Reviewer #2 (Public Review):

This paper uses the mouse mesoscale connectome, combined with data on the number and fraction of PV-type interneurons, to build a large-scale model of working memory activity in response to inputs from various sensory modalities. The key claims of the paper are two-fold. First, previous work has shown that there does not appear to be an increase in the number of excitatory inputs (spines) per pyramidal neuron along the cortical hierarchy (and this increase was previously suggested to underlie working memory activity occurring preferentially in higher areas along the cortical hierarchy). Thus, the claim is that a key alternative mechanism in the mouse is the heterogeneity in the fraction of PV interneurons. Second, the authors claim to develop novel cell type-specific graph theory.

I liked seeing the authors put all of the mouse connectomic information into a model to see how it behaved and expect that this will be useful to the community at large as a starting point for other researchers wishing to use and build upon such large-scale models. However, I have significant concerns about both primary scientific claims. With regard to the PV fraction, this does not look like a particularly robust result. First, it's a fairly weak result to start, much smaller than the simple effect of the location of an area along the cortical hierarchy (compare Figs. 2D, 2E; 3C, 3D). Second, the result seems to be heavily dependent upon having subdivided the somatosensory cortex into many separate points and focusing the main figures of the paper (and the only ones showing rates as a function of PV cell fraction) solely on simulations in which the sensory input is provided to the visual cortex. With regards to the claim of novel cell type-specific graph theory, there doesn't appear to be anything particularly novel. The authors simply make sure to assign negative rather than positive weights to inhibitory connections in their graph-theoretic analyses.

Major issues:<br /> 1) Weakness of result on effect of PV cell fraction. Comparing Figures 2D and 2E, or 3C and 3D, there is a very clear effect of cortical hierarchy on firing rate during the delay period in Figures 2D and 3C. However, in Figure 2E relating delay period firing rate to PV cell fraction, the result looks far weaker. (And similarly for Figs. 3C, 3D, with the latter result not even significant). Moreover, the PV cell fraction results are dominated by the zero firing rate brain regions (as opposed to being a nice graded set of rates, both for zeros and non-zeros, as with the cortical hierarchy results of Figures 2D), and these zeros are particularly contributed to by subdividing somatosensory (SS) into many subregions, thus contributing many points at the lower right of the graph.<br /> Further, it should be noted that Figure 2E is for visual inputs. In the supplementary Figure 2 - supplement 1, the authors do apply sensory inputs to auditory and somatosensory cortex...but then only show the result that the delay period firing rate increases along the cortical hierarchy (as in Figure 2D for the visual input), but strikingly omit the plots of firing rate versus PV cell fraction. This omission suggests that the result is even weaker for inputs to other sensory modalities, and thus difficult to justify as a defining principle.

2) Graph theoretic analyses. The main comparison made is between graph-theoretic quantities when the quantities account for or do not account for, PV cells contributing negative connection strengths. This did not seem particularly novel.

3) It was not clear to me how much the cell-type specific loop strength results were a result of having inhibitory cell types, versus were a result of the assumption ('counter-stream inhibitory bias') that there is a different ratio of excitation to inhibition in top-down versus bottom-up connections. It seems like the main results were more a function of this assumed asymmetry in top-down vs. bottom-up than it was a function of just using cell-type per se. That is, if one ignored inhibitory neurons but put in the top-down vs. bottom-up asymmetry, would one get the same basic results? And, likewise, if one didn't assume asymmetry in the excitatory vs. inhibitory connectivity in top-down versus bottom-up connections, but kept the Pyramidal and PV cell fraction data, would the basic result go away?

4) In the Discussion, there is a third 'main finding' claimed: "when local recurrent excitation is not sufficient to sustain persistent activity...distributed working memory must emerge from long-range interactions between parcellated areas". Isn't this essentially true by definition?

5) I don't know if it's even "CIB" that's important or just "any asymmetry (excitatory or inhibitory) between top-down vs. bottom-up directions along the hierarchy". This is worth clarifying and thinking more about, as assigning this to inhibition may be over-attributing a more basic need for asymmetry to a particular mechanism.

Other questions:<br /> 1) Is it really true that less than 2% of neurons are PV neurons for some areas? Are there higher fractions of other inhibitory interneuron types for these areas, and does this provide a confound for interpreting model results that don't include these other types?<br /> Maybe related to the above, the authors write in the Results that local excitation in the model is proportional to PV interneuron density. However, in the methods, it looks like there are two terms: a constant inhibition term and a term proportional to density. Maybe this former term was used to account for other cell types. Also, is local excitation in the model likewise proportional to pyramidal interneuron density (and, if not, why not?)?

2) Non-essential areas. The categorization of areas as 'non-essential' as opposed to, e.g. "inputs" is confusing. It seems like the main point is that, since the delay period activity as a whole is bistable, certain areas' contributions may be small enough that, alone, they can't flip the network between its bistable down and up states. However, this does not mean that such areas (such as the purple 'non-essential' area in Figure 5a) are 'non-essential' in the more common sense of the word. Rather, it seems that the purple area is just a 'weaker input' area, and it's confusing to thus label it as 'non-essential' (especially since I'd guess that, whether or not an area flips on/off the bistability may also depend on the assumed strength of the external input signal, i.e. if one made the labeled 'input area' a bit too weak to alone trigger the bistability, then the purple area might become 'essential' to cross the threshold for triggering a bistable-up state).

3) Relation between 'core areas' and loop strength. The measure underlying 'prediction accuracy = 0.93' in Figure 6D and the associated results seems incomplete by being unidirectional. It captures the direction: 'given high cell-type specific loop strength, then core area' but it does not capture the other direction: 'given a cell is part of a core area, is its predicted cell-type specific loop strength strong?'. It would be good to report statistics for both directions of association between loop strength and core area.

4) More justification would be useful on the assumption that the reticular nucleus provides tonic inhibition across the entire thalamus.

5) Is NMDA/AMPA ratio constant across areas and is this another difference between mice and monkeys? I am aware of early work in the mouse (Myme et al., J. Neurophys., 2003) suggesting no changes at least in comparing two brain regions' layer 2/3, but has more work been performed related to this?

6) Are bilateral connections between the left and right sides of a given area omitted and could those be important?

Reviewer #2 (Public Review):

This is a potentially important finding regarding the roles of O-GlcNAc cycling and one-carbon metabolism in nerve regeneration. In a previous paper (Taub et al. 2018) they showed that both ogt-1 and oga-1 mutants show strong activation of a neuronal regeneration phenotype. However, the different biological processes used for the neural regeneration phenotype differed between the ogt-1 and oga-1 mutants. Several small issues emerge in the present paper which will increase the interest in the findings presented.

In summary, this paper under review is a potentially important finding which upon further documentation will be an excellent contribution.

Reviewer #2 (Public Review):

This manuscript introduces an integrative framework for modelling and analysis in neuroscience called BrainPy. It describes the many tools and utilities for building a wide range of models with an accessible and extensible unified interface written in Python. Several illustrative examples are provided for common use cases, including how to extend the existing classes to incorporate new features, demonstrating its ease of use and adherence to Python's programming conventions for integrative modelling across multiple scales and paradigms. The provided benchmarks also demonstrate that despite the convenience of presenting a high-level interpreted language to the user, it provides orders of magnitude of computational speed-up relative to three popular alternative frameworks on the chosen simulations through the extensive use of several Just In Time compilers. Computational benchmarks are also provided to illustrate the speed-up gained from running the models on massively parallel processing hardware, including GPUs, suggesting leading computational performance across a wide range of use cases.

While the results presented are impressive, publishing further details of the benchmarks in an appendix would be helpful for evaluating the claims and the overall conclusion would be more convincing if the performance benefits were demonstrated on a wider selection of test cases. Unsatisfyingly, the authors gave up on making a direct comparison to Brian running on GPUs with GeNN which would have been a fairer comparison than CPU-based simulations. The code for the chosen benchmarks is also likely to be highly optimised by the authors for running on BrainPy but less so for the other platforms - a fairer test would be to invite the authors of the other simulators to optimise the same models and re-evaluate the benchmarks. Furthermore, the manuscript reads like an advertisement for the platform with very little discussion of its limitations, weaknesses, or directions for further improvement. A more frank and balanced perspective would strengthen the manuscript and give the reader greater confidence in the platform.

Since simulators wax and wane in popularity, it would be reassuring to see a roadmap for development with a proposed release cadence and a sustainable governance policy for the project. This would serve to both clearly indicate the areas of active development where community contributions would be most valuable and also to reassure potential users that the project is unlikely to be abandoned in the near future, ensuring that their time investment in learning to use the framework will not be wasted. Similarly, a complex set of dependencies, which need to be modified for BrainPy, will likely make the project hard to maintain and so a similar plan to those given for the CI pipeline and documentation generation for automation of these modifications would be a good addition. It is also important to periodically reflect on whether it still makes sense to combine all the disparate tools into one framework as the codebase grows and starts to strain under modifications required to maintain its unification.

Finally, a live demonstration would be a very useful addition to the project. For example, a Jupyter notebook hosted on mybinder.org or similar, and a fully configured Docker image, would each enable potential users to quickly experiment with BrainPy without having to install a stack of dependencies and troubleshoot version conflicts with their pre-existing setup. This would greatly lower the barrier to adoption and help to convince a larger base of modellers of the potential merits of BrainPy, which could be major, both in terms of the computational speed-up and ease of development for a wide range of modelling paradigms.

Reviewer #2 (Public Review):

Guo and her colleagues develop a new approach to map the category-selective functional topographies in individual participants based on their movie-viewing fMRI data and functional localizer data from a normative sample. The connectivity hyperalignment are used to derived the transformation matrices between the participants according to their functional connectomes during movies watching. The transformation matrices are then used to project the localizer data from the normative sample into the new participant and create the idiosyncratic cortical topography for the participant. The authors demonstrate that a target participant's individualized category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. The new approach allows researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate datasets from laboratories worldwide to map functional areas for individuals. The topic is of broad interest for neuroimaging community; the rationale of the study is straightforward and the experiments were well designed; the results are comprehensive. I have some concerns that the authors may want to address, particularly on the details of the pipeline used to map individual category-selective functional topographies.

1. How does the length of the scan-length of movie-viewing fMRI affect the accuracy in predicting the idiosyncratic cortical topography? Similarly, how does the number of participants in the normative database affect the prediction of the category-selective topography? This information is important for the researchers who are interested in using the approach in their studies.<br /> 2. The data show that category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. I'm wondering whether the functional connectome from resting state fMRI can do the same job as the movie-watching fMRI. If it is yes, it will expand the approach to broader data.<br /> 3. The authors averaged the hyper-aligned functional localizer data from all of subjects to predict individual category-selective topographies. As there are large spatial variability in the functional areas across subjects, averaging the data from many subjects may blur boundaries of the functional areas. A better solution might be to average those subjects who show highly similar connectome to the target subjects.<br /> 4. It is good to see that predictions made with hyperalignment were close to and sometimes even exceeded the reliability values measured by Cronbach's alpha. But, please clarify how the Cronbach's alpha is calculated.<br /> 5. Which algorithm was used to perform surface-based anatomical alignment? Can the state-of-the-art Multimodal Surface Matching (MSM) algorithm from HCP achieve better performance?<br /> 6. Is it necessary to project to the time course of the functional localizer from the normative sample into the new participants? Does it work if we just project the contrast maps from the normative samples to the new subjects?<br /> 7. Saygin and her colleagues have demonstrated that structural connectivity fingerprints can predict cortical selectivity for multiple visual categories across cortex (Osher DE et al, 2016, Cerebral Cortex; Saygin et al, 2011, Nat. Neurosci). I think there's a connection between those studies and the current study. If the author can discuss the connection between them, it may help us understand why CHA work so well.

Reviewer #2 (Public Review):

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

One potential limitation is the design of the expression system in this work. Even though the authors state (ln. 127-128) that "human cDNA is expressed under the control of the endogenous Uba5 enhancer and promoter", it is in fact the Gal4 gene that is expressed from the endogenous locus (which authors also note in the same paragraph 138-139), meaning that the cDNA expression level would inevitably be amplified in comparison. While I do not think this weakens the conclusions of this paper, it may impact the interpretation of future experiments that use these tools. Especially considering the authors argue that most disease variants of UBA5 are partial loss-of-functions, the amplification effect could potentially mask the phenotypes of milder hypomorphic alleles. Temperature sensitivity of Gal4 expression may allow calibrating levels to reduce the impact of this amplification, but the revised manuscript still does not openly acknowledge or discuss this potential caveat.

Reviewer #2 (Public Review):

More than 80 million people live at high altitude. This impacts health outcomes, including those related to pregnancy. Longer-lived populations at high altitudes, such as the Tibetan and Andean populations show partial protection against the negative health effects of high altitude. The paper by Yue sought to determine the mechanisms by which the placenta of Tibetans may have adapted to minimise the negative effect of high altitude on fetal growth outcomes. It compared placentas from pregnancies from Tibetans to those from the Han Chinese. It employed RNAseq profiling of different regions of the placenta and fetal membranes, with some follow-up of histological changes in umbilical cord structure and placental structure. The study also explored the contribution of fetal sex in these phenotypic outcomes.

A key strength of the study is the large sample sizes for the RNAseq analysis, the analysis of different parts of the placenta and fetal membranes, and the assessment of fetal sex differences.

A main weakness is that this study, and its conclusions, largely rely on transcriptomic changes informed by RNAseq. Changes in genes and pathways identified through bioinformatic analysis were not verified by alternate methods, such as by western blotting, which would add weight to the strength of the data and its interpretations. There is also a lack of description of patient characteristics, so the reader is unable to make their own judgments on how placental changes may link to pregnancy outcomes. Another weakness is that the histological analyses were performed on n=5 per group and were rudimentary in nature.

Reviewer #2 (Public Review):

Summary:<br /> This study addresses the factors affecting the loss of independent control of finger forces after stroke. As central and peripheral factors contribute to this impairment, the authors used a novel apparatus and task to rigorously quantify the specific features of loss of finger individuation across all digits. The analyses ruled out the role of biomechanical constraints and revealed that the loss of independent control of finger forces is primarily driven by the interaction of two factors: loss of complexity in finger control (shape of enslavement patterns) and involuntary coactivations of task-irrelevant fingers (flexion bias).

Strengths:<br /> 1. The device and 3D finger individuation task are major strengths of the study, setting this work apart from previous work and enabling novel insights.<br /> 2. The analyses are thorough and well-designed. Of particular value is the analysis of finger force control in 3D Cartesian space and the use of Representational Similarity Analysis of finger enslavement pattern magnitude and shape.<br /> 3. A major contribution of this work is the teasing out of the effects of top-down factors versus biomechanical constraints affecting impairment of finger force control.<br /> 4. I found the discussion about complexity of finger control (lines 541-553) very interesting. The topic of adaptability of finger coactivation patterns in the context of dexterous manipulation is a key topic in robotics and neuroscience. In robotics, finger forces are decomposed into a grasp and manipulation component. In human motor control studies, this approach has identified their temporal coordination (work by Latash and Zatsiorsky, e.g., Gao et al., 2005) and potentially distinct sensorimotor control mechanisms (Wu and Santello, 2023). The authors might wish to discuss how coactivation patterns might contribute to the coordination of grasp and manipulation forces.

Weaknesses:<br /> None (only minor clarifications, e.g., the term biomechanical constraints should be defined earlier in the paper).

Reviewer #2 (Public Review):

Summary:

In recent years, Auxin treatment has been frequently used for inducing targeted protein degradation in Drosophila and various other organisms. This approach provides a way to acutely alter the levels of specific proteins. In this manuscript, the authors examine the impact of Auxin treatment and provide strong evidence that Auxin treatment elicits alterations in feeding activity, survival rates, lipid metabolism, and gene expression patterns. Researchers should carefully consider these effects to design experiments and interpret their data.

Strengths:

Regarding the widespread usage of Auxin mediated gene manipulation method, it is important to address whether the application of Auxin itself causes any physiological changes. The authors provide evidence of several Auxin effects. Experiments are suitably designed with appropriate sample size and data analysis methods.

Weaknesses:

The provided information is limited and not very helpful for many applications. For example, although authors briefly mentioned aging research, feeding behavior, and lipid data, RNA seq data are provided only for short-term (48 hours) treatment. Especially, since ovary phenotype was examined with long-term treatment (15 days), authors should also show other data for long-term treatment as well.

Although the authors show that Auxin causes a change in gene expression patterns and suggests the possible alteration of Gal4 expression levels, no cell-type-specific data is provided. It would be informative if the authors could examine the expression level of major Gal4 drivers.<br /> Authors should discuss how severe these changes are by comparing them with other treatments or conditions, such as starvation or mutant data (ideally, comparing with reported data or their own data if any?).

Reviewer #2 (Public Review):

Summary:<br /> The work is potentially interesting as it outlines the role of satellite cells in supporting the functional decline of skeletal muscle due to the denervation process. In this context the authors analyze the functional and molecular characteristics of satellite cells in different muscle types differently affected by the degenerative process in the ALS model.

Strengths:<br /> The work illustrates a relevant aspect of the differences in stem cell potential in different skeletal muscles in a mouse model of the disease through a considerable amount of data and experimental models.

Weaknesses:<br /> However, there are some criticisms of the structuring of the results:

It is not clear how many animals were used in each experimental group (Figs 1 and 2, Fig. 2-9). In particular, it is unclear whether the dots in the histograms represent biological or technical replicates. Furthermore, the gender used in experimental groups is never specified. This last point appears to be important considering the gender differences observed in the SOD1G93A mouse model.

The first paragraph of the results lacks a functional analysis of the motor decline of the animals after the administration of sodium butyrate. The authors, in fact, administered NaBu around 90 days of age while in previous work the drug had been administered at a pre-symptomatic age. It would therefore be useful, to make the message more effective, to characterize the locomotor functions of the treated animals in parallel with the histological evidence of the integrity of the NMJ.

Figure 5 should be completed with the administration of NaBu also to the satellite cells isolated from the WT mouse, the same for figure 9 where AAV-CMV-Cxcl12 transduction of WT myotubes is missing.

In the experiment illustrated in Figure 8, treatment of cell cultures with NaBu would improve the outcome as well as the interference of Cxcl12 expression in myotubes derived from G93A EOM SC (Fig.9) would strengthen the specificity of this protein in axon guidance in this NMJ typical of a spared muscle in ALS.

In the "materials and methods" section the paragraph relating to the methods used for statistical analysis is missing.

Reviewer #2 (Public Review):

It is well known that as seasonal day length increases, molecular cascades in the brain are triggered to ready an individual for reproduction. Some of these changes, however, can begin to occur before the day length threshold is reached, suggesting that short days similarly have the capacity to alter aspects of phenotype. This study seeks to understand the mechanisms by which short days can accomplish this task, which is an interesting and important question in the field of organismal biology and endocrinology.

The set of studies that this manuscript presents is comprehensive and well-controlled. Many of the effects are also strong and thus offer tantalizing hints about the endo-molecular basis by which short days might stimulate major changes in body condition. Another strength is that the authors put together a compelling model for how different facets of an animal's reproductive state come "on line" as day length increases and spring approaches. In this way, I think the authors broadly fulfill their aims.

Reviewer #2 (Public Review):

As I indicated in the initial review, the experiments are well conceived and executed, and the data are clear. I also agree with the authors that this work represents a key first step toward understanding how Notch signaling contributes to temporal control of fly neuroblasts. It is my opinion that the authors fall short of demonstrating how Notch signaling and temporal identity genes at the chromatin levels. I find this disappointing given the availability of various tools for looking at dynamic regulation of gene activity at high resolution. Given these weaknesses, my opinion is that the study is descriptive and lacks mechanistic explanation.

Reviewer #2 (Public Review):

The phenotypic instability of in vitro-induced Treg cells (iTregs) has been discussed for a long time, mainly in the context of the epigenetic landscape of Treg-signature genes; e.g. Treg-specifically CpG-hypomethylated Foxp3 CNS2 enhancer region. However, it has been insufficiently understood the upstream molecular mechanisms, the particularity of intracellular signaling of natural Treg cells, and how they connect to stable/unstable suppressive function.

Huiyun Lv et al. addressed the issue of phenotypic instability of in vitro-induced regulatory T cells (iTregs), which is a different point from the physiological natural Treg cells and an obstacle to the therapeutic use of iTreg cells. The authors focused on the difference between iTreg and nTreg cells from the perspective of their control of store-operated calcium entry (SOCE)-mediated cellular signaling, and they clearly showed that the sustained SOCE signaling in iTreg and nTreg cells led to phenotypic instability. Moreover, the authors pointed out the correlation between the incomplete conversion of chromatin configuration and the NFAT-mediated control of effector-type gene expression profile in iTreg cells. These findings potentially cultivate our understanding of the cellular identity of regulatory T cells and may shed light on the therapeutic use of Treg cells in many clinical contexts.

The authors demonstrated the biological contribution of Ca2+ signaling with the variable methods, which ensure the reliability of the results and the claims of the authors. iTreg cells sustained SOCE-signaling upon stimulation while natural Treg cells had lower strength and shorter duration of SOCE-signaling. The result was consistent with the previously proposed concept; a certain range of optimal strength and duration of TCR-signaling shape the Treg generation and maintenance, and it provides us with further in-depth mechanistic understanding.

In the later section, authors found the incomplete installment of Treg-type open chromatin landscape in some effector/helper function-related gene loci in iTreg cells. These findings propose the significance of focusing on not only the "Treg"-associated gene loci but also "Teffector-ness"-associated regions to determine the Treg conversion at the epigenetic level.

Limitations;<br /> ・NFAT regulation did not explain all of the differences between iTregs and nTregs, as the authors mentioned as a limitation.<br /> ・Also, it is still an open question whether NFAT can directly modulate the chromatin configuration on the effector-type gene loci, or whether NFAT exploits pre-existing open chromatin due to the incomplete conversion of Treg-type chromatin landscape in iTreg cells. The authors did not fully demonstrate that the distinct pattern of chromatin regional accessibility found in iTreg cells is the direct cause of an effector-type gene expression.

Reviewer #2 (Public Review):

Summary:

Chew et al describe interaction of the flavivirus protein NS1 with HDL using primarily cryoEM and mass spec. The NS1 was secreted from dengue virus infected Vero cells, and the HDL were derived from the 3% FBS in the culture media. NS1 is a virulence factor/toxin and is a biomarker for dengue infection in patients. The mechanisms of its various activities in the host are incompletely understood. NS1 has been seen in dimer, tetramer and hexamer forms. It is well established to interact with membrane surfaces, presumably through a hydrophobic surface of the dimer form, and the recombinant protein has been shown to bind HDL. In this study, cryoEM and crosslinking-mass spec are used to examine NS1 secreted from virus-infected cells, with the conclusion that the sNS1 is predominantly/exclusively HDL-associated through specific contacts with the ApoA1 protein.

Strengths:

The experimental results are consistent with previously published data.

Weaknesses:

CryoEM:

Some of the neg-stain 2D class averages for sNS1 in Fig S1 clearly show 1 or 2 NS1 dimers on the surface of a spherical object, presumably HDL, and indicate the possibility of high-quality cryoEM results. However, the cryoEM results are disappointing. The cryo 2D class averages and refined EM map in Fig S4 are of poor quality, indicating sub-optimal grid preparation or some other sample problem. Some of the FSC curves (2 in Fig S7 and 1 in Fig S6) have extremely peculiar shapes, suggesting something amiss in the map refinement. The sharp drop in the "corrected" FSC curves in Figs S5c and S6c (upper) indicate severe problems. The stated resolutions (3.42 & 3.82 Å) for the sNS1ts-Fab56.2 are wildly incompatible with the images of the refined maps in Figs 3 & S7. At those resolutions, clear secondary structural elements should be visible throughout the map. From the 2D averages and 3D maps shown in the figures this does not seem to be the case. Local resolution maps should be shown for each structure.

The samples were clearly challenging for cryoEM, leading to poor quality maps that were difficult to interpret. None of the figures are convincing that NS1, Ab56.2 or Fab56.2 are correctly fit into EM maps. There is no indication of ApoA1 helices. Details of the fit of models to density for key regions of the higher-resolution EM maps should be shown and the models should be deposited in the PDB. An example of modeling difficulty is clear in the sNS1ts dimer with bound Fab56.2 (figs 3c & S7e). For this complex, the orientation of the Fab56.2 relative to the sNS1ts dimer in this submission (Fig 3c) is substantially different than in the bioRxiv preprint (Fig 3c). Regions of empty density in Fig 3c also illustrate the challenge of building a model into this map.

Mass spec:

Crosslinking-mass spec was used to detect contacts between NS1 and ApoA1, providing strong validation of the sNS1-HDL association. As the crosslinks were detected in a bulk sample, they show that NS1 is near ApoA1 in many/most HDL particles, but they do not indicate a specific protein-protein complex. Thus, the data do not support the model of an NS1-ApoA1 complex in Fig 4d. Further, a specific NS1-ApoA1 interaction should have evidence in the EM maps (helical density for ApoA1), but none is shown or mentioned. If such exists, it could perhaps be visualized after focused refinement of the map for sNS1ts-HDL with Fab56.2 (Fig S7d). The finding that sNS1-ApoA1 crosslinks involved residues on the hydrophobic surface of the NS1 dimer confirms previous data that this NS1 surface engages with membranes and lipids.

Sample quality:

The paper lacks any validation that the purified sNS1 retains established functions, for example the ability to enhance virus infectivity or to promote endothelial dysfunction. Peculiarities include the gel filtration profiles (Fig 2a), which indicate identical elution volumes (apparent MWs) for sNS1wt-HDL bound to Ab562 (~150 kDa) and to the ~3X smaller Fab56.2 (~50 kDa). There should also be some indication of sNS1wt-HDL pairs crosslinked by the full-length Ab, as can be seen in the raw cryoEM micrograph (Fig S5b).

Obtaining high quality structures is often more demanding of sample integrity than are activity assays. Given the low quality of the cryoEM maps, it's possible that the acidification step in immunoaffinity purification damaged the HDL complex. No validation of HDL integrity, for example with acid-treated HDL, is reported. Acid treatment is perhaps discounted by a statement (line 464) that another group also used immunoaffinity purification in a recent study (ref 20) reporting sNS1 bound to HDL. However the statement is incorrect; the cited study used affinity purification via a strep-tag on recombinant sNS1.

Discussion:

The Discussion reflects a view that the NS1 secreted from virus-infected cells is a 1:1 sNS1dimer:HDL complex with the specific NS1-ApoA1 contacts detected by crosslinking mass spec. This is inconsistent with both the neg-stain 2D class average with 2 sNS1 dimers on an HDL (Fig S1c) and with the recent study of Flamand & co-workers showing 1-3 NS1 dimers per HDL (ref 20). It is also ignores the propensity of NS1 to associate with membranes and lipids. It is far more likely that NS1 association with HDL is driven by these hydrophobic interactions than by specific protein-protein contacts. A lengthy Discussion section (lines 461-522) includes several chemically dubious or inconsistent statements, all based on the assumption that specific ApoA1 contacts are essential to NS1 association with HDL and that sNS1 oligomers higher than the dimer necessarily involve ApoA1 interaction, conclusions that are not established by the data in this paper.

Reviewer #2 (Public Review):

Summary: Larouche et al show that TEs are broadly expressed in thymic cells, especially in mTECs and pDCs. Their data suggest a possible involvement of TEs in thymic gene regulation and IFN-alpha secretion. They also show that at least some TE-derived peptides are presented by MHC-I in the thymus.

Strengths: The idea of high/broad TE expression in the thymus as a mechanism for preventing TE-mediated autoimmunity is certainly an attractive one, as is their involvement in IFN-alpha secretion therein. The analyses and experiments presented here are therefore a very useful primer for more in-depth experiments, as the authors point out towards the end of the discussion.

Weaknesses: Throughout the manuscript, most conclusions are presented as proven causal relationships that the current data do not demonstrate. In the abstract, results, and discussion, the following conclusions are drawn that are not supported by the data: a) TEs interact with multiple transcription factors in thymic cells, b) TE expression leads to dsRNA formation, activation of RIG-I/MDA5 and secretion of IFN-alpha, c) TEs are regulated by cell proliferation and expression of KZFPs in the thymus. All these statements derive from correlations. Only one TF has ChIP-seq data associated with it, dsRNA formation and/or IFN-alpha secretion could be independent of TE expression, and whilst KZFPs most likely regulate TEs in the thymus, the data do not demonstrate it. The authors also seem to suggest that AIRE, FEZF2, and CHD4 regulate TEs directly, but binding is not shown. The manuscript needs a thorough revision to be absolutely clear about the correlative nature of the described associations.

On the technical side, there are many dangers about analysing RNA-seq data at the subfamily level and without stringent quality control checks. Outputs may be greatly confounded by pervasive transcription (see PMID 31425522), DNA contamination, and overlap of TEs with highly expressed genes. Whether TE transcripts are independent units or part of a gene also has important implications for the conclusions drawn. I would say that for most purposes of this work, an analysis restricted to independent TE transcripts, with appropriate controls for DNA contamination, would provide great reassurances that the results from subfamily-level analyses are sound. Showing examples from the genome browser throughout would also help.

Reviewer #2 (Public Review):

Summary:

In this work, Mohamed Y. El-Naggar and co-workers present a detailed electronic characterization of cable bacteria from Southern California freshwater sediments. The cable bacteria could be reliably enriched in laboratory incubations, and subsequent TEM characterization and 16S rRNA gene phylogeny demonstrated their belonging to the genus Candidatus Electronema. Atomic force microscopy and two-point probe resistance measurements were then used to map out the characteristics of the conductive nature, followed by microelectrode four-probe measurements to quantify the conductivity.

Interestingly, the authors observe that some freshwater cable bacteria filaments displayed a higher degree of robustness upon oxygen exposure than what was previously reported for marine cable bacteria. Finally, a single nanofiber conductivity on the order of 0.1 S/cm is calculated, which matches the expected electron current densities linking electrogenic sulphur oxidation to oxygen reduction in sediment. This is consistent with hopping transport.

Strengths and weaknesses:

A comprehensive study is applied to characterise the conductive properties of the sampled freshwater cable bacteria. Electrostatic force microscopy and conductive atomic force microscopy provide direct evidence of the location of conductive structures. Four-probe microelectrode devices are used to quantify the filament resistance, which presents a significant advantage over commonly used two-probe measurements that include contributions from contact resistances. While the methodology is convincing, I find that some of the conclusions seem to be drawn on very limited sample sizes, which display widely different behaviour. In particular:

The authors observe that the conductivity of freshwater filaments may be less sensitive to oxygen exposure than previously observed for marine filaments. This is indeed the case for an interdigitated array microelectrode experiment (presented in Figure 5) and for a conductive atomic force microscopy experiment (described in line 391), but the opposite is observed in another experiment (Figure S1). It is therefore difficult to assess the validity of the conclusion until sufficient experimental replications are presented.

The calculation of a single nanofiber conductivity is based on experiment and calculation with significant uncertainty. E.g. for the number of nanofibres in a single filament that varies depending on the filament size (Frontiers in microbiology, 2018, 9: 3044.), and the measured CB resistance, which does not scale well with inner probe separation (Figure 5). A more rigorous consideration of these uncertainties is required.

Reviewer #2 (Public Review):

The authors set out to identify CAPs (Candidate Adaptive Polymorphyisms), i.e., simply put mutations that carry a potential functional advantage, and utilize computational methods based on the perturbation of C-alpha positions with an Elastic Network Model to determine if dynamics of CAP residues are different in any way.

In my opinion this manuscript *may* suffer from fundamental flaws in the detection of CAPs, and does not provide enough analysis and discussion to determine if the methodology is applicable. A highly expanded and rewritten manuscript may help clarify the results. Lastly, the authors severely ignore the vast literature and results already in the public domain, not only with respect to the use of normal-mode analysis methods as well as the detection of functionally relevant mutations in general and to understand the evolution of the SARS-CoV-2 Spike protein in particular.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript uses an interesting abstraction of epigenetic inheritance systems as partially stable states in biological networks. This follows on previous review/commentary articles by the author. Most of the molecular epigenetic inheritance literature in multicellular organisms implies some kind of templating or copying mechanisms (DNA or histone methylation, small RNA amplification) and does not focus on stability from a systems biology perspective. By contrast, theoretical and experimental work on the stability of biological networks has focused on unicellular systems (bacteria), and neglects development. The larger part of the present manuscript (Figures 1-4) deals with such networks that could exist in bacteria. The author classifies and simulates networks of interacting entities, and (unsurprisingly) concludes that positive feedback is important for stability. This part is an interesting exercise but would need to be assessed by another reviewer for comprehensiveness and for originality in the systems biology literature. There is much literature on "epigenetic" memory in networks, with several stable states and I do not see here anything strikingly new.

An interesting part is then to discuss such networks in the framework of a multicellular organism rather than dividing unicellular organisms, and Figure 5 includes development in the picture. Finally, Figure 6 makes a model of the feedback loops in small RNA inheritance in C. elegans to explain differences in the length of inheritance of silencing in different contexts and for different genes and their sensitivity to perturbations. The proposed model for the memory length is distinct from a previously published model by Karin et al. (ref 49).

Strengths:<br /> A key strength of the manuscript is to reflect on conditions for epigenetic inheritance and its variable duration from the perspective of network stability.

Weaknesses:<br /> - I found confusing the distinction between the architecture of the network and the state in which it is. Many network components (proteins and RNAs) are coded in the genome, so a node may not disappear forever.

- From the Supplementary methods, the relationship between two nodes seems to be all in the form of dx/dt = Kxy . Y, which is just one way to model biological reactions. The generality of the results on network architectures that are heritable and robust/sensitive to change is unclear. Other interactions can have sigmoidal effects, for example. Is there no systems biology study that has addressed (meta)stability of networks before in a more general manner?

- Why is auto-regulation neglected? As this is a clear cause of metastable states that can be inherited, I was surprised not to find this among the networks.

- I did not understand the point of using the term "entity-sensor-property". Are they the same networks as above, now simulated in a computer environment step by step (thus allowing delays)?

- The final part applies the network modeling framework from above to small RNA inheritance in C. elegans. Given the positive feedback, what requires explanation is how fast the system STOPs small RNA inheritance. A previous model (Karin et al., ref. 49) builds on the fact that factors involved in inheritance are in finite quantity hence the different small RNAs "compete" for amplification and those targeting a given gene may eventually become extinct.

The present model relies on a simple positive feedback that in principle can be modulated, and this modulation remains outside the model. A possibility is to add negative regulation by factors such as HERI-1, that are known to limit the duration of the silencing.

The duration of silencing differs between genes. To explain this, the author introduces again outside the model the possibility of piRNAs acting on the mRNA, which may provide a difference in the stability of the system for different transcripts.<br /> At the end, I do not understand the point of modeling the positive feedback.

- From the initial analysis of abstract networks that do not rely on templating, I expected a discussion of possible examples from non-templated systems and was a little surprised by the end of the manuscript on small RNAs.

Reviewer #2 (Public Review):

The manuscript presents a valuable investigation of genetic associations related to plant resistance against the turnip mosaic virus (TuMV) using Arabidopsis thaliana as a model. The study infects over 1,000 A. thaliana inbred lines with both ancestral and evolved TuMV and assesses four disease-related traits: infectivity, disease progress, symptom severity, and necrosis. The findings reveal that plants infected with the evolved TuMV strain generally exhibited more severe disease symptoms than those infected with the ancestral strain. However, there was considerable variation among plant lines, highlighting the complexity of plant-virus interactions.

A major genetic locus on chromosome 2 was identified, strongly associated with symptom severity and necrosis. This region contained several candidate genes involved in plant defense against viruses. The study also identified additional genetic loci associated with necrosis, some common to both viral isolates and others specific to individual isolates. Structural variations, including transposable element insertions, were observed in the genomic region linked to disease traits.

Surprisingly, the minor allele associated with increased disease symptoms was geographically widespread among the studied plant lines, contrary to typical expectations of natural selection limiting the spread of deleterious alleles. Overall, this research provides valuable insights into the genetic basis of plant responses to TuMV, highlighting the complexity of these interactions and suggesting potential avenues for improving crop resilience against viral infections.

Overall, the manuscript is well-written, and the data are generally high-quality. The study is generally well-executed and contributes to our understanding of plant-virus interactions. I suggest that the authors consider the following points in future versions of this manuscript:

1. Major allele and minor allele definition: When these two concepts are mentioned in the figure, there is no clear definition of the two words in the text. Especially for major alleles, there is no clear definition in the whole text. It is recommended that the author further elaborate on these two concepts so that readers can more easily understand the text and figures.

2. Possible confusion caused by three words (Major focus / Major association and major allele): Because there is no explanation of the major allele in the text, it may cause readers to be confused with these two places in the text when trying to interpret the meaning of major allele: major locus (line 149)/ the major association with disease phenotypes (line 183).

3. Discussion: The authors could provide a more detailed discussion of how the research findings might inform crop protection strategies or breeding programs.

Reviewer #2 (Public Review):

Faress et al. ask the question of how synaptic plasticity (i.e. long-term potentiation, LTP) induced at different time points and different synapses in relationship to learning can transform memories supported by these circuits. The authors adopted an experimental design developed by Nabavi et al, 2014 and used male mice to optogenetically induce a weak fear memory in thalamo-LA circuits by pairing an optical conditioned stimulus (CS) at thalamo-LA synapses with a footshock unconditioned stimulus (US), or subjected the mice to an unpairing of the opto-CS and the footshock US. They then investigated how homosynaptic (thalamo-LA)- or heterosynaptic (cortico-LA) high-frequency stimulation (HFS) -that would induce LTP- delivered at different time points before and after learning can transform the opto-fear memory by using state of the art in vivo dual-wavelength optogenetics. They find that homosynaptic HFS delivered before or after learning transforms weak memories into stronger ones, whereas heterosynaptic HFS can do so when delivered immediately after learning. Both homo- and hetero-HFS delivered after unpairing produce a 24 h fear memory for the opto-CS. Lastly, they show that synaptic potentiation accompanies the strengthening of fear memory induced by hetero HFS in freely moving mice.

The significance of the study lies in showing in vivo that plasticity induced at different times or different synapses than those engaged during learning can modify memory and the synaptic strength in a synaptic pathway related to that memory. While heterosynaptic and timing-dependent effects in synaptic plasticity have been described largely ex vivo on shorter time scales, the discovery of lasting behavioral effects on memory is novel.

A strength of the study is that it uses well-defined and elegant optogenetic manipulations of distinct neural pathways in awake-behaving mice combined with in vivo recordings, which allows the authors to directly manipulate and monitor synaptic strength and memory.

The conclusions of this paper are mostly well supported by the data, but there are some aspects that should be resolved:

1. The experimental design for assessing the effects of applying HFS 24 h after conditioning should be clarified, and it should be re-evaluated which experimental groups can be compared and how. The experimental schemes in Figs. 1 and 3 (and Fig. 4e and extended data 4a,b) show that one group of animals was subjected to retrieval in the test context at 24 h, then received HFS, which was then followed by a second retrieval session. With this design, it remains unclear what the HFS impacts when it is delivered between these two 24 h memory retrieval sessions. It would be nice to see these data parsed out in a clean experimental design for all experiments (in Figs 1, 3, and 4), that means 4 groups with different treatments that are all tested only once at 24 h, and the appropriate statistical tests (ANOVA). This would also avoid repeating data in different panels for different pairwise comparisons (Fig 1, Fig 3, Fig 4, and extended Fig 4).

2. The final experiment (Fig. 5a-c, extended data 5c) combines behavioral assessments with in vivo LFP recordings before and 24 h after hetero-HFS. While this experiment is demanding, it seems a bit underpowered and not well-controlled. It would be critical to know if LFPs change over 24 h in animals in which memory is not altered by HFS, and to see correlations between memory performance and LFP changes, as two animals displayed low freezing levels. Also, the slice experiments (Fig. 5d-f) are not well aligned with the in vivo experiments (juvenile animals, electrical vs. opto stimulation, different HFS protocols, timescale of hours). They would suggest that thalamo-LA potentiation occurs directly after learning+HFS (which could be tested) and is maintained over 24 h.

3. The statistical analyses need to be clarified. All statements should be supported with statistical testing (e.g. extended data 5c, pg 7 stats are missing). The specific tests should be clearly stated throughout. For ANOVAs, the post-hoc tests and their outcomes should be stated. In some cases, 2-way ANOVAs were performed, but it seems there is only one independent variable, calling for one-way ANOVA.

4. There are a number of details in the methods and procedures that need to be elaborated on and clarified for the reader. All of them will be listed in the recommendations to the authors.

Reviewer #2 (Public Review):

A number of previous reports have demonstrated that theta synchrony between the hippocampus (HPC) and prefrontal cortex (PFC) is associated with correct performance on spatial working memory tasks. The main goal of the current study is to examine this relationship by initiating trials either randomly (as is typically done) or during periods of high or low PFC-HPC coherence. To this end, they develop a 'brain-machine interface' (BMI) that provides real-time estimates of PFC-HPC theta coherence, which are then used to control trial onset using an automated figure-eight maze. Their main finding is that choice accuracy is significantly higher on trials initiated when theta coherence is high whereas performance on low coherence trials does not differ from randomly initiated control trials. They also observe a similar result using a non-working memory task in the same maze.

Overall the main experiments (Figures 1-4) are well designed and the BMI approach is convincingly validated. There are also appropriate controls and analyses to rule out behavioral confounds and the results clearly presented. The idea of triggering trial onset based on brain activity is an interesting idea and helps to examine how extremes in the distribution of brain states are associated with behavior, something that might be more difficult to examine if trials are initiated randomly. As such, the BMI is an interesting approach for studying the neuronal basis of behavior that could potentially be applied beyond the particular field of the study, something the authors could perhaps have elaborated on more.

That being said, although the authors have elegantly revealed an association between PFC-HPC theta synchrony and behavior using their BMI approach, it is not apparent whether these results add substantially to previous reports of similar associations, including from the author's own work. The authors sometimes seem to claim that they do; for example, in the discussion, after describing previous studies that reported an association between PFC-HPC theta synchrony and behavior, they raise the reasonable question "did mPFC-hippocampal theta coherence lead to, or coincide with, correct choice outcomes?" What they subsequently write gives the impression that their study has somehow addressed the question, whereas in fact their results still leave this question open. For example, it is entirely possible that during high-coherence trials an unobserved neural process is influencing both coherence and task performance. The authors could have made a more convincing case as to why their correlative results go beyond similar findings from previous studies, perhaps by including additional analysis to strengthen their case.

Sometimes, the authors also seem to suggest that their results establish a causal relationship between synchrony and behavior, for example when they say that they have "demonstrated for the first time that strong mPFC-hippocampal theta coherence ENHANCES memory-guided choice" (line 557, my emphasis). However, causal manipulations of PFC-HPC synchrony would be required to make such claims. I am not suggesting that the lack of such data is necessarily a weakness of the study, only that causal claims are not supported by the author's results.

In addition to the behavioral results described above, the authors also examine how HPC-PFC synchrony modulates synchrony with the ventromedial thalamus (VMT; Figure 5) and how optogenetic modulation of the VMT influences PFC-HPC synchrony (Figure 6). However, these results feel somewhat more preliminary and their relationship to the other findings in the manuscript is not always clear. For example, given that the authors demonstrate that "Prefrontal-hippocampal theta synchronization modulates prefrontal-thalamic interactions" (Figure 5) I would rather have expected the authors to manipulate HPC and/or PFC and see how this affects VMT in Figure 6. It is also difficult to draw strong conclusions about the effects of optogenetic VMT stimulation since the results presented by the authors come from only 2 rats (Figure 6D-K) and therefore feel somewhat anecdotal. I could also not find any statistical test supporting the increase in the proportion of phase-locked neurons during high theta states shown in Figure 5K.

Reviewer #2 (Public Review):

This interesting research commendably revealed irregular sleep-wake patterns are associated with higher mortality risk. However, as authors acknowledged, the analysis can not to accurately identify the cause and effect. In my opinion, the causality is important for this topic, cuz, sleep regularity and health (e.g. chronic disease) were long-term simultaneous status. especially given the participants are older. I suggest that the author could utilize MR analysis to find out for more evidence.

Reviewer #2 (Public Review):

Hage et al examine how the foraging behavior of marmoset monkeys in a laboratory setting systematically takes into account the reward value and anticipated effort cost associated with the acquisition and consumption of food. In an interesting comprehensive framework, the authors study how experimental and natural variation of these factors affect both the decisions and actions necessary to gather and accumulate food, as well as the actions necessary to consume the food.

The manuscript proposes a computational model of how the monkeys may guide all these aspects of behavior, by maximizing a food capture rate that trades off the food that can be gathered with the effort and duration of the underlying actions. They use this model to derive qualitative predictions for how monkeys should react to an increase in the effort associated with food consumption: Monkeys should work longer before deciding to consume the accumulated food, but should move more slowly. The model also predicts that monkeys should show a different reaction to an increase in reward value of the food, also working longer but moving faster. The authors test these predictions in an interesting experimental setup that requires monkeys to collect small increments of food rewards for successful eye movements to targets. The monkeys can decide freely when to interrupt work and consume the accumulated food, and the authors measure the speed of the eye movements involved in the food acquisition as well as the tongue movements involved in the food consumption.

By and large, the behavioral findings fall in line with the qualitative model predictions: When the effort involved in food consumption increases, monkeys collect more food before deciding to consume it, and they move slower both during food acquisition and food consumption. In a second test, the authors approximate the effects of reward value of the food at stake, by comparing monkey behavior during different days with natural variations in body weight. These quasi-experimental increases in the reward value of food also lead to longer work times before consumption, but to faster movements during food consumption. Finally, the authors show that these effects correlate with pupil size, with pupils dilating more for low-effort foraging actions with increased saccade speed and decreased work duration. The authors conclude that the effort associated with anticipated actions can lead to changes in global brain state that simultaneously affect decisions and action vigor.

The paper proposes an interesting model for how one unified action policy may simultaneously affect multiple types of decisions and movements involved in foraging. The methods employed to measure behavior and test these predictions are generally sound, and the paper is well written.

Reviewer #2 (Public Review):

Summary:<br /> This research shows compelling and detailed evidence showing that aging influences intrinsic membrane properties of peripheral sympathetic motor neurons such that they become more excitable. Furthermore, the authors present convincing evidence that the oral administration of the anti-aging drug Rapamycin partially reversed hyperexcitability in aged neurons. This study also investigates the molecular mechanisms underlying age-associated hyperexcitability in mouse sympathetic motor neurons. In that regard, the authors found an age-associated reduction of an outward current having properties similar to KCNQ2/Q3 potassium current. They suggested a reduction of KCNQ2/Q3 current density in aged neurons as a potential mechanism behind their overactivity.

Strengths:<br /> Detailed and rigorous analysis of electrical responses of peripheral sympathetic motor neurons using electrophysiology (perforated patch and whole-cell recordings). Most of the conclusions of this paper are well supported by the data.

Weaknesses:<br /> 1) The identity of the age-associated reduced current as KCNQ2/Q3 is not corroborated by pharmacology (blocking the current with the specific blocker XE-991).<br /> 2) The manuscript does not include a direct test of the reduction of KCNQ current as the mechanism behind age-induced hyperexcitability.

Reviewer #2 (Public Review):

Summary<br /> This work investigates how multiple regulatory elements combine to regulate gene expression. The authors use an episomal reporter assay which measures the transcriptional output of the reporter under the regulation of an enhancer-enhancer-promoter triple. The authors test all combinations of 8 promoters and 59 enhancers in this assay. The main finding is that enhancer pairs generally combine additively on reporter output. The authors also find that the extent to which enhancers increase reporter output is inversely related to the intrinsic strength of the promoter.

This manuscript presents a compact experiment that investigates an important open question in gene regulation. The results and data will be of interest to researchers studying enhancers. Given that my expertise is in modeling and computation, I will take the experimental results at face value and focus my review on the interpretation of the results and the computational methodology. I find the result of additivity between enhancers to be well supported. The findings on differential responsiveness between promoters are very interesting but the interpretation of such responses as 'non-linear' or 'following a power-law' may be misleading. More broadly, I think a more rigorous description of the mathematical methodology would increase the clarity and accessibility of this manuscript. A major unanswered question is whether the findings in this study apply to enhancers in their native genomic context. Regardless, investigating such questions in an episomal reporter assay is valuable.

Main comments<br /> Applicability to native genomic context: The applicability of the results in this paper to enhancers in their native genomic context is unclear. As the authors state in the discussion section, the reporter gene is not integrated into the genome, the spacing between enhancers does not match their native context etc. It is thus unclear whether this experimental design is able to detect the non-additivity between enhancers which is known to be present in the genome. This could be investigated by testing the enhancer-enhancer-promoter tuples for which non-additivity has been observed in the genome (references are given in the introduction) in this assay.

Interpretation of promoter responses as non-linear and following a power-law: In Fig 5, the authors demonstrate that enhancer-enhancer pairs boost reporter output more for weak promoters as opposed to strong promoters. I agree the data supports this finding, but I find the interpretation of such data as promoters scaling enhancers according to a power-law (as stated in the abstract) to be misleading. As mentioned on line 297, it is not possible to define an intrinsic measure of enhancer strength, thus the authors assign the base of the power-law to be the average boost index of the enhancer-enhancer pair across the 8 promoters. But this measure incorporates some aspect of a promoter and is not solely a property of enhancers. It would also be useful to know whether the results in Fig 5 apply to only enhancer-enhancer-promoter triples or also to enhancer-promoter pairs.

Enhancer-promoter selectivity: As a follow-up to a previous study (Martinez-Ara et al, Molecular Cell 2022) the authors mention that the data in this study also shows that enhancers show selectivity for certain promoters. The authors mention that both studies use the same statistical methodology and the data in this study is consistent with the data from the 2022 paper. However, I think the statistical methodology in both studies needs further exposition. This section of the review is thus meant to ensure that I understand the author's methodology, to guide the reader in understanding how the authors define 'selectivity', and to probe certain assumptions underlying the methodology.

My understanding of the approach is as follows: The authors consider an enhancer to be not selective if its 'boost index' is the same across a set of promoters. 'Boost index' is defined to be the ratio of the reporter output with the enhancer and promoter divided by the reporter output with just the promoter. Conceptually, I think that considering the boost index is a reasonable way to quantify selectivity.

The authors use a frequentist approach to classify each enhancer as selective or not selective. The null hypothesis is that the boost index of the enhancer is equal across a set of promoters. This can be visualized in Fig. 2C where the null hypothesis is that the mean of each vertical distribution is equal. Note that in Figure S4 of this paper (and in Figure 4B of their 2022 paper) the within-group variance is not plotted. Statistical significance is assessed using a Welch F-test. This is a parametric test that assumes that the observations within each vertical distribution in Fig 2C are normally distributed (this test does allow for heteroskedasticity - which means that the variance may differ within each vertical distribution). Does the normality assumption hold? This analysis should be reported. If this assumption does not hold, is the Welch test well calibrated?

Reviewer #2 (Public Review):

Human STEAPs form a family of transmembrane heme-bound proteins. They are implicated in cancer given their high expression levels in tumor cells. Previous work has revealed that STEAPs 1-4 are iron and copper reductases. The recent structure determination of STEAP1 and STEAP4 unveiled their trimeric arrangement. STEAP1 is an outlier because it lacks the cytosolic reductase domain present in STEAPs 2-5. The present work adds to our knowledge of the family. It reports on the cryoEM structure of STEAP2 that is similar to the known structures of STEAP4 and STEAP1. The structural analysis provides additional support to a FAD-dependent heme-reduction mechanism whereby FAD oscillates between two conformations. The excellent kinetics experiments show that STEAP1 can be promiscuous regarding the source of electron donors that it can use. Indeed, cytochrome b5 can directly reduce the heme prosthetic group of STEAP1 thereby establishing an electron transfer chain that conveys electrons from NADP(P)H to the extracellular iron. Remarkably, STEAP1 can also accept electrons from free reduced FAD. Most interestingly, the manuscript demonstrates that STEAP2 can be a source of reduced FAD so that STEAP2 can create the reducing power needed for its own activity and the activity of STEAP1. This work further convincingly shows that STEAP1 can reduce iron whereas STEAP2 is less effective in iron reduction. The manuscript indicates that STEAP2 might accept other substrates providing a hint about the distinct biochemical and physiological roles of the STEAP paralogs. The manuscript does not address this point that remains open for further investigations. Aside from this minor weakness, the manuscript will advance the fields of STEAP and iron biochemistry. It has benefited from the advice given by the Reviewers leading to a high-quality presentation and data analysis.

Reviewer #2 (Public Review):

Summary:<br /> Deng et al. investigate, for the first time to my knowledge, the role that hippocampal dentate gyrus mossy cells play in Fragile X Syndrome. They provide strong evidence that, in slice preparations from Fmr1 knockout mice, mossy cells are hypoactive due to increased Kv7 function whereas granule cells are hyperactive compared to slices from wild-type mice. They provide indirect evidence that the weakness of mossy cell-interneuron connections contributes to granule cell hyperexcitability, despite converse adaptations to mossy cell inputs. The authors show that application of the Kv7 inhibitor XE991 is able to rescue granule cell hyperexcitability back to wild-type baseline, supporting the overall conclusion that inhibition of Kv7 in the dentate may be a potential therapeutic approach for Fragile X Syndrome. However, any claims regarding specific circuit-based intervention or analysis are limited by the exclusively pharmacological approach of the manipulations.

Strengths:<br /> Thorough electrophysiological characterization of mossy cells in Fmr1 knockout mice, a novel finding.

Their electrophysiological approach is quite rigorous: patched different neuron types (GC, MC, INs) one at a time within the dentate gyrus in FMR1 KO and WT, with and without 'circuit blockade' by pharmacologically inhibiting neurotransmission. This allows the most detailed characterization possible of passive membrane/intrinsic cell differences in the dentate gyrus of Fmr1 knockout mice.

Provide several examples showing the use of Kv7 inhibitor XE991 is able to rescue excitability of granule cell circuit in Fmr1 knockout mice (AP firing in the intact circuit, postsynaptic current recordings, theta-gamma coupling stimulation).

Weaknesses:<br /> The implications for these findings and the applicability of the potential treatment for the disorder in a whole animal are limited due to the fact that all experiments were done in slices.

The authors' interpretation of the word 'circuit-based' is problematic - there are no truly circuit-specific manipulations in this study due to the reliance on pharmacology for their manipulations. While the application of the Kv7 inhibitor may have a predominant effect on the circuit through changes to mossy cell excitability, this manipulation would affect many other cells within the dentate and adjacent brain regions that connect to the dentate that express Kv7 as well.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors investigated the relationship between menopause (including status, type, and age of onset) with measures of brain health, including cognition, Alzheimer's disease (including age of onset), and structural brain imaging.

Strengths:<br /> A key strength is the use of propensity matching to address the confound of age. However, further clarification and justification regarding the study design, methodology, reporting, and discussion of the results is required.

Weaknesses:<br /> Overall, the strength of evidence is uncertain/incomplete, given the methodological limitations present in the design, analyses, and reporting of results. The findings are useful, however, much of the relevant literature in this area is missing and the findings have therefore not been appropriately contextualised nor compared with previous results, including those using the same dataset.

Reviewer #2 (Public Review):

Summary:

The work of Poudel et al. identified potential causal mutations related to the successful emergence of the virulent USA300 community-associated MRSA clone within clonal complex 8. To achieve this, the authors employed a methodology that combines the genome-wide association studies (GWAS) with the inference of a transcriptional regulatory network (TRN) through the independent component analysis (ICA) method from publicly available transcriptomic data. Thus, they identified genes with altered expression in the iModulons calculated by ICA and enriched mutations obtained from the De Bruijn graph genome-wide association study (DBGWAS) in the USA300 strains versus non-USA300 strains. The results revealed a deletion of 38 base pairs, containing a binding site for the Fur repressor, and an A→T mutation, both occurring in the upstream region of the isdH gene, whose expression level in USA300 strains exhibited a general increase compared to the other group. IsdH encodes the iron-regulated surface determinant protein H, which plays a crucial role in iron acquisition from heme and immune system evasion - two essential processes for the pathogenicity of S. aureus.

Strengths:

The clonal complex 8 (CC8), one of the most prevalent among S. aureus, encompasses strains responsible for both community-associated MRSA infections (CA-MRSA) and healthcare-associated (HA) infections (HA-MRSA and HA-MSSA). Within the CC8, one of the most prominent lineages is USA300, which emerged in the early 2000s and has since become a leading cause of CA-MRSA infections in the United States. The key genetic traits that characterize USA300 strains include the presence of the Panton-Valentine leukocidin (PVL) encoded by the genes lukF-PV and lukS-PV, the staphylococcal chromosomal cassette mec IVa (SCCmecIVa), and the arginine catabolic mobile element (ACME). Investigating the phenotypic impact of individual mutations on the success of epidemic strains through GWAS poses a challenge due to two main confounding factors: genome-wide linkage disequilibrium (LD) and population structure. The genome-wide LD is associated with false positives, where linked non-causal mutations are mistakenly identified as causal due to the same genomic backgrounds. Therefore, the strength of this work lies in the use of publicly available transcriptomic data to construct a TRN based on ICA. This approach validates the mutations enriched by GWAS and reduces the occurrence of false positives attributed to high genome-wide LD. By integrating various 'omics' data sources, this method enhances the reliability of the results and has successfully identified new potential genetic markers specific to USA300 strains. Furthermore, it revealed mutations within core genes and intergenic regulatory regions, findings that can be validated through experimental data.

Weaknesses:

GWAS aims to identify statistically significant associations that suggest a causal link between genotype and the specific phenotype of interest while simultaneously filtering out spurious associations caused by confounding factors. While the method described in this study minimizes the impact of genome-wide linkage disequilibrium (LD), it does not extend to addressing population structure. This is because the objective was precisely to identify mutations associated with the emergence of the USA300 clone. In this context, the confounding element arising from shared ancestry becomes the subject of analysis rather than an issue to be corrected. Therefore, it is essential to highlight that the method proposed in this work can not be applied to genome-wide association studies, where correction for population structure is critical for distinguishing genuine causal associations from spurious ones. This correction is crucial and necessary to most of the studied phenotypes of interest.

Another limitation is that, although the authors emphasize the mutation in the isdH gene, the analyses conducted in this study do not provide insight into any potential adaptive function associated with it. Similarly, like the other genes exhibiting distinct expression patterns associated with enriched mutations from DBGWAS in USA300 strains, isdH is among the potential markers related to the success of the clone. This group includes well-established markers, such as ACME, which carries relevant genes like the arc operon and the speG gene that contribute to virulence and survival at infection sites.

Finally, despite the availability of the codes on GitHub, the analysis itself is not easily reproducible or adaptable to other datasets.

Reviewer #2 (Public Review):

In this study, Torcq and colleagues make careful observations of the cellular morphology of haemogenic endothelium undergoing endothelial to haematopoietic transition (EHT) to become stem cells, using the zebrafish model. To achieve this, they used an extensive array of transgenic lines driving fluorescent markers, markers of apico-basal polarity (podocalixin-FP fusions), or tight junction markers (jamb-FP fusions). The use of the runx truncation to block native Runx1 only in endothelial cells is an elegant tool to achieve something akin to tissue-specific deletion of Runx1. Overall, the imaging data is of excellent quality. They demonstrate that differences in apico-basal polarity are strongly associated with different cellular morphologies of cells undergoing EHT from HE (EHT pol- and EHT pol+) which raises the exciting possibility that these morphological differences reflect the heterogeneity of HE (and therefore HSCs) at a very early stage. They then overexpress a truncated form of Runx1 (just the runt domain) to block Runx1 function and show that more HE cells abort EHT and remain associated with the embryonic dorsal aorta. They identify pard3aa and pard3ab as potential regulators of cell polarity. However, despite showing that loss of runx1 function leads to (late) decreases in the expression of these genes, no evidence for their role in EHT is presented. The FRAP experiments and the 2d-cartography, albeit very elegant, are difficult to interpret and not very clearly described throughout the text, making interpretation difficult for someone less familiar with the techniques. Finally, while it is clear that ArhGEF11 is playing an important role in defining cell shapes and junctions between cells during EHT, there is very little statistical evidence to support the limited data presented in the (very beautiful) images.

There is a sense that this work is both overwhelming in terms of the sheer amount of imaging data, and the work behind it to generate all the lines they required, and at the same time that there is very little evidence supporting the assertion that pard3 (and even ArhGEF11) are important mediators of cell morphology and cell fate in the context of EHT. For instance, the pard3 expression data, and levels after blocking runx1 (part of Figure 3 and Figure 4) don't particularly add to the manuscript beyond indicating that the pard3 genes are regulated by Runx1.

Weaknesses<br /> The writing style is quite convoluted and could be simplified for clarity. For example, there is plenty of discussion and speculation throughout the presentation of the results. A clearer separation of the results from this speculation/discussion would help with understanding. Figures are frequently presented out of order in the text; modifying the figures to accommodate the flow of the text (or the other way around) - would make it much easier to follow the narrative. While the evidence for the different cellular morphologies of cells undergoing EHT is strong, the main claim (or at least the title of the manuscript) that tuning apico-basal polarity and junctional recycling orchestrate stem cell emergence complexity is not well supported by the data.

Reviewer #2 (Public Review):

Summary:<br /> The paper by Kim et al. investigates the potential of stimulating the dopaminergic A13 region to promote locomotor restoration in a Parkinson's mouse model. Using wild-type mice, 6-OHDA injection depletes dopaminergic neurons in the substantia nigra pars compacta, without impairing those of the A13 region and the ventral tegmentum area, as previously reported in humans. Moreover, photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region improves bradykinesia and akinetic symptoms after 6-OHDA injection. Whole-brain imaging with retrograde and anterograde tracers reveals that the A13 region undergoes substantial changes in the distribution of its afferents and projections after 6-OHDA injection. The study suggests that if the remodeling of the A13 region connectome does not promote recovery following chronic dopaminergic depletion, photostimulation of the A13 region restores locomotor functions.

Strengths:<br /> Photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region promotes locomotion and locomotor recovery of wild-type mice 1 month after 6-OHDA injection in the medial forebrain bundle, thus identifying a new potential target for restoring motor functions in Parkinson's disease patients.

Weaknesses:<br /> Electrical stimulation of the medial Zona Incerta, in which the A13 region is located, has been previously reported to promote locomotion (Grossman et al., 1958). Recent mouse studies have shown that if optogenetic or chemogenetic stimulation of GABAergic neurons of the Zona Incerta promotes and restores locomotor functions after 6-OHDA injection (Chen et al., 2023), stimulation of glutamatergic ZI neurons worsens motor symptoms after 6-OHDA (Lie et al., 2022).

Although CAMKIIa is a marker of presumably excitatory neurons and can be used as an alternative marker of dopaminergic neurons, behavioral results of this study raise questions about the neuronal population targeted in the vicinity of the A13 region. Moreover, if YFP and CHR2-YFP neurons express dopamine (TH) within the A13 region (Fig. 2), there is also a large population of transduced neurons within and outside of the A13 region that do not, thus suggesting the recruitment of other neuronal cell types that could be GABAergic or glutamatergic.

Regarding the analysis of interregional connectivity of the A13 region, there is a lack of specificity (the viral approach did not specifically target the A13 region), the number of mice is low for such correlation analyses (2 sham and 3 6-OHDA mice), and there are no statistics comparing 6-OHDA versus sham (Fig. 4) or contra- versus ipsilesional sides (Fig. 5). Moreover, the data are too processed, and the color matrices (Fig. 4) are too packed in the current format to enable proper visualization of the data. The A13 afferents/efferents analysis is based on normalized relative values; absolute values should also be presented to support the claim about their upregulation or downregulation.

In the absence of changes in the number of dopaminergic A13 neurons after 6-OHDA injection, results from this correlation analysis are difficult to interpret as they might reflect changes from various impaired brain regions independently of the A13 region. There is no causal link between anatomical and behavioral data, which raises questions about the relevance of the anatomical data.

Overall, the study does not take advantage of genetic tools accessible in the mouse to address the direct or indirect behavioral and anatomical contributions of the A13 region to motor control and recovery after 6-OHDA injection.

Reviewer #2 (Public Review):

In this paper, Paladini and colleagues investigate the concerted motions within the Abl kinase that control its conformational transition between the active (disassembled) and inactive (assembled state). This work follows their previously published findings that binding of the type II inhibitor, imatinib to the active site of Abl, leads to kinase core disassembly via the force imposed by the P-loop and other regions of the N-lobe on the SH3 domain. Interestingly, imatinib-induced disassembly is prevented when an allosteric inhibitor, asciminib, binds to the myristate-binding pocket. Key to asciminib and myristate binding are motions of helix I, located in the C-lobe, and thus, helix I is hypothesized to be the sensor of the imatinib-induced changes. Specifically, bending of helix I upon engagement of myristate or asciminib was postulated to be important for re-assembly of the autoinhibited Abl core, and thus, reducing the "force" with which kinase N-lobe pushes against the SH2 domain upon binding imatinib.

The authors use NMR to measure conformational transitions in the several 15N-labeled Abl kinase constructs that display different degrees of helix I truncations. This analysis is slightly limited by the instability of the constructs that carry truncations beyond the helix I "bend". Nevertheless, it is sufficient to establish that truncation of helix I that removes its fragment, which is in contact with myristate or asciminib ligands, results in loss of the ability of helix I to impose "force" on the SH2 domain that results in kinase core disassembly, even in the presence of imatinib binding. In the absence of this force, the allosteric coupling between the helix I/SH2 and KD/SH3 interfaces is compromised. Principle component analysis is used to analyze the NMR data, and it is very clear and convincing.

A compelling evidence in support of the proposed allosteric mechanism comes from the analysis of the E528K disease mutation, identified in the Abl1 malformation syndrome. The authors show that this mutant, poised to break a salt bridge formed between E528 in the C-terminal portion of helix I and R479 on the kinase domain, increases helix I outward motions resulting in core disassembly and higher Abl kinase activity. Together, these results reinforce that helix I motions are central to the mechanism of kinase activation via core disassembly. I find that all authors' claims are supported by the experimental data. A couple of suggestions on how to expand and improve the discussion of the data are listed in specific feedback to the authors.

Reviewer #2 (Public Review):

In this manuscript, the authors present a novel interactome focused on human and fly alpha-arrestin family proteins and demonstrate its application in understanding the functions of these proteins. Initially, the authors employed AP/MS analysis, a popular method for mapping protein-protein interactions (PPIs) by isolating protein complexes. Through rigorous statistical and manual quality control procedures, they established two robust interactomes, consisting of 6 baits and 307 prey proteins for humans, and 12 baits and 467 prey proteins for flies. To gain insights into the gene function, the authors investigated the interactors of alpha-arrestin proteins through various functional analyses, such as gene set enrichment. Furthermore, by comparing the interactors between humans and flies, the authors described both conserved and species-specific functions of the alpha-arrestin proteins. To validate their findings, the authors performed several experimental validations for TXNIP and ARRDC5 using ATAC-seq, siRNA knockdown, and tissue staining assays. The experimental results strongly support the predicted functions of the alpha-arrestin proteins and underscore their importance.

Reviewer #2 (Public Review):

Summary:<br /> Previous work has shown subjects can use a form of short-term sensory memory, known as 'iconic memory', to accurately remember stimuli over short periods of time (several hundred milliseconds). Working memory maintains representations for longer periods of time but is strictly limited in its capacity. While it has long been assumed that sensory information acts as the input to working memory, a process model of this transfer has been missing. To address this, Tomic and Bays present the Dynamic Neural Resource (DyNR) model. The DyNR model captures the dynamics of processing sensory stimuli, transferring that representation into working memory, the diffusion of representations within working memory, and the selection of memory for report.

The DyNR model captures many of the effects observed in behavior. Most importantly, psychophysical experiments found the greater the delay between stimulus presentation and the selection of an item from working memory, the greater the error. This effect also depended on working memory load. In the model, these effects are captured by the exponential decay of sensory representations (i.e., iconic memory) following the offset of the stimulus. Once the selection cue is presented, residual information in iconic memory can be integrated into working memory, improving the strength of the representation and reducing error. This selection process takes time, and is slower for larger memory loads, explaining the observation that memory seems to decay instantly. The authors compare the DyNR model to several variants, demonstrating the importance of the persistence of sensory information in iconic memory, normalization of representations with increasing memory load, and diffusion of memories over time.

Strengths:<br /> The manuscript provides a convincing argument for the interaction of iconic memory and working memory, as captured by the DyNR model. The analyses are cutting-edge and the results are well captured by the DyNR model. In particular, a strength of the manuscript is the comparison of the DyNR model to several alternative variants.

The results provide a process model for interactions between iconic memory and working memory. This will be of interest to neuroscientists and psychologists studying working memory. I see this work as providing a foundation for understanding behavior in continuous working memory tasks, from either a mechanistic, neuroscience, perspective or as a high-water mark for comparison to other psychological process models.

Finally, the manuscript is well written. The DyNR model is nicely described and an intuition for the dynamics of the model is clearly shown in Figures 2 and 4.

Weaknesses:<br /> Despite its strengths, the paper does have some (relatively minor) weaknesses. In particular, the authors could consider the role of sensory processing, and its limitations, and variability in selecting an item from working memory as other factors affecting memory accuracy.

Reviewer #2 (Public Review):

The contribution of glial cells to the pathogenesis of amyotrophic lateral sclerosis (ALS) is of substantial interest and the investigators have contributed significantly to this emerging field via prior publications. In the present study, authors use a SOD1G93A mouse model to elucidate the role of astrocyte ephrinB2 signaling in ALS disease progression. Erythropoietin-producing human hepatocellular receptors (Ephs) and the Eph receptor-interacting proteins (ephrins) signaling is an important mediators of signaling between neurons and non-neuronal cells in the nervous system. Recent evidence suggests that dysregulated Eph-ephrin signaling in the mature CNS is a feature of neurodegenerative diseases. In the ALS model, upregulated Eph4A expression in motor neurons has been linked to disease pathogenesis. In the present study, authors extend previous findings to a new class of ephrinB2 ligands. Urban et al. hypothesize that upregulated ephrinB2 signaling contributes to disease pathogenesis in ALS mice. The authors successfully test this hypothesis and their results generally support their conclusion.

Major strengths of this work include a robust study design, a well-defined translational model, and complementary biochemical and experimental methods such that correlated findings are followed up by interventional studies. Authors show that ephrinB2 ligand expression is progressively upregulated in the ventral horn of the cervical and lumbar spinal cord through pre-symptomatic to end stages of the disease. This novel association was also observed in lumbar spinal cord samples from post-mortem samples of human ALS donors with a SOD1 mutation. Further, they use a lentiviral approach to drive knock-down of ephrinB2 in the central cervical region of SOD1G93A mice at the pre-symptomatic stage. Interestingly, in spite of using a non-specific promoter, authors note that the lentiviral expression was preferentially driven in astrocytes.

Since respiratory compromise is a leading cause of morbidity in the ALS population, the authors proceed to characterize the impact of ephrinB2 knockdown on diaphragm muscle output. In mice approaching the end stage of the disease, electrophysiological recordings from the diaphragm muscle show that animals in the knock-down group exhibited a ~60% larger amplitude. This functional preservation of diaphragm function was also accompanied with the preservation of diaphragm neuromuscular innervation. However, it must be noted that this cervical ephrinB2 knockdown approach had no impact on disease onset, disease duration, or animal survival. Furthermore, there was no impact of ephrinB2 knockdown on forelimb or hindlimb function. This is an expected result, given the fairly focal approach of ephrinB2 knockdown in C3-C5 spinal segments.

The major limitation of the study is the conclusion that the preservation of diaphragm output following ephrinB2 knockdown in SOD1 mice is mediated primarily (if not entirely) by astrocytes. The authors present convincing evidence that a reduction in ephrinB2 is observed in local astrocytes (~56% transduction) following the intraspinal injection of the lentivirus. However, the proportion of cell types assessed for transduction with the lentivirus in the spinal cord was limited to neurons, astrocytes, and oligodendrocyte lineage cells. Microglia comprise a large proportion of the glial population in the spinal grey matter and have been shown to associate closely with respiratory motor pools. This cell type, amongst the many other that comprise the ventral gray matter, have not been investigated in this study. Nonetheless, there is convincing evidence to suggest astrocytes play a significant role, as compared to oligodendrocytes in promoting ALS pathogenesis.

In summary, this study by Urban et al. provides a valuable framework for Eph-Ephrin signaling mechanisms imposing pathological changes in an ALS mouse model. The role of glial cells in ALS pathology is a very exciting and upcoming field of investigation. The current study proposes a novel astrocyte-mediated mechanism for the propagation of disease that may eventually help to identify potential therapeutic targets.

Reviewer #2 (Public Review):

Summary:

Using in vitro and in vivo approaches, the authors first demonstrate that BEST4 inhibits intestinal tumor cell growth and reduces their metastatic potential, possibly via downstream regulation of TWIST1.

They further show that HES4 positively upregulates BEST4 expression, with direct interaction with BEST4 promoter region and protein. The authors further expand on this with results showing that negative regulation of TWIST1 by HES4 requires BEST4 protein, with BEST4 required for TWIST1 association with HES4. Reduction of BEST1 expression was shown in CRC tumor samples, with correlation of BEST4 mRNA levels with different clinicopathological factors such as sex, tumor stage, and lymph node metastasis, suggesting a tumor-suppressive role of BEST4 for intestinal cancer.

Strengths:

• Good quality western blot data.<br /> • Multiple approaches were used to validate the findings.<br /> • Logical experimental progression for readability.<br /> • Human patient data / In vivo murine model / Multiple cell lines were used, which supports translatability / reproducibility of findings.

Weaknesses:

• Interpretation of figures and data (unsubstantiated conclusions).<br /> • Figure quality.<br /> • Figure legends lack information.<br /> • Lacking/shallow discussion.<br /> • Requires more information for reproducibility regarding materials and methods.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript examines the expression of orexin receptors in the midbrain - with a focus on dopamine neurons - and uses several fairly sophisticated manipulation techniques to explore the role of this peptide neurotransmitter in reward-related behaviors. Specifically, in situ hybridization is used to show that dopamine neurons predominantly express the orexin receptor 1 subtype and then go on to delete this receptor in dopamine neurons using a transgenic strategy. Ex vivo calcium imaging of midbrain neurons is used to show that in the absence of this receptor orexin is no longer able to excite dopamine neurons of the substantia nigra.

The authors proceed to use this same model to study the effect of orexin receptor 1 deletion on a series of behavioral tests, namely, novelty-induced locomotion and exploration, anxiety-related behavior, preference for sweet solutions, cocaine-induced conditioned place preference, and energy metabolism. Of these, the most consistent effects are seen in the tests of novelty-induced locomotion and exploration in which the mice with orexin 1 receptor deletion are observed to show greater levels of exploration, relative to wild-type, when placed in a novel environment, an effect that is augmented after icv administration of orexin.

In the final part of the paper, the authors use PET imaging to compare brain-wide activity patterns in the mutant mice compared to wildtype. They find differences in several areas both under control conditions (i.e., after injection of saline) as well as after injection of orexin. They focus on changes in the dorsal bed nucleus of stria terminalis (dBNST) and the lateral paragigantocellular nucleus (LPGi) and perform analysis of the dopaminergic projections to these areas. They provide anatomical evidence that these regions are innervated by dopamine fibers from the midbrain, are activated by orexin in control, but not mutant mice, and that dopamine receptors are present. Thus, they argue these anatomical data support the hypothesis that behavioral effects of orexin receptor 1 deletion in dopamine neurons are due to changes in dopamine signaling in these areas.

Strengths:<br /> Understanding how orexin interacts with the dopamine system is an important question and this paper contains several novel findings along these lines. Specifically:<br /> (1) The distribution of orexin receptor subtypes in VTA and SN is explored thoroughly.<br /> (2) Use of the genetic model that knocks out a specific orexin receptor subtype from only dopamine neurons is a useful model and helps to narrow down the behavioral significance of this interaction.<br /> (3) PET studies showing how central administration of orexin evokes dopamine release across the brain is intriguing, especially since two key areas are pursued - BNST and LPGi - where the dopamine projection is not as well described/understood.

Weaknesses:<br /> The role of the orexin-dopamine interaction is not explored in enough detail. The manuscript presents several related findings, but the combination of anatomy and manipulation studies does not quite tell a cogent story. Ideally, one would like to see the authors focus on a specific behavioral parameter and show that one of their final target areas (dBNST or LPGi) was responsible or at least correlated with this behavioral readout. In addition, some more discussion on what the results tell us about orexin signaling to dopamine neurons under normal physiological conditions would be very useful. For example, what is the relevance of the orexin-dopamine interaction blunting novelty-induced locomotion under wildtype conditions?

In some places in the Results, insufficient explanation and reporting is provided. For example, when reporting the behavioral effects of the Ox1 deletion in two bottle preference, it is stated that "[mutant] mice showed significant changes..." without stating the direction in which preference was affected.

The cocaine CPP results are difficult to interpret because it is unclear whether any of the control mice developed a CPP preference. Therefore, it is difficult to conclude that the knockout animals were unaffected by drug reward learning. Similarly, the sucrose/sucralose preference scores are also difficult to interpret because no test of preference vs. water is performed (although the data appear to show that there is a preference at least at higher concentrations, it has not been tested).

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

Summary:

In the manuscript 'Auditory cortical error signals retune during songbird courtship', Jones and Goldberg study auditory cortex in male zebra finches. They explore song-related responses in two different contexts, when the male is either alone or in the presence of a female. Social-context related responses are hypothesized based on previous results on downstream VTA neurons where such modulation is found. They play jamming stimuli through a loudspeaker to probe sensitivity of song-related neural responses to these external stimuli. They find a heterogeneity of responses, in line with auditory cortical neurons computing the social modulation of responses found in VTA.

Strengths:

In general, the work is interesting and sheds light onto auditory processing and self-perception mechanisms in songbirds.

Weaknesses:

Stability of responses has not been studied: some neurons seem to have responses that slowly drift in time, which could lead to observed differences between alone and with-female conditions. Also, possible motor confounds and sound-of-audience confounds should be addressed. The language is often imprecise.

Stability and Reversal: It is a bit unfortunate that stability of effects seemingly has not been studied by reversing experimental conditions. The work would be much stronger if authors could show that audience-dependent tuning is robust in individual cells. Did they record from some neurons during reversal back to the alone condition? Ideally, the responses should be identical before and after recording with an audience. This would control for possible non-stationarities in their neuron recordings/spike-sorting/circadian trends. If authors do not have such data, it would be worth wile to even just try to divide the dataset for each neuron and condition (either the audience or isolate condition) into two parts to verify that the response is the same in either part (provided sufficient song renditions are recorded). See also my comment below about Fig. 2A.

Motor responses: Does DAF playback change song? If so, especially if it applies only in one of the two conditions (audience/no audience), then the observed response differences could be motor-related rather than auditory responses. Analyses of song spectrograms right after DAF would presumably provide the answer.

Similarly, motif-aligned spiking activity was time warped to the median duration of undirected or directed motifs. Could the shorter motifs during directed song (as has been reported in other studies) lead to alignment differences that would account for the different error responses in alone/wfemale conditions? In other words, could increased error responses be due to the fixed 100 ms analysis window of the audience condition that extends into a song region beyond the 100 ms region of the no-audience condition where there is increased firing? And vice versa for observed decreases in error responses, i.e. is there a firing pause just after the offset of the 100 ms window in the no-audience condition that causes audience dependence of responses? A simple compensation of song tempo differences by shortening/stretching the analysis window in one of the two conditions would allow to test for this.

Audience versus sound of audience: In the first sentence of the discussion authors write: we discovered that auditory representations of an animal's own vocalizations change with an audience. Is it truly the audience that causes the difference in error responses or is it the sounds the audience makes? To control for that would be to play back stimuli that simulate a non-silent audience through a loudspeaker to see whether error responses depend on the soundscape created by a typical audience (either present or absent). Authors probably do not have such data and to record it would go beyond the scope of this study, but it would be important to discuss this possibility or perform some analysis in that vein.

Reviewer #2 (Public Review):

Summary:<br /> Hwang, Ran-Der et al utilized a CRISPR-Cas9 knockout in human retinal pigment epithelium (RPE1) cells to evaluate for suppressors of toxicity by the proteasome inhibitor MG132 and identified that knockout of dihydrolipoamide branched chain transacylase E2 (DBT) suppressed cell death. They show that DBT knockout in RPE1 cells does not alter proteasome or autophagy function at baseline. However, with MG132 treatment, they show a reduction in ubiquitinated proteins but with no change in proteasome function. Instead, they show that DBT knockout cells treated with MG132 have improved autophagy flux compared to wildtype cells treated with MG132. They show that MG132 treatment decreases ATP/ADP ratios to a greater extent in DBT knockout cells, and in accordance causes activation of AMPK. They then show downstream altered autophagy signaling in DBT knockout cells treated with MG132 compared to wild-type cells treated with MG132. Then they express the ALS mutant TDP43 M337 or expanded polyglutamine repeats to model Huntington's disease and show that knockdown of DBT improves cell survival in RPE1 cells with improved autophagic flux. They also utilize a Drosophila model and show that utilizing either a RNAi or CRISPR-Cas9 knockout of DBT improves eye pigment in TDP43M337V and polyglutamine repeat-expressing transgenic flies. Finally, they show evidence for increased DBT in postmortem spinal cord tissue from patients with ALS via both immunoblotting and immunofluorescence.

Strengths:<br /> This is a mechanistic and well-designed paper that identifies DBT as a novel regulator of proteotoxicity via activating autophagy in the setting of proteasome inhibition. Major strengths include careful delineation of a mechanistic pathway to define how DBT is protective. These conclusions are largely justified, but additional experiments and information would be useful to clarify and extend these conclusions.

Weaknesses:<br /> The large majority of the experiments are evaluating suppression of drug (MG132) toxicity in an in vitro epithelial cell line, so the generalizability to disease is unclear. Indeed, MG132 itself has been shown to modulate autophagy, and off-target effects of MG132 are not addressed. While this paper is strengthened by the inclusion of mouse-induced motor neurons, Drosophila models, and postmortem tissue, the putative mechanisms are minimally evaluated in these models.

Also, this effect is only seen with MG132 treatment, at a dose that causes markedly impaired cell survival. In this setting, it is certainly plausible that changes in autophagy could be the result of differences in cell survival, as opposed to an underlying mechanism for cell survival. Additional controls would be useful to increase confidence that DBT knockdown is protective via modulation of autophagy.

While the authors report increased DBT in postmortem ALS tissue as suggestive that DBT may modulate proteotoxicity in neurodegeneration, this point would be better supported with the evaluation of overexpression of DBT in their model.

Reviewer #2 (Public Review):

Summary:<br /> The authors have noted in preliminary work that tetrodotoxin (TTX), which inhibits NaV1.7 and several other TTX-sensitive sodium channels, has differential effects on nociceptors, dramatically reducing their excitability under certain conditions but not under others. Partly because of this coincidental observation, the aim of the present work was to re-examine or characterize the role of NaV1.7 in nociceptor excitability and its effects on drug efficacy. The manuscript demonstrates that a NaV1.7-selective inhibitor produces analgesia only when nociceptor excitability is based on NaV1.7. More generally and comprehensively, the results show that nociceptors can achieve equivalent excitability through changes in differential NaV inactivation and NaV expression of different NaV subtypes (NaV 1.3/1.7 and 1.8). This can cause widespread changes in the role of a particular subtype over time. The degenerate nature of nociceptor excitability shows functional implications that make the assignment of pathological changes to a particular NaV subtype difficult or even impossible.

Thus, the analgesic efficacy of NaV1.7- or NaV1.8-selective agents depends essentially on which NaV subtype controls excitability at a given time point. These results explain, at least in part, the poor clinical outcomes with the use of subtype-selective NaV inhibitors and therefore have major implications for the future development of Nav-selective analgesics.

Strengths:<br /> The above results are clearly and impressively supported by the experiments and data shown. All methods are described in detail, presumably allow good reproducibility, and were suitable to address the corresponding question. The only exception is the description of the computer model, which should be described in more detail.

The results showing that nociceptors can achieve equivalent excitability through changes in differential NaV inactivation and expression of different NaV subtypes are of great importance in the fields of basic and clinical pain research and sodium channel physiology and pharmacology, but also for a broad readership and community. The degenerate nature of nociceptor excitability, which is clearly shown and well supported by data has large functional implications. The results are of great importance because they may explain, at least in part, the poor clinical outcomes with the use of subtype-selective NaV inhibitors and therefore have major implications for the future development of Nav-selective analgesics.

In summary, the authors achieved their overall aim to enlighten the role of NaV1.7 in nociceptor excitability and the effects on drug efficacy. The data support the conclusions, although the clinical implications could be highlighted in a more detailed manner.

Weaknesses:<br /> As mentioned before, the results that nociceptors can achieve equivalent excitability through changes in differential NaV inactivation and NaV expression of different NaV subtypes are impressive. However, there is some "gap" between the DRG culture experiments and acutely dissociated DRGs from mice after CFA injection. In the extensive experiments with cultured DRG neurons, different time points after dissociation were compared. Although it would have been difficult for functional testing to examine additional time points (besides DIV0 and DIV4-7), at least mRNA and protein levels should have been determined at additional time points (DIV) to examine the time course or whether gene expression (mRNA) or membrane expression (protein) changes slowly and gradually or rapidly and more abruptly. It would also be interesting to clarify whether the changes that occur in culture (DIV0 vs. DIV4-7) are accompanied by (pro-)inflammatory changes in gene and protein expression, such as those known for nociceptors after CFA injection. This would better link the following data demonstrating that in acutely dissociated nociceptors after CFA injection, the inflammation-induced increase in NaV1.7 membrane expression enhances the effect of (or more neurons respond to) the NaV1.7 inhibitor PF-71, whereas fewer CFA neurons respond to the NaV1.8 inhibitor PF-24.

The results shown explain, at least in part, the poor clinical outcomes with the use of subtype-selective NaV inhibitors and therefore have important implications for the future development of Nav-selective analgesics. However, this point, which is also evident from the title of the manuscript, is discussed only superficially with respect to clinical outcomes. In particular, the promising role of NaV1.7, which plays a role in nociceptor hyperexcitability but not in "normal" neurons, should be discussed in light of clinical results and not just covered with a citation of a review. Which clinical results of NaV1.7-selective drugs can now be better explained and how?

Another point directly related to the previous one, which should at least be discussed, is that all the data are from rodents, or in this case from mice, and this should explain the clinical data in humans. Even if "impediment to translation" is briefly mentioned in a slightly different context, one could (as mentioned above) discuss in more detail which human clinical data support the existence of "equivalent excitability through different sodium channels" also in humans.

Although speculative, it would be interesting for readers to know whether a treatment regimen based on "time since injury" with NaV1.7 and NaV1.8 inhibitors might offer benefits. Based on the data, could one hypothesize that NaV1.7 inhibitors are more likely to benefit (albeit in the short term) in patients with neuropathic pain with better patient selection (e.g., defined interval between injury and treatment)?

Reviewer #2 (Public Review):

Summary<br /> This paper expands on the literature on spatial metamers, evaluating different aspects of spatial metamers including the effect of different models and initialization conditions, as well as the relationship between metamers of the human visual system and metamers for a model. The authors conduct psychophysics experiments testing variations of metamer synthesis parameters including type of target image, scaling factor, and initialization parameters, and also compare two different metamer models (luminance vs energy). An additional contribution is doing this for a field of view larger than has been explored previously.

General Comments<br /> Overall, this paper addresses some important outstanding questions regarding comparing original to synthesized images in metamer experiments and begins to explore the effect of noise vs image seed on the resulting syntheses. While the paper tests some model classes that could be better motivated, and the results are not particularly groundbreaking, the contributions are convincing and undoubtedly important to the field. The paper includes an interesting Voronoi-like schematic of how to think about perceptual metamers, which I found helpful, but for which I do have some questions and suggestions. I also have some major concerns regarding incomplete psychophysical methodology including lack of eye-tracking, results inferred from a single subject, and a huge number of trials. I have only minor typographical criticisms and suggestions to improve clarity. The authors also use very good data reproducibility practices.

Specific Comments

Experimental Setup<br /> Firstly, the experiments do not appear to utilize an eye tracker to monitor fixation. Without eye tracking or another manipulation to ensure fixation, we cannot ensure the subjects were fixating the center of the image, and viewing the metamer as intended. While the short stimulus time (200ms) can help minimize eye movements, this does not guarantee that subjects began the trial with correct fixation, especially in such a long experiment. While Covid-19 did at one point limit in-person eye-tracked experiments, the paper reports no such restrictions that would have made the addition of eye-tracking impossible. While such a large-scale experiment may be difficult to repeat with the addition of eye tracking, the paper would be greatly improved with, at a minimum, an explanation as to why eye tracking was not included.

Secondly, many of the comparisons later in the paper (Figures 9,10) are made from a single subject. N=1 is not typically accepted as sufficient to draw conclusions in such a psychophysics experiment. Again, if there were restrictions limiting this it should be discussed. Also (P11) Is subject sub-00 is this an author? Other expert? A naive subject? The subject's expertise in viewing metamers will likely affect their performance.

Finally, the number of trials per subject is quite large. 13,000 over 9 sessions is much larger than most human experiments in this area. The reason for this should be justified.

Model<br /> For the main experiment, the authors compare the results of two models: a 'luminance model' that spatially pools mean luminance values, and an 'energy model' that spatially pools energy calculated from a multi-scale pyramid decomposition. They show that these models create metamers that result in different thresholds for human performance, and therefore different critical scaling parameters, with the basic luminance pooling model producing a scaling factor 1/4 that of the energy model. While this is certain to be true, due to the luminance model being so much simpler, the motivation for the simple luminance-based model as a comparison is unclear.

The authors claim that this luminance model captures the response of retinal ganglion cells, often modeled as a center-surround operation (Rodieck, 1964). I am unclear in what aspect(s) the authors claim these center-surround neurons mimic a simple mean luminance, especially in the context of evidence supporting a much more complex role of RGCs in vision (Atick & Redlich, 1992). Why do the authors not compare the energy model to a model that captures center-surround responses instead? Do the authors mean to claim that the luminance model captures only the pooling aspects of an RGC model? This is particularly confusing as Figures 6 and 9 show the luminance and energy models for original vs synth aligning with the scaling of Midget and Parasol RGCs, respectively. These claims should be more clearly stated, and citations included to motivate this. Similarly, with the energy model, the physiological evidence is very loosely connected to the model discussed.

Prior Work:<br /> While the explorations in this paper clearly have value, it does not present any particularly groundbreaking results, and those reported are consistent with previous literature. The explorations around critical eccentricity measurement have been done for texture models (Figure 11) in multiple papers (Freeman 2011, Wallis, 2019, Balas 2009). In particular, Freeman 20111 demonstrated that simpler models, representing measurements presumed to occur earlier in visual processing need smaller pooling regions to achieve metamerism. This work's measurements for the simpler models tested here are consistent with those results, though the model details are different. In addition, Brown, 2023 (which is miscited) also used an extended field of view (though not as large as in this work). Both Brown 2023, and Wallis 2019 performed an exploration of the effect of the target image. Also, much of the more recent previous work uses color images, while the author's exploration is only done for greyscale.

Discussion of Prior Work:<br /> The prior work on testing metamerism between original vs. synthesized and synthesized vs. synthesized images is presented in a misleading way. Wallis et al.'s prior work on this should not be a minor remark in the post-experiment discussion. Rather, it was surely a motivation for the experiment. The text should make this clear; a discussion of Wallis et al. should appear at the start of that section. The authors similarly cite much of the most relevant literature in this area as a minor remark at the end of the introduction (P3L72).

White Noise:<br /> The authors make an analogy to the inability of humans to distinguish samples of white noise. It is unclear however that human difficulty distinguishing samples of white noise is a perceptual issue- It could instead perhaps be due to cognitive/memory limitations. If one concentrates on an individual patch one can usually tell apart two samples. Support for these difficulties emerging from perceptual limitations, or a discussion of the possibility of these limitations being more cognitive should be discussed, or a different analogy employed.

Relatedly, in Figure 14, the authors do not explain why the white noise seeds would be more likely to produce syntheses that end up in different human equivalence classes.

It would be nice to see the effect of pink noise seeds, which mirror the power spectrum of natural images, but do not contain the same structure as natural images - this may address the artifacts noted in Figure 9b.

Finally, the authors note high-frequency artifacts in Figure 4 & P5L135, that remain after syntheses from the luminance model. They hypothesize that this is due to a lack of constraints on frequencies above that defined by the pooling region size. Could these be addressed with a white noise image seed that is pre-blurred with a low pass filter removing the frequencies above the spatial frequency constrained at the given eccentricity?

Schematic of metamerism:<br /> Figures 1,2,12, and 13 show a visual schematic of the state space of images, and their relationship to both model and human metamers. This is depicted as a Voronoi diagram, with individual images near the center of each shape, and other images that fall at different locations within the same cell producing the same human visual system response. I felt this conceptualization was helpful. However, implicitly it seems to make a distinction between metamerism and JND (just noticeable difference). I felt this would be better made explicit. In the case of JND, neighboring points, despite having different visual system responses, might not be distinguishable to a human observer.

In these diagrams and throughout the paper, the phrase 'visual stimulus' rather than 'image' would improve clarity, because the location of the stimulus in relation to the fovea matters whereas the image can be interpreted as the pixels displayed on the computer.

Other<br /> The authors show good reproducibility practices with links to relevant code, datasets, and figures.

Reviewer #2 (Public Review):

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

The major strength of the study lies in the use of partial inactivation of hair cells to look at the effects on behaviors such as VOR and OKR. Some weaknesses stem from the fact that the effects of inactivation are highly variable across specimens and that there is no recovery of behavioral function.

Reviewer #2 Public Review:

Summary:<br /> In this manuscript, the authors ran a dual task. Subjects monitored a peripheral location for a target onset (to generate a saccade to), and they also monitored a foveal location for a foveal probe. The foveal probe could be congruent or incongruent with the orientation of the peripheral target. In this study, the authors manipulated the conspicuity of the peripheral target, and they saw changes in performance in the foveal task. However, the changes were somewhat counterintuitive.

Strengths:<br /> The authors use solid analysis methods and careful experimental design.

Weaknesses:<br /> I have some issues with the interpretation of the results, as explained below. In general, I feel that a lot of effects are being explained by attention and target-probe onset asynchrony etc, but this seems to be against the idea put forth by the authors of "foveal prediction for visual continuity across saccades". Why would foveal prediction be so dependent on such other processes? This needs to be better clarified and justified.

Specifics:<br /> The explanation of decreased hit rates with increased peripheral target opacity is not convincing. The authors suggest that higher contrast stimuli in the periphery attract attention. But, then, why are the foveal results occurring earlier (as per the later descriptions in the manuscript)? And, more importantly, why would foveal prediction need to be weaker with stronger pre-saccadic attention to the periphery? What is the function of foveal prediction? What of the other interpretation that could be invoked in general for this type of task used by the authors: that the dual task is challenging and that subjects somehow misattribute what they saw in the peripheral task when planning the saccade. i.e. foveal hit rates are misperceptions of the peripheral target. When the peripheral target is easier to see, then the foveal hit rate drops.

The analyses of Fig. 3C appear to be overly convoluted. They also imply an acknowledgment by the authors that target-probe temporal difference matters. Doesn't this already negate the idea that the foveal effects are associated with the saccade generation process itself? If the effect is related to target onset, how is it interpreted as related to a foveal prediction that is associated with the saccade itself? Also, the oscillatory nature of the effect in Fig. 3C for 59% and 90% opacity is quite confusing and not addressed. The authors simply state that enhancement occurs earlier before the saccade for higher contrasts. But, this is not entirely true. The enhancement emerges then disappears and then emerges again leading up to the saccade. Why would foveal prediction do that?

The interpretation of Fig. 4 is also confusing. Doesn't the longer latency already account for the lapse in attention, such that visual continuity can proceed normally now that the saccade is actually eventually made? In all results, it seems that the effects are all related to the dual nature of the task and/or attention, rather than to the act of making the saccade itself. Why should visual continuity (when a saccade is actually made, whether with short or long latency) have different "fidelity"? And, isn't this disruptive to the whole idea of visual continuity in the first place?

Small question: is it just me or does the data in general seem to be too excessively smoothed?

Reviewer #2 (Public Review):

Summary:<br /> The authors have generated human iPSC cells constitutively expressing the mNG21-10 and tested them by endogenous tagging multiple genes with mNG211 (several tagged iPS cell lines clones were isolated). With this tool, they have explored several weakly expressed cytokinesis genes and gained insights into how cytokinesis occurs.

Strengths:<br /> Human iPSC cells are used.

Weaknesses:<br /> i) The manuscript is extremely incremental, no improvements are present in the split-fluorescent (split-FP) protein variant used nor in the approach for endogenous tagging with split-FPs (both of them are already very well established and used in literature as well as in different cell types).

ii) The fluorescence intensity of the split mNeonGreen appears rather low, for example in Figure 2C the H2BC11, ANLN, SOX2, and TUBB3 signals are very noisy (differences between the structures observed are almost absent). For low-expression targets, this is an important limitation. This is also stated by the authors but image restoration could not be the best solution since a lot of biologically relevant information will be lost anyway.

iii) There is no comparison with other existing split-FP variants, methods, or imaging and it is unclear what the advantages of the system are.

Reviewer #2 (Public Review):

Summary:<br /> The authors here develop a novel Ovalbumin model peptide that can be labeled with a site-specific FlAsH dye to track agonist peptides both in vitro and in vivo. The utility of this tool could allow better tracking of activated polyclonal T cells particularly in novel systems. The authors have provided solid evidence that peptides are functional, capable of activating OTII T cells, and that these peptides can undergo trogocytosis by cognate T cells only.

Strengths:<br /> -An array of in vitro and in vivo studies are used to assess peptide functionality.<br /> -Nice use of cutting-edge intravital imaging.<br /> -Internal controls such as non-cogate T cells to improve the robustness of the results (such as Fig 5A-D).<br /> -One of the strengths is the direct labeling of the peptide and the potential utility in other systems.

Weaknesses:<br /> 1. What is the background signal from FlAsH?<br /> The baselines for Figure 1 flow plots are all quite different. Hard to follow. What does the background signal look like without FLASH (how much fluorescence shift is unlabeled cells to No antigen+FLASH?). How much of the FlAsH in cells is actually conjugated to the peptide? In Figure 2E, it doesn't look like it's very specific to pMHC complexes. Maybe you could double-stain with Ab for MHCII. Figure 4e suggests there is no background without MHCII but I'm not fully convinced. Potentially some MassSpec for FLASH-containing peptides.

2. On the flip side, how much of the variant peptides are getting conjugated in cells? I'd like to see some quantification (HPLC or MassSpec). If it's ~10% of peptides that get labeled, this could explain the low shifts in fluorescence and the similar T cell activation to native peptides if FlasH has any deleterious effects on TCR recognition. But if it's a high rate of labeling, then it adds confidence to this system.

3. Conceptually, what is the value of labeling peptides after loading with DCs? Why not preconjugate peptides with dye, before loading, so you have a cleaner, potentially higher fluorescence signal? If there is a potential utility, I do not see it being well exploited in this paper. There are some hints in the discussion of additional use cases, but it was not clear exactly how they would work. One mention was that the dye could be added in real-time in vivo to label complexes, but I believe this was not done here. Is that feasible to show?

4. Figure 5D-F the imaging data isn't fully convincing. For example, in 5F and 2G, the speeds for T cells with no Ag should be much higher (10-15micron/min or 0.16-0.25micron/sec). The fact that yours are much lower speeds suggests technical or biological issues, that might need to be acknowledged or use other readouts like the flow cytometry.

Reviewer #2 (Public Review):

Summary:<br /> This paper examined whether circulating platelets regulate oligodendrocyte progenitor cell (OPC) differentiation for the link with multiple sclerosis (MS). They identified that the interaction with platelets enhances OPC differentiation although persistent contact inhibits the process in the long-term. The mouse model with increased platelet levels in the blood reduced mature oligodendrocytes, while how platelets might regulate OPC differentiation is not clear yet.

Strengths:<br /> The use of both partial platelet depletion and thrombocytosis mouse models gives in vivo evidence. The presentation of platelet accumulation in a time-course manner is rigorous. The in vitro co-culture model tested the role of platelets in OPC differentiation, which was supportive of in vivo observations.

Weaknesses:<br /> How platelets regulate OPC differentiation is not clear. What the significance of platelets is in MS progression is not clear.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors reported a study to uncover that β-catenin inhibition disrupting the homeostasis of osteogenic/adipogenic differentiation contributes to the development of Glucocorticoid-induced osteonecrosis of the femoral head (GONFH). In this study, they first observed abnormal osteogenesis and adipogenesis associated with decreased β-catenin in the necrotic femoral head of GONFH patients, but the exact pathological mechanisms of GONFH remain unknown. They then performed in vivo and in vitro studies to further reveal that glucocorticoid exposure disrupted osteogenic/adipogenic differentiation bone marrow stromal cells (BMSCs) by inhibiting β-catenin signaling in glucocorticoid-induced GONFH rats, and specific deletion of β-catenin in Col2+ cells shifted BMSCs commitment from osteoblasts to adipocytes, leading to a full spectrum of disease phenotype of GONFH in adult mice.

Strengths:<br /> This innovative study provides strong evidence supporting that β-catenin inhibition disrupts the homeostasis of osteogenic/adipogenic differentiation that contributes to the development of GONFH. This study also identifies an ideal genetically modified mouse model of GONFH. Overall, the experiment is logically designed, the figures are clear, and the data generated from humans and animals is abundant supporting their conclusions.

Weaknesses:<br /> There is a lack of discussion to explain how the Wnt agonist 1 works. There are several types of Wnt ligands. It is not clear if this agonist only targets Wnt1 or other Wnts as well. Also, why Wnt agonist 1 couldn't rescue the GONFH-like phenotype in β-cateninCol2ER mice needs to be discussed.

Reviewer #2 (Public Review):

Summary: In the manuscript, the authors summarized and introduced the correlation between the non-core regions of RAG1 and RAG2 in BCR-ABL1+acute B lymphoblastic leukemia and off-target recombination which has certain innovative and clinical significance.

Reviewer #2 (Public Review):

Summary:

Jablonowski and colleagues studied key characteristics of MYC-driven cancers: dysregulated pre-mRNA splicing and altered metabolism. This is an important field of study as it remains largely unclear as to how these processes are coordinated in response to malignant transformation and how they are exploitable for future treatments. In the present study, the authors attempt to show that Jumonji Domain Containing 6, Arginine Demethylase And Lysine Hydroxylase (JMJD6) plays a central role in connecting pre-mRNA splicing and metabolism in MYC-driven neuroblastoma. JMJD6 collaborates with the MYC protein in driving cellular transformation by physically interacting with RNA-binding proteins involved in pre-mRNA splicing and protein regulation. In cell line experiments, JMJD6 affected the alternative splicing of two forms of glutaminase (GLS), an essential enzyme in the glutaminolysis process within the central carbon metabolism of neuroblastoma cells. Additionally, the study provides in vitro (and in silico) evidence for JMJD6 being associated with the anti-proliferation effects of a compound called indisulam, which degrades the splicing factor RBM39, known to interact with JMJD6.

Overall, the findings presented by Jabolonowski et al. begin to illuminate a cancer-promoting metabolic, and potentially, a protein synthesis suppression program that may be linked to alternative pre-mRNA splicing through the action of JMJD6 - downstream of MYC. This discovery can provide further evidence for considering JMJD6 as a potential therapeutic target for the treatment of MYC-driven cancers.

Strengths:

Alternative Splicing Induced by JMJD6 Knockdown: the study presents evidence for the role of JMJD6 in alternative splicing in neuroblastoma cells. Specifically, the RNA immunoprecipitation experiments demonstrated a significant shift from the GAC to the KGA GLS isoform upon JMJD6 knockdown. Moreover, a significant correlation between JMJD6 levels and GAC/KGA isoform expression was identified in two distinct neuroblastoma cohorts. This suggests a causative link between JMJD6 activity and isoform prevalence.

Physical Interaction of JMJD6 in Neuroblastoma Cells: The paper provides preliminary insight into the physical interactome of JMJD6 in neuroblastoma cells. This offers a potential mechanistic avenue for the observed effects on metabolism and protein synthesis and could be exploited for a deeper investigation into the exact nature, and implications of neuroblastoma-specific JMJD6 protein-protein interactions.

Weaknesses:

There are several areas that would benefit from improvements with regard to the current data supporting the claims of the paper (i.e., the conclusion presented in Figure 8).

Neuroblastoma Modelling Strategy: The study heavily relies on cell lines without incorporating patient-derived cells/biomaterials. Using databases to fill gaps in the experimental design can only fortify the observations to a certain extent. A critical oversight is the absence of non-cancerous control cells in many figures, and the rationale for selecting specific cell lines for assays/approaches remains somewhat unclear. A foundational control for such experiments should involve the non-transformed neural crest cell line, which the authors have readily available. Are the observed splicing and metabolic effects of JMJD6 specific to neuroblastoma? Is there a neuroblastoma-specific JMJD6 interactome? Is MYC function essential?

In Vivo Modelling: The inclusion of a genetic mouse model combined with an inducible JMJD6 knockdown, would enhance the study by allowing examination of JMJD6's role during both tumor initiation and growth in vivo. For instance, the TH-MYCN mice overexpressing MYCN in neural crest cells, could be a promising choice.

Dependence on Colony Formation Assay: The study leans on 2D and semi-quantitative colony formation assays to assess malignant growth. To validate the link between the mechanistic insights discussed (e.g., reduced protein synthesis) and JMJD6-mediated malignant growth as a potential therapeutic target, evidence from in vivo or representative 3D models would be crucial.

Data Presentation and Rigor: The presented data is predominantly qualitative and necessitates quantification. For instance, Western blots should be quantified. The RNAseq, metabolism, and pull-down data should be transparently and numerically presented. The figure legends seem elusive and their lack of transparency (often with regards to biological repeats, error bars, cell line used etc.) is concerning. Adequate citation and identification of all data sources, including online resources, are imperative. The manuscript would also benefit from a more rigorous depiction and quantification of RNA interference of both stable and transient knockdowns with quantitative validation at mRNA and protein levels.

Novelty Concerns: The emphasis on JMJD6 as a novel neuroblastoma target is contingent on the new mechanistic revelations about the JMJD6-centered link between splicing, metabolism, and protein synthesis. Given that JMJD6 has been previously linked to neuroblastoma biology, the rationale (particularly in Figure 1) for concentrating on JMJD6 may stem more from bias rather than data-driven reasoning.

Depth of Mechanistic Investigation: Current evidence lacks depth in key areas such as JMJD6-RNA binding. A more thorough approach would involve pinpointing specific JMJD6 binding sites on endogenous RNAs using techniques such as cross-linking and immunoprecipitation, paired with complementary proximity-based methodologies. Regarding the presented metabolism data, diving deeper into metabolic flux via isotope labeling experiments could shed light on dynamic processes like TCA and glutaminolysis. As it stands, the 'pathway cartoon' in Figure 6d appears overly qualitative.

Reviewer #2 (Public Review):

Summary:

In this work, the authors report a role for the well-studied GTPase Rab7 in gut homeostasis. The study combines cell culture experiments with mouse models and human ulcerative colitis patient tissues to propose a model where, Rab7 by delivering a key mucous component CLCA1 to lysosomes, regulates its secretion in the goblet cells. This is important for the maintenance of mucous permeability and gut microbiota composition. In the absence of Rab7, CLCA1 protein levels are higher in tissues as well as the mucus layer, corroborating with the anti-correlation of Rab7 (reduced) and CLCA1 (increased) from ulcerative colitis patients. The authors conclude that Rab7 maintains CLCA1 level by controlling its lysosomal degradation, thereby playing a vital role in mucous composition, colon integrity, and gut homeostasis.

Strengths:

The biggest strength of this manuscript is the combination of cell culture, mouse model, and human tissues. The experiments are largely well done and in most cases, the results support their conclusions. The authors go to substantial lengths to find a link, such as alteration in microbiota, or mucus proteomics.

Weaknesses:

There are also some weaknesses that need to be addressed. The association of Rab7 with UC in both mice and humans is clear, however, claims on the underlying mechanisms are less clear. Does Rab7 regulate specifically CLCA1 delivery to lysosomes, or is it an outcome of a generic trafficking defect? CLCA1 is a secretory protein, how does it get routed to lysosomes, i.e. through Golgi-derived vesicles, or by endocytosis of mucous components? Mechanistic details on how CLCA1 is routed to lysosomes will add substantial value.

Why does the level of Rab7 fluctuate during DSS treatment (Fig 1B)? Does the reduction seen in Rab7 levels (by WB) also reflect in reduced Rab7 endosome numbers? Are other late endosomal (and lysosomal) populations also reduced upon DSS treatment and UC? Is there a general defect in lysosomal function?

The evidence for lysosomal delivery of CLCA1 (Fig 7 I, J) is weak. Although used sometimes in combination with antibodies, lysotracker red is not well compatible with permeabilization and immunofluorescence staining. The authors can substantiate this result further using lysosomal antibodies such as Lamp1 and Lamp2. For Fig 7J, it will be good to see a reduction in Rab7 levels upon KD in the same cell. In this connection, Fig S3D is somewhat confusing. While it is clear that the pattern of Muc2 in WT and Rab7-/- cells are different, how this corroborates with the in vivo data on alterations in mucus layer permeability -- as claimed -- is not clear.

Overall, the work shows a role for a well-studied GTPase, Rab7, in gut homeostasis. This is an important finding and could provide scope and testable hypotheses for future studies aimed at understanding in detail the mechanisms involved.

Reviewer #2 (Public Review):

Despite the fact that CTLA-4 is a critical molecule for inhibiting the immune response, surprisingly individuals with heterozygous CTLA-4 mutations exhibit immunodeficiency, presenting with antibody deficiency secondary to B cell loss. Why the loss of a molecule that regulates T cell activation should lead to B cell loss has remained unclear. In this study, Muthana and colleagues use an anti-CTLA-4 antibody drug conjugate (aCTLA-4 ADC) to delete cells expressing high levels of CTLA-4, and show that this leads to a reduction in B cells. The aCTLA-4 ADC is found to delete a subset of Tregs, leading to hyperactivation of T cells that is associated with B cell depletion. Using blocking antibodies, the authors implicate TNFa in the observed B cell loss.

The reciprocal regulation of T and B cell homeostasis is an important research area. While it has been shown that Treg defects are associated with B cell loss, the mechanisms at play are incompletely understood. CTLA-4 is not normally expressed in B cells so an indirect mechanism of action is assumed. The authors show that the decrease in Treg following aCTLA-4 ADC treatment is associated with activation of T cells, and that B cell loss is blunted if T cells are depleted. A role for both CD4 and CD8 T cells is identified by selective CD4/CD8 depletion. T cells appear to require CD28 costimulation in order to mediate B cell loss, since the response is partially inhibited in the presence of the costimulation blockade drug belatacept (CTLA-4-Ig). Finally, experiments using the anti-TNFa antibody adalimumab suggest a potential role for TNFa in the depletion of B cells.

While the manuscript makes a useful contribution, a number of limitations remain. Perhaps most important is the extent to which this model mimics the natural situation in individuals with CTLA-4 mutations (or following CTLA-4-based clinical interventions). aCTLA-4 ADC treatment permits acute deletion of Treg expressing high levels of CTLA-4, whereas in patients the Treg population remains but is specifically impaired in CTLA-4 function. Secondly, although the requirement for T cells to mediate B cell loss is convincingly demonstrated, the incomplete reversal by TNFa blockade suggests additional unidentified factors contribute to this effect. Finally, although the manuscript favours peripheral killing of mature B cells over alterations to B cell lymphopoiesis, one concern is that this may simply reflect the model employed: the short-term (6 day) treatment used here may be too acute to alter B cell development, but this may nevertheless be a feature of prolonged immune dysregulation in humans.

Reviewer #2 (Public Review):

Summary: The authors present a technique for fitting diffusion magnetic resonance images (dMRI) to a sub-diffusion model of the diffusion process within brain imaging. The authors suggest that their technique provides robust and accurate calculation of diffusional kurtosis imaging parameters from which high quality images can be calculated from short dMRI data acquisitions at two diffusion times.

Strengths: If the authors can show that the dMRI signal in brain tissue follows a sub-diffusion model decay curve then their technique for accurately and robustly calculating diffusional kurtosis parameters from multiple diffusion times would be of benefit for tissue microstructural imaging in research and clinical arenas.

Weaknesses: The applied sub-diffusion model has two parameters that are invariant to diffusion time, D_β and β which are used to calculate the diffusional kurtosis measures of a diffusion time dependent D* and a diffusion time invariant K*. However, the authors do not demonstrate that the D_β, β and K* parameters are invariant to diffusion time in brain tissue. The authors' results visually show that there is time dependence of the K* measure (in Figure 6) that is more apparent in white matter with K* values being higher for diffusion times of ∆=49 ms than ∆ = 19 ms. The diffusion time dependence of K* indicates there is also diffusion time dependence of β. Furthermore, Figure 7 shows that there is a tissue specific root mean squared error in model fitting over the two diffusion times which indicates greater deviation from the model fit in white matter than grey matter. To show that the sub-diffusion model is robust and accurate (and consequently that K* is robust and accurate) the authors would have to demonstrate that there is no diffusion time-dependence in both D_β and β in application to brain imaging data for each diffusion time separately. Simulated data should not be used to demonstrate the robustness and accuracy of the sub-diffusion model or to determine optimization of dMRI acquisition parameters without first demonstrating that D_β and β are invariant to diffusion time. This is because simulated signals calculated by using the sub-diffusion charateristic equation of dMRI signal decay will necessarily have diffusion time invariant D_β and β parameters.

Without further information demonstrating diffusion time invariance of D_β, β and K* it is not possible to determine whether the authors have achieved their aims or that their results support their conclusions.

Ungenerous to take Alter to task for context which he might not have the background to comment upon.

Does Alter call it a "platitude" from it's historical context, or with respect to the modern context of Donald J. Trump and a wide variety of Republican Party members who are anything but Christian?

Reviewer #2 (Public Review):

Summary:

This is a well-written manuscript describing studies directed at identifying the cell type responsible for pacemaking in murine-collecting lymphatics. Using state-of-the-art approaches, the authors identified a number of different cell types in the wall of these lymphatics and then using targeted expression of Channel Rhodopsin and GCaMP, the authors convincingly demonstrate that only activation of lymphatic muscle cells produces coordinated lymphatic contraction and that only lymphatic muscle cells display pressure-dependent Ca2+ transients as would be expected of a pacemaker in these lymphatics.

Strengths:

The use of a targeted expression of channel rhodopsin and GCaMP to test the hypothesis that lymphatic muscle cells serve as the pacemakers in musing lymphatic collecting vessels.

Weaknesses:

The only significant weakness was the lack of quantitative analysis of most of the imaging data shown in Figures 1-11. In particular, the colonization analysis should be extended to show cells not expected to demonstrate colocalization as a negative control for the colocalization analysis that the authors present.

Reviewer #2 (Public Review):

DNA adenine methylation (6mA) is a rediscovered modification that has been described in a wide range of eukaryotes. However, 6mA presence in eukaryote remains controversial due to the low abundance of its modification in eukaryotic genome. In this manuscript, Boulet et al. re-investigate 6mA presence in drosophila using axenic or conventional fly to avoid contaminants from feeding bacteria. By using these flies, they find that 6mA is rare but present in the drosophila genome by performing LC/MS/MS. They also find that the loss of TET (also known as DMAD) does not impact 6mA levels in drosophila, contrary to previous studies. In addition, the authors find that TET is required for fly development in its enzymatic activity-independent manner.

The strength of this study is, that compared to previous studies of 6mA in drosophila, the authors employed axenic or conventional fly for 6mA analysis. These fly strains make it possible to analyze 6mA presence in drosophila without bacterial contaminant. Therefore, showing data of 6mA abundance in drosophila by performing LC-MS/MS in this manuscript is more convincing as compared with previous studies. Intriguingly, the authors find that the conserved iron-binding motif required for the catalytic activity of TET is dispensable for its function. This finding could be important to reveal TET function in organisms whose genomic 5mC levels are very low.

The manuscript in this paper is well written but some aspects of data analysis and discussion need to be clarified and extended.<br /> 1) It is convincing that an increase in 6mA levels is not observed in TETnull presented in Fig1. But it seems 6mA levels are altered in Ax.TET1/2 compared with Ax.TETwt and Ax.TETnull presented in Fig1f (and also WT vs TET1/2 presented in Fig1g). Is it sure that no statistically significant were not observed between Ax.TET1/2 and Ax.TETwt?<br /> 2) The representing data of in vitro demethylation assay presented in Fig.3 is convincing, but it is not well discussed and analyzed why these results are contrary to previous reports (Yao et al., 2018 and Zhang et al., 2015).

Reviewer #2 (Public Review):

The manuscript from deHaro-Arbona et al, entitled "Dynamic modes of Notch transcription hubs conferring memory and stochastic activation revealed by live imaging the co-activator Mastermind", uses single molecule microscopy imaging in live tissues to understand the dynamics and molecular determinants of transcription factor recruitment to the E(spl)-C locus in Drosophila salivary gland cells under Notch-ON and -OFF conditions. Previous studies have identified the major players that are involved in transcription regulation in the Notch pathway, as well as the importance of general transcriptional coregulators, such as CBP/P300 and the Mediator CDK module, but the detailed steps and dynamics involved in these processes are poorly defined. The authors present a wealth of single molecule data that provides significant insights into Notch pathway activation, including:

1. Activation complexes, containing CSL and Mam, have slower dynamics than the repressor complexes, containing CSL and Hairless.

2. Contribution of CSL, NICD, and Mam IDRs to recruitment.

3. CSL-Mam slow-diffusing complexes are recruited and form a hub of high protein concentrations around the target locus in Notch-ON conditions.

4. Mam recruitment is not dependent on transcription initiation or RNA production.

5. CBP/P300 or its associated HAT activity is not required for Mam recruitment.

6. Mediator CDK module and CDK8 activity are required for Mam recruitment, and vice-versa, but not CSL recruitment.

7. Mam is not required for chromatin accessibility but is dependent on CSL and NICD.

8. CSL recruitment and increased chromatin accessibility persist after NICD removal and loss of Mam, which confers a memory state that enables rapid re-activation in response to subsequent Notch activation.

9. Differences in the proportions of nuclei with both Pol II and with Mam enrichment, which results in transcription being probabilistic/stochastic. These data demonstrate that the presence of Mam-complexes is not sufficient to drive all the steps required for transcription in every Notch-ON nucleus.

10. The switch from more stochastic to robust transcription initiation was elicited when ecdysone was added.

Overall, the manuscript is well written, concise, and clear, and makes significant contributions to the Notch field, which are also important for a general understanding of transcription factor regulation and behavior in the nucleus. I recommend that the authors address my relatively minor criticisms detailed below.

Page 7, bottom. The authors speculate, "It is possible therefore that, once recruited, Mam can be retained at target loci independently of CSL by interactions with other factors so that it resides for longer." Is it possible that another interpretation of that data is that Mam is a limiting factor?

Page 9. The authors write, "A very low level of enrichment was evident for... for the CSL C-terminus..". The recruitment of CSL ct IDR does not appear to be statistically significant or there is no apparent difference (Figure S2C), suggesting the CSL ct IDR does not play a role in enrichment.

Page 9. The authors write, "Notably, MamnIDR::GFP fusion was present in droplets, suggesting it can self-associate when present in a high local concentration (Figure S2B)." Is this result only valid for Mam nIDR or does full-length Mam also localize into droplets, as has been previously observed for full-length mammalian Maml1 in transfected cells?

Previous studies in mammalian cells suggest that Maml1 is a high-confidence target for phosphorylation by CDK8, see Poss et al 2016 Cell Reports https://doi.org/10.1016/j.celrep.2016.03.030. By sequence comparison, does fly Mam have similar potential phosphorylation sites, and might these be critical for Mam/CDK module recruitment?

Page 11: The authors write, "The differences in the effects on Mam and CSL imply that the CDK module is specifically involved in retaining Mam in the hub, and that in its absence other CSL complexes "win-out", either because the altered conditions favour them and/or because they are the more abundant." Are the "other" complexes the authors are referring to Hairless-containing complexes? With the reagents the authors have in hand couldn't this be explicitly shown for CSL-complexes rather than speculated upon?

Page 12/13: The authors write, "Based on these results we propose that, after Notch activity decays, the locus remains accessible because when Mam-containing complexes are lost they are replaced by other CSL complexes (e.g. co-repressor complexes)." Again, why not actually test this hypothesis rather than speculate? The dynamics of Hairless complexes following the removal of Notch would be very interesting and build upon previously published results from the Bray lab.

Page 13: The authors write, "As Notch removal leads to a loss of Mam, but not CSL, from the hub, it should recapitulate the effects of MamDN." While the data in Figure 5B seem to support this hypothesis, it's not clear to me that the loss of Mam and MamDN should phenocopy each other, bc in the case of MamDN, NICD would still be present.

The temporal dynamics for Mam recruitment using the temperature- and optogenetic-paradigms are quite different. For example, in the optogenetic time course experiments, the preactivated cells are in the dark for 4 hours, while in the temperature-controlled experiments, there is still considerable enrichment of Mam at 4 hours. For the preactivated optogenetic experiments, how sure are the authors that Mam is completely gone from the locus, and alternatively, can the optogenetic experimental results be replicated in the temperature-controlled assays? My concern is whether the putative "memory" observation is just due to incomplete Mam removal from the previous activation event.

Reviewer #2 (Public Review):

A wide variety of assays are used to describe the new culture system and compare it both with those previously described and with the endometrial tissue itself. The three different cultures they used are control organoids (CTRL) cultured with described expansion media, secretory organoids (SEC, cultured with E2, MPA and cAMP inducing secretory phase as previously reported) and WOI organoids (cultured with E2, MPA, cAMP, prolactin (PRL), human chorionic gonadotropin (hCG) and human placental lactogen (hPL)). First, they performed morphological characterization of cultures using different antibodies, showing the presence of epithelial glandular cells and stromal cells, as well as their proliferation and absence of apoptosis. Glycogen secretion and progesterone receptor expression complete organoid characterization at the functional and hormone response levels respectively.

Then, they performed single-cell transcriptomics to analyse its composition in terms of cell type, comparing with different databases, but with an unknown "n". They detect stromal, epithelial, and immune cells (also by microscopy), and analyse gene expression and transcription regulation, showing similarities between WOI organoids and mid-secretory endometrium. With endometrial receptivity analysis, they suggest a successful formation of the implantation window in vitro, but this result is difficult to interpret.

Analyzing transcriptome and proteome information of WOI organoids, authors demonstrate a strong response to estrogen and progesterone, but some comparisons are made with CTRL and SEC, and others only with CTRL, which limits the power of some results. In the same way, some genes related to Cilia and pinopodes appear dominant in WOI organoids, but the comparison by electron microscopy is made only against CTRL organoids.

In subsequent analysis, WOI organoids showed a marked differentiation from proliferative to secretory epithelium, and from proliferative epithelium to EMT-derived stromal cells than SEC organoids. These statements are based on their upregulation of monocarboxylic acid and lipid metabolism, their enhanced peptide metabolism and mitochondrial energy metabolism, or their pseudotime trajectories. However, other analyses (such as the accumulation of secretory epithelium or decreased proliferative epithelium, the increased ciliated epithelium after hormonal treatment, or the presence of EMT-derived stromal cells) show only small differences between SEC and WOI organoids.

In summary, the development of an endometrial organoid culture methodology that allows targeting the endometrial situation in the window of implantation could change the experimental approaches of many studies, but more evidence is needed, and above all, more approaches on how different WOI organoids are from SEC organoids, to be sure if it is worth using them in implantation.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the membrane component of the sialic acid-specific TRAP transporter, SiaQM (HiSiaQM), from H. influenzae, is structurally characterized. TRAP transporters are substrate binding protein (SBP)-dependent secondary-active transporters, and HiSiaQM is the most comprehensively studied member of this family. While all previous work on fused TRAP transporter membrane proteins suggests that they are monomeric (including the previous structural characterization of HiSiaQM by a different group), a surprising finding from this work is the observation that HiSiaQM can form higher oligomers, consistent with it being a dimer. These higher oligomeric states were initially observed after extraction of the protein with LMNG detergent but were also observed in DDM detergent, amphipol and nanodiscs using analytical ultracentrifugation (AUC). Structural characterization of dimeric HiSiaQM revealed 2 arrangements, parallel and antiparallel arrangements, the latter of which is unlikely to be physiologically relevant.

The higher resolution of this new structure of HiSiaQM (2.2-2.7 Å compared to 4.7 Å for the previous structure) facilitated the assignment of bound lipids at the dimer interface and a lipid molecule embedded in each of the protomers; allowed for a clearer refinement of the Na+ and putative substrate binding sites, which differ slightly from the previous structure; and produced better-modelled side chains for the residues involved in the SBP:HiSiaQM interaction. The authors developed a useful AUC-based assay to determine the affinity for this interaction revealing an affinity of 65 µM. Finally, the authors make the very interesting observation that a sialic acid-specific SBP from a different TRAP transporter can utilize HiSiaQM for transport, contrary to previous observations, revealing for the first time that TRAP membrane components can recognize multiple SBPs.

Overall, this is a well-written and presented manuscript detailing some interesting new observations about this interesting protein family. One of the main findings, that the protein can form a dimer, is supported by data, but the physiological relevance of this is questionable, and the possibility that this is artefactual has not been ruled out. Conclusions regarding the mechanistic importance of the lipid-binding sites are not currently supported by the data.

Strengths:<br /> The main strength of this work is the increased resolution of HiSiaQM, which allows for a much more precise assignment of side chains and their orientation. This will be of importance for subsequent mechanistic studies on the contributions of these residues to Na+ and sialic acid binding and conformational changes.

The observation of the lipids, especially the lipid embedded near the fusion helix, is an intriguing observation, which lays the groundwork for future work to understand the lipid-dependence of these transporters. The development of the AUC-based approach to measure SBP affinity for the membrane component will likely prove useful to future studies.

Weaknesses:<br /> One of the main results from this work is the observation that HiSiaQM can form a dimer. Two arrangements were observed, parallel and antiparallel, the latter of which is almost certainly physiologically irrelevant as it would preclude essential interactions with the extracytoplasmic substrate-binding protein. As acknowledged by the author, this non-physiological arrangement is likely a consequence of protein preparation (overexpression, extraction, purification, etc.). However, if one dismisses the antiparallel arrangement as non-relevant and an artefact of protein preparation, it is difficult for the parallel arrangement to maintain its credibility, as it was also processed in the same way. This is especially true when one considers that there is only 100 Å2 buried surface area in the parallel arrangement that does not involve any sidechains; it is difficult to envisage this as a specific interaction, e.g. compared to related proteins that have ~2000 Å2 buried surface area. Unless this dimerization is observed in a bacterial membrane at physiological protein concentrations, it is difficult to rule out the possibility that the observed dimerization is merely an artefact caused by the expression, purification and concentration of the protein.

The manuscript contains some excellent structural analysis of this protein, whose higher resolution reveals some new and interesting insights. However, a weakness of the current work is a lack of validation of these observations using other approaches. For example, lipid interactions are observed in the structure that the authors claim is mechanistically important. However, without disrupting these interactions to look at the effect on transport, this conclusion is not supported. Similarly, the authors use their structure to predict residues that are important for the SBP:membrane protein interaction, and they develop an AUC-based binding assay to study this interaction, but they do not test their predictions using this approach.

Reviewer #2 (Public Review):

Summary:

This study uses transcriptome sequence from a dioecious plant to compare evolutionary rates between genes with male- and female-biased expression and distinguish between relaxed selection and positive selection as causes for more rapid evolution. These questions have been explored in animals and algae, but few studies have investigated this in dioecious angiosperms, and none have so far identified faster rates of evolution in male-biased genes (though see Hough et al. 2014 https://doi.org/10.1073/pnas.1319227111).

Strengths:

The methods are appropriate to the questions asked. Both the sample size and the depth of sequencing are sufficient, and the methods used to estimate evolutionary rates and the strength of selection are appropriate. The data presented are consistent with faster evolution of genes with male-biased expression, due to both positive and relaxed selection.

This is a useful contribution to understanding the effect of sex-biased expression in genetic evolution in plants. It demonstrates the range of variation in evolutionary rates and selective mechanisms, and provides further context to connect these patterns to potential explanatory factors in plant diversity such as the age of sex chromosomes and the developmental trajectories of male and female flowers.

Weaknesses:

The presence of sex chromosomes is a potential confounding factor, since there are different evolutionary expectations for X-linked, Y-linked, and autosomal genes. Attempting to distinguish transcripts on the sex chromosomes from autosomal transcripts could provide additional insight into the relative contributions of positive and relaxed selection.

Reviewer #2 (Public Review):

In their manuscript, McCormick, Cleary et al., explore the question of how the nucleotide state of the tubulin heterodimer affects the interaction between adjacent tubulins. They use a solid combination of biochemical reconstitution assays and modeling to reveal that the nucleotide at the interface of two tubulin dimers determines the strength of the interaction between two dimers. Overall, the findings will be valuable to the field of microtubule biology.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript is part of the Wright lab's ongoing studies that investigate whether the bumblebee B. terrestris can detect the presence of pesticides when feeding. Previously, they showed that B. terrestris cannot detect neonicotinoids and would prefer food containing neonicotinoids (Kessler et al. 2015). However, in that paper, they showed that B. terrestris cannot taste neonicotinoids but did not provide evidence on why B. terrestris prefer food containing neonicotinoids. In the current paper, the authors continue to suggest that B. terrestris cannot taste neonicotinoids as well as another insecticide, sulfoxaflor, based on additional behavioral experiments and electrophysiological experiments focusing on specific GRNs. While the data from these experiments continue to suggest that B. terrestris cannot taste these insecticides using their mouthparts, whether B. terrestris can actually perceive these insecticides, and why this species prefers food containing these compounds remains unknown.

Strengths:<br /> The authors provided additional evidence that B. terrestris cannot taste neonicotinoids with their mouthparts. The authors have addressed my concerns regarding overgeneralization and that parts of the manuscript were written in a way that sounded combative with studies from other groups that had come to slightly different conclusions from their previous paper.

Reviewer #2 (Public Review):

The study by Ellis et al. documents the development of a CRISPR interference (CRISPRi) screen aiming at identifying virulence-critical genes of Legionella pneumophila, the facultative intracellular bacterium causing Legionnaires' disease. L. pneumophila employs the Dot/Icm type IV secretion system to translocate more than 300 different "effector proteins" into host cells. Many effector proteins appear to have redundant functions, and therefore, depleting several of them is required to observe a strong intracellular replication phenotype. In the current study, Ellis et al. develop a "multiplex, randomized CRISPRi sequencing" (MuRCiS) approach to silence several effector genes simultaneously and randomly, thereby possibly causing synthetic lethality for L. pneumophila upon infection of host cells.

The MuRCiS approach comprises the ligation of different CRISPR spacers flanked by repeats in presence of "dead end" oligonucleotide pairs capping a random array of building blocks to be inserted into a library vector. Thus, spacer arrays with an average of 3.3 spacers per array were obtained. As a proof-of-concept, spacer arrays targeting 44 transmembrane effector-encoding L. pneumophila genes were employed to screen for intracellular growth defects in macrophages and amoeba. Consequently, novel pairs of synergistically functioning effector genes were identified by comparative next-generation sequencing of the input and output pools of spacer arrays.

A major strength of this well-written and straightforward study is the construction and use of random and multiplexed CRISPRi arrays, allowing an unbiased and comprehensive screen for multiple genes affecting the intracellular growth of L. pneumophila. The ingenious approach established by Ellis et al. will be useful for further genetic analysis of L. pneumophila infection and might also be adopted for other pathogens employing a large set of (functionally redundant) virulence factors.<br /> The reviewer's suggestion to test the single and double L. pneumophila effector mutant strains for growth in protozoa other than A. castellanii was considered beyond the scope of the current manuscript describing the optimization of the MuRCiS platform. The authors have satisfactorily addressed the minor points raised previously.

DOI: 10.1016/j.neuron.2023.09.022

Resource: ZFIN_ZDB-GENO-140423-2

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-140423-2

What is this?

Reviewer #2 (Public Review):

The authors present the OpenApePose database constituting a collection of over 70000 ape images which will be important for many applications within primatology and the behavioural sciences. The authors have also rigorously tested the utility of this database in comparison to available Pose image databases for monkeys and humans to clearly demonstrate its solid potential. However, the variation in the database with regards to individuals, background, source/setting is not clearly articulated and would be beneficial information for those wishing to make use of this resource in the future.

Reviewer #2 (Public Review):

Summary:

In the current manuscript, Tresenrider et al., present their recent study focusing on screening of small molecules to enhance the conversion from Müller cells (MG) to retina neurons induced by ectopic Ascl1 expression.

Strengths:

To analyze results from multiple treatment conditions in a single experiment, the authors employed a method called sci-Plex to perform scRNA-seq on mixed samples to investigate the effects of different durations of Ascl1 expression and screen for potential small molecules to promote reprogramming. Ultimately, they identified two compounds with intended activities on mouse retina. The findings may aid in future development of a cell replacement strategy for treating retinal degeneration.

Weaknesses:

The mechanistic insights are limited. Certain claims are confusing or superficial at this point, as detailed in issues/concerns.

Reviewer #2 (Public Review):

This is an interesting study examining the question of whether CSD sensitizes meningeal afferent sensory neurons leading to spontaneous activity or whether CSD sensitizes these neurons to mechanical stimulation related to locomotion. Using two-photon in vivo calcium imaging based on viral expression of GCaMP6 in the TG, awake mice on a running wheel were imaged following CSD induction by cortical pinprick. The CSD wave evoked a rise in intracellular calcium in many sensory neurons during the propagation of the wave but several patterns of afferent activity developed after the CSD. The minority of recorded neurons (10%) showed spontaneous activity while slightly larger numbers (20%) showed depression of activity, the latter pattern developed earlier than the former. The vast majority of neurons (70%) were unaffected by the CSD. CSD decreased the time spent running and the numbers of bouts per minute but each bout was unaffected by CSD. There also was no influence of CSD on the parameters referred to as meningeal deformation including scale, shear, and Z-shift. Using GLM, the authors then determine that there there is an increase in locomotion/deformation-related afferent activity in 51% of neurons, a decrease in 12% of neurons, and no change in 37%. GLM coefficients were increased for deformation related activity but not locomotion related activity after CSD. There also was an increase in afferents responsive to locomotion/deformation following CSD that were previously silent. This study shows that unlike prior reports, CSD does not lead to spontaneous activity in the majority of sensory neurons but that it increases sensitivity to mechanical deformation of the meninges. This has important implications for headache disorders like migraine where CSD is thought to contribute to the pathology in unclear ways with this new study suggesting that it may lead to increased mechanical sensitivity characteristic of migraine attacks.

Reviewer #2 (Public Review):

Summary:<br /> In this study, the authors investigate the effect of ACh on neuronal responses in the auditory cortex of anesthetized rats during an auditory oddball task. The paradigm consisted of two pure tones (selected from the frequency responses at each recording site) presented in a pseudo-random sequence. One tone was presented frequently (the "standard" tone) and the other infrequently (the "deviant" tone). The authors found that ACh enhances the detection of unexpected stimuli in the auditory environment by increasing or decreasing the neuronal responses to deviant and standard tones.

Strengths:<br /> The study includes the use of appropriate and validated methodology in line with the current state-of-the-art, rigorous statistical analysis, and the demonstration of the effects of acetylcholine on auditory processing.

Weaknesses:<br /> The study was conducted in anesthetized rats, and further research is needed to determine the behavioral relevance of these findings.

Reviewer #2 (Public Review):

This paper analyzes the effect of axon de-myelination and re-myelination on action potential speed, and propagation failure. Next, the findings are then incorporated in a standard spiking ring attractor model of working memory.

I think the results are not very surprising or solid and there are issues with method and presentation.<br /> The authors did many simulations with random parameters, then averaged the result, and found for instance that the Conduction Velocity drops in demyelination. It gives the reader little insight into what is really going on. My personal preference is for a well understood simple model rather than a poorly understood complex model. The link between the model outcome of WM and data remains qualitative, and is further weakened by the existence of known other age-related effects in PFC circuits.

* Both for the de/re myelination the spatial patterns are fully random. Why is this justified?<br /> * Similarly, to model the myelin parameters where drawn from uniform distributions, Table 1 (I guess). Again, why is this reasonable?

* The focus of most analysis is on the conduction velocity but in the end, this has no effect on WM, so the discussion of CV remains sterile.

* The more important effect of de/re myelination is on failure.<br /> However, the failure is, AFAIK, just characterized by a constant current injection of 380pA.<br /> From Fig 2 it seems however that the first spike is particularly susceptible to failure.<br /> In other words, it has not been justified that it is fine to use the failure rates from this artificial protocol in the I&F model. I would expect the temporal current trace to affect whether the propagation fails or not.<br /> I don't know if there are many axon-collaterals in the WM circuits and or distance dependence in the connectivity, but if so, then the current implementation of failure would be questionable.<br /> I would also advise against thresholding at 75% failure in Fig3C. Why don't the authors not simply plot the failure rate?

Regarding the presentation, there are a number of dead-end results that are not used further on. The paper is rather extensive, and it would be clearer if written up in half the space. In addition, much information is really supplementary. The issue of the CV I already mentioned, also the Lasso regression for instance remains unused.

Reviewer #2 (Public Review):

Lines et al investigated the integration of calcium signals in astrocytes of the primary somatosensory cortex. Their goal was to better characterize the mechanisms that govern the spatial characteristics of calcium signals in astrocytes. In line with previous reports in the field, they found that most events originated and stayed localized within microdomains in distal astrocyte processes, occasionally coinciding with larger events in the soma, referred to as calcium surges. As a single astrocyte communicates with hundreds of thousands of synapses simultaneously, understanding the spatial integration of calcium signals in astrocytes and the mechanisms governing the latter is of tremendous importance to deepen our understanding of signal processing in the central nervous system. The authors thus aimed to unveil the properties governing the emergence of calcium surges. The main claim of this manuscript is that there would be a spatial threshold of ~23% of microdomain activation above which a calcium surge, i.e. a calcium signal that spreads to the soma, is observed. Although the study provides data that is highly valuable for the community, the conclusions of the current version of the manuscript seem a little too assertive and general compared with what can be deduced from the data and methods used.

The major strength of this study is the experimental approach that allowed the authors to obtain numerous and informative calcium recordings in vivo in the somatosensory cortex in mice in response to sensory stimuli as well as in situ. Notably, they developed an interesting approach to modulating the number of active domains in peripheral astrocyte processes by varying the intensity of peripheral stimulation (its amplitude, frequency, or duration).

The major weakness of the manuscript is the method used to analyze and quantify calcium activity, which mostly relies on the analysis of averaged data and overlooks the variability of the signals measured. As a result, the main claims from the manuscript seem to be incompletely supported by the data. The choice of the use of a custom-made semi-automatic ROI-based calcium event detection algorithm rather than established state-of-the-art software, such as the event-based calcium event detection software AQuA (DOI: 10.1038/s41593-019-0492-2), is insufficiently discussed and may bias the analysis. Some references on this matter include: Semyanov et al, Nature Rev Neuro, 2020 (DOI: 10.1038/s41583-020-0361-8); Covelo et al 2022, J Mol Neurosci (DOI: 10.1007/s12031-022-02006-w) & Wang et al, 2019, Nat Neuroscience (DOI: 10.1038/s41593-019-0492-2). Moreover, the ROIs used to quantify calcium activity are based on structural imaging of astrocytes, which may not be functionally relevant.

For the reasons listed above, the manuscript would probably benefit from some rephrasing of the conclusions and a discussion highlighting the advantages and limitations of the methodological approach. The question investigated by this study is of great importance in the field of neuroscience as the mechanisms dictating the spatio-temporal properties of calcium signals in astrocytes are poorly characterized, yet are essential to understand their involvement in the modulation of signal integration within neural circuits.

Reviewer #2 (Public Review):

Schnell and colleagues trained rats on a two-alternative forced choice visual discrimination task. They used object pairs that differed in their concavity and the alignment of features. They found that rats could discriminate objects across various image transformations. Rat performance correlated best with late convolutional layers of an artificial neural network and was partially explained by factors of brightness and pixel-level similarity. In contrast, human performance showed the strongest correlation with higher, fully connected layers, indicating that rats employed simpler strategies to accomplish this task as compared to humans.

Strengths:<br /> 1. This is a methodologically rigorous study. The authors tested a substantial number of rats across a large variety of stimuli.<br /> 2. The innovative use of neural networks to generate stimuli with varying levels of complexity is a compelling approach that motivates principled experimental design.<br /> 3. The study provides important data points for cross-species comparisons of object discrimination behavior<br /> 4. The data strongly support the authors' conclusion that rats and humans rely on different visual features for discrimination tasks.<br /> 5. This is a valuable study that provides novel, important insights into the visual capabilities of rats.

Weaknesses:<br /> 1. The impact of rat visual acuity (~1cycle/degree) on the discriminability of stimuli could be more directly modeled and taken into consideration when comparing rat behavior to humans, who possess substantially higher acuity.<br /> 2. The distinction between low- and high-level visual behavior is coarse, and it remains uncertain which specific features rats utilized for discrimination. The correlations with brightness and pixel-level similarity do provide some insight.<br /> 3. The relatively weak correspondence between rat behavior and AlexNet raises the question of which network architecture, whether computational or biological, might better capture rat behavior, particularly to the level of cross-rat consistency.

Reviewer #2 (Public Review):

In this study, researchers aim to understand the computational principles behind attention allocation in goal-directed reading tasks. They explore how deep neural networks (DNNs) optimized for reading tasks can predict reading time and attention distribution. The findings show that attention weights in transformer-based DNNs predict reading time for each word. Eye tracking reveals that readers focus on basic text features and question-relevant information during initial reading and rereading, respectively. Attention weights in shallow and deep DNN layers are separately influenced by text features and question relevance. Additionally, when readers read without a specific question in mind, DNNs optimized for word prediction tasks can predict their reading time. Based on these findings, the authors suggests that attention in real-world reading can be understood as a result of task optimization.

Strengths of the Methods and Results:<br /> The present study employed stimuli consisting of paragraphs read by middle and high school students, covering a wide range of diverse topics. This choice ensured that the reading experience for participants remained natural, ultimately enhancing the ecological validity of the findings and conclusions.

In Experiments 1-3, participants were instructed to read questions before the text, while in Experiment 4 participants were instructed to read questions after the text. This deliberate manipulation allowed the paper to assess how different reading task conditions influence reading and eye movements.

Weaknesses of the Methods and Results:

While the study benefits from several strengths, it is important to acknowledge its limitations. Notably, recent months have seen significant advancements in Deep Neural Network (DNN) models, including the development of models such as GPT-3.5 and GPT-4, which have demonstrated remarkable capabilities in tasks resembling human cognition, like Theory of Mind. However, as the code for these cutting-edge models was not publicly accessible, they were unable to evaluate whether the attention mechanisms in the most up-to-date DNN models could provide improved predictions for human eye-movement data. This constraint represents a limitation in the investigation.

The methods and data presented in this study are valuable for gaining insights into the psychological mechanisms of reading. Moreover, the data provided in this paper may prove instrumental in enhancing the performance of future DNN models.

Reviewer #2 (Public Review):

Summary:<br /> This paper tests the idea that schooling can provide an energetic advantage over solitary swimming. The present study measures oxygen consumption over a wide range of speeds, to determine the differences in aerobic and anaerobic cost of swimming, providing a potentially valuable addition to the literature related to the advantages of group living.

Strengths:<br /> The strength of this paper is related to providing direct measurements of the energetics (oxygen consumption) of fish while swimming in a group vs solitary. The energetic advantages of schooling have been claimed to be one of the major advantages of schooling and therefore a direct energetic assessment is a useful result.

Weaknesses:<br /> The manuscript suffers from a number of weaknesses which are summarised below:

1) The possibility that fish in a school show lower oxygen consumption may also be due to a calming effect. While the authors show that there is no difference at low speed, one cannot rule out that calming effects play a more important role at higher speed, i.e. in a more stressful situation.

2) The ratio of fish volume to water volume in the respirometer is much higher than that recommended by the methodological paper by Svendsen et al (J Fish Biol 2016)

3) Because the same swimming tunnel was used for schools and solitary fish, schooling fish may end up swimming closer to the wall (because of less volume per fish) than solitary fish. Distances to the wall of schooling fish are not given, and they could provide an advantage to schooling fish.

4) The statistical analysis has a number of problems. The values of MO2 of each school are the result of the oxygen consumption of each fish, and therefore the test is comparing 5 individuals (i.e. an individual is the statistical unit) vs 5 schools (a school made out of 8 fish is the statistical unit). Therefore the test is comparing two different statistical units. One can see from the graphs that schooling MO2 tends to have a smaller SD than solitary data. This may well be due to the fact that schooling data are based on 5 points (five schools) and each point is the result of the MO2 of five fish, thereby reducing the variability compared to solitary fish. Other issues are related to data (for example Tail beat frequency) not being independent in schooling fish.

Reviewer #2 (Public Review):

The short-term administration of reprogramming factors to partially reprogram cells has gained traction in recent years as a potential strategy to reverse aging in cells and organisms. Early studies used Yamanaka factors in transgenic mice to reverse aging phenotypes, but chemical cocktails could present a more feasible approach for in vivo delivery. In this study, Mitchell et al sought to determine the effects that short-term administration of chemical reprogramming cocktails have on biological age and function. To address this question, they treated young and old mouse fibroblasts with chemical reprogramming cocktails and performed transcriptome, proteome, metabolome, and DNA methylation profiling pre- and post-treatment. For each of these datasets, they identified changes associated with treatment, showing downregulation of some previously identified molecular signatures of aging in both young and old cells. From these data, the authors conclude that partial chemical reprogramming can rejuvenate both young and old fibroblasts.

The main strength of this study is the comprehensive profiling of cells pre- and post-treatment with the reprogramming cocktails, which will be a valuable resource for better understanding the molecular changes induced by chemical reprogramming. The authors highlighted consistent changes across the different datasets that are thought to be associated with aging phenotypes, showing reduction of age-associated signatures previously identified in various tissues. However, from the findings, it remains unclear which changes are functionally relevant in the specific fibroblast system being used. Specifically:

1) The 4 month and 20 month mouse fibroblasts are designated "young" vs "old" in this study. An important analysis that was not shown for each of the profiled modalities was a comparison of untreated young vs old fibroblasts to determine age-associated molecular changes in this specific model of aging. Then, rather than using aging signatures defined in other tissues, it would be more appropriate to determine whether the chemical cocktails reverted old fibroblasts to a younger state based on the age-associated changes identified in this comparison.<br /> 2) Across all datasets, it appears that the global profiles of young vs old mouse fibroblasts are fairly similar compared to treated fibroblasts, suggesting that the chemical cocktails are not reverting the fibroblasts to a younger state but instead driving them to a different cell state. Similarly, in most cases where specific age-related processes/genes are being compared across untreated and treated samples, no significant differences are observed between young and old fibroblasts.<br /> 3) Functional validation experiments to confirm that specific changes observed after partial reprogramming are indeed reducing biological age is limited.<br /> 4) Partial reprogramming appears to substantially reduce biological age of the young (4 month) fibroblasts based on the aging signatures used. It is unclear how this result should be interpreted.

Reviewer #2 (Public Review):

Summary: In this study, the authors delve into the mechanisms responsible for the maintenance of two diptericin alleles within Drosophila populations. Diptericin is a significant antimicrobial peptide that plays a dual role in fly defense against systemic bacterial infections and in shaping the gut bacterial community, contributing to gut homeostasis.

Strengths: The study unquestionably demonstrates the distinct functions of these two diptericin alleles in responding to systemic infections caused by specific bacteria and in regulating gut homeostasis and fly physiology. Notably, these effects vary between male and female flies.

Weaknesses: Although the findings are highly intriguing and shed light on crucial mechanisms contributing to the preservation of both diptericin alleles in fly populations, a more comprehensive investigation is warranted to dissect the selection mechanisms at play, particularly concerning diptericin's roles in systemic infection and gut homeostasis. Unfortunately, the results from the association study conducted on wild-caught flies lack conclusive evidence.

Major Concerns:

Lines 120-134: The second hypothesis is not adequately defined or articulated. Please revise it to provide more clarity. Additionally, it should be explicitly stated that the first part of the first hypothesis (pathogen specificity), i.e., the superior survival of the S allele in Providencia infections compared to the R allele, has been previously investigated and supported by the results in the Unkless et al. 2016 paper. The current study aims to additionally investigate the opposite scenario: whether the R allele exhibits better survival in a different infection. Please consider revising to emphasize this point.

Figures and statistical analyses: It is essential to present the results of significant differences from the statistical analyses within Figures 1B, 2B, and 3. Additionally, please include detailed descriptions of the statistical analysis methods in the figure legends. Specify whether the error bars represent standard error or standard deviation, particularly in Figure 3, where assays were conducted with as few as 3 flies.

Lines 317-318 (as well as 320-328): The data related to P. rettgeri appear somewhat incomplete, and the authors acknowledge that bacterial load varies significantly, and this bacterium establishes poorly in the gut. These data may introduce more noise than clarity to the study. Please consider revising these sections by either providing more data, refining the presentation, or possibly removing them altogether.

Lines 335-387 and Figure 4: Although these results are intriguing and suggest interactions between functional diptericin and fly physiology, some mediated by the gut microbiome, they remain descriptive and do not significantly contribute to our understanding of the mechanism that maintains the diptericin alleles.

Lines 399-400: The contrast between this result and statement and the highly reproducible data presented in Figures 2-4 should be discussed.

Lines 422-429 and Figure 5D: The conclusion regarding an association between diptericin alleles and Morganellaceae bacteria is not clearly supported by Figure 5D and lacks statistical evidence.

Reviewer #2 (Public Review):

Summary:<br /> Winker and Delmore present a study on the demographic consequences of migratory versus resident behavior by contrasting the evolutionary history of lineages within the same songbird group (thrushes of the genus Catharus).

Strengths:<br /> I appreciate the test-of-hypothesis design of the study and the explicit formulation of three main expectations to test. The data analysis has been done with appropriate available tools.

Weaknesses:<br /> The current version of the paper, with the case study chosen, the results, and the relative discussion, is not satisfying enough to support or reject the hypotheses here considered.

The authors hypothesized that the wider realized breeding and ecological range characterising migrants versus resident lineages could be a major drive for increased effective population size and population expansion in migrants versus residents. I understand that this pattern (wider range in migrants) is a common characteristic across bird lineages and that it is viewed as a result of adapting to migration. A problem that I see in their dataset is that the breeding grounds range of the two groups are located in very different geographic areas (mainly South versus North America). The authors could have expanded their dataset to include species whose breeding grounds are from the two areas, regardless of their migratory behaviour, as a comparison to disentangle whether ecological differences of these two areas can affect the population sizes or growth rates.

As I understand from previous literature, the time-scale to population growth and estimates of effective population sizes considered in the present paper for the resident versus migratory clades seem to widely predate the times to speciation for the same lineages, which were reported in previous work of the same authors (Everson et al 2019) and others (Termignoni-Garcia et al 2022). This piece of information makes the calculation of species-specific population size changes difficult to interpret in the light of lineages' comparison. It is unclear what the authors consider to be lineage-specific in these estimates, as the clades were likely undergoing substantial admixture during the time predating full isolation.

Regarding the methodological difficulties in interpreting the impact of population structure on the estimates of effective population sizes with the PSMC approach, I would think that performing simulations to compare different scenarios of different degrees of structured populations would have helped substantially understand some of the outcomes.

Additionally, I have struggled to understand if migratory behaviour in birds is considered to be acquired to relieve species competition, or as a consequence of expanded range (i.e., birds expand their range but their feeding ground is kept where speciation occurred as to exploit a ground with higher quality and abundance of seasonal local resources).

The points raised above could be considered to improve the current version of the paper.

Reviewer #2 (Public Review):

Allison Coté et al. investigated the ordering and spatial distribution of nascent transcripts in several cells using smFISH, expansion microscopy, and live-cell imaging. They find that pre-mRNA splicing occurs post-transcriptionally at the clouds around the transcription start site, termed the transcription site proximal zone. They show that pre-mRNA may undergo continuous splicing when they pass through the zone after transcription. These data suggest a unifying model for explaining previously reported co-transcriptional splicing events and provide a direction for further study of the nature of the slow-moving zone around the transcription start site.

This paper is well-written. The findings are very important, and the data supports the conclusions well. However, some aspects of the image and description need to be clarified and revised.

The authors describe Figure 4E and 4F results in the main text as that "we performed RNA FISH simultaneously with immunofluorescence for SC35, a component of speckles, and saw that this compartmentalized pre-mRNA did indeed appear near nuclear speckles both before (Supplementary Figure 6C) and after (Figure 4E) splicing inhibition." However, no SC35 staining is shown in the Figure 4E. A similar situation happened in describing Figure 4F.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Bradley and his colleagues represented the cryo-EM structure of the nuclear cap-binding complex (CBC) in complex with an mRNA export factor, ALYREF, providing a structural basis for understanding CBC regulating gene expression.

Strengths:<br /> The authors successfully modeled the N-terminal region and the RRM domain of ALYREF (residues 1-183) within the CBC-ALYREF structure, which revealed that both the NCBP1 and NCBP2 subunits of the CBC interact with the RBM domain of ALYREF. Further mutagenesis and pull-down studies provided additional evidence to the observed CBC-ALYREF interface. Additionally, the authors engaged in a comprehensive discussion regarding other cellular complexes containing CBC and/or ALYREF components. They proposed potential models that elucidated coordinated events during mRNA maturation. This study provided good evidence to show how CBC effectively recruits mRNA export factor machinery, enhancing our understanding of CBC regulating gene expression during mRNA transcription, splicing, and export.

Weaknesses:<br /> No in vivo or in vitro functional data to validate and support the structural observations and the proposed models in this study. Cryo-EM data processing and structural representation need to be strengthened.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, "KinCytE- a Kinase to Cytokine Explorer to Identify Molecular Regulators and Potential Therapeutic", the authors present a web resource, KinCytE, that lets researchers search for kinase inhibitors that have been shown to affect cytokine and chemokine release and signaling networks. I think it's a valuable resource that has a lot of potential and could be very useful in deciding on statistical analysis that might precede lab experiments.

Opportunities:<br /> With the release of the manuscript and the code base in place, I hope the authors continue to build upon the platform, perhaps by increasing the number of cell types that are probed (beyond macrophages). Additionally, when new drug-response data becomes available, perhaps it can be used to further validate the findings. Overall, I see this as a great project that can evolve.

Strengths:<br /> The site contains valuable content, and the structure is such that growing that content should be possible.

Weaknesses:<br /> Only based on macrophage experiments, would be nice to have other cell types investigated, but I'm sure that will be remedied with some time.

Reviewer #2 (Public Review):

Summary:

The authors present a number of exploratory applications of current protein representations for remote homology search. They first fine-tune a language model to predict structural alphabets from sequence and demonstrate using these predicted structural alphabets for fast remote homology search both on their own and by building HMM profiles from them. They also demonstrate the use of residue-level language model amino acid predicted probabilities to build HMM profiles. These three implementations are compared to traditional profile-based remote homology search.

Strengths:

- Predicting structural alphabets from a sequence is novel and valuable, with another approach (ProstT5) also released in the same time frame further demonstrating its application for the remote homology search task.<br /> - Using these new representations in established and battle-tested workflows such as MMSeqs, HMMER, and HHBlits is a great way to allow researchers to have access to the state-of-the-art methods for their task.<br /> - Given the exponential growth of data in a number of protein resources, approaches that allow for the preparation of searchable datasets and enable fast search is of high relevance.

Weaknesses:

- The authors fine-tuned ESM-2 3B to predict 3Di sequences and presented the fine-tuned model ESM-2 3B 3Di with a claimed accuracy of 64% compared to a test set of 3Di sequences derived from AlphaFold2 predicted structures. However, the description of this test set is missing, and I would expect repeating some of the benchmarking efforts described in the Foldseek manuscript as this accuracy value is hard to interpret on its own.<br /> - Given the availability of predicted structure data in AFDB, I would expect to see a comparison between the searches of predicted 3Di sequences and the "true" 3Di sequences derived from these predicted structures. This comparison would substantiate the innovation claimed in the manuscript, demonstrating the potential of conducting new searches solely based on sequence data on a structural database.<br /> - The profile HMMs built from predicted 3Di appear to perform sub-optimally, and those from the ESM-2 3B predicted probabilities also don't seem to improve traditional HMM results significantly. The HHBlits results depicted in lines 5 and 6 in the figure are not discussed at all, and a comparison with traditional HHBlits is missing. With these results and presentation, the advantages of pLM profile-based searches are not clear, and more justification over traditional methods is needed.<br /> - Figure 3 and its associated text are hard to follow due to the abundance of colors and abbreviations used. One figure attempting to explain multiple distinct points adds to the confusion. Suggestion: Splitting the figure into two panels comparing (A) Foldseek-derived searches (lines 7-10) and (B) language-model derived searches (line 3-6) to traditional methods could enhance clarity. Different scatter markers could also help follow the plots more easily.<br /> - The justification for using Foldseek without amino acids (3Di-only mode) is not clear. Its utility should be described, or it should be omitted for clarity.<br /> - Figure 2 is not described, unclear what to read from it.

Reviewer #2 (Public Review):

Summary:<br /> In this work, Ibtisam and Kisselev explore the role of DDI2 in proteasome function recovery after a clinically relevant pulse dosing using different proteasome inhibitors and their corresponding PK properties. The authors report that despite the lack of NRF1 activation by DDI2 there was no difference in recovery from pulsed proteasome inhibition observed in DDI2 KO cells as compared to WT controls suggesting that DDI2 is not required for recovery in this system. They further show that transcription of the proteasome subunits is initiated only after partial recovery of proteasome activity is already observed suggesting that non-transcriptional mechanisms might be also involved. The authors further show that translation inhibition blocked the recovery from proteasome inhibitors.

Strengths:<br /> Overall, it is very important and informative to use a pulse treatment type approach (mimicking the PK properties of the drugs) to explore the biology of PIs as used in this study. The authors also provide convincing data that DDI2 is not required for proteasome activity recovery post-PI pulse treatment in the systems they explored.

Weaknesses:<br /> Many of the other conclusions are not supported by the data in the current form of the manuscript and are too speculative and ignore the major findings in the field that can present alternative mechanisms. In particular, the authors discuss the "levels" of the proteasomes post-PI treatment without measuring the actual protein level of the individual subunits or the different assembled proteasome complexes.

a large German dirigible airship of the early 20th century, long and cylindrical in shape and with a rigid framework. Zeppelins were used during World War I for reconnaissance and bombing, and after the war as passenger transports until the 1930s.

a violent or sudden change or disruption to something

without embarrassment or shame.

Reviewer #2 (Public Review):

It is recommended to use a blind sample test to determine the specimen's status using the AI they developed.<br /> Where these markers promote tumorigenesis or metastasis if tested in vivo?<br /> The article would be very valuable in the future to promote using AI to predict disease status and facilitate cancer screening.<br /> Much more improvement is required for data validation and presentation.

Reviewer #2 (Public Review):

Summary:<br /> The evolution of resistance to antimalarial drugs follows a seemingly counterintuitive pattern, in which resistant strains typically originate in regions where malaria prevalence is relatively low. Previous investigations have suggested that frequent exposures in high-prevalence regions produce high levels of partial immunity in the host population, leading to subclinical infections that go untreated. These subclinical infections serve as refuges for sensitive strains, maintaining them in the population. Prior investigations have supported this hypothesis; however, many of them excluded important dynamics, and the results cannot be generalized. The authors have taken a novel approach using a deterministic model that includes both general and adaptive immunity. They find that high levels of population immunity produce refuges, maintaining the sensitive strains and allowing them to outcompete resistant strains. While general population immunity contributed, adaptive immunity is key to reproducing empirical patterns. These results are robust across a range of fitness costs, treatment rates, and resistance efficacies. They demonstrate that future investigations cannot overlook adaptive immunity and antigenic diversity.

Strengths:<br /> Overall, this is a very nice paper that makes a significant contribution to the field. It is well-framed within the body of literature and achieves its goal of providing a generalizable, unifying explanation for otherwise disparate investigations. As such, this work will likely serve as a foundation for future investigations. The approach is elegant and rigorous, with results that are supported across a broad range of parameters.

Weaknesses:<br /> Although the title states that the authors describe resistance invasion, they do not support or even explore this claim. As they state in the discussion (line 351), this work predicts the equilibrium state and doesn't address temporal patterns. While refuges in partially immune hosts may maintain resistance in a population, they do not account for the patterns of resistance spread, such as the rapid spread of chloroquine resistance in Africa once it was introduced from Asia.

As the authors state in the discussion, the evolution of compensatory mutations that negate the cost of resistance is possible, and in vitro experiments have found evidence of such. It appears that their results are dependent on there being a cost, but the lower range of the cost parameter space was not explored.

The use of a deterministic, compartmental model may be a structural weakness. This means that selection alone guides the fixation of new mutations on a semi-homogenous adaptive landscape. In reality, there are two severe bottlenecks in the transmission cycle of Plasmodium spp., introducing a substantial force of stochasticity via genetic drift. The well-mixed nature of this type of model is also likely to have affected the results. In reality, within-host selection is highly heterogeneous, strains are not found with equal frequency either in the population or within hosts, and there will be some linkage between the strain and a resistance mutation, at least at first. Of course, there is no recourse for that at this stage, but it is something that should be considered in future investigations.

The authors mention the observation that patterns of resistance in high-prevalence Papua New Guinea seem to be more similar to Southeast Asia, perhaps because of the low strain diversity in Papua New Guinea. However, they do not investigate that parameter space here. If they did and were able to replicate that observation, not only would that strengthen this work, it could profoundly shape research to come.

Reviewer #2 (Public Review):

Summary:<br /> This study seeks to understand the connection between protein sequence and function in disordered regions enriched in polar amino acids (specifically Q, N, S and T). While the authors suggest that specific motifs facilitate protein-enhancing activities, their findings are correlative, and the evidence is incomplete. Similarly, the authors propose that the re-assignment of stop codons to glutamine-encoding codons underlies the greater user of glutamine in a subset of ciliates, but again, the conclusions here are, at best, correlative. The authors perform extensive bioinformatic analysis, with detailed (albeit somewhat ad hoc) discussion on a number of proteins. Overall, the results presented here are interesting, but are unable to exclude competing hypotheses.

Strengths:<br /> Following up on previous work, the authors wish to uncover a mechanism associated with poly-Q and SCD motifs explaining proposed protein expression-enhancing activities. They note that these motifs often occur IDRs and hypothesize that structural plasticity could be capitalized upon as a mechanism of diversification in evolution. To investigate this further, they employ bioinformatics to investigate the sequence features of proteomes of 27 eukaryotes. They deepen their sequence space exploration uncovering sub-phylum-specific features associated with species in which a stop-codon substitution has occurred. The authors propose this stop-codon substitution underlies an expansion of ploy-Q repeats and increased glutamine distribution.

Weaknesses:<br /> The preprint provides extensive, detailed, and entirely unnecessary background information throughout, hampering reading and making it difficult to understand the ideas being proposed.<br /> The introduction provides a large amount of detailed background that appears entirely irrelevant for the paper. Many places detailed discussions on specific proteins that are likely of interest to the authors occur, yet without context, this does not enhance the paper for the reader.

The paper uses many unnecessary, new, or redefined acronyms which makes reading difficult. As examples: (1) Prion forming domains (PFDs). Do the authors mean prion-like domains (PLDs), an established term with an empirical definition from the PLAAC algorithm? If yes, they should say this. If not, they must define what a prion-forming domain is formally. (2) SCD is already an acronym in the IDP field (meaning sequence charge decoration) - the authors should avoid this as their chosen acronym for Serine(S) / threonine (T)-glutamine (Q) cluster domains. Moreover, do we really need another acronym here (we do not). (3) Protein expression-enhancing (PEE) - just say expression-enhancing, there is no need for an acronym here.

The results suggest autonomous protein expression-enhancing activities of regions of multiple proteins containing Q-rich and SCD motifs. Their definition of expression-enhancing activities is vague and the evidence they provide to support the claim is weak. While their previous work may support their claim with more evidence, it should be explained in more detail. The assay they choose is a fusion reporter measuring beta-galactosidase activity and tracking expression levels. Given the presented data they have shown that they can drive the expression of their reporters and that beta gal remains active, in addition to the increase in expression of fusion reporter during the stress response. They have not detailed what their control and mock treatment is, which makes complete understanding of their experimental approach difficult. Furthermore, their nuclear localization signal on the tag could be influencing the degradation kinetics or sequestering the reporter, leading to its accumulation and the appearance of enhanced expression. Their evidence refuting ubiquitin-mediated degradation does not have a convincing control.

Based on the experimental results, the authors then go on to perform bioinformatic analysis of SCD proteins and polyX proteins. Unfortunately, there is no clear hypothesis for what is being tested; there is a vague sense of investigating polyX/SCD regions, but I did not find the connection between the first and section compelling (especially given polar-rich regions have been shown to engage in many different functions). As such, this bioinformatic analysis largely presents as many lists of percentages without any meaningful interpretation. The bioinformatics analysis lacks any kind of rigorous statistical tests, making it difficult to evaluate the conclusions drawn.

The methods section is severely lacking. Specifically, many of the methods require the reader to read many other papers. While referencing prior work is of course, important, the authors should ensure the methods in this paper provide the details needed to allow a reader to evaluate the work being presented. As it stands, this is not the case.

Overall, my major concern with this work is that the authors make two central claims in this paper (as per the Discussion).

The authors claim that Q-rich motifs enhance protein expression. The implication here is that Q-rich motif IDRs are special, but this is not tested. As such, they cannot exclude the competing hypothesis ("N-terminal disordered regions enhance expression"). The authors also do not explore the possibility that this effect is in part/entirely driven by mRNA-level effects (see Verma Na Comms 2019). As such, while these observations are interesting, they feel preliminary and, in my opinion, cannot be used to draw hard conclusions on how N-terminal IDR sequence features influence protein expression. This does not mean the authors are necessarily wrong, but from the data presented here, I do not believe strong conclusions can be drawn.

That re-assignment of stop codons to Q increases proteome-wide Q usage. I was unable to understand what result led the authors to this conclusion. My reading of the results is that a subset of ciliates has re-assigned UAA and UAG from the stop codon to Q. Those ciliates have more polyQ-containing proteins. However, they also have more polyN-containing proteins and proteins enriched in S/T-Q clusters. Surely if this were a stop-codon-dependent effect, we'd ONLY see an enhancement in Q-richness, not a corresponding enhancement in all polar-rich IDR frequencies? It seems the better working hypothesis is that free-floating climate proteomes are enriched in polar amino acids compared to sessile ciliates. Regardless, the absence of any kind of statistical analysis makes it hard to draw strong conclusions here.

Reviewer #2 (Public Review):

In this study, multiple biophysical techniques were employed to investigate the activation mechanism of BTK, a multi-domain non-receptor protein kinase. Previous studies have elucidated the inhibitory effects of the SH3 and SH2 domains on the kinase and the potential activation mechanism involving the membrane-bound PIP3 inducing transient dimerization of the PH-TH domain, which binds to lipids.

The primary focus of the present study was on three new constructs: a full-length BTK construct, a construct where the PH-TH domain is connected to the kinase domain, and a construct featuring a kinase domain with a phosphomimetic at the autophosphorylation site Y551. The authors aimed to provide new insights into the autoinhibition and allosteric control of BTK.

The study reports that SAXS analysis of the full-length BTK protein construct, along with cryoEM visualization of the PH-TH domain, supports a model in which the N-terminal PH-TH domain exists in a conformational ensemble surrounding a compact/autoinhibited SH3-SH2-kinase core. This finding is interesting because it contradicts previous models proposing that each globular domain is tightly packed within the core.

Furthermore, the authors present a model for an inhibitory interaction between the N-lobe of the kinase and the PH-TH domain. This model is based on a study using a tethered complex with a longer tether than a previously reported construct where the PH-TH domain was tightly attached to the kinase domain (ref 5). The authors argue that the new structure is relevant. However, this assertion requires further explanation and discussion, particularly considering that the functional assays used to assess the impact of mutating residues within the PH-TH/kinase domain contradict the results of the previous study (ref 5).

Additionally, the study presents the structure of the kinase domain with swapped activation loops in a dimeric form, representing a previously unseen structure along the trans-phosphorylation pathway. This structure holds potential relevance. To better understand its significance, employing a structure/function approach like the one described for the PH-TH/kinase domain interface would be beneficial.

Overall, this study contributes to our understanding of the activation mechanism of BTK and sheds light on the autoinhibition and allosteric control of this protein kinase. It presents new structural insights and proposes novel models that challenge previous understandings.

Reviewer #2 (Public Review):

Summary:<br /> This paper consists of mostly descriptive data, judged from alpha-mannosidase-treated samples, in which they found an increase in core fucose, a product of Fut 8.

Strengths:<br /> This paper is interesting in the clinical field, but unfortunately, the data is mostly descriptive and does not have a significant impact on the scientific community in general.

Weaknesses:<br /> If core fucose is increased, at least the target glycan molecules of core fucose should be evaluated. They also found an increase in NO, suggesting that inflammatory processes also play an important role in OA in addition to glycan changes.<br /> It has already been reported that core fucose is decreased by administration of alpha-mannosidase inhibitors. Therefore, it is expected that alpha-mannosidase administration increases core fucose.

Reviewer #2 (Public Review):

In this manuscript, the authors aim to study the PML-nucleoli association (PNAs) by different genotoxic stress and to determine the underlying molecular mechanisms.

First, from a diverse set of genotoxic stress conditions (topoisomerases, RNA Pol I, rRNA processing, and DNA replication stress), the authors have found that the inhibition of topoisomerases and RNA Polymerase I has the highest PNA formation associated with p53 stabilization, gamma-H2AX, and PAF49 segregation. It was further demonstrated that Rad51-mediated HR pathway but not NHEJ pathway is associated with the PNA formation. Immuno-FISH assays show that doxorubicin induces DSBs (53BP1 foci) in rDNA and PNA interactions with rDNA/DJ regions. Furthermore, endonuclease I-Ppol induced DSB at a defined location in rDNA and led to PNAs.

Most claims by the authors are supported by the data provided. However, below weaknesses/concerns may need to be addressed to improve the quality of the study.

1) Top2B toxin doxorubicin had the highest degree of elevating PNAs; however, Top2B-knockdown had almost no noticeable effects on PNAs. How to reconcile the different phenotypes targeting Top2B?

2) To test the role of Rad51 and DNA-PKcs in the PNA formation, Rad51 inhibitor B02 and DNA-PKcs inhibitor NU-7441 were chosen to use in the study. To further exclude the possible off-target of B02 and NU-7441, siRNA-mediated knockdown of Rad51 and DNA-PKcs would be an appropriate complementary approach to the pharmaceutical inhibitor approach.

3) Several previous studies have shown the activation of the nucleolar ATM-mediated DNA damage response pathway by I-Ppol-induced DSBs in rDNA. What is the role of nucleolar ATM in the regulation of PNAs?

Reviewer #2 (Public Review):

Summary:<br /> This manuscript by Latini et al describes a methodology to develop Boolean-based predictive logic models that can be applied to uncover altered protein/signalling networks in cancer cells and discover potential new therapeutic targets. As a proof-of-concept, they have implemented their strategy on a hematopoietic cell line engineered to express one of two types of FLT3 internal tandem mutations (FLT3-ITD) found in patients, FLT3-ITD-TKD (which are less sensitive to tyrosine kinase inhibitors/TKIs) and FLT3-ITD-JMD (which are more sensitive to TKIs).

Strengths:<br /> This useful work could potentially represent a step forward towards personalised targeted therapy, by describing a methodology using Boolean-based predictive logic models to uncover altered protein/signalling networks within cancer cells. However, the weaknesses highlighted below severely limit the extent of any conclusions that can be drawn from the results.

Weaknesses:<br /> While the highly theoretical approach proposed by the authors is interesting, the potential relevance of their overall conclusions is severely undermined by a lack of validation of their predicted results in real-world data. Their predictive logic models are built upon a set of poorly-explained initial conditions, drawn from data generated in vitro from an engineered cell line, and no attempt was made to validate the predictions in independent settings. This is compounded by a lack of sufficient experimental detail or clear explanations at different steps. These concerns considerably temper one's enthusiasm about the conclusions that could be drawn from the manuscript. Some specific concerns include:

1. It remains unclear how robust the logic models are, or conversely, how affected they might be by specific initial conditions or priors that are chosen. The authors fail to explain the rationale underlying their input conditions at various points. For example:<br /> - at the start of the manuscript, they assert that they begin with a pre-PKN that contains "76 nodes and 193 edges", though this is then ostensibly refined with additional new edges (as outlined in Fig 2A). However, why these edges were added, nor model performance comparisons against the basal model are presented, precluding an evaluation of whether this model is better.

- At a later step (relevant to Fig S4 and Fig 3), they develop separate PKNs, for each of the mutation models, that contain "206 [or] 208 nodes" and "756 [or] 782 edges", without explaining how these seemingly arbitrary initial conditions were arrived at. Their relation to the original parameters in the previous model is also not investigated, raising concerns about model over-fitting and calling into question the general applicability of their proposed approach. The authors need to provide a clearer explanation of the logic underlying some of these initial parameter selections, and also investigate the biological/functional overlap between these sets of genes (nodes).

2. There is concern about the underlying experimental data underpinning the models that were generated, further compounded by the lack of a clear explanation of the logic. For example, data concerning the status of signalling changes as a result of perturbation appears to be generated from multiplex LUMINEX assays using phosphorylation-specific antibodies against just 14 "sentinel" proteins. However, very little detail is provided about the rationale underlying how these 14 were chosen to be "sentinels" (and why not just 13, or 15, or any other number, for that effect?). How reliable are the antibodies used to query the phosphorylation status? What are the signal thresholds and linear ranges for these assays, and how would these impact the performance/reliability of the logic models that are generated from them?

In addition, there are publicly available quantitative proteomics datasets from FLT3-mutant cell lines and primary samples treated with TKIs. At the very least, these should have been used by the authors to independently validate their models, selection of initial parameters, and signal performance of their antibody-based assays, to name a few unvalidated, yet critical, parameters.

3. There is an overwhelming reliance on theoretical predictions without taking advantage of real-world validation of their findings. For example, the authors identified a set of primary AML samples with relevant mutations (Fig 5) that could potentially have provided a valuable experimental validation platform for their predictions of effective drug combination. Yet, they have performed Boolean simulations of the predicted effects, a perplexing instance of adding theoretical predictions on top of a theoretical prediction!

Additionally, there are datasets of drug sensitivity on primary AML samples where mutational data is also known (for example, from the BEAT-AML consortia), that could be queried for independent validation of the authors' models.

4. There are additional examples of insufficient experimental detail that preclude a fuller appreciation of the relevance of the work. For example, it is alluded that RNA-sequencing was performed on a subset of patients, but the entire methodological section detailing the RNA-seq amounts to just 3 lines! It is unclear which samples were selected for sequencing nor where the data has been deposited (or might be available for the community - there are resources for restricted/controlled access to deidentified genomics/transcriptomics data).

Similarly, in the "combinatory treatment inference" methods, it states "...we computed the steady state of each cell line best model....." and "Then we inferred the activity of "apoptosis" and "proliferation" phenotypes", without explaining the details of how these were done. The outcomes of these methods are directly relevant to Fig 4, but with such sparse methodological detail, it is difficult to independently assess the validity of the presented data.

Overall, the theoretical nature of the work is hampered by real-world validation, and insufficient methodological details limit a fuller appreciation of the overall relevance of this work.

Reviewer #2 (Public Review):

Summary:<br /> Chen Chen et al. investigated the interaction between GHR and PRLR at the cell surface using STORM-type super-resolution microscopy, proximity ligation assay, and mutagenesis. They found that GH and PRL change the surface expression of GHR and PRLR. Upon stimulation, the hGHR cluster size significantly increases in a transient manner, whereas changes in hPRLR occur more slowly. In their previous publication, the authors found that hGHR and hPRLR co-immunoprecipitate in the absence of ligands. Based on that finding and the observations here, the authors examined colocalization of hGHR and hPRLR in clusters with proximity ligation assays and found that the receptors form complexes on the surface of T47D cells, and that these complexes respond differently to the ligands. Remarkably, the experiments in cells lacking either hGHR or hPRLR showed that PRLR is necessary for the reduction of surface hGHR induced by PRL. Studies with truncation or deletion of hPRLR mutants, suggest the box 1 region in hPRLR plays a critical role in stabilizing the hGHR-hPRLR complexes. This region contains the JAK2 binding site, and the authors show that binding of JAK2 to hGHR is also required for hPRLR-mediated regulation of hGHR surface expression. Cytokine receptors have very important broad-ranging roles in regulating cells and physiological roles. Therefore, the new findings described here will significantly expand our understanding of the structure-function relationship that drives a core signalling mechanism in cell biology.

Strengths:<br /> I particularly appreciate that the authors used different angles to examine the mechanism of GHR-PRLR interaction and that they also checked the conclusions with CRISPR/Cas9 technology and with a cellular reconstitution system.

Weaknesses:<br /> I could not fully evaluate some of the data, mainly because several details on acquisition and analysis are lacking. It would be useful to know what the background signal was in dSTORM and how the authors distinguished the specific signal from unspecific background fluorescence, which can be quite prominent in these experiments. Typically, one would evaluate the signal coming from antibodies randomly bound to a substrate around the cells to determine the switching properties of the dyes in their buffer and the average number of localisations representing one antibody. This would help evaluate if GHR or PRLR appeared as monomers or multimers in the plasma membrane before stimulation, which is currently a matter of debate. It would also provide better support for the model proposed in Figure 8. Since many of the findings in this work come from the evaluation of localisation clusters, an image showing actual localisations would help support the main conclusions. I believe that the dSTORM images in Figures 1 and 2 are density maps, although this was not explicitly stated. Alexa 568 and Alexa 647 typically give a very different number of localisations, and this is also dependent on the concentration of BME. Did the authors take that into account when interpreting the results and creating the model in Figures 2 and 8? I believe that including this information is important as findings in this paper heavily rely on the number of localisations detected under different conditions. Including information on proximity labelling and CRISPR/Cas9 in the methods section would help with the reproducibility of these findings by other groups.

Reviewer #2 (Public Review):

Summary:<br /> In their manuscript titled "Stimulation-induced cytokine polyfunctionality as a dynamic concept," the authors investigate the dynamic nature of polyfunctional cytokine responses to established stimulants. The authors use their previously published single-cell encapsulation droplet-microfluidic platform to analyse the response of peripheral blood mononuclear cells (PBMCs) to different stimulants and measure the secretion dynamics of individual cytokines. This assay shows that polyfunctionality in cytokine responses is a complex but short-lived phenomenon that decreases with prolonged stimulation times. The study finds that polyfunctional cells predominantly display elevated cytokine concentrations with similar secretion patterns but higher secretion levels compared to their monocytokine-secreting counterparts. The method is promising to analyse the correlation between the secretion dynamics of different cytokines in primary samples and heterogeneous cell populations.

Strengths:<br /> This method provides single-cell-resolved and dynamic cytokine concentration information, which might be used to identify "fingerprints" of secretion patterns for selected cytokines. When extending the available data to more than one donor, this might be the basis for a diagnostic tool. The combination of established droplet microfluidics with an epi-fluorescence microscope-based readout makes it convincing that the method is transferable to other labs. Specifically, the dynamic analysis of cytokine concentrations is interesting, and the differences or similarities in secretion timepoints might be missed with end-point methods. The authors convincingly show that they detect up to three different cytokines in single cells.

Weaknesses:<br /> The conclusions of the study are based on samples from a single donor, which makes the conclusions on secretion patterns difficult to interpret. The choice of cytokines is explained, but the justification of the groupings of the antibodies into the two panels is missing. It would further be helpful to discuss how the single cell incubation might affect the sectration dynamics vs. the influence of co-culture of all cell types during the 24 h activation. The authors compare average secretion rates and levels. However, the right panel in Fig. 6 looks like there might be two different populations of mono- or polyfuntional cells that have two secretion rates. As the authors have single-cell data, I would find the separation into these populations more meaningful than comparing the mean values. In line with this comment, comparing the mean values for these cytokines instead of the mean of the populations with distinct seretion properties might actually show stronger differences than the authors report here. Is the plateau of the cytokine concentration caused by the fluorescence signal saturating the camera, saturation of the magnetic beads, exhaustion of the fluorescent antibodies, or constant cytokine concentrations? The high number of non-CSCs and the limited number of droplets decrease the statistical power of the method. The authors discuss their choice to use PBMCs and not solely T cells, but this aspect is missing in the discussion.

Reviewer #2 (Public Review):

Summary<br /> In this work, the authors seek to test a version of an old idea, which is that our perception of the world and our understanding of the objects in it are deeply influenced by the nature of our bodies and the kinds of behaviours and actions that those objects afford. The studies presented here muster three kinds of evidence for a discontinuity in the encoding of objects, with a mental "border" between objects roughly of human body scale or smaller, which tend to relate to similar kinds of actions that are yet distinct from the kinds of actions implied by human-or-larger scale objects. This is demonstrated through observers' judgments of the kinds of actions different objects afford; through similar questioning of AI large-language models (LLMs); and through a neuroimaging study examining how brain regions implicated in object understanding make distinctions between kinds of objects at human and larger-than-human scales.

Strengths <br /> The authors address questions of longstanding interest in the cognitive neurosciences -- namely how we encode and interact with the many diverse kinds of objects we see and use in daily life. A key strength of the work lies in the application of multiple approaches, as noted in the summary. Examining the correlations among kinds of objects, with respect to their suitability for different action kinds, is novel, as are the complementary tests of judgments made by LLMs.

Weaknesses <br /> A limitation of the tests of LLMs may be that it is not always known what kinds of training material was used to build these models, leading to a possible "black box" problem. Further, presuming that those models are largely trained on previous human-written material, it may not necessarily be theoretically telling that the "judgments" of these models about action-object pairs show human-like discontinuities. Indeed, verbal descriptions of actions are very likely to mainly refer to typical human behaviour, and so the finding that these models demonstrate an affordance discontinuity may simply reflect those statistics, rather than evidence that affordance boundaries can arise independently even without "organism-environment interactions" as the authors claim here.

The authors include a clever manipulation in which participants are asked to judge action-object pairs, having first adopted the imagined size of either a cat or an elephant, showing that the discontinuity in similarity judgments effectively moved to a new boundary closer to the imagined scale than the veridical human scale. The dynamic nature of the discontinuity suggests a different interpretation of the authors' main findings. It may be that action affordance is not a dimension that stably characterises the long-term representation of object kinds, as suggested by the authors' interpretation of their brain findings, for example. Rather these may be computed more dynamically, "on the fly" in response to direct questions (as here) or perhaps during actual action behaviours with objects in the real world.

Reviewer #2 (Public Review):

Summary:

The present work addresses the mechanisms linking the sex-dependent temporal GH secretion patterns to the robust sex differences in chromatin accessibility and transcription factor binding that ultimately regulate sexually dimorphic liver gene expression. Using DNAseq analysis genomic sites hypersensitive to cleavage by DNase I, DNase hypersensitive sites [DHS] were studied in hepatocytes from male and female mice. DHS in the genome corresponds to accessible chromatin regions and encompasses key regulatory elements, including enhancers, promoters, insulators, and silencers, often flanked by specific histone modifications, and all of these players were described in different settings of GH action. Importantly, the dynamics of sex-dependent and independent chromatin accessibility linked to STAT5 binding were evaluated. For that purpose, hepatic samples from mice were divided into STAT high and STAT low binding by EMSA screening. With this information changes in DHS related to STAT binding were calculated in both sexes, giving an approximation of chromatin opening in response to STAT5, or alternatively to hypophsectomy, or a single GH pulse. More the 800 male-biased DHS (from a total of more than 70000 DHS) regions were identified in the STAT5 high groups, implying that the binding of a plasma GH pulse activates STAT5, and evokes a dynamic cycle of male liver chromatin opening and closing at sites that comprised 31% of all male-biased DHS. This proves that the pulsatility of plasma GH stimulation confers significant male bias in chromatin accessibility, and STAT5 binding at a fraction of the genomic sites linked to sex-biased liver gene expression and liver disease. As a proof of concept, authors show that a single physiological replacement dose or pulse of GH given to hypophysectomized mice recapitulate, within 30 min, the pulsatile re-opening of chromatin seen in pituitary-intact male mouse liver.

In another male-biased DHS set (69% of male-biased DHS), chromatin accessibility was static, that is unchanged across the peaks and valleys of GH-induced liver STAT5 activity and mapped to a set of target genes and processes distinct though sometimes overlapping those of the dynamic male-biased DHS.

In view of these distinct dynamic and static DHS in males, authors evaluated key epigenetic features distinguishing the dynamic STAT5-driven mechanism of chromatin opening from that of static male-biased DHS, which are constitutively open in the male liver but closed in the female liver. The analysis of histone marks enriched at each class of sex-biased DHS indicated exquisite differences in the epigenetic mechanisms that mediate sex-specific gene repression in each sex. For example, H3K27me3 and H3K9me3, two widely used repressive histone marks, are used in a unique way in each sex to enforce sex differences in chromatin states at sex-biased DHS.

Finally, the work recapitulates and explains the classifications of sex dimorphic genes made in previous works. Sex-biased and pituitary hormone-dependent DHS act as regulatory elements with a positive enhancer potential, to induce or maintain gene expression in the intact liver by sustaining an open chromatin in the case of class I male-biased DHS and class I male-biased genes in the male liver. Contrariwise DHS may participate in the inhibition of gene expression by maintaining a closed chromatin state, as in the case of class II male-biased DHS and class II female-biased genes in male liver.<br /> These results as a whole present a complex mechanism by which GH regulates the sexual dimorphism of liver genes in order to cope with the metabolic needs of each sex. In a complete story, the information on chromatin accessibility, histone modification, and transcription factor binding was integrated to elucidate the complex patterns of transcriptional regulation, which is sexually dimorphic in the liver.

Strengths:

The work presents a novel insight into the fundamental underlying epigenetic mechanisms of sex-biased gene regulation.

Results are supported by numerous Tables, and Supplementary Tables with the raw data, which present the advantage that they may be reanalyzed in the future to prove new hypotheses.

Weaknesses:

It is a complicated work to analyze, even though the main messages are clearly conveyed.

Reviewer #2 (Public Review):

Summary:<br /> The study by Cullen et al presents intriguing data regarding the contribution of mTOR complex 1 (mTORC1) versus mTORC2 or both in Pten-null-induced macrocephaly and epileptiform activity. The role of mTORC2 in mTORopathies, and in particular Pten loss-off-function (LOF)-induced pathology and seizures, is understudied and controversial. In addition, recent data provided evidence against the role of mTORC1 in PtenLOF-induced seizures. To address these controversies and the contribution of these mTOR complexes in PtenLOF-induced pathology and seizures, the authors injected a AAV9-Cre into the cortex of conditional single, double, and triple transgenic mice at postnatal day 0 to remove Pten, Pten+Raptor or Rictor, and Pten+raptor+rictor. Raptor and Rictor are essentially binding partners of mTORC1 and mTORC2, respectively. One major finding is that despite preventing mild macrocephaly and increased cell size, Raptor knockout (KO, decreased mTORC1 activity) did not prevent the occurrence of seizures and the rate of SWD event, and aggravated seizure duration. Similarly, Rictor KO (decreased mTORC2 activity) partially prevented mild macrocephaly and increased cell size but did not prevent the occurrence of seizures and did not affect seizure duration. However, Rictor KO reduced the rate of SWD events. Finally, the pathology and seizure/SWD activity were fully prevented in the double KO. These data suggest the contribution of both increased mTORC1 and mTORC2 in the pathology and epileptic activity of Pten LOF mice, emphasizing the importance of blocking both complexes for seizure treatment. Whether these data apply to other mTORopathies due to Tsc1, Tsc2, mTOR, AKT or other gene variants remains to be examined.

Strengths:<br /> The strengths are as follows: 1) they address an important and controversial question that has clinical application, 2) the study uses a reliable and relatively easy method to KO specific genes in cortical neurons, based on AAV9 injections in pups. 2) they perform careful video-EEG analyses correlated with some aspects of cellular pathology.

Weaknesses:<br /> The study has nevertheless a few weaknesses: 1) the conclusions are perhaps a bit overstated. The data do not show that increased mTORC1 or mTORC2 are sufficient to cause epilepsy. However the data clearly show that both increased mTORC1 and mTORC2 activity contribute to the pathology and seizure activity and as such are necessary for seizures to occur. 2) the data related to the EEG would benefit from having more mice. Adding more mice would have helped determine whether there was a decrease in seizure activity with the Rictor or Raptor KO. 3) it would have been interesting to examine the impact of mTORC2 and mTORC1 overexpression related to point #1 above.

Reviewer #2 (Public Review):

The aim of this study was to relate functional alterations in patients with bvFTD to neurotransmitter maps provided by the JuSpace toolbox in order to better understand the underlying pathological mechanisms of this disease.

A strength of the study is the novelty of this aim. Some weaknesses are the different fMRI parameters of patients belonging to each centre and a better explanation of some methodological choices as well a better description of the JuSpace toolbox.

The authors have achieved their aims and the results seem to support some conclusions, although the results should be interpreted in light of a potential lack of proper control for multiple comparisons.

This work will increase the use of approaches that relate brain abnormalities to neurotransmitters and transcriptomics.

There is an increasing trend to assess the correspondence between neuroimaging alterations and detailed information of neurotransmitters across the brain. This work represents this trend and adds to an increasing body of work doing the same with transcriptomics.

Reviewer #2 (Public Review):

The goal of this study is to show that the superficial superior colliculus (sSC) of mouse signals figure-ground differences defined by contrast, orientation, and phase, and that these signals are necessary for the animal to detect such figure-ground differences. By inhibiting sSC while the animals perform a figure-ground detection task, the study shows that detection performance decreases when sSC activity is suppressed during the onset of the visual stimulus. The study then intends to show that sSC neurons exhibit surround suppression based on orientation differences, and that surround suppression is stronger when the animal detects the correct location of the figure on the background.

The major strength of this study is the use of a behavioural paradigm to test detection performance of figure-ground stimuli while manipulating neural activity in the sSC during different times after stimulus onset. This paradigm would show whether activity in the sSC is relevant for performing the task. Secondly, the study collected data to confirm previous findings: sSC neurons exhibit orientation specific surround suppression. Additionally, it is impressive that the authors were able to train mice to generalize their task performance across different stimulus categories (figure-ground differences in orientation and phase). This should be highlighted as it may inform future studies.

The study has, however, methodological and analytical weaknesses so that the stated conclusions are not supported by the presented results.

1) Optogenetic inhibition is not limited to sSC (even expression may not be limited)<br /> About 30% of inhibitory neurons in the sSC project to other areas, e.g. ventral LGN, parabigeminal nucleus and pretectum (Whyland et al, 2019, see ref in manuscript). This means that these areas receive direct inhibition when inhibitory sSC neurons are optogenetically stimulated. This fact is mentioned in the discussion but the consequences and implications for the results are ignored. This is a major flaw of the optogenetic experiments of this study. Additionally, no evidence is given that opsin expression was limited to the superficial layers (except for one histological slice), which the authors acknowledge in line 285. Deeper layers may have other inhibitory neurons with long-range projections.<br /> The finding that sSC neurons show no figure-ground modulation for phase while the optogenetic manipulation has behavioural effects may be an indication for other areas being affected by the optogenetic manipulation.

2) Could other behavioural variables explain the results?<br /> a) Are there any task events other than the visual stimuli that the mice could use to make their decisions? The authors state the use of a custom made lick spout but it is not clear how this spout works, i.e. how do mechanics of the spout deliver water to the right versus the left output and could the mouse perceive these mechanics?<br /> b) Could the different neural responses to figure versus ground shown in Fig 2I-J and Fig 3B be explained by behaviours varying between the trial types, e.g. by early lick movements (which are conceivable even if the spout is not present), eye movements or changes in pupil-linked arousal? A behavioural difference seems even more likely to occur between hit and error/miss trials (Fig 4). If these behaviours were not measured, the possibility of behavioural modulation should be discussed.

3) What is the behavioural strategy of the animals?<br /> Only licks beyond 200 ms after stimulus onset determine the choice of the animal because "mice made early random licks" from 0 to 200 ms. To better understand the behavioural strategies of the animals we need to see their behavioural data, i.e. left and right licks aligned to stimulus onset. It would be particularly interesting to see how number and latency of licks changes during optogenetic manipulation.

4) Data relating to misses should be included in analyses to provide a complete picture of behaviour and neural responses<br /> a) In the optogenetic manipulations, an increase in misses seems to dominate the decreased accuracy (please, explain when a response was counted as a miss). A separate analysis of miss trials may be more robust than of error trials and also offers a different interpretation of the data, namely that the mouse did not see the stimulus rather than perceiving the figure on the opposite side. However, if the mice reduced their lick rate in general during optogenetic stimulation, this begs the question whether their motor performance was affected by optogenetic manipulation. Can this possibility be excluded?<br /> b) Related to Fig 4, it would be equally interesting to see how FGM changes during misses. Do the changes support the observations for error trials?

5) Statistical tests do not support the conclusions, are missing or inadequate<br /> a) In Fig 1E, accuracy is significantly affected at only 1-2 time points in each task, specifically either the 1st and 3rd or the 2nd time point. How do the authors interpret these results? If inhibition starting at the 2nd time point has no significant effects, why would it be significant when inhibition starts later (at the 3rd time)? Furthermore, given that all other starting points of laser stimulation have no significant effects, there is no reason to trust the latency of inhibition effects based on mostly insignificant data points. This analysis in its current form should be removed, including a comparison of latencies between tasks, which was not tested for significance. It may be more meaningful to analyse accuracy for each animal separately. This may reduce variability.<br /> b) Analyses regarding the difference in neural response to figure and ground (Fig 2I-J, Fig 3B, Fig 4B, Fig 5C) would be more convincing and informative if the differences were analysed on the level of single neurons in response to the same orientation within their RF (or at the location where the figure is presented, for edge-RF neurons). A histogram of these differences would show how many neurons are affected and how large the effect is in single neurons.<br /> c) All statistical tests performed across neurons should account for dependencies due to simultaneous recordings (dependency on session) and due to recordings in the same animal (dependency on animal). This can be done in most cases by using linear mixed-effects models.<br /> d) There was no significant difference between model weights (Fig 3D), so the statement in line 210 (RF-edge neurons had higher weights) should be removed.<br /> e) Fig 4B compares FGM during correct and error trials. This comparison has to be performed with the same set of neurons in correct and error trials (not the case for orientation). Again, the most compelling and informative comparison would be on the level of single neurons: response difference between figure and ground (same visual features at figure position) during hits versus errors.<br /> f) There is no evidence that FGM for phase was different between hit and error trials as stated in line 234.<br /> g) It is not clear why and how the mixed linear effects model was used pooling data across tasks (Fig 4C and Fig 5D). Different neurons were recorded for each task, so the sample points (neurons) are not affected by both task effects (orientation and phase). Each task should be analysed separately.<br /> h) Bonferroni correction in Fig 1E should correct multiple comparisons across time points, not across tasks (see Table 1).<br /> i) What is the reason to perform some tests one-tailed, others two-tailed?

6) The results relating to "multisensory neurons" are ambiguous regarding their interpretation (if significant at all) and seem unrelated to the goal of the study. It is particularly likely that behaviours like licking or other movements cause the response differences between figure and ground.

7) What depth were neurons recorded from (Fig 3 and 4)?

DOI: 10.3390/ijms242015225

Resource: ZFIN_ZDB-ALT-040107-2

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-040107-2

What is this?

Reviewer #2 (Public Review):

Prior results established that Lepr, Calcr, and Cck neurons are non-overlapping neuronal populations in the NTS that individually suppress food intake when activated. This paper examines the consequences of activating or inhibiting two or three of these populations simultaneously. Activating two or three populations inhibits food intake a body weight more than each individually. Activation of Lepr and/or Calcr neurons is not aversive based on the conditioned taste aversion test, whereas activating all three is aversive by this test, indicating that aversion due to Cck neurons activation is dominant. Vertical sleeve gastrectomy (VSG) causes weight loss, but inhibiting each of these neurons individual or all three of them does not prevent weight loss. Overall, this paper provides a solid set of results but does not provide mechanistic insight into any of the phenomena examined.

Reviewer #2 (Public Review):

Summary:<br /> In this study, Wilmot et al., ran a series of experiments to describe a dopaminergic projection from LC to dHPC, and its functional role in trace fear conditioning (TFC). Using fiber photometry in LC, they show convincingly that the activity of LC TH neurons is increased to both cues and footshock, and that this increases with acquisition or TFC, and decreases during extinction of this association. Projections from LC to dHPC show a similar pattern of activity, and dopamine release (measured by the fluorescent sensor GRAB-DA) is also comparable to calcium activity from LC. While the authors do show that activity at the dopamine D1R/D5R is necessary for TFC, a direct test of the necessity of dopamine release from LC during TFC is not shown.

Strengths:<br /> • The authors clearly and effectively show that the LC-dHPC projection is activated by an aversive outcome (i.e. shock), and that activity in this pathway changes in response to learning about a neutral cue that predicts this shock (i.e. TFC). Furthermore, they show that increased dopamine release in dHPC can be observed if LC is chemogenetically activated. A critical role for dopamine receptors (but not β- and ⍺-adrenergic receptors) in TFC was demonstrated, and intra-dHPC injection of a D1R/D5R antagonist blocks this learning. Finally, dopamine release (measured by GRAB-DA) in dHPC was shown to also occur during trace fear conditioning.

• The authors have conclusively shown that activity at the dopamine receptors in the dPHC during trace fear conditioning is of the same pattern as calcium activity recorded both in LC cell bodies, but more importantly in the axonal projections from LC to dHPC. This is very good evidence that this pathway is recruited during TFC.

Weaknesses:<br /> • The claim that dopamine release in dHPC is caused by LC neurons is not directly tested. Unfortunately, the most critical experiment for the claims that dopamine release comes from LC during conditioning is not tested. A lack of dopamine signal in dHPC caused by inhibition of LC during TFC would show this. It is indeed an interesting observation that chemoegenetic activation of LC causes dopamine release in the dHPC. However, in the absence of concurrent VTA inhibition or lesion, it remains a possibility that the dopamine release is mediated through indirect actions on other dopamine-expressing neurons. The authors do a good job of arguing against this interpretation in the discussion, and the literature seems appropriate for this. However, the title is still an overstatement of the data presented in this study.

• The primary alternative interpretations of the phasic activation experiment are whether only stimulation to the cue events (both on and off), or whether only stimulation to the shock. Thus this experiment would benefit from additional data showing either a no shock control, to show that enhanced activity of the LC to the tone is not inherently aversive, or manipulations to the tone but not to the shock.

• Specificity of the GRAB-NE and GRAB-DA sensors should be either justified through additional experiments testing the alternative antagonist (i.e. GRAB-NE CNO+eticloprode / GRAB-DA CNO+yohimbine) or additional citations that have tested this already. It is critical for the claims of the paper to show that these sensors are specific to dopamine or norepinephrine.

• The role of dopamine in prediction error was tested through a series of conditions whereby the shock was presented either signaled (i.e. predicted), or not. However, another way that prediction error is signaled is through the absence of an expected outcome. Admittedly it might not be possible to observe a decrease in dopamine signaling with this methodology.

• The difference between Fig. 6E and 6H needs to be clarified. What is shown in Fig. 6E is that the response to the shock decreases through experience (i.e. by the 10th trial). However in Fig 6H, there is no difference between signaled and signaled shock, but this is during conditioning, and not after learning (based on my understanding of the methods, line 482).

• Unless I missed it, at no point in the manuscript is the number of subjects described. Please add the n per experiment within each section describing each experiment in the methods (Behavioral procedures). Some more details in the photometry statistical analysis would be helpful. For example, what is the n per group for every data set that is presented? How many trials per analysis?

Reviewer #2 (Public Review):

In the revised manuscript, the authors aim to investigate brain-wide activation patterns following administration of the anesthetics ketamine and isoflurane, and conduct comparative analysis of these patterns to understand shared and distinct mechanisms of these two anesthetics. To this end, they perform Fos immunohistochemistry in perfused brain sections to label active nuclei, use a custom pipeline to register images to the ABA framework and quantify Fos+ nuclei, and perform multiple complementary analyses to compare activation patterns across groups. This is an interesting line of research and a tour de force in brain-wide Fos quantification.

I appreciate many of the changes that were made in the revised manuscript, including FDR correction and transparency in showing their results with and without transformation. However, several key issues described in our first review have not been addressed.

1-Aside from issues with their data transformation (see below), (a) I think they have some interesting Fos counts data in Figures 4B and 5B that indicate shared and distinct activation patterns after KET vs. ISO based anesthesia. These data are far closer to the raw data than PC analyses and need to be described and analyzed in the first figures long before figures with the more abstracted PC analyses. In other words, you need to show the concrete raw data before describing the highly transformed and abstracted PC analyses. (b) This gets to the main point that when selecting brain areas for follow up analyses, these should be chosen based on the concrete Fos counts data, not the highly transformed and abstracted PC analyses.

2-Now, the choice of data transformation for Fos counts is the most significant problem. First, the authors show in the response letter that not using this transformation (region density/brain density) leads to no clustering. However, they also showed the region-densities without transformation (which we appreciate) and it looks like overall Fos levels in the control group Home (ISO) are a magnitude (~10-fold) higher than those in the control group Saline (KET) across all regions shown. This large difference seems unlikely to be due to a biologically driven effect and seems more likely to be due to a technical issue, such as differences in staining or imaging between experiments. Was the Homecage-ISO experiment or at least the Fos labeling and imaging performed at the same time as for the Saline-Ketamine experiment? Please state the answer to this question in the Results section one way or the other.

3-Second, they need to deal with this large difference in overall staining or imaging for these two (Home/ISO and Saline/KET) experiments more directly; their current normalization choice does not really account for the large overall differences in mean values and variability in Fos counts (e.g. due to labeling and imaging differences).

3a-I think one option (not perfect but I think better than the current normalization choice) could be z-scoring each treatment to its respective control. They can analyze these z-scored data first, and then in later figures show PC analyses of these data and assess whether the two treatments separate on PC1/2. And if they don't separate, then they don't separate, and you have to go with these results.

3b-Alternatively, they need to figure out the overall intensity distributions from the different runs (if that the main reason of markedly different counts) and adjust their thresholds for Fos-positive cell detection based on this. I would expect that the saline and HC groups should have similar levels of activation, so they could use these as the 'control' group to determine a Fos-positive intensity threshold that gets applied to the corresponding 'treatment' group.

3c- If neither 3a nor 3b is an option then they need to show the outcomes of their analysis when using the untransformed data in the main figures (the untransformed data plots in their responses to reviewer are currently not in the main or supplementary figs) and discuss these as well.

This suggests to me that there is a lot of proprietary layer 2-3 in IOT. Is this the case?

Reviewer #2 (Public Review):

Summary:<br /> This work tests the hypothesis that water coordination in WNK kinases is linked to allosteric control of activity. It is proposed that dimeric WNK is inactive and bound to some conserved water molecules, and that monomerization/activation involves departure of these waters. New data here include a crystal structure of monomeric WNK1 which shows missing waters compared to the dimeric structure, in support of the hypothesis. Mutant proteins of a different isozyme (WNK3) designed to disrupt water coordination were produced, and activity and quaternary structure were measured. The results with WNK3 do not clearly support or refute the hypothesis as there is no systematic correlation between mutations designed to disrupt water coordination and activity or quaternary structure.

Strengths:<br /> The most interesting result presented here is that P1 crystals of WNK1 convert to P21 in the presence of PEG400 and still diffract (rather than being destroyed as the crystal contacts change, as one would expect). All of the assays for activity and osmolyte sensing are carried out well.

Weaknesses:<br /> The rationale for using WNK3 for the mutagenesis study is that it is more sensitive to osmotic pressure than WNK1. I think that WNK1 would have been a better platform because of the direct correlation to the structural work leading to the hypothesis being tested. All of the crystallographic work is WNK1; it is not logical to jump to WNK3 without other practical considerations.

Osmolyte sensing was tested by measuring ATP consumption as a function of PEG400 (Figure 6). Data for the subset of mutants analyzed by this assay showed increasing activity. It is not clear why the same collection of mutant proteins analyzed in the experiments of Figure 5 was not also measured for osmolyte sensing in Figure 6.

The last set of data presented uses light scattering to test whether the WNK3 mutant proteins exhibit quaternary structural changes consistent with the monomer/dimer hypothesis. If they did, one would expect a higher degree of monomer for those that are activated by mutation, and a lower amount of monomer (like wt) for those that are not. Instead, one of the mutant proteins that showed the most chloride inhibition (Y346F) had a quaternary structure similar to the wt protein, and others have similar monomer/dimer mixtures but distinct chloride inhibition profiles (K307A and M301A). I don't see how the light scattering data contribute to this story other than to refute the hypothesis by showing a lack of correlation between quaternary structure, water binding, and activity. This is another reason why the disconnect between WNK1 and WNK3 could be a problem. All of the detailed structural work with WNK1 must be assumed with WNK3; perhaps the light scattering data are contradicting this assumption?

Reviewer #2 (Public Review):

Summary:

The manuscript's main claim is that the absence of H2-O, a component of the MHC II presentation pathway, promotes regulatory T cell development and function.

Unfortunately, the submitted material is not sufficient for proper evaluation of the manuscript, both in terms of the significance of the findings and the strength of the supporting evidence.

Major issues include:

- the scRNAseq (shown in Fig. 5) is too rudimentary to allow any conclusion. Statements in the text (eg "Principle Component Analysis (PCA) of the normalized scRNA-seq data identified 11 distinct CD4 T cell clusters", line 166) suggest that additional expertise should be leveraged for these analyses.

- Most flow cytometry data (Figs. 1 and 2) shows marginal (at best) differences on y-axis truncated bar graphs, with no original data plot, gating strategies, etc., severely challenging conclusions drawn from this data.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Glaser et al. describe a new selective plane illumination microscope designed to image a large field of view that is optimized for expanded and cleared tissue samples. For the most part, the microscope design follows a standard formula that is common among many systems (e.g. Keller PJ et al Science 2008, Pitrone PG et al. Nature Methods 2013, Dean KM et al. Biophys J 2015, and Voigt FF et al. Nature Methods 2019). The primary conceptual and technical novelty is to use a detection objective from the metrology industry that has a large field of view and a large area camera. The authors characterize the system resolution, field curvature, and chromatic focal shift by measuring fluorescent beads in a hydrogel and then show example images of expanded samples from mouse, macaque, and human brain tissue.

Strengths:<br /> I commend the authors for making all of the documentation, models, and acquisition software openly accessible and believe that this will help assist others who would like to replicate the instrument. I anticipate that the protocols for imaging large expanded tissues (such as an entire mouse brain) will also be useful to the community.

Weaknesses:<br /> The characterization of the instrument needs to be improved to validate the claims. If the manuscript claims that the instrument allows for robust automated neuronal tracing, then this should be included in the data.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Zhang and Speer examine changes in the spatial organization of synaptic proteins during eye-specific segregation, a developmental period when axons from the two eyes initially mingle and gradually segregate into eye-specific regions of the dorsal lateral geniculate. The authors use STORM microscopy and immunostain presynaptic (VGluT2, Bassoon) and postsynaptic (Homer) proteins to identify synaptic release sites. Activity-dependent changes in this spatial organization are identified by comparing the β2KO mice to WT mice. They describe two types of presynaptic organization based on Bassoon clustering, the complex and the simple synapse. By analyzing the relative densities and distances between these proteins over age, the authors conclude that the complex synapses promote the clustering of simple synapses nearby to form the future mature glomerular synaptic structure.

Strengths:<br /> The data presented is of good quality and provides an unprecedented view at high resolution of the presynaptic components of the retinogeniculate synapse during active developmental remodeling. This approach offers an advance to the previous mouse EM studies of this synapse because of the CTB label allows identification of the eye from which the presynaptic terminal arises. Using this approach, the authors find that simple synapses cluster close to complex synapses over age, that complex synapse density increases with age.

Weaknesses:<br /> From these data, the authors conclude that the complex synapse serves to "promote clustering of like-eye synapses and prohibit synapse clustering from the opposite eye". However, the authors show no causal data to support these ideas. There are a number of issues that the authors should consider:

1. Clustering of retinal synapses is in part due to the fact that retinal inputs synapse on the proximal dendrites. With increased synaptogenesis, there will be increased density of retinal terminals that are closely localized. And with development, perhaps simple synapses mature into complex synapses. Simple synapses may also represent ones that are in the process of being eliminated as previously described by Campbell and Shatz, JNeurosci 1992 (consider citing). Can the authors distinguish these scenarios from the ones that they conclude?

2. The argument that "complex" synapses are the aggregate of "simple" synapses (Fig 2, S2) is not convincing.

3. The authors use of the β2KO mice to assess changes in the organization of synaptic proteins in retinal terminals that have disrupted retinal waves. However, β2-nAChRs are also expressed in the dLGN and other areas of the brain and glutamatergic synapse development has been reported in the CNS independent of the disruption in retinal waves. This issue should be considered when interpreting the total reduced retinal synapse density in the dLGN of the mutant.

4. Outside of a total synapse density difference between WT and β2KO mice, the changes in the spatial organization of synaptic proteins over development do not seem that different. In fact % simple synapses near complex synapses from the non-dominant eye in the mutant is not that different from WT at P8 (Fig 3C), an age when eye-specific segregation is very different between the genotypes. Can the authors explain this discrepancy?

5. The authors use nomenclature that has been previously used and associated with other aspects of retinogeniculate properties. For example, the phrases "simple" and "complex" synapses have been used to describe single boutons or aggregates of boutons from numerous retinal axons, whereas in this manuscript the phrases are used to describe vesicle clusters/release sites with no knowledge of whether they are from single or multiple boutons. Likewise, the use of the word "glomerulus" has been used in the context of the retinogeniculate synapse to refer to a specific pattern of bouton aggregates that involves inhibitory and neuromodulatory inputs. It is not clear how the release sites described by the authors fit in this picture. Finally the use of the word "punishment" is associated with a body of literature regarding the immune system and retinogeniculate refinement-which is not addressed in this study. This double use of the phrases can lead to confusion in the field and should be clarified by clear definitions of how they are used in the current study.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript examines how humans walk over uneven terrain using vision to decide where to step. There is a huge lack of evidence about this because the vast majority of locomotion studies have focused on steady, well-controlled conditions, and not on decisions made in the real world. The author team has already made great advances in this topic, but there has been no practical way to map 3D terrain features in naturalistic environments. They have now developed a way to integrate such measurements along with gaze and step tracking, which allows quantitative evaluation of the proposed trade-offs between stepping vertically onto vs. stepping around obstacles, along with how far people look to decide where to step.

Strengths:<br /> 1. I am impressed by the overarching outlook of the researchers. They seek to understand human decision-making in real-world locomotion tasks, a topic of obvious relevance to the human condition but not often examined in research. The field has been biased toward well-controlled studies, which have scientific advantages but also serious limitations. A well-controlled study may eliminate human decisions and favor steady or periodic motions in laboratory conditions that facilitate reliable and repeatable data collection. The present study discards all of these usually-favorable factors for rather uncontrolled conditions, yet still finds a way to explore real-world behaviors in a quantitative manner. It is an ambitious and forward-thinking approach, used to tackle an ecologically relevant question.

2. There are serious technical challenges to a study of this kind. It is true that there are existing solutions for motion tracking, eye tracking, and most recently, 3D terrain mapping. However most of the solutions do not have turn-key simplicity and require significant technical expertise. To integrate multiple such solutions together is even more challenging. The authors are to be commended on the technical integration here.

3. In the absence of prior studies on this issue, it was necessary to invent new analysis methods to go with the new experimental measures. This is non-trivial and places an added burden on the authors to communicate the new methods. It's harder to be at the forefront in the choice of topic, technical experimental techniques, and analysis methods all at once.

Weaknesses:<br /> 1. I am predisposed to agree with all of the major conclusions, which seem reasonable and likely to be correct. Ignoring that bias, I was confused by much of the analysis. There is an argument that the chosen paths were not random, based on a comparison of probability distributions that I could not understand. There are plots described as "turn probability vs. X" where the axes are unlabeled and the data range above 1. I hope the authors can provide a clearer description to support the findings. This manuscript stands to be cited well as THE evidence for looking ahead to plan steps, but that is only meaningful if others can understand (and ultimately replicate) the evidence.

2. I wish a bit more and simpler data could be provided. It is great that step parameter distributions are shown, but I am left wondering how this compares to level walking. The distributions also seem to use absolute values for slope and direction, for understandable reasons, but that also probably skews the actual distribution. Presumably, there should be (and is) a peak at zero slope and zero direction, but absolute values mean that non-zero steps may appear approximately doubled in frequency, compared to separate positive and negative. I would hope to see actual distributions, which moreover are likely not independent and probably have a covariance structure. The covariance might help with the argument that steps are not random, and might even be an easy way to suggest the trade-off between turning and stepping vertically. This is not to disregard the present use of absolute values but to suggest some basic summary of the data before taking that step.

3. Along these same lines, the manuscript could do more to enable others to digest and go further with the approach, and to facilitate interpretability of results. I like the use of a neural network to demonstrate the predictiveness of stepping, but aside from above-chance probability, what else can inform us about what visual data drives that? Similarly, the step distributions and height-turn trade-off curves are somewhat opaque and do not make it easy to envision further efforts by others, for example, people who want to model locomotion. For that, clearer (and perhaps) simpler measures would be helpful.

I am absolutely in support of this manuscript and expect it to have a high impact. I do feel that it could benefit from clarification of the analysis and how it supports the conclusions.

Reviewer #2 (Public Review):

Summary:<br /> Seignette et al. investigated the potential roles of axo-axonic (chandelier) cells (ChCs) in a sensory system, namely visual processing. As introduced by the authors, the axo-axonic cell type has remained (and still is) somehow mysterious in its function. Seignette and colleagues leveraged the development of a transgenic mouse line selective for ChC, and applied a very wide range of techniques: transsynaptic rabies tracing, optogenetic input activation, in vitro electrophysiology, 2-photon recording in vivo, behavior and chemogenetic manipulations, to precisely determine the contribution of ChCs to the primary visual cortex network.

The main findings are 1) the identification of synaptic inputs to ChC, with a majority of local, deep layer principal neurons (PN), 2) the demonstration that ChC is strongly and synchronously activated by visual stimuli with low specificity in naive animals, 3) the recruitment of ChC by arousal/visuomotor mismatch, 4) the induction of functional and structural plasticity at the ChC-PN module, and, 5) the weak disinhibition of PNs induced by ChCs silencing. All these findings are strongly supported by experimental data and thoroughly compared to available evidence.

Strengths:<br /> This article reports an impressive range of very demanding experiments, which were well executed and analyzed, and are presented in a very clear and balanced manner. Moreover, the manuscript is well-written throughout, making it appealing to future readers. It has also been a pleasure to review this article.

In sum, this is an impressive study and an excellent manuscript, that presents no major flaws.

Notably, this study is one of the first studies to report on the activities and potential roles of axo-axonic cells in an active, integrated brain process, beyond locomotion as reported and published in V1. This type of research was much awaited in the fields of interneuron and vision research.

Weaknesses:<br /> There are no fundamental weaknesses; the latter mainly concern the presentation of the main results.

The main weakness may be that the different sections appear somehow disconnected conceptually.

Additionally, some parts deserve a more in-depth clarification/simplification of concepts and analytic methods for scientists outside the subfield of V1 research. Indeed, this paper will be of key interest to researchers of various backgrounds.

Reviewer #2 (Public Review):

Summary:<br /> The work by Klaassen & Rasch investigates the influence of word learning difficulty on sleep-associated consolidation and reactivation. They elicited reactivation during sleep by applying targeted memory reactivation (TMR) and manipulated word learning difficulty by creating words more similar (easy) or more dissimilar (difficult) to our language. In one group of participants, they applied TMR of easy words and in another group of participants, they applied TMR of difficult words (between-subjects design). They showed that TMR leads to higher memory benefits in the easy compared to the difficult word group. On a neural level, they showed an increase in spindle power (in the up-state of an evoked response) when easy words were presented during sleep.

Strengths:<br /> The authors investigate a research question relevant to the field, that is, which experiences are actually consolidated during sleep. To address this question, they developed an innovative task and manipulated difficulty in an elegant way.

Overall, the paper is clearly structured, and results and methods are described in an understandable way. The analysis approach is solid.

Weaknesses:<br /> 1.Sample size<br /> For a between-subjects design, the sample size is too small (N = 22). The main finding (also found in the title "Difficulty in artificial word learning impacts targeted memory reactivation") is based on an independent samples t-test with 11 participants/group.

The authors explicitly mention the small sample size and the between-subjects design as a limitation in their discussion. Nevertheless, making meaningful inferences based on studies with such a small sample size is difficult, if not impossible.

2.Choice of task<br /> Even though the task itself is innovative, there would have been tasks better suited to address the research question. The main disadvantage the task and the operationalisation of memory performance (d') have is that single-trial performance cannot be calculated. Consequently, choosing individual items for TMR is not possible.

Additionally, TMR of low vs. high difficulty is conducted between subjects (and independently of pre-sleep memory performance) which is a consequence of the task design.

The motivation for why this task has been used is missing in the paper.