Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

Summary

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

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

The methodology is clearly described and appropriate.

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Major points:

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

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

Figure 3: please show individual datapoints in all conditions.

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

2.) Major points for revision

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Strengths:

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

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

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

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

Weaknesses:

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

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

3. The rationale for analyzing a single oxidative modification (W14) as a D1 degradation signal is unclear. D1 N-terminus is modified at multiple sites. Please see Mckenzie and Puthiyaveetil, bioRxiv May 04 2023. Also, why is modification by only 1O2 considered while superoxide and hydroxide radicals can equally damage D1?

4. The D1 degradation assay seems not repeatable for the W14F mutant. High light minus CAM results in Fig. 3 shows a statistically significant decrease in D1 levels for W14F at multiple time points but the same assay in Fig. 4a does not produce a statistically significant decrease at 90 min of incubation. Why is this? Accelerated D1 degradation in the Phe mutant under high light is key evidence that the authors cite in support of their hypothesis.

5. The description of results at times is not nuanced enough, for e.g. lines 116-117 state "The oxidation levels in Trp-14 and Trp-314 increased 1.8-fold and 1.4-fold in var2 compared to the wild type, respectively (Fig. 1c)" while an inspection of the figure reveals that modification at W314 is significant only for NFK and not for KYN and OIA. Likewise, the authors write that CP43 mutant W353F has no growth phenotype under high light but Figure S6 reveals otherwise. The slow growth of this mutant is in line with the earlier observation made by Anderson et al., 2002. In lines 162-163, the authors talk about unchanged electron transport in some site-directed mutants and cite Fig. 2c but this figure only shows chl fluorescence trace and nothing else.

6. The authors rightly discuss an alternate hypothesis that the simple disassembly of the monomeric core into RC47 and CP43 alone may be sufficient for selective D1 degradation as in cyanobacteria. This hypothesis cannot yet be ruled out completely given the lack of some in vitro degradation data as mentioned in point 2. Oxidative protein modification indeed drives the disassembly of the monomeric core (Mckenzie and Puthiyaveetil, bioRxiv May 04 2023).

Reviewer #2 (Public Review):

The authors have used microfluidic channels to study the response of budding yeast to variable environments. Namely, they tested the ability of the cells to divide when the medium was repeatedly switched between two different conditions at various frequencies. They first characterized the response to changes in glucose availability or in the presence of hyper-osmotic stress via the addition of sorbitol to the medium. Subsequently, the two stresses were combined by applying the alternatively or simultaneously (in-phase). Interestingly, the observed that the in-phase stress pattern allowed more divisions and low levels of cell mortality compared to the alternating stresses where cells were dividing slowly and many cells died. A number mutants in the HOG pathway were tested in these conditions to evaluate their responses. Moreover, the activation of the MAPK Hog1 and the transcriptional induction of the hyper-osmotic stress promoter STL1 were quantified by fluorescence microscopy.

Overall, the manuscript is well structured and data are presented in a clear way. The time-lapse experiments were analyzed with high precision. The experiments confirm the importance of performing dynamic analysis of signal transduction pathways. While the experiments reveal some unexpected behavior, I find that the biological insights gained on this system remain relatively modest.

In the discussion section, the authors mention two important behaviors that their data unveil: resource allocation (between glycolysis and HOG-driven adaptation) and regulation of the HOG-pathway based on the presence of glucose. These behaviors had been already observed in other reports (Sharifan et al. 2015 or Shen et al. 2023, for instance). I find that this manuscript does not provide a lot of additional insights into these processes. One clear evidence that is presented, however, is the link between glycerol accumulation during the sorbitol treatment and the cell death phenotype upon starvation in alternating stress condition. However, no explanations or hypothesis are formulated to explain the mechanism of resource allocation between glycolysis and HOG response that could explain the poor growth in alternating stresses or the lack of adaptation of Hog1 activity in absence of glucose.

Another key question is to what extent the findings presented here can be extended to other types of perturbations. Would the use of alternative C-source or nitrogen starvation change the observed behaviors in dynamic stresses? If other types of stresses are used, can we expect a similar growth pattern between alternating versus in-phase stresses?

Reviewer #2 (Public Review):

The authors identify a bottleneck in cryoEM data collection, namely path optimization, and provide a method and software to attempt to solve this problem, then evaluate the solution based on several metrics including full downstream processing. In addition, the authors report on a cryoEM data collection simulator, which could be used to more efficiently train users and microscope operators if released. I have experience with cryo-EM and applications of machine learning to cryoEM. In my opinion, the results are convincing insofar as showing that the algorithm employed by cryoRL performs at least as well as humans and with greater consistency than humans. I think combining cryoRL with existing square & hole targeting algorithms and collection software has the potential to result in a complete and efficient automated solution for high-resolution cryoEM data collection.

Reviewer #2 (Public Review):

The authors provide solid molecular and cellular evidence that ULK4 and STK36 not only interact, but that STK36 is targeted (transported?) to the cilium by ULK4. Their data helps generate a model for ULK4 acting as a scaffold for both STK36 and its substrate, Gli2, which appear to co-localise through mutual binding to ULK4. This makes sense, given the proposed role of most pseuodkinases as non-catalytic signaling hubs. There is also an important mechanistic analysis performed, in which ULK4 phosphorylation in an acidic consensus by STK36 is demonstrated using IP'd STK36 or an inactive 'AA' mutant, which suggests this phosphorylation is direct.

The major strength of the study is the well-executed combination of logical approaches taken, including expression of various deletion and mutation constructs and the careful (but not always quantified in immunoblot) effects of depleting and adding back various components in the context of both STK36 and ULK3, which broadens the potential impact of the work. The biochemical analysis of ULK4 phosphorylation appears to be solid, and the mutational study at a particular pair of phosphorylation sites upstream of an acidic residue (notably T2023) is further strong evidence of a functional interaction between ULK4/STK36. The possibility that ULK4 requires ATP binding for these mechanisms is not approached, though would provide significant insight: for example it would be useful to ask if Lys39 in ULK4 is involved in any of these processes, because this residue is likely important for shaping the ULK4 substrate-binding site as a consequence of ATP binding; this was originally shown in PMID 24107129 and discussed more recently in PMID: 33147475 in the context of the large amount of ULK4 proteomics data released.

The discussion is excellent, and raises numerous important future work in terms of potential transportation mechanisms of this complex. It also explains why the ULK4 pseudokinase domain is linked to an extended C-terminal region. Does AF2 predict any structural motifs in this region that might support binding to Gli2?

A weakness in the study, which is most evident in Figure 1, where Ulk4 siRNA is performed in the NIH3T3 model (and effects on Shh targets and Gli2 phosphorylation assessed), is that we do not know if ULK4 protein is originally present in these cells in order to actually be depleted. Also, we are not informed if the ULK4 siRNA has an effect on the 'rescue' by HA-ULK4; perhaps the HA-ULK4 plasmid is RNAi resistant, or if not, this explains why phosphorylation of Gli2 never reaches zero? Given the important findings of this study, it would be useful for the authors to comment on this, and perhaps discuss if they have tried to evaluate endogenous levels of ULK4 (and Stk36) in these cells using antibody-based approaches, ideally in the presence and absence of Shh. The authors note early on the large number of binding partners identified for ULK4, and siRNA may unwittingly deplete some other proteins that could also be involved in ULK4 transport/stability in their cellular model.

The sequence of ULK4 siRNAs is not included in the materials and methods as far as I can see.

Reviewer #2 (Public Review):

This study explores the breadth of effects of one important metabolite, azelaic acid, on marine microbes, and reveals in-depth its pathway of uptake and catabolism in one model bacterial strain. This compound is known to be widely produced by phytoplankton and plants, and to have complex effects on associated microbiomes.

This work uses transcriptomics to assay the response of two strains that show contrasting responses to the metabolite: one catabolizes the compound and assimilates the carbon, while the other shows growth inhibition and stress response. A highly induced TRAP transporter, adjacent to a previously identified regulator, is inferred to be the specific uptake system for azelaic acid. However the transport function was not directly tested via genetic or biochemical methods. Nevertheless, this is a significant finding that will be useful for exploring the distribution of azelaic acid uptake capability across metagenomes and other bacteria.

The authors use pulse-chase style metabolomics experiments to beautifully demonstrate the fate of azelaic acid through catabolic pathways. They also measure an assimilation rate per cell, though it remains unclear how this measured rate relates to natural systems. The metabolomics approach is an elegant way to show carbon flux through cells, and could serve as a model for future studies.

The study seeks to extend the results from two model strains to complex communities, using seawater mesocosm experiments and soil/Arabidopsis experiments. The seawater experiments show a community shift in mesocosms with added azelaic acid. However, the mechanisms for the shift were not determined; further work is necessary to demonstrate which community members are directly assimilating the compound vs. benefitting indirectly or experiencing inhibition. In my opinion the soil and Arabidopsis experiments are quite preliminary. I appreciate the authors' desire to broaden the scope beyond marine systems, but I believe any conclusions regarding different modes of action in aquatic vs terrestrial microbial communities are speculative at this stage.

This work is a nice illustration of how we can begin to tease apart the effects of chemical currencies on marine ecosystems. A key strength of this work is the combination of transcriptomics and metabolomics methods, along with assaying the impacts of the metabolite on both model strains of bacteria and whole communities. Given the sheer number of compounds that probably play critical roles in community interactions, a key challenge for the field will be navigating the tradeoffs between breadth and depth in future studies of metabolite impacts. This study offers a good compromise and will be a useful model for future studies.

Reviewer #2 (Public Review):

The authors set out to show how hibernation is linked to brain size in frogs. If there were broader aims it is hard to decipher them. The authors present an extremely impressive dataset and a thorough set of cutting-edge analyses. However not all details are well explained. The main result about hibernation and brain size is fairly convincing, but it is hard to think of broader implications for this study. Overall, the manuscript is very confusing and hard to follow.

Reviewer #2 (Public Review):

The research study presented by Rice et al. set out to further profile the host defense properties of the mitochondrial protein MOTS-c. To do this they studied i. the potential antimicrobial effects of MOTS-c on common bacterial pathogens E.coli and MRSA, ii. the effects of MOTS-c on the stimulation and differentiation of monocytes into macrophages. This is a well performed study that utilizes relevant methods and cell types to base their conclusions on. However, there appear to be a few weaknesses to the current study that hold it back from more broad application.

Comment 1: From reading the manuscript methods and results, it is unclear exactly what the synthetic MOTS-c source is. Therefore it is hard to determine whether there may be any impurities in the production of this synthetic protein that may interfere with the results presented throughout the manuscript. Though, the data presented in Supplemental Figure 4F, where E.coli expressing intracellular MOTS-c inhibited bacterial growth certainly support MOTS-c specific effects. Similarly with the experiments showing endogenous MOTS-c levels rising in stimulation and differentiated macrophages (Figure 3).

Comment 2: It is interesting that the mice receiving bacteria coupled with MOTS-c lost about 10% of their body weight. It would have been interesting to demonstrate the cause of this weight loss since the effect appears to be separate from mere PAMPs as shown by using heat-killed MRSA in Supplemental Figure 5. Was inflammation changed? Is this due to changes in systemic metabolism? Would have been interesting to have seen CRP levels or circulating liver enzymes.

Despite these concerns, the data are well suited to answering their research question, and they open up the door to studying how mitochondrial peptides like MOTS-c could have roles outside of the mitochondria.

Reviewer #2 (Public Review):

The introduction is plotted with two parallel stories about PfKBP35 and FK506, with ribosome biogenesis as the central question at the end. In its current form, the manuscript suffers from two stories that are not entirely interconnected, unfinished, and somewhat confusing. I recommend focusing only on one story - either characterizing PfBP35 and its role in Plasmodium falciparum biology - future investigation of PfBP35 control of cellular processes or focusing on the actual targets of the FK506 drug (identified in figure 4). Both stories need additional experiments to make the manuscript(s) more complete. The results from PfFBP35 need more evidence for the proposed ribosome biogenesis pathway control. On the other hand, the results from the drug FK506 point to different targets with lower EC50, and other follow-up experiments are needed to substantiate the authors' claims. The strengths of the manuscript are the figures and experimental design. The combination of omics methods is informative and gives an opportunity for follow-up experiments.

Reviewer #2 (Public Review):

In this study, the investigators describe an unbiased phosphoproteomic analysis of cardiac-specific overexpression of adenylyl cyclase type 8 (TGAC8) mice that was then integrated with transcriptomic and proteomic data. The phosphoproteomic analysis was performed using tandem mass tag-labeling mass spectrometry of left ventricular (LV) tissue in TGAC8 and wild-type mice. The initial principal component analysis showed differences between the TGAC8 and WT groups. The integrated analysis demonstrated that many stress-response, immune, and metabolic signaling pathways were activated at transcriptional, translational, and/or post-translational levels.

The authors are to be commended for a well-conducted study with quality control steps described for the various analyses. The rationale for following up on prior transcriptomic and proteomic analyses is described. The analysis appears thorough and well-integrated with the group's prior work. Confirmational data using Western blot is provided to support their conclusions. Their findings have the potential of identifying novel pathways involved in cardiac performance and cardioprotection.

Reviewer #2 (Public Review):

Among ionotropic glutamate receptors, kainate receptors (KAR) are still the object of intense investigation to understand their role in normal and pathological excitatory synaptic transmission. Like other receptors, KAR appear under different splicing variants and their respective physiological function is still debated. In this manuscript Dhingra et al explored the impact of the presence and of the absence of Exon9 of the GluK1 receptors on the pharmacological, biophysical and structural properties of the receptors. They further investigated how it is impacted by the association of KAR with their cognate auxiliary subunit Neto 1 and 2. This study represents a large body of work and data. The authors addressed the issue in a very systematic and rigorous manner.

First, by exploring RNAseq database, authors showed that GluK1 transcripts containing the exon 9 are present in many brain structures and especially in the cerebellum suggesting that a large part of GluK1 contains effectively this exon9.<br /> Using HEK cells as an expression system, they characterized many gating and biophysical properties of GluK1 receptors containing or not the exon9. Evaluated parameters were desensitization, relative potency of glutamate versus kainite, polyamine block.

It is known that the association of GluK1 with auxiliary proteins Neto1/2 modulate the properties of the receptors. Authors investigated systematically whether Neto1 and 2 similarly alter GluK1 properties in function of the presence of exon9. This study provides many quantitative data that could be reused for modeling the role of kainate receptors. Given the change shown by the authors, the presence of exon in GluK1 is noticeable and likely should have an impact of synaptic transmission.<br /> Interestingly, authors used a mutational approach to identify critical residue encoded by exon9 that are responsible for the functional differences between the two splice variants. In many cases, the replacement of a single amino acid lead to the absence of current confirming the crucial role of the segment of the receptor. However, it made the comparison and the identification of critical residues more challenging.<br /> Authors attempted to establish the structure GluK1 receptors comprising the exon9 using different preparation methods. They succeeded in obtaining structures with equivalent or lower resolution compared with previous report on GluK1 and GluK2 receptors. However, the organization of the peptide coded by exon is poorly defined and limited possible analyses. Despite this they could observe that the presence of the exon9 does not alter significantly the structure of GluK1.

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):

Tuller et al. first made the curious observation, that the first ∼30-50 codons in most organisms are encoded by scarce tRNAs and appear to be translated slower than the rest of the coding sequences (CDS). They speculated that this has evolved to pace ribosomes on CDS and prevent ribosome collisions during elongation - the "Ramp" hypothesis. Various aspects of this hypothesis, both factual and in terms of interpretating the results, have been challenged ever since. Sejour et al. present compelling results confirming the slower translation of the first ~40 codons in S. cerevisiae but providing alternative explanation for this phenomenon. Specifically, they show that the higher amino acid sequence divergence of N-terminal ends of proteins and accompanying lower purifying selection (perhaps the result of de novo evolution) is sufficient to explain the prevalence of rare slow codons in these regions. These results are an important contribution in understanding how aspects of evolution of protein coding regions can affect translation efficiency on these sequences and directly challenge the "Ramp" hypothesis proposed by Tuller et al.

I believe the data is presented clearly and the results generally justify the conclusions. I do have one specific concern related to interpretating the data. The authors show that the conservation score of the last 40 codons is not dissimilar to the conservation score of the first 40 (Fig. 4 A & C). They also show that the calculated translational speed of the first 40 codons is significantly lower than the rest of the CDS. At the same time, they show lack of statistically significant decrease of calculated translational speed for the last 40 codons (Figure S1). If the poor conservation of the first 40 codon explains the slower speed of their translation what is the authors' explanation for the absence of statistically significant reduction of calculated translational speed for the last 40 codons?

"Although the reporter is GFP, the N- terminal region of this particular protein is derived from yeast HIS3, not GFP, and has little if any effect on the fluorescence of the GFP fused downstream."

The statement above is logical and reasonable; however, it is not supported by any reference or control experiments. At the very least this fact should be explicitly acknowledged. Also, the RNA levels of reporters were not measured, which means it cannot be categorically concluded that the observed effect is due to changes of translational efficiency. This is an important caveat.

Reviewer #2 (Public Review):

In this manuscript, the authors generated a novel transgenic C. elegans model with inducible expression and secretion of human GFP-tagged human Aβ1-42. Using this model, they investigated the role of ECM in the aggregation of Aβ. They identified collagens that regulate Aβ aggregate formation, and found the metalloproteases ADM-2 modulates ECM and assist in the removal of extracellular Aβ aggregates. The results suggest that ECM composition is critical for Aβ aggregate and removal. These data add in an interesting way to the ongoing discussion on the aggregation and clearance of amyloid through the extracellular matrix. However, some issues remain to be addressed.

1) The authors developed a novel C.elegans model for studying extracellular amyloid beta aggregation and is therefore likely to be taken up broadly by the field. However, the new model should be fully characterized. Throughout the manuscript, the only method to detect amyloid deposition was the GFP fluorescence intensity and morphology, while direct characterization of amyloid aggregates is lacking.

2) A targeted RNA interference (RNAi) screen was used to identify the key regulators of Aβ aggregation and clearance, which is one of the strengths of the study. There should be evidence that RNAi works to knockdown the specific genes. Similarly, there should be evidence indicating that ADM-2 is indeed expressed in the overexpression experiments.

3) It remains unknown whether ADM-2 directly degrades Aβ or facilitates the clearance of Aβ by remoulding the ECM. The effect of ADM-2 on ECM remodeing should be examined.

Reviewer #2 (Public Review):

During meiosis, mitotic cohesin complexes are replaced by meiosis-specific cohesins to enable a stepwise loss of sister chromatid cohesion. The identity of the cohesin complex is defined by its kleisin subunit. In the early meiotic prophase, the mitotic kleisin Scc1 is replaced by a meiotic counterpart Rec8. C. elegans expresses two additional meiotic kleisins, COH-3 and COH-4; however, how meiotic cohesin complexes differ in their loading and function has been unclear. In this paper, Castellano-Pozo and colleagues unveil their differential dynamics and functions using elegant approaches that include auxin-mediated depletion and TEV-mediated removal of meiotic kleisins. The association of COH-3/4 with chromosomes is dynamic and is under the control of two cohesin regulators, WAPL-1 and SCC-2, while REC-8 remains more stably associated. The authors established that COH-3/4 is involved in maintaining the structural integrity of chromosome axes, whereas the REC-8 cohesin is solely responsible for sister chromatid cohesion throughout meiosis. They further demonstrated the role of REC-8 in the repair of meiotic DSBs.

Overall, this solid work unequivocally establishes the distinct regulation and requirements for REC-8 and COH-3/4 cohesin complexes during C. elegans meiosis. However, as the authors acknowledged, the role of REC-8 cohesins in sister chromatid cohesion has been shown previously using genetic mutants (Crawley et al., 2016 eLife). While the authors highlighted the advantages of removing cohesin subunits in establishing their distinct requirements, many of the results were recapitulated from their previous work (e.g. rec-8; spo-11 and coh-3/4; spo-11). It might be helpful for the readers to compare the results between the two studies and point out uniquely illuminating results.

The role of REC-8 in DNA repair has also been shown in different contexts. Chromosomes fragmentation and DNA bridges are observed in rec-8; syp-1 or rec-8; syp-2 (RNAi) animals (Colaiacovo et al., 2003 Dev Cell; Crawley et al., 2016 eLife), suggesting a role of REC-8 in inter-sister repair. Persistent RAD-51 foci are also observed on asynapsed chromosomes in rec-8 mutants, suggesting a role for REC-8 in DNA repair (Cahoon et al., 2019 Genetics). The authors must cite these papers and discuss the results in the context of prior work.

Reviewer #2 (Public Review):

This manuscript tackles the important and vexing problem of mapping alleles for TB. It is a really important problem, and this paper presents the largest genetic data set. It does so by amalgamating data from multiple cohorts. The manuscript rightly points out that many studies have not produced reproducible results, and most alleles are population specific, and rarely seen in multiple studies.

1. Authors find a strong HLA associated SNP. They do conduct HLA imputation, but there is little effective fine-mapping. Authors should report which classical alleles are consistent with this allelic association (e.g. which classical alleles are in phase with it). Authors comment on DQA1-0301, but it isn't clear in the main text how significant it is. I think the authors should dig a little deeper. Imputing amino acids and assessing association might be useful. Finding classical alleles that explain the SNP associations and are seen across populations might be useful. If the authors think that the SNP might be a regulatory allele, the authors should make a case for that based on genomic annotations, eQTL analyses etc.<br /> 2. The authors comment on ancestry. Are ancestry components disease associated in any cohort? It might be interesting to demonstrate this.

Reviewer #2 (Public Review):

- Overall, the authors sought to determine whether children with autism spectrum disorder (ASD) or typical development (TD) would both benefit from a 5-day intervention designed to improve numerical problem-solving. They were particularly interested in how learning across training would be associated with pre-post intervention changes in brain activity, measured with functional magnetic resonance imaging (fMRI). They also examined whether brain-behavior associations driven by learning might be moderated by a classic cognitive inflexibility symptom in ASD ("insistence on sameness").

- The study is reasonably well-powered, uses a 5-day evidence-based intervention, and uses a multivariate correlation-based metric for examining neuroplastic changes that may be less susceptible to random variation over time than conventional mass univariate fMRI analyses.

- The study did have some weaknesses that draw into question the specific claims made based on the present set of analyses, as well as limit the generalizability of the findings to the significant proportion of individuals with ASD that are outside of the normative range of general cognitive functioning. The study also found minimal evidence for transfer between trained and untrained mathematical problems, limiting enthusiasm for the intervention itself.

- The majority of the authors' claims were rooted in the data and the team was generally able to accomplish their aims. I am sensitive to the fact that one of the main limitations I noted would have significant ethical implications-i.e. NOT offering potentially beneficial numerical training to children randomized to a sham or control group.

- I think the authors' work will represent a welcome addition to a growing corpus of studies showing similar neuropsychological test performance across several cognitive domains (e.g. learning, memory, proactive cognitive control, etc.) in ASD and TD. However, these relatively preserved cognitive functions still appear to be implemented by unique neural systems and demonstrate unique correlations to clinical symptoms in youth with ASD relative to TD, which may have implications for both educational and clinical contexts.

Reviewer #2 (Public Review):

Lindsay Fuzzell and her team of researchers have performed an extremely well-executed survey study, which captures a wide spectrum of providers who perform cervical cancer screening in the US. The researchers have captured a vast amount of demographic data in this study in attempting to determine whether cervical cancer screening continued to be reduced in the year immediately after the lockdown period caused by the COVID-19 pandemic.

The authors have uncovered some important and revealing concerns regarding the current state of cancer screening during the public health crisis caused by the COVID-19 pandemic. The most notable implication from their survey was a statistically higher reported reduction in cervical cancer screening in Internal medicine and family medicine providers as well as for community health and safety net clinics. These findings are important as they represent a large portion of primary care and a vulnerable patient population that has been shown to have worse cancer-related outcomes.

This study is more sobering information about the magnitude of ramifications of the COVID-19 pandemic on the US public health system. Decreases in cancer screening may have lasting implications for cancer-related mortality for many years to come. The implications of not going back to pre-pandemic cancer screening rates are daunting, to say the least.

The scope of this survey, the amount of data attained, and the sound methodology of the data acquisition and statistical analysis are the strengths of this study. Weaknesses are inherent to the study relying on survey answers rather than data from cervical cancer screening registries. Reporting biases are complex in surveys and answers given may not reflect the true rates of screening. The authors have also reported a disproportionate and statistically significant reduction in cervical cancer screening for Black and Asian providers. I would conclude more cautiously here with confidence intervals crossing one in both for this statistical analysis.

Overall, this is a survey study with a great magnitude, which has important implications for cancer screening and public health in the US.

Reviewer #2 (Public Review):

The authors seek to explore the mechanistic basis for enhancement binding to DNA by SsrB at lower pH. Their evidence supports the conclusions listed in the Evaluation Summary. Multiple additional conclusions are not supported by the data as described below:

1. The experiment displayed in Figure 5 is deeply flawed for multiple reasons and should be removed from the manuscript entirely. A Michaelis-Menton plot compares the initial rate of a reaction versus substrate concentration. Instead, the authors plotted the fraction of SsrB that is phosphorylated after 10 minutes at various substrate concentrations. Such a plot must reach saturation because the enzyme is limiting, whereas it is not always possible to achieve saturation in a genuine Michaelis-Menton plot. Because no reaction rates were measured, it is not possible to derive kcat values from the data. There are also at least three potential problems with the reaction conditions themselves: (i) Increasing the concentration of the phosphoramidite substrate increased ionic strength. Response regulator active sites contain many charged moieties and autophosphorylation of at least one response regulator (CheY) is inhibited by increasing ionic strength (PMID 10471801). (ii) Autophosphorylation with phosphoramidite is pH dependent because the nitrogen on the donor must be protonated to form a good leaving group (PMID 9398221). The pKa of phosphoramidite is ~8. Therefore, the fraction of phosphoramidite that is reactive (i.e., protonated) will be very different at pH 6.1 and 7.4. (iii) Response regulator autophosphorylation absolutely depends on the presence of a divalent metal ion (usually Mg2+) in the active site (PMID 2201404). There is no guarantee that the 20 mM Mg2+ included in the reaction is sufficient to saturate SsrB. Furthermore, as the authors themselves note, the amino acid at SsrB position 12 is likely to affect the affinity of Mg2+ binding. Therefore, the fraction of SsrB that is reactive (i.e. has Mg2+ bound) may differ between wildtype and the H12Q mutant, and/or between wildtype at different pHs (because the protonation state of His12 changes).

2. The data in Figures 1abcd and 3de are clearly sigmoidal rather than hyperbolic, indicating cooperativity. However, there are insufficient data points between the upper and lower bounds to accurately calculate the Hill coefficient or KD values. This limitation of the data means that comparisons of apparent Hill coefficient or KD values under different conditions cannot be the basis of credible conclusions.

3. There are hundreds of receiver domain structures in PDB. There is some variation, but to a first approximation receiver domain structures, all exhibit an (alpha/beta)5 fold. The structure of SsrB predicted by i-TASSER breaks the standard beta-2 strand into two parts, which throws off the numbering for subsequent beta strands. Given the highly conserved receiver domain fold, I am skeptical that the predicted i-TASSER structure is correct or adds any value to the manuscript. If the authors wish to retain the structure of the manuscript, then they should point out the unusual feature and the consequence of strand numbering.

4. The detailed predictions of active site structure in Supplementary Figure 5 are not physiologically relevant because Mg2+ was not included in the simulation. The presence of a divalent cation binding to Asp10 and Asp11 is likely to substantially alter interactions between Asp 10, Asp11, His12, and Lys109.

5. The authors present an AlphaFold model of an SsrB dimer, and note that His12 is at the dimer interface. However, the authors also believe that a higher-order oligomer of SsrB binds to DNA in a pH-dependent manner. Do the authors have any suggestions or informed speculation about how His12 might affect higher-order oligomerization than dimerization?

Reviewer #2 (Public Review):

This is a very interesting paper with several important findings related to the working mechanism of the cartwheel cells (CWC) in the dorsal cochlear nucleus (DCN). These cells generate spontaneous firing that is inhibited by the activation of α2-adrenergic receptors, which also enhances the synaptic strength in the cells, but the mechanisms underlying the spontaneous firing and the dual regulation by α2-adrenergic receptor activation have remained elusive. By recording these cells with the NALCN sodium-leak channel conditionally knocked, the authors discovered that both the spontaneous firing and the regulation by noradrenaline (NA) require NALCN. Mechanistically, the authors found that activation of the adrenergic receptor or GABAB receptor inhibits NALCN. Interestingly, these receptor activations also suppress the low [Ca2+] "activation" of NALCN currents, suggesting crosstalk between the pathways. The finding of such dominant contribution of the NALCN conductance to the regulation of firing by NA is somewhat surprising considering that NA is known to regulate K+ conductances in many other neurons.

The studies reveal the molecular mechanisms underlying well known regulations of the neuronal processes in the auditory pathway. The results will be important to the understanding of auditory information processing in particular, and, more generally, to the understanding of the regulation of inhibitory neurons and ion channels. The results are convincing and are clearly presented.

Reviewer #2 (Public Review):

Previous studies have shown that two hair cell transcription factors, Pou4f3 and Gfi1, are both necessary for the survival of cochlear hair cells, and that Gfi1 is regulated by Pou4f3. The authors have previously also shown that mosaic inactivation of the RNA-binding protein RBM24 leads to outer hair cell death.

In the present study, the authors show that hair cells die in Pou4f3 and Gfi1 mutant mice. They show that Gfi1 is regulated by Pou4f3. Both these observations have been published before. They then show that RBM24 is absent in Pou4f3 knockouts, but not Gfi1 knockouts. They ectopically activate RMB24 in the hair cells of Pou4f3 knockouts, but this does not rescue the hair cell death. Finally, the authors validate three RMB24 enhancers that are active in young hair cells and which have been previously shown to bind Pou4f3.

The experiments are well-executed and the data are clear. The results support the conclusions of the paper.

Much of the work in the paper has been reported before. The result that hair cell transcription factors operate in a network, with some transcription factors activating only a subset of hair cell genes, is an expected result. Since RMB24 is only one of many genes regulated directly by Pou4f3, it is not surprising that it cannot rescue the Pou4f3 knockout hair cell degeneration.

The identification of new hair cell enhancers may be of use to investigators wishing to express genes in hair cells.

In sum, this work, although carefully performed, does not shed significant new light on our understanding of hair cell development or survival.

Reviewer #2 (Public Review):

Kelly et al. strategically leverage state-of-the art scRNA-seq methods combined with unique strengths of the zebrafish larval model to identify gene expression patterns that underlie the different functional output of different neuronal circuits that converge on similar muscle groups. The results lead to the identification of ion channel and synapse associated genes that distinguish the neuronal components of a fast circuit mediating escape behavior from a rhythmic circuit mediating graded swimming.

The authors develop methods for isolation of single spinal cord neurons from 4 day post fertilization (dpf) zebrafish larvae. The 4 dpf neuronal circuits mediating escape vs. rhythmic swimming behavior have been extensively characterized allowing knowledge of the specific motor neuron and interneuron populations involved in one vs. the other circuit. (Work from the authors' research group has contributed to this strong starting point for this study.)

The transcriptomic analyses lead to the identification of clusters of cells sharing significant gene expression that distinguishes them from other clusters. Using well-known neuron subtype specific markers, the authors are able to assign a specific neuronal identity to about 2/3 of the cluster. Moreover, one other cluster results in the recognition in zebrafish of a neuronal cell type identified in the mammalian spinal cord, v0c, that they confirm to be present in zebrafish using solid markers. In addition, the results show that the zebrafish v0c population expressed markers of both cholinergic and glutamatergic neurons, while the mammalian v0c population is known to be cholinergic. (It is not clear whether the possibility that mammalian v0c neurons also express glutamatergic markers has been specifically tested, but it seems, at present, there is no evidence to suggest that might be the case.)

To zoom in on the question of molecular differences between the fast vs. rhythmic circuits, the authors focus on motor neurons as two different populations of neurons are involved in each circuit. (Along the way, they also identify markers that mark different subtypes of motor neurons.) They find that primary motor neurons (PMNs) involved in the fast circuit express a distinguishing cassette of ion channel and synapse associated genes. Moreover, the cassette of genes also is expressed by interneurons that function in the fast circuit. The results are illuminating and set the stage for many future exacting experiments.

As is true for significant work, the results open up and permit yet more rigorous and strategic analyses, running the gamut from specific molecules to behavior, of the circuit mechanisms underlying unique behaviors.

Overall, the work is carried out to high rigorous standards and the vast majority of conclusions are strongly supported by the results. However, there are a few instances of potential over-interpretation and points that could be further clarified/discussed:

1 - lines 412-414. The authors conclude that "Most importantly, and as detailed below, our scRNA seq revealed the ion channel and synaptic genes that serve to match specific neuronal function to behavior." That the authors have identified a gene cassette that distinguishes neurons of the fast escape circuit is a laudable finding. However, at this stage, to say that this gene cassette is the basis for unique circuit function and resultant behavior is a well-supported hypothesis that requires rigorous testing and not yet a solid conclusion. (Maybe that is what the authors meant, and I have misinterpreted the sentence.)

2 - lines 323-324: Given that ~ 6 hrs separates PMN from SMN birthdates (Myers et al. 1986) and that the study was done using 4dpf larval tissue, the possibility that the higher level of expression of transcription factors and RNA-biding factors in SMNs reflects "the less well differentiated state that accompanies the later birthdate of the SMns" seems unlikely.

3 - Fig 5 and Sup Fig 1:The authors mention that the unidentified cluster in the motor neuron set shares markers with non-skeletal muscle. I realize that this cluster is tangential to their focus. However, given that this cluster predominantly arises from the FACS sorted cells, it is worth considering that the cells might correspond to the pancreas.

4 - lines 113-115 and Fig. 1: The authors indicate that three clusters reflect cells that have mixed glial and neuronal cell expression. Is there any possibility that in a few instances, in the final single cell capture, that two rather than one cell were collected? (Again, not a major focus of the study but the cluster is commented on.)

Finally, as the transcriptomic information about glial cells will be of interest to many in the field, the authors are to be commended for depositng the data in congratulations to the authors for depositing the data in the publicly accessible Gene Expression Omnibus.

Reviewer #2 (Public Review):

The authors analysed functional MRI recordings of brain activity at rest, using state-of-the-art methods that reveal the diverse ways in which the information can be integrated in the brain. In this way, they found brain areas that act as (synergistic) gateways for the 'global workspace', where conscious access to information or cognition would occur, and brain areas that serve as (redundant) broadcasters from the global workspace to the rest of the brain. The results are compelling and consisting with the already assumed role of several networks and areas within the Global Neuronal Workspace framework. Thus, in a way, this work comes to stress the role of synergy and redundancy as complementary information processing modes, which fulfill different roles in the big context of information integration.

In addition, to prove that the identified high-order interactions are relevant to the phenomenon of consciousness, the same analysis was performed in subjects under anesthesia or with disorders of consciousness (DOC), showing that indeed the loss of consciousness is associated with a deficient integration of information within the gateway regions.

However, there is something confusing in the redundancy and synergy matrices shown in Figure 2. These are pair-wise matrices, where the PID was applied to identify high-order interactions between pairs of brain regions. I understand that synergy and redundancy are assessed in the way the brain areas integrate information in time, but it is still a little contradictory to speak about high-order in pairs of areas. When talking about a "synergistic core", one expects that all or most of the areas belonging to that core are simultaneously involved in some (synergistic) information processing, and I do not see this being assessed with the currently presented methodology. Similarly, if redundancy is assessed only in pairs of areas, it may be due to simple correlations between them, so it is not a high-order interaction. Perhaps it is a matter of language, or about the expectations that the word 'synergy' evokes, so a clarification about this issue is needed. Moreover, as the rest of the work is based on these 'pair-wise' redundancy and synergy matrices, it becomes a significative issue.

Reviewer #2 (Public Review):

This article is focused on investigating incremental speech processing, as it pertains to building higher-order syntactic structure. This is an important question because speech processing in general is lesser studied as compared to reading, and syntactic processes are lesser studied than lower-level sensory processes. The authors claim to shed light on the neural processes that build structured linguistic interpretations. The authors apply modern analysis techniques, and use state-of-the-art large language models in order to facilitate this investigation. They apply this to a cleverly designed experimental paradigm of EMEG data, and compare neural responses of human participants to the activation profiles in different layers of the BERT language model.

Strengths:

[1] The study aims to investigate an under-explored aspect of language processing, namely syntactic operations during speech processing

[2] The study is taking advantage of technological advancements in large language models, while also taking linguistic theory into account in building the hypothesis space

[3] The data combine EEG and MEG, which provides a valuable spatio-temporally resolved dataset

[4] The use of behavioural validation of high/low transitive was an elegant demonstration of the validity of their stimuli

Weaknesses:

[1] The manuscript is quite hard to understand, even for someone well-versed in both linguistic theory and LLMs. The questions, design, analysis approach, and conclusions are all quite dense and not easy to follow.

[2] The analyses end up seeming overly complicated when the underlying difference between sentence types is a simple categorical distinction between high and low transitivity. I am not sure why tree depth and BERT are being used to evaluate the degree to which a sentence is being processed as active or passive. If this is necessary, it would be helpful for the authors to motivate this more clearly.

[3] The main data result figures comparing BERT and the EMEG brain data are hard to evaluate because only t-values are provided, and those, only for significant clusters. It would be helpful to see the full 600 ms time course of rho values, with error bars across subjects, to really be able to evaluate it visually. This is a summary statistic that is very far away from the input data

[4] Some details are omitted or not explained clearly. For example, how was BERT masked to give word-by-word predictions? In its default form, I believe that BERT takes in a set of words before and after the keyword that it is predicting. But I assume that here the model is not allowed to see linguistic information in the future. How were the auditory stimuli recorded? Was it continuous speech or silences between each word? How was prosody controlled? Was it a natural speaker or a speech synthesiser?

It is difficult for me to fully assess the extent to which the authors achieved their aims, because I am missing important information about the setup of the experiment and the distribution of test statistics across subjects.

Reviewer #2 (Public Review):

Language skills are traditionally associated with a network of brain regions in the left hemisphere. In this intriguing study, Esteban Villar-Rodríguez and collaborators examined if atypical hemispheric lateralization for language determines the functional and structural organisation of the network for inhibitory control as well as its relationship with schizotypy and autistic spectrum traits. The results suggest that individuals who have atypical lateralisation of the language function have also an atypical (mirrored) lateralisation of the inhibitory control network, compared to the typical group (individuals with left-lateralised language function). Furthermore, the atypical organization of language production is associated with a greater white matter volume of the corpus callosum, and atypical lateralization of inhibitory control is related to a higher interhemispheric functional coupling of the IFC, suggesting a link between atypical functional lateralisation (language and inhibitory control) and structural and functional changes in the brain.

This study also provides interesting evidence on how atypical language lateralisation impacts some aspects of language behaviour (reading), i.e., atypical lateralization predicts worse reading accuracy. Furthermore, the results suggest an association between atypical lateralization and increased schizotypy and autistic traits.

The strength of this work is that it presents a collection of measurements on the same individuals (including task-related behavioural, functional and structural neuroimaging measures) to reveal if (and how) atypical language lateralisation might be associated with: (1) atypical neural organisation of other non-linguistic cognitive systems, (2) behavioural performance associated with language tasks, and finally (3) personality traits. As such the results presented in this manuscript have the potential to be informative for various disciplines. For instance, if clarifications/corrections are provided (see below), the results might provide some insight into the role of the right hemisphere for language processing in healthy individuals as well as patient populations with acquired linguistic impairment including stroke and dementia.

One important weakness of this manuscript is that several areas, including the characteristics of participants tested, and the hypotheses/predictions, are underspecified or incomplete. Furthermore, in some cases the types of analysis do not seem to be appropriate for addressing the questions of the present study and very little explanation for those choices is provided.

Reviewer #2 (Public Review):

The manuscript by Est and Murphy tested the feasibility of using brain microvascular endothelial-like cells (BMECs) derived from induced pluripotent stem cells (iPSCs) as a model for studying retinoid uptake and transport across the blood-brain barrier (BBB). Establishing this experimental model is an important step towards obtaining greater mechanistic insight into the specificity of retinol trafficking between blood and retinoid-dependent tissues. The authors validated the iPSC-derived BMECs by detecting the expression of specific protein markers for BBB. They also demonstrated that BMECs form a tight barrier when cultured in a Transwell chamber, allowing for the quantification of permeability across the cells rather than through paracellular leakage. Finally, they confirmed the expression of the transporter (STRA6), binding protein (CRBP1), and enzyme (LRAT), which are key elements of the molecular machinery involved in the cellular uptake of circulating retinol. The carefully established model of the human BBB served as an experimental platform for the authors to investigate the uptake and transcellular transport of retinol. For this purpose, they compared the kinetics and efficiency of retinoid accumulation delivered to the cell as free retinol, retinol bound to serum retinol-binding protein (RBP), or retinol-RBP in complex with transthyretin (TTR), a physiological binding partner for retinol-loaded RBP.

Although the development and thorough characterization of the experimental model of the BBB have great value and meaningfully contribute to ongoing efforts to better understand the mechanisms of retinoid homeostasis, the premise and interpretation of cellular uptake appear controversial. In particular:

1. The authors assume that there is a significant fraction of free ROL, 20% for ROH/RBP and 7% for RBP/TTR complexes (summarized in Table 1). This implies that at the physiological concentration of ROH/RBP in the plasma of 2 uM, free ROL represents 0.4 uM. However, the concentration of free ROL is limited by its poor solubility in the aqueous phase, which is around 0.06 uM (Szuts EZ, 1991, Arch Biochem Biophys). Moreover, taking into account the large concentration of other potential nonspecific carriers for lipids, it is safe to assume that there is virtually no free ROH in the plasma. There is also an important physiological reason for the limited amount of free ROL. Its rapid and nonspecific partition into cells (also observed in this study) would work against the highly specific RBP/STRA6-dependent ROH uptake pathway, undermining its physiological function.

2. The advantage of the experimental system used in this report is that it allows for the assessment of the permeability across BMECs. Interestingly, the basolateral accumulation of ROH represented only a small fraction (1 - 1.5%) of the total ROH taken up by the cells. Moreover, the overall permeability was comparable regardless of the source of ROL added at the apical side. However, a question remains: would the outcome of the experiment be different if the basolateral chamber contained an ROH acceptor (retinol-binding proteins) rather than Hank's balanced salt solution, to which the partition of ROL is limited by its water solubility? In fact, the maximum concentration of ROH on the basolateral side did not exceed 40 nM (Fig 5D and 7C), which is roughly the maximum water solubility of ROH. Thus, this experimental design limits extrapolation of the data to in vivo conditions.

3. The authors claim that transthyretin (TTR) increases BMECs permeability when compared to ROH/RBP. However, the mechanistic explanation for this phenomenon remains unclear. Do the authors imply the presence of a putative TTR receptor whose signaling could affect the efflux of ROL at the basolateral side of BMECs? TTR is an ubiquitous plasma protein. The concentration of TTR is tightly regulated and maintained between 300 - 330 mg/L. Therefore, it is questionable how TTR can serve as a signaling molecule modulating retinoid homeostasis in the brain.

4. Although overexpression of LRAT in response to increased uptake of ROH is well-documented, the postulate that TTR stimulates the expression of LRAT in an RBP-independent manner is puzzling, for the reasons mentioned in point 3. Moreover, LRAT is a highly efficient enzyme that operates under physiological conditions with substrate concentrations below the Km value. The rate of esterification is primarily limited by the intracellular transport of ROH to the ER. Therefore, without kinetic studies, it is unclear whether an increased number of LRAT copies (x2) would have a significant effect on the rate of accumulation of retinyl esters (REs).

5. The conclusion that cellular uptake of ROH is biphasic appears to be correct. However, the proposed interpretation of the mechanistic principles of this phenomenon is oversimplified. It assumes that loading CRBP1 with ROL to its capacity triggers the synthesis of REs. However, the saturation of CRBP1 with ROH is not required for REs formation. In fact, studies on CRBP1-deficient mice indicate that this protein is not necessary for the efficient esterification of ROL but rather affects the intracellular turnover of retinoids. It is likely that with increasing concentration of ROH, the specific and controlled mechanism of intracellular retinoid transport becomes saturated, allowing for spontaneous diffusion-driven partitioning of retinoids within cells.

Additional technical issues that could affect the experimental outcomes:

1. The formation of the ROH/RBP-TTR complex should be confirmed and purified using gel filtration to separate free TTR and ROH/RBP. Only fractions containing the complex should be used in the experiments. Assuming that the complex is formed with 100% efficiency is overly optimistic.

2. Reloading RBP with isotopically labeled ROH requires an additional purification step. Stripping ROL from the ROH/RBP complex with organic solvent (diethyl ether) is appropriate but relatively harsh, causing partial unfolding of a fraction of RBP. Therefore, assuming that 100% of stripped RBP remains functional and can be reloaded with ROH is inaccurate. Reloading apo-RBP with a stoichiometric amount of ROH without an additional purification step (e.g., ion exchanger) leads to an excess of free ROL and/or its nonspecific association with nonfunctional RBP fractions. Measuring absorbance at 330 nm is not sufficient proof of binding since free ROH also absorbs at the same wavelength.

Reviewer #2 (Public Review):

The authors tried to diagnose cancers and pinpoint tissues of origin using cfDNA. To achieve the goal, they developed a framework to assess methylation, CNA, and other genomic features. They established discovery and validation cohorts for systematic assessment and successfully achieved robust prediction power.

Still, there are places for improvement. The diagnostic effect can be maximized if their framework works well in early stage cancer patients. According to Table 1, about 10% of the participants are stage I. Do these cancers also perform well as compared to late stage cancers?

Can authors show a systematic comparison of their method to other previous methods to summarize what their algorithm can achieve compared to others.

Reviewer #2 (Public Review):

Ghasemahmad et al. report findings on the influence of salient vocalization playback, sex, and previous experience, on mice behaviors, and on cholinergic and dopaminergic neuromodulation within the basolateral amygdala (BLA). Specifically, the authors played back mice vocalizations recorded during two behaviors of opposite valence (mating and restraint) and measured the behaviors and release of acetylcholine (ACh), dopamine (DA), and serotonin in the BLA triggered in response to those sounds.

Strength: The authors identified that mating and restraint sounds have a differential impact on cholinergic and dopaminergic release. In male mice, these two distinct vocalizations exert an opposite effect on the release of ACh and DA. Mating sounds elicited a decrease of Ach release and an increase of DA release. Conversely, restraint sounds induced an increase in ACh release and a trend to decrease in DA. These neurotransmission changes were different in estrus females for whom the mating vocalization resulted in an increase of both DA and ACh release.

Weaknesses: The behavioral analysis and results remain elusive, and although addressing interesting questions, the study contains major flaws, and the interpretations are overstating the findings.

Reviewer #2 (Public Review):

In the manuscript, the authors highlighted the importance of T-cell receptor (TCR) analysis and the lack of amino acid embedding methods specific to this domain. The authors proposed a novel bi-directional context-aware amino acid embedding method, catELMo, adapted from ELMo (Embeddings from Language Models), specifically designed for TCR analysis. The model is trained on TCR sequences from seven projects in the ImmunoSEQ database, instead of the generic protein sequences. They assessed the effectiveness of the proposed method in both TCR-epitope binding affinity prediction, a supervised task, and the unsupervised TCR clustering task. The results demonstrate significant performance improvements compared to existing embedding models. The authors also aimed to provide and discuss their observations on embedding model design for TCR analysis: 1) Models specifically trained on TCR sequences have better performance than models trained on general protein sequences for the TCR-related tasks; and 2) The proposed ELMo-based method outperforms TCR embedding models with BERT-based architecture. The authors also provided a comprehensive introduction and investigation of existing amino acid embedding methods. Overall, the paper is well-written and well-organized.

The work has originality and has potential prospects for immune response analysis and immunotherapy exploration. TCR-epitope pair binding plays a significant role in T cell regulation. Accurate prediction and analysis of TCR sequences are crucial for comprehending the biological foundations of binding mechanisms and advancing immunotherapy approaches. The proposed embedding method presents an efficient context-aware mathematical representation for TCR sequences, enabling the capture and analysis of their structural and functional characteristics. This method serves as a valuable tool for various downstream analyses and is essential for a wide range of applications.

Reviewer #2 (Public Review):

In this project, Schmidig, Ruch and Henke examined whether word pairs that were presented during slow-wave sleep would leave a detectable memory trace 12 and 36 hours later. Such an effect was found, as participants showed a bias to categorize pseudowords according to a familiar word that they were paired with during slow-wave sleep. This behavior was not accompanied by any sign of conscious understanding of why the judgment was made, and so demonstrates that long-term memory can be formed even without conscious access to the presented content. Unconscious learning occurred when pairs were presented during troughs but not during peaks of slow-wave oscillations. Differences in brain responses to the two types of presentation schemes, and between word pairs that were later correctly- vs. incorrectly-judged, suggest a potential mechanism for how such deep-sleep learning can occur.

The results are very interesting, and they are based on solid methods and analyses. Results largely support the authors' conclusions, but I felt that there were a few points in which conclusions were not entirely convincing:

1) As a control for the critical stimuli in this study, authors used a single pseudoword simultaneously played to both ears. This control condition (CC) differs from the experimental condition (EC) in a few dimensions, among them: amount of information provided, binaural coherence and word familiarity. These differences make it hard to conclude that the higher theta and spindle power observed for EC over CC trials indicate associative binding, as claimed in the paper. Alternative explanations can be made, for instance, that they reflect word recognition, as only EC contains familiar words.

2) The entire set of EC pairs were tested both following 12 hours and following 36 hours. Exposure to the pairs during test #1 can be expected to have an effect over memory one day later, during test #2, and so differences between the tests could be at least partially driven by the additional activation and rehearsal of the material during test #1. Therefore, it is hard to draw conclusions regarding automatic memory reorganization between 12 and 36 hours after unconscious learning. Specifically, a claim is made regarding a third wave of plasticity, but we cannot be certain that the improvement found in the 36 hour test would have happened without test #1.

3) Authors claim that perceptual and conceptual processing during sleep led to increased neural complexity in troughs. However, neural complexity was not found to differ between EC and CC, nor between remembered and forgotten pairs. It is therefore not clear to me why the increased complexity that was found in troughs should be attributed to perceptual and conceptual word processing, as CC contains meaningless vowels. Moreover, from the evidence presented in this work at least, I am not sure there is room to infer causation - that the increase in HFD is driven by the stimuli - as there is no control analysis looking at HFD during troughs that did not contain stimulation.

Reviewer #2 (Public Review):

Chakraborty et al. present a comprehensive analysis of the role of the IP3R in regulating SOCE in neuronal cells starting with human neurons derived from stem cells and continuing with SH-SY5Y cells after careful characterization of the maintenance of the inhibitory role of IP3R. They also show differential effects in non-neuronal cell lines. The work is careful and the data convincing. The conclusion that IP3Rs somehow stabilize ER-PM MCS to enhance SOCE is supported by the findings especially the surprising finding that the IP3R effect does not require a functional pore but does require IP3 binding to IP3R. Overall this is a careful, well-done analysis. However, the conclusion that IP3R stabilizes ER-PM MCS is mostly inferred from the current data. The authors need to extend the finding by directly assessing the size, density, and the number of ER-PM MCS using endogenous STIM1 (there are reliable antibodies for STIM1) to confirm their conclusion that when IP3R is knocked down ER-PM MCS are smaller/less dense. Another interesting experiment that would support their conclusion is expressing tagged STIM1 and Orai1 and observing their interaction in real time after store depletion. These experiments would need to be carefully controlled to select cells with low levels of expression of STIM1-Orai1 as there are hints from their current data that high expressors would not exhibit the IP3R dependence on SOCE. So, some independent experimental evidence that IP3R knockdown is affecting ER-PM MCS and not STIM1-Orai1 interaction directly to support the presented PLA data would greatly support the final conclusion of the paper. From the PLA assay alone it is difficult to differentiate between poor direct STIM1-Orai1 interaction versus stability of ER-PM MCS.

Reviewer #2 (Public Review):

McCormick, Cleary et al., explore the question of how the nucleotide state of the tubulin heterodimer affects the interaction between adjacent tubulins.

(1) The setup of the authors' model, which attributes the dynamic properties of the growing microtubule only to the differences in interface binding affinities, is unrealistic. They excluded the influence of the nucleotide-dependent global conformational changes even in the 'Self-Acting Nucleodide' model (Fig. 1A). As the authors have found earlier, tubulin in its unassembled state may be curved irrespective of the species of the bound nucleotide (Rice et al., 2008, doi: 10.1073/pnas.0801155105), but at the growing end of microtubules, the situation could be different. Considering the recently published papers from other laboratories, it may be more appropriate to include the nucleotide-dependent change in the tubulin conformation in the Self-Acting Nucleotide model.

(2) The result that the minus end is insensitive to GDP (Fig. 2) was previously published in a paper by Tanaka-Takiguchi et al. (doi: 10.1006/jmbi.1998.1877). The exact experimental condition was different from the one used in Fig. 2, but the essential point of the finding is the same. The authors should cite the preceding work, and discuss the similarities and differences, as compared to their own results.

Reviewer #2 (Public Review):

Schmid et al present a lovely study looking at the effect of passive auditory exposure on learning a categorization task.

The authors utilize a two-alternative choice task where mice have to discriminate between upward and downward-moving frequency sweeps. Once mice learn to discriminate easy stimuli, the task is made psychometric and additional intermediate stimuli are introduced (as is standard in the literature). The authors introduce an additional two groups of animals, one that was passively exposed to the task stimuli before any behavioral shaping, and one that had passive exposure interleaved with learning. The major behavioral finding is that passive exposure to sounds improves learning speed. The authors show this in a number of ways through linear fits to the learning curves. Additionally, by breaking down performance based on the "extreme" vs "psychometric" stimuli, the authors show that passive exposure can influence responses to sounds that were not present during the initial training period. One limitation here is that the presented analysis is somewhat simplistic, does not include any detailed psychometric analysis (bias, lapse rates etc), and primarily focuses on learning speed. Ultimately though, the behavioral results are interesting and seem supported by the data.

To investigate the neural mechanisms that may underlie their behavioral findings, the authors turn to a family of artificial neural network models and evaluate the consequences of different learning algorithms and schedules, network architectures, and stimulus distributions, on the learning outcomes. The authors work through five different architectures that fail to recapitulate the primary behavior findings before settling on a final model, utilizing a combination of supervised and unsupervised learning, that was capable of reproducing the key aspects of the experiments. Ultimately, the behavioral results presented are consistent with network models that build latent representations of task-relevant features that are determined by statistical properties of the input distribution.

Reviewer #2 (Public Review):

Knowles et al. investigated the developmental roles of Erk1/2 expression in cells from the Nkx2.1-lineage, which includes the PV and SST classes of cortical inhibitory interneurons (CINs) and glial subtypes. They find that embryonic expression of Erk1/2 regulates the number of Nkx2.1-derived oligodendrocytes and astrocytes, but not CINs, observed in postnatal mice. However, Erk1/2 is necessary for the expression of SST in subset of Nkx2.1-derived CINs, which can be partially rescued by postnatal depolarization via chemogenetic stimulation with DREADDs. Finally, loss of Erk1/2 from these cells impairs activity-dependent expression of FOSB. Collectively, this revised paper demonstrates differential roles of Erk1/2 for the development of glia and neurons. Furthermore, it suggests SST CINs may be particularly vulnerable to loss of Erk1/2 signaling during both early embryonic and later postnatal developmental stages.

Strengths:<br /> This paper uses multiple transgenic mouse lines to investigate the contributions of Erk1/2 loss and over-expression and MEK overexpression for interneuron and glial development. Furthermore, they consider how Erk1/2 signaling may evolve over the course of development from embryonic to postnatal juvenile and adult stages. Thus, they investigate Erk1/2's early role in cell differentiation and its later role in activity dependent signaling. This approach to studying gene function throughout development is important but not often attempted within a single study.

The authors investigate Erk1/2 using several techniques, including immunohistochemistry, sequencing of translated genes using the Ribotag method, electrophysiology, and chemogenetic stimulation using DREADDs. Thus, they aim to apply a comprehensive battery of approaches to assay Erk1/2 signaling in Nkx2.1-derived cells throughout development.

Weaknesses:<br /> This paper describes a series of mostly separate observations that are not directly linked. The mechanisms underlying their observations and the significance of the findings are often unclear.

The authors use Erk1-/-; Erk2fl/wt; Nkx2.1Cre as "het" controls throughout the manuscript. However, there is no explanation for why this is a valid control except for a statement that they are "grossly intact", without elaboration. It is unclear why the authors did not use Nkx2.1Cre mice for their control. Figure 1 - Supplemental Figure 1 provides the only comparison between Erk1-/-; Erk2fl/wt; Nkx2.1Cre and Erk1-/-; Erk2wt/wt; Nkx2.1Cre mice. This figure shows a single example of immune staining for Erk2, but it is not obvious that Nkx2.1 control or "het control" cells even express Erk2 in this image. There is no quantification. Thus, their choice of control condition is not obviously appropriate.

Reviewer #2 (Public Review):

In "Behavioral entrainment to rhythmic auditory stimulation can be modulated by tACS depending on the electrical stimulation field properties" Cabral-Calderin and collaborators aimed to document 1) the possible advantages of personalized tACS montage over standard montage on modulating behavior; 2) the inter-individual and inter-session reliability of tACS effects on behavioral entrainment and, 3) the importance of the induced electric field properties on the inter-individual variability of tACS.

To do so, in two different sessions, they investigated how the detection of silent gaps occurring at random phases of a 2Hz- amplitude modulated sound could be enhanced with 2Hz tACS, delivered at different phase lags. In addition, they evaluated the advantage of using spatially optimized tACS montages (information-based procedure - using anatomy and functional MRI to define the target ROI and simulation to compare to a standard montage applied to all participants) on behavioral entrainment. They first show that the optimized and the standard montages have similar spatial overlap to the target ROI. While the optimized montage induced a more focal field compared to the standard montage, the latter induced the strongest electric field. Second, they show that tACS does not modify the optimal phase for gap detection (phase of the frequency-modulated sound) but modulates the strength of behavioral entrainment to the frequency-modulated sound in a phase-lag specific manner. However, and surprisingly, they report that the optimal tACS lag, and the magnitude of the phasic tACS effect were highly variable across sessions. Finally, they report that the inter-individual variability of tACS effects can be explained by the strength of the inward electric field as a function of the field focality and on how well it reached the target ROI.

The article is interesting and well-written, and the methods and approaches are state-of-the-art.

Strengths:<br /> - The information-based approach used by the authors is very strong, notably with the definition of subject-specific targets using a fMRI localizer and the simulation of electric field strength using 3 different tACS montages (only 2 montages used for the behavioral experiment).<br /> - The inter-session and inter-individual variability are well documented and discussed. This article will probably guide future studies in the field.

Weaknesses:<br /> - The addition of simultaneous EEG recording would have been beneficial to understand the relationship between tACS entrainment and the entrainment to rhythmic auditory stimulation.<br /> - It would have been interesting to develop the fact that tACS did not "overwrite" neural entrainment to the auditory stimulus. The authors try to explain this effect by mentioning that "tACS is most effective at modulating oscillatory activity at the intended frequency when its power is not too high" or "tACS imposes its own rhythm on spiking activity when tACS strength is stronger than the endogenous oscillations but it decreases rhythmic spiking when tACS strength is weaker than the endogenous oscillations". However, it is relevant to note that the oscillations in their study are by definition "not endogenous" and one can interpret their results as a clear superiority of sensory entrainment over tACS entrainment. This potential superiority should be discussed, documented, and developed.<br /> - The authors propose that "by applying tACS at the right lag relative to auditory rhythms, we can aid how the brain synchronizes to the sounds and in turn modulate behavior." This should be developed as the authors showed that the tACS lags are highly variable across sessions. According to their results, the optimal lag will vary for each tACS session and subtle changes in the montage could affect the effects.<br /> - In a related vein, it would be very useful to show the data presented in Figure 3 (panels b,d,e) for all participants to allow the reader to evaluate the quality of the data (this can be added as a supplementary figure).

Reviewer #2 (Public Review):

In their article titled "Brain mechanisms of reversible symbolic reference: a potential singularity of the human brain", van Kerkoerle et al address the timely question of whether non-human primates (rhesus macaques) possess the ability for reverse symbolic inference as observed in humans. Through an fMRI experiment in both humans and monkeys, they analyzed the bold signal in both species while observing audio-visual and visual-visual stimuli pairs that had been previously learned in a particular direction. Remarkably, the findings pertaining to humans revealed that a broad brain network exhibited increased activity in response to surprises occurring in both the learned and reverse directions. Conversely, in monkeys, the study uncovered that the brain activity within sensory areas only responded to the learned direction but failed to exhibit any discernible response to the reverse direction. These compelling results indicate that the capacity for reversible symbolic inference may be unique to humans.

In general, the manuscript is skillfully crafted and highly accessible to readers. The experimental design exhibits originality, and the analyses are tailored to effectively address the central question at hand. Although the first experiment raised a number of methodological inquiries, the subsequent second experiment thoroughly addresses these concerns and effectively replicates the initial findings, thereby significantly strengthening the overall study. Overall, this article is already of high quality and brings new insight into human cognition.

I identified three weaknesses in the manuscript:<br /> - One major issue in the study is the absence of significant results in monkeys. Indeed, authors draw conclusions regarding the lack of significant difference in activity related to surprise in the multi-demand network (MDN) in the reverse congruent versus reverse incongruent conditions. Although the results are convincing (especially with the significant interaction between congruency and canonicity), the article could be improved by including additional analyses in a priori ROI for the MDN in monkeys (as well as in humans, for comparison).<br /> - While the authors acknowledge in the discussion that the number of monkeys included in the study is considerably lower compared to humans, it would be informative to know the variability of the results among human participants.<br /> - Some details are missing in the methods.

Reviewer #2 (Public Review):

The manuscript by Bull et al investigates the relationship between metabolic features, in particular different lipoproteins and fatty acids, and colorectal cancer. They combine different data sources to analyze forward and reverse Mendelian Randomization associations in children and adults. Their results indicate that polyunsaturated fatty acids may be implicated in the risk for colorectal cancer.

Overall, the paper is well-written, and the methods used are solid. The use of different data (cohort individual data and summary stats) and stratifications strengthens the analyses. The conclusions drawn from the results are balanced and supported by the data although the novelty of the findings is modest.

Reviewer #2 (Public Review):

This study presents novel findings on the metabolic fuel preference shift regulated by PTPMT1, a target of interest, in skeletal and cardiac muscle cells.

Zheng et al. have investigated the effects of PTPMT1 Knock-out on cellular metabolic flexibility. Since the authors used several types of appropriate tissue-specific mouse models, it seems to be a broad significance at the first glance. However, most of the data lack the quantification, consequently they don't provide statistical significance. In addition, the functional data such as echocardiography shows partial and limited data.<br /> Therefore, it is only a matter of speculation that the absence of PTPMT1 inhibits glucose (pyruvate) utilization and promotes FAO.

Reviewer #2 (Public Review):

Members of the EphB family of tyrosine kinase receptors are involved in a multitude of diverse cellular functions, ranging from the control of axon growth to angiogenesis and synaptic plasticity. In order to provide these diverse functions, it is expected that these receptors interact in a cell-type-specific manner with a diverse variety of downstream signalling molecules.

The authors have used proteomics approaches to characterise some of these molecules in further detail. This molecule, myc-binding protein 2 (MYCBP2) also known as highwire, has been identified in the context of establishment of neural connectivity. Another molecule coming up on this screen was identified as FBXO45.

The authors use classical methods of co-IP to show a kinase-independent binding of MYCBP2 to EphB2. They further showed that FBXO45 within a ternary complex increased the stability of the EphB2/MYCBP2 complex.

To define the interacting domains, they used clearly designed swapping experiments to show that the extracellular and transmembrane domains are necessary and sufficient for the formation of the ternary complex.

Using a cellular contraction assay, the authors showed the necessity of MYCBP2 in mediating the cytoskeletal response of EphB2 forward signalling. Furthermore, they used the technically challenging stripe assay of alternating lanes of ephrinB-Fc and Fc to show that also in this migration-based essay MYCBP2 is required for EphB mediated differential migration pattern.

MYCBP2 in addition is necessary to stabilize EphB2, that is in the absence of MYCBP2, EphB2 is degraded in the lysosomal pathway.

Interestingly, the third protein in this complex, Fbxo45, was further characterized by overexpression of the domain of MYCBP2, known to interact with Fbxo45. Here the authors showed that this approach led to the disruption of the EphB2 / MYCBP2 complex, and also abolished the ephrinB-mediated activation of EphB2 receptors and their differential outgrowth on ephrinB2-Fc / Fc stripes.

Finally, the authors demonstrated an in vivo function of this complex using another model system, C elegans where they were able to show a genetic interaction.

Data shows in a nice set of experiments a novel level of EphB2 forward signalling where a ternary complex of this receptor with multifunctional MYCBP2 and Fbxo45 controls the activity of EphB2, allowing a further complex regulation of this important receptor. Additionally, the authors challenge pre-existing concepts of the function of MYCBP2 which might open up novel ways to think about this protein.

Of interest is this work also in terms of the development of the retinotectal projection in zebrafish where MYCBP2/highwire plays a crucial role, and thus might lead to a better understanding of patterning along the DV axis, for which it is known that EphB family members are crucial.

Overall, the experiments are classical experiments of co-immunoprecipitations, swapping experiments, collapse assays, and stripe assays which all are well carried out and are convincing.

Reviewer #2 (Public Review):

In this manuscript, Smith et al. delineated novel mechanistic insights into the structure-function relationships of the C-terminal repeat domains within the mouse DUX protein. Specifically, they identified and characterised the transcriptionally active repeat domains, and narrowed down to a critical 6aa region that is required for interacting with key transcription and chromatin regulators. The authors further showed how the DUX active repeats collaborate with the C-terminal acidic tail to facilitate chromatin opening and transcriptional activation at DUX genomic targets.

Reviewer #2 (Public Review):

In this study, Yan et al. report that a cleaved form of METTL3 (termed METTL3a) plays an essential role in regulating the assembly of the METTL3-METTL14-WTAP complex. Depletion of METTL3a leads to reduced m6A level on TMEM127, an mTOR repressor, and subsequently decreased breast cancer cell proliferation. Mechanistically, METTL3a is generated via 26S proteasome in an mTOR-dependent manner.

The manuscript follows a smooth, logical flow from one result to the next, and most of the results are clearly presented. Specifically, the molecular interaction assays are well-designed. This model represents a significant addition to the current understanding of m6A-methyltransferase complex formation.

Reviewer #2 (Public Review):

In this study, the authors utilize a compendium of public genomic data to identify transcription factors (TF) that can identify their DNA binding motifs in the presence of nuclosome-wrapped chromatin and convert the chromatin to open chromatin. This class of TFs are termed Pioneer TFs (PTFs). A major strength of the study is the concept, whose premise is that motifs bound by PTFs (assessed by ChIP-seq for the respective TFs) should be present in both "closed" nucleosome wrapped DNA regions (measured by MNase-seq) as well as open regions (measured by DNAseI-seq) because the PTFs are able to open the chromatin. Use of multiple ENCODE cell lines, including the H1 stem cell line, enabled the authors to assess if binding at motifs changes from closed to open. Typical, non-PTF TFs are expected to only bind motifs in open chromatin regions (measured by DNaseI-seq) and not in regions closed in any cell type. This study contributes to the field a validation of PTFs that are already known to have pioneering activity and presents an interesting approach to quantify PTF activity.

For this reviewer, there were a few notable limitations. One was the uncertainty regarding whether expression of the respective TFs across cell types was taken into account. This would help inform if a TF would be able to open chromatin. Another limitation was the cell types used. While understandable that these cell types were used, because of their deep epigenetic phenotyping and public availability, they are mostly transformed and do not bear close similarity to lineages in a healthy organism. Next, the methods used to identify PTFs were not made available in an easy-to-use tool for other researchers who may seek to identify PTFs in their cell type(s) of interest. Lastly, some terms used were not defined explicitly (e.g., meaning of dyads) and the language in the manuscript was often difficult to follow and contained improper English grammar.

Reviewer #2 (Public Review):

Dr. Kia Davis and colleagues present a thoughtful analysis of disruptions to cancer care during COVID-19 in the article, "Understanding disruptions in cancer care to reduce increased cancer burden: a cross-sectional study." The article is based on an online survey of 680 residents in the Siteman Cancer Center catchment area in Summer 2020. The authors aim to characterize demographic differences in cancer care disruptions. Information about the causes and distribution of care disruption can help reduce the impacts of COVID-19 and guide the recovery of programs and services. The article provides a clear and detailed assessment of factors associated with care disruption and return to care during the first six months of the pandemic.

A strength of the study is the focus on the catchment area of the cancer center during a period of dramatic change. The results would provide timely and actionable data to address emerging barriers to care and associated social or contextual factors. This information helps the Community Outreach and Engagement efforts to be responsive to community priorities despite rapidly evolving circumstances.

The analysis would benefit from greater detail in three areas. First, it would be helpful to have more information about how the outcome measures were originally developed or tested. Second, for the regression analysis, it would be helpful to show the demographic characteristics of the two strata to better understand the sample composition. Third, the authors should demonstrate that the data do not violate the assumptions for conducting logistic regression to improve confidence in the findings.

COVID-19 affected all aspects of the cancer continuum. The study reports factors associated with postponing or canceling cancer-related appointments during the pandemic. It will be of great interest to researchers and practitioners in cancer prevention and control.

Reviewer #2 (Public Review):

In this manuscript, the authors study the generation of joint torques in stick insects under external electrical excitation. The goal of this paper is to develop a model for the relationship between torque and excitation period, with a specific focus on accounting for inter-individual variances in the model. The long-term motivation for this work is to be able to generate controlled external excitation of insect muscle to create "cyborg" systems where computer-controlled electronics generate movement of living systems.

The authors performed measurements of joint torque generated from three different muscles across two excitation parameters (voltage and excitation time). The authors study the relationship between excitation parameters and muscle torque comparing a linear relationship, and a non-linear (power-law) relationship between torque and voltage. In addition, the authors also compare a hierarchical version of the model which includes inter-individual differences, with a pooled model that ignores individual differences. The authors use an information criteria metric to then identify the best model.

I believe that the methods of this paper and the findings are all sound; however, I have the following comments and questions.

Main questions:<br /> 1. It is interesting to find that inter-individual differences are important in the torque output from the joint. However, in some sense, this is what I would have expected. I am curious if these inter-individual differences can be related to any distinct differences among the insects studied: for example body mass, limb length, cross-sectional muscle area, and age all would likely influence torque. Now I am not advocating that all of the above parameters (age, size, etc) be added into a more complex model because I don't think that is necessarily the right path. However, I do think it would be beneficial to present the known information about the variance in individual size/age/etc, some of which may be unknown.

2. Line 145 states that "Models 1-2 and 2-1 most accurately predicted the posterior predictive distribution.", but is this not a typo? I thought Models 1-2 and 2-2 are the best as they are the linear and nonlinear models with hierarchical slopes.

In the paragraph starting at line 147 and the subsequent paragraph it is argued that while the nonlinear model 2-2 worked well, the linear model is still better. "The comparison of the linear model (model 1-2) with the nonlinear model (model 2-2) using the WAIC for all conditions (muscle type and applied voltage) resulted in lower values for the linear model." But certainly, both are quite close in WAIC, and my question is, might there be reasons from muscle physiology on stick insects to expect a non-linear model? While the linear model had the lowest WAIC (marginally from looking at Fig 2) without any prior assumptions about the torque-duration curve, certainly much is known about the effect of stimulation on force production, and might including that information validate the non-linear model over linear?

Alternatively, if the goal is to just model the data under 500ms stimulation because this is the relevant timescale for walking behavior (line 181) then the linear model is fine. But reading the manuscript I got the impression the goal was to best model the torque-voltage relationship, which I would think includes the full excitation range and incorporates known information from muscle physiology.

3. Fig 3 is a bit confusing as this is meant to compare the experimental data with the hierarchical model distribution. However, all the model distributions across the 10 insects look identical. I thought the point of the hierarchical model is that the slope parameter varies across individuals (isn't this what Fig 4 demonstrates?). So shouldn't the distributions and green fit lines all be different for the individuals?

I have some questions that should be clarified about the methods:<br /> 4. It is stated that 20 insects were tested, but all the plots show only 10. Is this just because the other 10 were not presented? Or were observations discarded from the other 10 insects for some reason? This is important to describe so that readers can assess the results.

5. More information should be provided about the ordering of the different excitation experiments. The methods do not describe what the time duration between excitations was, how many were performed over what time period, etc. Additionally, it looks like four different voltage amplitudes were performed which I could only observe from figures 2 and 4. It would be beneficial to describe in detail the full sequence of data collection on an insect.

6. What is the order of presentation of different voltages? It is stated that muscle fatigue should be negligible for under 50 stimulations, but the range of the 2V experiments alone was between 49-79 stimulations. So were another ~50 stimulations performed at the three other voltages? And if so was fatigue a possible issue?<br /> Also, were there "warm up" effects too where the muscle force increased with subsequent stimulations? It would be useful to provide some characterization of this.

Reviewer #2 (Public Review):

In the present study, Castano et al. discovered a chemical inhibitor that is specifically effective against the kinase activity of CDKL5 and applied it in the in vitro and the brain slice culture to reveal the acute effects of the loss of function (LOF) of CDKL5. LOF has been modeled in gene knockout mice, but these are loss-of-function models with the added developmental time effects of the absence of CDKL5 from developmental stages. The present authors' approach is the fastest timescale study to date, examining CDKL5 LOF effects in seconds to minutes.

The authors showed that chemical inhibition of CDKL5 kinase activity suppresses postsynaptically derived LTP in rat brain slice experiments, indicating that the previously controversial results of CDKL5 LOF on LTP in knockout mice and rats are possibly due to combined effects of the loss of the kinase and compensation by other factors.

The authors employed state-of-the-art methodologies and presented their data clearly and convincingly.

Reviewer #2 (Public Review):

In this work, the authors examine the antineoplastic effects of a combined treatment with the impridone ONC201/Tic10 and everolimus against ER+ breast cancer models. The combination was shown to have enhanced activity against everolimus resistant cells especially in 3D models as well as against primary cells derived from patients that have received treatment with everolimus in the past.

The authors address the important issue of drug resistance in ER+ breast cancer by using resistant cell models. Moreover, patient-derived cells were used in this work. From a molecular point of view, current mechanisms of action of ONC201/Tic10 were explored including effects on ERK/AKT pathways, integrated stress response and oxphos. Overall, this interesting work opens a venue for further exploration of imipridones in ER+ breast cancer resistant to current first- and second-line therapies.

Reviewer #2 (Public Review):

Past systems for identifying and tracking rodent vocaliztions have relied on triangulating positions using only a few high-quality ultrasonic microphones. There are also large arrays of less sensitive microphones, called acoustic cameras that don't capture the detail of the sounds, but do more accurately locate the sound in 3D space. Therefore the key innovation here is that the authors combine these two technologies by primarily using the acoustic camera to accurately find the emitter of each vocalization, and matching it to the high-resolution audio and video recordings. They show that this strategy (HyVL) is more accurate than other methods for identifying vocalizing mice and also has greater spatial precision. They go on to use this setup to make some novel and interesting observations. The technology and the study are timely, important, and have the potential to be very useful. As machine learning approaches to behavior become more widespread in use, it is easy to imagine this being incorporated and lowering entry costs for more investigators to begin looking at rodent vocalizations. I have a few comments.

1) What is the relationship of the current manuscript to this: https://www.biorxiv.org/content/10.1101/2021.10.22.464496v1 which has a number of very similar figures and presents a SLIM-only method that reportedly has lower precision than the current HyVL approach. Is this superseded by the submitted paper?

2) Can the authors provide any data showing the accuracy of their system in localizing sounds emitted from speakers as a function of position and amplitude? I am imagining that it would be relatively easy to place multiple speakers around the arena as ground truth emitting devices to quantify the capabilities of the system.

3) How is the system's performance affected by overlapping vocalizations? It might be useful to compare the accuracy of caller identification for periods where only one animal is calling at a time vs. periods where multiple animals are simultaneously calling.

4) Can the authors comment on how sound shadows cast by animals standing between the caller and a USM4 affect either the accuracy of identification or the fidelity of the vocal recording?

5) I'm a bit confused about how the algorithm uses the information from the video camera. Reading through the methods, it seems like they primarily calculate competing location estimates by the two types of microphone data and then make sure that a mouse is in close proximity to one location, discarding the call if there isn't. Why did the authors choose this procedure rather than use the tracked position of the snouts as constrained candidate locations and use the microphone data to arbitrate between them? Do they think that their tracking data are not reliable or accurate enough?

6) I guess the authors have code that we can run, but I couldn't access it. The manuscript describes the algorithms and equations that are used to calculate the location, but this doesn't really give me a feel for how it works. If you want to have the broadest impact possible, I think you would do well to make the code user-friendly (maybe it is, I don't know). In pursuit of that goal, I would suggest that the authors devote some of the paper to a guided example of how to use it.

Reviewer #2 (Public Review):

In the present study, Castano et al. discovered a chemical inhibitor that is specifically effective against the kinase activity of CDKL5 and applied it in the in vitro and the brain slice culture to reveal the acute effects of the loss of function (LOF) of CDKL5. LOF has been modeled in gene knockout mice, but these are loss-of-function models with the added developmental time effects of the absence of CDKL5 from developmental stages. The present authors' approach is the fastest timescale study to date, examining CDKL5 LOF effects in seconds to minutes.

The authors showed that chemical inhibition of CDKL5 kinase activity suppresses postsynaptically derived LTP in rat brain slice experiments, indicating that the previously controversial results of CDKL5 LOF on LTP in knockout mice and rats are possibly due to combined effects of the loss of the kinase and compensation by other factors.

The authors employed state-of-the-art methodologies and presented their data clearly and convincingly.

Reviewer #2 (Public Review):

Schmit et al. analyze and compare different strategies for the allocation of funding for insecticide-treated nets (ITNs) to reduce the global burden of malaria. They use previously published models of Plasmodium falciparum and Plasmodium vivax malaria transmission to quantify the effect of ITN distribution on clinical malaria numbers and the population at risk. The impact of different resource allocation strategies on the reduction of malaria cases or a combination of malaria cases and achieving pre-elimination is considered to determine the optimal strategy to allocate global resources to achieve malaria eradication.

Strengths:<br /> Schmit et al. use previously published models and optimization for rigorous analysis and comparison of the global impact of different funding allocation strategies for ITN distribution. This provides evidence of the effect of three different approaches: the prioritization of high-transmission settings to reduce the disease burden, the prioritization of low-transmission settings to "shrink the malaria map", and a resource allocation proportional to the disease burden.

Weaknesses:<br /> The analysis and optimization which provide the evidence for the conclusions and are thus the central part of this manuscript necessitate some simplifying assumptions which may have important practical implications for the allocation of resources to reduce the malaria burden. For example, seasonality, mosquito species-specific properties, stochasticity in low transmission settings, and changing population sizes were not included. Other challenges to the reduction or elimination of malaria such as resistance of parasites and mosquitoes or the spread of different mosquito species as well as other beneficial interventions such as indoor residual spraying, seasonal malaria chemoprevention, vaccinations, combinations of different interventions, or setting-specific interventions were also not included. Schmit et al. clearly state these limitations throughout their manuscript.

The focus of this work is on ITN distribution strategies, other interventions are not considered. It also provides a global perspective and analysis of the specific local setting (as also noted by Schmit et al.) and different interventions as well as combinations of interventions should also be taken into account for any decisions. Nonetheless, the rigorous analysis supports the authors' conclusions and provides evidence that supports the prioritization of funding of ITNs for settings with high Plasmodium falciparum transmission. Overall, this work may contribute to making evidence-based decisions regarding the optimal prioritization of funding and resources to achieve a reduction in the malaria burden.

Reviewer #2 (Public Review):

Marmor et al. mine a previously published dataset to examine whether recent reward/stimulus history influences responses in sensory (and other) cortices. Bulk L2/3 calcium activity is imaged across all of the dorsal cortex in transgenic mice trained to discriminate between two textures in a go/no-go behavior. The authors primarily focus on comparing responses to a specific stimulus given that the preceding trial was or was not rewarded. There are clear differences in activity during stimulus presentation in the barrel cortex along with other areas, as well as differences even before the second stimulus is presented. These differences only emerge after task learning. The data are of high quality and the paper is clear and easy to follow. My only major criticism is that I am not completely convinced that the observed difference in response is not due to differences in movement by the animal on the two trial types. That said, the demonstration of differences in sensory cortices is relatively novel, as most of the existing literature on trial history effect demonstrates such differences only in higher-order areas.

Major:

1a. The claim that body movements do not account for the results is in my view the greatest weakness of the paper - if the difference in response simply reflects a difference in movement, perhaps due to "excitement" in anticipation of reward after not receiving one on CR-H vs. H-H trials, then this should show up in movement analysis. The authors do a little bit of this, but to me, more is needed.

First, given the small sample size and use of non-parametric tests, you will only get p<.05 if at least 6 of the 7 mice perform in the same way. So getting p>.05 is not surprising even if there is an underlying effect. This makes it especially important to do analyses that are likely to reveal any differences; using whisker angle and overall body movement, which is poorly explained, is in my opinion insufficient. An alternative approach would be to compare movements within animals; small as the dataset is, it is feasible to do an animal-by-animal analysis, and then one could leverage the large trial count to get much greater statistical power, foregoing summary analyses that pool over only n=7.

The authors only consider a simple parametrization of movement (correlation across successive frames), and given the high variability in movement across animals, it is likely that different mice adopt different movements during the task, perhaps altering movement in specific ways. Aggregating movement across different body parts after an analysis where body parts are treated separately seems like an odd choice - perhaps it is fine, but again, supporting evidence for this is needed. As it stands, it is not clear if real differences were averaged out by combining all body parts, or what averaging actually entails.

If at all possible, I would recommend examining curvature and not just the whisker angle, since the angle being the same is not too surprising given that the stimulus is in the same place. If the animal is pressing more vigorously on CR-H trials, this should result in larger curvature changes.

Finally, the authors presumably have access to lick data. Are reaction times shorter on CR-H trials? Is lick count or lick frequency shorter?

If movement differs across trial types, it is entirely plausible that at least barrel cortex activity differences reflect differences in sensory input due to differences in whisker position/posture/etc. This would mitigate the novelty of the present results.

1b. Given the importance of this control to the story, both whisker and body movement tracking frames should be explicitly shown either in the primary paper or as a supplement. Moreover, in the methods, please elaborate on how both whisker and body tracking were performed.

2. Did streak length impact the response? For instance, in Fig. 1f "Learning", there is a 6-trial "no-go" streak; if the data are there, it would be useful to plot CR-H responses as a function of preceding unrewarded trials.

Reviewer #2 (Public Review):

In this manuscript, Franco et al show that the mitofusin 2 mutation MFN2 Q400 impaires mitochondrial fusion with normal GTPase activity. MFN2 Q400 fails to recruit Parkin and further disrupts Parkin-mediated mitophagy in cultured cardiac cells. They also generated MFN2 Q400 knock-in mice to show the development of lethal perinatal cardiomyopathy, which had an impairment in multiple metabolic pathways.

The major strength of this manuscript is the in vitro study that provides a thorough understanding in the characteristics of the MFN2 Q400 mutant in function of MFN2, and the effect on mitochondrial function. However, the in vivo MFN2 Q/Q400 knock-in mice are more troubling given the split phenotype of MFN2 Q/Q400a vs MFN2 Q/Q400n subtypes. Their main findings towards impaired metabolism in mutant hearts fail to distinguish between the two subtypes.

While the data support the conclusion that MFN2 Q400 causes cardiomyopathy, several experiments are needed to further understand mechanism. This manuscript will likely impact the field of MFN2 mutation-related diseases and show how MFN2 mutation leads to perinatal cardiomyopathy in support of previous literature.

Reviewer #2 (Public Review):

MCM8 and MCM9 together form a hexameric DNA helicase that is involved in homologous recombination (HR) for repairing DNA double-strand breaks. The authors have previously reported on the winged-helix structure of the MCM8 (Zeng et al. BBRC, 2020) and the N-terminal structure of MCM8/9 hexametric complex (MCM8/9-NTD) (Li et al. Structure, 2021). This manuscript reports the structure of a near-complete MCM8/9 complex and the conformational change of MCM8/9-NTD in the presence of its binding protein, HROB, as well as the residues important for its helicase activity.

The presented data might potentially explain how MCM8/9 works as a helicase. However, additional studies are required to conclude this point because the presented MCM8/9 structure is not a DNA-bound form and HROB is not visible in the presented structural data. Taking into these accounts, this work will be of interest to biologists studying DNA transactions.

A strength of this paper is that the authors revealed the near-complete MCM8/9 structure with 3.66A and 5.21A for the NTD and CTD, respectively (Figure 1). Additionally, the authors discovered a conformational change in the MCM8/9-NTD when HROB was included (Figure 4) and a flexible nature of MCM8/9-CTD (Figure S6 and Movie 1).

The revised version of "Structural and mechanistic insights into the MCM8/9 helicase complex" by Weng et al. includes only very minor changes in the text and incorporates two additional supplementary figures (S8 and S11) illustrating the size of MCM8/9 mutants.

In the previous version, I raised two important concerns that required addressing. 1) The presented structures exclusively depicted the unbound forms of DNA. It is crucial to elucidate the structure of a DNA-bound form. 2) The MCM8/9 activator, HROB, was not visible in the structural data. Although HROB induced a conformational change in MCM8/9-NTD, it is essential to visualize the structure of an MCM8/9-HROB complex.

The authors neither addressed nor provided new data in response to these issues. Consequently, I maintain my initial stance and have no further comments on the revised version.

Reviewer #2 (Public Review):

The authors present an important study on the potential of small extracellular vesicle (sEV)-derived RNAs as biomarkers for the early detection of colorectal cancer (CRC) and precancerous adenoma (AA). The authors provide a detailed analysis of the RNA landscape of sEVs isolated from participants, identifying differentially expressed sEV-RNAs associated with T1a stage CRC and AA compared to normal controls. The paper further categorises these sEV-RNAs into modules and constructs a 60-gene model that successfully distinguishes CRC/AA from NC samples. The authors also validate their findings using RT-qPCR and propose an optimised classifier with high specificity and sensitivity. Additionally, the authors discuss the potential of sEV-RNAs in understanding CRC carcinogenesis and suggest that a comprehensive biomarker panel combining sEV-RNAs and proteins could be promising for identifying both early and advanced CRC patients. Overall, the study provides valuable insights into the potential clinical application of sEV-RNAs in liquid biopsy for the early detection of CRC and AA.

Major strengths:<br /> 1. Comprehensive sEV RNA profiling: The study provides a valuable dataset of the whole-transcriptomic profile of circulating sEVs, including miRNA, mRNA, and lncRNA. This approach adds to the understanding of sEV-RNAs' role in CRC carcinogenesis and facilitates the discovery of potential biomarkers.

2. Detection of early-stage CRC and AA: The developed 60-gene t-SNE model successfully differentiated T1a stage CRC/AA from normal controls with high specificity and sensitivity, indicating the potential of sEV-RNAs as diagnostic markers for early-stage colorectal lesions.

3. Independent validation cohort: The study combines RNA-seq, RT-qPCR, and modelling algorithms to select and validate candidate sEV-RNAs, maximising the performance of the developed RNA signature. The comparison of different algorithms and consideration of other factors enhance the robustness of the findings.

Major weaknesses:<br /> 1. Lack of analysis on T1-only patients in the validation cohort: While the study identifies key sEV-RNAs associated with T1a stage CRC and AA, the validation cohort is only half of the patients in T1(25 out of 49). It would be better to do an analysis using only the T1 patients in the validation cohort, so the conclusion is not affected by the T2-T3 patients.

2. Lack of performance analysis across different demographic and tumor pathology factors listed in Supplementary Table 12. It's important to know if the sEV-RNAs identified in the study work better/worse in different age/sex/tumor size/Yamada subtypes etc.

Reviewer #2 (Public Review):

In this study, the authors identified the complex TOR, HOG, and CWI signaling networks-involved genes that relatively modulate the development, aflatoxin biosynthesis and pathogenicity of A. flavus by gene deletions combined with phenotypic observation.

They also analyzed the specific regulatory process and proposed that the TOR signaling pathway interacts with other signaling pathways (MAPK, CWI, calcineurin-CrzA pathway) to regulate the responses to various environmental stresses. Notably, they found that FKBP3 is involved in sclerotia and aflatoxin biosynthesis and rapamycin resistance in A. flavus, and that the conserved site K19 of FKBP3 plays a key role in regulating the aflatoxin biosynthesis. In general, there is a heavy workload task carried in this study and the findings are interesting and important for understanding or controlling aflatoxin biosynthesis. However, findings have not been deeply explored and conclusions mostly are based on parallel phenotypic observations. In addition, there are some concerns that exist surrounding the conclusions.

Reviewer #2 (Public Review):

This study by Syed et al identifies Prmt5 as a novel and broad modulator of gene expression and genome architecture during the early stages of adipogenesis. Specifically, Prmt5 is reported to be required to maintain strong insulation at TAD boundaries.

This is a logically and clearly conducted study that relies on the integration of public datasets (PCHi-C) to identify chromatin loops, with its own new genomics datasets, including Prmt5 ChIPseq and Hi-C data in control and Prmt5 kd cells. Despite showing relatively model effects of Prmt5 kd on genome architecture, the results are informative and contribute to advancing our knowledge of chromatin-linked processes during early adipogenesis.

The manuscript would benefit from incorporating ATACseq data (public or own) to better appreciate binding profiles of Prmt5 at H3K27ac sites. A more detailed analysis of these relative enrichments would also be useful, particularly if linked to a transcription factor footprint from ATAC data.

Reviewer #2 (Public Review):

The Kinesin superfamily motors mediate the transport of a wide variety of cargos which are crucial for cells to develop into unique shapes and polarities. Kinesin-3 subfamily motors are among the most conserved and critical classes of kinesin motors which were shown to be self-inhibited in a monomeric state and dimerized to activate motility along microtubules. Recent studies have shown that different members of this family are uniquely activated to undergo a transition from monomers to dimers.

Niwa and colleagues study two well-described members of the kinesin-3 superfamily, unc104 and KLP6, to uncover the mechanism of monomer to dimer transition upon activation. Their studies reveal that although both Unc104 and KLP6 are both self-inhibited monomers, their propensities for forming dimers are quite different. The authors relate this difference to a region in the molecules called CC2 which has a higher propensity for forming homodimers. Unc104 readily forms homodimers if its self-inhibited state is disabled while KLP6 does not.

The work suggests that although mechanisms for self-inhibited monomeric states are similar, variations in the kinesin-3 dimerization may present a unique form of kinesin-3 motor regulation with implications on the forms of motility functions carried out by these unique kinesin-3 motors.

Reviewer #2 (Public Review):

Yu et al. investigated the structural landscape of 'secreted in xylem' (SIX) effector (virulence and avirulence) proteins from the plant-pathogenic fungus, Fusarium oxysporum f. sp. lycopersici (Fol), with the goal of better understanding effector function and recognition by host (tomato) immune receptors. In recent years, several experimental and computational studies have shown that many effector proteins of plant-associated fungi can be assigned to one of a few major structural families. In the study by Yu et al., X-ray crystallography was used to show that two avirulence effectors of Fol, Avr1 (SIX4) and Avr3 (SIX1), which are recognized by the tomato immune receptors I and I-3, respectively, form part of a new structural family, the Fol dual-domain (FOLD) family, found across three fungal divisions. Using AlphaFold2, an ab initio structural prediction tool, the authors then predicted the structures of all proteins within the Fol SIX effector repertoire (and other effector candidates) and provided evidence that two other effectors, SIX6 and SIX13, also belong to this family.

In addition to identifying members of the FOLD family, structural prediction revealed that proteins of the Fol effector repertoire can largely be classified into a reduced set of structural families. Examples included four members of the ToxA-like family (including Avr2 (SIX3) and SIX8), as well as four members of a new family, Family 4 (including SIX5 and PSL1). Given previous studies had demonstrated that Avr2 (ToxA-like) and SIX5 (Family 4) interact and function together and that the genes encoding these proteins are divergently transcribed, and because homologues of SIX8 (ToxA-like) and PSL1 (Family 4) from another Fusarium pathogen are functionally dependent on each other and, in the case of Fol, are encoded by genes that are next to each other in the genome, the authors hypothesized that SIX8 and PSL1 may also physically interact. In line with this, co-incubation of the SIX8 and PSL1 proteins, followed by size exclusion chromatography (SEC), gave elution and gel migration profiles consistent with interaction in the form of a heterodimer. AlphaFold2-Multimer modelling then suggested that this interaction was mediated through an intermolecular disulfide bond. Such a prediction was subsequently confirmed through mutational analysis of the relevant cysteine residue in each protein in conjunction with SEC.

Finally, using a variant (homologue) of Avr1 from another Fusarium pathogen, as well as chimeric forms of this protein that integrated regions of Avr1 from Fol, Yu et al. determined through co-expression assays in Nicotiana benthamiana with the I immune receptor, as well as subsequent ion leakage assays, that the C-domain of Avr1 is recognized by the I immune receptor. Furthermore, through these assays, the authors were also able to show that surface-exposed residues in the C-domain enable Avr1 to evade recognition by a variant of the I receptor in Moneymaker tomato that does not provide resistance to Fol.

Overall, the manuscript presents a large body of work that is well supported by the data. A key strength of the manuscript is the validation (benchmarking) of protein structures predicted using AlphaFold2, which is a first for largescale effector structure prediction papers published to date. Another key strength is the use of largescale effector structure predictions to make hypotheses about functional relationships or interactions that are then tested (i.e. the SIX8-PSL1 protein interaction and recognition of Avr1 by the I immune receptor). This testing again goes above and beyond the large scale effector structure prediction papers published to date. Taken together, this showcases how experimental and computational experiments can be effectively combined to provide biologically relevant data for the plant protection and molecular plant-microbe interactions fields.

In terms of weaknesses, the manuscript could have validated the SIX8-PSL1 protein interaction with in planta experiments, such as co-immunoprecipitation assays or co-localization experiments in conjunction with confocal microscopy, to provide support for the interaction in a plant setting. However, given what is already known about the Avr2-SIX5 interaction, these additional experiments are not crucial and could instead form part of a follow-up study. With regards to the Agrobacterium tumefaciens-mediated transient expression assays involving co-expression of the Avr1 effector and I immune receptor, the authors need to make clear how many biological replicates were performed as this information is only provided for the ion leakage assay.

Reviewer #2 (Public Review):

In this manuscript, the authors address how cerebellar Purkinje cells (PC) control the firing of nuclear cells (CbN), the output stage of the cerebellar. They used patch-clamp recordings in acute cerebellar slices, and combined dynamic clamp with simulations of nuclear cell firing rate.

This article addresses one of the most fundamental unresolved question of the cerebellar physiology: how inhibitory PCs control the output stage of the cerebellum?<br /> They first described a developmental evolution of the that PC-CbN synapses. Inhibitory synaptic weights become highly variable after three weeks of age, with a group of very large PC inputs. They used dynamic clamp to examine the influence of these variable inputs on CbN firing rate. They demonstrate that while all input size affect CbN discharge, larger ones can stop them for a few milliseconds. Using a distribution of variable input size, they showed that increasing the variability of PC inputs favor CbN discharge, while increasing the magnitude of a constant inhibitory conductance decrease their firing rate. By varying the frequency of PC inputs, they suggest that CbNs faithfully transmit rate code, but larger inputs are more effective to decrease their firing rate. Finally, addressing how synchrony of variable PC inputs influence CbN discharge, dynamic clamp studies and simulations showed that input synchronization enhance firing, but driven by the total charge of the inhibitory input.

The keystone observations that PC inputs are highly variable is very interesting and convincing and open new questions about PC-CbN plasticity. More importantly the combination of dynamic clamp and simulations is a real strength of the study, allowing the authors to test many combinations of inputs in real cells and extrapolating their hypotheses in silico. Weaknesses result from the assumptions made on the construction of the distribution of inputs and the many different conditions explored. The organization of the article could be difficult to read for a non-specialist of cerebellar physiology.

Reviewer #2 (Public Review):

The authors of this paper use a "digital twin" computational model of electrophysiology to investigate the pathology of Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) in several patients undergoing Electro-Physiological Studies (EPS) to treat Ventricular Tachycardias (VTs). The digital twin computational models are customised to the individual patient in two ways. Firstly, information on the patient's heart geometry and muscle/fibrous structure is extracted from Late Gadolium-Enhanced Magnetic Resonance Image (LGE-MRI) scans. Secondly, information from the patient's genotype is used to decide the particular electrophysiological cell model to use in the computational model. The two patient genotypes investigated include a Gene Ellusive (GE) group characterised by abnormal fibrous but normal cell electrical physiology and a palakophilin-2 (PKP2) group in which patients have abnormal fibrotic remodelling and distorted electrical conduction. The computational model predicts the locations and pathways of re-entrant circuits that cause VT. The model results are compared to previous recordings of induced VTs obtained from EPS studies.

The paper is very well written, and the modelling study is well thought out and thorough and represents an exemplar in the field. The major strengths of the paper are the use of a personalised patient model (geometry, fibrous structure and genotype) in a clinically relevant setting. Such a comprehensive personal model puts this paper at the forefront of such models in the field. The main weaknesses of the paper are more of a reflection on what is required for creating such models than on the study itself. As the authors acknowledge, the number of patients in each group is small. Additional patients would allow for statistical significance to be investigated.

The paper's authors set out to demonstrate the use of a "digital twin" computational model in the clinical setting of ARVC. The main findings of the paper were threefold. Firstly, the locations of VTs could be accurately predicted. There was a difference in the abnormal fibrous structure between the two genotype groups. Finally, there was an interplay between the fibrous structure of the heart and the cellular electrophysiology in that the fibrous remodelling was responsible for VTs in the GE group, but in the PKP2 group VTs were caused by slowed electrical conduction and altered restitution. The study successfully met the aims of the paper.

The major impact of the paper will be in demonstrating that a personalised computational model can a) be developed from available measurements (albeit at the high end of what would normally be measured clinically) and b) generate accurate results that may prove helpful in a clinical setting. Another impact is the finding in the paper that the cause of VTs may be different for the two genotypes investigated. The different interplay between fibrous and electrophysiology suggested by the modelling results may provide insights into different treatments for the different genotypes of the pathology. The authors use open-source software and have deposited all non-confidential data in publically available repositories.

Reviewer #2 (Public Review):

The mechanisms that mediate female aggression remain poorly understood. Chiu, Schretter, and colleagues, employed circuit dissection techniques to tease apart the specific roles of particular doublesex and fruitless expressing neurons in the fly Drosophila in generating a persistent aggressive state. They find that activating the fruitless positive alPg neurons, generated an aggressive state that persisted for >10min after the stimulation ended. Similarly, activating the doublesex positive pC1de neurons also generated a persistent state. Activating pC1d or pC1e individually did not induce a persistent state. Interestingly, while neural activation of alPGs and pC1d+e neurons induced persistent behavioural states it did not induce persistent activity in the neurons being activated.

The conclusions of this paper are well supported by the data, there were only a few points where clarification might help:

1) Figure 3 is a little confusing. This is a circuit behavioural epistasis experiment where the authors activate alPg with CsChrimson while inhibiting pC1d with Kir2.1. In Fig. 2 flies were separated for 10 min following stimulation which allowed for identification of a persistent state. However, in Fig 3 it appears as if flies were allowed to freely interact during and immediately post-stimulation. It is unclear why flies were not separated as in Fig. 2, which makes it difficult to compare the two results. Some discussion of this point would help. Also, from the rasters it appears as if inhibition of pC1d reduced aggression induced by alPg during the stimulation period. Is this true?

2) pC1e neurons also have recurrent connectivity with alPg neurons. It might help to also discuss the potential role of this arm of the microcircuit.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behavior performance on a virtual task. Mice were trained to stop at a rewarded location along a virtual linear track. The rewarded location could be marked by distinct visual stimuli or be unmarked. When the rewarded location was unmarked, the animal had to estimate its distance run from the beginning of the trial to know where to stop. When the mouse reached the end of the virtual track, it was teleported back to the start of the virtual track.

The authors found that grid cells could fire in at least two modes. In the "virtual position" mode, grid firing fields had stable positions relative to the virtual track. In the "distance run" mode, grid fields were decoupled from the virtual cues and appeared to be located as a function of distance run on the running wheel. Importantly, on trials in which the rewarded location was unmarked, the behavioral performance of mice was better when grid cells fired in the "virtual position" mode.

This study is very timely as there is a pressing need to identify/delimitate the contribution of grid cells to spatial behaviors. More studies in which grid cell activity can be associated with navigational abilities are needed. The link proposed by Clark and Nolan between "virtual position" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behavior. It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioral performance on this task. In a previous study by the same research group, it was shown that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

When interpreting the "position" and "distance" firing mode of grid cells, it is important to appreciate that the "position" code likely involves estimating distance. The visual cues on the virtual track appear to provide mainly optic flow to the animal. Thus, the animal has to estimate its position on the virtual track by estimating the distance run from the beginning of the track (or any other point in the virtual world).

It is also interesting to consider how grid cells could remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. The resetting means that a specific virtual track position is mapped to a constant position on the torus. Thus, the "virtual position" mode of grid cells may involve 1) a trial-by-trial resetting process anchoring the grid pattern to the virtual cues and 2) a path integration mechanism. Just like the "virtual position" mode of grid cell activity, successful behavioral performance on non-beaconed trials requires the animal to anchor its spatial behavior to VR cues.

One main conclusion of this study is that better performance on the VR task was observed when the grid cells were anchored to the reference frame that was the most behaviorally relevant.

Reviewer #2 (Public Review):

Schwarz et al. have presented a study aiming to investigate whether circulating factors in sera of subjects are able to synchronize depending on age, circadian rhythms of fibroblast. The authors used human serum taken from either old (age 70-76) or young (age 25-30) individuals to synchronise cultured fibroblasts containing a clock gene promoter driven luciferase reporter, followed by RNA sequencing to investigate whole gene expression.

This study has the potential to be very interesting, as evidence of circulating factors in sera that mediate peripheral rhythms has long been sought after. Moreover, the possibility that those factors are affected by age which could contribute to the weaken circadian rhythmicity observed with aging.

Here, the authors concluded that both old and young sera are equally competent at driving robust 24 hour oscillations, in particular for clock genes, although the cycling behaviour and nature of different genes is altered between the two groups, which is attributed to the age of the individuals. This conclusion could however be influenced by individual variabilities within and between the two age groups. The groups are relatively small, only four individual two females and two males, per group. And in addition, factors such as food intake and exercise prior to blood drawn, or/and chronotype, known to affect systemic signals, are not taken into consideration. As seen in figure 4, traces from different individuals vary heavily in terms of their patterns, which is not addressed in the text. Only analysing the summary average curve of the entire group may be masking the true data. More focus should be attributed to investigating the effects of serum from each individual and observing common patterns. Additionally, there are many potential causes of variability, instead or in addition to age, that may be contributing to the variation both, between the groups and between individuals within groups. All of this should be addressed by the authors and commented appropriately in the text.

The authors also note in the introduction that rhythms in different peripheral tissues vary in different ways with age, however the entire study is performed on only fibroblast, classified as peripheral tissue by the authors. It would be very interesting to investigate if the observed changes in fibroblast are extended or not to other cell lines from diverse organ origin. This could provide information about whether circulating circadian synchronising factors could exert their function systemically or on specific tissues. At the very least, this hypothesis should be addressed within the discussion.

In addition to the limitations indicated above I consider that the data of the study is an insufficiently analysis beyond the rhythmicity analysis. Results from the STRING and IPA analysis were merely descriptive and a more comprehensive bioinformatic analysis would provide additional information about potential molecular mechanism explaining the differential gene expression. For example, enrichment of transcription factors binding sites in those genes with different patters to pinpoint chromatin regulatory pathways.

Reviewer #2 (Public Review):

The authors investigate the transcriptional regulation of cysteine dioxygenase (CDO-1) in C. elegans and its role in maintaining cysteine homeostasis. They show that high cysteine levels activate cdo-1 transcription through the hypoxia-inducible transcription factor HIF-1. Using transcriptional and translational reporters for CDO-1, the authors propose a negative feedback pathway involving RHY-1, CYSL-1, EGL-9, and HIF-1 in regulating cysteine homeostasis.

Genetics is a notable strength of this study. The forward genetic screen, gene interaction, and epistasis analyses are beautifully designed and rigorously conducted, yielding solid and unambiguous conclusions on the genetic pathway regulating CDO-1. The writing is clear and accessible, contributing to the overall high quality of the manuscript.

Addressing the specifics of cysteine supplementation and interpretation regarding the cysteine homeostasis pathway would further clarify the paper and strengthen the study's conclusions.

First, the authors show that the supplementation of exogenous cysteine activates cdo-1p::GFP. Rather than showing data for one dose, the author may consider presenting dose-dependency results and whether cysteine activation of cdo-1 also requires HIF-1 or CYSL-1, which would be important data given the focus and major novelty of the paper in cysteine homeostasis, not the cdo-1 regulatory gene pathway. While the genetic manipulation of cdo-1 regulators yields much more striking results, the effect size of exogenous cysteine is rather small. Does this reflect a lack of extensive condition optimization or robust buffering of exogenous/dietary cysteine? Would genetic manipulation to alter intracellular cysteine or its precursors yield similar or stronger effect sizes?

Second, there remain several major questions regarding the interpretation of the cysteine homeostasis pathway. How much specificity is involved for the RHY-1/CYSL-1/EGL-9/HIF-1 pathway to control cysteine homeostasis? Is the pathway able to sense cysteine directly or indirectly through its metabolites or redox status in general? Given the very low and high physiological concentrations of intracellular cysteine and glutathione (GSH, a major reserve for cysteine), respectively, there is a surprising lack of mention and testing of GSH metabolism. In addition, what are the major similarities and differences of cysteine homeostasis pathways between C. elegans and other systems (HIF dependency, transcription vs post-transcriptional control)? These questions could be better discussed and noted with novel findings of the current study that are likely C. elegans specific or broadly conserved.

Reviewer #2 (Public Review):

In this paper Sasani, Quinlan and Harris present a new method for identifying genetic factors affecting germline mutation, which is particularly applicable to genome sequence data from mutation accumulation experiments using recombinant inbred lines. These are experiments where laboratory organisms are crossed and repeatedly inbred for many generations, to build up a substantial number of identifiable germline mutations. The authors apply their method to such data from mice, and identify two genetic factors at two separate genetic loci. Clear evidence of such factors has been difficult to obtain, so this is an important finding. They further show evidence of an epistatic interaction between these factors (meaning that they do not act independently in their effects on the germline mutation process). This is exciting because such interactions are difficult to detect and few if any other examples have been studied.

The authors present a careful comparison of their method to another similar approach, quantitative trait locus (QTL) analysis, and demonstrate that in situations such as the one analysed it has greater power to detect genetic factors with a certain magnitude of effect. They also test the statistical properties of their method using simulated data and permutation tests. Overall the analysis is rigorous and well motivated, and the methods explained clearly.

The main limitation of the approach is that it is difficult to see how it might be applied beyond the context of mutation accumulation experiments using recombinant inbred lines. This is because the signal it detects, and hence its power, is based on the number of extra accumulated mutations linked to (i.e. on the same chromosome as) the mutator allele. In germline mutation studies of wild populations the number of generations involved (and hence the total number of mutations) is typically small, or else the mutator allele becomes unlinked from the mutations it has caused (due to recombination), or is lost from the population altogether (due to chance or perhaps selection against its deleterious consequences).

Nevertheless, accumulation lines are a common and well established experimental approach to studying mutation processes in many organisms, so the new method could have wide application and impact on our understanding of this fundamental biological process.

The evidence presented for an epistatic interaction is convincing, and the authors suggest some plausible potential mechanisms for how this interaction might arise, involving the DNA repair machinery and based on previous studies of the proteins implicated. However as with all such findings, given the higher degree of complexity of the proposed model it needs to be treated with greater caution, perhaps until replicated in a separate dataset or demonstrated in follow-up experiments exploring the pathway itself.

Reviewer #2 (Public Review):

Qin, Sanbo and Zhou, Huan-Xiang created a model, SeqDYN, to predict nuclear magnetic resonance (NMR) spin relaxation spectra of intrinsically disordered proteins (IDPs), based primarily on amino acid sequence. To fit NMR data, SeqDYN uses 21 parameters, 20 that correspond to each amino acid, and a sequence correlation length for interactions. The model demonstrates that local sequence features impact the dynamics of the IDP, as SeqDYN performs better than a one residue predictor, despite having similar numbers of parameters. SeqDYN is trained using 45 IDP sequences and is retrained using both leave-one-out cross validation and five-fold cross validation, ensuring the model's robustness. While SeqDYN can provide reasonably accurate predictions in many cases, the authors note that improvements can be made by incorporating secondary structure predictions, especially for alpha-helices that exceed the correlation length of the model. The authors apply SeqDYN to study nine IDPs and a denatured ordered protein, demonstrating its predictive power. The model can be easily accessed via the website mentioned in the text.

While the conclusions of the paper are primarily supported by the data, there are some points that could be extended or clarified.

1. The authors state that the model includes 21 parameters. However, they exclude a free parameter that acts as a scaling factor and is necessary to fit the experimental data (lambda). As a result, SeqDYN does not predict the spectrum from the sequence de-novo, but requires a one parameter fitting. The authors mention that this factor is necessary due to non-sequence dependent factors such as the temperature and magnetic field strength used in the experiment. Given these considerations, would it be possible to predict what this scaling factor should be based on such factors?

2. The authors mention that the Lorentzian functional form fits the data better than a Gaussian functional form, but do not present these results.

3. The authors mention that they conducted five-fold cross validation to determine if differences between amino acid parameters are statistically significant. While two pairs are mentioned in the text, there are 190 possible pairs, and it would be informative to more rigorously examine the differences between all such pairs.

Reviewer #2 (Public Review):

Jarysta and colleagues set out to define how similar GNAI/O family members contribute to the shape and orientation of stereocilia bundles on auditory hair cells. Previous work demonstrated that loss of particular GNAI proteins, or inhibition of GNAIs by pertussis toxin, caused several defects in hair bundle morphogenesis, but open questions remained which the authors sought to address. Some of these questions include whether all phenotypes resulting from expression of pertussis toxin stemmed from GNAI inhibition; which GNAI family members are most critical for directing bundle development; whether GNAI proteins are needed for basal body movements that contribute to bundle patterning. These questions are important for understanding how tissue is patterned in response to planar cell polarity cues.

To address questions related to the GNAI family in auditory hair cell development, the authors assembled an impressive and nearly comprehensive collection of mouse models. This approach allowed for each Gnai and Gnao gene to be knocked out individually or in combination with each other. Notably, a new floxed allele was generated for Gnai3 because loss of this gene in combination with Gnai2 deletion was known to be embryonic lethal. Besides these lines, a new knockin mouse was made to conditionally express untagged pertussis toxin following cre induction from a strong promoter. The breadth and complexity involved in generating and collecting these strains makes this study unique, and likely the authoritative last word on which GNAI proteins are needed for which aspect of auditory hair bundle development.

Appropriate methods were employed by the authors to characterize auditory hair bundle morphology in each mouse line. Conclusions were carefully drawn from the data and largely based on excellent quantitative analysis. The main conclusions are that GNAI3 has the largest effect on hair bundle development. GNAI2 can compensate for GNAI3 loss in early development but incompletely in late development. The Gnai2 Gnai3 double mutant recapitulates nearly all the phenotypic effects associated with pertussis toxin expression and also reveals a role for GNAIs in early movement of the basal body. Although these results are not entirely unexpected based on earlier reports, the current results both uncover new functions and put putative functions on more solid ground.

Based on this study, loss of GNAI1 and GNAO show a slight shortening of the tallest row of stereocilia but no other significant changes to bundle shape. Antibody staining shows no change in GNAI localization in the Gnai1 knockout, suggesting that little to no protein is found in hair cells. One caveat to this interpretation is that the antibody, while proposed to cross-react with GNAI1, is not clearly shown to immunolabel GNAI1. More than anything, this reservation mostly serves to illustrate how challenging it is to nail down every last detail. In turn, the comprehensive nature of the current study seems all the more impressive.

Reviewer #2 (Public Review):

Aw et al presents a new stability-guided fine-mapping method by extending the previously proposed PICS method. They applied their stability-based method to fine-map cis-eQTLs in the GEUVADIS dataset and compared it against what they call residualization-based method. They evaluated the performance of the proposed method using publicly available functional annotations and claimed the variants identified by their proposed stability-based method are more enriched for these functional annotations.

While the reviewer acknowledges the contribution of the present work, there are a couple of major concerns as described below.

Major:

1. It is critical to evaluate the proposed method in simulation settings, where we know which variants are truly causal. While I acknowledge their empirical approach using the functional annotations, a more unbiased, comprehensive evaluation in simulations would be necessary to assess its performance against the existing methods.

2. Also, simulations would be required to assess how the method is sensitive to different parameters, e.g., LD threshold, resampling number, or number of potential sets.

3. Given the previous studies have identified multiple putative causal variants in both GWAS and eQTL, I think it's better to model multiple causal variants in any modern fine-mapping methods. At least, a simulation to assess its impact would be appreciated.

4. Relatedly, I wonder what fraction of non-matching variants are due to the lack of multiple causal variant modeling.

5. I wonder if you can combine the stability-based and the residualization-based approach, i.e., using the residualized phenotypes for the stability-based approach. Would that further improve the accuracy or not?

6. The authors state that confounding in cohorts with diverse ancestries poses potential difficulties in identifying the correct causal variants. However, I don't see that they directly address whether the stability approach is mitigating this. It is hard to say whether the stability approach is helping beyond what simpler post-hoc QC (e.g., thresholding) can do.

7. For non-matching variants, I wonder what the difference of posterior probabilities is between the stable and top variants in each method. If the difference is small, maybe it is due to noise rather than signal.

8. It's a bit surprising that you observed matching variants with (stable) posterior probability ~ 0 (SFig. 1). What are the interpretations for these variants? Do you observe functional enrichment even for low posterior probability matching variants?

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