechanism of voltage-sensing in P2X2 receptor. These receptors are primarily activated by ligand, ATP but their activity is also regulated to some extent by voltage even though they lack a canonical voltage-sensing domain. To address this question, the authors introduce unnatural fluorescent amino acid throughout the structure of the P2X2 receptor. The interaction between excited state dipole and electric fields can cause shift in the fluorescence emission and excitation spectra. For a given probe, the extent of these shifts are directly proportional to the strength of the electric field. The authors exploit this phenomenon to determine the strength of the electric field in the various regions of the P2X2 receptor. The underlying premise is that the regions which sense the largest electric field are likely to be the primary sensors of membrane voltage.

Strengths:

The approach to localize the putative voltage-sensing region is novel and maybe broadly applicable to other voltage-regulated channels which lack canonical voltage-sensors.

Unnatural amino acid, ANAP was introduced and tested at 96 positions in the structure of P2X2 receptor. This is an insane amount of work and has to be a tour de force.

Weakness:

The main limitation of this approach is that ANAP is not going to be incorporated with equal efficiency at all sites and therefore, it is likely that some of the potential where the electric field is strong may remain undetected.

Overall, using ANAP scanning approach, they were able to identify couple of sites in TM2 helix which exhibits large electrochromic signals. Furthermore, they find that the interaction between Ala 337 and Phe44 is critical for voltage-dependent response. These studies lay the groundwork for further investigations of the mechanism of voltage-sensing these physiologically important ion channels.

Reviewer #1 (Public Review):

This manuscript presents new data and a model that extend our understanding of color vision. The data are measurements of activity in human primary visual cortex in response to modulations of activity in the L- and M-cone photoreceptors. The model describes the data with impressive parsimony. This elegant simplification of a complex data set reveals a useful organizing principle of color processing in the visual cortex, and it is an important step towards construction of a model that predicts activity in the visual cortex to more complex visual patterns.

Strengths of the study include the innovative stimulus generation technique (which avoided technical artifacts that would have otherwise complicated data interpretation), the rigor of experimental design, the clear and even-handed data presentation, and the success of the QCM.

The study could be improved by a more thorough vetting of the QCM and additional discussion on the biological substrate of the activation patterns.

Reviewer #1 (Public Review):

Patients with myotonia congenita caused by loss-of-function mutations in ClC-1 experience muscle stiffness (due to hyperexcitability) as well as transient muscle weakness. This study examines the mechanisms underlying the transient muscle weakness seen myotonia congenita. The authors show that a ClC-1 null mouse exhibits the transient weakness after muscle stimulation observed in humans. Current clamp recordings of muscle fibers from ClC-1-null mice showed indicated myotonia after electrical stimulation that often terminated in a plateau potential for varying periods, during which the muscle was unexcitable, before repolarization to the resting membrane potential. The myotonia and plateau potentials could be recapitulated in wild type muscle fibers with acute pharmacological inhibition of ClC-1. Experiments in fibers from a non-conducting Cav1.1 knockin mouse indicated Ca2+ influx is important for sustaining, but not initiating, plateau potentials. Ranolazine blocked both the myotonia and development of a plateau potential in isolated muscle fibers, as well as the in vivo transient muscle weakness observed in ClC-1-null mice, implicating Na+ persistent inward currents through Nav1.4 (NAPIC) as the molecular mechanism.

Overall, the experiments presented in this work are well-executed and the results convincing. While the role of NAPIC in the development of myotonia in ClC mice has been previously reported this work provides the new insight that it is also responsible for the development of plateau potentials that underlie muscle weakness in myotonia congenita.

Joint Public Review:

The presented manuscript takes a very comprehensive look at the molecular underpinnings of the differential outcomes of IL-27 and IL-6 signaling. Both cytokines engage GP130 as a cellular receptor, however while IL-6 uses homodimers of this signal transducing receptor, IL-27 signals through a heterodimer of GP130 and IL-27Ra. Both receptor complexed lead to the phosphorylation and activation of STAT1 and STAT3 and, hence, to a similar transcriptional program. Strikingly, however, IL-27 responses lean more towards an anti-inflammatory nature (suppressing Th17 and supporting Treg responses), and IL-6 stimulates a classical inflammatory response (inhibiting Treg differentiation, supporting Th17 generation). The presented study deals with elucidating this functional pleiotropy of similar or identical signal transducers.

The authors follow a comprehensive and elaborated approach, combining in vitro experiments in cell lines and human Th1 cells with (phospho-)proteomics, transcriptome sequencing and mathematical modeling, which gives rise to an impressive data set presented in this manuscript. The large body of experimental work is complemented by mathematical modelling of the signaling pathway(s), which is used to discriminate feasibility of distinct hypothesis in terms of mechanisms behind differential STAT activation.

The major finding of the study is that IL-27, at least in certain cells (Th-1), leads to the stronger and more sustained activation of STAT1 as compared to IL-6, and that this higher activation of STAT1 is the basis of the differential transcriptional result. The subsequent -omics analyses support differences in signaling outcome between IL-6 and IL-27, and provide an interesting data base for the community. Finally, data re-analysis in a cohort of patients suffering from the autoimmune disease Systemic lupus erythematosus (SLE), reproduced the effects expected by the mathematical model, potentially pointing to differences in their response to different cytokines.

Overall, the extensive and complex study presents a comprehensive analyses of IL-6 and IL-27 signaling, puzzling together pieces that may have been around before but not put into meaningful context. It provides a compelling overall idea and model of how cytokine receptors make differential use of STAT proteins.

Reviewer #1 (Public Review):

In this manuscript, Levi-Ferber et al use C elegans to study how germline cells maintain pluripotency and avoid GED (germline ectopic differentiation) before fertilization. The authors previously showed that activation of the ER stress sensor Ire1 (but not its major downstream target Xbp1) enhances GED, and here they explore the mechanism of this effect.

The authors convincingly – and surprisingly – show that the Ire1-mediated GED increase results not from Ire1 activity in the germline but in the nervous system, specifically in certain sensory neurons. Worms lacking a specific neuropeptide (FLP-6) or a particular neuron that produces this peptide (ASE) also displayed increased GED. Although FLP-6 deficiency did not induce ER stress, ER stress did lead to a reduction of FLP-6 transcript (and protein) levels in an Ire1-dependent manner, suggesting this RNA is a target of Regulated Ire1-dependent decay (RIDD). The authors then go on to map out the signaling cascade that begins with FLP6 reduction in ASE by Ire1 and is transmitted to the gonad via an ASE-AIY-HSN circuit, including serotonin produced by HYE.

This paper is quite interesting and for the most part the data are very convincing and support the model. The demonstration that Ire1 and the ER stress response have non-cell autonomous effects is of particular interest, and is very well supported here. The description of this circuit linking particular neurons and signaling molecules to gonad pluripotency is also very strong.

A weakness of the paper is the link between RIDD of FLP6 and the disruption of this circuit. The data presented do clearly support the model. However, additional information would strengthen this considerably. The authors show that FLP6 mRNA levels are reduced in Ire1+ but not Ire-/- animals subjected to ER stress. They also show that GED results from the nuclease activity of Ire1 in the ASE; and that loss of FLP6 can also induce a similar effect. However, they do not show as clearly that Ire1's effects on GED are mediated primarily through FLP6.

Reviewer #1 (Public Review):

Drs. Chen and colleagues report that augmentation of the integrated stress response (ISR) increases the oligodendrocytes and myelination during recovery after experimental demyelination in the presence of inflammation. Homozygous GADD43 KO mice or Sephin1 are used, respectively, to genetically and pharmacologically augment the ISR. Sephin1 treatment in mice with experimental autoimmune encephalomyelitis (EAE) shows increased remyelination in the spinal cord after inflammatory demyelination. Cuprizone administration to GFAP/tre;TRE/IFN-gamma double transgenic mice produced corpus callosum demyelination and CNS inflammation, with release of interferon-gamma initiated by removal of doxycycline from the drinking water. GADD43 KO did not change overall severity of cuprizone demyelination based on loss of oligodendrocytes and demyelination in corpus callosum after 5 weeks of cuprizone with ectopic interferon-gamma. The authors state that GADD43 KO enhanced the recovery of oligodendrocytes and remyelination during the 3 weeks after removal of cuprizone from the diet, but an incorrect figure prevents evaluation of this result. In double transgenic mice, with initiation of CNS inflammation, but without the GADD43 null mutation, pharmacologically enhancing the ISR with Sephrin1, increased recovery of oligodendrocytes and remyelination at 3 weeks after removal of cuprizone from the diet. These effects of genetically or pharmacologically enhancing ISR were not observed in the absence of ectopic interferon-gamma. Genetic and pharmacologic enhancement of the ISR did not appear to significantly alter the progenitor or microglial response to cuprizone demyelination. The combination of Sephin1 with bazedoxifene (BZA) enhanced the oligodendrocyte density and remyelination during the recovery period to a similar extent as either treatment alone. The authors provide several results supporting their interpretation that augmenting the ISR can overcome inhibitory effects of inflammation to enhance oligodendrocyte density and remyelination. Clarifications of the methods, correction of missing data, and additional experiments are needed to support the authors' conclusions that the potentially significant findings that combination of Sephin1 and BZA protects remyelinating oligodendrocytes and promotes remyelination even in the presence of inflammation.

Major concerns:

1) The experimental design and interpretation of the results would be strengthened by examining an indicator of the ISR to allow the reader to interpret the extent of ISR activation and the effect of the genetic and pharmacologic modulators of the ISR. This analysis would be particularly helpful in the corpus callosum in conditions with and without cuprizone.

2) Cuprizone is started at 6 weeks of age which is designated as week 0 (W0). The studies use W0 for comparison to the treatment groups that are analyzed at W5 or W8. The authors refer to W0 as pre-lesion or baseline levels, which is appropriate. The authors' statements related to the vehicle condition are appropriate as is. However, it is not clear why the W8 age-match (non-cuprizone and non-IFN-gamma) was not used to more directly interpret the extent of recovery. Using W0, the comparison is 6 versus 14 weeks of age. Myelinated axons continue to significantly increase during this age interval in mice.

3) The data graphed in panel 3C for the KO genetic prolongation of the ISR is exactly the same and the data graphed in panel 5C for the Seph pharmacologic enhancement of the ISR. The graph in 3C is actually labeled for Seph and so must have been inadvertently inserted when the graph of the KO data was intended.

4) The combined Sephin1/BZA treatment does not appear to work through remyelination, based on the definition of thinly myelinated axons (g-ratio >0.8) as used by the authors. The authors state that the data shows the after cuprizone demyelination, mice treated with Sephin1/BZA "reached myelin thickness levels comparable to pre-lesion levels" and "restored myelin thickness to baseline levels". To support this interpretation, the authors would need to include analysis of the Sephin1/BZA mice at 5 weeks of cuprizone to show that the combined treatment, which is initiated at 3 weeks of cuprizone, did not protect oligodendrocytes or reduce demyelination during weeks 3-5 of cuprizone and Sephin1/BZA treatment.

5) Conditions during which augmenting ISR is protective of mature oligodendrocytes or protecting remyelinating oligodendrocytes should be more clearly presented in the Discussion. The prior EAE results are reported as protecting mature oligodendrocytes. The results (Figures 3B and 5B) show that genetically or pharmacologically augmenting the ISR did NOT protect from mature oligodendrocyte loss at 5W cuprizone. The results (Figure 5B) show increased oligodendrocytes at 8W cuprizone. The current results are interpreted as protecting remyelinating oligodendrocytes, which are presumably mature as well.

Reviewer #1:

Patients with posttraumatic stress disorder show impaired fear extinction that leads to persistent fear memories. The CA1 subregion of the hippocampus has been implicated in the acquisition and extinction of contextual fear memories, and both mechanisms depend on glutamatergic synaptic plasticity in this region. Postsynaptic density protein 95 (PSD-95) is known to regulate structural and functional changes in glutamatergic synapses, but whether PSD-95 participates in the acquisition and extinction of contextual fear memories remains unclear. To address this question, here Ziółkowska and coworkers used nanoscale-resolution analyses of PSD-95 protein in the CA1 combined with genetic and chemogenetic manipulations in mice exposed to a classical Pavlovian contextual fear conditioning paradigm. The study revealed that PSD-95-dependent synaptic plasticity in the dorsal CA1 area is not necessary for fear acquisition or the initial phase of fear extinction, but is critical for updating a partially extinguished fear memory. In addition, phosphorylation of PSD-95 at serine 73 is necessary for contextual fear extinction-induced PSD-95 expression and remodeling of dendritic spines in this region, suggesting a potential mechanism for fear memory persistence.

This timely study provides important and novel findings with regard to the role of PSD-95 protein in fear extinction formation and helps to advance our understanding of how dendritic changes in the hippocampus regulates fear maintenance. The present findings should be of general interest to the scientific community because extinction-based therapies are the gold-standard treatment for many fear-related disorders. The manuscript is clear, and the experiments were well-designed and executed. While the study is elegant, there are several important points including data interpretation that need to be clarified.

Major points:

1) The authors identified changes in PSD-95 expression levels and spine density after both fear acquisition and fear extinction. Similarly, S73-dependent phosphorylation of PSD-95 and changes in spine density were also reported following both phases. How do the authors explain the lack of effects on fear acquisition and extinction after the infusion of S73-deficient PSD-95 expressing virus? Does this suggest that the observed dynamics of PSD-95 are not important for the fear memory expression? The interpretation of these findings should be clarified in the discussion.

Previous studies have demonstrated a key role of dorsal hippocampus CA1 area on fear retrieval and extinction acquisition using either lesion (e.g., Ji and Maren 2008, PMID: 18391185), or optogenetic tools (e.g., Sakagushi et al, 2015, PMID: 26075894). However, in the present study, chemogenetic inhibition of this same region had no effect on fear retrieval or extinction acquisition (Figures 5 and 6). How do the authors reconcile the lack of effects on fear retrieval and extinction acquisition with the previous literature? Similarly, previous studies on the role of hippocampal PSD-95 protein in extinction memory should be described and the main differences in the experimental design and findings should be discussed (e.g.; Nagura et al, 2012, PMID: 23268962; Cai et al, 2018; PMID: 30143658; Li et al 2017, PMID: 28888982)

2) The authors have used scanning electron microscopy to analyze the ultrastructure of dendritic spines and determine whether PSD-95 regulates extinction-induced synaptic growth. In addition, the authors complemented these studies by investigating the effect of PSD-95-overexpression and fear extinction training on synaptic transmission in the dorsal CA1 ex vivo. However, it is hard to understand what does the observed changes in dendritic spines and amplitude of EPSCs mean if the behavior of the animals was the same. This point should be discussed in the article.

3) In Figure 5, the authors showed that chemogenetic inactivation of CA1 changed PSD-95 expression in all the 3 subregions of CA1 (stOri, stRad and stLM). However, the extinction training behavior in Figure 1 demonstrated an effect only in 2 subregions (stOri and stLM). The authors should clarify this discrepancy. In addition, in the same series of experiments (Fig. 5Ciii), it is unclear whether the reduction in PSD-95 expression induced by chemogenetic inactivation is sufficient to bring the PSD-95 expression to the same post-conditioning levels.

4) The authors showed an interesting behavioral effect in the second part of the extinction phase (Figure 6C), similar to the results in Figure 4C. However, to confirm that phosphorylated PSD-95 is crucial for the maintenance of extinction memory, the authors may want to consider a direct comparison between the levels of phosphorylated PSD-95 right after extinction 1 and extinction 2. Differences in the expression would clarify whether the phosphorylated PSD-95 expression is further increased after additional extinction training, which would help to link the effect of chemogenetic inactivation on behavior. At least some discussion is needed for this part.

5) The authors used immunostaining and confocal tools to analyze 3 domains of dendritic tree of dorsal CA1 area in Thy1-GFP(M) mice (stOri, stRad and stLM) on different fear phases (conditioning and extinction). They found a significant decrease of PSD-95 expression, spine density and spine area in stOri and stRad during conditioning and a rescue of such decrease during extinction. However, the authors’ interpretation is that extinction resulted in an upregulation of PSD-95, which doesn't seem to be the case if you compare the numbers with the naïve group. Please clarify this point.

Reviewer #1 (Public Review):

The manuscript by Parker and colleagues presents an extensive body of work on characterizing the role of FPA in the choice of polyadenylation sites in transcripts of A. thaliana. Investigation on the mechanistic details that FPA engages on the mRNA processing was first initiated with the in vivo pull-down followed by LC-MS/MS, which revealed the its protein interactome relevant for 3'-end processing. The main dataset pertaining to the manuscript title comes from the comparative transcriptome analysis of Col-0, fpa-8 mutant and the overexpressor of FPA, 35S:FPA:YFP. The strength of this work lies in the use of nanopore DRS by demonstrating the layers of FPA-dependent transcripts, including its own, and its comparison to datasets by Illumina RNA-Seq and Helicos DRS. The systematic analysis uncovered unexpected complexity in the A. thaliana NLR transcriptome under the control of FPA and thus delivers a new insight on NLR biology. Several studies anecdotally have reported the importance of using genomic DNA, but not a single cDNA species, for addressing full functionality of NLR genes. Recent advances in NLRome sequencing from multiple genomes of a species and NLR structure/function studies also highlight the importance of understanding modular nature of NLR. As alluded with the modular diversity of NLRs kept in the genomes of a species in recent studies, NLR genes are prone to reshuffle in the genome to generate different variants, including partial entities with the loss of some parts of the proteins or even chimeras, supposedly maximizing the repertoire for defense. This work adds the level of transcript diversity on that of genomic diversity; FPA, an essential factor for transcription termination determinant, targets numerous NLRs to control the layers of NLR transcriptome of an individual plant. Although it is yet to be clarified for the regulatory significance of FPA-mediated NLR transcript changes under biotic or abiotic conditions, the authors succeeded in employing fine genetic schemes utilizing FPA-defective vs. -overexpressing lines along with long-read nanopore DRS technology for the first time to uncover the breadth of differential transcript generation focused on 3'-end choices. This work is timely and impactful for NLR research owing to the above-mentioned recent advances in NLR field.

As this work is the first of its kind in utilizing nanopore DRS to address NLR transcriptome, several technical concerns can be addressed to corroborate the claims made in the manuscript, which authors can find in the following section (1-8). Regarding the organization of the manuscript, the authors may consider to rebalance the two parts: FPA interactome vs. FPA targets and NLRs. Overall, the manuscript can be seen as combining two stories; first to characterize FPA function in 3'-end processing of transcripts inferred by interacting proteomes and meta-analysis of ChIP-seq data; second part includes detailed analysis of NLR transcripts and others. Although the first half of the analysis is a necessary prelude to the following NLR analysis, the current title and academic novelty mainly lies, or were intended by the authors, on the NLR analysis. However, current manuscript has relatively enlarged section of the first with NLR analysis packed into a series of supplementary dataset. If authors wishes to opt for highlighting NLR analysis, the following suggestions would help (9-14).

1) Earth mover distance (EMD) has been applied to identify a locus with alternative polyadenylation. What is the basis of using EMD value of 25 as a cutoff? According to Figure 4 B,D, EMD can range from 0-4000. One would also wonder if the distance unit equals bp. In addition, EMD values of some genes (e.g. FPA and representative NLRs) can be specified in the main dataset so that significance of the cut-off values shall be appreciated.

2) Regarding the manual annotation of alternatively polyadenylated NLR genes (L1160-): Genes with alternative polyadenylation were identified and the ending location was supported when there were minimum four DRS reads. It would be relevant to provide the significance of "the four" based on read coverage statistics, for example, with average read number covering an annotated NLR transcript with the specification of an average size.

3) Figure 4E shows that Ilumina-RNAseq dataset detects the number of loci with a different order of magnitude compared with the other two methods. Reference-agonistic pipeline shall be appreciated, however, the method engaged might have elevated the counting of paralogous reads mapped to different locations than they should be. Along with paralogous read collapsing, this is always a problem with tandemly repeated genes, such as NLRs by and large. For example, NLR paralogs in a complex cluster with conserved TIR/NBS but diversified LRRs would have higher coverage in the first two domains but drop in the diversified parts. The authors need to specify their bioinformatic consideration to avoid such problems.

Although the tone of the Illumina read section was careful and the main 3'-end processing conclusion was made by nanopore DRS, the authors are also advised to clearly state the limitation of using Illumina-RNAseq to address alternative polyadenylating sites at the beginning of the section, for example what to be maximally taken out from Figure 4 E and 4F. This will give relative weights to each dataset generated by different methods. One advantage of using Illumina data would be that the expression level changes can be associated with changes in processing, it seems.

4) At the RPP7 locus, At1g58848 is identical in sequences with At1g59218 as is At1g58807 with At1g59214 (two twins in the RPP7 cluster by tandem duplication). It would be good to check whether the TE At1g58889 readthrough indeed occurs in the sister duplicate with a potential TE in the downstream of At1g59218. If not, it can be used as an example of duplication and neofunctionalization through an alternative polyadenylation site choices.

5) HMM search shall be revisited to confirm if they are to detect the TIR domain. Given that a large proportion of NLRs in A. thaliana carry TIR at their N-terminal ends and the specified examples included TIR-NLR, it is surprising to see no TIR domain in Figure 5.

6) L659-668: how does the new data relate to the previously TAIR annotated At1g58602.1 vs At1g58602.2 (Figure 6, Inset 1)? It would be good to see these clearly stated in the main text as compared to newly identified ones. From the nanopore profiling, At1g58602.2 appears to be the dominant form.

7) One thing to note is that in the overexpressor of which Hiks1 R is suppressed, there was hardly any At1g58602.1 produced in addition to the large reduction of At1g58602.2. Thus, relative functional importance of the two transcripts shall be discussed in line with the Hpa resistance data. Accordingly, L740-741 phrasing shall be revised to include the possibility of absolute or relative "depletion" of functional transcript(s) contributing to the compromise in Hpa resistance.

8) It would be necessary to state in the main text the implication of phosphorylation on the two Ser residues on Pol II at L245. A clear description distinguishing the effect of the two phosphorylation and the specificity of the antibodies is desirable, as the data was interpreted as if the two sites made differences, such that Ser2 was heavily emphasized (e.g. subtitle). Albeit low level, Ser5 data also shows an overlap with FPA ChIP-seq coverage at the 3' end. If there is a statistical significance to be taken account to interpret the coverage, please state it. Given that elongation occurs progressively, I wonder how much should be taken out from the distinction.

9) Figures presentation for RPP4 and RPP7 are great in detailing the FPA-dependent NLR transcript complexity. To make the functional link more evident, the authors may consider bringing up parts of the Figure 5-supplement to a main Figure to detail the revised annotation of NLRs. Given recent advances in NLR structure and function studies, extra domain fusion, fission and truncated versions of NLRs require a great deal of attention. For example, potential functional link to the NMD-mediated autoimmunity and revised annotation of At5g46470 (RPS6) needs a clear visual guidance preferably with a main figure (Figure 5-Supplement 3).

10) The section "FPA controls the processing of NLR transcripts" includes dense information and can be broken down to several categories. To this end, Supplement File 3 (NLR list) shall be revised to deliver the categorical classes and further details and converted to a main table.

For NLR audience, for example, it would be important to associate the information to raw reads to assess where the premature termination would occur. At least, the ways to retrieve dataset or to curate the termination sites shall be guided.

On the contrary, there is no need to include other genes in Figure 4 Sup4-8 under this section. They are not NLRs.

11) Figure 7 and IBM1 section can be spared to the supplement.

12) The list of "truncated NLR transcripts" in particular, either by premature termination within protein-coding or with intronic polyadenylation, should be made as a main table. The table can be preferably carrying details in which degree the truncation is predicted to be made. With current sup excel files, it is difficult to assess the breadth of the FPA effect on the repertoire of NLRs and their function. This way, functional implication of differential NLRs transcriptome can be better emphasized.

13) FPA-mediated NLR transcript controls, as to promote transcript diversity, is expected to exert its maximum effect if FPA level or activity is subject to the environmental stresses, such as biotic or abiotic stresses. The discussion on effectors targeting RNA-binding proteins (L909-918) is a great attempt in broadening the impact of this research. In addition, if anything is known to modulate FPA activity, such as biotic or abiotic stresses or environmental conditions, please include in the discussion.

14) NLR transcript diversity as source of cryptic variation contributing to NLR "evolution" is an interesting concept, however, evolutionary changes require processes of genic changes affecting transcript layers or stabilizing transcriptome diversity. In the authors' proposition in looking into accessions, potential evolutionary processes can be further clarified.

Reviewer #1 (Public Review):

The authors investigate the role of Condensin and its loading in ensuring appropriate chromosome dynamics in the model organism Bacillus subtilis. The data are of high quality and generally support the ultimate conclusions.

The demonstration of collisions between ectopically-loaded Condensin and their negative impact on cellular viability are important insights, particularly in light of the recent single-molecular in vitro experiments demonstrating the ability of 2 Condensins to pass one another and thereby form Z-structures on DNA.

The main caveat is that the work lacks direct quantization of the levels of chromosome-associated Condensin—inclusion of experiments to evaluate this parameter would go a long way to validating (or refuting) the authors' conclusions.

Reviewer #1 (Public Review):

This manuscript by Gabor Tamas' group defines features of ionotropic and metabotropic output from a specific cortical GABAergic cell cortical type, so-called neurogliaform cells (NGFCs), by using electrophysiology, anatomy, calcium imaging and modelling. Experimental data suggest that NGFCs converge onto postsynaptic neurons with sublinear summation of ionotropic GABAA potentials and linear summation of metabotropic GABAB potentials. The modelling results suggest a preferential spatial distribution of GABA-B receptor-GIRK clusters on the dendritic spines of postsynaptic neurons. The data provide the first experimental quantitative analysis of the distinct integration mechanisms of GABA-A and GABA-B receptor activation by the presynaptic NGFCs, and especially gain insights into the logic of the volume transmission and the subcellular distribution of postsynaptic GABA-B receptors. Therefore, the manuscript provides novel and important information on the role of the GABAergic system within cortical microcircuits.

Reviewer #1 (Public Review):

This paper focuses on the role of historical evolutionary patterns that lead to genetic adaptation in cytokine production and immune mediated diseases including infectious, inflammatory, and autoimmune diseases. The overall goal of this research was to track the evolutionary trajectories of cytokine production capacity over time in a number of patients with different exposure to infectious organisms, infectious disease, autoimmune and inflammatory diseases using the 500 Functional Genomics cohort of the Human Functional Genomics Project. The identified cohort is made up of 534 individuals of Western European ancestry. Much of this focus is on the impact and limitations of certain datasets that they have chosen to use such as the "average genotyped dosage" to be substituted for missing variants and data interpretation. Moreover, some data pairings in the data set are not complete or had varying time points . Similarly, a split was done to look at before and after the Neolithic era and the linear regression correspond to those two eras. However, the authors do not comment or show the data to demonstrate why they choose that specific breakpoint as opposed to looking at every historical era transition, i.e., from early upper paleolithic to late upper paleolithic to Mesolithic to Neolithic to post-Neolithic to modern. Lastly, the authors should highlight additional limitations of this current study in terms of the generalizability to other populations or to clearly state that this is limited to the European population at the specified latitude and longitudes used.

Reviewer #1 (Public Review):

In the manuscript by Willoughby et al. the authors examine the role of Rab25 in early embryogenesis in zebrafish. They implicate Rab25 activity in abscission and show various defects including delayed epiboly and altered cell behaviors associated with defective acting dynamics. This is an interesting and well-written paper that uses reverse genetics and microscopy to analyze the function of Rab25, a GTPase previously implicated in membrane recycling, in vivo. Their work illustrates how defects in cytokinesis affect epiboly and establish an interesting link to acto-myosin regulation of the mechanical properties of the EVL. While these pehnotypes are described and demonstrated clearly, the implication of membrane recycling is not fully supported in the present work. It is also unclear whether Rab25 plays a role in oogenesis that may account for some of the observed phenotypes.

Reviewer #1 (Public Review):

The authors provide a novel case-study of the skeletal consequences of queen-only breeding in Damaraland mole-rats, one of the few eusocial mammals. Out of a population of adults, a queen will be selected as the sole female to breed with a male, and the non-breeders will provide support in the highly cooperative society. Once selected, a new queen will undergo a rapid skeletal transformation in which lumbar vertebrae expand. Supporting closely-timed pregnancies and lactation, mineral reserves will be excavated by bone-specific macrophages along the inner, or endosteal, lining of some limb bones. Unlike most other mammals, the skeletons of queens do not typically recover to their pre-pregnancy phenotype as rapid sequential pregnancies continually erode the limbs, leaving them vulnerable to fracture.

To understand the molecular mechanisms driving these phenotypic changes associated with breeding in queens, the authors artificially selected queens in captivity, recreated a eusocial society, and then tracked gene expression along with skeletal phenotypes throughout breeding cycles. After lumbar expansion in queens had completed only long bones showed gene expression consistent with breeding status. Specifically, results showed upregulation of differentiation and activity of bone-specific macrophages, call osteoclasts. These cells liberate minerals from bone and make components of the extracellular matrix available metabolism and development of embryos.

To understand if these changes were driven by the presence of sex-steroids, multiple cell types were harvested from the marrow of lumbar vertebrae and limb bones and treated with estradiol. No significant effect was found. Data, therefore, suggest that mechanisms shaping the postcranial skeleton were not consequences of sex-steroid mediated signaling pathways.

Non-recoverable bone loss in queens is unusual among mammals and is a vulnerability that potentially limits the number of pups a queen can produce. Vulnerable queens may therefore be protected in cooperative societies in which non-breeders can work more and offer queens more rest.

This study furthers the field of skeletal biology by exploring how enduring bone resorption contributes to the greater fecundity of one of the world's few eusocial mammals but has a potentially life-long consequence on limb performance and fracture resistance. The authors weave together multiple lines of evidence to better illustrate the enormous and rapid changes that occur as a female ascends to queen status, and what she sacrifices to build her colony. Results offer compelling and transdisciplinary insights into an extreme skeletal strategy and the impact of this work can be bolstered by only minor changes.

Reviewer #1 (Public Review):

The authors set out to test a variety of factors that could impact poladenylation site (PAS) selection in yeast. To that end, they rigorously tested a collection of temperature-sensitive mutations in polyadenylation machinery components and utilized a custom 3'-end sequencing method to assess PAS selection genome-wide. The most common result associated with polyadenylation machinery dysfunction was global switching to a more distal PAS. Further, the authors test an interesting phenomenon of cordecypin-induced switching to the distal PAS and reveal through metabolomics that enhanced nucleotide biosynthesis may be the root cause. The enhanced nucleotide pools was found to alter elongation rate leading to alterations in PAS choice. Finally, the authors find that convergent genes are influenced by the nucleosome landscape to impact APA events.

Overall, this is a rigorous and thorough study that brings together multiple regulatory components that impact PAS selection. The model presented by the authors is supported by their work and provides the field with a clear picture of the complex nature of cleavage and polyadenylation in yeast.

Reviewer #1 (Public Review):

This manuscript reports data from unique experiments in which a paralysed person reported sensations evoked by microstimulation of the somatosensory cortex. The main emphasis of this paper is on the effects of increase in stimulation frequency. It was discovered that depending on the electrode used, the peak intensity was felt at different frequencies. Accordingly, the electrodes and stimulation sites were divided into three groups-Low, Intermediate and High frequency preferring. Overall, it was noticed that in most electrodes increasing stimulation frequency beyond about 100 Hz led to less intense sensation. Without knowing the exact somatosensory circuits involved in processing, the connection with recently discovered human vibrotactile psychophysics phenomena and cortical recordings in mice are speculative, but are in close agreement with the current observation and thus the manuscript would benefit from expanding discussion on this. I personally don't think there is any contradiction with non-human primate studies, as the authors state, rather it should be viewed as a significant extension to those studies and warrants viewing them in a new light.

A very interesting observation is that three types of frequency-intensity effects are associated with different perceptual qualities. However, types of seemingly distinct sensations might be attributed to semantics describing sensation of periodic stimulation at different intensities. Subjective reports of one subject are very valuable to set future directions for this kind of investigation, but may not be enough to generalise those findings just yet.

The location of electrodes belonging to three different frequency-intensity effect groups appeared to be not at random, but whether it reflects cortical organisation or some other factors like systematic variation in electrode depth might have influenced the result, needs to be confirmed. Only a small number of electrodes was tested - 8 in the Medial Array and 11 in the Lateral Array.

Three frequency-intensity effect group electrodes also differed in median intensity reported across all frequencies, which cautions that the reported perceptual quality differences at least partly might be attributed to the overall level of intensity sensation. It has to be noted that the overall frequency-intensity response profile did not change by changing the stimulation current, however some shifts seems to be present. Alternatively, such frequency-intensity effect profiles represent circuits tuned to detection of specific features of stimuli. This possibility is indeed very intriguing.

As those experiments performed on a human subject with implanted electrodes are absolutely unique, the data are exceptionally interesting regardless of limitations generalising those findings. Unlike animal experiments humans can describe sensations evoked by cortical microstimulation so there is no substitution for these experiments and every piece of evidence is highly valuable. These results give ground for new hypotheses to better understand how the somatosensory system works and generate ideas for designing future human psychophysics and animal model experiments. From a practical point of view, it is exceptionally valuable for informing the design of stimulation protocols for bidirectional brain-computer interfaces (BCIs).

Reviewer #1 (Public Review):

In this study, Hallast and colleagues performed a detailed genetic analysis of the AZFc region of the Y-chromosome in a large cohort of 1190 Estonian men with idiopathic infertility and >1100 controls from the same population. They focused on partial deletions of the AZFc regions, because their clinical significance remains controversial and published reports are often contradictory. The authors performed a comprehensive genetic analysis, which in addition to a standard AZFc deletion protocol with gene dosage of the key AZFc genes, included also Y-haplogroup determination and re-sequencing of the retained DAZ, BPY2 and CDY genes. The authors showed that gr/gr deletions were enriched in infertile men, thus confirming that this deletion is a risk factor for impaired spermatogenesis. An important novel finding is identification of a previously unknown structural variant: a long r2/r3 inversion, which likely destabilizes two palindromes and leads to deletions. This variant is fixed in the Y lineage R1a1-M458, which is common in some Central European populations. In the Estonian study group, nearly all patients with this variant and a gr/gr deletion, had a severe impairment of spermatogenesis. The authors mentioned that the variant largely 'destroys' two palindromes, P1 and P2. One would like to see more discussion what are the structural and functional consequences - e.g. are any loci for e.g. non-coding RNA affected by a deletion in men with this inversion in comparison to those without?

The authors also speculated in the discussion that deletion on this background might lead to progressive worsening of the reproductive phenotype. This is based on just one control individual, a young man with borderline reproductive parameters, and corroborating this hypothesis would require further studies, including repeated evaluation of the same individuals over a long period of time.

This is a high quality study, performed by collaborators from the UK and Estonia, with an excellent track record in the analysis of the Y-chromosome structure and evolution, and in reproductive genetics and clinical andrology, respectively. The data presentation and figures are very informative and convincing. Among the strengths of the study, I have to emphasise a detailed phenotypic evaluation of the study subjects, including several parameters of testis function, semen analysis, and reproductive hormone profiles. Hence, the results and conclusions are valuable and add to the understanding of the consequences of the partial AZFc deletions. The authors also provided useful guidelines how to identify men with this variant in labs performing genetic analysis of infertile couples.

Reviewer #1 (Public Review):

In this study, Lee et al. reanalyzed a previous fMRI dataset (Aly et al., 2018) in which participants watched the same 90s movie segment six times. Using event-segmentation methods similar to Baldassano et al. (2017), they show that event boundaries shifted for the average of the last 5 viewings as compared to the first viewing, in some regions by as much as 12 seconds. Results provide evidence for anticipatory neural activity, with apparent differences across brain regions in the timescale of this anticipation, in line with previous reports of a hierarchy of temporal integration windows.

– One of the key findings of the paper – long-timescale anticipatory event reinstatement – overlaps with the findings of Baldassano et al., 2017. However, the previous study could not address the multiple time scales/hierarchy of predictions. Considering that this is the novel contribution of the current study, more statistical evidence for this hierarchy should be provided.

– The current hierarchy of anticipation is closely linked to (and motivated by) previous studies showing evidence of a hierarchy of temporal integration windows. Indeed, the question of the study was "whether this hierarchy also exists in a prospective direction". This question is currently addressed somewhat indirectly, by displaying above-threshold brain regions, but without directly relating this hierarchy to previous findings of temporal integration windows, and without directly testing the claimed "posterior (less anticipation) to anterior (more anticipation) fashion" (from abstract).

– The analysis is based on averaging the data of the 5 repeated viewings and comparing this average with the data of the first viewing. This means that the repeated viewing condition had much more reliable data than the initial viewing condition. This could potentially affect the results (e.g. better fit to HMM). To avoid this bias, the 5 repeated viewings could be entered separately into the analysis (e.g., each separately compared to the first viewing) and results averaged at the end. Alternatively, only the 6th viewing could be compared to the first viewing (as in Aly et al., 2018).

– Correlation analysis (Fig 6). "we tested whether these correlations were significantly positive for initial viewing and/or repeated viewing, and whether there was a significant shift in correlation between these conditions". It was not clear to me how we should interpret the correlation results in Figure 6. Might a lower correlation for repeated viewing not also reflect general suppression (e.g. participants no longer paying attention to the movie)? Perhaps comparing the correlations at the optimal lag (for each cluster) might help to reduce this concern; that is, the correlation difference would only exist at lag-0.

– Correlation analysis (Figure 6). "For both of these regions the initial viewing data exhibits transitions near the annotated boundaries, while transitions in repeated viewing data occur earlier than the annotated transitions" How was this temporal shift statistically assessed?

– Not all clusters in Figure 2/6 look like contiguous and meaningful clusters. For example, cluster 9 appears to include insula as well as (primary?) sensorimotor cortex, and cluster 4 includes both ventral temporal cortex and inferior parietal cortex/TPJ. It is thus not clear what we can conclude from this analysis about specific brain regions. For example, the strongest r-diff is in cluster 4, but this cluster includes a very diverse set of regions.

– In previous related work, the authors correlated time courses within and across participants, providing evidence for temporal integration windows. For example, in Aly et al., 2018 (same dataset), the authors correlated time courses across repeated viewings of the movie. Here, one could similarly correlate time courses across repeated viewings, shifting this time course in multiple steps and testing for the optimal lag. This would seem a more direct (and possibly more powerful) test of anticipation and would link the results more closely to the results of the previous study. If this analysis is not possible to reveal the anticipation revealed here, please motivate why the event segmentation is crucial for revealing the current findings.

Joint Public Review:

Hsiang-Chun Chang et al. investigated the role of ALR, component of the mitochondrial MIA40/ALR protein import apparatus, in cytosolic Fe/S cluster biogenesis performing loss-of-function (silencing) and gain-of-function (over-expression) experiments with MEFs (mouse embryonic fibroblast) and HEK293 (human embryonic kidney) cells. They find that downregulation of ALR impairs maturation of cytosolic Fe/S cluster proteins, while activities of mitochondrial Fe/S cluster proteins such as complex I and II are unaffected. Furthermore by reducing ALR expression cells up-regulate cellular iron transporter transferrin receptor 1 (Tfrc) and consequently cellular iron levels increase. The authors reveal that ALR down-regulation post-transcriptionally regulates Trfc through stabilization of Trfc mRNA mediated by IRP1, which is activated by absence of its mature Fe/S cluster. Additionally they demonstrate that only over- expression of full-length ALR, mainly located in the mitochondria and not the cytosolic short from ALR can reverse cytosolic Fe/S cluster maturation and therefore IRP1 activity and cellular iron levels. In the last part of their manuscript the authors present evidence about the mechanism by which ALR carries out this function. They find that ALR enables mitochondrial import of ABCB8 but not ABCB7, two mitochondrial proteins involved in the maturation of cytoplasmic Fe/S clusters. This transport into mitochondria requires functional MIA40/ALR in the IMS and further the TIM23 complex to the inner mitochondrial membrane. ABCB8 interacts directly with MIA40 by 5 cysteines (difulfide bond formation) and therefore these conserved cysteins are necessary for recognition and binding, which is not the case for ABCB7. These data add an interesting view on how ALR expression is linked to Fe/S cluster protein maturation, cellular iron homeostasis and their potential impact on related dieases.

The strength of the manuscript are the well designed and performed experiments presenting evidence of how mitochondrial function of ALR is linked to the sulfur redox homeostasis and cellular iron regulation. Interestingly, reduction in cytsolic Fe/S cluster maturation and therefore increased cellular iron levels is also associated with increased sensitivity of cells to oxidative stress and this might be a plausible explanation for the previously described impact of full length ALR expression on oxidative stress in various disease models (PMID: 30579845).

The drawn conclusions that the mechanistic studies about the role of ALR for Fe/S cluster maturation and cellular iron uptake may parallel the disease phenotype of patients with mutations in ALR gene GFER may be in parts speculative. The reported ALR mutations are varying and result either in partial functional or truncated protein expression (PMID: 20593814, PMID: 25269795). ALR is expressed in several isoforms (varying between two or three depending on the organ) of different size (15kDa, 21kDa, 23kDa). Most of the data showing the short form ALR (15kDa) solely in the cytosol and the full length ALR (23 kDa) as wells a second immuno-reactive band of 21 kDa ALR, both in cytosol and mitochondria (PMID: 30579845). While over-expressing full length ALR the authors show in the manuscript higher expression level in the cytosol than in the mitochondria fraction (w-blot, which is not reflected in the graph of Fig. S3 B). It was reported earlier that continuous over-expression of full length ALR in mammalian cells leads to the accumulation of full length ALR not only in the mitochondria but also in the cytosol (PMID: 23676665), which is also in agreement to observations of cytosolic occurrence of full length ALR (see above). This raises the question whether the conclusions made in the manuscript may be due to its cytosolic accumulation rather than or in addition to its mitochondrial localization. The presented study refers at several points to a study by Lange et al 2001 demonstrating that ALR rescues cytoplasmic Fe/S cluster maturation defects in Erv1- null yeast. There has been contradictory evidence published about the role of ALR in the maturation and export of cytosolic Fe-S cluster proteins. Lange et al. claimed that ALR interacts with Atm1 (an ABC transporter in the inner membrane of the mitochondria) and facilitates the export of Fe-S proteins to the cytosol. However, later it was suggested that, in yeast cells, ALR plays neither a direct nor an indirect role in cytosolic Fe-S cluster assembly and iron homeostasis. It is claimed that Iron homeostasis is independent of Erv1/Mia40 function in various yeast strains (Erv1 mutant) and that the finding by Lange et al. is based on only one Erv1 mutant strain, mainly due to strongly decreased glutathione (GSH) levels (PMID: 26396185).

Additionally, this statement is reinforced by a study in human cells, demonstrating that depletion of ALR does not impact the maturation of cytosolic Fe-S proteins assembled via the CIA pathway (PMID: 25012650). Furthermore, this study in mammalian cells has pointed out the role of ALR in exporting MitoNEEt to the outer mitochondrial membrane (OMM). MitoNEEt is a Fe-S protein that is synthesized in the mitochondrial matrix. Upon synthesis, MitoNEEt translocates through the inner membrane of the mitochondria by ABCB7 and then through the IMS by ALR to the OMM where it contributes to cell proliferation (PMID: 25012650).

Reviewer #1 (Public Review):

Mobile genetic elements like phages, transposons, plasmids, and conjugative elements are widespread in prokaryotes and confer important traits to their hosts, including antibiotic resistance and virulence. In this study, the authors convincingly demonstrate that the mobile element ICEBs1 of Bacillus subtilis confers a fitness advantage to its host by delaying entry into metabolically costly developmental processes (biofilm formation and sporulation). The gene devI is identified as being responsible for delaying initiation of development, but the mechanistic basis for this could be further explored. Their results show that, in addition to conferring novel phenotypes, mobile elements exert influence by tuning existing host pathways, a paradigm that could be extended to many other prokaryotes.

Strengths:

The paper is written very clearly, the experimental data is convincing, the interpretations and conclusions are justified by the data.

The authors implemented clever genetic approaches to quantitatively compare the fitness of strains harboring or lacking ICEBs1 in co-culture. I appreciated the use of the conjugation mutant (comEK476E) to prevent ICE transfer that would confound the analysis. Similarly, the authors genetically separate the developmental pathways under which ICEBs1 confers an advantage (biofilm formation and sporulation), by deleting the spo0A promoter under sigH control to prevent sporulation but retain biofilm formation. Finally, to assess the contribution of ICE-encoded genes to fitness, the authors take advantage of a "locked-in" ICE variant (∆attR, oriT*) that cannot excise and replicate - thereby eliminating the confounding variable of gene dosage from ICE replication.

As mentioned above, the effects of ICEBs1 on development set an important precedent for how mobile genetic elements interact with their hosts. They are often regarded as autonomous elements, but the authors provide an example of how these elements can influence host pathways.

Suggestions for improvement:

The authors show that the gene devI is necessary and sufficient for ICE-mediated delay of development initiation. Gene expression analyses suggest this delay affects the earliest stages of development (genes under control of spo0A, the master regulator of sporulation, are affected). I think the authors could investigate the mechanism of spo0A inhibition in more detail. Which aspect of spo0A function is affected by DevI? Starvation sensing, spo0A expression, activation of upstream kinases (KinA?), phosphorelay, or binding of Spo0A~P to promoters?

Ectopically expressed DevI (Fig 5) seemed to have a stronger inhibition of sporulation than ICEBs1 alone (Figure 2) - does the constitutively expressed protein block rather than delay sporulation? I wonder if the authors would like to comment on how, in the wild-type ICEBs1 context, DevI activity is eventually overcome by cells that eventually do sporulate after a delay. Furthermore, will cells that successfully transfer ICEBs1 be relieved of DevI-mediated sporulation inhibition?

The data in Fig 4 suggest that devI is not the only ICEBs1-encoded factor providing a fitness advantage. Do the unknown factor(s) also delay development, or do they work via other mechanisms: i.e. does the ∆devI mutant have a sporulation delay? Any idea what the other factors might be (from bioinformatics for example)?

Reviewer #1 (Public Review):

Ruberto et al. utilize hepatocyte-specific Klf10 knock-out mice to demonstrate expression changes of rhythmic transcripts, highlighting dysregulated glucose and lipid metabolism as an enriched gene set. They demonstrate that KLF10 is necessary for proper glycemic control in mice and that KLF10 coordinates suppression of metabolic gene expression in the liver in response to high sugar diet. The authors corroborate their findings by analyzing gene expression changes of primary hepatocytes stimulated with fructose and high glucose. Finally, the authors identify KLF10 target genes using ChIP-seq and validate Acss2 and Acacb as target genes suppressed in mice following a high sugar diet. Novel aspects of this work include the metabolic characterization of a hepatocyte-specific Klf10 knock-out mouse, identification of KLF10 target genes in hepatocytes using ChIP-seq, and description of circadian transcript expression with Klf10 loss.

Reviewer #1 (Public Review):

This manuscript titled "Human Erbb2-induced Erk Activity Robustly Stimulates Cycling and Functional Remodeling of Rat and Human Cardiomyocytes" directly compared a number of previously identified candidate mitogenic genes in different cardiomyocytes and different maturity status and investigated the pathway involved. The authors found that the human Erbb2 triggers the strongest proliferative effect in both human-induced Pluripotent Stem Cells and Neonatal Rat Ventricular Myocyte, and was associated with the Erk pathway. The authors then proved this association by demonstrating that inhibition with Mek inhibitor and Erk inhibitor attenuates the human Erbb2-induced response. In addition, the authors found that Yap8SA failed to trigger proliferation in the cardiomyocyte tested due to negative feedback loop. Thus, this study provides helpful information regarding the relative effectiveness of a number of candidate genes.

Strengths:

— This study investigates five candidate genes in different species and different maturation status of cardiomyocyte. In each setting, all genes are studied. Therefore, direct comparison regarding their effectiveness can be made.

— Furthermore, this study demonstrated the mechanism on how the differing responses arose, providing in-depth information.

Weakness:

— Although this study showed induced proliferation of cardiomyocyte following candidate genes expression, the authors did not present sufficient proof that the function would improve. Cardiomyocyte harbor differing functions and parameters that represents it should ideally be investigated.

Joint Public Review:

The manuscript by Liu and colleagues is a very elegant study demonstrating the emergence of ectopic beta cells after beta cell specific ablation in zebrafish pancreas in a context in which vascularization of the larvae was altered in either npas4l mutants or etv2 morphants. Provocatively, the authors demonstrate the mesodermal origin of ectopic and functional beta cells using 2 mesodermal mapping strategies. This study is very well conducted with appropriate controls and rigorous statistical analyses. This study will likely impact the field of pancreas regeneration providing a novel source for beta cells within the adjacent mesodermal tissue.

Joint Public Review:

Although sensory neurons are thought to be the primary detectors of environmental stimuli in skin, it is more and more appreciated that non-neuronal cell types also play important roles. Previous work from the Stucky group (and others) has shown stimulation of optical excitation of keratinocytes can evoke action potentials in sensory neurons and behavioral responses suggesting functional connectivity. Earlier work from the Stucky group provided evidence that keratinocytes are thermosenstive and required for normal temperature sensation.

Here, they look into whether these cells are also important for mechanosensation. Using K14-Cre-dependent conditional KO mice, functional assays and behavioral analysis, Moehring and collaborators report that the mechanosensitive channel Piezo1 is expressed in keratinocytes in mice and humans and claim that it contributes to normal touch sensation. The in vitro data convincingly show that keratinocytes have mechanically evoked currents mediated by Piezo1. Interestingly, this work shows that recruitment of epidermal, non-neuronal Piezo1 by mechanical stimulation of keratinocytes could contribute significantly to touch through activation of cutaneous sensory fibers (mechanoreceptors). Specifically, they provide evidence that removing Piezo1 from keratinocytes reduces the frequency of spiking in select types of sensory neurons to punctate and dynamic touch stimuli. Finally, they supply quite surprising data documenting significant behavioral deficits in Krt-conditional knockout mice.

Overall, this work provides an intriguing series of observation and potentially fundamental discovery. However, concerns remain as to how the relatively subtle differences in the skin-nerve recordings result in such profound behavioral effects? Similarly, it is hard to understand how loss of the related channel Piezo2 in sensory neurons completely abolishes many touch responses if mechanosensitivity of keratinocytes is sufficient to evoke touch behaviors (as their experiments applying Yoda-1 to the hindpaw of mice would suggest). Altogether, this work suggests a novel role for epidermal Piezo1 in normal touch but the key neuro-epithelial signaling remains to be identified.

Reviewer #1 (Public Review):

The manuscript by Rosello et al., describes the application of cytosine base editing to efficiently introduce known and predictable mutations into disease genes in vivo in zebrafish, and examine signaling pathways and model disease. The majority of the data presented is analysis of editing precision and efficiency in somatically targeted embryos, with one example of a precise edited germline allele recovered. A direct comparison of the cytosine base editor BE4 and an improved version ancBE4max indicates both are highly efficient at somatic base editing. ancBE4max reduces alteration of bases outside the base editing window, and the data suggests loci for which BE4 base editing has failed can be targeted with ancBe4max. The authors demonstrate efficient base editing in embryos at multiple cancer genes (up to 91%), introducing activating mutations into oncogenes and nonsense mutations in a number of tumor suppressors. A S33L allele was introduced into the b-catenin gene ctnnb1 to activate the wnt signaling pathway as evidenced by expression of the wnt reporter Tg(tcf:GFP). Another novel aspect of this study is that the authors have expanded base editing target site selection by switching out the ancBe4max SpCas9 PAM-interacting motif domain with the domain from Spymac, which recognizes an NAA PAM. ancBe4maxSpymac editing efficiency was modest (16-19%). The method reported here has strong potential for effective combinatorial mutagenesis to map complex genetic interactions that underly disease pathogenesis. Overall, this study demonstrates cytosine base editing is an efficient and powerful method for introducing precise in vivo edits into the zebrafish genome.

Reviewer #1 (Public Review):

While I am not sufficiently qualified to comprehensively assess the molecular dynamics simulations, all interpretations seem careful and remain within the described limitations of the various metrics that the authors report.

The experiments are well executed; the results are presented clearly and interpreted carefully. This is a rigorous and important biophysical study that provides a solid foundation for the investigation of archaeal genome biology. The authors' new findings raise interesting questions, and although addressing them is outside the scope of this study, the article would perhaps benefit from a more detailed discussion of the biological implications of the results. The manuscript does not indicate whether the cryo-EM maps and atomic models were deposited in the EMDB and PDB. I strongly encourage the authors to do that: it would add a lot of value not only for the readers of this study, but also for the wider structural biology community.

Reviewer #1 (Public Review):

Previous research showed a close link between sub-clinical AFib (Atrial Fibrillation) and ESUS (Embolic Stroke of Undetermined Source). As such, current established clinical care for ESUS patients is long-term monitoring for evidence of AFib and anticoagulant treatment for an individual with high risk for AFib. Nevertheless, questions are still unanswered about who the individuals with high-risk for ESUS are and how to properly identify this population.

This research tries to identify the fibrotic properties of ESUS patients and its pro-arrhythmic potential using computational modeling of patient's left atria reconstructed from cardiac LGE-MRI (Late-Gadolinium Enhanced Magnetic Resonance Imaging). Ultimately, their results of the comparison between left atria of ESUS and AFib patients revealed that the fibrotic substrate that could induce arrhythmia in ESUS and AFib patients are indistinguishable, raising more questions that would need to be addressed in further studies.

This study uses a sophisticated personalized computational modeling approach that has been validated in previously published papers. This study is also well designed, clearly written, with robust data and proper statistical analysis.

What is left unclear is what is unique about the fibrotic substrate in ESUS patients in comparison to AFib patients in the future.

Reviewer #1 (Public Review):

In the days of the COVID-19 pandemic vaccines, mechanisms of vaccine administration are important and of broad interest. Vaccines are most often given into the skin. Antigen-presenting cells of the skin are responsible for eliciting the immune response in draining lymph nodes. Langerhans cells, the dendritic cell variant of the epidermis, are one of these cutaneous antigen presenting cells that are believed to do this job. They migrate from the skin, the site of antigen/vaccine uptake to the draining lymph nodes, where lymphocytes are located and where the immune reaction will be initiated. With their sophisticated experiments, the authors challenge this view. They use leading edge methodology (mouse models) that strongly suggest that there may be yet another subset of skin antigen presenting cells, that is responsible for carrying antigen from skin to lymph — at least in the steady-state skin. This population resides in the dermis (the connective tissue part of skin), as opposed to the classical Langerhans cells, which sit in the epidermis. This may be relevant to the maintenance of immunologic tolerance to innocuous substances in the absence of an overt inflammation. The data suggest that Langerhans cells may not play the crucial role they were thought to play. This is certainly a conceptual advance that — like always in science, especially when experimental systems are complex, as they are here — needs to be underpinned by future studies. In the long run, it will be very interesting (but much more difficult to study) to see whether this also holds true for human skin.

Reviewer #1:

The manuscript by Rae et al. reports the development of a new protocol for labeling genetically-tagged proteins of interest with heavy atom particles for visualization by electron microscopy. The optimized protocol builds on the established use of the enzyme APEX fused to the target of interest. APEX oxidizes diaminobenzidine, DAB, which in turn converts silver and gold metal salts to particulates in close proximity to the APEX-fused protein of interest. The optimized protocol is related to the contrast-enhancement method reported by Sedmak et al., 2009 and Mavlyutov et al., 2017. The changes to the method may improve the proportionality of the signal such that the number of APEX tags present in a sample is better correlated with the number of heavy atom particles. While the study appears to be sound, it is an extension of an established labeling method.

Reviewer #1:

This study takes on the question of the roles of the many pathways leading to ERK activation in long-term potentiation. This is an advance: few models consider more than a couple of input pathways. The authors consider two aspects: how pathways sum to give strong responses, and distinct temporal pattern selectivity. They show that both summation linearity, and pattern selectivity, are strongly governed by which pathways are engaged in driving the response.

The model and analysis is potentially interesting, but the paper would be much strengthened if there were more convincing validation of the properties of the model by way of simulations to compare with experiments. Further, the pathways chosen are already one step into the synapse. Thus the actual combination of pathway activations would not be quite as cleanly separated if they were driven by synaptic input.

Reviewer #1:

Hofmann et al. investigate the link between two phenomena, emotional arousal and oscillatory alpha activity in the cerebral cortex, which is of central interest in their respective fields. Although alpha activity is tightly linked to the first reports of electric activity in the brain nearly 100 years ago, a comprehensive characterization of this phenomenon is elusive. One of the reasons is that EEG, the major method to investigate electric activity in the human brain, is susceptible to motion artifacts and, thus, mostly used in laboratory settings. Here, the authors combine EEG with a virtual reality setup to give experimental participants a roller-coaster ride with high immersion. The ride, literally, leads to large ups and downs in emotional arousal, which is quantified by the subjects during a later rerun. Next, the authors decode the degree of emotional arousal as stated in the rerun based on the EEG signals recorded during the VR session. They demonstrate convincingly a negative dependence of alpha activity with the degree of emotional arousal. Further, they demonstrate the differential involvement of parietal and occipital regions in this process. The sequencing of the description of methods and results could be improved upon, is, however, as such perfectly ok. This investigation comes timely, makes an important contribution to our understanding of the relation of emotions and sensory processing.

Reviewer #1:

The manuscript entitled "An evolutionary model identifies the main selective pressures for the evolution of genome-replication profiles" is an examination of the principles shaping evolution of replication origin placement. Overall I found the manuscript to be engaging and interesting, and the topic of general importance. It is quite compelling that with just two parameters, origin efficiency and distance between origins, a good model can be built to describe the dynamics of origin birth and death. While this work on its own is sufficiently important for publication, it would be very interesting to see whether the model can be updated in the future to address whether there are fork-stalling or origin-generating mechanisms that shape evolution of specific inter-origin spaces. This work provides a very good foundation for such efforts.

I have a few major, general concerns I would like the authors to address.

If I'm interpreting the methods correctly, it seems the parameters used in these simulations, such as mean birth rate, mean death rate, gamma, and beta, were fit to the data once, and used as point estimates during simulation. If true, I expect the simulations to be yielding estimates of birth and death rates with a much narrower distribution of outcomes than is likely to be realistic given what an appropriate level of confidence in those parameter estimates would be. Could the parameters be fit to data in such a way that we attain an estimate of confidence in the parameter values, from which a distribution could be generated and sampled from during simulation?

Closely related to my prior concern, I would like the authors to demonstrate the general predictive value of their model on out-of-sample data. Can the model be applied to other data on replication timing? Without such an attempt to demonstrate the model's applicability to out-of-sample prediction, the reader cannot ascertain whether the model is overfit to the Lachancea data from Agier et al, 2018. Also, keeps the parameter estimates here from being overfit to better predict origin birth and death events in closely related branches of the Lachancea tree in Figure S1? Are gamma and beta inferred in a way that accounts for the higher correlation in birth and death events in closer-related branches than in distal branches, or has the fit ignored those correlations?

The authors state that their model identifies selective pressures. The authors imply, and specifically state in lines 238-242, that increased death rate of origins which happen to be nearby highly efficient origins represents selective pressure against the less efficient origins. It isn't until the discussion that the authors raise the possibility that there may simply be a lack of selective pressure to retain inefficient origins that are near highly efficient origins. In my view, it's more likely that selection for the existence of an inefficient origin is simply lower than the drift barrier, so mutagenesis and drift can passively remove such origins over time without the need to invoke selection against inefficient origins.

Figure 3 is intended to show that the stall-aversion and interference model performs better at predicting correlations between efficiency of lost origins and their nearest neighbor. I agree, but I do not think Figure 3 presents a strong case for this conclusion. Fig S6 presents stronger evidence to me. While fig 3 does qualitatively suggest that the joint model may predict the correlation between neighboring origin efficiency and origin loss better than the double-stall model alone, it almost appears to me that the model with fork stalling and interference has significantly overestimated the correlation. Is there a quantitative way, perhaps using information criteria, though I admittedly am not sure how one would go about doing that with simulations such as these, to demonstrate that the model with both effects has better predictive value than the one with only fork stalling?

There are a couple of assumptions of the model that I would like the authors to examine in further detail. First, that origin birth events occur in the middle of an inter-origin space. I am not aware of evidence pointing to this being a good a priori assumption. Can you re-run the simulations, allowing origins to arise at a random site within the inter-origin space into which it is born? Second, is it reasonable to expect origin firing rates to reshuffle to a new value randomly, without any dependence on their prior rate? Perhaps I'm mistaken, but it seems to me that an origin's firing rate should evolve more gradually, and should have a higher probability of sampling from values near its current value than from values very far from its current value.

Reviewer #1:

In this manuscript, Bakken et al use single cell and single nucleus RNA-sequencing to conduct comparative analysis of dLGN in humans, macaques and mice. dLGN exhibits a dramatic reorganization and lamination in primates relative to mice. Other components of the visual system (retina, V1) have previously been explored with cross-species transcriptomic analyses to reveal species-specific or evolutionary modifications. How dLGN fits in this picture, and the extent to which differences amongst previously identified cell types can be discerned from transcriptomic data, is an important question.

The conclusions are supported by the data, but the paper could better motivate what the main questions or debates are.

Strengths:

The authors use highly sensitive SMART-seq v4 to collect and analyze thousands of cells from dLGN and some adjacent nuclei. The gene detection rate using this method is impressive, and the plate/strip-based workflow has distinct advantages in terms of lower ambient contamination and risk of doublets compared to microfluidics-based single cell platforms. Cells or nuclei are sorted to enrich for neurons, which are the main focus of this paper. Key results are validated by smFISH or by examining publicly available Allen Brain Atlas ISH data. By examining conservation and divergence of cell types and evolutionarily conserved thalamic nucleus that has nonetheless undergone dramatic anatomical reorganization, these data and analyses add to our understanding of how cell types evolve in mammalian brains. They also contribute nuance to the ongoing debate of the extent to which transcriptomic data alone can be used to identify and discriminate cell types that have been described using other methodologies.

Weaknesses:

The Introduction does a nice job of describing what is known about the anatomy and cell types of the dLGN in each species, but it is less obvious what the motivating cross-species question is. Similarly, the Discussion focuses on technical details but the take-away is not clear.

dLGN is collected from all species, but in some species (macaque, mouse), additional thalamic nuclei are also collected. These are useful for examining cell type correspondences across regions or shifts between species, but their inclusion in cross-species integrations can also distort results (e.g. with some integration approaches, inclusion of very different, dataset-specific cell types can distort integration of more similar types). Analyses could be done to better distinguish the evolutionary comparisons within dLGN itself vs. what is additionally learned from inclusion of extra-dLGN nuclei.

One major evolutionary difference can involve differences in cell type proportions. Some proportion results are described but mainly for individual species (some of which include extra-dLGN regions) rather than in the integrated maps, so they can't be compared across species. The FISH results could also be used to corroborate proportion changes when such data are available.

Parameters for clustering analysis (using CCA/Seurat) are not described. Often changes in parameters can change the clusters, and it would be important to know if species integration results robust across a range of parameters and inclusion of extra-dLGN regions.

Some expected genes (PVALB) are barely detected in the macaque neurons, raising the question of whether this is due to tissue or annotation/alignment quality.

Reviewer #1:

This is an interesting manuscript which covers an important topic in the field of computational neuroscience - the 'temporal signatures' of individual neurons. The authors set out to address several important questions using a single-neuron electrophysiology dataset, recorded from monkeys, which has previously been published. The behavioural paradigm is well designed, and particularly well suited to investigating the functional importance of different temporal signatures - as it simultaneously requires the subjects to monitor feedback across a short timescale, as well as integrate multiple outcomes across a longer timescale. The neural data are of high quality, and include recordings from lateral prefrontal cortex (LPFC) and mid-cingulate cortex (MCC). First, the authors modify an existing method to quantify the temporal signatures of individual neurons. This modification appears helpful, and an improvement on similar previously published methods, as the authors are able to capture the temporal signatures of the vast majority of neurons they recorded from. The temporal signatures differ across brain regions, and according to the neurons' spike width. The authors argue that the temporal signatures of a subset of neurons are modified by the subjects' degree of task engagement, and that neurons with different temporal signatures play dissociable roles in task-related encoding. However, I have several concerns about these conclusions which I will outline below. The authors then present a biophysical network model, and show that by varying certain parameters in their model (AHP and GABAB conductances) the temporal signatures of the monkey data can be reproduced. Although I cannot comment on the technical specifics of their models, this seems to be an important advance. Finally, they perform a Hidden-Markov Model analysis to investigate the metastability of activity in MCC, LPFC, and their network model. However, there are a few important differences between the model and experimental data (e.g. neurons recorded asynchronously, and the network model not performing a task) that limit the interpretation of these analyses. Overall, I found the manuscript interesting - and the insights from the biophysical modelling are exciting. However, in its current form, the conclusions drawn from the experimental data are not supported by sufficient evidence.

Major Comments:

1) The authors use a hierarchical clustering algorithm to divide neurons into separate groups according to their spike width and amplitude (Fig 1C). There are three groups: FS, RS1, and RS2. The authors ultimately pool RS1 and RS2 groups to form a single 'RS' category. They then go on to suggest that RS neurons may correspond to pyramidal neurons, and FS neurons to interneurons. I have a few concerns about this. Firstly, the suggestion that spike width determined from extracellular recordings in macaques can be used as an indicator of cell type is controversial. A few studies have presented evidence against this idea (e.g. Vigneswaran et al. 2011 JNeuro; Casale et al. 2015 JNeuro). The authors should at least acknowledge the limitation of the inference they are making in the discussion section. Secondly, visualising the data alone in Fig 1C, it is far from clear that there are three (or two) relatively distinct clusters of neurons to warrant treating them differently in subsequent analyses. In the methods section, the authors mention some analyses they performed to justify the cluster boundaries. However, this data is not presented. A recent study approached this problem by fitting one gaussian to the spike waveform distribution, then performing a model comparison to a 2-gaussian model (Torres-Gomez et al. 2020 Cer Cortex). Including an analysis such as this would provide a stronger justification for their decision to divide cells based on spike waveform.

2) The authors conclude that the results in Fig 3 show that MCC temporal signatures are modulated by current behavioural state. However, this conclusion seems a bit of a stretch from the data currently presented. I can understand why the authors used the 'pause' periods as a proxy for a different behavioural state, but the experiment clearly was not designed for this purpose. As the authors acknowledge, there is only a very limited amount (e.g. a few minutes) of 'pause' data available for the fitting process compared with 'engage' data. Do the authors observe the same results if they constrain the amount of included 'engage' data to match the length of the 'pause' data? Also, presumably the subjects are more likely to 'pause' later on in the behavioural session once they are tired/sated. Could this difference between 'pause' and 'engage' data be responsible for the difference in taus? For instance, there may have been more across-session drift in the electrode position by the time the 'pause' data is acquired, and this could possibly account for the difference with the 'engage' data. Is the firing rate different between 'pause' and 'engage' periods - if so, this should be controlled for as a covariate in the analyses. Finally it is not really clear to me, or more importantly addressed by the authors, as to why they would expect/explain this effect only being present in MCC RS neurons (but not FS or LPFC neurons).

3) At many points in the manuscript, the authors seem to be suggesting that the results of Fig 4 demonstrate that neurons with longer (shorter) timescales are more involved in encoding the task information which is used across longer (shorter) behavioural timescales (e.g. "long TAU were mostly involved in encoding gauge information", and "population of MCC RS units with short TAU was mostly involved in encoding feedback information"). However, I disagree that this conclusion can be reached based on the way the analysis has currently been performed. A high coefficient simply indicates that the population is biased to be more responsive depending on a particular trial type / condition - i.e. the valence of encoding. This does not necessarily tell us how much information the population of neurons is encoding, as the authors suggest. For instance, every neuron in the population could be extremely selective to a particular parameter (i.e. positive feedback), but if half the neurons encode this attribute by increasing their firing but the other half of neurons encode it by decreasing their firing, the effects will be lost in the authors' regression model (i.e. the beta coefficient would equal 0). I would suggest that the authors consider using an alternative analysis method (e.g. a percentage of explained variance or coefficient of partial determination statistic for each neuron) to quantify coding strength - then compare this metric between the high and low tau neurons.

4) Similarly, in Fig 4 the authors suggest that the information is coded differently in the short and long tau neurons. However, they do not perform any statistical test to directly compare these two populations. One option would be to perform a permutation test, where the neurons are randomly allocated into the 'High TAU' or 'Low TAU' group. A similar comment applies to the different groups of neurons qualitatively compared in panel Fig 4C.

5) The authors make an interesting and well-supported case for why changing the AHP and GABA-B parameters in their model may be one mechanism which is sufficient to explain the differences in temporal signatures they observed between MCC and LPFC experimentally. However, I think in places the conclusions they draw from this are overstated (e.g. "This suggests that GABA-B inhibitory - rather than excitatory - transmission is the causal determinant of longer spiking timescales, at least in the LPFC and MCC."). There are many other biophysical differences between different cortical regions - which are not explored in the authors' modelling - which could also account for the differences in their temporal signatures. These could include differences in extra-regional input, the position of the region in an anatomical hierarchy, proportion of excitatory to inhibitory neurons, neurotransmitter receptor/receptor subunit expression, connectivity architecture etc. I think the authors should tone down the conclusions a little, and address some more of these possibilities in more detail in their discussion.

6) For the Hidden Markov Model, I think there are a couple of really important limitations that the authors only touch upon very briefly. Firstly, the authors are performing a population-level analysis on neurons which were not simultaneously recorded during the experiment (only mentioned in the methods). This really affects the interpretation of their results, as presumably the number of states and their duration is greatly influenced by the overall pattern of population activity which the authors are not able to capture. At this stage of the study, I am not sure how the authors can address this point. Secondly, the experimental data is compared to the network model which is not performing any specific task (i.e. without temporal structure). The authors suggest this may be the reason why their predictions for the state durations (Fig 7B) are roughly an order of magnitude out. Presumably, the authors could consider designing a network model which could perform the same task (or a simplified version with a similar temporal structure) as the subjects perform. This would be very helpful in helping to relate the experimental data to the model, and may also provide a better understanding of the functional importance of the metastability they have identified in behaviour.

7) It is not clear to me how many neurons the authors included in their dataset, as there appear to be inconsistencies throughout the manuscript (Line 73, Fig 1A-B: MCC = 140, LPFC = 159; line 97-98: MCC = 294, LPFC=276; Fig2: MCC = 266, LPFC = 258; Methods section line 734-735 and Fig 2S2: MCC = 298, LPFC = 272). While this is likely a combination of typos and excluding some neurons from certain analyses, this will need to be resolved. It will be important for the authors to check their analyses, and also add a bit more clarity in the text as to which neurons are being included/excluded in each analysis, and justify this.

Reviewer #1:

Astrocyte glutamate transporter, GLT1, plays a crucial role in confining the levels of extrasynaptic glutamate, and therefore, understanding the cellular basis by which surface dynamics of GLT1 is regulated has implications in regulating glutamatergic transmission. Here, Michaluk et al. perform FRAP experiments using pHluorin (SEP)-tagged GLT1, and present a careful quantitative characterization of GLT1 surface dynamics that takes into account both lateral diffusion and exocytic delivery. The authors report that 25-30% of surface GLT1 represent immobile fraction which may be subject to slower exchange via exocytic delivery from intracellular compartments. In addition, the cytoplasmic domain of GLT1 plays a role in regulating GLT1 subcellular localization patterns and its activity-dependent dynamics. While the roles for mGluR and calcium-signaling mechanisms are explored, given the drugs have been applied under conditions in which neurons are equally affected, whether mGluR and calcium signaling involving calcineurin are engaged in astrocytes to impact GLT1 remains to be established. In addition, the super-resolution imaging, which does not discriminate between surface and intracellular pool of GLT1, is not well connected to the FRAP results, which is performed blind to the location of synapses.

Reviewer #1:

The strength of this paper is its coupling of careful phylogenetic work with genomics to demonstrate the take-home message: all afrotherians are equal, but some are more equal than others with respect to mechanisms that reduce cancer risk. This is a significant advance in our understanding of the evolution of cancer risk with body size, and in so doing it considerably lengthens the list of genes of interest. It also has interesting examples illustrating the logical criteria of consistency, necessity, and sufficiency that will make it quite useful in teaching critical thinking to students.

Reviewer #1:

Vandaele et al. probe the mechanisms of decision making in rats when making a forced choice between drug and non-drug reward. The authors have led the field in this domain. In this manuscript, a retrospective analysis of choice response times from many rats in their past work is used to tease out potential decision-making mechanisms. We know already from decades of work that choice response times are almost always log-normally distributed (humans, non-human primates, rodents). The question here is whether differences in the mean and dispersion of these distributions can be used to derive insights into nature of the decision-making mechanism - a deliberative comparison versus a race model - and how this may differ for rats that prefer cocaine over saccharin and how this might be altered by more extended training. These questions are framed in terms of the differences between goal-directed and habitual behavior which, to be frank, I found less compelling (these response time data are of significant interest in their own right). I enjoyed reading this manuscript. It was thoughtful and well presented. I have only two comments.

First, much, if not all, of the absolute differences between latencies in sample and choice phases appear to be carried by the sample rather than the choice phase. Choice latencies for cocaine preferring rats, saccharin preferring rats, and the indifferent rats are all very similar. In contrast, the sampling latencies for cocaine preferring rats and the indifferent rats are longer. I am not sure why this should be. My reading was that the authors were more concerned with the choice side of the experiment being different, not the sample phase. Is this predicted by the models being tested? I struggled to understand why an SCM-like model would predict the difference being in the sample phase. Either way, the authors could be clearer about where the difference is expected to lie and why the sample phase is so obviously different in some conditions and the choice phase so similar.

Second, the main and real issue for me is whether the differences between response latencies in the sample versus choice phases plausibly reflect operation of different decision making mechanisms (race model versus deliberative processing) or different operation of the same decision-making mechanism. I don't know the answer, but I could not really derive the answer from the data and modelling provided. The authors frame the differences in response time as being uniquely predicted or explained by different forms of choice. The models that the authors are using are closely linked to, and intellectually derived from, models of human choice reaction time. The most successful of these models are the diffusion model (DDM) (Ratcliff, R., Smith, P.L., Brown, S.D., and McKoon, G. (2016). Diffusion Decision Model: Current Issues and History. Trends in Cognitive Sciences 20, 260-281) and the linear ballistic accumulator (LBA) (Brown, S.D., and Heathcote, A. (2008). The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology 57, 153-178.2008).

Even though the DDM and LBA adopt different architectures to each other (but the same architectures as those in Supp Fig 1A), they are intended to explain the same data. Of relevance, the same model (a DDM or an LBA) can explain differences in both the response distribution and the mean response time via changes in the starting point of evidence accumulation, rate of evidence accumulation, and/or the boundary or threshold at which evidence is translated into choice behavior. So, for either a difference accumulator model (DDM) or a race model (LBA), the difference between sampling and choice performance could reflect changes in how the model is operating between these two phases, including a change in the starting point of the decision [bias], a change in rate of accumulation [evidence], a change in threshold [caution] or collapsing boundary scenario, rather than reflecting operation of a completely different decision-making mechanism.

In thinking of a way forward I readily concede I could be wrong and the authors may effectively rebut this point. Another option could be to acknowledge this possibility and discuss it. E.g., does it really matter if it is a qualitatively different decision-making process or different operation of the same decision-making mechanism? I don't really think the action-habit distinction lives or dies by reaction/response time data, this distinction is almost certainly far less absolute than often portrayed in the addiction literature, and it is generally intended as an account of what is learned rather than an account of how that learning is translated into behaviour (even if an S-R mechanism provides an account of both). Response time data tell me, at least, something different about how what has been learned is translated into behaviour. The third, marginally more difficult but more interesting option, would be to explore these issues formally and to move beyond simple descriptive or LDA analyses of response time distributions. The LBA has a full analytical solution and there are reasonable approximations for the DDM. Formal modelling of choice response times (e.g., Bayesian parameter estimation for a race model or DDM) could indicate whether a single decision-making mechanism (LBA or DDM or something else) can explain response times under both sample and choice conditions or not. This is a standard approach in cognitive modelling. This would be compelling if it showed the dissociation the authors argue - i.e. one model cannot be fit to both sample and choice datasets for all animals. However, if one model can be fit to both, then formal modelling would show which decision making parameters change between the sample and choice conditions for cocaine v saccharin v individual animals to putatively cause the differences in response times observed. Either way, more formal modelling would provide a platform towards identification of those specific features of the decision-making mechanisms that are being affected.

Reviewer #1:

The authors report the analysis of a Mga deletion and provide convincing evidence that Mga functions as a tumor suppressor during lung carcinogenesis. The data shown are clear, the message is important and the discussion is very careful. There is a certain overlap with a recent study by Llabata et al., but there is sufficient novelty in the current study.

Comments:

It seems that the investigation of publicly available datasets is essentially identical to the Schaub et al . analysis and not new data. If the authors want to maintain this, they would need to better explain what is new. One important piece of information that seems to be missing is whether the mutations are homozygous or heterozygous. So data on MGA and MYC protein expression in human tumors would greatly strengthen this part.

Conceptually, one would to know whether tumor development in an MGA-delete situation depends on MYC. One would also like to know whether the polycomb complex that is assembled by MGA is tumor-suppressive. Therefore,the authors should perform a similar analysis as they did for MGA (introduce sgRNAs into the lung models) and score the phenotypes they get. Both experiments could be done in cell lines established from this model and either in vitro (that would allow a mechanistic analysis, e.g. RNA seq) or upon re-transplantation. This would also prevent simply reporting negative results.

The interpretation of the VENN diagram and the heatmaps in Figure 5A,B is somewhat uncertain. If one plots these for MYC, occupancy often simply parallels occupancy by RNAPII, so essentially being bound by MYC simply says the promoter is open/active. Is this the case for MGA and its complex partners? Or is there a specificity in binding? The authors should do RNAPII ChipSeqs in these cells, preferentially +/- MGA, and then show these alongside (and plot a correlation between MYC, RNAPII and MGA occupancy).

Along these lines, it is hard to understand how one obtains the extreme p-values shown in figure 5E and 5H, I would challenge this. If the authors want to maintain this, they should not use ENCODe data, but simply determine what genes are active in the cells (e.g. what promoters are bound by RNAPII) and then use those as background list and calculate P-values for overlap between MYC, MAX and E2F6.

Based on the description, the ChIPSeq analyses are not spike-normalized and I could not find information about the number of repeats. If it is n=1, the authors need to find a way to exclude that the differences are due to experimental variation.

I think the Llabata reference is missing in the list.

Reviewer #1:

Bacterial chemotaxis is a well-studied process at many levels, from the chemical networks that control the rotation of the flagella to the fluid dynamics of the motility itself. In the present paper the authors address the widely held view that ligand sensing is responsible only for changing the rotational bias of the motor driving flagellar motion, and not its speed. Using a well-established method of quantifying motor activity by monitoring the rotation of the cell body when the flagella are stuck to a surface, a fluorescent labelling technique to determine the membrane potential, a mutant with fluorescently labelled stator units, and direct measurements of swimming speed, the authors show that the sensing of a non-metabolizable analogue of glucose leads to a momentary increase in motor speed and stator unit numbers. At the same time, control experiments make it clear that this is purely as a consequence of ligand sensing. This behaviour is indeed contrary to the accepted view, and although the fundamental mechanism is as yet unclear, this is an important result.

On the whole I am very supportive of this work, which has been done with great care and clear logic. My only suggestion for improvement would be to make quantitative the changes in chemotactic behaviour that would be expected as a consequence of the motor speed changes revealed in this research. That is, can the authors put some numbers into a standard analysis of run-and-tumble dynamics to quantify any improvement in chemotactic efficiency or speed under such changes?

Reviewer #1:

The current study by Ohara et al. describes differences in the connectivity patterns between LVb to LVa. The study builds on the authors previous study (Ohara et al., 2018) where they showed the intrinsic connectivity of LVb neurons in the MEC and LEC. The focus of the current study is the difference the authors observed in the strengths of connectivity between LVb and LVa in the MEC and LEC. The authors suggest that the in MEC Vb neurons do not provide substantial direct input to LVa neurons. The manuscript emphasizes the functional importance of difference as the authors suggest that "...hippocampal -cortex output circuit is present only in LEC, suggesting that episodic systems consolidation predominantly uses LEC-derived information and not allocentric spatial information from MEC." The study uses a newly developed mouse line to investigate connectivity differences, this is a nice technical approach and the experimental data is of high quality. While the data is solid, the authors tend to over-interpret their findings from the functional point of view. While the observed difference is quite interesting, it is unclear what the impact is on information flow in the MEC and LEC and to which degree they differ functionally. The authors assume major differences and their work is framed based on these expected differences, but the manuscript does not provide data that would demonstrate functionally distinct features.

Major Comments:

1) Throughout the text the authors treat their findings as if it was 'all-or-none' i.e the LEC has a direct connection between LVb and LVa while the MEC does not. This does not seem to be the case based on their data, the data shows that connectivity in the MEC is less robust but it is definitely there. The difference seems to be quantitative and not qualitative.

2) Due to this problem, the authors seem to be over-interpreting their data by suggesting that the information flow must be significantly different conceptually in the LEC and the MEC and this would have important implications for memory consolidation. It is impossible to draw these conclusions based on the data presented, as there are no experiments investigating the functional, network level consequences of these connectivity differences.

3) The electrophysiology experiments provide information about the basic parameters of the investigated cells, but these lack a physiological context that would allow the authors to evaluate the consequences of these differences on information flow and/or processing in the MEC and the LEC.

4) The study is using a novel transgenic mouse line to differentiate between LVb and LVa neurons, while this is definitely a strength of the study, this strategy allows the authors to visualize ~50% of LEC and ~30% MEC neurons. Since the authors aim to prove a negative (MEC does not have direct connection) the fact that ~70% of the neurons are not labelled could be problematic.

Reviewer #1:

In the article "Glypicans specifically regulate Hedgehog signalling through their interaction with Ihog in cytonemes" Simon et al. elucidate the function of Glypicans in Hh transport via cytonemes. The manuscript describes convincingly that the fly glypicans Dally and Dally-like are required to maintain the expression of the Hh co-receptor Ihog. Ihog - in turns - stabilises Hh cytonemes through its interaction with Glypicans to establish the Hh gradient in the wing imaginal disc. The authors further carried out an extensive molecular analysis of Ihog and identified the relevant domains within the protein required for interactions with Glypicans, Patched, and Hh. In general, this is a very thorough, detailed analysis of Ihog function. The images and videos are excellent. However, prior publication, there are two major criticisms, which needs to be addressed, in my opinion.

Firstly, the first part of the manuscript, the molecular analysis of Ihog (Fig.1-4) seems to be detached from the second cytoneme-focussed part (Fig. 5, 6). Independent evidence is needed to show support for the idea that the Ihog-Gly mediated stabilisation of cytonemes is responsible for the expansion of the signalling gradient. Are the static cytonemes involved in a flattened gradient or are the receiving cells just sensitised for Hh? Can cytonemes be (de-) stabilised w/o interfering with Hh components to untangle these observations? The authors write "Intriguingly, the same Ihog domains that regulate cytoneme dynamics are those also involved in the recruitment of Hh ligand, glypicans and the reception complex."

My concern is that cytoneme dynamics and Hh gradient formation could be two parallel, independent events -> one needs to show this interdependency in a clear way. I could imagine an analysis of the consequences when Ihog is overexpressed, and cytoneme formation is inhibited (by other means). Consistently, could one stabilise cytonemes in an Ihog-reduced background and analyse gradient formation?

Secondly, the authors demonstrate an effect of Ihog alterations on the formation of the gradient. However, what is the physiological relevance? What are the consequences of Ihog/Gly-mediated cytoneme stabilisation and gradient formation on tissue patterning and wing formation? If this is not possible to show experimentally, this needs to be discussed.

Reviewer #1:

The authors present a novel framework for running CPM simulations in the web browser. The CPM framework is a well-established model methodology for cells and tissues. Several well established other simulation platforms exist, however they do not run in the web browser, and require varying amounts of setup. This often presents an insurmountable roadblock since many researchers do not have the required software packages or expertise to read, execute, and run models in different formats. Artistoo on the other hand promises a zero-install experience for end-users, and ease of model construction for modellers.

The unique feature of Artistoo is that it runs in the web browser. This allows users to execute simulations in a zero-install setting. In the web browser users can change model parameters, and observe resulting effects instantly. Extending or modifying models requires the user to know JS. Artistoo implements core modern CPM features. Artistoo is successfully benchmarked against the existing software of Morpheus. The source code is available on github. A wiki with an apparent complete and extensive documentation is available.

The authors argue for three main avenues of impact: (1) accelerated feedback loops on models with experimental collaborators, (2) science communication, and (3) in teaching.

The authors' points have merit, point (1) in particular. Installation and execution of tissue modelling software by non-experts is a well known challenge. Artistoo elegantly avoids this issue, by allowing models to be shared via the web browser. The non-expert is able to gain insights into model dynamics, and can explore the model's parameter space at ease. This approach has the potential of stimulating more frequent feedback between experimentalists and modellers, and maybe even the adoption of such a model by experimentalists.

There is no markup language support. The software package Morpheus describes simulations using a markup language, allowing non-expert users to assemble complex models without writing a single line of C++, while at the same time preserving exact details of each simulation run. Morpheus is (as far as I know) the only based on a markup language. It would be fantastic if Artistoo could read and execute Morpheus ML files. From a technical point of view this should be possible. This would mean, all `Morpheus' models become "Artistoo' models, meaning that Artistoo would become the standard for sharing CPM models with collaborators. Finally the markup language would allow novices to implement new models without being discouraged by the JS requirement. Adopting a common markup language between projects would be the first example of standardization across open-source CPM software packages.

I can see Artistoo being adopted by ``CPM modellers', who want to share models with collaborators, a wider audience (science communication). It may also find adoption in teaching. At the same time, the adoption of Artistoo faces some challenges: (1) Among modellers existing platforms have more features, are familiar, and have similar computational efficiency; (2) existing models are to be rewritten in the Artistoo framework.

Reviewer #1:

The authors devised clinical criteria for identifying Zebrafish larvae with high or low T. cassari infections in order to track. Using transgenic fish line marking macrophages and neutrophils, the authors showed that both groups of larvae increase macrophage (and to lesser extent neutrophil) levels in response to infection. However, the macrophages in high parasitaemia animals migrated into the capillaries and had elevated levels of inflammatory markers (TNF, IL-1) and lipids, indicative of a foamy phenotype. The authors conclude that a measured inflammatory response allows animals to control the initial infection, while an exaggerated inflammatory response leads to an environment in which the bloodstream trypanosomes can proliferate. The findings support and extend data from murine models of infection, by allowing direct visualization of host immune response.

Reviewer #1:

These investigators examine how lactic acid producing E. coli impact age-related decline in neurological function through the use of temperature-food associative learning or thermotaxis. In particular, they screen a panel of different lactate producing E. coli and identify a particular clade of bacteria, Lactobacilli, that are able to suppress age-dependent decline in thermotaxis in a daf-16 dependent manner. Moreover, they uncouple improvement in neurological function from lifespan determination and locomotion. Overall, this group presents an interesting phenomenon regarding the effects of the lactic acid producing bacteria. However, it is not clear what is happening in the worm to elicit this neurological response and much work remains to determine this mechanism of action.

While I can appreciate the careful nature of these worm behavioral assays including a host of different controls, these studies lack cellular and molecular details, which reduce my overall excitement for the story. It is interesting that a clade of lactic acid bacteria (LAB) can improve associative learning in C. elegans. However, I was very underwhelmed when I got to the final figure, which very briefly touched on molecular mechanism (only to give DAF-16 dependence). Since it has previously been shown that daf-16 mutant animals impact taste avoidance learning (Nagashima et al. PLOS Genetics, 2019), the dependence of DAF-16 and its role in associative learning seemed predictable. For future submissions, this previous study on DAF-16 should be referenced in the manuscript. Moreover, data regarding dietary restriction and the eat-2 mutation appear to be misinterpreted. Thus, more attention and analysis should be dedicated to the effects of dietary restriction on their paradigm. I thought that it was interesting that a clade of LAB consistently reduced expression of PHA-4 transcription factor and the authors might benefit for expanding upon this observation.

In addition to molecular characterization, the manuscript provides little explanation at the cellular level. It is unclear what neurons or neuronal circuit are responsible for this phenomenon. Although mentioned in the discussion, this manuscript would benefit by close examination of the thermosensory circuit including the AFD and AIY neurons. How are these lactic acid producing E. coli ultimately signaling to the neurons? Do the LAB slow the rate of degeneration of either neuron? Is this phenomenon the result of lactic acid production or something else in the bacteria? Would it be possible to supplement lactic acid to worm media and produce the same result?

This is an interesting phenomenon and requires more in-depth cellular and molecular characterization.

Reviewer #1:

This manuscript describes a longitudinal study of the adolescent structural connectome. The authors find strong effects of expansion of structural connectomes in transmodal brain regions during adolescence. They also report findings centered on the caudate and thalamus, and supplement the structural connectivity analyses with transcriptome association analyses revealing genes enriched in specific brain regions. Finally, intelligence measures are predicted from baseline structural measures. This is an interesting and comprehensive set of analyses on an important topic. Overall, the figures are lovely. The sensitivity analyses are particularly commendable. Some suggestions and points for clarification are below.

There is not much in the introduction about why co-localized gene sets are of interest to explore. What is already known about brain development using this approach, and how does the current work fill a gap in our knowledge?

Similarly, the introduction states that the study aims to "predict future measures of cognitive function". What cognitive functions specifically were of interest in this study, and why? No rationale or background is provided for conducting these analyses.

The authors claim that their study examines "the entire adolescent time period", however some would argue that age 14 does not represent the earliest age at which adolescence onsets. I think it would be more accurate to say the study covers the mid to late adolescent period.

In the results (page 4) it is stated that three eigenvector explained approximately 50% of the variance in the template affinity matrix. Here it would be helpful to report exactly how much of the variance was explained by each (E1, E2, E3).

Pubertal development occurs across the age range investigated, and affects brain structure and function. Was information on pubertal stage of participants available? Did some participants undergo changes in pubertal status from timepoint 1 to timepoint 2?

The introduction does not mention cortical thickness much, therefore these analyses come as a bit of surprise in the results.

As in the introduction, there is not much interpretation of the transcriptome findings in the discussion.

For constructing the structural connectome, the Schaefer 7-network atlas was utilized. Can the authors comment on why a functional atlas (rather than a structural atlas) was used here?

Reviewer #1:

The authors provide interesting evidence on the properties of CSF-contacting neurons, referred to as 'CSFcNs' in their manuscript, using 2 photon calcium imaging in mice.

Their work relies on calcium imaging using 2 photon microscopy in slices of the mouse spinal cord. The authors observed calcium transients with two different amplitudes and propose that these transients reflect the activation of different voltage dependent calcium channels (T and L).

Although the work is of interest, there are throughout the manuscript numerous issues: -shortcuts and oversimplified assumptions (calcium transients do not equal spikes!) (see title of Figure 2, 3) -the massive ignorance of the relevant literature for this small field on CSF-cNs in mice. In particular, but not only, the authors should know and refer to the work of Orts Dell'Immagine, Wanaverbecq, Trouslard who have shown since 2012 that CSF-cN in mice are chemosensory cells whose spontaneous activity is driven by the channel PKD2L1.

Major comments

1) The authors assume that calcium transients equal to firing (Figure 2) or calcium spikes (Figure 3) but these are far from being the same. No deconvolution algorithm can use calcium transients to infer spiking with better than 70% accuracy.

In the recordings of the Wanaverbecq group, spontaneous firing in slices was 0.4Hz in control and 0.1Hz in PKD2L1 KO. The authors find here calcium transients occurring at 0.16Hz (n = 63 cells), suggesting that some of the sparse firing activity is missed by the authors.

Since calcium transients reflect spiking but not in a linear manner, a calibration is necessary via cell attached or loose patch recordings in order to infer on CSF-cN spiking, or perforated patch to validate the evidence for calcium spikes.

2) This assumption of calcium = firing does not hold in cells that have an input resistance of GOhms and whose activity has been shown to be driven by the opening of the channel PKD2L1 (Orts Del Immagine et al Neuropharmacology 2016). In particular, observations of the TTX insensitive calcium transients may be due to the PKD2L1 channel.

=> The authors need to combine recordings with perforated Patch Clamp together with the 2P calcium imaging in order to tackle the question of the role of the channel openings in the generation of the different calcium transients observed in WT or KO for PKD2L1.

From introduction to discussion, the authors should properly cite the work of the Wanaverbecq group as well as other groups in the field, whose contributions were relevant and ignored.

3) Activation leading to calcium spikes (K+, ATPergic, Cholinergic inputs, ...) was done without blockage of the neurotransmission in the slices and could therefore originate from indirect sources, including activation of metabotropic receptors presynaptically.

The authors need to solve these issues.

4) In Figure 7, there are diverse responses that the authors should better illustrate. Many cells appear to not respond for multiple stimuli tested : what is the rational criterion to define that a cell responded or not? Can the authors quantify the proportion of cells responding? Did the author take into account the high level of spontaneous activity? Can the negative dip in response possibly from a motion artifact in panel G and H?

Reviewer #1:

I'm quite enthusiastic about the care the authors have taken in designing this cutting edge hybrid environment, and the effort they've gone through to describe it in detail. I believe that this endeavor has great merit, and that seeing the advancements in animal welfare and experimentation should be of interest to the general reader. However, at present, the stated interpretations are not fully justified by the results, and this must be addressed.

The manuscript should be amended and updated in one of two possible ways: the interpretations of the scientific result here should be tamped down significantly, or additional evidence should be presented for some of the claims in the originally submitted manuscript. I am confident that the authors should be able to carry out either of these to a satisfying degreen.

Major issues:

1) Throughout the manuscript, stating that the third monkey learned the task "merely by observing two other trained monkeys" is misleading. The naive monkey may have learned very important details about the cognitive testing set-up from observation. But the third monkey learned the task of a unique behavioural shaping paradigm that included -but was not limited to- watching trained monkeys. The authors trained the third monkey on the cognitive task in the absence of the other monkeys, and do not show that the third monkey learned the specific cognitive task from watching other monkeys. Over-interpreting the anecdotal observations here hinders obfuscates what is novel and notable in this manuscript.

2) The authors repeatedly state that the third monkey learned the task faster than the previous two monkeys. It is quite difficult to parse exactly what the authors mean by this, and exactly what the data is that supports that claim.

The authors go on to state that M2 learned the "task structure" faster than M1/M3. However, "task structure" is not defined, so it is difficult for a reader to know precisely what was learned faster under social observation. Furthermore, the data showing that M2 learned the task structure faster than M1/M3 is not clear, and it is not known how M1/M3 learned the task structure in isolation. Description of which training steps may be aided by observation of trained monkeys must be clarified. The authors allowed M2 to observe M1 and M3 during initial familiarization of the experimental set-up, but it seems that observation may not have aided M2 in learning the complex same-different task at all.

Even though M2 may have learned the task structure faster than M1/M3, these observations are anecdotal and should not be over-interpreted. If there is a clear difference in the time to learn basic task structure, it may be due to social observation, but the authors should not favor that interpretation without considering alternatives as well. E.g., monkeys have widely varying personalities (see e.g. Capitanio 1999, Am J Primatology), and this has important implications for the curiosity, exploration behavior, and likelihood to accept and complete new challenges in training. To what extent could the differences in learning rate also be explained by these differences across these 3 monkeys? To what extent does the different training regimen in the task explain differences in learning rate across monkeys (e.g. M2 got two days of repeating correction trials, which significantly alters learning rates)?

3) There is a vast literature in ethological settings where the gaze of nonhuman primates has been tracked using noninvasive methods that the authors do not acknowledge. Instead, authors state that most infrared eye trackers require head restraint (line 32), though this is demonstrably not the case. For review, see Hopper et al. 2020, Behav Res Methods.

4) Some important details for introducing monkeys to the testing apparatus during Tailored Automated Training should be described. For example, were animals water-restricted, or on any sort of fluid restriction when TAT began? How did the authors entice the animals to initially explore the testing apparatus?

Reviewer #1:

General Assessment:

The work presented by Knight et al. in "Major genetic discontinuity and novel toxigenic species in Clostridioides difficile taxonomy" is of excellent quality and spans several of the themes of eLife. The manuscript provides a thorough and robust examination of publicly available C. difficile genomes, to deliver a much-needed update of C. difficile phylogeny, in particular the cryptic clades of C. difficile. However, there are some further clarifications could be included to confirm if the cryptic clades of C. difficile, and the 26 unclassified STs (which seemingly form 4 distinct clusters) should indeed be assigned to the Clostridioides genus, distinct from both C. mangenotii and C. difficile.

Specific comments:

Lines 96-97 and Figure 2: Figure 2 suggests the 26 unclassified STs form at least 4 distinct clusters, yet these STs are classified as outliers. Could you please comment on why these are considered outliers? Or do these STs represent new cryptic clades? C-IV, C-V etc.? And do these unclassified STs also fit into the criteria for the novel independent Clostridioides genomospecies?

Lines 161-162; Table 1: C. mangenotii is referred to as Clostridioides mangenotii on lines 161-162, but has been listed as Clostridium mangenotii in table 1. Was this intentional? Or should this be Clostridioides mangenotii as C. difficile is also listed as Clostridioides difficile?

Figure 6: Many of the numbers and symbols on the figure are difficult to see e.g. Figure 6A the values listed above each data point are extremely small. Can these values/symbols be increased?

Lines 224-225: Given that C. difficile strains lacking tcdA and tcdB can still cause infections, consider rephrasing "indicating their ability to cause CDI".

Figure 7: As with Figure 6, many of the numbers and symbols on the figure are difficult to see. Can these values/symbols be increased?

General comments:

Were the unclassified STs included in the species wide ANI analyses in Figure 3? If similar analyses were performed for these STs and given the clusters that are presented in Figure 2 would this support the idea that they may also fit into the criteria for the novel independent Clostridioides genomospecies?

Similarly, were these same unclassified STs included in the BactDating and BEAST analyses? Or the pairwise ANI and 16S rRNA value comparisons in Figure 5? Or the pangenome and toxin gene analysis also presented in Figures 6 and 7? And would this add further strength to the idea that these "outliers" could be the first typed representatives of additional genomospecies?

Lastly, your conclusions are a little too on the fence. You have presented sufficient evidence to suggest that the cryptic clades of C. difficile likely represent novel independent Clostridioides genomospecies, but dilute out the importance of this throughout the discussion and conclusions. Although controversial, the evidence provided gives credence to these claims, and the text should be changed to reflect this.

Reviewer #1:

In the study by Buus et al., the authors set out to address an important need to understand how oligo-conjugated antibodies should be optimally utilized in droplet-based scRNA-seq studies. These techniques, often referred to as CITE-seq, complement techniques such as flow cytometry and mass cytometry yet also further extend them by the ability to jointly measure intra-cellular RNA-based cell states together with antibody-based measurements. As is the case with flow cytometry, manufacturers provide staining recommendations, yet encourage users to titrate antibodies on their specific samples in order to derive a final staining panel. Based on the ability to stain with hundreds of antibodies jointly, few studies to date have assessed how the antibodies present in these pre-made staining panels respond to a standard titration curve. In order to address this point, this study tests two dilution factors, staining volume, cell count, and tissue of origin to understand the relationships between signal and background for a commercially available antibody panel. They arrive at the general recommendation that these panels could be improved, grouping various antibodies into distinct categories.

This study is of general interest to the scRNA-seq and CITE-seq communities as it draws attention to this important aspect of CITE-seq panel design. However, it would stand to be substantially improved by not only providing suggestions but also testing at least one, if not more, of their suggestions from Supplementary Table 2, and preferably performing experiments using more technical replicates or biological replicates. As it stands now, the study is largely based on one PBMC and one lung sample, that were stained once with each manipulation as far as can be gathered from the Methods.

Major comments:

1) Given the title is improving oligo-conjugated antibody... it would be important to functionally test one of the suggestions. We would suggest a full titration curve of selected antibodies, perhaps one from each of the categories, but if cost is a concern at least two or three antibodies, to identify how titration impacts antibodies, and especially those in categories labeled as in need of improvement. Relatedly, if the idea is that if antibodies (such as gD-TCR) do not have a cognate receptor leading to general background spread, does spiking in a cell that is a known positive in increasing ratios remedy this issue by acting as a target for the antibodies? Does adding extra washes help to remedy these issues of background?

2) Another way of improving these panels is through reducing the costs spent on both staining but perhaps more importantly the sequencing-based readouts. Several times in the manuscript (at line 77 for example or line 277) it is alluded to that the background signal of antibodies can make up a substantial cost of sequencing these libraries. However, no formal data on cost is presented, which would be important to formalize the author's points. It would be important to provide cost calculations and recommendations on sequencing depth of ADT libraries based on variation of staining concentration. Relatedly, in the methods, sequencing platform and read depth for ADT libraries was not discussed, nor is the RNA-seq quality control metrics provided other than a mention of ~5,000 reads/cell targeted. This is important to report in all transcriptomic studies, and especially a methods development study.

3) One of the powerful elements of joint multi-modal profiling, as mentioned in the title, is to be able to measure protein and RNA from a single cell. This study does not formally look at correlation of protein and RNA levels, and whether a decrease in concentration of antibody either improves or diminishes this correlation. This would be important to test within this study to ensure that decreasing antibody levels does not then adversely affect the power of correlating protein with RNA, and whether it may even improve it.

4) How was the lack of antibody binding determined for Category E? CD56 is frequently detected on NK cells in peripheral blood, CD117 should be detected on mast cells in the lung, and CD127 should be found on T cells, particularly CD8+ T cells. From inspecting Figure 1E, it appears as if all three of these markers are detected on small but consistent cell subsets. As the clusters are only numbered and no supplementary table is provided to help the reader in their interpretation, it is difficult to determine if these represent rare but specific binding, or have not bound with any specificity.

5) References: At 14 references, the paper overall could benefit from a more comprehensive citation of related literature including flow cytometry and/or CyTOF best practices for antibody staining and dealing with background, and joint RNA and protein measurement from single cells.

Reviewer #2:

In this paper, Numssen and co-workers focus on the functional differences between hemispheres to investigate the "domain-role" of IPL in different types of mental processes. They employ multivariate pattern-learning algorithms to assess the specific involvement of two IPL subregions in three tasks: an attentional task (Attention), a semantic task (Semantics) and a social task (Social cognition). The authors describe how, when involved in different tasks, each right and left IPL subregion recruits a different pattern of connected areas.

The employed tasks are "well established", and the results confirm previous findings. However, the novelty of the paper lies in the fact that the authors use these results as a tool to observe IPL activity when involved in different domains of cognition.

The methodology is sound, well explained in the method section, the analyses are appropriate, and the results clear and well explained in the text and in graphic format.

However, a solid experimental design is required to provide strong results. To the reviewer's view, the employed design can provide interesting results about functional connectivity, but not about the functional role of IPL in the investigated functions.

I think the study would be correct and much more interesting if only based on functional connectivity data. Note that rewriting the paper accordingly would lead to a thorough discussion about how anatomical circuits are differently recruited based on different cognitive demands and about the variable role of cortical regions in functional tasks. This issue is neglected in the present discussion, and this concept is in disagreement with the main results, suggesting (probably beyond the intention of the authors) that different parts of the right and left IPL are the areas responsible for the studied functions.

Major points:

1) The 3 chosen tasks explore functions that are widespread in the brain, and are not specifically aimed at investigating IPL. The results (see. e.g. fig 1) confirm this idea, but the authors specifically focus on IPL. This seems a rather arbitrary and not justified choice. If they want to explore the lateralization issue, they should consider the whole set of involved areas or use tasks showing all their maximal activation in IPL.

2) The authors aims to study lateralization using an attentional task, considering the violation of a prevision (invalid>valid), a linguistic task, looking for an activation related to word identification (word>pseudoword) and a social task, considering correct perspective taking (false belief>true belief), but they do not consider that in all cases a movement (key press) is required. It is well known that IPL is a key area also for creating motor commands and guiding movements. Accordingly, the lateralization bias observed could be due more to the unbalance between effectors while issuing the motor command, than to a different involvement of IPL regions in the specific tasks functions.

3) Like point 2, the position of keys is also crucial if the authors want to explore lateralization. This is especially important if one considers that IPL plays a major role in spatial attention (e.g. Neglect syndrome). In the Methods, the authors simply say "Button assignments were randomized across subjects and kept identical across sessions", this should be explained in more detail.

4) The authors show to know well the anatomical complexity of IPL, however their results are referred to two large-multiareal-regions. This seems to the reader at odds with all the descriptions related to fig.2. If they don't find any more subtle distinction within these 2 macro-regions, they should at least discuss this discrepancy.

5) The part about Task-specific network connectivity is indeed very interesting, I would suggest to the authors to focus exclusively on this part. (Note that the results of this part seems to confirm that only the linguistic task is able to show a clear lateralization).

Reviewer #1:

The authors have performed a rare feat in the study of the posterior parietal cortex, which is to achieve a functional parcellation of this crucial area on the basis of its response during a diverse set of tasks. The variety of tasks and the analytical approach married to it are very strong and lead to a division that agrees well with data from patients with lesions and studies in homologous areas of non-human primates.

Readers are encouraged to note the analytical approach, with particular regard for the permutation testing that establishes the differences between the tasks in the functional connectivity of the area.

Conceptually, this paper is another strong argument for understanding the broad role of the posterior parietal across tasks and point at the flexibility of its functional response in supporting those roles.

This manuscript lays out a series of fMRI investigations and analyses centered on examining the response of the IPL during three different tasks (attention, semantics, social cognition). The analyses are largely data-driven and examine functional response and connectivity, to make the argument for a functional parcellation of the IPL into at least two distinct subregions. The manuscript is well-written and the analyses well described. There are some concerns about the analyses that dampen enthusiasm slightly and a lack of consideration of the associated literature in non-human primates, but these problems seem imminently correctable.

The analyses begin with a data-driven cluster analysis across an anatomically constrained IPL ROI, searching for cluster solutions that efficiently parcellate IPL on the basis of the response of voxels across the three tasks. This analysis is fine, but does constrain the average activity in the identified clusters to differ across the tasks. That makes the univariate activation in 3b a bit circular and hard to interpret. Either the error bars should be removed and a note added that the univariate activity is purely descriptive or the univariate data should be displayed from a slice of the data that did not contribute to the derivation of the clusters. The strongest version of this analysis would hold out entire participants.

The predictive coding analysis is potentially informative but the details were a bit unclear. In the one versus rest analysis the strongest test would be to build the model on the data from n-1 participants and then test it on the trials of the held-out participant. If this was not done, some justification for not doing it would be in order.

Finally, the authors should also consider integrating some of the non-human primate literature as it only strengthens their case. In the human literature the IPL has proved a tough nut to crack, but the single unit physiology has revealed strong differences in the homologous areas of macaque, some of which directly map onto the division argued for here.

Reviewer #1:

Xiao et al describes a kinetic model of enhance-promoter interactions, which the authors use to explain the changes in transcription levels upon disruption of genomic contacts within topologically associated domains (TADs). The model uses the law of mass action to describe activity of promoters and enhancers, which are proposed to be able to accommodate multiple transcription activation tags. The authors use the model to explain the nonlinear relationship between the genomic contact frequencies within TADs and their corresponding transcription rates. They recapitulate the superlinear relationship between the changes in genomic contact probabilities and transcription rates within TADs observed in their recent experiments (Mateo et al, 2019). Inspired by the futile cycle of cell signaling, their model incorporates multiple tagging of promoters allowing for transient amplification of transcription rates.

Conceptually, this work is interesting and the model suggests possible reconciliation of seemingly contradictory experimental observations reported earlier.

However, the manuscript in its current form fails to substantiate many of its claims.

Here are my major concerns:

1) The presentation of the model is unclear. It is currently present in the text, lines 110-122, in pure qualitative description. Authors define only rates in the text; definitions of other model parameters are not present. For example, E and a are not specifically defined in the text or Methods section. Since both terms "enzyme" and "enhancer" are being used and in fact "enzyme tagging" and "enhancer tagging" occur simultaneously in the model, it is not possible to say for sure when do authors call which one in the model and thus the methods section can be interpreted in different ways. Moreover, the cartoon is missing a legend confirming, which molecular player is which. The figure caption mentions only green triangles being the tags, but no other parts of the cartoon are being explained. Taken together, this makes it very difficult to verify the mechanics of the model.

• The authors should provide a detailed technical description of their model directly in the text, including description of their parameters, list their constitutive equations and identify all parameters in their cartoon Fig. 1C.
• Axes labels in all figures should be expressed in the parameters/variables of the model (as in Fig. 6C-D) directly connecting to inputs/outputs of the model.

2) Due to the lack of description, in many sections it is not clear what are the specific inputs and outputs of the model (e.g. Fig. 2).

3) The Methods section describes the chemical kinetics of the suggested reactions and the insulation score calculations. But it is not clear how do these inform each other, how are contact-frequency maps chosen/computed and cross-referenced with the local E-P kinetics?

4) In the Methods section, it appears that in lines 577-580 of the model description, the mass is not conserved.

5) In 587-588, the index of k is 2(n+1), which equals to 2n+2, but then in the next line the following assumption is made 2n+1 → n+1

6) The authors make assumptions that their kinetic considerations hold for n>2. What is the evidence?

7) The authors observe hysteresis in median transcription rate as a function of enhancer contact frequency. However, the presented violin plots suggest a presence of two states, one with low and one with high transcription rates. In the intermediate regime of enhancer contact frequency, where authors report hysteresis, the violin plots show bimodal distributions suggesting coexistence of these two states. This would suggest that the system exists in and switches between two distinct states with a discontinuous transition, instead of a continuous hysteretic behavior as suggested by the median behavior.

8) The language of the paper is often not technically precise with qualifiers missing, which could lead to ambiguities and misinterpretations. Here are some examples:

• *p. 1, line 10, "difference in contact across TAD borders is usually less than twofold"
• *p. 1, line 17, "results from recent cohesion disruption"
• *p. 2, line 71, "A simple model of hypersensitivity to changes in contact frequency"

9) On p. 13, line 483, authors define Ostwald ripening as given by weak multivalent interactions; however, Ostwald ripening is a thermodynamic process. In addition, they propose that liquid condensates become larger due to Ostwald ripening, but there are also other processes that may occur, such as coalescence of condensates, which would also lead to larger condensates.

10) At the beginning of the Discussion section authors state they will propose future experiments in each section. However, in some of the sections it is not clear what specifically authors are proposing. These suggestions should be made clearer.

Reviewer #1:

This paper investigates the modulation of spatial signals in higher order visual areas. A number of the findings are novel and interesting, including that signals in higher visual areas are not more influenced by spatial position that signals in V1, that this modulation is not a general feature of the entire visual circuit (i.e. LGN boutons in L4 of V1, as well as LGN units, show very little spatial modulation, and that spatial modulation decreases when mice are watching a replay of tunnel traversals. Overall, I think this paper provides new insight regarding position coding in visual systems. However, there are some points that should be addressed.

1) The imaging data is from mice with different genetic backgrounds, as well as a mixture of gcamp6f and 6s. In addition, different reward protocols were used for different mice. Although the authors state in the methods that none of these factors impact their results, it would be good to include some quantifications to this effect (e.g. they could show the distribution of SMI for 6f data vs 6s data). While I don't expect the major observations to change if it turns out that some of these factors have as systematic effect, it could affect portions of the results where the dataset is split up - for example in the comparison between different higher visual areas, and the observation that spatial modulation appears to vary with receptive field location.

2) The authors state that it is to be expected that LGN neurons respond more strongly in the first half of the corridor due to contrast adaption mechanisms. However, I did not see any quantification that could support this statement?

3) When looking at the spatial modulation index, the authors switch between using median (e.g. Fig 1 and 2) and mean (Fig 4), t-test and rank-sum - and sometimes there is missing information regarding which (mean or median) they are reporting. The authors need to include more detail regarding these statistics.

4) It was not clear to me if the authors are only imaging from layer 2/3 or if they also attempted to image deeper layers.

5) Throughout the paper, the authors use 'firing rate' to refer to deconvolved calcium signal. Although this is stated in the methods, this wording can be misleading, especially since the paper also contains extracellular recordings of spiking activity.

6) It was not clear to me how the dotted lines (e.g. Fig 1 b) were calculated.

Reviewer #1:

This manuscript reports the results of two timing experiments. The experimental paradigm asks participants to judge the time of target items in an unfilled interval between two landmark stimuli. In experiment 1, there is one item that must be judged. In experiment 2, there are two items to be judged. The basic empirical result is that relative order judgments in experiment 2 are more accurate than one might expect from the absolute timing judgments of experiment 1. A model is presented.

My overall reaction is that this paper does not present a sufficiently noteworthy empirical result. I can't imagine that there is a cognitive psychologist studying memory who would be surprised by the finding that relative order judgments in the second experiment are more accurate than one might expect from the absolute judgments in experiment 1. On the encoding side, in these really short lists (with no secondary task), there is nothing preventing the participant from noting and encoding the order as the items are presented (not unlike the recursive reminding). On the retrieval side, we've known for a very long time that judgments of serial position use temporal landmarks (see for instance a series of remarkable studies by Hintzman and colleagues circa 1970).

Methodologically, this paper falls short of the standards one would expect for a cognitive psychology paper. There are basically no statistics or description of the distribution of the effect across participants. Although I'm pretty well-convinced that the basic finding (distributions in experiment 2 are different from experiment 1), I could not begin to guess at an effect size. The model is not seriously evaluated. The bimodal distributions are a large qualitative discrepancy that is not really discussed.

Although the title of the paper invites us to understand these results as telling us something about episodic memory, the empirical burden of this claim is not carried. Amnesia patients (and animals with hippocampal lesions) show relatively subtle differences in timing tasks. There is no evidence presented here, nor literature review, to convince the reader of this point.

Reviewer #1:

This study examines MEG activity in a picture categorization task (decide living or non-living) in a sample of 18 patients with semantic variant PPA, compared to 18 controls. As svPPA is a rare (but scientifically informative) disorder, the sample size is impressive, and given that relatively few MEG studies exist in PPA at all, this is an interesting dataset. The authors show differences in engagement of oscillatory activity, specifically increased low-gamma ERS in occipital cortex and increased beta ERD in the superior temporal gyrus. The authors interpret this as reflecting increased engagement of / reliance on early perceptual mechanisms for completing the task, as opposed to semantic identification of the picture.

Major concerns:

1) My biggest methodological issue with this paper relates to a very old debate in neuroimaging that still comes up all the time: the choice of statistical threshold. Using a high threshold prevents false positives, but may also lead to false negatives, and I fear that is the case here, with the high threshold contributing to an unrealistic impression of spatial specificity in MEG. It is obvious from the average responses in both groups that these oscillatory responses are widespread through the brain. Indeed the alpha and beta responses are significant in the majority of cortical voxels. This basic property of the responses should be presented clearly and prominently in the paper - I don't think it's appropriate to put it in supplementary information where only a minority of readers will even see it. The authors then use what I think is an extremely high and conservative statistical threshold to contrast differences between the two groups. P<.005 uncorrected is a highly conservative threshold already, even before cluster-thresholding is added (although with data as smooth as MEG beamforming solutions, cluster-thresholding is unlikely to change anything). Basically this makes the only the strongest part of the activation survive, and it is valid to conclude that a significant group difference exists there (protected from Type 1 error), but this can give a false impression of the difference is specific to that region. I think a more realistic characterization of the results would involve measuring differences in the strength of the responses between groups on a broader level, possibly the sensors or in large ROIs - and not ROIs pre-selected to show a dramatic difference by first searching the whole brain for the most significant effects - that is the classic "double-dipping" fallacy in neuroimaging.

2) Similarly, the ERD/ERS in each frequency band is treated as a separate entity, ignoring the fact that these bands are arbitrary and frequency is a continuous quantity. This matters because much is made of the fact that PPA participants exhibited greater ERS in the low-gamma range, and that this was correlated with reaction time. Supplementary figure 1 shows that both groups had strong occipital ERS in the high-gamma range, but only PPA showed it in the low gamma range as well. This suggests that the ERS in the PPA group may simply have been shifted to a lower frequency range. A more fulsome characterization of these group differences via time-frequency analysis and/or power spectral analysis would help clarify what is going on here.

3) It is surprising that PPA participants only exhibited increased MEG responses compared to controls - assuming that both gamma ERS and beta ERD can be interpreted as increased neural activation, which is a reasonable assumption based on the literature. No decreases in the PPA group are found, and thus the observed increases can be plausibly attributed to compensatory processes as framed by the authors. However, I am concerned about the role of certain analysis choices in producing this data pattern. In particular, the authors state (line 611): "To remove potential artifacts due to neurodegeneration or eye movement (lacking electrooculograms), we masked statistical maps using patients' ATL atrophy maps (see section MRI protocol and analyses), as well as a ventromedial frontal mask."

It is not clear whether this masking was done in group space from average atrophy maps, or on an individual level. In either case, I don't think this is well justified. I don't know any physical mechanism by which tissue undergoing neurodegeneration can be said to generate an artifactual signal. Atrophied tissue still contains living neurons with ionic currents; these are real signals not artifacts, and furthermore, atrophy is a continuous process with tissue further from the epicenter also undergoing similar neurodegenerative mechanisms. Atrophied tissue may well generate electromagnetic signals that are different from healthy tissue, and such differences should be included in this paper. I think that there may be regions of hypoactivation as well as hyperactivation in this PPA group. If the hypoactivation localizes to atrophied tissue and the hyperactivation to other regions, that will bolster the case that we are seeing compensatory processes, but it isn't certain with half the story masked. I also don't really see statistical masking of the frontal region as a valid solution to eye movement artifacts. The authors would have to present evidence that the region that they masked corresponds to the region potentially affected by eye movements. However, many studies have found that beamforming already does a pretty good job of removing ocular artifacts from estimated brain signals, except for very close to the eyes.

4) The correlation with reaction time in the occipital cortex is consistent with the idea that the ERS there may reflect compensatory overreliance on perceptual information, but it isn't conclusive. The authors suggest that PPA patients are able to categorize the stimuli correctly based on visual features, but are unable to name them. What about testing for correlations with the out-of-scanner behavioural measures that established that the patients have a naming deficit? It would strengthen the case if atrophy or hypoactivation (see comment above) correlated with the naming deficit.

Reviewer #1:

Perez-Ortega and colleagues performed rigorous experiments to determine if the activity of neurons in the visual cortex is similar across days, in particular comparing spontaneous activity in the absence of visual stimuli across days, which was previously not examined to my knowledge. The paper claims that evoked ensembles are more stable than spontaneous ensembles, but more convincing quantitative analyses are required to support these claims.

Major Comments:

1) There is only one mention of prior work with multi-day imaging in the visual cortex (Ranson 2017). Another related study to cite and compare your results to would be Jeon, ..., Kuhlman 2018 (and I think a comment about how similar/different your results are from this study + Ranson would be useful for the reader). I would also recommend mentioning that there are studies that have observed differences in evoked activity across learning in V1 (e.g. Poort, Khan et al 2015; Henschke, Dylda et al 2020). Do you think there was adaptation across days to the stimulus that you repeated?

2) Some GCaMP6f mice have aberrant cortical activity (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604087/). In the raw data (Fig 1F) it doesn't look present, but it would be useful to show more time and sort the neurons by their first PC weights perhaps to see the activity structure.

3) The approach of 3 plane imaging taking the maximum projection seems useful for tracking cells across days. There is a claim that some cells are no longer found / no longer active. Based on Fig 1G it appears there may have been some Z-movement from day 10 to day 46. This Z movement may explain some of the lost active cells. As a sanity check I would recommend plotting the Z-plane on which the cells were maximally active on day 1 vs the Z-plane on which the cells were maximally active on day n.

4) There is an emphasis on analyzing the data as ensembles but I think this may be missing other slow, gradual changes. The definition of stable is at least 50% of neurons were preserved across days. However, the fitting procedure of finding ensembles may produce different ensembles even if those neurons are still correlated to each other. I would recommend two possible additional analyses: 1) compare the correlation matrices for common neurons across days (unless there are too few neurons for this); 2) look at changes in single neuron statistics across days. For 2) this may include reliability of neural responses to the visual stimuli, the weights of the neuron onto the first principal component of spontaneous activity, or the correlation of a neuron with running speed. I think these results may solidify your ensemble result (evoked-related statistics change less across time).

Reviewer #1:

This study presents a detailed and focused study of the structural basis for a regulation strategy used by a human iron-sulfur cluster biosynthesis system, elucidated by artificial installation of new amino acids into a bacterial system that lacks the allosteric elements of the human enzyme. The work includes quaternary structure analysis and activity assays of variant bacterial proteins. It is performed competently and supports the conclusions. But the focus may be too narrow for a general audience. To bring the work over the bar, the authors could test whether installing the bacterial residues into human NFS1 restores activity without frataxin (inactivated in the human genetic disorder Friedrich's Ataxia). Furthermore, some elements of the study could be presented more clearly/rigorously to communicate the significance of the work to a general audience. These suggestions are listed below.

1) It would be useful for an unfamiliar reader to include a diagram of the bacterial and human iron-sulfur cluster biogenesis pathway. It would also be helpful to depict the mechanism of the IscS/NFS1 cysteine desulfurase reaction - essentially a picture to go along with the description of the PLP-dependent transformations described in paragraph 2.

2) In the first paragraph of the results section - I would be interested to see more details about the selection of the three residues targeted for mutagenesis. For example, did the authors inspect the interfaces of existing crystal structures of these complexes? Did they create sequence alignments for multiple eukaryotic/prokaryotic cysteine desulfurases and select sites conserved in bacterial proteins but not eukaryotic ones? More description of the experimental or bioinformatics basis for selecting these three sites would be important for convincing the reader that the basis for this work is sound.

3) The structural basis for the dimer interaction and the enhanced activity isn't completely clear - how do the changed interactions enhance the enzyme activity? A good description of the different quaternary forms and why they are more/less active is given on page 4-5 - but perhaps another link could be made between the exact residues targeted for substitution and the features of the system important for catalysis.

4) On page 10, the authors describe changes in IscS quaternary structure as a function of concentration. What is the estimated copy number or concentration inside the cell? Which concentration ranges would be most physiologically relevant?

5) Addition of any helper protein appears to increase the proportion of variant IscS dimer and activity. Is there any reason to believe that this phenomenon is simply a crowding effect? If the same amount of an unrelated protein is added - does the activity/dimer fraction change compared to variant IscS alone?

6) I found the color scheme in Figure 1 hard to follow - could the authors keep the subunit colors consistent and use text labels directly on the figure panels for the subunits and forms (open, ready, etc). I also don't think the "Clash!!" labels are necessary. A more effective approach might be to use zoomed-in insets for each clash.

7) In Figures 4-6 - could the authors include a more complete description of the error bars? What kind of error is shown? Are the replicates different experiments done on different days? These presentations might also benefit from showing the actual data points on top of the bars/error bars.

Reviewer #1:

Dingens et al. report a timely complementary study to map neutralizing and binding responses in polyclonal rabbit sera induced by immunization with the BG505 SOSIP Env trimer. Neutralizing responses are mapped using libraries of replication-competent HIV expressing all mutants of the BG505 Env, an approach developed in the Bloom laboratory. Binding responses were mapped using an EM-based method, EMPEM, developed in the Ward laboratory. The Env mutations that affect neutralization of the autologous BG505 strain in the BG505-SOSIP-immunized animals were largely known from other studies, as were the binding (not necessarily neutralizing) responses - the strength of this study is the combination of the two approaches. It is especially useful that the complex datasets have been deposited on-line where they can be interactively explored, including mapping onto Env trimer and monomer structures. Although results were anticipated, it is very nice to directly compare the neutralization epitopes to the binding epitopes determined by EMPEM. This is a well-written and beautifully illustrated paper.

Reviewer #1:

The manuscript by Badhiwala et al. is an interesting study using the emerging model system Hydra, which has many advantages for studying the entire nervous system of an animal during simple behavior. Some of the foundational neuroscience papers in this field have only come out in the past few years, and new studies such as the one here, might have the potential to contribute to an important early literature. Despite clear reasons for enthusiasm, the many shortcomings in this work greatly diminished my enthusiasm and support for this study. Although I appreciate building the microfluidic devise with simultaneous pan-neuronal imaging, the nature of the new biological insights provided here seems quite limited and easily predicted based on prior studies in hydra and other model systems. Moreover, the crude nature of some experiments inhibits my ability to make fair judgement of potential findings.

Major concerns:

1) The pressurized stimulation of the hydra appeared to be specific to the center of the body. The authors don't mention why this region was chosen, which seems critical to this study. Relatedly, why didn't they test multiple areas across the hydra with this system? Might we expect to see different sensorimotor behaviors, and thus different neural outputs?

2) The authors reference a recent single cell study characterizing multiple neuronal cell types in hydra. This work would greatly benefit by using some cell-type resolution studies to determine the functional nature of the neurons being activated as opposed to solely using pan-neuronal GCAMP imaging. If they can put GCAMP in all neurons, why not put it in specific subsets of neurons based on cellular identity? This point becomes more salient because a major take-home from this paper is that the spontaneous behavior and firing patterns is nearly identical to the stimulus evoked patterns, except for an apparent increase in firing rate. The true nature of the mechanosensory response might be revealed with cell-type specific experiments.

3) Although the authors reference whole animal imaging, they focus imaging analysis on peduncle and hypostomal nerve rings, despite the videos showing calcium activity in other areas throughout the body. Moreover, are the authors certain their pan-neuronal genetic strategy equally samples neurons throughout the body? In other words, is the apparent increase in activity in the nerve ring over other areas driven by a technical artifact of these neurons being labeled better?

4) While I appreciate the resection studies to get at "loss-of-function" experiments, this approach seems rather crude, and potentially confounding to clear interpretation. Exactly which neurons are killed and to what extent, and how many, if any began to regenerate throughout this process? My alarm here is raised especially in light of the author's surprising finding that "footless" animals show that the aboral nerve ring is not required for spontaneous or mechanosensory responses. What if residual activity from neurons not ablated is driving this response?

Reviewer #1:

The authors investigated the importance of visual and vestibular sensory cues and the underlying motion dynamics to the accuracy of spatial navigation by human subjects. A virtual environment coupled with a 6-degrees of motion platform, as described in prior studies, allowed precise control over sensory cues and motion dynamics. The research builds on previous work in several important ways: 1) the authors demonstrate that reliance on vestibular cues leads to an undershooting of trajectories to hidden goal locations, 2) manipulation of the underlying motion dynamics (the time constant) during navigation alters the accuracy of trajectories particularly when subjects are reliant on vestibular cues, 3) probabilistic models were used to demonstrate that path integration errors can be explained by mis-estimates of the underlying motion time constants, and 4) time constant estimates were improved when visual cues were available. Overall, the analyses are appropriate, the conclusions are judicious, and the authors provide an important contribution to understanding the sensory mechanisms underlying human spatial navigation.

1) Some minor methodological clarifications: how many trials were performed per subject? How many of the trials were performed in each condition (visual, vestibular, combined)?

2) The study tested performance by both male and female subjects. Could the authors comment as to whether sex differences were observed across performance measures? Perhaps sex can be indicated in some of the scatter plots.

3) Figure 2A. It would be helpful if the authors identified the start-point of the trajectory and also provided more explanation of the schematic in the caption.

4) Figure 2B-C. It would be helpful if the authors could expand this section to show some example trajectories and the relationship between examples and plotted data points. This could be done by presenting measures (radial distance, angular eccentricity, grain) for each example trajectory.

5) Because the range of sampled time-constants can vary across subjects, it would be nice to show plots as in Figure 3B for each subject (i.e., in supplementary material).

6) Discussion. The broader implications of the findings from the models are not sufficiently discussed. In addition, some comparison could also be made to other recent efforts to model path integration error (e.g., PMC7250899).

Reviewer #1:

This manuscript by Gould et al presents highly novel data which is logically presented and is likely to have both clinical and fundamental implications. Of relevance to the bone field, it defines a new mechanism by which one of the most important clinical targets for the treatment of osteoporosis is endogenously regulated. Beyond bone, I am not aware of any other examples of stimulus-directed acute lysosomal degradation of a secreted canonical Wnt antagonist as a mechanism to provide rapid de-repression. What seems lacking is a careful analysis of the physiological consequences of the acute degradation of sclerostin.

1) A landmark paper which convinced many in the field that sclerostin down-regulation is necessary for osteoanabolic responses to loading was based on a transgenic model from the Bellido lab (Tu et al, Bone, 2012). In that study, expression of Sost from the DMP-1 promoter precluded its transcriptional and protein-level down-regulation at late time points. That was sufficient to largely prevent bone gain following loading. Several other groups interpreted this as indicating Sost transcript regulation is required for bone's adaptation to loading, calling into question the physiological relevance of transient post-translational degradation described here. Can the authors reconcile that study with their own?

2) One way the authors attempt to demonstrate in vivo relevance is through western blotting of mechanically loaded mouse ulnas, showing previously-undocumented acute reductions in lysate sclerostin levels. It is standard practice in the field to quantify sclerostin positive osteocytes histologically, rather than by western blotting. This is because mechanical loading can rapidly increase blood flow to the limb (even in this study, the authors implicate the vasodilator NO) as well as having inflammatory effects, diluting the proportion of osteocyte-specific proteins in the lysate. Demonstrating protein-level sclerostin down-regulation specifically in osteocytes rapidly following loading would be a major addition to this study.

3) A long-stranding, reproducible finding which has always been very perplexing is that the largest transcriptomic responses to osteogenic mechanical loading occur very quickly, within an hour of loading, before Sost is down-regulated. Even in UMR106 cells in vitro, B-catenin is stabilised before Sost is down-regulated following exposure to substrate strain. The current findings may explain this temporal discrepancy. The authors should responses to sclerostin degradation such as quantifying Wnt target genes to provide physiologically-relevant readouts of their findings.

4) Figure 3 shows co-localisation of endogenous or ever-expressed sclerostin with lysosomal markers. Does this co-localisation change following FSS or PTH?

5) It is not clear whether early lysosomal degradation which transiently decreases sclerostin is triggered by the same mechanoresponsive pathways which subsequently down-regulate its RNA levels, or whether the two responses are distinct. Can the authors clarify this? For example, does Sost decrease in the BafA1-treated cells 8 hours after FSS or PTH treatment?

6) Discussion "that the rapid and transient nature of sclerostin degradation may be critical to the precise anatomical positioning of new bone formation following an anabolic stimulus" is very unclear. How do the authors propose that lysosomal sclerostin degradation produces regionalised responses to a greater degree than the previously-reported transcriptional mechanisms?

7) The evidence of lysosomal involvement in sclerostin down-regulation is largely based on pharmacological compounds of limited selectivity. A degree of genetic evidence is indirectly provided by the Gaucher cell line, but this is based on a single patient line. Can the authors provide direct genetic evidence that lysosomal function is necessary for sclerostin down-regulation, and ideally for bone formation?

8) References to previous studies which described mechanisms and relevance of Sost down-regulation are sparse. For example, see previous implications of NO signalling from the Vanderschueren lab (Callewaert et al, JBMR, 2010), protein-level down-regulation of sclerostin in the context of ageing from the Price lab (Meakin et al, JBMR, 2014) relevant to the discussion in the current manuscript, as well as work from the Ferrari lab on sclerostin regulation following both PTH and mechanical loading (e.g. Bonnet et al, JBC 2009; Bonnet et al, PNAS 2012).

Reviewer #1:

In this study, Feilong and colleagues showed that hyper-aligned fine-grained cortical connectivity profiles can be used to strongly predict general intelligence in individual participants. This is an important study demonstrating the utility of previously developed connectivity hyperalignment and highlighting the behavioral importance of fine-grained connectivity which is typically ignored in more standard functional connectivity analysis.

1) How does the bootstrapping handle the family structure in the data? More details are needed.

2) The authors mentioned that "the code for performing hyperalignment and nuisance regression was adapted from PyMVPA". One of the most important contributions of this study is the impressive demonstration of prediction performance improvement using hyperalignment and fine-grained connectivity profiles. Therefore, it is important that the adapted code and code utilized for the current study be made publicly available. While connectivity hyperalignment code from the previous study is available in PyMVPA, my experience is that it is not easy to use. If no code from the current study is made available, I believe it will be very difficult to replicate this study.

Reviewer #1:

The manuscript by Allio et al. tries to justify that roadkill can be a useful source for genomic sequencing and even genome assembly level data. The authors cover all aspects of using this resource, from a new protocol to extract DNA, through generating a hybrid short- and long-read genome assembly and to various applications, showing that this data can be used in phylogenomic and population genomic analyses. Although I think that the manuscript is useful in highlighting how this resource can be analysed, it covers a lot of different topics and covers them in varying depth, which makes it difficult to follow and understand the real importance of the different sections.

Major comments:

-Overall, this manuscript left me a bit confused about what is the main scope. It covers a lot of different topics from the laboratory-end of the spectrum, e.g. protocol used to get good DNA out of roadkills and how to assemble these genomes with a hybrid genome assembly, and crossing into a phylogenomic analyses making taxonomic suggestions and an analyses of the complete carnivora group, plus a demographic analyses showing the changes of population size over time.

I was left with an impression that the authors tried to cover a lot of different topics but did not go deep enough in any of those. As a consequence, the results section ends up sounding somewhat shallow, while the discussion takes up a lot of space.

What I would suggest if this manuscript is indeed to serve as a roadmap to roadkill genomics, is to add a figure showing the pipeline and then adjust the structure of the manuscript accordingly. For example, one box in the figure would correspond to one heading in the results/methods, where the DNA analyses is explained - reasoning why a special protocol is needed, what is the main difference to existing methods, how does the yield compare to other methods, etc.

And then the different topics explored in this manuscript could be shown as different examples of the application - taxonomic questions on intra-/inter-species level, higher level taxonomic analyses (of the whole Carnivora), population genomic analyses, etc. Highlighting this as examples of the potential use of the roadkill genomes would make it understandable why this paper is trying to cover aardwolf and bat-eared fox genomics from so many ends.

-Even though showing that roadkill samples can be useful for analysing particular species for which obtaining samples is difficult in other ways, I'm missing a discussion of how difficult it is to obtain roadkill samples and what are the ramifications. Can this approach be generally applied due to legislation reasons, do you need permits, do you find enough roadkill to rely on this source or do you only see it as an opportunistic sampling scheme?

-Genome assembly is not exactly my field of expertise; therefore, I would like the authors to better explain how their hybrid, short- and long-read genome assembly approach is novel. My impression was that such hybrid assemblies are now a rather common and well-established practice. But a lot of space is dedicated to explaining this topic in the introduction and again in the discussion, which to me is something obvious and reads more like a review than a research article. But maybe I'm missing something obvious here, in which case I'd like the authors to make it clearer.

Reviewer #1:

The evidence that sumoylation of K68 in the PIE-1 zinc finger protein is important for HDA-1 type 1 histone deacetylase association and sumoylation seems reasonable, and, is important because as shown in the co-submitted paper HDA-1 sumoylation leads to its association with MEP-1 and LET-418/NuRD complex thus accelerating H3K9ac deacetylation, and silencing gene expression.

The evidence that PIE-1 is needed for sumoylation of HDA-1, presumably through association of PIE-1 with the UBC-9 SUMO E2, is reasonable. However, several aspects of the authors' model remain unclear, and there is an absence of biochemical assays to establish the role of sumoylated PIE-1 in HDA-1 sumoylation, and the effects of sumoylation on HDA-1 HDAC activity.

1) How sumoylation of K68 in PIE1 affects its function was not worked out. Can the deleterious effect of the K68R mutation on PIE-1 function be reversed by generating a SUMO-PIE-1 fusion, as was done for HDA-1 in the co-submitted paper? K68 maps to the N-terminal side of ZF1 in the PIE-1 protein in what appears to be an unstructured region. Does the SUMO residue play a role in the interaction of PIE-1 with HDA-1? Are the zinc fingers required for PIE-1 interaction with HDA-1 or UBC-9? No zinc finger mutations were tested. Does HDA-1 have a SIM that would allow it to interact selectively with sumoylated PIE-1? Another possibility is that the PIE-1 SUMO moiety is important because it interacts with the non-covalent SUMO-binding site on the backside of UBC-9 (Capill and Lima, JMB 369:606, 2007), which might stabilize the interaction. The backside interaction of SUMO with UBC-9 is proposed to promote UBC-9-mediated sumoylation of target proteins with SUMO consensus sites that are directly recognized by UBC-9. In this scenario, SUMO-PIE-1 would in effect be acting as an E3 SUMO ligase for HDA-1 by serving as a recruitment "factor". In this regard, the authors could test biochemically whether recombinant PIE-1 or K68SUMO-PIE-1 stimulates sumoylation of HDA-1 by UBC-9, using recombinant WT and KKRR mutant HDA-1 as substrates. These issues deserve discussion.

2) What is the SUMO E3 ligase that sumoylates PIE-1? Is it possible that through association with UBC-9, perhaps through its zinc fingers, PIE-1 is sumoylated in cis within a PIE-1/UBC-9 complex?

3) In many places, including the title, the authors make the claim that PIE-1 promotes sumoylation and activation of HDA-1. While it is clear that PIE-1 does increase sumoylation of HDA-1, in a manner requiring K68, and that H3K9ac levels are decreased as a result, the authors do not provide any direct evidence that this process increases HDA-1 catalytic activity, as is implied in the title and elsewhere. As indicated in the review of the co-submitted paper, this would need to be established by carrying out an HDAC assay on control and sumoylated HDA-1 in vitro. Instead of enzymatic activation, it is possible that the PIE-1 interaction and HDA-1 sumoylation results in relocalization of HDA-1 within the nucleus to facilitate more efficient H3K9ac deacetylation.

Reviewer #1:

The evidence that sumoylation of HDA-1, a type 1 HDAC, plays a key role in establishing transcriptional silencing of piRNA-regulated genes in C. elegans is quite convincing. The genetic analysis demonstrating that the SUMO pathway is involved in piRNA silencing is strong, and the mutational evidence that this involves sumoylation of two Lys in the tail of HDA-1 is reasonable. Likewise, the finding that HDA-1 sumoylation promotes association with NuRD complex components and association of MEP-1, an HDA-1 interactor, with chromatin regulators is convincing. In addition, the evidence that HDA-1 sumoylation increases H3K9ac deacetylation in vivo, leading to negative regulation of hundreds of target genes, and plays a role in the inherited RNAi pathway is solid.

While the overall conclusion provides an interesting advance in understanding mechanisms of piRNA-mediated gene silencing in C. elegans, the paper is lacking any biochemical analysis of the effects of sumoylation on HDA-1 activity and its association with other transcriptional regulators.

1) The authors mapped two sumoylation sites close to the C terminus of HDA-1, K444 and K459, based on extremely weak homology with two established sumoylation sites in human HDAC1 that are reported to be important for transcriptional repression (N.B. the authors should indicate here that David et al. reported that K444/476R HDAC1 had reduced transcriptional repression activity in reporter assays.). While the two human sites conform to the sumoylation site consensus, ψKXE, neither K444 nor K459 in HDA-1 fits this consensus (possibly one could argue that K444 is in an inverted motif). The fact that the KKRR mutant HDA-1 is no longer sumoylated is consistent with these two Lys being sumoylated, but it would be reassuring to have direct MS evidence that K444 and K459 are indeed sumoylated, which could be achieved using a SUMO Thr91Arg mutant that generates a GlyGly stub upon trypsin digestion, among other methods.

2) It remains unclear how sumoylated HDA-1 is recognized by MEP-1 for assembly into the NuRD complex. Does MEP-1, or another NuRD subunit, have a SIM that could facilitate direct interaction of MEP-1 and sumoylated HDA-1?

3) As the authors discuss, it is surprising that the HDA-1(KKRR)::SUMO protein, which in effect is a constitutively sumoylated form of HDA-1 that will interact constitutively with MEP-1/NuRD, does not have more deleterious effects on the organism, since according to the data in Figure 2B, the stoichiometry of endogenous HDA-1 sumoylation was extremely low. Of course, low sumoylation stoichiometry, which is a general issue with sumoylation studies, means that only a very small fraction of the HDA-1 endogenous population will be able to engage with the silencing complexes at any one time. This point is also worth discussion.

4) Page 5: Here, and elsewhere, the authors claim that sumoylation of the two C-terminal Lys activates HDA-1 histone deacetylase activity, but provide no direct evidence for this statement. There are no HDAC assays, and it is unclear how C-terminal SUMO residues distant from the catalytic domain would alter its enzymatic activity, unless there is a SIM motif in HDA-1 that might allow for intramolecular interaction with SUMO residues at the tail leading to a conformation change. Did the authors check for a SIM motif in HDA-1? The fact that adding SUMO to the C-terminus rather than one or both of the two Lys would also have to be taken into account in determining bow sumoylation might "activate" HDA-1. To demonstrate that sumoylation activated HDA-1 in vitro deacetylation assays would need to be carried out comparing the activities of unmodified and sumoylated HDA-1. Instead of enzymatic activation, it is possible that the PIE-1 interaction and HDA-1 sumoylation results in relocalization of HDA-1 within the nucleus to facilitate more efficient H3K9ac deacetylation.

Reviewer #1:

In this manuscript, Soucy and colleagues present a novel innervated system which they use to model the effects of prenatal nicotine and opioid exposure. Using the system they provide potentially interesting insights on how prenatal nicotine and opioid exposure could impact release of catecholamines. However, following careful review of the manuscript,I recommend that the authors provide substantial additional data and evidence to support the biological relevance of their findings.

Major points:

1) A main pillar of this manuscript is the assumption that the adrenal medulla is innervated. To substantiate their claims the authors cite books/book chapters, rather than citing convincing primary evidence. In fact, other than old EM images showing vesicular densities akin to synapses, I have not found published images of convincing axonal arborization in the adrenal medulla - if such images exist the authors should at least try to reproduce them for internal consistency of their study. This is particularly relevant if they wish to draw parallels between in vitro and in vivo systems. As this is a major pillar upon which this research stands, the lack of supporting histological evidence, which could be easily done, undermines the validity of this manuscript. Presenting primary evidence (i.e. not a textbook diagram) is essentia.

2) Multiple experiments lack appropriate controls. See comments on Figure 2B, 2D, Supplementary Figure 2.

Reviewer #1:

The authors report a model about the confidence-effort tradeoff; explaining how subjects invest effort depending on how confident they want to be in their decision (and how costly this is). They fit their model to behavioural data and report qualitative similarities between model and data.

I find this an interesting model, with interesting links between timely topics of interest, such as confidence, effort, and cost optimisation. But I have several requests for clarification.

Major Comments:

Line 274: Without loss of generality: what does it mean here? I guess that with a different cost function, not all conclusions remain the same?

The model assumes that it is "rewarding" to choose the correct (highest-value) option (B = R*P). But is this realistic? If the two options have approx the same value, then R should be small (it doesn't matter which one you choose); if the options have different values, it is important to choose the correct one. Of course, the probability P_c continuously differentiates between the two options, but that is not the same as the reward. Can the predictions generalise toward a more general R that depends on value difference?

In Figure 2, I guess that the important quantity to decide is a standardised delta-mu (similar to d' in signal detection theory). It might be useful to also plot that (essentially combining the current two plots). Or alternatively, plot P_c(z), which relates more directly to the theory.

The section Probabilistic model fit is unclear. Are the MCD variables y the 5 variables mentioned above? Do different y's share the same alpha, beta, gamma? Are different transformation parameters a and b fitted for each y? Is estimation done per subject? It is mentioned that VBA is used, but what distribution is approximated exactly using VBA? Is it a mean-field approximation, optimised with gradient descent? Is the goal function a posterior across the 5 parameters? It would also be good then to have an intuition on the estimated model parameters (e.g., their standard error or Bayesian equivalent). Is there an estimate of model fit (in addition to checking qualitative predictions)? Figure S3 is a good start (and I think it is worth putting in the main MS), but it would be nice, for example, to see model comparisons where one or more parameters are restricted.

Figure 4, 5, 6 should be better annotated. I have a hard time trying to fill in what is plotted exactly (eg, scale of the color bar). Why are the data grouped in percentiles? Also in Figure 4 legend, I guess that "beta" is not used as the MCD model parameter? Please avoid overloading definitions.

Figure 7: It seems that "spreading" of alternatives occurs in the model only for alternatives that are initially close together? Is this consistent with their discussion around equation (14)? (I may be overlooking something; if so, consider making this more explicit.)

I find it a really interesting feature of the model that it can explain spreading of alternatives from a statistical perspective. So I think it's worth commenting on it in the Discussion. For example, does the current model capture trends in the literature? To what extent is the effect (also in empirical data) dependent on initial value differences?

Reviewer #1:

This paper attempts to explain perinatal risk factors and the associated risk of developing pediatric asthma in the mid-childhood and early teenage years. The authors found that some maternal characteristics such as atopy, BMI, race/ethnicity and demographics such as newborn sex, and birth characteristics such as birthweight, gestational age, and mode of delivery were associated with risks of subsequent asthma development in the pediatric population. The paper then goes on to demonstrate the differences in immune response during the different time frames of pregnancy. Throughout the majority of the pregnancy, fetal hematopoiesis generates mostly lymphoid and erythroid lineages. Towards term, the immune cells are predominantly neutrophils and monocytes. Pre-term is characterized primarily by lymphocytes. It was seen during term deliveries that the myeloid response produces several cytokines that shift CD4+ T- cells away from the Th2 response. Enhanced production of IFN gamma by leukocytes stimulation early in life is associated with reduced susceptibility to infections. However, the author states that these findings do not extend to asthma diagnosis in childhood.

Major comments:

I would have liked the paper to readjust the introduction; a lot of emphasis is placed on IFN/infection/asthma, but after this fact, it seems neglected going forward and the paper explores another topic. Instead, the paper's focus was on determining the biological nature, serologically, with a granulocytic luminal marker (PGLYRP-1) and a membrane-bound marker (sIL6Ra) and its association to pediatric asthma.

The take-home message for the paper - that there appears to be an inverse relationship between serum levels of PGLYRP-1 and overall risk for pediatric asthma - should be explored in relation to the whether a therapeutic role for such proteins is possible since they can accurately predict risk factors for disease and assess pulmonary function. Other proteins, like the sIL6Ra, have no association with disease predictability and have no association with predicting pulmonary outcomes. This should be explored/explained in greater detail.

Minor comments:

As part of the validation efforts of the study - the rationale for using three different cohorts to assess pediatric asthma risk was not clearly explained.

One of the main findings of the analysis was the conclusion that patients with higher levels of myeloid cells in their CBMCs are at lower risk of developing pediatric asthma, and vice versa. Furthermore, CBMC neutrophil abundance was negatively associated with the number of risk factors. (patients with more risk factors, as mentioned above, were found to have lower levels of neutrophils in their CBMC, and more at risk of pediatric asthma). This was further elucidated with measuring CBMC plasma levels of PGLYRP-1 with levels of mRNA and correlating it with risk of developing pediatric asthma. Increased levels of mRNA for the PGLYRP-1 protein was associated with an increased serum concentration of the protein. However, this was inversely correlated with risk factors. Patients with reduced risk factors for development of pediatric asthma were found to have increased levels of the protein and its mRNA.

The measurement and correlation of PGLYRP-1 (present in neutrophil specific granules) and sIL6Ra (derived from neutrophils, but not present in granules) to pediatric asthma at mid-childhood and early-teen years was determined. There were two follow-up points where asthma outcomes and pulmonary function by way of the FEV1/FVC ratio was determined. It was found that increased levels of PGLYRP-1 were significantly associated with current asthma at mid-childhood. However, there was no association between levels at the early-teen follow-up.

In terms of correlations between each protein level and pulmonary function - the sIL6Ra protein was NOT associated with the FEV1/FVC ratio or a bronchodilator response at either age group. However, it was found that increased levels of PGLYRP-1 were associated with an INCREASED FEV1/FVC ratio (not indicative of asthma) and reduced odds of developing pediatric asthma at each age group.

This analysis makes sense as increased production of neutrophil granules, PGLYRP-1, serves a protective effect against infection, reducing incidence of disease states. The paper, however, should explore the rationale behind the no-response to the sIL6Ra protein. In terms of understanding, since this protein is NOT associated with neutrophilic granules, it can be inferred, that is it may not have a role in protecting against infection. However, this could have been explored in more detail in the paper.

Reviewer #1:

A very interesting paper testing the biological embedding model in a wild long-lived mammal using an impressive dataset. However, the results for immature orphans are not entirely straight forward. The effect on the HPA axis is in the opposite direction to humans and there seems to be no significant increase in cortisol compared to non-orphans overall - it depends on time since maternal loss. The paper would be improved by communicating this more clearly and discussing exactly why this pattern may be different to that in humans. Some of the evolutionary ideas discussed in the paper also need to be more clearly conveyed or thought through.

Substantive concerns:

1) There are important sections in the introduction (L125-128 particularly) and discussion (L403-409) about the evolution of the HPA response and differences between humans and other mammals that are unclear. Greater detail on the evolutionary logic being used, the precise hypotheses being suggested and references to back the ideas up are required (further details in minor comments).

2) Table2/Model 1a doesn't directly test whether orphans have higher cortisol than non-orphans (or no p-value reported in table 2) and CIs in table 1 suggest that there is not a significant difference. Therefore, categorical statements that orphans have higher cortisol levels don't seem to be entirely justified. However, model 1B demonstrates that cortisol declines with years since maternal loss and figure 3 supports the idea that orphans do have higher cortisol than non-orphans in the first 2 years following maternal loss but that this declines to levels similar to those of non-orphans after 2 years. Could a statistical test be run to back this up? Perhaps instead of using a binary variable for orphan status (yes/no) it could be analysed as categories (orphaned within 2 years, orphaned more than 2 years ago, not orphaned as an immature) which could be used to directly test this and back up statements e.g. recently orphaned immatures had higher cortisol levels than non-orphans. A broader concern is why likelihood ratio tests have been used to calculate p values (and for only some of the predictors) rather than reporting the output from the models themselves. Could you explain what the benefit of this is over reporting values from the actual models and/or also provide the model outputs?

3) The effect on cortisol slopes found in this study is in the opposite direction to that in humans. This is discussed in some detail but is lacking clarity in places and I think it would help to make this difference more obvious - it is really a key finding of the paper not a secondary point. The expected pattern is very nicely set out in the introduction so it would be good to format the discussion so there is a paragraph that outlines exactly how the results differ from hypothesized:

(a) that the effect on cortisol slopes is in the opposite direction

(b) that only the cortisol levels of recently orphaned immatures are significantly different to non-orphan immatures and then brings in the ideas discussed about why these differences may be present. I think this would really help communicate the findings more clearly, bringing the discussion more inline with what is set out in the introduction.

Reviewer #1:

This is overall a well written and methodologically sound study researching how educational achievement can be predicted using genomic data when the sample is stratified to those without and those with diagnoses of common psychiatric disorders. I think that it is a very important study area, the study is well powered using a fantastic representative sample and offers some insights into aetiology of associations between psychiatric traits and educational achievement.

I suggest some minor adjustments for the authors to consider, mainly addressing the conclusions and implications of the findings. I also recommend some clarifications in the methods and the results sections; these suggestions might require some very modest additional analyses and rethinking/rewording some of the conclusions.

• The major issue I have is that you discuss family SES as a purely environmental factor throughout the manuscript. However, we know that this is not the case and that there is substantial heritability for SES. It follows from what SES composite is made out of, in your case parental education and occupation, both of which are highly heritable (as you rightly note in the manuscript yourself). This needs to be addressed and discussed throughout the manuscript.

• The major conclusion in the manuscript, even if you acknowledge that this is speculation, is that the attenuation of the association between EA-PGS and school grades after correcting for SES can be explained by genetic nurture. I agree, this can be one of the explanations, however, here you also control (partially) for transmitted genes, that is educationally related genetic variants present in both generations (so without genotyped trios here you cannot distinguish between direct and indirect genetic effects). In addition, this attenuation can also be explained by gene and environment correlation (not only passive which is addressed by genetic nurture hypothesis) but also active and evocative rGE. In addition, in your design, you need to consider assortative mating. I suggest directly addressing this in the manuscript.

• I also think that you should address that you are dealing with diagnosed disorders only. It is a great strength of the paper, and you are using a fantastic resource, but we know that these disorders are quantitative traits and your study does not allow to take that into account, so there are possibly individuals with high ADHD symptoms are included in the control group; similarly, you cannot take into account the symptom severity. In terms of symptom level data, I see you have referenced Selzam et al., 2019 paper that, among other things, related EA-PGS to ADHD symptoms and vice versa, and also controlled for SES.

• In the introduction, you rightly state that individual differences are explained by genetic and environmental factors and the interplay between them, however, I suggest rephrasing it, because "much of the variance can be explained" is incorrect, all of the individual differences can be explained by the combination of these factors.

• You report low rG between schizophrenia and E1, can you specify how this was calculated

• You state that your prediction in the control sample is lower than the other studies and offer a possible solution of the inclusion or exclusion of 23andMe data in the summary statistics, please note that other studies have not used 23ndme statistics either (for example TEDS publications). You also discuss genetic heterogeneity; I think that the difference can be explained by both genetic and environmental heterogeneity. What is the rG between EA in your sample and GWAS sample?

• I think that the conclusion that the impact of low EA-PGS is comparable to the impact of ADHD is too strong, your data does not support this strong conclusion. I suggest rephrasing it, especially as we're not aware of the associated mechanisms. Note that people with ADHD in your sample also have lower EA-PGS compared to control conditions. In addition, symptom severity of ADHD varies greatly.

• I also do not agree with the statement that having wealthy parents does not boost the performance as much for children with ADHD as compared to children without for the reasons mentioned above.

• I think that you have fantastic data, and you have data available about how many of your participants have multiple diagnoses. I suggest adding a stratified group with multiple diagnoses to the analyses, that is adding groups with 2, 3 or 4 and more psychiatric diagnoses and checking their polygenic score prediction to EA.

• I suggest making it clearer what covariates were used in every analysis (you say first that you added psychiatric diagnoses as covariate among the usual covariates, but later only that covariates were included 'as before', I assume you did not include diagnoses in later analyses, but this is not clear). In addition, it is not clear to me why you control for psychiatric diagnoses in the first set of analyses, I would have wanted to see full results without this covariate.

Overall, this is a beautiful study and it was a pleasure to read/review it.

Reviewer #1:

General Assessment:

I found the studies to be well motivated and thoughtfully designed to disentangle competing interpretations in the extant literature on visual perception in ASD. The first two experiments provided compelling evidence that prior choices affect perceptual decision making in ASD, but the outcome of the response invariant condition suggests that the authors' interpretation goes beyond the data.

Substantive Concerns:

"In summary, we found here that individuals with ASD demonstrated an increased influence of recent prior choices on perceptual decisions (vs. controls),..." is the major finding in the paper, quoted here in the concluding paragraph. It seems, however that the data support a narrower (and potentially less interesting) conclusion that individuals with ASD demonstrated an increased influence of recent button presses/motor responses, as the finding which forms the basis of the summary went away when different keys were used to report prior vs. test responses (i.e., in the response invariant condition). I understand that the authors present these data as challenges to theories of attenuated priors in ASD, but they seem to sidestep the issue that these data make their general conclusion more complicated.

For completeness, it would be helpful to present some information on the stimulus values for the test stimuli, as these were set individually using a staircase. Where did these staircases converge? Were there group differences?

Reviewer #1:

In this manuscript, the authors show they can accomplish imaging in complex specimens using 3- and 4-photon excitation, deeper in the specimen than comparable optics can accomplish with 2-photon excitation laser scanning microscopy. This is a clear advantage for imaging optically hostile specimens such as cultured organoids or spheroids, or in challenging in vivo settings. I am excited about these findings, but I am not at all supportive of the current version of the manuscript being used to present these lovely findings.

There are two strong reasons for my opinion:

i. The manuscript presents the findings in a manner that will only be understandable by the readers who are familiar with the topic, and who are likely to already have heard of the capabilities of 3- and 4-photon excitation to image deeper into specimens.

ii. The results are not presented in a way that the large body of potential readers can understand. They will be unable to grasp the way that the experiments were performed, or understand what the figures are showing, or critically evaluate the results that are presented.

Thus, there is a disconnect between the quality of the work and the quality of the presentation. There are many areas of quantitative imaging and intravital imaging that are well known to those that know about them (or use them), and that are a complete mystery to the vast majority of those that don't know about the tools or use them. The authors must take this as an opportunity to reach the many workers that could benefit from this powerful approach, rather than writing for the group that already knows (and even uses) the approaches presented.

1) Provide needed background and present important things first. The authors should give the reader a clear view into the issues in imaging biological tissues with the longer wavelengths that are used for confocal laser scanning microscopy (CLSM) and for two-photon laser scanning microscopy (TPLSM). There are several factoids presented, all seemingly true, but not presented in an accessible manner. Rather than starting with a mention of the expected temperature rise due to the dramatically higher absorbance by water of 1300nm and 1700nm light, the paper first presents the major absorbance of the light (~2/3 loss) and that this isn't a problem because there is sufficient laser power. For most readers, the need for a larger laser won't be their first question; instead it will be the viability after/during the imaging session. The expected temperature rise, and an indirect mention of burn marks (!), comes at the end of the section.

2) Explain and perform cell viability tests. Calcium imaging for assessing tissue viability is not the technique of choice for most readers, and is presented in a way that assumes general knowledge that simply does not exist. Membrane patency assays using membrane-impermeant DNA dyes, or other live-dead assays are far more common, but not presented in this study. I am not insistent that the authors use any particular assay, but I am insistent that the authors present the need for viability assay(s), teach the reader the principles of the assay(s) used, and present the results in an understandable manner.

3) Present the finding and the figures in an accessible manner. The figures are simply not digestible by the readers who do not perform this sort of work, and the legends do not help sufficiently. For those of us who do perform work of this sort, the figures are not as convincing as they should be, or presented in a way that they can be critically evaluated.

Consider the legend for Figure 1: "Microscopy with simultaneous 2-, 3- and 4 photon processes excited in fluorescent skin tumor xenografts in vivo. Representative images were selected from median-filtered (1 pixel) z-stacks, which were taken in the center of fluorescent tumors through a dermis imaging window. a) Excitation at 1300nm (OPA) in day-10 tumor at 145 μm imaging depth with a calculated 3.3 nJ pulse energy at the sample surface, 24 μs pixel integration time and 0.36 μm pixel size. For calculation of pulse energy at the sample surface see Figure S3. b) Excitation at 1650 nm (OPA) in day-13 tumor at 30 μm depth with a calculated 6.3 nJ pulse energy at the sample surface, 12 μs pixel integration time and 0.46 μm pixel size. c) Excitation at 1650 nm (OPA) in day-14 tumor at 85 μm depth, with a calculated 5.4 nJ pulse energy at the sample surface, 12 μs pixel integration time and 0.46 μm pixel size. Cell nuclei containing a mixture of mCherry and Hoechst appear as green."

If I gave any of the figures and legends to the people in my lab, the half that don't do multiphoton imaging (but that have sat through many lab meetings) would just hand them back to me with quizzical expressions on their faces.

The figures are not as compelling as the results, and defer to the body of the paper to explain what was done or what was shown, and assumes that the average reader remembers the differences between OPO and OPA , for example (which they won't). The power plots showing nJ and mW in Figure 3 are inaccessible to most readers, and not well described.

I should mention that the figures, legends and text are not satisfying for the readers who are familiar with 2-, 3- and 4-photon imaging either. These are fantastic findings, and deserve figures that are as lovely as the results, and are compelling. Some of these issues are due to typos: "Consistently, multiparameter recordings were achieved inside the tumor at 350 μm depth using excitation at 1650 nm and 1300 nm, but 1180 nm (Figure 3b). "

However, the greater problem is that the text doesn't present the findings in a straightforward, convincing fashion and then interpret them. Instead, the conclusion often leads the evidence: "In line with an improved depth range, the signal-to-noise ratio (SNR) of 3PE TagRFP outperformed the SNR of 2PE TagRFP at depths beyond 150 μm (Figure 3c). Because H2B-eGFP expression in HT1080 tumors was very high, 3PE eGFP emission reached the highest SNR."

The legend and figure that it describes should be able to stand on their own, and convince a skeptical reader with the help of the text in the body of the manuscript.

In summary, these are lovely and important results that I am excited about. They are presented in a fashion that will make it difficult for most to appreciate because the body of the paper is not fashioned to teach the reader, and the figures themselves are challenging, and the legends inadequately present what is shown in the figures. Careful expansion and editing should resolve all of these issues and make the manuscript into the presentation these excellent findings deserve.