Reviewer #3 (Public Review):

The authors, in their research manuscript, dissected the role of Metformin in bone healing under type-2 diabetics conditions. The authors used three classic bone fracture models to assess the impacts of Metformin in bone healing under hyperglycemic conditions. In all three models, Metformin treatment showed bone formation. At the cellular level, the authors showed the effect of Metformin on promoting bone healing using BMSCs in vitro. The authors in the paper demonstrated that Metformin promotes bone growth only in hyperglycemic conditions. The experiments were appropriately well-defined and carried out to support the role of Metformin in bone healing. The use of three different bone-defective rat models to study the role of Metformin in skeletal tissues is convincing.

Reviewer #1 (Public Review):

In the manuscript the authors identify new players regulating cell state transitions. They show that heme biosynthesis is required for the naïve-to-primed pluripotency transition. In particular, they provide a link between heme biosynthesis inhibition and failure to activate TGFβ and MAPK pathways, two crucial regulators of the exit from the naïve pluripotent state. Heme biosynthesis inhibition increases the percentage of 2CLCs within the mESC population. The authors further show that this increased level of 2CLCs depends on the accumulation of succinate in non-mitochondrial cell compartments as a consequence of heme biosynthesis inhibition. Based on experiments using chemical inhibitors, the authors conclude that succinate acts in a paracrine and autocrine manner to enhance reprogramming of mESC into 2CLCs.

The observations provided by the authors are interesting and potentially relevant in the field of pluripotent cell state transitions. However, in the present state, neither the role of heme biosynthesis on FGF-ERK and TGF beta signalling and exit from naïve pluripotency nor the role of heme biosynthesis in controlling the 2CLC state are clear.

Below I list my main concerns:

1. The authors claim that the heme metabolic pathway is important for the naïve-to-primed transition. Interfering with its proper function appears to have developmental consequences. However, it is unclear how exactly this pathway is regulated during the exit from naïve pluripotency in WT cells. Hence the physiological relevance remains unclear. Are the levels of heme itself, or the mentioned 7 enzymes of the heme pathway regulated during differentiation?

2. The claim that "the roles of this [heme] biosynthetic pathway and this metabolite have never been studied in the context of pluripotency" is a bit misleading. It has been reported that HO1 is regulated by Oct4 (https://doi.org/10.1002/1873-3468.14138).

3. The link between heme biosynthesis and the TGFβ and MAPK pathways remains unclear. Is there any evidence for a direct link, or are these two observations simply linked through an altered cell state? Without further experiments it remains unclear whether the lack of proper Tgf beta and Fgf-ERK signalling activation are cause or consequence of the observed differentiation defects. Results must be discussed with this limitation in mind.

4. The fact that MEKi did not recapitulate the phenotype of SA treatment to prevent EpiLC differentiation should already be clarified in the results section. Moreover, the fact that SMAD inhibition seems to delay downregulation of naïve markers more than SA treatment, and the fact that SMAD inhibition combined with MEK inhibition seems weaker than SMAD inhibition alone seem counterintuitive and needs explanation. Can the authors attempt to titrate pathway inhibition to a similar level as observed in heme pathway deficient ESCs? Furthermore, can the differentiation defect be rescued upon overstimulation of Fgf-ERK and TGF beta to reach WT levels?

5. To better characterize the direct effect of heme biosynthesis inhibition it is necessary to in depth analyse any possible cell proliferation, viability of cell cycle defects after SA (or AA5) treatment. If there is an impact on cellular health, this needs to be reported and taken into careful consideration when interpreting results.

6. The authors claim that SA pre-treatment of mESC is able to enhance their differentiation ability into throphoblast like cells; however, they do not show statistically significant differences in terms of throphoblast expression markers between SA-treated and control cells (Supplemental Fig.2d). Furthermore, the variance of measurements in 2iL are not shown. Expression levels in TS cells or in trophoblast tissue must be used as control to judge the effect size. 2iL cells do also generate cells similar in morphology to 2iL+SA cells (Suppl Fig. 2b). Should these also be giant cells; this would be very surprising. Together, this makes drawing conclusions from these experiments impossible. If the statement that SA treatment expands the lineage potential of ESCs is made, it will need to be supported by appropriate and statistically strong data. A mere increase in some marker genes (which are not really specific for the TE lineage but also expressed in embryonic tissues) and statements based on morphology are no good support for this hypothesis.

7. What is the reason for calling the increase in 2C like cells 2C-like reprogramming? Is there any evidence that this is indeed a reprogramming event? There is no evidence for disruption of heme biosynthesis directly instructing cells to take on a 2CLC state, there is simply an increase in the Zscan4 expressing population, for a reason that remains unclear.

8. Addition of AA5 or SA results in absolutely striking changes in the epigenetic state of ESCs. H3K4me3, H3K27me3 and H3K9me3 together with 5mC levels appear drastically increased based on IF images in Supp. Fig. 5a. I wonder how physiological these levels are. How much data directly meaningful for 'normal' differentiation can be obtained from such a massively perturbed system? This needs to be appropriately acknowledged and discussed.

Reviewer #1 (Public Review):

In this manuscript, Dhekne et al. sought to identify modulators of LRRK2 activity. Thereto, the authors performed a FACS-based genome-wide pooled CRISPR/Cas9 screen in NIH-3T3 cells monitoring phosphorylation of the Rab GTPase Rab10 which is a well-characterized target of LRRK2. Besides Rab10 and LRRK2, this unbiased screen surprisingly uncovered another Rab GTPase, Rab12, as one of the most significant hits. Validation experiments with Rab12 knockout (KO) NIH-3T3 cells, the LRRK2 inhibitors MLi-2, and Rab12 KO mice unanimously confirmed the dependency of Rab10 phosphorylation on Rab12. Conversely, the authors used A549 LRKK2 and PPM1H KO cells to show that overexpression of Rab12 but not Rab29 which is another LRRK2 modulator leads to elevated phospho-Rab10 levels in a manner dependent on LRRK2 but insensitive to pathogenic mutations thereof. Moreover, the authors mapped E240 and S244 of LRRK2 as specific binding sites for Rab12 and showed that these residues are important to sustain full LRKK2 activity towards Rab10. Lastly, the authors showed that RAB12-dependent LRRK2 activation occurs under (patho)physiological stress conditions, namely endolysosomal membrane damage. Together, this comprehensive and elegant study of Dhekne and colleagues reveals exciting new mechanistic insights into the regulation of LRRK2 which have therapeutic potential for Parkinson's disease.

Reviewer #2 (Public Review):

Dhekne and colleagues present an unbiased genome-wide screen by systematic CRISPR-Cas9 gene knock-out in mouse NIH-3T3 fibroblasts to identify regulators of the LRRK2 pathway which is relevant for Parkinson's disease. The screen identified Rab12 as the most potent regulator of the LRRK2 activity. Phosphorylation of the well-established LRRK2 substrate Rab10 has been used as a read-out. To allow a large-scale screen, the authors established a flow cytometry-based assay using phospho-Rab10-specific antibodies. Subsequently, Rab12 has been confirmed as an upstream effector of LRRK2 acting in a similar way as Rab29. Using computational modelling by Alphafold in conjunction with Colabfold the authors could model the Rab12:LRRK2 complex and identify a third Rab binding site within the N-terminal Armadillo repeats which is distinct from the two sites, previously identified for Rab8a/Rab10 and Rab29. The predicted interaction epitope could be experimentally confirmed by systematic mutational analysis.

The experimental setting and the data presented are overall sound. It should however be considered that the selected cell model is most likely not covering the full set of LRRK2 pathway regulators as these are likely expressed in a tissue and cell-type-specific manner. It could therefore be interesting to also include more disease-relevant models, such as neuronal or immune cells. Nevertheless, Rab12 is an important effector, which is also expressed in cell types relevant to Parkinson's disease.<br /> To validate their computational model of the Rab12 binding epitope within the N-terminal Armadillo domain of LRRK2, the authors determined the binding affinity of Rab12 which is in the lower µM range and similar to the affinities of Rab10 and Rab29 to LRRK2. The authors conducted a mutational screen mutating surface exposed residues within the predicted Rab12 binding epitope in the N-terminus of LRRK2. The study could identify critical residues, which significantly contribute to the affinity of LRRK2 for Rab12. Corresponding alanine mutations could significantly reduce the enhanced LRRK2-mediated Rab10 phosphorylation observed upon Rab12 co-expression. The effect size is similar to the previously identified Rab29 effector. Furthermore, the authors could convincingly demonstrate that Rab12 and Rab29 bind to different LRRK2 epitopes.

Noteworthy, besides disrupting mutations targeting the predicted Rab12 binding epitope, the authors also found one mutation enhancing the cellular effect of Rab12 overexpression demonstrated by increased phospho-Rab10 levels. For a better evaluation of the presented computational model of the Rab12:LRRK2 complex, it would be interesting, if the authors could study the binding affinity of that mutant (F283A), as well.

Overall, the authors could convincingly demonstrate that Rab12, previously identified as LRRK2 substrate, acts upstream of LRRK2 similar to Rab29 but via a distinct binding site. The site located within the N-terminal Ankyrin domain has been predicted by a computational 3D model of the complex structure and experimentally validated. The interaction epitope might be an interesting target for the future development of allosteric modulators to treat LRRK2-mediated PD.

Reviewer #3 (Public Review):

Dhekne et al. set out to identify novel activators of the LRRK2 kinase. They developed a flow cytometry assay to separate pools of unmodified and phosphorylated Rab10 (pRab10) from mouse NIH-3T3 cells. They then used this methodology to perform a CRISPR-based genome-wide screen to identify genes responsible for increased pRab10 levels. Candidates were validated with knock-out experiments. As far as we know, LRRK2 is the only kinase that phosphorylates the Switch II motif in Rab10. Therefore, the genes affecting pRab10 levels were classified into positive and negative LRRK2 regulators. Knocking out a positive LRRK2 regulator led to a decrease in pRab10 while knocking out a negative regulator led to an increase in pRab10. The authors found several interesting, previously unknown modulators of LRRK2 activity, including SPTLC2 and CERT1, which are involved in ceramide synthesis.

The major finding of this work is the unexpected effect of Rab12 on pRab10 levels in cells. Knocking out Rab12 resulted in a five-fold decrease in pRab10 levels. This observation was validated in an animal model. Conversely, overexpression of Rab12 led to a ten-fold increase in pRab10 levels. To exclude the possibility that other kinases were responsible for modifying Rab10, the authors overexpressed Rab12 in A549 cells lacking LRRK2; no increase in pRab10 was observed in these cells.

Dhenke et al. then used AlphaFold to model possible interaction between LRRK2 and Rab12 and identified a putative binding site for Rab12 in its Armadillo domain. This is the third Rab binding site in the domain of LRRK2. To validate this interaction, they mutated E240 and S244, both of which are involved in the interface; they observed no changes in pRab10 levels in cells expressing LRRK2 carrying the E240R and S244R mutations. The previously reported site #1 and site #2, both of which also bind Rabs and are involved in feed-forward LRRK2 activation, seem to be unrelated to the binding of Rab12 to site #3. The authors propose that site #3 might open the kinase of LRRK2 to increase its activity.

Finally, the authors point out the important role of Rab12 in lysosomal damage by showing that LLOME- or Nigericin-induced cellular stress increases LRRK2 activity in a Rab12-dependent manner.

Reviewer #1 (Public Review):

For many years it has been understood that transposable elements (TEs) are an important source of natural variation. This is because, in addition to simple knockouts of genes, TEs carry regulatory sequences that can, and sometimes do, affect the expression of genes near the TEs. However, because TEs can be difficult to map to reference genomes, they have generally not been used for trait mapping. Instead, single nucleotide polymorphisms are widely used because they are easy to detect when using short reads. However, improvements in sequencing technology, as well as an increased appreciation of the importance of TEs to both linked to favorable alleles and are more likely to be causing the changes that make those alleles beneficial in a given environment. Further, because TE activity can occur after bottlenecks, they can provide polymorphisms in the absence of variation in point mutations.

In this manuscript, the authors carefully examine insertion polymorphisms in rice and demonstrate linkage to differences in expression. To do this, they used expression quantitative trait locus (eQTL) GWAS using TIPs as genetic markers to examine variation in 208 rice accessions. Because they chose to focus on genes that were expressed in at least 10% of the accessions, presumably because more rare variants would end up lacking statistical power. This is an understandable decision, but it says that recent insertions, such as the MITE elements detailed in a previous paper, would not be included. Importantly, although TIPs associated with differentially expressed genes are far less common than SNPs' traditional eQTLs, there were a significant number of eQTLs that showed linkage to TIPs but not to QTL.

The authors then show that of the eQTLs associated with both TIPs and SNPs, TIPs are more tightly linked to the eQTL, and are more likely to be associated with a reduction in expression, with variation in the effects of various TEs families supporting that hypothesis. Here and throughout, however, the distance of the TEs could be an important variable. It is also worth noting the relative numbers in order to assess the claim in the title of the paper. The total number of eQTL-TIPs is ten-fold less than the number of eQTL-SNPs, and, of the eQTLs that have both, there are a significant number of eQTL-TIPs that are not more tightly linked to the expression differences than the eQTL.

The authors show that eQTL-TIPs are more likely to be in the promoter-proximal region, but this may be due to insertion bias, which is well documented in DNA-type elements. Here and throughout the authors are careful to state that the data is consistent with the hypothesis that TEs are the cause of the change, but do not claim that the data demonstrate that they are.

Throughout the rest of the manuscript, the authors systematically build the case for a causal role for TEs by showing, for instance, that eQTL-TIPs show much stronger evidence for selection, with increased expression being more likely to be selected than decreased expression. The authors provide examples of genes most likely to have been affected by TE insertions.

Overall, the authors build a convincing case for TEs being an important source of regulatory information. I don't have any issues with the analysis, but I am concerned about the sweeping claims made in the title. Once you get rid of eQTLs that could be altered by either SNPs or TIPs and include only those insertions that show strong evidence of selection, the number of genes is reduced to only 30. And even in those cases, the observed linkage is just that, not definitive evidence for the involvement of TEs. Although clearly beyond the scope of this analysis, transgenic constructs with the TEs present or removed, or even segregating families, would have been far more convincing.

The fact that many of the eQTL-TIPs were relatively old is interesting because it suggests that selection in domesticated rice was on pre-existing variation rather than new insertions. This may strengthen the argument because those older insertions are less likely to be purged due to negative effects on gene expression. Given that the sequence of these TEs is likely to have diverged from others in the same family, it would have been interesting to see if selection in favor of a regulatory function had caused these particular insertions to move away from more typical examples of the family.

Reviewer #2 (Public Review):

In this manuscript, Castanera et al. investigated how transposable elements (TEs) altered gene expression in rice and how these changes were selected during the domestication of rice. Using GWAS, the authors found many TE polymorphisms in the proximity of genes to be correlated to distinct gene expression patterns between O. sativa ssp. japonica and O. sativa ssp. indica and between two different growing conditions (wet and drought). Thereby, the authors found some evidence of positive selection on some TE polymorphisms that could have contributed to the evolution of the different rice subspecies. These findings are underlined by some examples, which illustrate how changes in the expression of some specific genes could have been advantageous under different conditions. In this work, the authors manage to show that TEs should not be ignored when investigating the domestication of rise as they could have played an important role in contributing to the genetic diversity that was selected. However, this study stops short of identifying causations as the used method, GWAS, can only identify promising correlations. Nevertheless, this study contributes interesting insights into the role TEs played during the evolution of rice and will be of interest to a broader audience interested in the role TEs played during the evolution of plants in general.

Reviewer #1 (Public Review):

In this manuscript the authors proposed a novel system by which they can suppress the expression of any gene of interest precisely and efficiently with a pre-validated, highly specific and efficient synthetic short-hairpin RNA. The idea of identifying potent artificial RNAi (ARTi) triggers is intriguing, and the authors successfully identify six ARTi with robust knockdown efficiency and limited to no off-target effects. As a proof-of-concept, the authors examined three oncology targets for validation, including EGFRdel19 (which already has a clinically approved drug for validation), KRASG12R (for which there are no in vivo compatible inhibitors yet) and STAG1 (which has a synthetic lethal interaction with recurrent loss-of-function mutations of STAG2). The authors demonstrated significant suppression of colony formation and in vivo tumor growth for all three oncology targets.

This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).

ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:

• The authors claim in the introduction that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the methods does not, as the authors acknowledge, eliminate the possibility of off-target effects.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins. This is not, as the authors point out, a replacement for other LOF methods, and there are other ways to achieve gene-specific regulation via, for example, degrons. However it is an effective orthogonal approach that many users may find compelling for their applications.

Reviewer #2 (Public Review):

In this manuscript, Hoffmann et al. introduce a novel and innovative method to validate and study the mechanism of action of essential genes and novel putative drug targets. In the wake of many functional genomics approaches geared towards identifying novel drug targets or synthetic lethal interactions, there is a dire need for methods that allow scientists to ablate a gene of interest and study its immediate effect in culture or in xenograft models. In general, these genes are lethal, rendering conventional genetic tools such as CRISPR or RNAi inept.

The ARTi system is based on expression of a transgene with an artificial RNAi target site in the 3'-UTR as well as a TET-inducible miR-E-based shRNAi. Using this system, the authors convincingly show that they can target strong oncogenes such as EGFRdel19 or KRasG12 as well as synthetic lethal interactions (STAG1/2) in various human cancer cell lines in vivo and in vitro.

The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful.

Reviewer #1 (Public Review):

This is a carefully performed and well documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.Overall this is a nicely done study with nice quantitation.

Reviewer #2 (Public Review):

Fuijino et al provide interesting data describing the RNA-binding protein, FUS, for its ability to bind the RNA produced from the hexanucleotide repeat expansion of GGGGCC (G4C2). This binding correlates with reductions in RNA foci formation, the production of toxic dipeptides and concomitant reductions in toxic phenotypes seen in (G4C2)30+ expressing Drosophila. Both FUS and G4C2 repeats of >25 are associated with ALS/FTD spectrum disorders. Thus, these data are important for increasing our understanding of potential interactions between multiple disease genes.

Reviewer #3 (Public Review):

In this manuscript Fujino and colleagues used C9-ALS/FTD fly models to demonstrate that FUS modulates the structure of (G4C2) repeat RNA as an RNA chaperone, and regulates RAN translation, resulting in the suppression of neurodegeneration in C9-ALS/FTD. They also confirmed that FUS preferentially binds to and modulates the G-quadruplex structure of (G4C2) repeat RNA, followed by the suppression of RAN translation. The potential significance of these findings is high, since C9ORF72 repeat expansion is the most common genetic cause of ALS/FTD, especially in Caucasian populations and the DPR proteins have been considered the major cause of the neurodegenerations.

1) While the effect of RBP as an RNA chaperone on (G4C2) repeat expansion is supposed to be dose-dependent according to (G4C2)n RNA expression, the first experiment of the screening for RBPs in C9-ALS/FTD flies lacks this concept. It is uncertain if the RBPs of the groups "suppression (weak)" and "no effect" were less or no ability of RNA chaperone or if the expression of the RBP was not sufficient, and if the RBPs of the group "enhancement" exacerbated the toxicity derived from (G4C2)89 RNA or the expression of the RBP was excessive. The optimal dose of any RBPs that bind to (G4C2) repeats may be able to neutralize the toxicity without the reduction of (G4C2)n RNA.

2) In relation to issue 1, the rescue effect of FUS on the fly expressing (G4C2)89 (FUS-4) in Figure 4-figure supplement 1 seems weaker than the other flies expressing both FUS and (G4C2)89 in Figure 1 and Figure 1-figure supplement 2. The expression level of both FUS protein and (G4C2)89 RNA in each line is important from the viewpoint of therapeutic strategy for C9-ALS/FTD.

3) While hallmarks of C9ORF72 are the presence of DPRs and the repeat-containing RNA foci, the loss of function of C9ORF72 is also considered to somehow contribute to neurodegeneration. It is unclear if FUS reduces not only the DPRs but also the protein expression of C9ORF72 itself.

4) In Figure 5E-F, it cannot be distinguished whether FUS binds to GGGGCC repeats or 5' flanking region. Same experiment should be done by using FUS-RRMmut to elucidate whether FUS binding is the major mechanism for this translational control. Authors should show that FUS binding to long GGGGCC repeats is important for RAN translation.

5) It is not possible to conclude, as the authors have, that G-quadruplex-targeting RBPs are generally important for RAN translation (Figure 6), without showing whether RBPs which do not affect to (G4C2)89 RNA levels lead to decreased DPR protein level or RNA foci.

Reviewer #1 (Public Review):

Based on a recent report of spontaneous and reversible remapping of spatial representations in the enthorhinal cortex (Low et al 2021), this study sets out to examine possible mechanisms by which a network can simultaneously represent a positional variable and an uncorrelated binary internal state. To this end, the authors analyse the geometry of activity in recurrent neural networks trained to simultaneously encode an estimate of position in a one-dimensional track and a transiently-cued binary variable. They find that network activity is organised along two separate ring manifolds. The key result is that these two manifolds are significantly more aligned than expected by chance, as previously found in neural recordings. Importantly, the authors show that this is not a direct consequence of the design of the model, and clarify scenarios by which weaker alignment could be achieved. The model is then extended to a two-dimensional track, and to more than two internal variables. The latter case is compared with experimental data that had not been previously analysed.

Strengths:

• rigorous and careful analysis of activity in trained recurrent neural networks

• particular care is taken to show that the obtained results are not a necessary consequence of the design of the model

• the writing is very clear and pleasant to read

• close comparison with experimental data

• extensions beyond the situations studied in experiments (two-dimensional track, more than two internal states)

Weaknesses:

• no major weaknesses

• (minor) the comparison with previous models of remapping could be expanded

Altogether the conclusions claimed by the authors seem to be strongly supported and convincing.

Reviewer #2 (Public Review):

This important work presents an example of a contextual computation in a navigation task through a comparison of task driven RNNs and mouse neuronal data. Authors perform convincing state of the art analyses demonstrating compositional computation with valuable properties for shared and distinct readouts. This work will be of interest to those studying contextual computation and navigation in biological and artificial systems.

This work advances intuitions about recent remapping results. Authors trained RNNs to output spatial position and context given velocity and 1-bit flip-flops. Both of these tasks have been trained separately, but this is the first time to my knowledge that one network was trained to output both context and spatial position. This work is also somewhat similar to previous work where RNNs were trained to perform a contextual variation on the Ready-Set-Go with various input configurations (Remington et al. 2018). Additionally findings in the context of recent motor and brain machine interface tasks are consistent with these findings (Marino et al in prep). In all cases contextual input shifts neural dynamics linearly in state space. This shift results in a compositional organization where spatial position can be consistently decoded across contexts. This organization allows for generalization in new contexts. These findings in conjunction with the present study make a consistent argument that remapping events are the result of some input (contextual or otherwise) that moves the neural state along the remapping dimension.

The strength of this paper is that it tightly links theoretical insights with experimental data, demonstrating the value of running simulations in artificial systems for interpreting emergent properties of biological neuronal networks. For those familiar with RNNs and previous work in this area, these findings may not significantly advance intuitions beyond those developed in previous work. It's still valuable to see this implementation and satisfying demonstration of state of the art methods. The analysis of fixed points in these networks should provide a model for how to reverse engineer and mechanistically understand computation in RNNs.

I'm curious how the results might change or look the same if the network doesn't need to output context information. One prediction might be that the two rings would collapse resulting in completely overlapping maps in either context. I think this has interesting implications about the outputs of the biological system. What information should be maintained for potential readout and what information should be discarded? This is relevant for considering the number of maps in the network. Additionally, I could imagine the authors might reproduce their current findings in another interesting scenario: Train a network on the spatial navigation task without a context output. Fix the weights. Then provide a new contextual input for the network. I'm curious whether the geometric organization would be similar in this case. This would be an interesting scenario because it would show that any random input could translate the ring attractor that maintains spatial position information without degradation. It might not work, but it could be interesting to try!

I was curious and interested in the authors choice to not use activity or weight regularization in their networks. My expectation is that regularization might smooth the ring attractor to remove coding irrelevant fluctuations in neural activity. This might make Supplementary Figure 1 look more similar across model and biological remapping events (Line 74). I think this might also change the way authors describe potential complex and high dimensional remapping events described in Figure 2A.

Overall this is a nice demonstration of state-of-the-art methods to reverse engineer artificial systems to develop insights about biological systems. This work brings together concepts for various tasks and model organisms to provide a satisfying analysis of this remapping data.

Reviewer #3 (Public Review):

This important work provides convincing evidence that artificial recurrent neural networks can be used to model neural activity during remapping events while an animal is moving along a one-dimensional circular track. This will be of interest to neuroscientists studying the neural dynamics of navigation and memory, as well as the community of researchers seeking to make links between artificial neural networks and the brain.

Low et al. trained artificial recurrent neural networks (RNNs) to keep track of their location during a navigation task and then compared the activity of these model neurons to the firing rates of real neurons recorded while mice performed a similar task. This study shows that a simple set of ingredients, namely, keeping track of spatial location along a one-dimensional circular track, along with storing the memory of a binary variable (representing which of the two spatial maps are currently being used), are enough to obtain model firing rates that reproduce features of real neural recordings during remapping events. This offers both a normative explanation for these neural activity patterns as well as a potential biological implementation.

One advantage of this modeling approach using RNNs is that this gives the authors a complete set of firing rates that can be used to solve the task. This makes analyzing these RNNs easier, and opens the door for analyses that are not always practical with limited neural data. The authors leverage this to study the stable and unstable fixed points of the model. However, in this paper there appear to be a few places where analyses that were performed on the RNNs were not performed on the neural data, missing out on an opportunity to appreciate the similarity, or identify differences and pose challenges for future modeling efforts. For example, in the neural data, what is the distribution of the differences between the true remapping vectors for all position bins and the average remapping vector? What is the dimensionality of the remapping vectors? Do the remapping vectors vary smoothly over position? Do the results based on neural data look similar to the results shown for the RNN models (Figures 2C-E)?

There are many choices that must be made when simulating RNNs and there is a growing awareness that these choices can influence the kinds of solutions RNNs develop. For example, how are the parameters of the RNN initialized? How long is the RNN trained on the task? Are the firing rates encouraged to be small or smoothly varying during training? For the most part these choices are not explored in this paper so I would interpret the authors' results as highlighting a single slice of the solution space while keeping in mind that other potential RNN solutions may exist. For example, the authors note that the RNN and biological data do not appear to solve the 1D navigation and remapping task with the simplest 3-dimensional solution. However, it seems likely that an RNN could also be trained such that it only encodes the task relevant dynamics of this 3-dimensional solution, by training longer or with some regularization on the firing rates. Similarly, a higher-dimensional RNN solution may also be possible and this would likely be necessary to explain the more variable manifold misalignment reported in the experimental data of Low et al. 2021 as opposed to the more tightly aligned distribution for the RNNs in this paper. However, thanks to the modeling work done in this paper, the door has now been opened to these and many other interesting research directions.

Reviewer #2 (Public Review):

The ATPase protein machine cohesin shapes the genome by loop extrusion and holds sister chromatids together by topological entrapment. When executing these functions, cohesin is tightly regulated by multiple cofactors, such as Scc2/Nipbl, Pds5, Wapl, and Eco1/Esco1/2, and it undergoes dynamic conformational changes with ATP binding and hydrolysis. The mechanisms by which cohesin extrudes DNA loops and medicates siter-chromatid cohesion are still not understood. A major reason for the lack of understanding of cohesin dynamics and regulation is the failure to capture the structures of intact cohesin in different nucleotide-bound states and in complex with various regulators. So far only the ATP state cohesin bound to NIPBL and DNA have been experimentally determined.

In this manuscript, Nasmyth et al. made use of the powerful protein structure prediction tool, AlphaFold2 (AF), to predict the models of tens of cohesin subcomplexes from different species. The results provide important insight into how the Smc3-Scc1 DNA exiting gate is opened, how Pds5 and Wapl maintain the opened gate, how Pds5 and Scc3/SA recruit different cofactors, how Eco1 and Sororin antagonize Wapl, and how Scc2/Nipbl interacts with Scc3/SA. The models are for the most part consistent with published mutations in these proteins that affect cohesin's functions in vitro and in vivo and raise testable hypotheses of cohesin dynamics and regulation. This study also serves as an example of how to use AF to build models of protein complexes that involve the docking of flexible regions to globular domains.

Major points<br /> (1) As it stands, the manuscript is simply too long and not readable. The authors should streamline their presentations and remove excessive speculations and models of minor importance.

(2) AF has been accurate in predicting both the fold and sidechain conformations of globular domains. It is less accurate in predicting structural regions with conformational flexibility. Comparisons of predicted and determined structures of large protein complexes have shown considerable differences, particularly with respect to regions lacking tertiary fold. The authors should be more cautious in interpreting some of their models, particularly when the predicted models are inconsistent with determined structures and published biochemical data. For example, human WAPL-C in isolation does not interact with the SA-SCC1 complex while the N-terminal region of WAPL does.

(3) The predicted SA/Scc3-Pds5-Scc1-WaplC quaternary complex is fascinating. Can the authors provide some experimental evidence to support the formation of this quaternary complex or at least the formation of the SA/Scc3-Pds5-WaplC ternary complex? In vitro pulldown or gel filtration can be used to test their predictions.

Reviewer #1 (Public Review):

There are a number of outstanding questions concerning how cohesin turnover on DNA is controlled by various accessory factors and how such turnover is controlled by post-translational modification. In this paper, Nasmyth et al. perform a series of AlphaFold structure predictions that aim to address several of these outstanding questions. Their structure predictions suggest that the release factor WAPL forms a ternary complex with PDS5 and SA/SCC3. This ternary complex appears to be able to bind the N-terminal end of SCC1, suggesting how formation of such a complex could stabilize an open state of the cohesin ring. Additional calculations suggest how the Eco/ESCO acetyltransferases and Sororin engage the SMC3 head domain and thereby protect against WAPL-mediated release.

This work thus demonstrates the power of AF prediction methods and how they can lead to a number of interesting and testable hypotheses that can transform our understanding of cohesin regulation. These findings require orthogonal experimental validation, but the authors argue convincingly that such validation should not be a pre-requisite to publication.

As the authors did not systematically include model confidence scores it is difficult for the reader to evaluate the reliability of the models obtained. The caveat is that many readers will by default assume that the presented models are correct, when in fact, some of them may score poorly and require careful assessment. As numerous readers will not be very familiar with the AF confidence scoring mechanisms, it would be important to include such metrics and indicate what these scores mean for the different interfaces (Acceptable, Medium and High confidence?). pLDDT and PAE plots should be included. When they report on a key interaction (E.g. WAPL-SCC1) they should indicate the key region (SCC1 N-terminus) on the PAE plot. False positives are always possible even with good scores, especially when many protein pairs are tried. It would therefore be important to also include a table showing the global scores for pTM and ipTM to summarise the confidence scores of interfaces.

It is exciting to see AF-multimer predictions being applied to cohesin. As some of the reported interactions are not universally conserved and some involve relatively small interfaces the possibility arises that these interfaces show poor or borderline confidence scores. As some of these interfaces map to mutants that have previously been obtained by hypothesis-free genetic screens and mutational analyses they appear nevertheless valid. Thus, an important point to make is that even interfaces that show modest confidence scores may turn out to be valid while others may be not. The authors therefore should emphasize that the proposed models are just predictions and that additional orthogonal validations are required.

IAPWE Review: Should You Join?

Reviewer #1 (Public Review):

This work by Gonzalez-Segarra et al. greatly extends previous research from the same group that identified ISNs as a key player in balancing nutrition and water ingestion. Using well-balanced sets of exploratory anatomical analyses and rigorous functional experiments, the authors identify and compile various peptidergic circuits that modulate nutrient and/or water ingestion. The findings are convincing and the experiments rigorous.

Strengths:

- The authors complement anatomically-reconstructed and functionally-validated neuronal connectivity with extensive and intensive morphological and synaptic reconstruction.

- Neurons and genes involved in specific components of feeding control are undoubtedly challenging, because numerous neurons and circuits redundantly and reciprocally regulate the same components of feeding behavior. This work dissociates how multiple, parallel and interconnected, peptidergic circuits (dilp3, CCHa2, CCAP) modulate sucrose and water ingestion, in tandem and in parallel.

- The authors address some of the incongruencies / discrepancies in current literature (IPCs) and try to provide explanations, rather than ignoring inconsistent findings.

Weaknesses:

- Is "function" of the ISNs to balance "nutrient need" or osmolarity? Balancing hemolymph osmolarity for physiological homeostasis is conceptually different from balancing thirst and hunger.

- The final schematic nicely sums up how the different peptidergic pathways might work together, but it is unclear which connections are empirically-validated or speculative. It would be informative to show which parts of the model are speculative versus validated. For example, does FAFB volume synapse = functional connectivity and not just anatomical proximity? A bulk of the current manuscript relies on "synapses of relatively high confidence" (according to Materials and methods: line 522). I recommend distinguishing empirically tested & predicted connections in the final schematic, and maybe reword/clarify throughout the manuscript as "predicted synaptic partners"

Reviewer #2 (Public Review):

In this manuscript, González-Segarra et al. investigated how ISNs regulate sugar and water ingestion in Drosophila.

Strengths:

• In their previous paper, authors have shown that inhibiting neurotransmission in ISNs has opposite effects on sugar and water ingestion. In this new manuscript, they investigated the downstream neurons connected to ISNs.

• The authors first identified the effector molecules released by ISNs. Their RNAi screen found that, surprisingly, ISNs use ilp3 as a neuromodulator.

• Next, using light and electron microscopy, they investigated the downstream neural circuits ISNs connect with to regulate water or sugar ingestion. These analyses identified a new group of neurons named Bilateral T-shaped neurons (BiT) as the main output of ISNs, and several other peptidergic neurons as downstream effectors of ISNs. While BiT activity regulated both sugar and water ingestion, BiT downstream neurons, such as CCHa2R, only impacted water ingestion.

• These results suggested that ISNs might interact with distinct neural circuits to control sugar or water ingestion.

• The authors also investigated other ISN downstream neurons, such as ilp2 and CCAP, and revealed that their activity also contributes to ingestive behaviors in flies.

Areas for further development:

• Does BIT inhibit all of the IPCs or some of them? I think it is critical to indicate the ROIs used for each neuron in the methods. Which part of the neuron is used for imaging experiments? Dendrites, cell bodies, or synaptic terminals?

• The discussion section is not giving big picture explanation of how these neurons work together to regulate sugar and water ingestion. Silencing and activation experiments are good, but without showing the innate activity of these neural groups during ingestion, it is not clear what their functions are in terms of regulating fly behavior.

Reviewer #1 (Public Review):

This paper presents extensive numerical simulations using a model that incorporates up to second-order epistasis to study the joint effects of microscopic epistasis and clonal interference on the evolutionary dynamics of a microbial population. Previous works that explicitly modeled microscopic epistasis typically assumed strong selection & weak mutation (SSWM), a condition that is generally not met in real-life evolutionary processes. Alternatively, another class of models coarse-grained the effects of microscopic epistasis into a generic distribution of fitness effects. The framework introduced in this paper represents an important advance with respect to these previous approaches, allowing for the explicit modeling of microscopic epistasis in non-SSWM scenarios. The modeling framework presented promises to be a valuable tool to study microbial evolution in silico.

Reviewer #2 (Public Review):

This paper presents an extensive numerical study of microbial evolution using a model of fitness inspired by spin glass physics. It places special emphasis on elucidating the combined effects of microscopic epistasis, which dictates how the fitness effect of a mutation depends on the genetic background on which it occurs, and clonal interference, which describes the proliferation of and competition between multiple strains. Both microscopic epistasis and clonal interference have been observed in microbial evolution experiments, and are chief contributors to the complexity of evolutionary dynamics. Correlations between random mutations and nonlinearities associated with interactions between sub-populations consisting of competing strains make it extremely challenging to make quantitative theoretical predictions for evolutionary dynamics and associated observables such as the mean fitness. While the body of theoretical and computational research on modeling evolutionary dynamics is extensive, most theoretical efforts rely on making simplifications such as the strong selection weak mutation (SSWM) limit, which neglects clonal interference, or assumptions about the distribution of fitness effects that are not experimentally verifiable.

The authors have addressed this challenge by running a numerical microbial evolution experiment over realistic population sizes (~ 100 million cells) and timescales (~ 10,000 generations) using a spin glass model of fitness that considers pairwise interactions between mutations on distinct genetic loci. By independently tuning mutation rate as well as the strength of epistasis, the authors have shown that epistasis generically slows down the growth of fitness trajectories regardless of the amount of clonal interference. On the other hand, in the absence of epistasis, clonal interference speeds up the growth of fitness trajectories, but leaves the growth unchanged in the presence of epistasis. The authors quantitatively characterize these observations using asymptotic power law fits to the mean fitness trajectories. Further, the authors employ more simplified macroscopic models that are informed by their empirical findings, to reveal the mechanistic origins of the epistasis mediated slowing down of fitness growth. Specifically, they show that epistasis leads to a broadening of the distribution of fitness increments, leading to the fixation of a large number of mutations that confer small benefits. Effectively, this leads to an increase in the number of fixed mutations required to climb the fitness peak. This increased number of required beneficial mutations together with the decreasing availability of beneficial mutations at high fitness lead to the slowdown of fitness growth. The authors' data analysis is quite solid and their conclusions are well supported by quantitative macroscopic models. The paper can be strengthened further by conducting a deeper analysis of correlations between mutations, using tools for analyzing dynamical correlations developed in the spin glass literature.

One of the highlights of this paper is the author's astute choice of model, which strikes an impressive balance between complexity, flexibility, and numerical accessibility. In particular, the authors were able to achieve results over realistic population sizes and timescales largely because of the amenability of the model to the implementation of an efficient simulation algorithm. At the same time, the strength of epistasis and clonal interference can be tuned in a facile manner, enabling the authors to map out a phase diagram spanning these two axes. One could argue that the numerical scheme employed here would only work for a specific class of models, and is therefore not generalizable to all models of evolutionary dynamics. While this is likely true, the model is capable of recapitulating several complex aspects of microbial evolution, and is therefore not unduly restrictive.

Spin glass physics has already provided significant insights into a wide range of topics in the life sciences including protein folding, neuroscience, ecology and evolution. The present work carries this approach forward, with immediate implications for microbial evolution, and potential implications in related areas of research such as microbial ecology. In addition to the theoretical value of spin glass physics, the high performance algorithm developed in this work lays the foundation for formulating data driven approaches aimed at understanding evolutionary dynamics. In the future, there is considerable scope for utilizing data generated by such models to train machine learning algorithms for quantifying parameters associated with epistasis, clonal interference, and the distribution of fitness effects in laboratory experiments.

Reviewer #1 (Public Review):

Ibar and colleagues investigate the function of spectrin in Drosophila wing imaginal discs and its effect on the Hippo pathway and myosin activity. The authors find that both βH-Spec and its canonical binding partner α-Spec reduce junctional localization of the protein Jub and thereby restrict Jub's inhibitory effect on Hippo signaling resulting in activation of the Hippo effector Yorkie regulating tissue shape and organ size. From genetic epistasis analysis and analysis of protein localization, the authors conclude that βH-Spec and α-Spec act independently in this regulation. The major point of this study is that the apical localization of βH-Spec and myosin is mutually exclusive and that the proteins antagonize each other's activity in wing discs. In vitro co-sedimentation assays and in silico structural modeling suggest that this antagonization is due to a competition of βH-Spec and myosin for F-actin binding.

The study's strengths are the genetic perturbation that is the basis for the epistasis analysis which includes specific knockdowns of the genes of interest as well as an elegant CRISPR-based overexpression system with great tissue specificity. The choice of the model for such an in-depth analysis of pathway dependencies in a well-characterized tissue makes it possible to identify and characterize quantitative differences between closely entangled and mutually dependent components. The method of quantifying protein localization and abundance is common for multiple figures which makes it easy to assess differences across experiments. The flow of experiments is logical and in general, the author's conclusions are supported by the presented data. The findings are very well embedded into the context of relevant literature and both confronting and confirming literature are discussed.

The study shows how components of the cytoskeleton are directly involved in the regulation of the mechanosensitive Hippo pathway in vivo and thus ultimately regulate organ size supporting previous data in other contexts. The molecular mechanism regulating myosin activity by out-competing it for F-actin binding has been observed for small actin-binding proteins such as cofilin but is a new mode for such a big, membrane-associated actin-binding protein. This may inspire future experiments in different morphogenetic contexts for the investigation of similar mechanisms. For example, the antagonistic activity of βH-Spec and myosin in this tissue context might help explain phenomena in other systems such as spectrin-dependent ratcheting of apical constriction during mesoderm invagination (as the authors discuss). Against the classical view, the work shows that βH-Spec can act independently of α-Spec. Together the results will be of interest to the cell biology community with a focus on the cytoskeleton and mechanotransduction.

Reviewer #2 (Public Review):

Ibar and colleagues address the role of the spectrin cytoskeleton in the regulation of tissue growth and Hippo signaling in an attempt to elucidate the underlying molecular mechanism(s) and reconcile existing data. Previous reports in the field have suggested three distinct mechanisms by which the Spectrin cytoskeleton regulates Hippo signaling and this is, at least in part, due to the fact that different groups have mainly focused on different spectrins (alpha, beta, or beta-heavy) in previous reports.

The authors start their investigation by trying to reconcile their previous data on the role of Ajuba in the regulation of Hippo signaling via mechanotransduction and previous observations suggesting that Spectrins affect Hippo signaling independently of any effect on myosin levels or Ajuba localization. Contrary to previous reports, the authors reveal that, indeed, depletion of alpha- and beta-heavy-spectrin leads to an increase in myosin levels at the apical membrane. Moreover, the authors also reveal that the depletion of spectrins leads to an increase in Ajuba levels.

Reviewer #1 (Public Review):

This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision, and in the use of optogenetics to manipulate cell signaling in vascular biology more generally. The authors establish the role of Rho-family GTPases in controlling the cytoskeletal-plasma membrane interface as it relates to endothelial barrier integrity and function, and adequately motivate the need for optogenetic tools for global and local signaling manipulation to study endothelial barriers.

Throughout the work, the optogenetic assays are conceptualized, described, and executed with exceptional attention to detail, particularly as it relates to potential confounding factors in data analysis and interpretation. Comparison across experimental setups in optogenetics is notoriously fraught, and the authors' control experiments and measurements to ensure equal light delivery and pathway activation levels across applications is very thorough. In demonstrating how these new opto-GEFs can be used to alter vascular barrier strength, the authors cleverly use fluorescent-labeled dextran polymers of different sizes and ECIS experiments to demonstrate the physiological relevance of BOEC monolayers to in vivo blood vessels. Of particular note, the resiliency of the system to multiple stimulation cycles and longer time course experiments is promising for use in vascular leakage studies.

Given that dozens of Rho GTPase-activating GEFs exist, expanded rationale for the selection of p63, ITSN1, and TIAM1 in the form of discussion and literature citations would be helpful to motivate their selection as protein effectors in the engineered tools. Extensive tool engineering studies demonstrate the superiority of iLID over optogenetic eMags or rapamycin-based chemogenetic tools for these purposes. However, as the utility of iLID and eMags has been demonstrated for manipulation of a variety of signaling pathways, the iSH-Akt demonstration does not seem necessary for these systems.

The demonstration of orthogonality in GTPase- and VE-cadherin-blocking antibody-mediated barrier function decreases and is compelling, even without full elucidation of the role of cell size or overlap in barrier strength. The discussion section presents a mature and thoughtful description of the limitations, remaining questions, and potential opportunities for the tools and technology developed in this work. Importantly, this manuscript demonstrates a commitment to scientific transparency in the ways in which the data are visualized, the methods descriptions, and the reagent and code sharing it presents, allowing others to utilize these tools to their full potential.

Reviewer #2 (Public Review):

This manuscript reports on the use of Optogenetics to influence endothelial barrier integrity by light. Light-induced membrane recruitment of GTPase GEFs is known to stimulate GTPases and modulate cell shape, and here this principle is used to modulate endothelial barrier function. It shows that Rac and CDc42 activating constructs enhance barrier function and do this even when a major junctional adhesion molecule, VE-cadherin, is blocked. Activation of Rac and Cdc42 enhanced lamellipodia formation and cellular overlaps, which could be the basis for the increase in barrier integrity.

The authors aimed at developing a light driven technique with which endothelial barrier integrity can be modulated on the basis of activating certain GTPases. They succeeded in using optogenetic tools that recruit GEF exchange domains to membranes upon light induction in endothelial cell monolayers. Similar tools were in principle known before to modulate cell shape/morphology upon light induction, but were used here for the first time as regulators of endothelial barrier integrity. In this way it was shown that the activation of Cdc42 and Rac can increase barrier integrity even if VE-cadherin, a major adhesion molecule of endothelial junctions, is blocked. Although it was shown before that stimulation of S1P1 receptor or of Tie-2 can enhance endothelial barrier integrity in dependence of Cdc42 or Rac1 and can do this independent of VE-cadherin, the current study shows this with tools directly targeting these GTPases.

Furthermore, this study presents very valuable tools. The immediate and repeatable responses of barrier integrity changes upon light-on and light-off switches are fascinating and impressive. It will be interesting to use these tools in the future in the context of analyzing other mechanisms which also affect endothelial barrier function and modulate the formation of endothelial adherens junctions.

Reviewer #3 (Public Review):

Mahlandt et al. report the design and proof of concept of Opto-RhoGEF, a new set of molecular tools to control the activation by light of the three best known members of the Rho GTPase family, RhoA, Rac1 and Cdc42.

The study is based on the optogenetically-controlled activation of chimeric proteins that target to the plasma membrane guanine nucleotide exchange factors (GEFs) domains, which are natural activators specific for each of these three Rho GTPases. Membrane-targeted GEFs encounter and activate endogenous Rho proteins. Further investigation on the effect of these tools on RhoGTPase signaling would have strengthened the report.

These three Opto-RhoGEFs are reversible and enable the precise spatio-temporal control of Rho-regulated processes, such as endothelial barrier function, cell contraction and plasma membrane extension. Hence, these molecular tools will be of broad interest for cell biologists interested in this family of GTPases.

Mahlandt et al. design and characterize three new optogenetic tools to artificially control the activation of the RhoA, Rac1 and Cdc42 by light. These three Rho GTPases are master regulators of the actin cytoskeleton, thereby regulating cell-cell contact stability or actin-mediated contraction and membrane protrusions.

The main strength of this new experimental resource lies in the fact that, to date, few tools controlling Rho activation by reversibly targeting Rho GEFs to the plasma membrane are available. In addition, a comparative analysis of the three Opto-RhoGEFs adds value and further strengthens the results, given the fact that each Opto-GEF produces different (and somehow expected) effects, which suggest specific GTPase activation. The design of the tools is correct, although the membrane targeting could be improved, since the Lck N-terminus used to construct the recombinant proteins contains myristoylation and palmitoylation sites, which has the potential to target the chimeric protein to lipid rafts. As a consequence, this may not evenly translocate these Rho-activating domains.

An additional technical feature that must be highlighted is an elegant method to activate Opto-RhoGEFs in cultured cells, independent of laser and microscopes, by using led strips, which notably expands the possibilities of this resource, potentially allowing biochemical analyses in large numbers of cells.

The experimental evidence clearly indicates that authors have achieved their aim and designed very useful tools. However, they should have taken more advantage of this remarkable technical advance and investigate in further detail the spatiotemporal dynamics of Rho-mediated signaling. Although the manuscript is a "tool and resource", readers may have better grasped the potential benefits of tuning GTPase activity with this tool by learning about some original and quantitative insights of RhoA, Rac1 and Cdc42 function.

One of such insights may have come from the set of data regarding the contribution of adherens junctions. The effect of other endothelial cell-cell junctions, such as tight junctions, may also contribute to barrier function, as well as junctional independent, cell-substratum adhesion. These optogenetic tools will undoubtedly impact on these future studies and help decipher whether these other adhesion events that are important for endothelial barrier integrity are also under control of these three GTPases. Overall, the manuscript is sound and presents new and convincing experimental strategies to apply optogenetics to the field of Rho GTPases.

Joint Public Review:

In this study, Anthoney and coworkers continue an important, unique, and technologically innovative line of inquiry from the van Swinderen lab aimed at furthering our understanding of the different sleep stages that may exist in Drosophila. Here, they compare the physiological and transcriptional hallmarks of sleep that have been induced by two distinct means, a pharmacological block of GABA signaling and optogenetic activation of dorsal fan-shaped-body neurons. They first employ an incredibly impressive fly-on-the-ball 2-photon functional imaging setup to monitor neural activity during these interventions, and then perform bulk RNA sequencing of fly brains at different stages. These transcriptomic analyses leads them to (a) knocking out nicotinic acetyl-choline receptor subunits and (b) knocking down AkhR throughout the fly brain testing the impact of these genetic interventions on sleep behaviors in flies. Based on this work, the authors present evidence that optogenetically and pharmacologically induced sleep produces highly distinct brain-wide effects on physiology and transcription.

The study is of significant interest, is easy to read, and the figures are mostly informative. However there are features of the experimental design and the interpretation of results that diminish enthusiasm.

a - Conditions under which sleep is induced for behavioral vs neural and transcriptional studies

1) There is a major conceptual concern regarding the relationships between the physiological and transcriptomic effects of optogenetic and pharmacological sleep promotion, and the effects that these manipulations have on sleep behavior. The authors show that these two means of sleep-induction produce remarkably distinct physiological and transcriptional responses, however, they also show that they produce highly similar effects on sleep behavior, causing an increase in sleep through increases in the duration of sleep bouts. If dFB neurons were promoting active sleep, the sleep it produces should be more fragmented than the sleep induced by the drug, because the latter is supposed to produce quiet sleep. Yet both manipulations seem to be biasing behavior toward quiet sleep.

2) The authors show that the pharmacological block of GABA signaling and the optogenetic activation of dorsal fan-shaped-body neurons cause different responses on brain activity. Based on these recordings and the behavioral and brain transcriptomic data they then claim that these responses correspond to different sleep states and are associated with the expression and repression of a different constellation of genes. Nevertheless, neural activity in animals was recorded following short stimulations whereas behavioral and transcriptomic data were obtained following chronic stimulation. In this regard, it would be interesting to determine how the 12-hour pharmacological intervention they employed for their transcriptomic analysis changes neural activity throughout the brain - 12 hours will likely be too long for the open-cuticle preps, but an in-between time-point (e.g. 1h) would probably be equally informative.

b - Efficiency of THIP treatment under different conditions

1) There are no data to quantify how THIP alters food consumption. It is evident that flies consume it otherwise they would not show increased sleep. However, they may consume different amounts of food overall than the minus THIP controls. This might have an influence on the animal's metabolism, which could at least explain the fact that metabolism-related genes are regulated (Figure 5). Therefore, in the current state, it is not possible to be certain that gene regulation events measured in this experiment are solely due to THIP effects on sleep.

2) A similar problem exists in the sleep deprivation experiments. If flies are snapped every 20 seconds, they may not have the freedom to consume appropriate amounts of food, and therefore their consumption of THIP or ATR may be smaller than in non-sleep deprived controls. Thus, it would be crucial to know whether the flies that are sleep-deprived (i.e. shaken every 20 seconds for 12 hours) actually consume comparable amounts of food (and therefore THIP) as those that are undisturbed. If not, then perhaps the transcriptional differences between the two groups are not sleep-specific, but instead reflect varying degrees of exposure to THIP.

3) The authors should further discuss the slow action of THIP perfusion vs dFB activation, especially as flies only seem to fall asleep several minutes after THIP is being washed away. Is it a technical artifact? If not, it may not be unreasonable to hypothesize that THIP, at the concentration used, could prevent flies from falling asleep, and that its removal may lower the concentration to a point that allows its sleep-promoting action. The authors could easily test this by extending THIP treatment for another 4-5 minutes.

c - Comments regarding the behavioral assays

1) L319-322: the authors conclude that dFB stimulation and THIP consumption have similar behavioral effects on sleep. However, this is inaccurate as in Figure S1 they explain that one increases bout number in both day and night and the other one only during the day.

2) The behavioral definitions used for active and quiet sleep do not fit well with strong evidence that deep sleep (defined by lowered metabolic rates) is probably most closely associated with bouts of inactivity that are much longer than the >5min duration used here, i.e., probably 30min and longer (Stahl et al. 2017 Sleep 40: zsx084). Given that the authors are providing evidence that quiet sleep is correlated with changes in the expression of metabolism related genes, they should at least discuss the fact that reductions in metabolism have been shown to occur after relatively long bouts of inactivity and might reconsider their behavioral sleep analysis (i.e., their criteria for sleep state) with this in mind.

d - Comments regarding the recordings of neuronal activity

1) There is an additional concern regarding the proposed active and quiet sleep states that rest at the heart of this study. Here these two states in the fly are compared to the REM and NREM sleep states observed in mammals and the parallels between active fly sleep and REM and quiet fly sleep and NREM provide the framework for the study. The establishment of such parallel sleep states in the fly is highly significant and identifying the physiological and molecular correlates of distinct sleep stages in the fly is of critical importance to the field. However, the proposal that the dorsal fan shaped body (dFB) neurons promote active sleep runs counter to the prevailing model that these neurons act as a major site of sleep homeostasis. If quiet sleep were akin to NREM, wouldn't we expect the major site of sleep homeostasis in the brain to promote it? Furthermore, the authors state that the effects of dFB neuron excitation on transcription have "almost no overlap" (line 500) with the transcriptomic effects of sleep deprivation (Supplementary Table 3), which is not what would be expected if dFB neurons are tracking sleep pressure and promoting sleep, as suggested by a growing body of convergent work summarized on page four of the manuscript. Wouldn't the 10h excitation of the dFB neurons be predicted to mimic the effects of sleep deprivation if these neurons "...serve as the discharge circuit for the insect's sleep homeostat..." (line 60)? Shouldn't their prolonged excitation produce an artificial increase in sleep drive (even during sleep) that would favor deep, restorative sleep? How do the authors interpret their results with regard to the current prevailing model that dFB neurons act as a major site of sleep homeostasis? This study could be seen as evidence against it, but the authors do not discuss this in their Discussion.

2) Regarding the physiological effects of Gaboxadol, to what extent is the quieting induced by this drug reminiscent of physiology of the brains of flies spontaneously meeting the behavioral criterion for quiet sleep? Given the relatively high dose of the drug being delivered to the de-sheathed brain in the imaging experiments (at least when compared to the dose used in the fly food), one worries that the authors may be inducing a highly abnormal brain state that might bear very little resemblance to the deeply sleeping brain under normal conditions. As the authors acknowledge, it is difficult to compare these two situations. Comparing the physiological state of brains put to sleep by Gaboxadol and brains that have spontaneously entered a deep sleep state therefore seems critical.

3) There are some issues with Figure 3, in particular 3C-D. It is not clear whether these panels show representative traces or an average, however both the baseline activity and fluorescence are different between C and D, in particular in their amplitude. Therefore, it is difficult to attribute the differences between C and D to the stimulation itself or to the previously different baseline. In addition, the fact that flies with dFB activation seem to keep a basal level of locomotor activity whereas THIP-treated ones don't is quite striking, however it is not being discussed. Finally, the authors claim that the flies eventually wake up from THIP-induced sleep (L360-361), however there are no data to support this statement.

4) In Figure 4C, it is strange that the SEM is always exactly the same across the whole experiment. Readers should be aware that there might have been an issue when plotting the figure.

e - Comments regarding the transcript analyses

1) General comment: the title of this manuscript is inaccurate - the "transcriptome" commonly refers to the entirety of all transcripts in a cell/tissue/organ/animal (including genes that are not differentially expressed following their interventions), and it is therefore impossible to "engage two non-overlapping transcriptomes" in the same tissue. Perhaps the word "transcriptional programs" or transcriptional profiles" would be more accurate here?

2) Given the sensitivity of transcriptomic methods, there is a significant concern that the optogenetic experiments are not as well controlled as they could be. Given the need for supplemental all-trans retinal (ATR) for functional light gating of channelrhodopsins in the fly, it is convenient to use flies with Gal4-driven opsin that have not been given supplemental ATR as a negative control, particularly as a control for the effects of light. However, there is another critical control to do here. Flies bearing the UAS-opsin responder element but lacking the GAL4 driver and that have been fed ATR are critical for confirming that the observed effects of optogenetic stimulation are indeed caused by the specific excitation of the targeted neurons and not due to leaky opsin expression, or the effect of ATR feeding under light stimulation or some combination of these factors. Given the sensitivity of transcriptomic methods, it would be good to see that the candidate transcripts identified by comparing ATR+ and ATR- R23E10GAL4/UAS-Chrimson flies are also apparent when comparing R23E10GAL4/UAS-Chrimson (ATR+) with UAS-Chrimson (ATR+) alone.

3) Figures about qPCR experiments (5G and 6G) are problematic. First, whereas the authors seem satisfied with the 'good correspondence' between their RNA-seq and qPCR results, this is true for only ~9/19 genes in 5G and 2/6 genes in 6G. Whereas discrepancies are not rare between RNA-seq and qPCR, the text in L460-461 and 540-541 is misleading. In addition, it is unclear whether the n=19 in L458 refers to the number of genes tested or the number of replicates. If the qPCR includes replicates, this should be more clearly mentioned, and error bars should be added to the corresponding figures.

4) There is a lack of error bars for all their RNAseq and qPCR comparisons, which is particularly surprising because the authors went to great lengths and analyzed an applaudably large amount of independent biological replicates, yet the variability observed in the corresponding molecular data is not reported.

Reviewer #1 (Public Review):

Ritvo and colleagues present an impressive suite of simulations that can account for three findings of differentiation in the literature. This is important because differentiation-in which items that have some features in common, or share a common associate are less similar to one another than are unrelated items-is difficult to explain with classic supervised learning models, as these predict the opposite (i.e., an increase in similarity). A few of their key findings are that differentiation requires a high learning rate and low inhibitory oscillations, and is virtually always asymmetric in nature.

This paper was very clear and thoughtful-an absolute joy to read. The model is simple and elegant, and powerful enough to re-create many aspects of existing differentiation findings. The interrogation of the model and presentation of the findings were both extremely thorough. The potential for this model to be used to drive future work is huge. I have only a few comments for the authors, all of which are relatively minor.

1. I was struck by the fact that the "zone" of repulsion is quite narrow, compared with the zone of attraction. This was most notable in the modeling of Chanales et al. (i.e., just one of the six similarity levels yielded differentiation). Do the authors think this is a generalizable property of the model or phenomenon, or something idiosyncratic to do with the current investigation? It seems curious that differentiation findings (e.g., in hippocampus) are so robustly observed in the literature despite the mechanism seemingly requiring a very particular set of circumstances. I wonder if the authors could speculate on this point a bit-for example, might the differentiation zone be wider when competitor "pop up" is low (i.e., low inhibitory oscillations), which could help explain why it's often observed in hippocampus? This seems related a bit to the question about what makes something "moderately" active, or how could one ensure "moderate" activation if they were, say, designing an experiment looking at differentiation.

2. With real fMRI data we know that the actual correlation value doesn't matter all that much, and anti-correlations can be induced by things like preprocessing decisions. I am wondering if the important criterion in the model is that the correlations (e.g., as shown in Figure 6) go down from pre to post, versus that they are negative in sign during the post learning period. I would think that here, similar to in neural data, a decrease in correlation would be sufficient to conclude differentiation, but would love the authors' thoughts on that.

3. For the modeling of the Favila et al. study, the authors state that a high learning rate is required for differentiation of the same-face pairs. This made me wonder what happens in the low learning rate simulations. Does integration occur? This paradigm has a lot of overlap with acquired equivalence, and so I am thinking about whether these are the sorts of small differences (e.g., same-category scenes and perhaps a high learning rate) that bias the system to differentiate instead of integrate.

4. For the simulations of the Schlichting et al. study, the A and B appear to have overlap in the hidden layer based on Figure 9, despite there being no similarity between the A and B items in the study (in contrast to Favila et al., in which they were similar kinds of scenes, and Chanales et al., in which they were similar colors). Why was this decision made? Do the effects depend on some overlap within the hidden layer? (This doesn't seem to be explained in the paper that I saw though, so maybe just it's a visualization error?)

5. It seems as though there were no conditions under which the simulations produced differentiation in both the blocked and intermixed conditions, which Schlichting et al. observed in many regions (as the present authors note). Is there any way to reconcile this difference?

6. A general question about differentiation/repulsion and how it affects the hidden layer representation in the model: Is it the case that the representation is actually "shifted" or repelled over so it is no longer overlapping? Or do the shared connections just get pruned, such that the item that has more "movement" in representational space is represented by fewer units on the hidden layer (i.e., is reduced in size)? I think, if I understand correctly, that whether it gets shifted vs. reduce would depend on the strength of connections along the hidden layer, which would in turn depend on whether it represents some meaningful continuous dimension (like color) or not. But, if the connections within the hidden layer are relatively weak and it is the case that representations become reduced in size, would there be any anticipated consequences of this (e.g., cognitively/behaviorally)?

Reviewer #2 (Public Review):

This paper addresses an important computational problem in learning and memory. Why do related memory representations sometimes become more similar to each other (integration) and sometimes more distinct (differentiation)? Classic supervised learning models predict that shared associations should cause memories to integrate, but these models have recently been challenged by empirical data showing that shared associations can sometimes cause differentiation. The authors have previously proposed that unsupervised learning may account for these unintuitive data. Here, they follow up on this idea by actually implementing an unsupervised neural network model that updates the connections between memories based on the amount of coactivity between them. The goal of the authors' paper is to assess whether such a model can account for recent empirical data at odds with supervised learning accounts. For each empirical finding they wish to explain, the authors built a neural network model with a very simple architecture (two inputs layers, one hidden layer, and one output layer) and with prewired stimulus representations and associations. On each trial, a stimulus is presented to the model, and inhibitory oscillations allow competing memories to pop up. Pre-specified u-shaped learning rules are used to update the weights in the model, such that low coactivity leaves model connections unchanged, moderate coactivity weakens connections, and high coactivity strengthens connections. In each of the three models, the authors manipulate stimulus similarity (following Chanales et al), shared vs distinct associations (following Favila et al), or learning strength (a stand in for blocked versus interleaved learning schedule; following Schlichting et al) and evaluate how the model representations evolve over trials.

As a proof of principle, the authors succeed in demonstrating that unsupervised learning with a simple u-shaped rule can produce qualitative results in line with the empirical reports. For instance, they show that pairing two stimuli with a common associate (as in Favila et al) can lead to *differentiation* of the model representations. Demonstrating these effects isn't trivial and a formal modeling framework for doing so is a valuable contribution. Overall, the authors do a good job of both formally describing their model and giving readers a high level sense of how their critical model components work, though there are some places where the robustness of the model to different parameter choices is unclear. In some cases, the authors are very clear about this (e.g. the fast learning rate required to observe differentiation). However, in other instances, the paper would be strengthened by a clearer reporting of the critical parameter ranges. For instance, it's clear from the manipulation of oscillation strength in the model of Schlichting et al that this parameter can dramatically change the direction of the results. The authors do report the oscillation strength parameter values that they used in the other two models, but it is not clear how sensitive these models are to small changes in this value. Similarly, it's not clear whether the 2/6 hidden layer overlap (only explicitly manipulated in the model of Chanales et al) is required for the other two models to work. Finally, though the u-shaped learning rule is essential to this framework, the paper does little formal investigation of this learning rule. It seems obvious that allowing the u-shape to collapse too much toward a horizontal line would reduce the model's ability to account for empirical results, but there may be other more interesting features of the learning rule parameterization that are essential for the model to function properly.

There are a few other points that may limit the model's ability to clearly map onto or make predictions about empirical data. The model(s) seems very keen to integrate and do so more completely than the available empirical data suggest. For instance, there is a complete collapse of representations in half of the simulations in the Chanales et al model and the blocked simulation in the Schlichting et al model also seems to produce nearly complete integration. Even if the Chanales et al paper had observed some modest behavioral attraction effects, this model would seem to over-predict integration. The author's somewhat implicitly acknowledge this when they discuss the difficulty of producing differentiation ("Practical Advice for Getting the Model to Show Differentiation") and not of producing integration, but don't address it head on. Second, the authors choice of strongly prewiring associations in the Chanales and Favila models makes it difficult to think about how their model maps onto experimental contexts where competition is presumably occurring while associations are only weakly learned. In the Chanales et al paper, for example, the object-face associations are not well learned in initial rounds of the color memory test. While the authors do justify their modeling choice and their reasons have merit, the manipulation of AX association strength in the Schlichting et al model also makes it clear that the association strength has a substantial effect on the model output. Given the effect of this manipulation, more clarity around this assumption for the other two models is needed.

Overall, this is strong and clearly described work that is likely to have a positive impact on computational and empirical work in learning and memory. While the authors have written about some of the ideas discussed in this paper previously, a fully implemented and openly available model is a clear advance that will benefit the field. It is not easy to translate a high-level description of a learning rule into a model that actually runs and behaves as expected. The fact that the authors have made all their code available makes it likely that other researchers will extend the model in numerous interesting ways, many of which the authors have discussed and highlighted in their paper.

Reviewer #3 (Public Review):

This paper proposes a computational account for the phenomenon of pattern differentiation (i.e., items having distinct neural representations when they are similar). The computational model relies on a learning mechanism of the nonmonotonic plasticity hypothesis, fast learning rate and inhibitory oscillations. The relatively simple architecture of the model makes its dynamics accessible to the human mind. Furthermore, using similar model parameters, this model produces simulated data consistent with empirical data of pattern differentiation. The authors also provide insightful discussion on the factors contributing to differentiation as opposed to integration. The authors may consider the following to further strengthen this paper:

The model compares different levels of overlap at the hidden layer and reveals that partial overlap seems necessary to lead to differentiation. While I understand this approach from the perspective of modeling, I have concerns about whether this is how the human brain achieves differentiation. Specifically, if we view the hidden layer activation as a conjunctive representation of a pair that is the outcome of encoding, differentiation should precede the formation of the hidden layer activation pattern of the second pair. Instead, the model assumes such pattern already exists before differentiation. Maybe the authors indeed argue that mechanistically differentiation follows initial encoding that does not consider similarity with other memory traces?

Related to the point above, because the simulation setup is different from how differentiation actually occurs, I wonder how valid the prediction of asymmetric reconfiguration of hidden layer connectivity pattern is.

Although as the authors mentioned, there haven't been formal empirical tests of the relationship between learning speed and differentiation/integration, I am also wondering to what degree the prediction of fast learning being necessary for differentiation is consistent with current data. According to Figure 6, the learning rates lead to differentiation in the 2/6 condition achieved differentiation after just one-shot most of the time. On the other hand, For example, Guo et al (2021) showed that humans may need a few blocks of training and test to start showing differentiation.

Related to the point above, the high learning rate prediction also seems to be at odds with the finding that the cortex, which has slow learning (according to the theory of complementary learning systems), also shows differentiation in Wammes et al (2022).

More details about the learning dynamics would be helpful. For example, equation(s) showing how activation, learning rate and the NMPH function work together to change the weight of connections may be added. Without the information, it is unclear how each connection changes its value after each time point.

In the simulation, the NMPH function has two turning points. I wonder if that is necessary. On the right side of the function, strong activation leads to strengthening of the connectivity, which I assume will lead to stronger activation on the next time point. The model has an upper limit of connection strength to prevent connection from strengthening too much. The same idea can be applied to the left side of the function: instead of having two turning points, it can be a linear function such that low activation keeps weakening connection until the lower limit is reached. This way the NMPH function can take a simpler form (e.g., two line-segments if you think the weakening and strengthening take different rates) and may still simulate the data.

Reviewer #1 (Public Review):

The authors present a carefully controlled set of experiments that demonstrate an additional complexity for GPCR signalling in that endosomal signalling make be different when beta-arrestin is or isn't associated with a G protein-bound V2 vasopressin receptor. It uses state of the art biosensor-based approaches and beta-arrestin KO lines to assess this. It adds to a growing body of evidence that G proteins and beta-arresting can associate with GPCR complexes simultaneously. They also demonstrate the possibility that Gq might also be activated by the V2 receptor. My sense is one thing they may need to be considered is the possibility of such "megacomplexes" might actually involve receptor dimers or oligomers.

Reviewer #2 (Public Review):

This manuscript by Daly et al., probes the emerging paradigm of GPCR signaling from endosomes using the V2R as a model system with an emphasis on Gq/11 and β-arrestins. The study employs cellular imaging, enzyme complementation assays and energy transfer-based sensors to probe the potential formation of GPCR-G-protein-β-arrestin megaplexes. While the study is certainly very interesting, it appears to be very preliminary at many levels, and clearly requires further development in order to make robust conclusions.

1. The use of mini-G-proteins in these experiments is a major concern as these are highly engineered and may not represent the true features of G-proteins. While these have been used as a readout in other publications, their use in demonstrating megaplex formation is sub-optimal, and native, full-length G-proteins should be used.<br /> 2. The interpretation of complementation (NanoLuc) or proximity (BRET) as evidence of signaling not appropriate, especially when overexpression system and engineered constructs are being used.<br /> 3. After the original work from the same corresponding authors on megaplex formation, the major challenge in the field is to demonstrate the existence and relevance of megaplex formation at endogenous levels of components, and the current study focuses solely on showing the proximity of Gq and β-arrestins.<br /> 4. The study lacks a coherent approach, and the assays are often shifted back and forth between the two β-arrestin isoforms (1 and 2), for example, confocal vs. complementation etc.<br /> 5. In every assay, only the G-proteins and β-arrestins are monitored without a direct assessment of the presence of receptor, and absent that data, it is difficult to justify calling these entities megaplexes.

In conclusion, the authors should consider expanding on this work further to make the points more convincingly to make the work solid and impactful. The two corresponding authors are among the leaders in the field having demonstrated the existence of megaplexes, and building on the work in a systematic fashion should certainly move the paradigm forward. As the work presented in the current manuscript is already pre-printed, the authors should take this opportunity to present a completer and more comprehensive story to the field.

Reviewer #3 (Public Review):

The manuscript by Daly et al examines endosomal signaling of the vasopressin type 2 receptors using engineered mini G protein (mG proteins) and a number of novel techniques to address if sustained G protein signaling in the endosomal compartment is enhanced by β arrestin. Employing these interesting techniques they have how V2R could activates Gαs and Gα in the endosomal compartments and how this modulation could occur in arrestin dependent and independent manner. Although the phenomenon of endosomal signaling is complex to address the authors have tried their best to examine these using a number of well controlled set of experiments.

Reviewer #2 (Public Review):

This study highlights the importance of including not only spatio-termporal scales to biodiversity assessments, but also to include some of the possible drivers of biodiversity loss and to study their joint contribution as environmental stressors.

Introduction - Well written and placed within the current trends of unprecedented biodiversity loss, with an emphasis on freshwater ecosystems. The authors identify three important points as to why biodiversity action plans have failed. Namely, community changes occur over large spatio-temporal scales and monitoring programs capture a fraction of these long-term dynamics (e.g. few decades) which although good at capturing trends in biodiversity change, they often fail at identifying the drivers of these changes. Additionally, most of these rely on manual sorting of samples, overlooking cryptic diversity, or state-of-the-art techniques such as sedimentary DNA (sedaDNA) which allow studying decade-long dynamics, usually focus on specific taxonomic groups unable to represent community-level changes. Secondly, the authors identify that biodiversity is threatened by multiple factors and are rarely studied in tandem. Finally, the authors stress the need for high-throughput approaches to study biodiversity changes since historically, most conservation efforts rely on highly specialized skills for biodiversity monitoring, and even well-studied species have relatively short time series data. The authors identify a model freshwater lake (Lake Ring, Denmark) - suitable due to its well-documented history over the last 100 years - to present a comprehensive framework using metabarcoding, chemical analysis and climatic records for identifying past and current impacts on this ecosystem arising from multiple abiotic environmental stressors.

Results - They are brief and should expand some more. Particularly, there are no results regarding metabarcoding data (number of reads, filtering etc.). These details are important to know the quality of the data which represents the bulk of the analyses. Even the supplementary material gives little information on the metabarcoding results (e.g. number of ASVs - whether every ASV of each family were pooled etc.). The drivers of biodiversity change section could be restructured and include main text tables showing the families positively or negatively correlated with the different variables (akin to table S2 but simplified).

Discussion<br /> The discussion is well written, identifying first some of the possible caveats of this study, particularly regarding the classification of metabarcoding data, its biases and the possible DNA degradation of ancient sediment DNA. The authors discuss how their results fit to general trends showing how agricultural runoff and temperature drive changes in freshwater functional biodiversity primarily due to their synergistic effects on bioavailability, adsorption, etc. The authors highlight the advantage of using a system-level approach rather than focusing on taxa-specific studies due to their indicator status. Similarly, the authors justify the importance of studying community composition as far back as possible since it reveals unexpected patterns of ecosystem resilience. Lake Ring, despite its partially recovered status, has not returned to its semi-pristine levels of biodiversity and community assemblage. Additionally, including enzyme activity allows to assess the functional diversity of the studied environment, although reference databases of these pathways are still lacking. Finally, the authors discuss the implications of their findings under a conservation and land management framework suggesting that by combining these different approaches, drivers of biodiversity stressors can be derived with high accuracy allowing for better-informed mitigation and conservation efforts.

Reviewer #1 (Public Review):

This study presents a conceptual and analytical framework for tracking the impacts of human activities on freshwater ecosystems over time. It demonstrates the application of the framework to a 100-year record of community-level biodiversity, climate change, and chemical pollution from sediments cores of Lake Ring, Denmark. By reconstructing biodiversity using environmental DNA (eDNA) and pollutant inputs using mass spectrometry, the authors identify the taxonomic groups responding positively and negatively in different phases of the lake's environmental history. Furthermore, they identify the independent and additive effects of climate variables and pollutants on biodiversity throughout the 100-year record.

Strengths:

The advances in paired molecular and machine learning analyses are an important step towards a better understanding of 20th/21st century trajectories of biodiversity and pollution.

The finding that taxonomic groups so central to ecosystem assessment in Europe (i.e., diatoms) do not appear to respond to degradation or amelioration - providing at least a partial explanation as to why "ecological status" (as defined under the EU Water Framework Directive) has proved so difficult to improve.

The framework shows how both taxonomic and functional indicators can be used to better understand ecological degradation and recovery.

The identification of individual biocides and climate variables driving observed changes is a particular strength.

Limitations:

The analytical framework is not sufficiently explained in the main text.

The significance of findings in relation to functional changes is not clear. What are the consequences of enrichment of RNA transport or ribosome biogenesis pathways between pesticides and recovery stages, for example?

The impact of individual biocides and climate variables, and their additive effects, are assessed but there is no information offered on non-additive interactions (e.g., synergistic, antagonistic).

The level of confidence associated with results is not made explicit. The reader is given no information on the amount of variability involved in the observations, or the level of uncertainty associated with model estimates.

The major implications of the findings for regulatory ecological assessment are missed. Regulators may not be primarily interested in identifying past "ecosystem shifts". What they need are approaches which give greater confidence in monitoring outcomes by better reflecting the ecological impact of contemporary environmental change and ecosystem management. The real value of the work in this regard is that: (1) it shows that current approaches are inappropriate due to the relatively stable nature of the indicators used by regulators, despite large changes in pollutant inputs; (2) it presents some better alternatives, including both taxonomic and functional indicators; and (3) it provides a new reference (or baseline) for regulators by characterizing "semi-pristine" conditions.

Reviewer #2 (Public Review):

Chen et. al investigated the effects of natural tannins, proanthocyanidins, and punicalagin, against infection by the SARS-CoV-2 virus and its variants. The authors found that these two compounds affect different parts of the SARS-CoV-2 viral infection mechanisms, namely that punicalagin may act ACE2-spike protein interaction and repress Main protease activity, whereas tannic acid and OPC inhibits TMPRSS2 activity. Additionally, the authors show that these tannic compounds can act upon multiple variants of the virus, which suggests a pan-inhibitory effect on SARS-CoV-2 viruses. The studies performed herein present a novel alternative to inhibiting viral infection by SARS-CoV-2 which may be of interest to patients with concerns about reinfection.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) All compounds should be tested in vivo to test not only safety but efficacy and whether these compounds elicit any acute liver toxicity when administered in proposed doses.

2) Efficacy in vaccinated patients would be of great interest, especially since many reinfections occur in the vaccinated population (especially by variants such as Delta).

Reviewer #1 (Public Review):

I feel that this study has potentially high public health significance and should be made known to the public, especially the usefulness of a natural chemical product, oligomeric proanthocyanidins, in preventing SARS-CoV2 infection. The studies are very well designed, using the first 5 figures to compare carefully the effects of tannic acid, punicalagin, and oligomeric proanthocyanidins in disrupting the interaction of the virus with host cells and in inhibiting the enzymatic activity of transmembrane serine protease 2 required for viral entry. I am especially impressed by the work done in Figures 6 and 7 in which the investigators put their efforts into quantitating the amounts of oligomeric proanthocyanidins, tannic acid, and punicalagin present in the grape seed, peel, flesh as well as juice. I also appreciate the translational application in which the investigators prepared grape seed extract capsules (200 mg and 400 mg), recruited healthy human subjects to take these capsules once or twice, and showed that the sera from randomized human subjects taking grape seed extract capsules indeed exert does-dependent and time-dependent activities in suppressing the infection rate of various SARS-CoV2 variants using in vitro studies. The study in Figure 7 is indeed very well-designed and quite elegant. The manuscript is also well-written.

Reviewer #2 (Public Review):

The molecular mechanisms by which monoaminergic antidepressants exert their therapeutic effects are unknown. An emerging hypothesis in this regard is that these antidepressants work by modulating the glutamatergic system, yet the precise links remain unclear. In this manuscript, Lin et al. describe one such link. First, they observe that the small nucleolar RNA (snoRNA), SNORD90 is consistently elevated following antidepressant treatments in peripheral blood samples, in postmortem brain samples of individuals that received antidepressant treatments, mouse models of depression, and in induced neurons treated with antidepressants in culture. To test whether the elevation of SNORD90 could be significant for antidepressive-like behaviors, the authors perform bilateral injections of viral vectors carrying either SNORD90 or scrambled controls into the mouse cg1/2 and show that overexpression of SNORD90 reduces anxiety and depressive-like behaviors. Using in-silico analysis of base complementarity, the authors predict that the growth factor, neuregulin 3 (NRG3), could be a potential target of SNORD90, and they then validate this prediction by directly showing that SNORD90 overexpression results in the reduction of NRG3 in human neural progenitor cells, whereas knockdown of SNORD90 upregulates NRG3. The authors then show that the binding of SNORD90 to NRG3 pre-mRNA and mature mRNA results in their methylation and subsequent decay. Finally, they show that SNORD90 overexpression in the mouse anterior cingulate cortex is sufficient to increase the levels of glutamatergic neurotransmission.

Overall, the experiments described in the manuscript are well executed and their conclusions are fairly drawn. The observations that SNORD90 overexpression is sufficient to reduce anxiety and depression-like behaviors are indeed exciting, as are the links between SNORD90, and m6A methylation of NRG3, and glutamatergic neurotransmission. There are a few weaknesses in the data and the text, but these should be addressable by the authors.

Reviewer #1 (Public Review):

The authors of this manuscript are interested in identifying the molecular mechanisms underlying antidepressant action. Though most antidepressants target the serotonin system, regulation of glutamate neurotransmission has been associated with rapid treatment response. Here the authors find that monoaminergic targeted antidepressants are associated in some patients with expression of a small nucleolar RNA that they go on to show results in alterations to glutamate neurotransmission in a mouse model. These data offer a molecular mechanism that can link traditional monoaminergic targeted antidepressants with glutamatergic regulation and could offer a new way to estimate the efficacy of these drugs.

Reviewer #1 (Public Review):

In their research article, Sapiro et al. overcome the technical burden of low B. burgdorferi numbers during infection by physically enriching for spirochetes prior to RNA-sequencing/mass spectrometry. This technology, which has potential broad applications, was applied to B. burgdorferi-infected ticks, generating datasets for future studies.

Sapiro et al. addressed many of the reviewers' comments including the addition of experimental details, comparisons to other studies and some caveats to their approach. The manuscript has been significantly improved and I appreciate the efforts to address our critiques. There are a few remaining comments that the authors should consider before creating the final Version of Record.

The authors sought to develop technology for a transcriptomic analysis of B. burgdorferi directly from infected ticks. The methodology has exciting implications to better understand pathogen RNA profiles during specific infection timepoints, even beyond the Lyme spirochete. The authors demonstrate successful sequencing of the B. burgdorferi transcriptome from ticks and perform mass spectrometry to identify possible tick proteins that interact with B. burgdorferi. This technology and first dataset will be useful for the field. The study is limited in that no transcripts/proteins are followed-up by additional experiments and no biological interactions/infectious-processes are investigated.

Remaining critiques:

Experimental data regarding the sensitivity of this approach are missing. What is the limit of detection for this protocol? While the authors have stated that they were unable to sequence B. burgdorferi from unfed nymphs, the number of bacteria needed for antibody enrichment are not tested. The starting CFU in their infected nymphal ticks was also not reported (the authors only report reisolation data from 12 ticks). Page 18, line 458 the authors claim their approach "captured the vast majority" of Bb inside of the tick. Data are missing to demonstrate this. Understanding the limits of this approach will be critical for future applications, especially when using B. burgdorferi infected material with low bacterial burden.

The authors should clarify the term "genes" in the abstract and throughout the manuscript. I think they actually mean "open reading frames" or "annotated mRNAs".

More information regarding the efficacy of RNA-seq coverage is still warranted and lacking from the results, especially on page 6. The authors skip right to differential expression analysis without fully examining sequencing effectiveness. This is especially important given their development of a new technique. What was the numbers of detected genes for each sample? How is this affected by bacterial burden of the sample? What is the distribution of reads among tRNAs, mRNAs, UTRs, and sRNAs? How reproducible is the coverage for one gene across replicates? A few browser images of RNA-seq data (ex. of BAM files) across different genes would be useful to visualize the read coverage per gene.

Downregulated genes are largely ignored and should be commented on further.

Page 11, line 258-260: authors state Rpos, Rrp1, and RelBbu are the "three main Bb regulatory programs active in the tick." Yes, these three regulons have been well studied but there could be other uncharacterized regulatory programs. Please consider changing the language.

Reviewer #2 (Public Review):

This work is significant as it provides insights into the global transcriptomic changes of Borrelia burgdorferi during tick feeding. The manuscript also provides methodological advances for the study of the transcriptome of Borrelia burgdorferi in the tick host.

This manuscript documents the study of the transcriptome of Borrelia burgdorferi at 1, 2, 3 and 4 days post-feeding in nymphs of Ixodes scapularis. The authors use antibody-based pull-downs to separate bacteria from tick and mouse cells to perform an enrichment. The data presented support that the transcriptome of B. burgdorferi changes over time in the tick. This work is important as, until now, only limited information on specific genes had been collected. The methodological advances described in this study are valuable for the field.

Reviewer #2 (Public Review):

This paper explores the mechanisms by which cells in tissues use the extracellular matrix (ECM) to reinforce and establish connections. This is a mechanistic and quantitative paper that uses imaging and genetics to establish that the Type IV collagen, DDR-2/collagen receptor discoidin domain receptor 2, signaling through Ras to strengthen an adhesion between two cell types in C. elegans. This connection needs to be strong and robust to withstand the pressure of the numerous eggs that pass through the uterus. The major strengths of this paper are in crisply designed and clear genetic experiments, beautiful imaging, and well supported conclusions. I find very few weaknesses, although, perhaps the evidence that DDR-2 promotes utse-seam linkage through regulation of MMPs could be stronger. This work is impactful because it shows how cells in vivo make and strengthen a connection between tissues through ECM interactions involving collaboration between discoidin and integrin.

Reviewer #2 (Public Review):

Numerous neurodegenerative diseases are thought to be driven by the aggregation of proteins into insoluble filaments known as "amyloids". Despite decades of research, the mechanism by which proteins convert from the soluble to insoluble state is poorly understood. In particular, the initial nucleation step is has proven especially elusive to both experiments and simulation. This is because the critical nucleus is thermodynamically unstable, and therefore, occurs too infrequently to directly observe. Furthermore, after nucleation much faster processes like growth and secondary nucleation dominate the kinetics, which makes it difficult to isolate the effects of the initial nucleation event. In this work Kandola et al. attempt to surmount these obstacles using individual yeast cells as microscopic reaction vessels. The large number of cells, and their small size, provides the statistics to separate the cells into pre- and post-nucleation populations, allowing them to obtain nucleation rates under physiological conditions. By systematically introducing mutations into the amyloid-forming polyglutamine core of huntingtin protein, they deduce the probable structure of the amyloid nucleus. This work shows that, despite the complexity of the cellular environment, the seemingly random effects of mutations can be understood with a relatively simple physical model. Furthermore, their model shows how amyloid nucleation and growth differ in significant ways, which provides testable hypotheses for probing how different steps in the aggregation pathway may lead to neurotoxicity.

In this study Kandola et al. probe the nucleation barrier by observing a bimodal distribution of cells that contain aggregates; the cells containing aggregates have had a stochastic fluctuation allowing the proteins to surmount the barrier, while those without aggregates have yet to have a fluctuation of suitable size. The authors confirm this interpretation with the selective manipulation of the PIN gene, which provides an amyloid template that allows the system to skip the nucleation event.

In simple systems lacking internal degrees of freedom (i.e., colloids or rigid molecules) the nucleation barrier comes from a significant entropic cost that comes from bringing molecules together. In large aggregates this entropic cost is balanced by attractive interactions between the particles, but small clusters are unable to form the extensive network of stabilizing contacts present in the larger aggregates. Therefore, the initial steps in nucleation incur an entropic cost without compensating attractive interactions (this imbalance can be described as a surface tension). When internal degrees of freedom are present, such as the conformational states of a polypeptide chain, there is an additional contribution to the barrier coming from the loss of conformational entropy required to the adopt aggregation-prone state(s). In such systems the clustering and conformational processes do not necessarily coincide, and a major challenge studying nucleation is to separate out these two contributions to the free energy barrier. Surprisingly, Kandola et al. find that the critical nucleus occurs within a single molecule. This means that the largest contribution to the barrier comes from the conformational entropy cost of adopting the beta-sheet state. Once this state is attained, additional molecules can be recruited with a much lower free energy barrier.

There are several caveats that come with this result. First, the height of the nucleation barrier(s) comes from the relative strength of the entropic costs compared to the binding affinities. This balance determines how large a nascent nucleus must grow before it can form interactions comparable to a mature aggregate. In amyloid nuclei the first three beta strands form immature contacts consisting of either side chain or backbone contacts, whereas the fourth strand is the first that is able to form both kinds of contacts (as in a mature fibril). This study used relatively long polypeptides of 60 amino acids. This is greater than the 20-40 amino acids found in amyloid-forming molecules like ABeta or IAPP. As a result, Kandola et al.'s molecules are able to fold enough times to create four beta strands and generate mature contacts intramolecularly. The authors make the plausible claim that these intramolecular folds explain the well-known length threshold (L~35) observed in polyQ diseases. The intramolecular folds reduce the importance of clustering multiple molecules together and increase the importance of the conformational states. Similarly, manipulating the sequence or molecular concentrations will be expected to manipulate the relative magnitude of the binding affinities and the clustering entropy, which will shift the relative heights of the entropic barriers.

The authors make an important point that the structure of the nucleus does not necessarily resemble that of the mature fibril. They find that the critical nucleus has a serpentine structure that is required by the need to form four beta strands to get the first mature contacts. However, this structure comes at a cost because residues in the hairpins cannot form strong backbone or zipper interactions. Mature fibrils offer a beta sheet template that allows incoming molecules to form mature contacts immediately. Thus, it is expected that the role of the serpentine nucleus is to template a more extended beta sheet structure that is found in mature fibrils.

A second caveat of this work is the striking homogeneity of the nucleus structure they describe. This homogeneity is likely to be somewhat illusory. Homopolymers, like polyglutamine, have a discrete translational symmetry, which implies that the hairpins needed to form multiple beta sheets can occur at many places along the sequence. The asparagine residues introduced by the authors place limitations on where the hairpins can occur, and should be expected to increase structural homogeneity. Furthermore, the authors demonstrate that polyglutamine chains close to the minimum length of ~35 will have strict limitations on where the folds must occur in order to attain the required four beta strands.

A novel result of this work is the observation of multiple concentration regimes in the nucleation rate. Specifically, they report a plateau-like regime at intermediate regimes in which the nucleation rate is insensitive to protein concentration. The authors attribute this effect to the "self-poisoning" phenomenon observed in growth of some crystals. This is a valid comparison because the homogeneity observed in NMR and crystallography structures of mature fibrils resemble a one-dimensional crystal. Furthermore, the typical elongation rate of amyloid fibrils (on the order of one molecule per second) is many orders of magnitude slower than the molecular collision rate (by factors of 10^6 or more), implying that the search for the beta-sheet state is very slow. This slow conformational search implies the presence of deep kinetic traps that would be prone to poisoning phenomena. However, the observation of poisoning in nucleation during nucleation is striking, particularly in consideration of the expected disorder and concentration sensitivity of the nucleus. Kandola et al.'s structural model of an ordered, intramolecular nucleus explains why the internal states responsible for poisoning are relevant in nucleation.

To achieve these results the authors used a novel approach involving a systematic series of simple sequences. This is significant because, while individual experiments showed seemingly random behavior, the randomness resolved into clear trends with the systematic approach. These trends provided clues to build a model and guide further experiments.

Reviewer #1 (Public Review):

The authors take on the challenge of defining the core nucleus for amyloid formation by polyglutamine tracts. This rests on the assertion that polyQ forms amyloid structures to the exclusion of all other forms of solids. Using their unique assay, deployed in yeast, the authors attempt to infer the size of the nucleus that templates amyloid formation by polyQ. Further, through a series of sequence titrations, all studied using a single type of assay, the authors converge on an assertion stating that a single polyQ molecule is the nucleus for amyloid formation, that 12-residues make up the core of the nucleus, that it takes ca. 60 Qs in a row to unmask this nucleation potential, and that polyQ amyloid formation belongs to the same universality class as self-poisoned crystallization, which is the hallmark of crystallization from polymer melts formed by large, high molecular weight synthetic polymers. Unfortunately, the authors have decided to lean in hard on their assertions without a critical assessment of whether their findings stand up to scrutiny. If their findings are truly an intrinsic property of polyQ molecules, then their findings should be reconstituted in vitro. Unfortunately, careful and rigorous experiments in vitro show that there is a threshold concentration for forming fibrillar solids. This threshold concentration depends on the flanking sequence context on temperature and on solution conditions. The existence of a threshold concentration defies the expectation of a monomer nucleus. The findings disagree with in vitro data presented by Crick et al., and ignored by the authors. Please see: https://doi.org/10.1073/pnas.1320626110. These reports present data from very different assays, the importance of which was underscored first by Regina Murphy and colleagues. The work of Crick et al., provides a detailed thermodynamic framework - see the SI Appendix. This framework dove tails with theory and simulations of Zhang and Muthukumar, which explains exactly how a system like polyQ might work (https://doi.org/10.1063/1.3050295). The picture one paints is radically different from what the authors converge upon. One is inclined to lean toward data that are gleaned using multiple methods in vitro because the test tube does not have all the confounding effects of a cellular milieu, especially when it comes to focusing on sequence-intrinsic conformational transitions of a protein. In addition to concerns about the limitations of the DAmFRET method, which based on the work of the authors in their collaborative paper by Posey et al., are being stretched to the limit, there is the real possibility that the cellular milieu, unique to the system being studied, is enabling transitions that are not necessarily intrinsic to the sequence alone. A nod in this direction is the work of Marc Diamond, which showed that having stabilized the amyloid form of Tau through coacervation, there is a large barrier that limits the loss of amyloid-like structure for Tau. There may well be something similar going on with the polyQ system. If the authors could show that their data are achievable in vitro without anything but physiological buffers one would have more confidence in a model that appears to contradict basic physical principles of how homopolymers self-assemble. Absent such additional evidence, numerous statements seem to be too strong. There are also several claims that are difficult to understand or appreciate.

Reviewer #3 (Public Review):

Kandola et al. explore the important and difficult question regarding the initiating event that triggers (nucleates) amyloid fibril growth in glutamine-rich domains. The researchers use a fluorescence technique that they developed, dAMFRET, in a yeast system where they can manipulate the expression level over several orders of magnitude, and they can control the length of the polyglutamine domain as well as the insertion of interfering non-glutamine residues. Using flow cytometry, they can interrogate each of these yeast 'reactors' to test for self-assembly, as detected by FRET.

In the introduction, the authors provide a fairly thorough yet succinct review of the relevant literature into the mechanisms of polyglutamine-mediated aggregation over the last two decades. The presentation as well as the illustrations in Figure 1A and 1B are difficult to understand, and unfortunately, there is no clear description of the experimental technique that would allow the reader to connect the hypothetical illustrations to the measurement outcomes. The authors do not explain what the FRET signal specifically indicates or what its intensity is correlated to. FRET measures distance between donor and acceptor, but can it be reliably taken as an indicator of a specific beta-sheet conformation and of amyloid? Does the signal increase with both nucleation and with elongation, and is the signal intensity the same if, e.g., there were 5 aggregates of 10 monomers each versus 50 monomeric nuclei? Is there a reason why the AmFRET signal intensity decreases at longer Q even though the number of cells with positive signal increases? Does the number of positive cells increase with time? The authors state later that 'non-amyloid containing cells lacked AmFRET altogether', but this seems to be a tautology - isn't the lack of AmFRET taken as a proof of lack of amyloid? Overall, a clearer description of the experimental method and what is actually measured (and validation of the quantitative interpretation of the FRET signal) would greatly assist the reader in understanding and interpreting the data.

The authors demonstrate that their assay shows that the fraction of cells with AmFRET signal increases strongly with an increase in polyQ length, with a 'threshold around 50-60 glutamines. This roughly correlates with the Q-length dependence of disease. The experiments in which asparagine or other amino acids are inserted at variable positions in the glutamine repeat are creative and thorough, and the data along with the simulations provide compelling support for the proposed Q zipper model. The experiments shown in Figure 5 are strongly supportive of a model where formation of the beta-sheet nucleus is within a monomer. This is a potentially important result, as there are conflicting data in the literature as to whether the nucleus in polyQ is monomer.

I did not find the argument, that their data shows the Q zipper grows in two dimensions, compelling; there are more direct experimental methods to answer this question. I was also confused by the section that Q zippers poison themselves. It would be easier for the reader to follow if the authors first presented their results without interpretation. The data seem more consistent with an argument that, at high concentrations, non-structured polyQ oligomers form which interfere with elongation into structured amyloid assemblies - but such oligomers would not be zippers.

Although some speculation or hypothesizing is perfectly appropriate in the discussion, overall the authors stretch this beyond what can be supported by the results. A couple of examples: The conclusion that toxicity arises from 'self-poisoned polymer crystals' is not warranted, as there is no relevant data presented in this manuscript. The authors refer to findings 'that kinetically arrested aggregates emerge from the same nucleating event responsible for amyloid formation', but I cannot recall any evidence for this statement in the results section.

Reviewer #1 (Public Review):

This paper looks at nutrient-responsive Ca++ flux in islet cells of eight genetically diverse mouse strains. The investigators correlate Ca++ flux with insulin secretory capacity, demonstrating that calcium parameters in response to different nutrients are a better predictor of insulin secretory capacity than average calcium. They also correlate Ca++ flux with previously collected islet protein abundance followed by integration with human genome-wide association studies. This integration allows them to identify a sub-set of proteins that are both relevant to human islet function and that may play a causal role in regulating islet Ca++ oscillations. All data have been deposited in a searchable public database. There are many strengths to this paper. To my knowledge, this is the first work to assess the genetics of nutrient-responsive Ca++ flux in islets. Given the importance of Ca++ for beta cell insulin secretion, this work is of high importance. Investigators also use the founders of two powerful genetic mouse models: the diversity outbred and collaborative, opening up several avenues of future research into the genetics of Ca++ flux. By looking at multiple parameters of Ca++ flux, investigators are able to start to understand which parameters may be driving low or high insulin secretion. Integration with protein abundance and human GWAS has allowed identification of proteins with known roles in insulin secretory capacity, as well as several novel regulators, again opening up several avenues of future research. Finally, the public database is likely to be useful to multiple investigators interested in following up specific protein targets or in conducting future genetic studies. I found only minor weaknesses in this paper, mainly regarding clarity in certain areas. One specific area to be improved is Figure 4A, B where in addition to the heat maps, it would be useful to see regression plots that show the differences per sex and strain for the insulin secretion vs Ca++ parameters.

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. One major critique is that 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 #1 (Public Review):

Secondary cell walls support vascular plants and conduct water throughout the plant body, but are also important resources for lignocellulosic feedstocks. Secondary cell wall synthesis is under complex transcriptional control, presumably because it must only be initiated after cell growth is complete. Here, the authors found that two Musashi-type RNA-binding proteins, MSIL2 and MSIL4 are redundantly required for secondary cell wall development in Arabidopsis. The plant phenotypes could be complemented by the wild-type version of either protein, but not by a MSIL4 version that carries mutations in the conserved RNA-binding domains, and the authors localized MSIL2 & 4 to stress granules, implicating the RNA-binding function of MSIL4 in the cell wall phenotype. Upon closer inspection, the secondary cell wall phenotypes included changes in vasculature morphology, and minor changes to lignin and hemicellulose (glucuronoxylan). While there were no changes to likely cell wall target genes in the transcriptome of msil2msil4 plants, proteomics experiments found glucuronoxylan biosynthesis components were upregulated in the mutants, and they detected an increase in substituted xylan via several methods. Finally, they documented MSIL4 binding to RNA encoding one of these targets, suggesting that MSIL2 and MSIL4 act to post-transcriptionally regulate glucuronoxylan modification. Altogether, this is a new mechanism by which cell wall composition could be regulated.

Overall, the manuscript is well-written, the data are generally high-quality, and the authors typically use several independent methods to support each claim. However, several important questions remain unanswered by this work in its current state and the model presented in Figure 7 is quite speculative. For example, the link between the striking plant phenotype and GXM misregulation is unclear since GXM overexpression doesn't alter plant phenotypes or lignin content (Yuan et al 2014 Plant Science), so misregulation of GXMs in msil2msil4 mutants clearly is not the whole story. It also remains to be determined why one particular secondary cell wall synthesis enzyme is regulated likely post-transcriptionally, while so much of the pathway is regulated at the transcriptional level. There are likely other targets for MSIL2- and MSIL4-mediated regulation since it seems that MSIL2 and MSIL4 are expressed in tissues that are not synthesizing secondary cell walls.

Reviewer #2 (Public Review):

This work explored the biological functions of a small family of RNA-binding proteins that was previously studied in animals, but was uncharacterized in plants. Combinatorial T-DNA insertional mutants disrupting the expression of the four Mushashi-like (MSIL) genes in Arabidopsis revealed that only the msil2 msil4 double mutant visibly alters plant development. The msil2/4 plants produced stems that could not stand upright. Transgene complementation, site-directed mutagenesis of MSIL4 conserved RNA-binding motifs, and in vitro RNA binding assays support the conclusion that the loss of MSIL2 and MISL4 function is responsible for the observed morphological defects. MSIL2/4 interact with proteins associated with mRNA 3'UTR binding and translational regulation.

The authors present compelling biochemical evidence that Mushashi-like2 (MSIL2) and MSIL4 jointly regulate secondary cell wall biosynthesis in the Arabidopsis stem. Quantitative analyses of proteins and transcripts in msil2/4 stems uncovered upregulation of several xylan-related enzymes (despite WT-like RNA levels). Consistent with MALDI-TOF data for released xylan oligosaccharides, the authors propose a model in which MSIL2/4 negatively regulate the translation of GXM (glucuronoxylan methyltransferase), a presumed rate-limiting step. The molecular links between overmethylated xylans and the observed stem defects (which include subtle reductions in lignin and increases beta-glucan polymer distribution) warrants further investigation in future studies. Similarly, as the authors point out, it is intriguing that the loss of the broadly expressed MSIL2/4 genes only significantly affects specific cell types in the stem.

Reviewer #3 (Public Review):

The manuscript by Kairouani et al. investigates the function of a small family of plant RNA binding proteins with similarity to the well-studied Musashi protein in animals, and, therefore, called MUSASHI-LIKE1-4 (MSL1-4). Studies on the biological importance of post-transcriptional control of gene expression via RNA-binding proteins in plants are not numerous, and advances in this important field are much needed. The thorough work presented in this manuscript is such an advance.

The central observations of the paper are:

- Knockout of any MSL gene alone does not produce a phenotype.<br /> It is of note that basic characterization of knockout mutations is really well done - for example, the authors have taken care to raise specific antibodies to each of the MSL proteins and use them to demonstrate that each of the T-DNA insertion mutants used actually does knock out protein production from the corresponding gene.

- Knockout of MSL2/4 (but no other double mutant) produces a clear leaf phenotype, and a remarkable stem phenotype in which the mutants collapse as they are unable to support upright growth

- The phenotypes of knockout mutants persist in point mutants defective in RNA-binding, indicating that RNA-binding is required for biological activity. Consistent with this, and associate physically with other RNA-binding proteins and translation factors.

- MSL proteins are cytoplasmic

- The msl2/4 mutants present multiple defects in secondary cell wall composition and structure, probably explaining their inability to grow upright. I did not examine the cell wall analyses in detail as I am no specialist in this field.

- Msl2/4 mutants show transcriptomic changes with at large two big categories of differentially expressed genes compared to wild type.<br /> (1) Genes related to cell wall metabolism<br /> (2) Genes associated with defense against herbivores and pathogens

- Two of the mRNAs encoding cell wall factors with significant upregulation in msl2/4 mutants compared to wild type also associate physically with MSL4 as judged by RNA-immunoprecipitation-RT-PCR assays, and this physical association is abrogated in the RNA-binding deficient MSL4 mutant.

Altogether, the study shows clear biological relevance of the MSL family of RNA-binding proteins, and provides good arguments that the underlying mechanism is control of mRNAs encoding enzymes involved in secondary cell wall metabolism (although concluding on translational control in the abstract is perhaps saying too much - post-transcriptional control will do given the evidence presented). One observation reported in the study makes it vulnerable to alternative interpretation, however, and I think this should be explicitly treated in the discussion:

The fact that immune responses are switched on in msl2/4 mutants could also mean that MSL2/4 have biological functions unrelated to cell wall metabolism in wild type plants, and that cell wall defects arise solely as an indirect effect of immune activation (that is known to involve changes in expression of many cell wall-modifying enzymes and components such as pectin methylesterases, xyloglucan endotransglycosylases, arabinogalactan proteins etc. Indeed, the literature is rich in examples of gene functions that have been misinterpreted on the basis of knockout studies because constitutive defense activation mediated by immune receptors was not taken into account (see for example Lolle et al., 2017, Cell Host & Microbe 21, 518-529).

With the evidence presented here, I am actually close to being convinced that the primary defect of msl2/msl4 mutants is directly related to altered cell wall metabolism, and that defense responses arise as a consequence of that, not the other way round. But I do not think that the reverse scenario can be formally excluded with the evidence at hand, and a discussion listing arguments in favor of the direct effect proposed here would be appropriate. Elements that the authors could consider to include would be the isolation of a cellulose synthase mutant as a constitutive expressor of jasmonic acid responses (cev1) as a clear example that a primary defect in cell wall metabolism can produce defense activation as secondary effect. The interaction of MSL4 with GXM1/3 mRNAs is also helpful to argue for a direct effect, and it would strengthen the argument if more examples of this kind could be included.

Reviewer #1 (Public Review):

In this study, the authors use prospective sorting and microarray analyses, extended by single-cell RNA sequencing, in the neural stem cell niche of the subventricular zone (SVZ) to identify and refine a series of states along the continuum from quiescent neural stem cells to mature progeny. Of note, changes in the levels and subgroups of RNA splicing regulators are detailed across this continuum. Using in vitro proliferation and differentiation assays, coupled with in vivo engraftment of some prospectively sorted subsets, the authors argue that a stage they define as immature neuroblasts (iNBs) retain proliferative and multilineage differentiation capacity that is not seen in the mature neuroblast population, and is unexpected based on prior models for lineage progression in this system. This iNB stage is accompanied by a change in RNA splicing regulator expression, which is of interest due to the emerging roles for RNA processing and preferential translation within this niche.

These data complement several additional sc-RNAseq studies of this stem cell niche, and use a different, but similar, sorting strategy to isolate and profile subpopulations of stem/progenitor cells and neuroblast progeny. The claim that immature neuroblasts retain multipotency - the ability to generate glia and neurons - is surprising and somewhat controversial given that this has largely not been reported before under homeostatic conditions. Some factors to consider when interpreting these data are that the "immature neuroblast" populations are studied in some experiments using a transcriptional signature and a functional assay, namely the timing of reappearance of these cells after use of agents that kill rapidly dividing cells (in this case, radiation), leading to reconstitution of the lineage by previously quiescent stem cells. In a separate set of experiments, a tamoxifen-inducible labeling system is used in combination with cell-surface markers to prospectively isolate and study the differentiation potential of neuroblast populations that are assumed to be equivalent to those found in transcriptional experiments. It would be of interest in future to confirm that the exact sorted populations (using CD24/EGFR/DCX-CreERT2::CAG) have the same transcriptional profile as those studied in earlier experiments within the paper, and to confirm the purity of the sorted populations. Finally, while elegant use is made of engraftment of the sorted populations to study the differentiation and lineage potential of these immature neuroblasts, a remaining question is the relative abundance of each lineage (neurons/astrocytes/oligodendrocytes) produced by the engrafted cells - is production of glia rare, or common? Could this be due to factors such as alteration of lineage potential due to culture conditions, a disconnect between transcript expression and protein expression, or an incompletely purified starter population?

Overall, this manuscript presents an intriguing possible refinement of models for SVZ neurogenesis, and highlights the role of RNA splicing at specific stages in the lineage. It will be of interest to see if additional groups confirm these findings and whether multiplexed immunostaining, highly multiplexed flow cytometry, or other approaches focused at the proteomic level confirm and extend these findings, particularly given recent data in the developing brain that suggest transcript and protein levels are relatively poorly correlated in stem/progenitor populations.

A final point on terminology: "iNB", "A cells", and "D1/D2 cells" are all used in the manuscript to denote different stages along the continuum from TAP/C cells to mature neuroblasts; however, historically "D cells" refers to neuroblasts in the dentate gyrus, not those derived from the SVZ. In this case, the authors are exclusively studying SVZ-derived neuroblasts.

Reviewer #2 (Public Review):

Bernou et al use a FACS-based method to sort different cells along the neurogenesis trajectory. They identify cells that are LeX+EGFR+CD24+ which they call i-NBs. The authors suggest these cells proliferate performing neurosphere assays, and that they can make all NSC-derived differentiated cell types through transplantation into mice. They performed microarrays on the different cell subtypes, which led them to their interest in RNA splicing proteins. They additionally performed single-cell analyses to try to identify the cluster of i-NBs compared to other cell types. Further, they performed an irradiation experiment to initiate quiescence exit and depletion of the dividing cell types to create a directionality in the progression through cell types. Comparison with other published sequencing datasets of the same cell type revealed that the i-NBs were most similar to Mitotic TAPs. The authors use their single cell sequencing data to observe expression changes of the RNA splicing factors in different clusters. They also suggest that the i-NB population is heterogeneous in their DCX mRNA levels, with a high group and a low group that have different characteristics. They erroneously use a DCX-Cre-ERT2 line to identify GFP+ or GFP- cells to transplant, and find no GFP+ cells at the end of 5 weeks after transplantation, and draw the conclusion that the high DCX cells don't have the same NSC potential. The authors propose they have identified a new cell type, and that there should be a rewrite of the SVZ neurogenesis cascade to include this population.

Summary of response<br /> This manuscript postulates the identification of a new cell type in the adult neurogenesis cascade. However, all of the author's analyses point to this population of sorted cells being the late mitotic TAPs on their way to becoming neuroblasts. This would suggest that these cells are in the trajectory between TAPs and NBs, so a pivot point, but not a unique cell type in its own. In their sequencing analyses, cell cycle becomes the defining factor of the clustering. Indeed, their cell type as compared to other datasets suggests this population is a mitotic TAP, which is supported by their own transcriptome data (Fig S2) showing that i-NBs are just further in mitosis than the TAPs.

Reviewer #1 (Public Review):

This study by Park et al. describes an interesting approach to disentangle gene-environment pathways to cognitive development and psychotic-like experiences in children. They have used data from the ABCD study and have included PGS of EA and cognition, environmental exposure data, cognitive performance data and self-reported PLEs. Although the study has several strengths, including its large sample size, interesting approach and comprehensive statistical model, I have several concerns:

- The authors have included follow-up data from the ABCD Study. However, it is not very clear from the beginning that longitudinal paths are being explored. It would be very helpful if the authors would make their (analysis) approach clearer from the introduction. Now, they describe many different things, which makes the paper more difficult to read. It would be of great help to see the proposed path model in a Figure and refer to that in the Method.

- There is quite a lot of causal language in the paper, particularly in the Discussion. My advice would be to tone this down.

- It's a bit unclear to me why the authors chose the PEs phenotype as their outcome of interest. They mainly speak about child development and mental health in general in the Introduction, so why focus on PLE? Aren't genes and environments also relevant for other types of mental health problems? Relatedly, in the Discussion the authors seem to conflate PLEs with psychosis. There is a large body of literature highlighting the differences between PLEs and psychosis, and this should - in my opinion - be adjusted throughout the introduction and discussion.

- I feel that the limitation section is a bit brief, and can be developed further.

- I like that the assessment of CP and self-reports PEs is of good quality. However, I was wondering which 4 items from the parent-reported CBCL were used and how did they correlate with the child-reported PEs? And how was distress taken into account in the child self-reported PEs measurement? Which PEs measures were used?

- What was the correlation between CP and EA PGSs?

- Regarding the PGS: why focus on cognitive performance and EA? It should be made clearer from the introduction that EA is not only measuring cognitive ability, but is also a (genetic) marker of social factors/inequalities. I'm guessing this is one of the reasons why the EA PGS was so much more strongly correlated with PEs than the CP PGS. See the work bij Abdellaoui and the work by Nivard.

- Considering previous work on this topic, including analyses in the ABCD Study, I'm not surprised that the correlation was not very high. Therefore, I don't think it makes a whole of sense to adjust for the schizophrenia PGS in the sensitivity analyses, in other words, it's not really 'a more direct genetic predictor of PLEs'.

- How did the FDR correction for multiple testing affect the results?

Overall, I feel that this paper has the potential to present some very interesting findings. However, at the moment the paper misses direction and a clear focus. It would be a great improvement if the readers would be guided through the steps and approach, as I think the authors have undertaken important work and conducted relevant analyses.

Reviewer #2 (Public Review):

This paper tried to assess the link between genetic and environmental factors on psychotic-like experiences, and the potential mediation through cognitive ability. This study was based on data from the ABCD cohort, including 6,602 children aged 9-10y. The authors report a mediating effect, suggesting that cognitive ability is a key mediating pathway in the link between several genetic and environmental (risk and protective) factors on psychotic-like experiences.

While these findings could be potentially significant, a range of methodological unclarities and ambiguities make it difficult to assess the strength of evidence provided.

Strengths of the methods:

The authors use a wide range of validated (genetic, self- and parent-reported, as well as cognitive) measures in a large dataset with a 2-year follow-up period. The statistical methods have the potential to address key limitations of previous research.

Weaknesses of the methods:

The rationale for the study is not completely clear. Cognitive ability is probably a more likely mediator of traits related to negative symptoms in schizophrenia, rather than positive symptoms (e.g., psychosis, psychotic-like symptom). The suggestion that cognitive ability might lead to psychotic-like symptoms in the general population needs further justification.

Terms are used inconsistently throughout (e.g., cognitive development, cognitive capacity, cognitive intelligence, intelligence, educational attainment...). It is overall not clear what construct exactly the authors investigated.

Not the largest or most recent GWASes were used to generate PGSes.<br /> It is not fully clear how neighbourhood SES was coded (higher or lower values = risk?). The rationale, strengths, and assumptions of the applied methods are not fully clear. It is also not clear how/if variables were combined into latent factors or summed (weighted by what). It is not always clear when genetic and when self-reported ethnicity was used. Some statements might be overly optimistic (e.g., providing unbiased estimates, free even of unmeasured confounding; use of representative data).

It appears that citations and references are not always used correctly.

Strengths of the results:

The authors included a comprehensive array of analyses.

Weaknesses of the results:

Many results, which are presented in the supplemental materials, are not referenced in the main text and are so comprehensive that it can be difficult to match tables to results. Some of the methodological questions make it challenging to assess the strength of the evidence provided in the results.

Appraisal:

The authors suggest that their findings provide evidence for policy reforms (e.g., targeting residential environment, family SES, parenting, and schooling). While this is probably correct, a range of methodological unclarities and ambiguities make it difficult to assess whether the current study provides evidence for that claim.

Impact:

The immediate impact is limited given the short follow-up period (2y), possibly concerns for selection bias and attrition in the data, and some methodological concerns.

Reviewer #2 (Public Review):

The manuscript by Petroccione et al., examines the modulatory role of the neuronal glutamate transporter EAAC1 on glutamatergic and GABAergic synaptic strength at D1- and D2-containing medium spiny neurons within the dorsolateral striatum. They find that pharmacological and genetic disruption of EAAC1 function increases glutamatergic synaptic strength specifically at D1-MSNs. They show that this is due to a structural change in release sites, not release probability. They also show that EAAC1 is critical in maintaining lateral inhibition specifically between D1-MSNs. Taken together, the authors conclude that EAAC1 functions to constrain D1-MSN excitation. Using a computational modeling technique, they posit that EAAC1's modulatory role at glutamatergic and GABAergic inputs onto D1-MSNs ultimately manifests as a reduction of gain of the input-output firing relationship and increases the offset. They go on to show that EAAC1 deletion leads to enhanced switching behavior in a probabilistic operant task. They speculate that this is due to a dysregulated E/I balance at D1-MSNs in the DLS.

Overall, this is a very interesting study focused on an understudied glutamate transporter. Generally, the study is done in a very thorough and methodical manner and the manuscript is well written.

Major Comments/Concerns:<br /> 1. Regional/Local manipulations in behavior study: The manuscript would be greatly improved if they provided data linking the ex vivo electrophysiological findings within the DLS with the behavior. Although they are using a DLS-dependent task, they are nonetheless, using a constitutive EAAC1 KO mouse. Thus, they cannot make a strong conclusion that the behavioral deficits are due to the EAAC1 dysfunction in the DLS (despite the strong expression levels in the DLS).

2. Statistics used in the study: There are some missing details regarding the precise stats using for the different comparisons. I am particularly concerned that the electrophysiology studies that were a priori designed as a 2-factor analysis did not have 2-way ANOVAs performed, but rather a series of t-tests. For example, in Figure 3b, the two factors are 1) cell type and 2) genotype. Was a 2-way ANOVA performed? It is hard for me to tell from the text.

Moderate Concerns:<br /> 3. Control mice: I am moderately concerned that littermates were not used for controls for the EAAC1 KO, but rather C57Bl/6NJ presumably ordered from a vendor. It has been shown that issues like transit and rearing conditions can have long term affects on behavior. Were the control mice reared in house? How long was the acclimation time before use?

4. OCD framework: I generally find the OCD framework unnecessary, particularly in the introduction. Compulsive behaviors are not restricted to OCD. Indeed, the link between the behavioral observations and OCD phenotype seems a bit tenuous. In addition, studying the mechanisms of behavioral flexibility in and of itself is interesting. I don't think such a strong link needs to be made to OCD throughout the entirety of the paper. The authors should consider tempering this language or restricting it to the discussion and end of the abstract.

Reviewer #1 (Public Review):

This manuscript reports new findings about the role of the glutamate transporter EAAC1 in controlling neural activity in the striatum. The significance is two-fold - it addresses gaps in knowledge about the functional significance of EAAC1, as well as provides a potential explanation for how EAAC1 mutations contribute to striatal hyperexcitability and OCD-associated behaviors. The manuscript is clearly presented, and the well-designed experiments are rigorously performed and analyzed. The main results showing that EAAC1 deletion increases the dendritic arbor of MSN D1 neurons and increases excitatory synaptic connectivity, as well as reduces D1-to-D1 mediated IPSCs are convincing. These results clearly demonstrate that EAAC1 deletion can alter excitatory and inhibitory synaptic function. Modelling the potential consequences for these changes on D1 MSN neural activity, and the behavior changes are interesting. Minor weaknesses include incomplete support for the conclusions about how EAAC1 regulates GABAergic transmission.

Reviewer #1 (Public Review):

Park et al demonstrate that cells on either side of a BM-BM linkage strengthen their adhesion to that matrix using a positive feedback mechanism involving a discoidin domain receptor (DDR-2) and integrin (INA-1 + PAT-3). In response to its extracellular ligand (Collagen IV/EMB-9), DDR-2 is endocytosed and initiates signaling that in turn stabilizes integrin at the membrane. DDR-2 signaling operates via Ras/LET-60. This work's strength lies in its excellent in vivo imaging, especially of endogenously tagged proteins. For example, tagged DDR-2:mNG could be seen relocating from seam cell membranes to endosomes. I also think a second strength of this system is the ability to chart the development of BM-BM linkage over time based on the stages of worm larval development. This allows the authors to show DDR signaling is needed to establish linkage, rather than maintain it. It likely is relevant to many types of cells that use integrin to adhere to BM and left me pondering a number of interesting questions. For example: (1) Does DDR-2 activation require integrin? Perhaps integrin gets the process started and DDR-2 positively reinforces that (conversely is DDR-2 at the top of a linear pathway)? (2) In ddr-2(qy64) mutants, projections seem to form from the central portion of the utse cell. Does this reveal a second function for DDR-2, regulating perhaps the cytoskeleton? And (3) can you use the forward genetic tools available in C. elegans to find new genes connecting DDR-2 and integrin? The authors discuss these ideas in their response to the reviews, and I look forward to hearing about their future work on these questions.

I do see two areas where the manuscript could be improved. First, the authors rely on imprecise genetic methods to reach their conclusions (i.e. systemic RNAi, or expression of dominant negative constructs.) I think their conclusion would be stronger if they used tissue specific degradation to block ddr-2 function specifically in the utse or seam cells. Methods to do this are now regularly used in C. elegans and the authors have already developed the necessary tissue-specific promoters. Second, the manuscript is presented in the introduction as a study on formation and function of BM-BM linkage. However, their results actually demonstrate a mechanism by which cells adhere to BM. Since ddr-2 appears to function equally in both utse + seam cells (based on their dominant negative data), there are likely three layers of adhesion (utse-BM, BM-BM, BM-seam) and if any of those break down, you get a partially penetrant rupture phenotype. I pointed this out in my initial review, and after reading the revised manuscript, I do still feel the authors' introduction presents the paper as dealing with how basement membranes link together. But, I wonder if this might this be a question of terminology/language use? Maybe I am operating on a strict definition of linkage, and the authors use it more inclusively. What term(s) should we use to differentiate two basement membranes that are linked together, versus tissues that are connected through a basement membrane linkage? This is something that could be clarified in future publications.

These concerns do not undercut the significance of this work, which identifies an interesting mechanism cells use to strengthen adhesion during BM linkage formation. In fact, I am excited to read future papers detailing the connection between DDR-2 and integrin. But before undertaking those experiments the authors should be certain which cells require DDR-2 activity, and that should not be determined based solely on mis expression of a dominant negative.

Reviewer #2 (Public Review):

This paper explores the mechanisms by which cells in tissues use the extracellular matrix (ECM) to reinforce and establish connections. This is a mechanistic and quantitative paper that uses imaging and genetics to establish that the Type IV collagen, DDR-2/collagen receptor discoidin domain receptor 2, signaling through Ras to strengthen an adhesion between two cell types in C. elegans. This connection needs to be strong and robust to withstand the pressure of the numerous eggs that pass through the uterus. The major strengths of this paper are in crisply designed and clear genetic experiments, beautiful imaging, and well supported conclusions. I find very few weaknesses, although, perhaps the evidence that DDR-2 promotes utse-seam linkage through regulation of MMPs could be stronger. This work is impactful because it shows how cells in vivo make and strengthen a connection between tissues through ECM interactions involving collaboration between discoidin and integrin.

Reviewer #1 (Public Review):

The authors examine signaling factors that differentiate parallel routes to activating phosphoinositide 3-kinase gamma (PI3Kγ). Dissecting the convergent pathways that control PI3Kγ activity is critical because PI3Kγ is a therapeutic target for treating inflammatory disease and cancer. Here, the authors employ a multipronged approach to reveal new aspects for how p84 and p101 pair with p110γ to activate the PI3Kγ heterodimer. The key instigator to this study is a previously reported inhibitory Nanobody, NB7. The hypothesized mechanism for NB7 allosteric inhibition of p84- p110γ was previously proposed to involve blockage of the Ras-binding domain. The authors revise the allosteric inhibition model based on meticulous profiling of various PI3Kγ complex interactions with NB7. In parallel, a cryo-EM-derived model of NB7 bound to the p110γ subunit convincingly reveals a Nanobody interaction pocket involving the helical domain and regulatory motifs of the kinase domain. This revelation shifts the focus to the helical domain, a known target of PKC phosphorylation. While the connections between NB7 interactions and the effects of PKC phosphorylation are sometimes tenuous, it could be argued that the Nanobody served as a tool to reveal the importance of the helical domain to p110γ regulation.

The sites of PKC-mediated p110γ helical domain phosphorylation were unexpectedly inaccessible in the available structural models. Nevertheless, mass spectrometry (MS)-based phosphorylation profiling indicates that PKC can phosphorylate the helical domain of p110γ and p84/p110γ (but not p101/p110γ) in vitro. The authors hypothesize that helical domain dynamics dictate susceptibility to PKC phosphorylation. To explore this notion, carefully executed, rigorous H/D exchange MS (HDX-MS) experiments were performed comparing phosphorylated vs. unphosphorylated p110γ. Notably, this design reveals more about the consequences of p110γ phosphorylation, rather than the mechanisms of p84/p101 promoting/resisting phosphorylation. Nevertheless, HDX-MS is very well suited to exploring secondary structure dynamics, and helical domain phosphorylation strikingly increases dynamics consistent with increased regional accessibility. The increased dynamics also nicely map to the pocket enveloped by the inhibitory NB7 Nanobody.

Ultimately, this study reveals an unexpected p110γ pocket that allows an engineered Nanobody to allosterically inhibit PI3Kγ complexes. The cryo-EM characterization of the interaction inspired an HDX-MS investigation of known sites of phosphorylation in the region. These insights could be linked to differences/convergences of p84 and p101 complex formation and activation of PI3Kγ, and future work may clarify these mechanisms further. The data presented herein will also be useful for broadening the target surface for future therapeutic developments. New allosteric connections between effector binding sites and post-translational modifications are always welcome.

Reviewer #2 (Public Review):

Harris et al. have described the cryo-EM structure of PI3K p110gamma in a complex with a nanobody that inhibits the enzyme. This provided the first structure of full-length of PI3Kgamma in the absence of a regulatory subunit. This nanobody is a potent allosteric inhibitor of the enzyme, and might provide a starting point for developing allosteric, isotype-specific inhibitors of the enzyme. One distinct effect of the nanobody is to greatly decrease the dynamics of the enzyme as shown by HDX-MS, which is consistent with a growing body of observations suggesting that for the whole PI3K superfamily, enzyme activators increase enzyme dynamics.

The most remarkable outcome of the study is that upon observing the site of nanobody binding, the authors searched the literature and found that there was a previous report of a PKCbeta phosphorylation of PI3Kgamma in the helical domain that is near the nanobody binding site. This led the authors to re-examine the consequence of the phosphorylation armed with better structural models and the tools to study the effects of this phosphorylation on enzyme dynamics. They found that the site of phosphorylation is buried in the helical domain, suggesting that a large conformational change would have to take place to enable the phosphorylation. HDX-MS showed that phosphorylation at three sites clustered in the helical domain generate a distinctly different conformation with rapid deuterium exchange. This suggests that the phosphorylation locks the enzyme in a more dynamic state. Their enzyme kinetics show that the phosphorylated, dynamic enzyme is activated.

While this phosphorylation was reported before, the authors have provided a mechanism for why this activates the enzyme, and they have shown why binders that stabilise the helical domain (such as binding to the p101 regulatory subunit and the nanobody) prevent the phosphorylation. It is this insight into the dynamics of the PI3Kgamma that will likely be the long-lasting influence of the work.

The paper is well written and the methods are clear.

Reviewer #2 (Public Review):

In recent years, the role of the ECM in synaptic organization has been increasingly studied, leading to a better appreciation of how proteins that comprise the ECM influence synaptic structure and function. How the ECM affects neuronal structure and axonal biology is less well understood, however. Guss and colleagues begin to remedy this by assessing the role of Perlecan in the maintenance of NMJ terminals in the fly. They demonstrate a role for Perlecan in synaptic NMJ stability - loss of Perlecan results in a drastic increase in synaptic retractions. These retractions occur as a result of multiple non-cell-autonomous sources of Perlecan, as neither one tissue RNAi induces phenotypes nor does neuronal cDNA rescue a mutant. They advocate that multiple cellular mechanisms, including Wallerian degeneration and Wnt signaling, are not involved and demonstrate cytoskeletal and functional deficits. They also show that entire nerve bundles degenerate in a coordinated manner, likely due to the disruption of the neural lamella.

This is a strong and thorough genetic analysis of the role of Perlecan in neuronal stability and axonal retraction. The conclusions are largely valid, and the controls and experiments reasonable to answer the stated questions. I have some requests for additional experiments to bolster the existing conclusions.

Reviewer #3 (Public Review):

The spindle checkpoint ensures the accuracy of chromosome segregation by sensing unattached kinetochores during mitosis and meiosis and delays the onset of anaphase. Unattached kinetochores catalyze the conformational activation of the latent open MAD2 (O-MAD2) to the active closed MAD2 (C-MAD2). C-MAD2 is then incorporated into the mitotic checkpoint complex (MCC), which inhibits the anaphase-promoting complex or cyclosome (APC/C) to delay anaphase. When all kinetochores are properly unattached, the MAD2-binding protein p31comet and the ATPase TRIP13 extract C-MAD2 from the MCC, leading to MCC disassembly and the conversion of C-MAD2 back to O-MAD2. This action turns off the spindle checkpoint, resulting in APC/C activation and anaphase onset. Cells deficient in p31comet exhibit mitotic delays.

In the current study, Huang et al. have linked p31comet mutations to female infertility. Biallelic loss-of-function alleles of p31comet cause delays in the exiting metaphase of meiosis I and polar body extrusion. The p31comet mutant proteins contain C-terminal truncations and fail to bind to MAD2. Reintroducing full-length p31comet into patient oocytes can bypass the metaphase arrest. Together with a previous study that showed biallelic mutations of TRIP13 caused female infertility, this work established a critical role of the p31comet-TRIP13 module in regulating meiotic progression during oogenesis. As such, this is a significant study.

Reviewer #3 (Public Review):

In this work, Eccleston et. al. use a computational method involving the Rosetta (Flex ddG) suite to infer epistasis in binding free energy changes for combinatorial sets of mutations in the DHFR gene and the drug pyrimethamine. They use this to estimate the most likely path of stepwise mutation accumulation in the evolution of antimalarial drug resistance. The authors also infer likely pathways from different geographical regions from isolated data using a method based on mutation frequencies. They report that these results are broadly consistent with their computational predictions as well.

In contrast to machine learning approaches, the Rosetta Flex ddG method uses physical models at the atomic scale to compute various macromolecular properties. The present paper, therefore, uses atomic-scale molecular properties to make predictions at the population level. As acknowledged by the authors, their method has the limitation that chemical factors other than the free energy changes are largely ignored, as are complications arising from complex population dynamics. Nonetheless, there is reasonable agreement between their predictions and the experimental data, especially at high drug concentrations.

The authors also infer likely trajectories of mutation acquisition from isolate data from various parts of the world. The inference method is based on a simple ranking scheme of mutation frequencies. It is difficult to gauge the reliability of this method, given the complexity of infectious disease dynamics, including confounding factors introduced by varied drug treatment regimens. However, predictions from the computational method are still able to capture some of the general trends in the inferred pathways from isolates, inspiring some confidence in both approaches. The authors emphasize the importance of geographic variation in evolutionary pathways, but their computational method is limited in its ability to provide quantitative insights into the origins of such variation.

A few limitations of the work should be mentioned. It suffers from a lack of summary metrics that quantify the performance of its computational method, which is important for a clearer understanding of its accuracy. While the work is a useful indicator of the potential usefulness of the Rosetta Flex ddG method in enabling evolutionary predictions through macromolecular modeling, the method is applied to a well-studied system and the work remains limited in the novelty of the insights it generates into the dynamics of the evolution of antimalarial drug resistance.

Reviewer #1 (Public Review):

The authors sought to understand the neurocomputational mechanisms of how acute stress impacts human effortful prosocial behavior. Functional neuroimaging during an effort-based decision task and computational modeling were employed. Two major results are reported: 1) Compared to controls, participants who experienced acute stress were less willing to exert effort for others, with a more prominent effect for those who were more selfish; 2) More stressed participants exhibited an increase in activation in the dorsal anterior cingulate cortex and anterior insula that are critical for self-benefiting behaviour. The authors conclude that their findings have important insights into how acute stress affects prosociality and its associated neural mechanisms.

Overall, there are several strengths in this well-written manuscript. The experimental design along with acute stress induction procedures were well controlled, the data analyses were reasonable and informative, and the results from the computational modeling provide important insights (e.g., subjective values). Despite these strengths, there were some weaknesses regarding potential confounding factors in both the experimental design and methodological approach, including selective reporting of only some aspects of this complex dataset, and the interpretation of the observations. These detract from from the overall impact of the manuscript. In particular, the stress manipulation and pro-social task are both effortful, raising the possibility that stressed participants were more fatigued. Other concerns include the opportunity for social dynamics or cues during task administration, the baseline social value orientation (SVO) in each group, and the possibility of a different SVO in individuals with selfish tendencies. Finally, Figure 4 should specify whether the depicted prosocial choices include all five levels of effort.

Reviewer #2 (Public Review):

This manuscript describes an interesting study assessing the impact of acute stress on neural activity and helping behavior in young, healthy men. Strengths of the study include a combination of neuroimaging and psychoneuroendocrine measures, as well as computational modeling of prosocial behavior. Weaknesses include complex, difficult to understand 3-way interactions that the sample size may not be large enough to reliably test. Nonetheless, the study and results provide useful information for researchers seeking to better understand the influence of stress on the neural bases of complex behavior.

The stressor was effective at eliciting physiological and psychological stress responses as shown in Figure 2.

Higher perceived stress in more selfish participants (lower social value orientation (SVO) angle) was associated with lower prosocial responding (Figure 4). How can we reconcile this finding with the finding (presented on page 15) that those with a more prosocial SVO showed a significant decline in dACC activation to subjective value at increasing levels of perceived stress? This seems contrary to the behavioral response.

A larger issue with the study is that the power analysis presented on page 23 is based on a 2 (between: stress v. control) by 2 (within: self v. other) design. Most of the reported findings come from analyses of 3-way interactions. How can the readers have confidence in the reliability of results from 3-way interaction analyses, which were not powered to detect such effects?

Reviewer #1 (Public Review):

Wang et al., present a paper aiming to identify NALCN and TRPC6 channels as key mechanisms regulating VTA dopaminergic neuron spontaneous firing and investigating whether these mechanisms are disrupted in a chronic unpredictable stress model mouse.

Major strengths:

-This paper uses multiple approaches to investigate the role of NALCN and TRPC6 channels in VTA dopaminergic neurons.

Major weaknesses:

-The pharmacological tools used in this study are highly non-selective. Gd3+, used here to block NALCN is actually more commonly used to block TRP channels. 2-APB inhibits not only TRPC channels, but also TRPM and IP3 receptors while stimulating TRPV channels (Bon and Beech, 2013), while FFA actually stimulates TRPC6 channels while inhibiting other TRPCs (Foster et al., 2009).

Are the author's claims supported by the data?

-The multimodal approach including shRNA knockdown experiments alleviates much of the concern about the non-specific pharmacological agents. Therefore, the author's claim that NALCN is involved in VTA dopaminergic neuron pacemaking is well-supported.

-However, the claim that TRPC6 is the key TRPC channel in VTA spontaneous firing is somewhat, but not completely supported. As with NALCN above, the pharmacology alone is much too non-specific to support the claim that TRPC6 is the TRP channel responsible for pacemaking. However, unlike the NALCN condition, there is an issue with interpreting the shRNA knockdown experiments. The issue is that TRPC channels often form heteromers with TRPC channels of other types (Goel, Sinkins and Schilling, 2002; Strübing et al., 2003). Therefore, it is possible that knocking down TRPC6 is interfering with the normal function of another TRPC channel, such as TRPC7 or TRPC4.

-The claim that TRPC6 channels in the VTA are involved in the depressive-like symptoms of CMUS is supported.

- However, the connection between the mPFC-projecting VTA neurons, TRPC6 channels, and the chronic unpredictable stress model (CMUS) of depression is not well supported. In Figure 2, it appears that the mPFC-projecting VTA neurons have very low TRPC6 expression compared to VTA neurons projecting to other targets. However, in figure 6, the authors focus on the mPFC-projecting neurons in their CMUS model and show that it is these neurons that are no longer sensitive to pharmacological agents non-specifically blocking TRPC channels (2-APB, see above comment). Finally, in figure 7, the authors show that shRNA knockdown of TRPC6 channels (in all VTA dopaminergic neurons) results in depressive-like symptoms in CMUS mice. Due to the low expression of TRPC6 in mPFC-projecting VTA neurons, the author's claims of "broad and strong expression of TRPC6 channels across VTA DA neurons" is not fully supported. Because of the messy pharmacological tools used, it cannot be clamed that TRPC6 in the mPFC-projecting VTA neurons is altered after CMUS. And because the knockdown experiments are not specific to mPFC-projecting VTA neurons, it cannot be claimed that reducing TRPC6 in these specific neurons is causing depressive symptoms.

Impact:

It is valuable to compare pacemaking mechanisms in VTA and SNc neurons and this paper convincingly shows that NALCN contributes to VTA pacemaking, as it is known to contribute to SNc pacemaking. It also shows that TRPC6 channels in VTA dopamine neurons contribute to the depressive-like symptoms associated with CMUS.

It is important to note that the experiments presented in Figure 1 have all been previously performed in VTA dopaminergic neurons (Khaliq and Bean, 2010) including showing that low calcium increases VTA neuron spontaneous firing frequency and that replacement of sodium with NMDG hyperpolarizes the membrane potential.

Additional context:

-The authors explanation for the increase in firing frequency in 0 calcium conditions is that calcium-activated potassium channels would no longer be activated. However, there is a highly relevant finding that low calcium enhances the NALCN conductance through the calcium sensing receptor from Dejian Ren's lab (Lu et al., 2010) which is not cited in this paper. This increase in NALCN conductance with low calcium has been shown in SNc dopaminergic neurons (Philippart and Khaliq, 2018), and is likely a factor contributing to the low-calcium-mediated increase in spontaneous VTA neuron firing.

-One of the only demonstrations of the expression and physiological significance of TRPCs in VTA DA neurons was published by (Rasmus et al., 2011; Klipec et al., 2016) which are not cited in this paper. In their study, TRPC4 expression was detected in a uniformly distributed subset of VTA DA neurons, and TRPC4 KO rats showed decreased VTA DA neuron tonic firing and deficits in cocaine reward and social behaviors.

- Out of all seven TRPCs, TRPC5 is the only one reported to have basal/constitutive activity in heterologous expression systems (Schaefer et al., 2000; Jeon et al., 2012). Others TRPCs such as TRPC6 are typically activated by Gq-coupled GPCRs. Why would TRPC6 be spontaneously/constitutively active in VTA DA neurons?

-A new paper from the group of Myoung Kyu Park (Hahn et al., 2023) shows in great detail the interactions between NALCN and TRPC3 channels in pacemaking of SNc DA neurons.

References

Bon, R.S. and Beech, D.J. (2013) 'In pursuit of small molecule chemistry for calcium-permeable non-selective TRPC channels -- mirage or pot of gold?', British Journal of Pharmacology, 170(3), pp. 459-474. Available at: https://doi.org/10.1111/bph.12274.

Foster, R.R. et al. (2009) 'Flufenamic acid is a tool for investigating TRPC6-mediated calcium signalling in human conditionally immortalised podocytes and HEK293 cells', Cell Calcium, 45(4), pp. 384-390. Available at: https://doi.org/10.1016/j.ceca.2009.01.003.

Goel, M., Sinkins, W.G. and Schilling, W.P. (2002) 'Selective association of TRPC channel subunits in rat brain synaptosomes', The Journal of Biological Chemistry, 277(50), pp. 48303-48310. Available at: https://doi.org/10.1074/jbc.M207882200.

Hahn, S. et al. (2023) 'Proximal dendritic localization of NALCN channels underlies tonic and burst firing in nigral dopaminergic neurons', The Journal of Physiology, 601(1), pp. 171-193. Available at: https://doi.org/10.1113/JP283716.

Jeon, J.-P. et al. (2012) 'Selective Gαi subunits as novel direct activators of transient receptor potential canonical (TRPC)4 and TRPC5 channels', The Journal of Biological Chemistry, 287(21), pp. 17029-17039. Available at: https://doi.org/10.1074/jbc.M111.326553.

Khaliq, Z.M. and Bean, B.P. (2010) 'Pacemaking in dopaminergic ventral tegmental area neurons: depolarizing drive from background and voltage-dependent sodium conductances', The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30(21), pp. 7401-7413. Available at: https://doi.org/10.1523/JNEUROSCI.0143-10.2010.

Klipec, W.D. et al. (2016) 'Loss of the trpc4 gene is associated with a reduction in cocaine self-administration and reduced spontaneous ventral tegmental area dopamine neuronal activity, without deficits in learning for natural rewards', Behavioural Brain Research, 306, pp. 117-127. Available at: https://doi.org/10.1016/j.bbr.2016.03.027.

Lu, B. et al. (2010) 'Extracellular calcium controls background current and neuronal excitability via an UNC79-UNC80-NALCN cation channel complex', Neuron, 68(3), pp. 488-499. Available at: https://doi.org/10.1016/j.neuron.2010.09.014.

Philippart, F. and Khaliq, Z.M. (2018) 'Gi/o protein-coupled receptors in dopamine neurons inhibit the sodium leak channel NALCN', eLife, 7. Available at: https://doi.org/10.7554/eLife.40984.

Rasmus, K. et al. (2011) 'Sociability is decreased following deletion of the trpc4 gene', Nature Precedings, pp. 1-1. Available at: https://doi.org/10.1038/npre.2011.6367.1.

Schaefer, M. et al. (2000) 'Receptor-mediated regulation of the nonselective cation channels TRPC4 and TRPC5', The Journal of Biological Chemistry, 275(23), pp. 17517-17526. Available at: https://doi.org/10.1074/jbc.275.23.17517.

Strübing, C. et al. (2003) 'Formation of novel TRPC channels by complex subunit interactions in embryonic brain', The Journal of Biological Chemistry, 278(40), pp. 39014-39019. Available at: https://doi.org/10.1074/jbc.M306705200.

Reviewer #2 (Public Review):

This paper describes the results of a set of complementary and convergent experiments aimed at describing roles for the non-selective cation channels NALCN and TRPC6 in mediating subthreshold inward depolarizing currents and action potential generation in VTA DA neurons under normal physiological conditions. That said, some datasets are underpowered, and general flaws in statistical reporting make assessment difficult. There is also a lack of clarity at various points throughout the manuscript, as well as overinterpretation of the data generated in these experiments. Specific comments follow:

1. These results do not show that TRPC6 mediates stress effects on depression-like behavior. As stated by the authors in the first sentence of the final paragraph, "downregulation of TRPC6 proteins was correlated with reduced firing activity of the VTA DA neurons, the depression-like behaviors, and that knocking down of TRPC6 in the VTA DA neurons confer the mice with depression behaviors." Therefore, the results show associations between TRPC6 downregulation and stress effects on behavior, occlusion of the effects of one by the other on some outcome measures, and cell manipulation effects that resemble stress effects. There is no experiment that shows reversal of stress effects with cell/circuit-specific TRPC6 manipulations. Please adjust the title, abstract and interpretation accordingly.<br /> 2. Statistical tests and results are unclear throughout. For all analyses, please report specific tests used, factors/groups, test statistic and p-value for all data analyses reported. In some cases, the chosen test is not appropriate. For example, in Figure 6E, it is not clear how an experiment with 2 factors (stress and drug) can be analyzed with a 1-way RM ANOVA. The potential impact of inappropriate statistical tests on results makes it difficult to assess the accuracy of data interpretation.<br /> 3. Why were only male mice used? Please justify and discuss in the manuscript. Also, change the title to reflect this.<br /> 4. Number of recorded cells is very low in Figure 1. Where in VTA did recordings occur? Given the heterogeneity in this brain region, this n may be insufficient. Additional information (e.g., location within VTA, criteria used to identify neurons) should be included. Report the number of mice (i.e., n = 6 cells from X mice) in all figures.<br /> 5. Authors refer to VTA DA neurons as those that are DAT+ in line 276, although TH expression is considered the standard of DAergic identity, and studies (e.g., Lammel et al, 2008) have shown that a subset of VTA DA neurons have low levels of DAT expression. Authors should reword/clarify that these are DAT-expressing VTA DA neurons.<br /> 6. Neuronal subtype proportions should be quantified and reported (Fig. 1Aii).<br /> 7. In addition to reporting projection specificity of neurons expressing specific channels, it would be ideal to report these data according to spatial location in VTA.<br /> 8. The authors state that there are a small number of Glut neurons in VTA, then they state that a "significant proportion" of VTA neurons are glutamatergic.<br /> 9. It is an overstatement that VTA DA neurons are the key determinant of abnormal behaviors in affective disorders.

Reviewer #3 (Public Review):

The authors of this study have examined which cation channels specifically confer to ventral tegmental area dopaminergic neurons their autonomic (spontaneous) firing properties. Having brought evidence for the key role played by NALCN and TRPC6 channels therein, the authors aimed at measuring whether these channels play some role in so-called depression-like (but see below) behaviors triggered by chronic exposure to different stressors. Following evidence for a down-regulation of TRPC6 protein expression in ventral tegmental area dopaminergic cells of stressed animals, the authors provide evidence through viral expression protocols for a causal link between such a down-regulation and so-called depression-like behaviors. The main strength of this study lies on a comprehensive bottom-up approach ranging from patch-clamp recordings to behavioral tasks. However, the interpretation of the results gathered from these behavioral tasks might also be considered one main weakness of the abovementioned approach. Thus, the authors make a confusion (widely observed in numerous publications) with regard to the use of paradigms (forced swim test, tail suspension test) initially aimed (and hence validated) at detecting the antidepressant effects of drugs and which by no means provide clues on "depression" in their subjects. Indeed, in their hands, the authors report that stress elicits changes in these tests which are opposed to those theoretically seen after antidepressant medication. However, these results do not imply that these changes reflect "depression" but rather that the individuals under scrutiny simply show different responses from those seen in nonstressed animals. These limits are even more valid in nonstressed animals injected with TRPC6 shRNAs (how can 5-min tests be compared to a complex and chronic pathological state such as depression?). With regard to anxiety, as investigated with the elevated plus-maze and the open field, the data, as reported, do not allow to check the author's interpretation as anxiety indices are either not correctly provided (e.g. absolute open arm data instead of percents of open arm visits without mention of closed arm behaviors) or subjected to possible biases (lack of distinction between central and peripheral components of the apparatus).

Reviewer #1 (Public Review):

Li et al report that upon traumatic brain injury (TBI), Pvr signalling in astrocytes activates the JNK pathway and up-regulates the expression of the well-known JNK target MMP1. The FACS sort astrocytes, and carry out RNAseq analysis, which identifies pvr as well as genes of the JNK pathway as particularly up-regulated after TBI. They use conventional genetics loss of function, gain of function and epistasis analysis with and without TBI to verify the involvement of the Pvr-JNK-MMP1 signalling pathway.

The strengths are that multiple experiments are used to demonstrate that TBI in their hands damaged the BBB, induced apoptosis and increased MMP1 levels. The RNAseq analysis on FACS sorted astrocytes is nice and will be valuable to scientists beyond the confines of this paper. The functional genetic analysis is conventional, yet sound, and supports claims of JNK and MMP1 functioning downstream of Pvr in the TBI context.

However, the weaknesses are that novelty and insight are both rather limited, some data are incomplete and other data do not support some claims. Some approaches used lacked resolution and some experiments lacked rigour. The authors may wish to improve some of their data as this would make their case more convincing. Alternatively, they should remove unsupported claims.

Novelty and insight:<br /> Others had previously published that both JNK signalling and MMP1 were activated upon injury, in multiple contexts (as well as the articles cited by the authors, they should also see Losada-Perez et al 2021). That Pvr can regulate JNK signalling was also known (Ishimaru et al 2004). And it was also known that astrocytes can respond to injury by proliferating, both in larval ventral nerve cords and adult brains (Kato et al 2011; Losada-Perez et al 2016; Harrison et al 2021; Simoes et al 2022). The authors argue that the novelty of the work is the investigation of the response of astrocytes to TBI. However, this is of somewhat limited scope. The authors mention that Mmp1 regulates tissue remodelling, the inflammatory process and cancer. Exploring these functions further would have been an interesting addition, but the authors do not investigate what consequences the up-regulation of Mmp1 after injury has in repair or regeneration processes.

Incomplete or unconvincing data:<br /> The authors failed to detect PCNA-GFP and pH3 in brains after TBI and conclude that that TBI does not induce astrocyte proliferation. However, this is a surprising claim, as it would be rather different from all previous prevalent observations of cell proliferation induced by injury. Cell proliferation can be notoriously difficult to detect (ie due to timing and sample size), thus instead this raises doubts on the experimental protocol or execution.<br /> Others have previously reported: cells in S- phase using PCNA-GFP and other reporters (eg BrdU, EdU, FUCCI) in the intact adult brain (Kato et al 2009; Foo et al 2017; Li et al 2020; Fernandez-Hernandez et al 2013; Simoes et al 2022); that injury to the adult brain and VNC induces cell proliferation that can be detected with cell proliferation markers like BrdU, Myc, FUCCI and the mitotic marker pH3 (Kato et al 2009; Fernandez-Hernandez et al 2013; Losada-Perez et al 2021; Simoes et al 2022); and that injury to the brain and CNS induces glial proliferation in adult and larval brains/CNS, specifically of astrocytes (Kato et al 2011; Losada-Perez et al 2016; Fernandez-Hernandez et al 2013; Simoes et al 2022). Thus, the fact that they did not observe PCNAGFP+ cells in control, intact adult brains nor after TBI could suggest that they had technical, experimental difficulties. Detecting mitotic cells with anti-pH3 is difficult because M phase is very brief, but others have succeeded (Simoes et al 2022). Given that in all previous reports mentioned above cells were seen to proliferate after injury in the CNS, it would be rather surprising if no cell proliferation occurred after TBI. Resolving this conflicting result is important, as it could imply that TBI induces very different cellular responses from various other lesions or injury types. It is conceivably not impossible, but the most parsimonious start point would be that multiple injury types could cause equivalent responses in cells. Thus, the authors ought to consider whether technical or experimental design problems affected their experimental outcome instead.

Other claims not supported by data:<br /> (1) astrocyte hypertrophy, as the tools used do not have the resolution to support this claim;

(2) localisation of anti-Pvr to specific cells, as the images show uniform signal or background instead;

(3) astrocytes do not engulf cell debris after TBI, as the tools and images do not have the resolution to make this claim.

The authors could improve these data with alternative experiments to maintain the claims; alternatively, these unsupported claims should be removed.

Statistical analysis:<br /> The statistical analysis needs revising as it is wrong in multiple places. Revising the statistics will also require revision of the validity of the claims and adjusting interpretations accordingly.

Altogether, this is an interesting and valuable addition to the repertoire of articles investigating neuron-glia communication and glial responses to injury in the Drosophila central nervous system (CNS). It is good and important to see this research area in Drosophila grow. This community together is building a compelling case for using Drosophila and its unparalleled powerful genetics to investigate nervous system injury, regeneration and repair, with important implications. Thus, this paper will be of interest to scientists investigating injury responses in the CNS using Drosophila, other model organisms (eg mice, fish) and humans.

Reviewer #2 (Public Review):

In this study, Li et al. examined the involvement of astrocyte-like glia in responding to traumatic brain injury in Drosophila. Using a previously-established method that induces high-impact, whole-body trauma to flies (HIT device), the authors observed increased blood-brain-barrier permeabilization, neuronal cell death, and hypertrophy of astrocyte-like cells in the fly brain following injury. The authors provide compelling evidence implicating a signaling pathway involving the PDGF/VEGF-like Pvr receptor tyrosine kinase, the AP-1 transcription factor, and the matrix metalloprotease Mmp1 in the astrocytic cell response to TBI. The authors' data was generally high-quality data and combined multiple experimental approaches (microscopy, RNA sequencing, and transgenic), increasing the rigor of the study. The identification of injury-induced gene expression changes in astrocytic cells helps increase our limited understanding of roles this glial subtype plays in the adult fly brain. Limitations of the study include a reliance on RNAi-mediated gene silencing without validation via genetic mutants and a limited examination of how astrocyte-like and ensheathing glia could interact following TBI, especially given that several genes identified in this study are known to mediate ensheathing glial responses to axotomy. The conclusions are generally well-supported by the presented data, however some further clarification of quantitative methods and analyses will help to strengthen the findings:

1. The significance and quantification method for the astrocytic cell body sizes in Fig. 2C, D and appearance of GFP+ accumulations in Fig. 2F should be better defined - how were cell bodies and GFP+ puncta identified relative to other astrocytic cell structures, are they homogeneous in size/intensity in different brain regions following injury, and what could the GFP+ puncta represent?<br /> 2. The relative contributions of astrocyte-like and ensheathing glia in the brain's response to TBI is unclear. RNA sequencing identified Mmp1 and Draper as genes upregulated following TBI, however, these genes have previously been implicated in ensheathing glial (and not astrocytic) responses to acute nerve injury. The authors provide convincing evidence that their transcriptomic data is devoid of neuronal genes, but what about the possibility of ensheathing glial contaminants? Figures 2I-Q suggest that the majority of Mmp1 protein co-localizes with ensheathing rather than astrocytic glial membranes following TBI. Does knockdown of Pvr, Jra, or kay in ensheathing glia affect Mmp1 upregulation following injury? A closer examination of how these two glial subtypes contribute to and interact-and what proportion of Mmp1 is cell autonomous to astrocytes-during injury responses would be valuable.<br /> 3. The authors rely on RNAi and overexpression methods to manipulate expression of candidate genes in Figures 4, 5, and 7. In most cases, only a single RNAi line is used to reduce expression of a candidate gene, increasing the possibilities of off-target effects or insufficient gene knockdown. These data could be strengthened by using multiple RNAi lines as well as mutants to validate findings for Pvr, Jra, and kay knockdown in Figures 4 and 5, and perhaps confirmation of knockdown efficiency, particularly in Fig. 7.<br /> 4. Single channel images should be included in Fig. 1L and M to help strengthen the conclusion that Dcp-1+ puncta are elav+ and repo-.<br /> 5. Sample sizes and a description of power analysis should be included in figure legends/methods. Based on the graphs, some sample sizes appear low (e.g., Fig. 1H+K and 2D+Q).

Reviewer #3 (Public Review):

In this study, authors used the Drosophila model to characterize molecular details underlying traumatic brain injury (TBI). The authors used the transcriptomic analysis of astrocytes collected by FACS sorting of cells derived from Drosophila heads following brain injury and identified upregulation of multiple genes, such as Pvr receptor, Jun, Fos, and MMP1. Additional studies identified that Pvr positively activates AP-1 transciption factor (TF) complex consisting of Jun and Fos, of which activation leads to the induction of MMP1. Finally, authors found that disruption of endocytosis and endocytotic trafficking facilitates Pvr signaling and subsequently leads to induction of AP-1 and MMP1.

Overall, this study provides important clues to understanding molecular mechanisms underlying TBI. The identified molecules linked to TBI in astrocytes could be potential targets for developing effective therapeutics. The obtained data from transcriptional profiling of astrocytes will be useful for future follow-up studies. The manuscript is well-organized and easy to read. However, I would like to request the authors to address the following issue to improve the quality of their study.

It is unclear why the authors did not explore the involvement of the JNK pathway in their study. While they described the potential involvement of the JNK pathway based on previous literature, they did not include any evidence on the JNK pathway in their own study.

It is important to note that the mechanism by which JNK activates AP-1 is primarily through phosphorylation, not the quantitative control of amounts, as much as I know. This raises questions about the authors' proposed hierarchical relationship between Pvr and AP-1 and the potential involvement of the JNK pathway in mediating this relationship.

Given the significance of the mechanistic link between Pvr and AP-1 in solidifying the authors' conclusion, it would have been beneficial for them to explore the involvement of the JNK pathway in their study, even if only minimally. The lack of such exploration may weaken the overall strength of their findings and the potential implications for understanding TBI.

Reviewer #1 (Public Review):

Jackson and Giacomassi et al. investigated the impact of repeated topical application of the TLR-7/8 agonist R848, mimicking single-stranded RNA viral infection, on circulating monocytes. Interestingly in this murine model of skin inflammation, they find that there is a striking increase in vascular patrolling (Ly6Clow) monocytes in the blood. In the majority of inflammatory settings so far described, it is the classical (Ly6Chi) monocyte population that is augmented. They found that this Ly6Clow monocyte expansion occurred in response to stimulation by R848 at epithelial barrier surfaces (skin and gut) and not following systemic administration of R848. Of note, the Ly6Clow increase was not dependent on type I or type II IFNs or CCR2, all factors that are important for Ly6Chi monocyte expansion in response to life-threatening infections, such as Toxoplasma gondii. Positive factors driving Ly6Clow augmentation are not identified. Alterations to circulating monocytes may have implications for secondary infection as R848-treated animals were less susceptible to flu infection. This research furthers our understanding of how tissues and organs have distinct mechanisms of communication in response to inflammatory and infectious stimuli and the implications this can have on circulating immune populations.

The conclusions of this paper are generally well supported by the data presented, however, some aspects of the study need to be clarified or extended. Additionally, some of the findings could be better discussed in the context of the current literature.

1) CSF-1 availability is described, initially by Yona et al. (DOI: 10.1016/j.immuni.2012.12.001), as an important factor extending the half-life of Ly6Clow monocytes in circulation. Given the expansion of Ly6Clow monocytes and their upregulation of CD115 in circulation, it would have been relevant to measure CSF-1 to assess whether this may be a candidate factor for the phenotype observed.

2) The conclusion that the altered monocyte compartment enhances protection against secondary infection is underdeveloped. The key experiment presented involves treating animals with R848 and demonstrating that they have an altered response to flu infection. This approach does not specifically assess the importance of monocytes. From these studies, it is only possible to conclude there is an association between monocyte alterations and secondary infection.

Reviewer #2 (Public Review):

Jackson et al present a study focused on the role of TLR7 in emergency myelopoiesis following infection or injury. The investigators observe that TLR7 stimulation to the skin with the TLR7 agonist R848 causes an increase in circulating monocytes. This effect appears to require stimulation at an epithelial surface as it occurred with skin or intestinal administration but not intraperitoneal or intravenous administration. They demonstrate TLR7 specificity using TLR7-/- mice and the requirement for TLR7 expression in hematopoietic cells, likely myeloid cells. To determine if other TLR ligands can stimulate myelopoiesis, they compared skin administration with other TLR ligands (LPS, Poly I:C, CpG) or a general pro-inflammatory stimuli (TPA). None of these resulted in increased myelopoiesis, further highlighting TLR7 specificity. They confirm that this TLR7-mediated myelopoiesis occurs in the bone marrow as opposed to extramedullary sites (i.e. spleen) and differentiation occurs through the HSPC-MDP-cMoP pathway as opposed to a GMP-mediated differentiation. In addition to myelopoiesis, they demonstrate that R848 facilitates the transition of Ly6c high monocytes to Ly6c-low monocytes and tissue macrophages and this effect requires the Ly6c high monocytes. Furthermore, these effects occur independently of Ccr2 and Cx3cr1, known monocyte chemoattractant receptors. Finally, they identified that R848 administration enhances anti-viral responses. In mice topically treated with R848, they then exposed these mice to RSV and/or influenza. They observed that the R848 treated mice had reduced viral responses (defined by a decrease in weight loss and reduced viral replication). Overall, the data support that TLR7 administration to epithelial surfaces drives an increase in circulating monocytes, and this required TLR7 expression in myeloid cells. This is an interesting study that has implications for our understanding of how immune signals at peripheral sites drive the expansion of monocytes required to respond to infections and/or inflammation.

The conclusions are largely supported by the data, and several aspects of TLR7-mediated myelopoiesis are explored. However, there are some limitations to the data that need to be considered and reduce the generalizability of the conclusions made by the authors.

1. Data convincingly demonstrates that skin administration of the TLR7 agonist R848 causes an increase in circulating monocytes, particularly Ly6c low monocytes. In addition, this requires TLR7 expression and specifically TLR7 expression on myeloid cells. However, this raises an important question that is not answered by the present investigations. Specifically, the connection between local TLR7 administration requiring myeloid cells and how this directly leads to emergency myelopoiesis. Presumably, there is some factor released from local myeloid cells that then stimulates the bone marrow response and then a response that leads to the differentiation of Ly6c high monocytes to Ly6c low monocytes and infiltrating tissue macrophages. It is not clear if this is one factor or several factors. Presumably, this would be a circulating factor, though this is also not clear from the data. This appears to be a critical piece to tie in the connection between local TLR7 and emergency myelopoiesis. Furthermore, it is not clear how the dermal administration of R848 impacts the skin and if this is a critical feature of the response. Presumably, it generates local inflammation as evidenced by the data in 3C showing the proportions of monocytes and neutrophils. However, the impact on skin structure/function is not clear nor is there a definition of how this changes over the time course of the treatments.

2. The requirement for TLR7 stimulation on the skin is convincing. However, it is not clear how generalizable it is to all epithelial surfaces. The authors administer R848 in the drinking water and this causes myelopoiesis. However, the data supporting this as a direct effect of intestinal epithelial exposure is not explicitly demonstrated. The data using IP injections would seem to suggest that this is not a generic "epithelial surface response". IP injections are an administration to the peritoneum, an epithelial surface. The lack of an IP injection response would seem to argue that TLR7 responses are only to specific epithelial surfaces. This limits the generalizability of the observation. Alternatively, differences could be attributed to differences in TLR7 doses required at the distinct epithelial barrier sites. Further exploration of the specific epithelial interface requirements would provide better insight into the specific mechanism of how TLR7 stimulation works.

3. The authors demonstrate that dermal TLR7 and not other TLR ligands cause the increase in monocytes. Though the data is convincing for TLR7, the lack of a response with the other TLR ligands requires additional experiments to clarify if this is really TLR7-specific. Specifically, dose ranging experiments are needed to clarify if a lack of effect is simply due to differences in the sensitivity of TLR ligands to dermal exposure as opposed to being a TLR7 only effect.

4. The evidence of increased Ly6C low monocytes following dermal TLR7 in CCR2 null mice is intriguing. This suggests that TLR7-mediated emergency myelopoiesis is occurring independently of CCR2. However, this data is confusing as the authors also report that Ly6C low monocytes are generated from a Ly6C high monocyte intermediate. The data in Figure 6A supports that CCR2 null mice have baseline monocytopenia (a known feature of these mice) and then fail to generate Ly6C high monocytes following R848 exposure. Then how does this lead to an increase in Ly6C low monocytes if there are no Ly6C high monocytes as shown in the third panel of 6A? This is not clarified but critical to making this argument. There are also missing vehicle controls that would be important to interpreting these provocative results.

5. Data is lacking for direct TLR7 effects on the lung. These would appear to be important, given the focus on RSV and influenza responses in the study. As presented, the TLR7 protection from respiratory viral responses is via dermal TLR7 exposure followed by respiratory viral infection. This is unlikely to be clinically relevant, raising the significance of this model to human respiratory viral infection. An improved experimental design would incorporate respiratory TLR7 stimulation followed by pathogen exposure. In addition, given the focus on monocytes and macrophages, elucidating the impact on monocytes and lung macrophages, prior to and following infection would better define the connection between TLR7 exposure at epithelial barrier sites, emergency myelopoiesis, and respiratory viral infection.

Reviewer #1 (Public Review):

This study examines the effects of Ca2+ and NHE1 peptide binding on the conformation of CHP3, one of three related calcineurin-homologous proteins. One question that is addressed is whether Ca2+ binding triggers membrane association of the myristoyl group, a so-called "Ca2+-myristoyl switch". This is convincingly demonstrated to not be the case by the experiment in Figure 6B: unlike myristoylated recoverin, mCHP3 does not show enhanced association with liposomes. In the presence of a target peptide, however, myristoylation enhances membrane association. Curiously, this interaction is not Ca2+ dependent, but the membrane association of the non-myristoylated CHP3 is Ca2+-dependent.

My concerns with this study relate to physiological relevance. First, it is unclear if Ca2+ binding has a regulatory function in any of the CHP proteins. The authors state that CHP1 and CHP2 have Ca2+ binding affinities <100 nM, so these proteins are likely saturated with Ca2+ under all physiological conditions. On the other hand, CHP3 binds Ca2+ with a Kd of 8 micromolar (in the presence of physiological concentrations of Mg2+) so it will be largely unbound under most normal cellular concentrations of Ca2+ which are in the submicromolar range. Free Ca2+ rarely reaches 1 micromolar under non-pathological concentrations, and if it does, the fraction of CHP3 bound to Ca2+ should be estimated for context. Given these caveats, I am not convinced that experiments done with millimolar concentrations of Ca2+ (e.g., Figures 2, 3, 6) are physiologically informative.

Reviewer #2 (Public Review):

The manuscript by Becker and coworkers describes a target-binding myristoyl switch in the calcium-binding EF hand protein CHP3 using one of its targets, the NHE1. The work uses a suite of biophysical methods including SEC, nanoDSF, fluorescence, and native MS, to address conformations, ligand binding (Ca2+, Mg2+, NHE1), and liposome association, pinpointing a conformation switch which they term a target-dependent myristoyl switch. The strength of the manuscript is a convincing mapping of the different conformations and the conclusion that target binding, and not Ca2+ binding is necessary to expel the lipid from the protein, and that this jointly enhances membrane binding. It would have been even stronger if additional structural data had been included to address the properties of the different states and hence support if there indeed are changes in dynamics and flexibility.

Reviewer #3 (Public Review):

This work provides new insights into the regulation of the intracellular effector protein Calcineurin B homologous protein 3 (CHP3). The authors precisely delineate how intracellular calcium signals and myristoylation affect the binding of CHP3 to lipid membranes and the sodium/proton exchanger NHE1. Different mechanisms are known to trigger the exposure of the myristoyl-moiety in the calcium-binding protein family and CHP3 was proposed to use a "calcium-myristoyl switch", which leads to exposure of the myristoyl group due to conformational changes in the protein triggered by calcium-binding. Becker and Fuchs et al. now demonstrate that CHP3 uses a novel mechanism, in which not calcium-binding but binding to the target protein NHE1 triggers exposure of its myristoyl-group. This paper represents a detailed functional characterization of CHP3 and the maximum level of mechanistic interpretation that can be achieved without high-resolution structural information.

The conclusions of this paper are fully supported by the data.

Strengths<br /> The protein biochemistry is of an exceptionally high level, both with respect to the quality of the material and the stringency with which the authors assess and assure the protein quality. The authors purify CHP3 without any affinity tags, and thus in its most representative relevant state. Their validations indicate that complete myristoylation of CHP3 is achieved and that all protein is functional with respect to calcium binding.

The authors go to extensive lengths to convince themselves of the quality of their data and their interpretation. They use an extensive amount of replicates, including both biological and technical replicates. Assays and experimental procedures are verified using model proteins, such as Recoverin. In addition, the authors employ an extensive set of complementary approaches to assure their observations are universal.

Weaknesses<br /> A small weakness is the fact that the interpretation in terms of mechanistic insights contributed by some of the assays employed is rather limited, resulting in comparably unprecise descriptions of the state of the protein such as "affects the conformation and/or flexibility of CHP3" or the "open" and "closed" conformations. As indicated by the authors, structural studies are required to precisely detail the conformational states and delineate their mechanism of action.

The authors imply that the major form of CHP3 is the myristoylated state. However, it remains unclear whether the source of the biological material, which appears to be membrane-only, already implies a significant experimental bias that only allows (or highly favors) the identification of myristoylated CHP3. Without a calcium-signal, unmyristoylated CHP may not associate with membranes, or be less strong, resulting in its depletion upon isolation of the vesicles.

Reviewer #1 (Public Review):

This is the most complete genomic overview of the epidemiology of Salmonella enterica serovar Typhi including close to 13,000 genmoes from multiple countries, clearly demonstrating the geographical differences in molecular epidemiology and antibiotic resistance traits. This database could serve as the global reference for the future with constant addition of new information.

This is a descriptive study, not providing fundamentally new mechanistic insights of the disease, but providing an overview of the global epidemiology of this bacterium.<br /> Open-ended questions remain the generalizability of the findings, which is linked to the completeness of the surveillance systems, as well as the linkage of genotypes to clinical disease presentation (severity) and of linkage of local antibiotic use and the prevalence of the different resistance traits.

Publication of these data will be very helpful for all those interested in the molecular epidemiology of Salmonella and may stimulate not-yet participating institutes to add information for future analyses. It may also stimulate investigators to use the data for deriving more insights in clinical disease presentations, associations with antibiotic use and input for mathematical modelling.

The (lenghty) introduction is textbook epidemiology of the emergence of antimicrobial resistance in Typhi.

Reviewer #2 (Public Review):

The authors present a thorough and comprehensive analysis of 13000 Typhi genomes sampled globally over the last 21 years. The paper is an important example of how to perform meta-analysis of large numbers of published genomes while keeping credit equitable and including all original investigators as authors. This should be commended and maintained by the genomics community as the correct protocol when performing meta-analyses of this kind.

The study presents important findings on the emergence, maintenance and dynamics of AMR in different Typhi lineage backgrounds globally. This is extremely important for surveillance and appropriate adjustments to empirical therapy guidelines.

The study was also able to deduce new findings on the emergence of XDR Typhi in Pakistan and to date the first case to much earlier than previously thought. This is a good demonstration of why collating and re-analysing data in this fashion can be so valuable.

The authors present interesting evidence that settings where MDR is chromosomally integrated has remained at high prevalence whereas it seems to be declining in settings where MDR is plasmid-borne. I found Figure S11 particularly interesting. As noted by the authors, this is consistent with the hypothesis that the IncHI1 MDR plasmid is associated with a fitness cost that is removed when the MDR transposon becomes chromosomally-integrated.

This study also represents a good demonstration of why patient travel information can be such a useful metadata field for genomic studies and the potential for its use in helping to survey areas where no genomic studies have taken place yet. Other studies (e.g. https://www.medrxiv.org/content/10.1101/2022.08.23.22279111v1) have used this information from UKHSA to similarly represent the phylogeography of a different serovar of Salmonella and have found that data collected in this way can provide broader global coverage and more uniform sampling than what is currently available on NCBI. This data should be encouraged to be shared and this study goes a long way in proving its general utility for surveillance studies in public health.

Reviewer #3 (Public Review):

The authors present a study of 13000, Salmonella Typhi genomes from across the globe. Here, they present an overview of the global genomic epidemiology of Salmonella Typhi, in the context of the evolution of antimicrobial resistance. The authors present the temporal trends in the prevalence of Salmonella Typhi genotypes in select regions/ countries as well as the prevalence and antimicrobial resistance. The authors cite travel isolates of Salmonella Typhi as a useful proxy for surveillance in high burden settings where there exists a paucity of genomic data. While the authors acknowledge the limitations of their study, there remain major concerns over sampling bias and representativeness that question the generalizability of their findings.

Based on the methods section, the authors did not make mention of adjusting their prevalence estimates for outbreak investigations. When conducting a population analysis, including outbreak samples can lead to an overestimation of the prevalence of the outbreak strain. First, outbreaks tend to be sampled more densely than isolates from routine surveillance of endemic disease, secondly in an outbreak, you are essentially sampling the same strain multiple times. This needs to be taken into consideration when estimating the prevalence of genotypes in the population. Treating outbreak investigations and routine surveillance equally in calculating prevalence can be misleading if the proportion of outbreak isolates sequenced is greater than the proportion of isolates in the surveillance area that are sequenced.

There are concerns regarding the validity of the results presented in Figures 1-3. These results require a nuanced assessment of the factors that are likely to influence genotypic diversity including type of study, duration of sampling and total number of genomes sequenced. In big Countries like Nigeria and India, where can be heterogeneity in different regions of the country and this needs to also be considered in inferring the prevalence of genotypes.

This heterogeneity in prevalence of genotypes was observed in countries with multiple laboratories. In India for example, the prevalence of lineage 4.3.1.2 ranged from 39% to 82%, in different cities/ regions. The authors have not provided sufficient context on the underlying source of this variation in prevalence. In order to understand the reason for observing these differences there needs to be a discussion around when the samples in each place/ region were conducted, how long the study was conducted, how many isolates were collected and whether this was a routine surveillance, outbreak investigation or other type of study. Similar variability is observed in Nigeria where most isolates were from Abuja (Zankli Medical Center, n=105, 2010-2013) and other sources included Ibadan (University of Ibadan, n=14, 2017-2018), and reference laboratories in England (n=15, 2015-2019) and the USA (n=10, 2016-2019). Given the small sample sizes and the fact that the time periods for sample collection varied, using this dataset to get a snapshot of the prevalence of genotypes in Nigeria can be potentially misleading.

Moreover, the authors cite that 70% of cases in Pakistan are caused by XDR. Is this based on the proportions of isolates that are XDR in this dataset? Klemm et al 2018 sequenced primarily XDR isolates, therefore that dataset is not representative of the wider population. Rasheed et al included on 27 genomes, which were isolated at hospitals. Hospitals isolates may give an overestimated XDR burden because susceptible isolates are likely to get treated successfully with antibiotics alleviating the need for hospitalization. Similarly, Yousafzai et al 2019 was an investigation into an outbreak of ceftriaxone-resistant Salmonella Typhi in Hyderabad, which is a densely sample dataset and not necessarily a representation of the wider population. Aggregating these data may lead to an accumulation of bias that gives a distorted snap shot of the diversity on genotypes. Also, it is unclear whether the number of isolates collected from each of these studies was consistent with time. Thus, changes in the prevalence may be representative of a change in the proportion of genomes that were sampled from individual studies.

One of the major recommendations from this study was that travel associated isolates can be a proxy for surveillance in high burden regions where there is paucity of data. The authors have not demonstrated a rigorous test for representativeness of the travel associated samples. The test conducted by the authors looked at how well the travel isolates correlated with the isolates from other studies conducted in the source population. However, they have not factored in potential biases associated with the studies conducted in the host countries. Also, travel is more likely to encompass a specific socio-economic demography of people who can afford to travel. This leads to underrepresentation of low income individuals and communities, especially in low-income countries. Moreover, the authors have not shown that the phylogenetic placement of the travel isolates supports the claim that they originated from that country. Conclusions drawn from travel associated isolates need to be tempered, while it can be a useful tool for early detection of potentially virulent lineages or lineages that have novel resistance mechanisms, using it to determine prevalence can be misleading.

Other minor observations include:

Introduction needs to trim significantly to be more concise. The authors can demonstrate that Salmonella typhi accumulates resistance genotypes over time and as new antibiotics are introduced resistance mutations become selected for and fixed in the population.

Figure 4 is very similar to Figure 1 of Klemm et al 2018, does not add any new insights.

Reviewer #1 (Public Review):

The manuscript by Mau et al. describes a sophisticated method to follow enhancer activity in both live embryos and fixed embryos in Tribolium. The authors identified putative enhancers via comparative ATAC-seq of embryos divided into different regions and at different developmental time points. As an experimental piece of work, this is excellent. However, the framing and presentation in this manuscript would need to be improved to avoid misrepresentation of existing ideas and over-interpretation of results. The manuscript would require significant re-writing. This can be done without additional data or analyses, but simply more careful writing.

The Introduction starts by setting up a straw-man argument, claiming that the assumption is that gene expression is set up as stable expression domains that undergo little or no subsequent change. I don't think that any current developmental biologist thinks this is true. The references used to support this claim are from the 1990s up to the early 2000s. There are numerous examples since then that show that developmental gene expression is dynamic as a rule.

The Introduction then continues as a rather detailed review of enhancers, Tribolium methodology, tools for identifying enhancers, and more. The Introduction cites 99 references, which seems excessive for what is essentially an experimental paper. Significant parts of the Introduction can be trimmed or removed. There is no need to mention all the tools available for Tribolium if they are not used in the described experiments. A thorough analysis of the advantages and disadvantages of different modes of ATAC-seq is also beyond the scope of the Introduction. The authors should explain why they chose the tools they chose without excessive background. Having said that, the Introduction actually overlooks a lot of significant work that is relevant to the subject of the paper. Specifically, the authors completely ignore all of the work on development in hemimetabolous insects such as Oncopeltus and Gryllus - the omission is glaring. There has been a lot of relevant work on dynamic gene expression patterns coming out of these species.

The experimental setup involves cutting embryos into three sections at two time points. The results then discuss differences in "space" and "time" but there is no discussion of the embryological meaning of these terms. What is happening at the two time points from a developmental perspective? What is the difference between the three sections? There is a lot of relevant development going on at these stages and important regional differences, which have been well-studied in Tribolium and in other insects but are not even mentioned.

In the preliminary results of the ATAC-seq analysis, it is clear that there are significant differences between the sections, which should come as no surprise, but fairly minor differences between the same section at the two time points. This could be because the two time points are pretty close together at a stage when there is a lot of repetitive patterning going on. A possible interpretation, which the authors don't mention because it goes against their main thesis, is that maybe most of the processes that are taking place at this stage are not dynamic enough to show up at the temporal resolution they have applied. This is worth at least a mention.

The authors link each accessible site to the nearest gene when looking at putative enhancer function. This is a risky assumption since there are many examples of enhancer sites that are far upstream or downstream of the target gene and often closer to an unrelated gene than to the target gene. The authors should at least acknowledge this problem with their functional annotation.

In the Discussion, the authors claim that contrary to how it may seem, the question they are addressing is not a "fringe problem". Once again, I think this is a straw man. No active researcher thinks that the question of dynamic regulation of gene expression during development is a fringe problem. On the contrary, most researchers will accept that this is one of the most interesting and important questions in current developmental biology.<br /> Perhaps the most significant problem with the manuscript is that it is all built around the premise of enhancer switching between dynamic enhancers and static enhancers. The authors find one site that is consistent with their prediction for a dynamic enhancer and one site - regulating a different gene - that is consistent with their prediction for a static enhancer and claim that they have provided support for their model. I think this claim is grossly exaggerated. They present data that can be seen as consistent with their model but are a long way from providing evidence for it.<br /> Like the Introduction, the Discussion includes long paragraphs (lines 450-480) that are more suitable for a review/hypothesis paper. The data presented in this manuscript has little relevance to the question of kinematic vs. trigger waves, and therefore there is no real reason for the question to be discussed here.

Reviewer #2 (Public Review):

Embryonic development requires differential gene expression, which is regulated by enhancer elements. Regulatory proteins bind to these DNA elements to regulate close-by promoters. Key insights into the molecular mechanisms of enhancer function have been gained by studying fly segmentation, where a hierarchical cascade of gene regulation subdivides the embryo into ever smaller units. However, segmentation in other insects and arthropods as well as in vertebrates relies on a much more dynamic process where repetitive gene expression patterns appear to migrate across tissues similar to waves. Only recently, models have been proposed that make predictions on the underlying gene regulatory networks (GRN) and the properties of the respective enhancers. Specifically, the previously suggested model of the authors, the enhancer switching model, predicted that each gene expression wave should actually be regulated by two GRNS - one based on a "dynamic enhancer", which directs the early wave-like pattern and the other involving a "static enhancer", which directs the more stable expression defining the segment anlagen at the end of each cycle. However, these predicted enhancer types have not been described so far. In flies, where the respective methodology would be available, the segmentation does not show prominent wave-like patterns. In beetles, where pronounced wave-like patterns have been described, the respective methodology has been missing.

With this work, the authors establish a genomic resource and a transgenic line in the red flour beetle in order to establish it as a model system to tackle questions on enhancers driving dynamic expressions during development. First, they determine the open chromatin at early embryonic stages thereby generating a valuable resource for enhancer detection. They did so by dissecting the embryos of two temporal stages into three parts (head, middle part, and growth zone) and then determined open chromatin via ATAC-seq. This setup allowed for a comparison across tissues and stages to identify dynamically regulated chromatin. Indeed, Mau et al. find that dynamic chromatin regulation is a good criterion to enrich for active enhancers.

Second, they established an enhancer reporter system, which allows for visualization of de novo transcription by both in situ hybridization and in vivo. This MS2 system has for the first time been implemented in this beetle and the authors convincingly show its functionality. Indeed, the expression dynamics can be very nicely visualized in vivo at blastoderm stages.

Combing these two resources, they predicted enhancers based on the criterion of dynamic chromatin regulation (from their ATAC-seq resource) and tested them using their novel MS2 system. Out of 9 tested enhancers located close to the gap genes hunchback and Krüppel and the pair-rule gene runt, 4 drove expression. Combining these data with previously published beetle enhancers, they show that DNA regions with differential accessibility were indeed enriched in active enhancers (appr. 60%), providing a good selection criterion.

Finally, they characterize two of the newly identified enhancers that reflect wave-like expression patterns in fixed embryos and in vivo by using the MS2 system to test predictions of the enhancer switching model. The results are compared with an elaboration of their previously suggested enhancer-switching model, which predicts different patterns for static vs. dynamic enhancers. Indeed, they think that the runB enhancer fits the predictions of a static enhancer.

The authors have generated a genomic resource that will be of very high value to the community in the future. The fact that they dissected the embryos for that purpose makes it even more precise and valuable. Likewise, the transgenic system that allows for testing enhancer activity in vivo will be very valuable for the highly active research field dealing with the prediction and analyses of enhancers.

The analysis of the identified enhancers provides partial confirmation for their model. As the authors state in the discussion, finding at least one pair of such enhancers for one gene would be a great test of their hypothesis - finding pairs of static and dynamic enhancers in several genes would be strong support. Unfortunately, they found only one of the two enhancer types in runt and one in hunchback, respectively. Hence, the prediction of the model remains to be tested in the future.

The authors provide a lot of high-quality data visualized nicely in the figures. The text would profit from some re-formulation, re-structuring, and shortening.

Open questions:<br /> What happens with the runB enhancer at later stages of embryogenesis? With what kind of dynamics do the anterior-most stripes fade and does that agree with the model? Do they show the same dynamics throughout segmentation? I think later stages need to be shown because the prediction from the model would be that the dynamics are repeated with each wave. I am not so sure about the prediction for ageing stripes - yet it would have been interesting to see the model prediction and the activity of the static enhancer.<br /> I understand that the mRNA of the reporter gene yellow is more stable than the runt mRNA. This might interfere with the possibility to test your prediction for static enhancers: The criterion is that the stripes should increase in strength as the wave migrates towards the anterior. You show this for runB - but given that yellow has a more stable transcript - could this lead to a "false positive" increase in intensity with the slower migration and accumulation of transcripts? I would feel more comfortable with the statement that this is a static enhancer if you could exclude that the signal is blurred by an artifact based on different mRNA stability. What about re-running the simulation (with the parameters that have shown to well reflect endogenous runt mRNA levels) but increasing the parameter for the stability of the mRNA? Are static and dynamic enhancers still distinguishable? The claim of having found a static enhancer rests on this increase in signal, hence, other explanations need to be excluded carefully.<br /> What about the head domain of the runB enhancer (e.g. Fig. 6A lowest row): This seems to be different from endogenous expression in your work and in Choe et al. Is that aspect different from endogenous expression and can this be reconciled with your model?<br /> The claim of similar dynamics of expression visualized by in situ and MS2 in vivo relies on comparing Fig. 6C with 6A. To compare these two panels, I would need to know to what stage in A the embryo in C should be compared. Actually, the stripe in 6C appears more crisp than the stripes in 6A.<br /> Were the enhancer dynamics tested in vivo at later stages as well? I would appreciate a clear statement on what stages can be visualized and where the technical boundaries are because this will influence any considerations by others using this system.<br /> How do the reported accessibility dynamics of runA enhancer correlate with the activity of the reporter: E.g. is the enhancer open in the middle body region but closed at the posterior part of the embryo? Or is it closed at the anterior - and if so: why is there a signal of the reporter in the head?<br /> You show that chromatin accessibility dynamics help in identifying active enhancers. Is this idea new or is it based on previous experience with Drosophila (e.g. PMID: 29539636 or works cited in https://doi.org/10.1002/bies.201900188)? Or in what respect is this novel?

Reviewer #3 (Public Review):

The authors study the spatio-temporal dynamics of gap and pair-rule pattern formation in the Tribolium embryo. Their main contributions are (1) to perform DNA accessibility profiles at multiple time points and in three domains along the A/P axis, (2) to establish a reporter gene system to examine reporter gene expression driven by candidate enhancers (including live imaging), (3) identify at least three new enhancers, and (4) provide some evidence in favor of the "Enhancer Switching" model.

This is an interesting study that marks solid progress towards an organizing principle of pattern formation. The two practical contributions of the work are impactful: (1) germband region-specific accessibility profiling provides a novel view of the epigenome, especially when combined with profiling of temporal variation. (2) the live imaging system has been powerful in Drosophila studies and this work establishes this system for Tribolium, which has certain advantages as a model.

I have two major concerns: First, the claim about differential accessibility being related to enhancer activity is not really established from the presented data, in my view. This needs to be clarified. (I do believe in the claim to some extent, but not based on presented evidence.) Second, the evidence in support of the Enhancer Switching model for runt should be accompanied by identification of and spatiotemporal profiling of the "speed regulator", if this is not established yet. In addition to these two concerns, the simulations of the Enhancer Switching model need to be described, at least in the outline, in the Methods section.

Reviewer #1 (Public Review):

This is a very interesting and timely manuscript investigating the roles of root-emitted secondary metabolites in mediating plant-soil feedback in a realistic and agricultural context (maize - wheat rotation). I find this article to be an important contribution to the field as the roles played by soil chemical legacies in mediating plant-soil feedbacks have been largely overlooked so far, particularly in the field. I found this manuscript to be extremely well-written and clear. I was impressed by the number of response variables measured by the authors to characterise how wheat plants responded to the soil legacies created by different maize genotypes.<br /> The article presents the results of a plant-soil feedback experiment in which two maize genotypes (wild type or benzoxazinoid-deficient bx1 mutant plant) conditioned field soil for one growing season. Monocultures of each genotype occupied alternate strips in the field. At the end of this conditioning phase, the authors analysed benzoxazinoids in the soil and found that the soil conditioned by WT maize was characterized by greater concentrations of several benzoxazinoids. In fact, most benzoxazinoids were below the detection limit for soil conditioned by bx1 mutant plants. These differences in soil chemical legacies were associated with differences in bacterial and fungal communities in the roots and rhizosphere of maize. Soon after the maize harvest, monocultures of three wheat varieties were grown in soil that was conditioned by either WT or bx1 maize plants. This factorial design allowed the authors to study the response of different wheat varieties to soil legacies created by maize genotypes that differ in their ability to produce and release benzoxazinoids into the soil. Although root and rhizosphere microbial communities were mainly driven by wheat genotype (and not by maize soil conditioning), soil conditioning effects on benzoxazinoid concentrations were still visible at the end of the feedback phase, but only for specific compounds (e.g. AMPO). In comparison to wheat grown on bx1-conditioned soil, the authors found that wheat plants grown on benzoxazinoid-conditioned soil had better emergence and were taller and more productive. In addition, benzoxazinoid soil conditioning reduced infestation by the cereal leaf beetle Oulema melanopus (particularly in one wheat variety) but did not affect weed pressure. The authors also found that wheat grown on benzoxazinoid-conditioned soil had more reproductive tillers, which led to greater grain yield (+4-5%). Grain quality, however, was not affected by maize soil conditioning.

I appreciated that the authors carefully interpreted the results of their experiment, although data analysis could be improved to take repeated measures within a plot into account. Overall, this is a compelling study, with rigorous and numerous measurements and state-of-the-art methods in plant/soil ecology. This study is unique in that it demonstrates the important role that soil chemical legacies can play in mediating plant-soil interactions and influencing the fitness of the following crop in a realistic agricultural setting. Therefore, I believe that this work will be of broad interest to plant and soil ecologists, as well as to agronomists.

Reviewer #2 (Public Review):

Gfeller et al. performed an experiment to test the mechanism underlying plant-soil feedback-induced effects on crop yield using two common rotation partners, corn and wheat, that are grown in sequence with one another in agricultural fields across years. The authors use a benzoxazinoid-deficient corn genotype to show that, compared to soil conditioned in year one by a wild-type (normal) corn variety, wheat growth, and yield decreased in year two. As part of this experiment, the authors also showed that benzoxazinoids exuded from corn roots are persistent over time (i.e., they can be detected in the soil long after corn was harvested), resulting in changes to the structure of bacterial and fungal communities, and reduce insect feeding damage to wheat. These effects were replicated across three different wheat cultivars. Weed pressure (benzoxazinoids have previously been shown to be allelopathic towards other plants) and wheat quality were unaffected in the experiment.

Strengths:

The authors use a large-scale field experiment to test their hypothesis. This is a very important aspect of the study. Most plant-soil feedback studies are conducted using potted plants or, at best, small-plot trials. This experiment was performed using large field plots, which is essential for making reliable inferences about crop rotations and yields in agriculture.

The study does a nice job of testing the underlying chemical mechanisms of how plant-soil feedbacks operate. Many studies have shown that conditioning the soil with one plant species affects the performance of a second plant species sharing that soil, but in virtually all cases we don't know, and can only speculate, what mechanisms are causing this effect.

The data reported are impressive for a few reasons. First, the authors make it a point to measure a wide variety of variables, making the findings particularly robust. I was impressed with the breadth of phenotyping considered by the authors. For example, their plant growth measurements were highly detailed, going from early-season crop performance (e.g., seedling emergence, chlorophyll, height, biomass, water content) to late-season yield effects (e.g., tiller density per plant and unit area, kernel weight) and even considering crop quality (e.g., protein, dough stability), which is usually ignored and assumed to not differ. This was clearly a ton of work! As part of this, they comprehensively measured variables related to plants, insects, weeds, soil microbes, etc., making this highly interdisciplinary work. And a second factor related to the data - the treatment effects were very consistent and impressive in magnitude. While not all variables were significantly affected, the ones that showed effects were consistent and not trivial (i.e., they were biologically significant).

Weaknesses:

While corn and wheat are common rotation partners around the world, it still seems like wheat was an odd choice for this experiment. The main reason I say this is that, as the authors point out, both plants produce benzoxazinoids. This makes it difficult to ascertain the effects of corn-derived benzoxazinoids since wheat is also exuding these compounds from its roots. A non-benzoxazinoid crop like soybean seems like it would've been a better choice since you wouldn't have the confounding effect of both the conditioning and feedback plants producing the same secondary metabolites. On the other hand, the fact that wheat produces benzoxazinoid could be a factor driving its yield response (i.e., crops that don't produce benzoxazinoids may show allelopathic-negative reactions).

The authors show that experimentally eliminating benzoxazinoids has a negative effect on the subsequent crop. While this is interesting from a mechanistic standpoint, it's less compelling than if the reverse was true. In other words, the authors simply show why corn is a good rotation crop for wheat, which has been known for a very long time, even if the mechanism was unclear. The authors argue that this could open the door for breeding that targets benzoxazinoids, which may very well be true; however, the outcomes would be more interesting if the study was showing that existing practices result in low yields and they were paving a path for how to ameliorate this.

In the end, it remains unclear whether the effect is driven by a direct effect from benzoxazinoids on wheat or an indirect effect caused by changes in soil microbes. The authors do a good job of speculating on the likelihood of these two mechanisms in the Discussion; however, they can't say with certainty. They would have to use sterilized soil as a separate treatment to differentiate these mechanisms.

Reviewer #1 (Public Review):

In this work Indurthy and Auerbach investigate the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of the nicotinic acetylcholine receptor (nAChR). The conclusions are based on a very large pool of experimental data that are interpreted with great mechanistic insight.

Specifically, the authors define "efficacy" (eta) of a ligand as the fractional change in binding free energy between the open and the closed states of the channel. They construct a log-log scatter plot of efficacy vs. affinity which represents 23 different agonists acting on the WT receptor, plus a subset of the same agonists acting on various nAChR mutants. They go on to show that these largely scattered dots can be partitioned into 5 distinct clusters ("eta-classes") within which the dots are linearly arranged. They interpret these clusters in terms of a mechanistic gating model (the "catch&hold LFER model"), and suggest that a different model accounts for each different eta-class. Put in simple terms, the interpretation is that 5 different subtypes of gating isomerization exist for the nAChR, the choice among which depends on the agonist used.

These types of study are necessary to advance conceptual understanding in biophysics. I have some reservations regarding the mechanistic interpretation of the data set and the uniqueness of the proposed model.

1. One concern regards the clustering of the data sets in Fig. 5 into exactly 5 eta-classes. First, two clusters contain only two data points each. Second, the proposed "catch&hold LFER model" (Fig. 2) does not predict the existence of a discrete number of such eta-classes. How strong is the evidence that there are exactly 5 classes as opposed to a continuum of possible eta values.

2. The authors do not discuss the uniqueness of the proposed model. In fact, it seems to me that the existence of eta-classes might be explained just as well by an alternative model which assumes a single gating mechanism for the receptor, but distinct patterns of ligand-protein interactions for the different agonists. The pore opening-associated increase in agonist affinity is typically caused by a tightening of the substrate binding site (often called clamshell closure) which brings further protein side chains into the vicinity of the ligand, thereby allowing further ligand-protein interactions to form (or further strengthening interactions that exist also in the closed-pore state). Thus, at a first approximation, the ratio between binding free energies in the open- and closed-pore states reflects the ratio of the numbers (and strengths) of ligand-protein bonds in those two states.

As an illustration, consider the following simplified model for a channel and a given ligand. In the open-pore state the number of ligand-protein interactions is n(o), and all those interactions are comparably strong. Out of those interactions only a subset is formed in the closed-pore state, their number is n(c) (where n(c)<br /> The maximal possible values of n(c) and n(o) are determined by the number and spatial arrangement of protein chemical groups that surround the substrate binding site. On the other hand, depending on the number and arrangement of matching chemical groups on the ligand, different ligands will be able to "exploit" different subsets of these possible ligand-protein interactions, resulting in different values of eta. Furthermore, ligands for which the absolute values of n(o) are different, but the ratio n(c)/n(o) is similar, will form apparent "eta-classes", i.e., will be arranged on a "eta-plot" along a straight line. (See attached image file for a graphical representation of the model.)

This model would suggest that there is a single gating mechanism (i.e., the actual protein conformational change is similar regardless of which agonist is bound), but the relative stabilities of the ligand-bound closed and open states are agonist-dependent. Wouldn't such a mechanism equally well explain all the data shown? The authors should either acknowledge this possibility or discuss available structural or functional evidence to exclude it.

Reviewer #2 (Public Review):

This is an interesting manuscript with a worthwhile approach to receptor mechanisms. The paper contains an impressive amount of new data. These single molecule concentration response curves have been compiled with care and the authors deserve great credit for obtaining these data. I judge the main result to be that there are different values of the recently-proposed agonist-related quantity "efficiency". These values are clustered into 5 quite closely spaced groups. The authors propose that these groups are the same whether considering mutations in the binding site or different agonists.

It was unclear to me in several places, what new data and what old data are included in each figure. Therefore readers may have difficulty judging the claimed advance. This difficulty is not helped by the discussion, which includes some previous findings as "results".

A further weakness is that it is unclear how general or how specific these concepts are. The authors assert that they are, by definition, completely universal. However, we do not have reference to previous work or current data on any other receptor than the muscle nicotinic. I could not square the concept that "every receptor works like this" with the evident lack of desire to demonstrate this for any other receptor.

On one hand, if the framework can be extended, this can be a very important concept, and in some sense, could be the missing link to understanding concentration response curves. On the other, if it proves not to be general, or not to be generally applicable because of circumstances.

Reviewer #3 (Public Review):

This work attempts to introduce a new attribute of the receptor- efficiency, a fraction of an agonist binding energy consumed by conformational transition of the receptor from resting to active (open) states. Furthermore, the authors use an impressive set of experimental data (single channel recordings with 23 agonists and 53 mutations) to measure the efficiency for each agonist and mutant receptor. All the estimated efficiencies fall into a few groups and inside each of the efficiency groups there is a strong correlation between agonist affinity and receptor opening efficacy.

The main finding in this study is that estimated efficiencies fall into 5 groups. There is no clear description of the method how the efficiencies were allocated into different groups. Most importantly, it is not clear if the method used takes into account the uncertainty of the efficiency estimate. The study does not show any statistical metrics of the efficiency estimates as well as any other calculated variable such as dissociation equilibrium constants to resting or open states. Surely, the uncertainty of the efficiency should matter especially considering how near the efficiency group values are (eg. difference about 10% between 0.51 and 0.56 or 0.41 and 0.45).

All the tested agonists fell into groups according to the efficiency value attributed to them. It is difficult to see why some of the agonists belong to the same group. For example, it is not obvious at all why such agonists as epibatidine, decamethonium and TMP are in the same group. The question, I guess, arises if this grouping based on efficiency has any predictability value. Furthermore, if a series of mutations with the same agonist fall into different groups, the prediction power of this approach is very limited if one attempts to design a new agonist or look for a new mutation.

Reviewer #1 (Public Review):

This manuscript will interest cognitive scientists, neuroimaging researchers, and neuroscientists interested in the systems-level organization of brain activity. The authors describe four brain states that are present across a wide range of cognitive tasks and determine that the relative distribution of the brain states shows both commonalities and differences across task conditions.

The authors characterized the low-dimensional latent space that has been shown to capture the major features of intrinsic brain activity using four states obtained with a Hidden Markov Model. They related the four states to previously-described functional gradients in the brain and examined the relative contribution of each state under different cognitive conditions. They showed that states related to the measured behavior for each condition differed, but that a common state appears to reflect disengagement across conditions. The authors bring together a state-of-the-art analysis of systems-level brain dynamics and cognitive neuroscience, bridging a gap that has long needed to be bridged.

The strongest aspect of the study is its rigor. The authors use appropriate null models and examine multiple datasets (not used in the original analysis) to demonstrate that their findings replicate. Their thorough analysis convincingly supports their assertion that common states are present across a variety of conditions, but that different states may predict behavioural measures for different conditions. However, the authors could have better situated their work within the existing literature. It is not that a more exhaustive literature review is needed-it is that some of their results are unsurprising given the work reported in other manuscripts; some of their work reinforces or is reinforced by prior studies; and some of their work is not compared to similar findings obtained with other analysis approaches. While space is not unlimited, some of these gaps are important enough that they are worth addressing:

1. The authors' own prior work on functional connectivity signatures of attention is not discussed in comparison to the latest work. Neither is work from other groups showing signatures of arousal that change over time, particularly in resting state scans. Attention and arousal are not the same things, but they are intertwined, and both have been linked to large-scale changes in brain activity that should be captured in the HMM latent states. The authors should discuss how the current work fits with existing studies.<br /> 2. The 'base state' has been described in a number of prior papers (for one early example, see https://pubmed.ncbi.nlm.nih.gov/27008543). The idea that it might serve as a hub or intermediary for other states has been raised in other studies, and discussion of the similarity or differences between those studies and this one would provide better context for the interpretation of the current work. One of the intriguing findings of the current study is that the incidence of this base state increases during sitcom watching, the strongest evidence to date is that it has a cognitive role and is not merely a configuration of activity that the brain must pass through when making a transition.<br /> 3. The link between latent states and functional connectivity gradients should be considered in the context of prior work showing that the spatiotemporal patterns of intrinsic activity that account for most of the structure in resting state fMRI also sweep across functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/33549755/ ). In fact, the spatiotemporal dynamics may give rise to the functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/35902649/ ). HMM states bear a marked resemblance to the high-activity phases of these patterns and are likely to be closely linked to them. The spatiotemporal patterns are typically obtained during rest, but they have been reported during task performance (https://pubmed.ncbi.nlm.nih.gov/30753928/ ) which further suggests a link to the current work. Similar patterns have been observed in anesthetized animals, which also reinforces the conclusion of the current work that the states are fundamental aspects of the brain's functional organization.

Reviewer #2 (Public Review):

In this study, Song and colleagues applied a Hidden Markov Model to whole-brain fMRI data from the unique SONG dataset and a grad-CPT task, and in doing so observed robust transitions between low-dimensional states that they then attributed to specific psychological features extracted from the different tasks.

The methods used appeared to be sound and robust to parameter choices. Whenever choices were made regarding specific parameters, the authors demonstrated that their approach was robust to different values, and also replicated their main findings on a separate dataset.

I was mildly concerned that similarities in some of the algorithms used may have rendered some of the inter-measure results as somewhat inevitable (a hypothesis that could be tested using appropriate null models).

This work is quite integrative, linking together a number of previous studies into a framework that allows for interesting follow-up questions.

Overall, I found the work to be robust, interesting, and integrative, with a wide-ranging citation list and exciting implications for future work.

Reviewer #3 (Public Review):

My general assessment of the paper is that the analyses done after they find the model are exemplary and show some interesting results. However, the method they use to find the number of states (Calinski-Harabasz score instead of log-likelihood), the model they use generally (HMM), and the fact that they don't show how they find the number of states on HCP, with the Schaeffer atlas, and do not report their R^2 on a test set is a little concerning. I don't think this perse impedes their results, but it is something that they can improve. They argue that the states they find align with long-standing ideas about the functional organization of the brain and align with other research, but they can improve their selection for their model.

Strengths:

- Use multiple datasets, multiple ROIs, and multiple analyses to validate their results<br /> - Figures are convincing in the sense that patterns clearly synchronize between participants<br /> - Authors select the number of states using the optimal model fit (although this turns out to be a little more questionable due to what they quantify as 'optimal model fit')<br /> - Replication with Schaeffer atlas makes results more convincing<br /> - The analyses around the fact that the base state acts as a flexible hub are well done and well explained<br /> - Their comparison of synchrony is well-done and comparing it to resting-state, which does not have any significant synchrony among participants is obvious, but still good to compare against.<br /> - Their results with respect to similar narrative engagement being correlated with similar neural state dynamics are well done and interesting.<br /> - Their results on event boundaries are compelling and well done. However, I do not find their Chang et al. results convincing (Figure 4B), it could just be because it is a different medium that explains differences in DMN response, but to me, it seems like these are just altogether different patterns that can not 100% be explained by their method/results.<br /> - Their results that when there is no event, transition into the DMN state comes from the base state is 50% is interesting and a strong result. However, it is unclear if this is just for the sitcom or also for Chang et al.'s data.<br /> - The involvement of the base state as being highly engaged during the comedy sitcom and the movie are interesting results that warrant further study into the base state theory they pose in this work.<br /> - It is good that they make sure SM states are not just because of head motion (P 12).<br /> - Their comparison between functional gradient and neural states is good, and their results are generally well-supported, intuitive, and interesting enough to warrant further research into them. Their findings on the context-specificity of their DMN and DAN state are interesting and relate well to the antagonistic relationship in resting-state data.

Weaknesses:

- Authors should train the model on part of the data and validate on another<br /> - Comparison with just PCA/functional gradients is weak in establishing whether HMMs are good models of the timeseries. Especially given that the HMM does not explain a lot of variance in the signal (~0.5 R^2 for only 27 brain regions) for PCA. I think they don't report their own R^2 of the timeseries<br /> - Authors do not specify whether they also did cross-validation for the HCP dataset to find 4 clusters<br /> - One of their main contributions is the base state but the correlation between the base state in their Song dataset and the HCP dataset is only 0.399<br /> - Figure 1B: Parcellation is quite big but there seems to be a gradient within regions<br /> - Figure 1D: Why are the DMNs further apart between SONG and HCP than the other states<br /> - Page 5 paragraph starting at L25: Their hypothesis that functional gradients explain large variance in neural dynamics needs to be explained more, is non-trivial especially because their R^2 scores are so low (Fig 1. Supplement 8) for PCA<br /> - Generally, I do not find the PCA analysis convincing and believe they should also compare to something like ICA or a different model of dynamics. They do not explain their reasoning behind assuming an HMM, which is an extremely simplified idea of brain dynamics meaning they only change based on the previous state.<br /> - For the 25- ROI replication it seems like they again do not try multiple K values for the number of states to validate that 4 states are in fact the correct number.<br /> - Fig 2B: Colorbar goes from -0.05 to 0.05 but values are up to 0.87<br /> - P 16 L4 near-critical, authors need to be more specific in their terminology here especially since they talk about dynamic systems, where near-criticality has a specific definition. It is unclear which definition they are looking for here.<br /> - P16 L13-L17 unnecessary<br /> - I think this paper is solid, but my main issue is with using an HMM, never explaining why, not showing inference results on test data, not reporting an R^2 score for it, and not comparing it to other models. Secondly, they use the Calinski-Harabasz score to determine the number of states, but not the log-likelihood of the fit. This clearly creates a bias in what types of states you will find, namely states that are far away from each other, which likely also leads to the functional gradient and PCA results they have. Where they specifically talk about how their states are far away from each other in the functional gradient space and correlated to (orthogonal) components. It is completely unclear to me why they used this measure because it also seems to be one of many scores you could use with respect to clustering (with potentially different results), and even odd in the presence of a log-likelihood fit to the data and with the model they use (which does not perform clustering).<br /> - Grammatical error: P24 L29 rendering seems to have gone wrong

Questions:

- Comment on subject differences, it seems like they potentially found group dynamics based on stimuli, but interesting to see individual differences in large-scale dynamics, and do they believe the states they find mostly explain global linear dynamics?<br /> - P19 L40 why did the authors interpolate incorrect or no-responses for the gradCPT runs? It seems more logical to correct their results for these responses or to throw them out since interpolation can induce huge biases in these cases because the data is likely not missing at completely random.

Reviewer #1 (Public Review):

The goal of this study is to identify transcription factors that mediate stem cell transitions during differentiation. To achieve this, the authors examine the type II Drosophila neuroblast lineage, using single-cell RNA sequencing to examine all cell types in the type II lineage. There are known patterns of expression for neurons in this lineage, so they can identify clusters in their data set that are in the developmental state of transitioning from neuroblast to immature intermediate neuronal progenitor. They have outlined a set of expression criteria for transcription factors that are candidates for fine-tuning stem cell fate. They find that an isoform of Fruitless, called FruC, is a candidate transcription factor. Using microscopy and several genetic perturbation conditions the authors find that FruC is expressed in neuroblasts and can alter the number of cells in the lineage. To determine the mechanism that FruC uses to influence stemness the authors examine genomic occupancy of FruC, changes in histone modifications in FruC loss-of-function studies, and examination of DNA occupancy of proteins that function in chromatin modification. The authors argue that FruC functions to promote low-level H3K27me3 enrichment to maintain stemness based on comparisons across these data sets. The identification of transcription factors and the mechanisms used to maintain or differentiate stem cells is an important goal and is still a fundamental question in biology. The Drosophila model is poised for this type of analysis, given the knowledge of gene expression across cell fate that the authors use in this study.

Comments the authors should address:<br /> This is a valuable study that relies on several state-of-the-art genomic data sets to examine the mechanism that drives stemness. However, the authors should be using statical approaches to support their major conclusion regarding FruC and the role of H3K27me3. The study presents peak data in genome browser tracks of a handful of loci in the Notch pathway that show the pattern of reduced HK3K27me3 and not the other modifications they examine. However, it is not clear if the majority of FruC target genes in the genomic analyses have this pattern, though they argue they do. The major conclusion that FruC promotes a stem cell fate is based on the overlap between the list of genes they identify bound by FruC and the lists of genes that have changes in histone modifications (H3K27ac, H3K4me3, and H3K27me3). The limited use of statistical approaches to draw these conclusions is a weakness of the study. The authors do not use statistics to find changes in chromatin modification at loci, instead relying on 2-fold change calculations. Furthermore, the authors don't indicate if the genes with altered histone modification/binding peaks are significantly enriched (or not) with FruC targets, with no quantitative assessments of these data. The data in Figures 5,6 and S4 should have statistics/quantification to support the major conclusions of their study that FruC targets differ in H3K27me3, but not H3K27ac, and H3K4me3.

Reviewer #2 (Public Review):

This study addresses the molecular mechanism by which the FruC transcription factor regulates neurogenesis in Drosophila. The authors combine genetics and genomics to profile FruC genomic binding along with that of trithorax-like (Trl) and Su(H) and several histone modifications including H3K27me3. They propose that Fru acts to fine-tune the expression of Notch effector genes and they show that this regulation does not involve changes in H3K27ac, nor H3K4me3, but rather that FruC-regulated gene expression is correlated with changes in H3K27me3 levels at Fru target genes. While the study is well conducted and combines state-of-the-art techniques, there are several aspects that could be improved. The authors propose that Fru fine-tunes the expression of Notch effector genes, but they do not directly measure gene expression in any of the genetic backgrounds. It is important to do this to have some type of precise measure of transcriptional changes (what does 'fine tune' really mean), as the authors' model is based on subtle changes in H3K27me3. It would be important to quantify and correlate both processes more precisely. Similarly, the authors claim that Fru promotes 'low levels' of H3K27me3 at its bound loci throughout the genome, but they do not describe the criteria that define 'low levels' versus high levels of HK27me3.

In the authors' model, FruC likely functions together with PRC2 to regulate gene expression, and local low-level enrichment of repressive histone marks act to fine-tune gene expression. However, in the absence of experiments directly addressing the molecular mechanisms by which Fru regulates transcription, it would be more accurate to claim that changes in H3K27me3 correlate with altered gene expression.

Reviewer #3 (Public Review):

Rajan et al. used scRNAseq to identify transcription factors responsible for fine-tuning stemness gene expression in neural stem cells (neuroblasts), identifying Fruitless (fru) as a putative regulator of this process. Specifically, loss of the fru isoform C (fruc) results in increased stemness gene expression, while its overexpression leads to the opposite effect. Consistently, overexpression of fruc in a brat-null neuroblast-tumor background is sufficient to partially restore differentiation. Furthermore, by performing extensive genome-wide binding studies, the authors show that Fruc preferentially binds to cis-regulatory elements of stemness genes, with evidence that this transcription factor regulates the Notch-pathway via co-binding with Notch-target genes. The overall impact of FruC on transcription was not assessed.

Their data also shows that instead of regulating the deposition of histone marks associated with active transcription, such as H3K27ac or H3K4me3, loss of fruc results in decreased levels of the repressive mark H3K27me3, namely in the Notch locus or in Notch downstream effector genes, indicating that FruC fine-tunes the expression of their bound genes through maintenance of low-levels of repressive marks at cis-regulatory elements of its target genes. Given the extensive binding profile of FruC the effects promoted by its misexpression in neuroblasts are likely multifactorial.

In addition, the authors also show that PRC2 subunits, Caf1 and Su(z)12, the multisubunit complex responsible for catalyzing H3k27me3 deposition, (1) co-localize with Fru in Fruc-bound regions and (2) their loss partially phenocopies the previous results obtained for fruc depletion. The authors propose a model in which Fruc, via synergistic work with PRC2, is capable of fine-tuning the expression of stemness genes, in particular, Notch and Notch targets in neuroblasts by promoting low levels of transcriptionally repressive histone marks at their target cis-regulatory elements. If FruC and PRC2 functionally interact, and if the recruitment of one factor affects the binding of the other remains unknown.

The authors present an assortment of results that will be useful for those working in transcription and chromatin regulation, namely in the field of Drosophila neural stem cells (neuroblasts). Specifically, the authors provide robust single-cell RNA sequencing results and analysis that can be used by researchers interested in trying to understand the transcriptional state of neuroblasts and their progeny. Additionally, genome-wide binding studies for FruC or PRC2 subunits, together with the profiling of active/repressive histone marks, offer new insights regarding transcription factor or transcriptional repressor binding and the respective read-out in terms of histone modifications. Moreover, the authors propose an interesting model via which transcription regulation of Notch and Notch downstream effectors is rendered via fine-tuning of the transcriptional output. Hence, FruC restrains and limits the levels of its target genes within neuroblasts, avoiding the segregation of high levels of stemness-associated proteins to the progeny, which would incur in fate and differentiation defects. The model proposed here highlights how transcription regulation by histone marks is much more dynamic and layered, other than being dictated only by the mutually exclusive presence of either active or repressive marks.

Reviewer #1 (Public Review):

This study optimized a protocol for analyzing microplastics (MPs) in bovine and human follicular fluid. The authors identified the most common plastic polymers in the follicular fluid and assessed the impact of polystyrene beads on bovine oocyte maturation based on the concentration of MPs in follicular fluid. The authors found a decrease in maturation rate in the presence of polystyrene beads and conducted proteomic analysis of oocytes treated with and without MPs, revealing protein alterations.

Strengths:<br /> • The optimization of the protocol for analyzing MPs in follicular fluid, which is important for future research in this area.<br /> • Investigating the effects of MPs on oocyte maturation and proteomic profiles is significant.

Weaknesses:<br /> • The effects of polystyrene beads on oocyte maturation and proteomic profiles are not directly demonstrated, and insufficient analysis is performed to support the claims made in the manuscript.<br /> • The use of polystyrene beads does not fully mimic the concentration and interaction of MPs in follicular fluid, which warrants careful interpretation and discussion.<br /> • A major weakness is the lack of mechanism. Determining the cause of meiotic arrest (decreased maturation rate) would be needed to strengthen the paper. Are spindle morphology, chromosome morphology/alignment and/or spindle assembly checkpoint mechanism perturbed in MPs-treated oocytes?<br /> • Functional assays to validate one or more of the pathways suggested by the proteomic analysis would be necessary to strengthen the paper.<br /> • The analysis of broken zona pellucida is not sufficiently convincing. Definitely the breakage of zona pellucida is most likely a result of oocyte denudation. However, this may indicate increased fragility of polystyrene beads-treated oocytes. Investigating cytoskeletal components in oocytes treated with or without polystyrene beads would strengthen this paper.<br /> • The percentage of degenerated oocytes in control group is abnormally high which raises concern that the oocytes are not healthy.<br /> • The small font size of the figures (such as Fig. 1C) affects the quality of the manuscript.<br /> • Finally, the authors should cite previous publications on the effects of MPs on female reproduction, as this is not a novel area of research, despite the use of different concentrations. For example, "Polystyrene microplastics lead to pyroptosis and apoptosis of ovarian granulosa cells via NLRP3/Caspase-1 signaling pathway in rats (DOI: 10.1016/j.ecoenv.2021.112012)".

Reviewer #2 (Public Review):

This study presents valuable findings including the use of an improved method of Raman spectroscopy to measure accumulation of microplastics in ovarian follicular fluid obtained from cows and women and demonstration that experimental direct exposure of bovine eggs to biologically relevant levels of polystyrene, a microplastic found in both cows and women's follicular fluid, negatively influenced ova maturation status and the abundance of proteins involved in oxidative stress, DNA damage, apoptosis, and oocyte maturation. The evidence supporting the claims of the authors is solid but inclusion of human population from which the follicular fluid was obtained (e.g., demographics, reason for assisted reproduction), and details about quality control for proteome profiling experiments (i.e., peptide count cut-off for significant proteins) would have strengthened the study. The work will be of interest to exposure scientists, reproductive toxicologists, regulatory scientists, and reproductive health clinicians.

Reviewer #3 (Public Review):

The study from Grechi et al showed that emerging environmental microplastics (MPs) are present in both human and bovine follicular fluid. Moreover, based on the characterization and quantification data, authors treated bovine oocytes with environmentally relevant levels of polystyrene (PS) MPs and found that PS MPs interfered with oocyte maturation in vitro. This study is novel, particularly the first part of MP characterization and quantification, and for the first time confirms the presence of MPs in follicular fluid of humans and large farm animals. These results provide a possible mechanism by which the female infertility rate has been increasing in both humans and large farm animals. The session of exposing MPs to bovine and related oocyte health evaluation can be further improved. For example, authors examined the morphology of the oocyte zona pellucida (ZP) and degeneration and stained oocyte DNA to determine the meiotic maturation status. However, a much more comprehensive oocyte health evaluation can be performed including but not limited to the examination of oocyte spindle morphology, meiotic division, fertilization, early embryo development, mitochondria, and accumulation of ROS. These additional endpoints can provide more robust evidence to determine the impact of MPs on oocyte health. While the oocyte proteomic analysis identified altered proteins, more functional studies and causation experiments can be performed. In addition, authors exposed cumulus-oocyte-complexes (COCs) but not denuded oocytes with MPs, it is crucial to determine whether MPs accumulate in cumulus cells or oocytes or both as well as the compromised oocyte quality is caused by the direct effect of MPs or the indirect impact on somatic cumulus cells to cause a secondary effect on the oocytes.

Reviewer #1 (Public Review):

The authors performed a meta-analysis of GC concentrations and metabolic rates in birds and mammals. They found close associations for all studies showing a positive association between these two traits. As GCs have been viewed with close links to "stress," authors suggest that this overlooks the importance of metabolism and perhaps GC variation does not relate to "stress" per se but an increase in metabolism instead.

This is an important meta-analysis, as most researchers acknowledge the link between GCs and metabolism, metabolism is often overlooked in studies. The field of conservation physiology is especially focused on GCs being a "stress" hormone, which overlooks the importance of GCs in mediating energy balance, i.e., an animal that has high GC concentrations may not be doing that poorly compared to an animal with low GC concentrations, it might just be expending more energy, e.g., caring for young. The results, with overwhelming directionality and strong effect sizes, support the link for a positive association with these two variables.

My main concern lies in that most of the studies come from a few labs, therefore there may be limited data to test this relationship. I would include lab as a random effect to see how strong this effect might be. Furthermore, I would like to see a test of the directionality of the two variables. Authors suggest that changes in metabolism affect GC levels but likely changes in GC levels would affect metabolism. Why not look into studies that have altered GC levels experimentally and see the effect on metabolism? Based on the close link, authors suggest that GCs may not play a role outside of "stress" beyond the stressor's effect on metabolic rate. However, if they were to investigate manipulations of GCs on metabolic rate, the link may or may not be there, which would be interesting to look at. I firmly believe that GCs are tightly linked to metabolism; however, I also think that GCs have a range of effects outside of metabolism as well, depending on the course and strength of the stressor.

This work helps in the thinking that GCs are not the same as a "stress" hormone or labelling hormones with only one function. As hormones are naturally pleiotropic, the view of any one hormone being X is overly simplistic.

Reviewer #2 (Public Review):

Where this study is interesting is that the authors do a meta-analysis of studies in which metabolic rate was experimentally manipulated and both this rate and glucocorticoid levels were simultaneously measured. Unsurprisingly, there are relatively few such studies and many are from the lab of Michael Romero. While the results of the analysis are compelling, they are not surprising. That said, this work is important.

It is worth noting that in this analysis, the majority of the studies, if not all, are dealing with variation in baseline levels of glucocorticoids. That means the hormone is mostly acting metabolically at these lower levels and not as a stress response hormone as it does when levels are much higher. This difference is probably due to differences in receptors being activated. This could be discussed.

Reviewer #1 (Public Review):

The paper titled 'A dual function of the IDA peptide in regulating cell separation and modulating plant immunity at the molecular level' by Olsson Lalun et al., 2023 aims to understand how IDA-HAE/HSL2 signalling modulates immunity, a pathway that has previously been implicated in development. This is a timely question to address as conflicting reports exist within the field. IDL6/7 have previously been shown to negatively regulate immune signalling, disease resistance and stress responses in leaf tissue, however IDA has been shown to positively regulate immunity through the shedding of infected tissues. Moreover, recently the related receptor NUT/HSL3 has been shown to positively regulate immune signalling and disease resistance. This work has the potential to bring clarity to this field, however the manuscript requires some additional work to address these questions. This is especially the case as it contracts some previous work with IDL peptides which are perceived by the same receptor complexes.

Can IDA induce pathogen resistance? Does the infiltration of IDA into leaf tissue enhance or reduce pathogen growth? Previously it has been shown that IDL6 makes plants more susceptible. Is this also true for IDA? Currently cytoplasmic calcium influx and apoplastic ROS as overinterpreted as immune responses - these can also be induced by many developmental cue e.g. CLE40 induced calcium transients. Whilst gene expression is more specific is also true that treatment with synthetic peptides, which are recognised by LRR-RKs, can induce immune gene expression, especially in the short term, even when that is not there in vivo function e.g. doi.org/10.15252/embj.2019103894.

This paper shows that receptors other than hae/hsl2 are genetically required to induce defense gene expression, it would have been interesting to see what phenotype would be associated with higher order mutants of closely related haesa/haesa-like receptors. Indeed recently HSL1 has been shown to function as a receptor for IDA/IDL peptides. Could the triple mutant suppress all response? Could the different receptors have distinct outputs? For example for FRK1 gene expression the hae hsl2 mutant has an enhanced response. Could defence gene expression be primarily mediated by HSL1 with subfunctionalisation within this clade?

One striking finding of the study is the strong additive interaction between IDA and flg22 treatment on gene expression. Do the authors also see this for co-treatment of different peptides with flg22, or is this unique function of IDA? Is this receptor dependent (HAE/HSL1/HSL2)?

It is interesting how tissue specific calcium responses are in response to IDA and flg22, suggesting the cellular distribution of their cognate receptors. However, one striking observation made by the authors as well, is that the expression of promoter seems to be broader than the calcium response. Indicating that additional factors are required for the observed calcium response. Could diffusion of the peptide be a contributing factor, or are only some cells competent to induce a calcium response?<br /> It is interesting that the authors look for floral abscission phenotypes in cngc and rbohd/f mutants to conclude for genetic requirement of these in floral abscission. Do the authors have a hypothesis for why they failed to see a phenotype for the rbohd/f mutant as was published previously? Do you think there might be additional players redundantly mediating these processes?

Can you observe callose deposition in the cotyledons of the 35S::HAE line? Are the receptors expressed in native cotyledons? This is the only phenotype tested in the cotyledons.

Are flg22-induced calcium responses affected in hae hsl2?

Reviewer #2 (Public Review):

Lalun and co-authors investigate the signalling outputs triggered by the perception of IDA, a plant peptide regulating organs abscission. The authors observed that IDA perception leads to a transient influx of Ca2+, to the production of reactive oxygen species in the apoplast, and to an increase accumulation of transcripts which are also responsive to an immunogenic epitope of bacterial flagellin, flg22. The authors show that IDA is transcriptionally upregulated in response to several biotic and abiotic stimuli. Finally, based on the similarities in the molecular responses triggered by IDA and elicitors (such as flg22) the authors proposed that IDA has a dual function in modulating abscission and immunity. The manuscript is rather descriptive and provide little information regarding IDA signalling per se. A potential functional link between IDA signalling and immune signalling remains speculative.

Reviewer #3 (Public Review):

Previously, it has been shown the essential role of IDA peptide and HAESA receptor families in driving various cell separation processes such as abscission of flowers as a natural developmental process, of leaves as a defense mechanism when plants are under pathogenic attack or at the lateral root emergence and root tip cell sloughing. In this work, Olsson et al. show for the first time the possible role of IDA peptide in triggering plant innate immunity after the cell separation process occurred. Such an event has been previously proposed to take place in order to seal open remaining tissue after cell separation to avoid creating an entry point for opportunistic pathogens. The elegant experiments in this work demonstrate that IDA peptide is triggering the defense-associated marker genes together with immune specific responses including release of ROS and intracellular CA2+. Thus, the work highlights an intriguing direct link between endogenous cell wall remodeling and plant immunity. Moreover, the upregulation of IDA in response to abiotic and especially biotic stimuli are providing a valuable indication for potential involvement of HAE/IDA signalling in other processes than plant development.

Strengths:<br /> The various methods and different approaches chosen by the authors consolidates the additional new role for a hormone-peptide such as IDA. The involvement of IDA in triggering of the immunity complex process represents a further step in understanding what happens after cell separation occurs. The Ca2+ and ROS imaging and measurements together with using the haehsl2 and haehsl2 p35S::HAE-YFP genotypes provide a robust quantification of defense responses activation. While Ca2+ and ROS can be detected after applying the IDA treatment after the occurrence of cell separation it is adequately shown that the enzymes responsible for ROS production, RBOHD and RBOHF, are not implicated in the floral abscission.<br /> Furthermore, IDA production is triggered by biotic and abiotic factors such as flg22, a bacterial elicitor, fungi, mannitol or salt, while the mature IDA is activating the production of FRK1, MYB51 and PEP3, genes known for being part of plant defense process.

Weaknesses:<br /> Even though there is shown a clear involvement of IDA in activating the after-cell separation immune system, the use of p35S:HAE-YFP line represent a weak point in the scientific demonstration. The mentioned line is driving the HAE receptor by a constitutive promoter, capable of loading the plant with HAE protein without discriminating on a specific tissue. Since it is known that IDA family consist of more members distributed in various tissues, it is very difficult to fully differentiate the effects of HAE present ubiquitously.<br /> The co-localization of HAE/HSL2 and FLS2 receptors is a valuable point to address since in the present work, the marker lines presented do not get activated in the same cell types of the root tissues which renders the idea of nanodomains co-localization (as hypothetically written in the discussion) rather unlikely.

Reviewer #1 (Public Review):

In this study, Le Moigne and coworkers shed light on the structural details of the Sedoheptulose-1,7-Bisphosphatase (SBPase) from the green algae Chlamydomonas reinhardtii. The SBPase is part of the Calvin cycle and catalyzes the dephosphorylation of sedoheptulose-1,7-bisphosphate (SBP), which is a crucial step in the regeneration of ribulose-1,5-bisphosphate (RuBP), the substrate for Rubisco. The authors determine the crystal structure of the CrSBPase in an oxidized state. Based on this structure, potential active site residues and sites of post-translational modifications are identified. Furthermore, the authors determine the CrSBPase structure in a reduced state revealing the disruption of a disulfide bond in close proximity to the dimer interface. The authors then use molecular dynamics (MD) to gain insights into the redox-controlled dynamics of the CrSBPase and investigate the oligomerization of the protein using small-angle X-ray scattering (SAXS) and size-exclusion chromatography. Despite the difference in oligomerization, disruption of this disulfide bond did not impact the activity of CrSBPase, suggesting additional thiol-dependent regulatory mechanisms modulating the activity of the CrSBPase.

The authors provide interesting new findings on a redox-mechanism that modulates the oligomeric behavior of the SBPase, however without investigating this potential mechanism in more detail. The conclusions of this manuscript are mostly supported by the data, but they should be more carefully evaluated in respect to what is known from other systems as e.g. the moss Physcomitrella patens. This is especially of interest, as SBPase was previously reported to be dimeric, whereas for FBPase a dimer/tetramer equilibrium has been observed.

1.) Given that PpSBPase has been already characterized in detail, the authors should provide a more rigorous comparison to the existing data on SBPases. This includes a more conclusive structural comparison but also the enzymatic assays should be compared to the findings from P. patens. Do the authors observe differences between the moss and the chlorophyte systems, maybe even in regard to the oligomerization of the SBPase?

2.) The authors should include the control experiments (untreated SBPase) and the assays performed with mutant versions of the SBPase, which are currently only mentioned in the text or not shown at all.

3.) The representation of the structure in figures (especially Figures 1 and 3) should be adjusted to match the author's statements. In Figure 1, the angle from which the structure is displayed changes over the entire figure making it difficult to follow especially as a non-structural biologist. Furthermore, important aspects of the structure mentioned in the text are not labeled and should be highlighted, by e.g. a close-up. Same holds true for Figure 3 that currently mostly shows redundant information.

4.) The authors state that mutation of C115 and C120 to serine destabilize the dimer formation, while more tetramer and monomer is formed. As the tetramer is essentially a dimer of dimers, the authors should elaborate how this might work mechanistically. In my opinion, dimer formation is a prerequisite for tetramer formation and the two mutations rather stabilize the tetramer instead of destabilizing the dimer.

Reviewer #2 (Public Review):

The central theme of the manuscript is to report on the structure of SBPase - an enzyme central to the photosynthetic Calvin-Benson-Bassham cycle. The authors claim that the structure is first of its kind from a chlorophyte Chlamydomonas reinhardtii, a model unicellular green microalga. The authors use a number of methods like protein expression, purification, enzymatic assays, SAXS, molecular dynamics simulations and xray crystallography to resolve a 3.09 A crystal structure of the oxidized and partially reduced state. The results are supported by the claims made in the manuscript. One of the main weakness of the work is the lack of wider discussion presented in the manuscript. While the structure is the first from a chlorophyte, it is not unique. Several structures of SBPase are available. As the manuscript currently reads, the wider context of SBPase structures available and comparisons between them is missing from the manuscript. Another important point is that the reported structure of crSBPase is 0.453A away from the alphafold model. Though fleetingly mentioned in the methods section, it should be discussed to place it in the wider context.

Reviewer #1 (Public Review):

The authors used mathematical models to explore the mechanism(s) underlying the process of polar tube extrusion and the transport of the sporoplasm and nucleus through this structure. They combined this with experimental observations of the structure of the tube during extrusion using serial block face EM providing 3 dimensional data on this process. They also examined the effect of hyperosmolar media on this process to evaluate which model fit the predicted observed behavior of the polar tube in these various media solutions. Overall, this work resulted in the authors arriving at a model of this process that fit the data (model 5, E-OE-PTPV-ExP). This model is consistent with other data in the literature and provides support for the concept that the polar tube functions by eversion (unfolding like a finger of a glove) and that the expanding polar vacuole is part of this process. Finally, the authors provide important new insights into the bucking of the spore wall (and possible cavitation) as providing force for the nucleus to be transported via the polar tube. This is an important observation that has not been in previous models of this process.

Reviewer #2 (Public Review):

Microsporidia has a special invasion mechanism, which the polar tube (PT) ejects from mature spores at ultra-fast speeds, to penetrate the host and transfer the cargo to host. This work generated models for the physical basis of polar tube firing and cargo transport through the polar tube. They also use a combination of experiments and theory to elucidate possible biophysical mechanisms of microsporidia. Moreover, their approach also provided the potential applications of such biophysical approaches to other cellular architecture.

The conclusions of this paper are mostly well supported by data, but some analyses need to be clarified.

According to the model 5 (E-OE-PTPV-ExP) in P42 Fig. 6, is the posterior vacuole connected with the polar tube? If yes, how does the nucleus unconnected with the posterior vacuole enter the polar tube? In Fig. 6, would the posterior vacuole become two parts after spore germination? One part is transported via the polar tube, and the other is still in the spore. I recommend this process requires more experiments to prove.

Reviewer #3 (Public Review):

Abstract:

The paper follows a recent study by the same team (Jaroenlak et al Plos Pathogens 2020), which documented the dramatic ejection dynamics of the polar tube (PT) in microsporidia using live-imaging and scanning electron microscopy. Although several key observations were reported in this paper (the 3D architecture of the PT within the spore, the speed and extent of the ejection process, the translocation dynamics of the nucleus during germination), the precise geometry of the PT during ejection remain inaccessible to imaging, making it difficult to physically understand the phenomenon.

This paper aims to fill this gap with an indirect "data-driven" approach. By modeling the hydrodynamic dissipation for different unfolding mechanisms identified in the literature and by comparing the predictions with experiments of ejection in media of various viscosities, authors shows that data are compatible with an eversion (caterpillar-like) mechanism but not compatible with a "jack-in-the-box" scenario. In addition, the authors observe that most germinated spores exhibit an inward bulge, which they attribute to buckling due to internal negative pressure and which they suggest may be a mean of pushing the nucleus out of the PT during the final stage of ejection.

Major strengths:

Probably the most impressive aspect of the study is the experimental analysis of the ejection dynamics (velocity, ejection length) in medium of various viscosities over 3 orders of magnitudes, which, combined with a modeling of the viscous drag of the PT tube, provides very convincing evidence that the unfolding mechanism is not a global displacement of the tube but rather an apical extension mechanism, where the motion is localized at the end of the tube. The systematic classification of the different unfolding scenarios, consistent with the previous literature, and their confrontation with data in terms of energy, pressure and velocity also constitute an original approach in microbiology where in-situ and real time geometry is often difficult to access.

Major weaknesses:

1) While the experimental part of the paper is clear, I had (and still have) a hard time understanding the modeling part. Overall, the different unfolding mechanisms should be much better explained, with much more informative sketches to justify the dissipation and pressure terms, magnifying the different areas where dissipation occurs, showing the velocity field and pressure field, etc. In particular, a key parameter of eversion models is the geometry of the lubrication layers inside and outside the spore (h_sheath, h_slip). Where do the values of h_sheath and h_slip come from? What is the physical process that selects these parameters? For clarity, the figures showing the unfolding mechanics in the different scenario should be in the main text, not in the supplemental materials.

2) The authors compute and discuss in several places "the pressure" required to ejection, but no pressure is indicated in the various sketches and no general "ejection mechanism" involving this pressure is mentioned in the paper. What is this "required pressure" and to what element does it apply? I understand that the article focuses on the dissipation required to the deployment of the PT but I find it difficult to discuss the unfolding mechanism without having any idea on the driving mechanism of the movement. How could eversion be initiated and sustained?

3) Finally, the authors do not explain how pressure, which appears to be a positive, driving quantity at the beginning of the process, can become negative to induce buckling at the end of ejection. Although the hypothesis of rapid translocation induced by buckling is interesting, a much better mechanistic description of the process is needed to support it.

Reviewer #1 (Public Review):

The goal of the authors is to use whole-exome sequencing to identify genomic factors contributing to asthenoteratozoospermia and male infertility. Using whole-exome sequencing, they discovered homozygous ZMYND12 variants in four unrelated patients. They examined the localization of key sperm tail components in sperm from the patients. To validate the findings, they knocked down the ortholog in Trypanosoma brucei. They further dissected the complex using co-immunoprecipitation and comparative proteomics with samples from Trypanosoma and Ttc29 KO mice. They concluded that ZMYND12 is a new asthenoteratozoospermia-associated gene, bi-allelic variants of which cause severe flagellum malformations and primary male infertility.

The major strengths are that the authors used the cutting-edge technique, whole-exome sequencing, to identify genes associated with male infertility, and used a new model organism, Trypanosoma brucei to validate the findings; together with other high-throughput tools, including comparative proteomics to dissect the protein complex essential for normal sperm formation/function. The major weakness is that limited samples could be collected from the patients for further characterization by other approaches, including western blotting and TEM.

In general, the authors achieved their goal and the conclusion is supported by their results. The findings not only provide another genetic marker for the diagnosis of asthenoteratozoospermia but also enrich the knowledge in cilia/flagella.

Reviewer #2 (Public Review):

The manuscript "Novel axonemal protein ZMYND12 interacts with TTC29 and DNAH1, and is required for male fertility and flagellum function" by Dacheux et al. interestingly reported homozygous deleterious variants of ZMYND12 in four unrelated men with asthenoteratozoospermia. Based on the immunofluorescence assays in human sperm cells, it was shown that ZMYND12 deficiency altered the localization of DNAH1, DNALI1, WDR66 and TTC29 (four of the known key proteins involved in sperm flagellar formation). Trypanosoma brucei and mouse models were further employed for mechanistic studies, which revealed that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1. Their findings are solid, and this manuscript will be very informative for clinicians and basic researchers in the field of human infertility.

Reviewer #3 (Public Review):

In this study, the authors identified homozygous ZMYND12 variants in four unrelated patients. In sperm cells from these individuals, immunofluorescence revealed altered localization of DNAH1, DNALI1, WDR66, and TTC29. Axonemal localization of ZMYND12 ortholog TbTAX-1 was confirmed using the Trypanosoma brucei model. RNAi knock-down of TbTAX-1 dramatically affected flagellar motility, with a phenotype similar to ZMYND12-variant-bearing human sperm. Co-immunoprecipitation and ultrastructure expansion microscopy in T. brucei revealed TbTAX-1 to form a complex with TTC29. Comparative proteomics with samples from Trypanosoma and Ttc29 KO mice identified a third member of this complex: DNAH1. The data presented revealed that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1, which is critical for flagellum function and assembly in humans, and Trypanosoma. The manuscript is informative for the clinical and basic research in the field of spermatogenesis and male infertility.

Joint Public Review:

In this study, Porter et al report on outcomes from a small, open-label, pilot randomized clinical trial comparing dornase-alfa to the best available care in patients hospitalized with COVID-19 pneumonia. As the number of randomized participants is small, investigators describe also a contemporary cohort of controls and the study concludes about a decrease of inflammation (reflected by CRP levels) after 7 days of treatment but no other statistically significant clinical benefit.

Suggestions to the authors:<br /> • The RCT does not follow CONSORT statement and reporting guidelines<br /> • The authors have chosen a primary outcome that cannot be at least considered as clinically relevant or interesting. After 3 years of the pandemic with so much research, why investigate if a drug reduces CRP levels as we already have marketed drugs that provide beneficial clinical outcomes such as dexamethasone, anakinra, tocilizumab and baricitinib.<br /> • Please provide in Methods the timeframe for the investigation of the primary endpoint<br /> • Why day 35 was chosen for the read-out of the endpoint?<br /> • The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.<br /> • Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.<br /> • It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.<br /> • In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatments as BAT received by those patients except for dexamethasone.<br /> • In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.<br /> • Propensity-score selected contemporary controls may introduce bias in favor of the primary study analysis, since controls are already adjusted for age, sex and comorbidities.<br /> • The authors do not clearly present numerically survivors and non-survivors at day 34, even though this is one of the main secondary outcomes.<br /> • It is unclear why another cohort (Berlin) was used to associate CRP with mortality. CRP association with mortality should (also) be performed within the current study.

Reviewer #1 (Public Review):

Levy and Hasselmo investigated the representational codes of dorsal hippocampus neurons in episodic memory and spatial navigation. Specifically, how new learning affects previously acquired spatial memory. They asked if the hippocampal representational codes evolve in a different manner when two tasks governed by different rules are learnt in a single environment vs. when each rule is learnt in a separate environment. The two rules they used were based on the classical Packard & McGaugh (1996) experiment. In the original 1996 experiment there was a striatal-dependent response-based task vs. a hippocampal-dependent map-based task. In the current paper they either trained the two types of rules (response vs. map based) in two different contexts (Two-Maze), or in a single context (One-Maze). They found that the remapping of the second time in the response-based rule task was greater in the One-Maze variant of the experiment than in the Two-Maze variant, and they interpreted this by suggesting that in the One-Maze variant, the different intermediate map-based task interfered with the representation in the second response-based task, while in the Two-Maze variant no such interference occurred, and thus the hippocampal map remained more stable.

The results of this paper are well supported by data; however, we believe the conclusion of paper should be different than the conclusion the authors have arrived at.

Major issue:<br /> 1. The main claim is that a new behavioral rule in a familiar environment leads to an increase in the level of remapping of hippocampal activity when returning to the original rule in the same environment. However, we are worried that the result is not due to the interference by a different task in the same environment, but rather by the fact that the mouse spent more time in the environment, causing a larger representational drift. Consider, for example, the change in correlation in Figure 4E over days. In all cases, there is a maze-dependent reduction in correlation from day to day. This reduction continues in the One-maze case also when changing the rule, suggesting that what determined the larger reduction is the time spent in each context, and not the actual change of rule or behavior. Thus it is probable that the fact that the mouse was longer in the first maze in the one-maze variant was enough to create a difference in correlation. See also Khatib et al., bioRxiv, 2022 on the issue of context-dependent drift. To actually control for that, we suggest that the mouse spends twice the time in the first maze during the first Turn-Right session, in the Two-maze variant, and then the comparison will be more valid, by equating the amount of time spent in the first maze in-between comparisons, in the two types of experiments.

Additional points:<br /> 2. Figure 1.d: While behaving differently, is there a difference in the representation? (e.g. mouse 2 on 7th day showed in the beginning very bad behavior). What is the relation between the reduction in performance and the change in representation?<br /> 3. Figure 3.c: We suggest to get a better estimate of the significance of the effect here using shuffling. Specifically, it could be a good idea to distinguish between signal correlations (derived from the overlapping spatial fields) vs. noise correlations. To what extent are the correlations dependent on spatial overlap? It could be worthwhile to determine the type of correlation: Is it due to the fact that the maps are similar for overlapping place cells, or is there noise correlations between these cells?<br /> 4. Figure 4.a: What is the explanation for the reduction in correlation between days 5 and 6?<br /> 5. Supplementary Figure 2: Higher correlations in all arms - note the higher correlation in all arms in the Two-Maze vs. the One-Maze, suggesting again that the effect is related to the longer time in the context, and not so much to the rule-change.<br /> 6. Methods: the researchers note that the animals were previously used in a different study. This should be stated clearly also in the results.

Reviewer #2 (Public Review):

In this study, the authors design a study to examine how place cell representations in the hippocampus change when the rules of a navigational task change. In one group of animals (group 1), the rules change in the same environment as the initial task was performed, and in the second group of animals (group 2), the task with the new rules is presented in a different environment, and then the animals are returned to the first environment with the original rule. (Briefly, on a cross maze, animals first learned to turn right, then the task rule changed to require turning east, and then the rule changed back to turning right). Broadly, using one photon calcium imaging with head mounted mini microscopes, the authors show that, at both the single cell and population level, more remapping occurs in group 1 animals in the initial environment than in group 2 animals.

This work is bolstered by the unique and rigorous way in which the authors track cells across days, in which they compare the rotation angles of crossed-registered groups of cells-I will definitely be using this in the future! The work also benefits from the extensive analysis of both temporal and spatial correlations of cellular activity. However, there are several shortcomings of the behavioral setup and learning conditions that need to be addressed in order to fully support the conclusions of the authors:

First, group 1 animals spend significantly more time in maze 1 than group 2 animals, since group two animals were switched to a different maze when the rule was changed. It is thus difficult to make direct comparison between the two groups, particularly in the last phase of experimentation when although both groups are in the first environment with the task rule, group 1 has experienced maze one for 6 days while group 2 has only experienced in for 3 days. It is therefore potentially difficult to disentangle differences caused by task changes versus length of environmental exposure.

Secondly, and similarly, during the task period, group 1 animals only have exposure to one environment while group 2 animals have exposure to 2 environments. Ideally, group 1 animals would also be exposed to environment 2, to rule out any potential effects of experiencing a novel environment may have on place cell representations, otherwise this cannot be disentangled from the effect of a task rule change.

Third, two concerns about how the animals are trained: First, if I am interpreting the methods correctly, both Group 1 and Group 2 animals are trained so turn-right is on one maze and turn east is on another way. As such, both groups thus have an "original understanding" that different rules are associated with different mazes. This seems potentially confounding given that it is consistent with the future training of Group 2 but not Group 1 mice. Additionally confounding is the fact that, because of the pretraining, group 1 mice have actually experienced the task in 3 different environments; I am unclear if and how this might be expected to affect results. Additionally, it is methodically unclear why pre-training occurs in a different environment than testing does, and what the criterion is for switching the animals from pre-training to training.

It would additionally be useful to discuss the results of this study in the context of spatial and non-spatial tasks. The authors, usefully, spend a significant portion of the paper comparing their results to results seen during fear extinction. It might be worth contextualizing the differences in how fear conditioning has a contextual "background" (i.e., the animals are conditioned to the context) while in their experiment the entire task is based entirely on navigation.

Overall, this is an interesting manuscript that attempts to address how contextual representations change as task parameters change. While the paper contains thorough statistical analysis but could benefit from more discussion of behavior in the context of learning as well as more rigorous behavioral controls. This work will be of interest to researchers studying hippocampus, navigation, and learning.

Reviewer #3 (Public Review):

The fundamental question that the authors address in this work is how our brain encodes when two events occur in the same context as against two events occurring in different contexts. Often in life, we encounter situations where it is difficult to alter the memory specifically associated with a place, for e.g. when we try to find our favourite brand of soap after the shopkeeper rearranges the shelves. Here the authors hypothesise that the acquisition of new memory, bound by a context /space, results in extensive remapping. Presumably, such remapping is manifested as an increased difficulty in acquiring new memories that are linked through space, especially when we have to remember both the old and the new. Using a combination of a modified behavioural task and in vivo imaging of neuronal activity, the authors test this hypothesis in mice. The spatial task requires the animal to learn two navigational rules of when to make a turn. Rule 1 requires the animal to make a turn with respect to itself (turn right), and Rule 2 requires the animal to turn with respect to the outside world (turn East). This is achieved by training the mice in two distinct contexts (mazes). Having trained the mice, they acquire the neuronal activation data and analysis through i) correlation matrices and ii) population vectors they test and show that the hypothesis is true. The manuscript is well-written and easy to follow in general. One of the important aspects of this manuscript is the clarity and detail with which the methods are described. The descriptions are unambiguous and complete in detail. This needs to be appreciated.

One of the soft spots of the study is the following: The animal learns to perform the task in two different contexts. It could also be interpreted as a change in context triggering the change in rule rather than a specific context predicting a specific rule as interpreted. I would like to know the authors' views on this. Additionally, the data is from one experiment with six mice, and the data is analyzed through different frameworks to glean information. This is both the boon and bane of the study. Independent/additional cross-validation of the overall effect would be nice to establish the observed phenomena. For e.g., the use of IEGs to identify the ensembles across the two scenarios, and/or inactivation of CA1 to show that rule change is affected or the first memory is also affected.

Reviewer #1 (Public Review):

In their manuscript, Brischigliaro et al. show that the disruption of respiratory complex assembly results in Drosophila melanogaster results in the accumulation of respiratory supercomplexes. Further, they show that the change in the supercomplex abundance does not impact respiratory function suggesting that the main role of supercomplex formation is structural. Overall, the manuscript is well written and the results and conclusion are supported. The D. melanogaster system, in which the abundance of supercomplexes can be altered through the genetic disruption of the assembly of the individual complexes, will be important for the field to discover the role of the supercomplexes. This manuscript will be of broad interest to the field of mitochondrial bioenergetics. The findings are valuable and the evidence is convincing.

Strengths:

The system developed in which the relative levels of SCs can be varied will be extremely useful for studying SC physiology.

The experiments are clearly described and interpreted.

Weaknesses:

The statement in the abstract regarding low amounts of SCs in "insect tissues" needs further support or should be narrowed. I am only aware of detailed characterization of the mitochondrial SC composition from D. melanogaster, which is insufficient to make a broad statement about the large and diverse category of insects. This should be rewritten.

In the introduction (line 76) and discussion (line 283), the authors reference the CoQ binding sites in CI and CIII2 being "too far apart" to allow for substrate channeling. The distance between the active sites, though significant, is insufficient to rule out substrate channeling. A stronger argument arises from the fact that the CoQ sites of both CI and CIII2 are open to the membrane and that there are no clear barriers for the free exchange of CoQ with the membrane pool.

Line 195, the slight elevation in CI amounts referred to here, does not appear to be statistically significant and therefore should not be discussed a being altered relative to the control.

Figure 4H, the assignments of the observed larger bands seem incorrect. The largest band (currently assigned as SC I1+III2+IV1) represents too large of a shift for only the addition of CIV and the band currently assigned at SC I1+III2 appears to also contain CIV. The identity of these bands should be reevaluated and additional experiments are needed to definitively prove their identity. This uncertainty should be addressed experimentally or made more explicit in the text.

Line 302, the authors state that the structural basis for less SC in D. melanogaster is "due to a more stable association of the NDUFA11 subunit..." However, this would not result is a less stable SC association and only explains why NDUFA11 is more stably associated with CI in the absence of CIII2. The more likely structural reason for the observation of less SC in D. melanogaster is the N-terminal truncation of Dm-NDUFB4 relative to mammalian NDUFB4. This truncation results in the loss of a major SC interaction site between CI and CIII2 in the matrix.

Reviewer #2 (Public Review):

Respiratory chain complexes assemble in higher-ordered structures termed supercomplexes or respirasomes. The functional significance of these assemblies is currently investigated, there are two main hypothesis tested, namely that supercomplexes provide kinetic advantages or structural stability. Here, the authors use the fruitfly to reveal that, while the respiratoy chain in the organism normally does not form higher-order assemblies, it does so under conditions when their assembly is impaired. Because the rather moderate increase in supercomplex formation does not change oxygen consumption stimulated by CI or CII substrate, the authors conclude that supercomplex formation has more a structural than a functional role. The main strength of this work is that the technical quality of the experiments is high and that the authors induced defects in respiratory chain assembly through sets of well-controlled genetic models. The obtained data are mostly descriptive using standard approaches and are very well executed. The authors claim that their experiments allow to conclude that the role of supercomplex formation is restricted to a structural role and, hence, exclude a function directly related to electron transport efficiency. However, while the authors can show convincingly that supercomplexes form in the mutants, but not in the wild type, their main claim is not well supported by data and both the structural mechanism of supercompelx formation and their significance remain unknown. While the supercomplex formation observed only in mitochondrial mutants per se is interesting, it would be good to great to define structural aspects of supercomplex formation and their potential impact on the stability of the respiratory chain complexes in these mutants.

Reviewer #2 (Public Review):

Yanagihara and colleagues investigated the immune cell composition of bronchoalveolar lavage fluid (BALF) samples in a cohort of patients with malignancy undergoing chemotherapy and with with lung adverse reactions including Pneumocystis jirovecii pneumonia (PCP) and immune-checkpoint inhibitors (ICIs) or cytotoxic drug induced interstitial lung diseases (ILDs). Using mass cytometry, their aim was to characterize the cellular and molecular changes in BAL to improve our understanding of their pathogenesis and identify potential biomarkers and therapeutic targets. In this regard, the authors identify a correlation between CD16 expression in T cells and the severity of PCP and an increased infiltration of CD57+ CD8+ T cells expressing immune checkpoints and FCLR5+ B cells in ICI-ILD patients.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) The authors should elaborate on why different set of markers were selected for each analysis step. E.g., Different set of markers were used for UMAP, CITRUS and viSNE in the T cell and myeloid analysis.

2) The authors should state if a normality test for the distribution of the data was performed. If not, non-parametric tests should be used.

3) The authors should explore the correlation between CD16 intensity and the CTCAE grade in T cell subsets such as EMRA CD8 T cells, effector memory CD4, etc as identified in Figure 1B.

4) The authors could use CITRUS to better assess the B cell compartment.

Reviewer #3 (Public Review):

The authors collected BALF samples from lung cancer patients newly diagnosed with PCP, DI-ILD or ICI-ILD. CyTOF was performed on these samples, using two different panels (T-cell and B-cell/myeloid cell panels). Results were collected, cleaned-up, manually gated and pre-processed prior to visualisation with manifold learning approaches t-SNE (in the form of viSNE) or UMAP, and analysed by CITRUS (hierarchical clustering followed by feature selection and regression) for population identification - all using Cytobank implementation - in an attempt to identify possible biomarkers for these disease states. By comparing cell abundances from CITRUS results and qualitative inspection of a small number of marker expressions, the authors claimed to have identified an expansion of CD16+ T-cell population in PCP cases and an increase in CD57+ CD8+ T-cells, FCRL5+ B-cells and CCR2+ CCR5+ CD14+ monocytes in ICI-ILD cases.

By the authors' own admission, there is an absence of healthy donor samples and, perhaps as a result of retrospective experimental design, also an absence of pre-treatment samples. The entire analysis effectively compares three yet-established disease states with no common baseline - what really constitutes a "biomarker" in such cases? The introduction asserts that "y characterizing the cellular and molecular changes in BAL from patients with these complications, we aim to improve our understanding of their pathogenesis and identify potential therapeutic targets" (lines 82-84). Given these obvious omissions, no real "changes" have been studied in the paper. These are very limited comparisons among three, and only these three, states.

Even assuming more thorough experimental design, the data analysis is unfortunately too shallow and has not managed to explore the wealth of information that could potentially be extracted from the results. CITRUS is accessible and convenient, but also make a couple of big assumptions which could affect data analysis - 1) Is it justified to concatenate all FCS files to analyse the data in one batch / small batches? Could there be batch effects or otherwise other biological events that could confuse the algorithm? 2) With a relatively small number of samples, and after internal feature selection of CITRUS, is the regression model suitable for population identification or would it be too crude and miss out rare populations? There are plenty of other established methods that could be used instead. Have those methods been considered?

Colouring t-SNE or UMAP (e.g. Figure 6C) plots by marker expression is useful for quick identification of cell populations but it is not a quantitative analysis. In a CyTOF analysis like this, it is common to work out fold changes of marker expressions between conditions. It is inadequate to judge expression levels and infer differences simply by looking at colours.

The relatively small number of samples also mean that most results presented in the paper are not statistical significant. Whilst it is understandable that it is not always possible to collect a large number of patient samples for studies like this, having several entire major figures showing "n.s." (e.g. Figures 3A, 4B and 5C), together with limitations in the comparisons themselves and inadequate analysis, make the observations difficult to be convincing, and even less so for the single fatal PCP case where N = 1.

It would also be good scientific practice to show evidence of sample data quality control. Were individual FCS files examined? Did the staining work? Some indication of QC would also be great.

This dataset generated and studied by the authors have the potential to address the question they set out to answer and thus potentially be useful for the field. However, in the current state of presentation, more evidence and more thorough data analysis are needed to draw any conclusions, or correlations, as the authors would like to frame them.

Reviewer #1 (Public Review):

Cytotoxic agents and immune checkpoint inhibitors are the most commonly used and efficacious treatments for lung cancers. However their use brings two significant pulmonary side-effects; namely Pneumocystis jirovecii infection and resultant pneumonia (PCP), and interstitial lung disease (ILD). To observe the potential immunological drivers of these adverse events, Yanagihara et al. analysed and compared cells present in the bronchoalveolar lavage of three patient groups (PCP, cytotoxic drug-induced ILD [DI-ILD], and ICI-associated ILD [ICI-ILD]) using mass cytometry (64 markers). In PCP, they observed an expansion of the CD16+ T cell population, with the highest CD16+ T proportion (97.5%) in a fatal case, whilst in ICI-ILD, they found an increase in CD57+ CD8+ T cells expressing immune checkpoints (TIGIT+ LAG3+ TIM-3+ PD-1+), FCRL5+ B cells, and CCR2+ CCR5+ CD14+ monocytes. Given the fatal case, the authors also assessed for, and found, a correlation between CD16+ T cells and disease severity in PCP, postulating that this may be owing to endothelial destruction. Although n numbers are relatively small (n=7-9 in each cohort; common numbers for CyTOF papers), the authors use a wide panel (n=65) and two clustering methodologies giving greater strength to the conclusions. The differential populations discovered using one or two of the analytical methods are robust: whole population shifts with clear and significant clustering. These data are an excellent resource for clinical disease specialists and pan-disease immunologists, with a broad and engaging contextual discussion about what they could mean.

Strengths:<br /> • The differences in immune cells in BAL in these specific patient subgroups is relatively unexplored.<br /> • This is an observational study, with no starting hypothesis being tested.<br /> • Two analytical methods are used to cluster the data.<br /> • A relatively wide panel was used (64 markers), with particular strength in the alpha beta T cells and B cells.<br /> • Relevant biomarkers, beta-D-glucan and KL-6 were also analysed<br /> • Appropriate statistics were used throughout.<br /> • Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD) but these are difficult samples to collect and so in relative terms, and considering the use of CyTOF, these are good numbers.<br /> • Beta-D-glucan shows potential as a biomarker for PCP (as previously reported) whilst KL-6 shows potential as a biomarker for ICI-ILD (not reported before). Interestingly, KL-6 was not seen to be increased in DI-ILD patients.<br /> • Despite the relatively low n numbers and lack of matching there are some clear differentials. The CD4/CD8+CD16+HLA-DR+CXCR3+CD14- T cell result is striking - up in PCP (with EM CD4s significantly down) - whilst the CD8 EMRA population is clear in ICI-ILD and 'non-exhausted' CD4s, with lower numbers of EMRA CD8s in DI-ILD.<br /> • The authors identify 17/31 significantly differentiated clusters of myeloid cells, eg CD11bhi CD11chi CD64+ CD206+ alveolar macrophages with HLA-DRhi in PCP.<br /> • With respect to B cells, the authors found that FCRL5+ B cells were more abundant in patients with ICI-ILD compared to those with PCP and DI-ILD, suggesting these FCRL5+ B cells may have a role in irAE.<br /> • One patient's extreme CD16+ T cell (97.5% positive) and death, led the authors to consider CD16+ T cells as an indicator of disease severity in PCP. This was then tested and found to be correct.<br /> • Authors discuss results in context of literature leading them to suggest that CD16+ T cells may target endothelial cells and wonder if anti-complement therapy may be efficacious in PCP.<br /> • Great discussion on auto-reactive T cell clones where the authors suggest that in ICI-ILD CD8s may react against healthy lung, driving ILD.<br /> • An observation of CXCR3 in different CD8 populations in ICI-ILD and PCP lead the authors to hypothesise on the chemoattractants in the microenvironment.<br /> • Excellent point suggesting CD57 may not always be a marker of senescence on T cells - reflective of growing change within the community.<br /> • Well considered suggestion that FCRL5+ B cells may be involved in ICI-ILD driven autoimmunity.<br /> • The authors discuss the main weaknesses in the discussion and stress that the findings detailed in the paper "demonstrate a correlation rather than proof of causation".<br /> • Figures and legends are clear and pleasing to the eye.

Weaknesses:<br /> • This is an observational study, with no starting hypothesis being tested.<br /> • Only patients who were able to have a lavage taken have been recruited.<br /> • One set of analysis wasn't carried out for one subgroup (ICI-ILD) as PD1 expression was negative owing to the use of nivolumab.<br /> • Some immune cell subsets wouldn't be picked up with the markers and gating strategies used; e.g. NK cells.<br /> • Some immune cells would be disproportionately damaged by the storage, thawing and preparation of the samples; e.g. granulocytes.<br /> • Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD), sex, age and adverse event matching wasn't performed, and treatment regimen are varied and 'suspected' (suggesting incomplete clinical data) - but these are difficult samples to collect. These numbers drop further for some analyses e.g. T cell clustering owing to factors such as low cell number.<br /> • The disease comparisons are with each other, there is no healthy control.<br /> • Samples are taken at one time point.<br /> • The discussion on probably the stand out result - the CD16+ T cells in PCP - relies on two papers - leading to a slightly skewed emphasis on one paper on CD16+ cells in COVID. There are other papers out there that have observed CD16+ T cells in other conditions. It is also worth being in mind that given the markers used, these CD16+ T cell may be gamma deltas.<br /> • The discussion on ICI patient consistently showing increased PD1, could have been greater, as given the ICI is targeting PD1, one would expect the opposite as commented on, and observed, in the methods section.

Reviewer #1 (Public Review):

This article describes the development and refinement of an open-source software framework that is used to track how the COVID-19 pandemic impacted healthcare use in England over a range of key healthcare use indicators.

Important strengths of this study include the high coverage of 99% of practices in England, the development of health care indicators with the input of a clinical advisory group, extensive online documentation, and rigorous safeguards for the protection of patient confidentiality.

Perhaps the largest limitation is that only high-level descriptive data on the monthly volume of health outcomes are presented. It is not clear whether the system could be used to generate more fine-grained or stratified information, ex. weekly or daily data, or data stratified by important characteristics of practices or of patient characteristics. As such, the utility of the system for answering new scientific questions is unclear, and also what the utility and long-term potential uses of this system will be past the COVID-19 pandemic.

Reviewer #2 (Public Review):

The authors developed 11 key measures of clinical activity in primary care and measured changes in the frequency of these measures throughout the first 1.5 years of the COVID-19 pandemic. The biggest strength of the study is the data source, which comprises records from 99% of general practices in England. The biggest limitation lies in the analysis of the data: The authors used only descriptive statistics for the investigation of time trends and have not accounted for long-term time trends (only one "control year" was considered). Still, owing to the large study size, the time trends observed are convincing. The work is of high significance to the field because the OpenSAFELY platform will enable the continuous and real-time monitoring of primary care activity.

Reviewer #3 (Public Review):

This manuscript by Fisher and colleagues documents the change in clinical activity in English general practices during the COVID-19 pandemic according to a set of indicators of clinical activity. The indicators include measures of clinical reviews (e.g. blood pressure, asthma, chronic obstructive pulmonary disease, medication, and cardiovascular risk reviews), blood tests (e.g. cholesterol, liver function, thyroid function, full blood counts, diabetes monitoring blood tests, and kidney function). All these measures saw a drop during the pandemic, to a varying degree, and some recovered afterwards but others did not.

Clinical activity was measured using SNOMED CT codes, which are standard codes used for recording clinical events in UK GP records.

Strengths:

This is a large and comprehensive study including data from 99% of general practices in England. The indicators are clinically relevant, cover a broad range of disease areas, and have been chosen in a sensible manner, involving relevant stakeholders such as GPs, pharmacists, and pathologists.

The OpenSAFELY platform has the ability to enable federated analyses to be run on raw coded data of almost all patients registered with a GP in England.

The study demonstrates the value of OpenSAFELY in being able to monitor clinical activity in general practice at a detailed level, which is essential for planning and improving health services. The statistical methodology is broadly sound.

Weaknesses:

The measures are all related to chronic physical diseases in adults, with a particular focus on cardiometabolic and respiratory conditions. There are no measures related to mental health, maternal or child health.

The description of the measures does not distinguish between different types of clinical activity e.g. lab tests, clinical measurements, or diagnoses, and all are lumped together as 'codes'. This is a peculiarity of the way that information is recorded in GP systems - many different types of clinical information (such as diagnoses and lab tests) are recorded using a SNOMED CT 'code', and only the exact code differentiates what type of information is in the record.

The codelists were broad and comprehensive, but it is unclear how necessary this is because for some measures e.g. lab tests, laboratories typically record a particular type of test using a single standardised code. Instead of using a broad set of codes in the analysis, the authors could have initially verified which codes are associated with the clinical activity being measured (e.g. a numerical value of a blood pressure measurement) in all practices, as I would expect the same single or small number of codes would be used in all practices. This would have provided a smaller and simpler final codelist.

Reviewer #1 (Public Review):

In this study, Fang H et al. describe a potential pathway, ITGB4-TNFAIP2-IQGAP1-Rac1, that may involve in the drug resistance in triple negative breast cancer (TNBC). Mechanistically, it was demonstrated that TNFAIP2 bind with IQGAP1 and ITGB4 activating Rac1 and the following drug resistance. The present study focused on breast cancer cell lines with supporting data from mouse model and patient breast cancer tissues. The study is interesting. The experiments were well controlled and carefully carried out. The conclusion is supported by strong evidence provided in the manuscript. The authors may want to discuss the link between ITGB4 and Rac 1, between IQGAP1 and Rac1, and between TNFAIP2 and Rac1 as compared with the current results obtained. This is important considering some recent publications in this area (Cancer Sci 2021, J Biol Chem 2008, Cancer Res 2023).

Reviewer #3 (Public Review):

In this manuscript, Fang and colleagues found that IQGAP1 interacts with TNFAIP2, which activates Rac1 to promote drug resistance in TNBC. Furthermore, they found that ITGB4 could interact with TNFAIP2 to promote TNBC drug resistance via the TNFAIP2/IQGAP1/Rac1 axis by promoting DNA damage repair.

This work has good innovation and high potential clinical significance.

Reviewer #1 (Public Review):

Current experimental work reveals that brain areas implicated in episodic and spatial memory have a dynamic code, in which activity representing familiar events/locations changes over time. This paper shows that such reconfiguration is consistent with underlying changes in the excitability of cells in the population, which ties these observations to a physiological mechanism.

Delamare et al. use a recurrent network model to consider the hypothesis that slow fluctuations in intrinsic excitability, together with spontaneous reactivations of ensembles, may cause the structure of the ensemble to change, consistent with the phenomenon of representational drift. The paper focuses on three main findings from their model: (1) fluctuations in intrinsic excitability lead to drift, (2) this drift has a temporal structure, and (3) a readout neuron can track the drift and continue to decode the memory. This paper is relevant and timely, and the work addresses questions of both a potential mechanism (fluctuations in intrinsic excitability) and purpose (time-stamping memories) of drift.

The model used in this study consists of a pool of 50 all-to-all recurrently connected excitatory neurons with weights changing according to a Hebbian rule. All neurons receive the same input during stimulation, as well as global inhibition. The population has heterogeneous excitability, and each neuron's excitability is constant over time apart from a transient increase on a single day. The neurons are divided into ensembles of 10 neurons each, and on each day, a different ensemble receives a transient increase in the excitability of each of its neurons, with each neuron experiencing the same amplitude of increase. Each day for four days, repetitions of a binary stimulus pulse are applied to every neuron.

The modeling choices focus in on the parameter of interest-the excitability-and other details are generally kept as straightforward as possible. That said, I wonder if certain aspects may be overly simple. The extent of the work already performed, however, does serve the intended purpose, and so I think it would be sufficient for the authors to comment on these choices rather than to take more space in this paper to actually implement these choices. What might happen were more complex modeling choices made? What is the justification for the choices that are made in the present work?

The two specific modeling choices I question are (1) the excitability dynamics and (2) the input stimulus. The ensemble-wide synchronous and constant-amplitude excitability increase, followed by a return to baseline, seems to be a very simplified picture of the dynamics of intrinsic excitability. At the very least, justification for this simplified picture would benefit the reader, and I would be interested in the authors' speculation about how a more complex and biologically realistic dynamics model might impact the drift in their network model. Similarly, the input stimulus being binary means that, on the single-neuron level, the only type of drift that can occur is a sort of drop-in/drop-out drift; this choice excludes the possibility of a neuron maintaining significant tuning to a stimulus but changing its preferred value. How would the use of a continuous input variable influence the results.

Result (1): Fluctuations in intrinsic excitability induce drift<br /> The two choices highlighted above appear to lead to representations that never recruit the neurons in the population with the lowest baseline excitability (Figure 1b: it appears that only 10 neurons ever show high firing rates) and produce networks with very strong bidirectional coupling between this subset of neurons and weak coupling elsewhere (Figure 1d). This low recruitment rate need may not necessarily be problematic, but it stands out as a point that should at least be commented on. The fact that only 10 neurons (20% of the population) are ever recruited in a representation also raises the question of what would happen if the model were scaled up to include more neurons.

Result (2): The observed drift has a temporal structure<br /> The authors then demonstrate that the drift has a temporal structure (i.e., that activity is informative about the day on which it occurs), with methods inspired by Rubin et al. (2015). Rubin et al. (2015) compare single-trial activity patterns on a given session with full-session activity patterns from each session. In contrast, Delamare et al. here compare full-session patterns with baseline excitability (E = 0) patterns. This point of difference should be motivated. What does a comparison to this baseline excitability activity pattern tell us? The ordinal decoder, which decodes the session order, gives very interesting results: that an intermediate amplitude E of excitability increase maximizes this decoder's performance. This point is also discussed well by the authors. As a potential point of further exploration, the use of baseline excitability patterns in the day decoder had me wondering how the ordinal decoder would perform with these baseline patterns.

Result (3): A readout neuron can track drift<br /> The authors conclude their work by connecting a readout neuron to the population with plastic weights evolving via a Hebbian rule. They show that this neuron can track the drifting ensemble by adjusting its weights. These results are shown very neatly and effectively and corroborate existing work that they cite very clearly.

Overall, this paper is well-organized, offers a straightforward model of dynamic intrinsic excitability, and provides relevant results with appropriate interpretations. The methods could benefit from more justification of certain modeling choices, and/or an exploration (either speculative or via implementation) of what would happen with more complex choices. This modeling work paves the way for further explorations of how intrinsic excitability fluctuations influence drifting representations.

Reviewer #2 (Public Review):

In this computational study, Delamare et al identify slow neuronal excitability as one mechanism underlying representational drift in recurrent neuronal networks and that the drift is informative about the temporal structure of the memory and when it has been formed. The manuscript is very well written and addresses a timely as well as important topic in current neuroscience namely the mechanisms that may underlie representational drift.

The study is based on an all-to-all recurrent neuronal network with synapses following Hebbian plasticity rules. On the first day, a cue-related representation is formed in that network and on the next 3 days it is recalled spontaneously or due to a memory-related cue. One major observation is that representational drift emerges day-by-day based on intrinsic excitability with the most excitable cells showing highest probability to replace previously active members of the assembly. By using a day-decoder, the authors state that they can infer the order at which the reactivation of cell assemblies happened but only if the excitability state was not too high. By applying a read-out neuron, the authors observed that this cell can track the drifting ensemble which is based on changes of the synaptic weights across time. The only few questions which emerged and could be addressed either theoretically or in the discussion are as follows:

1. Would the similar results be obtained if not all-to-all recurrent connections would have been molded but more realistic connectivity profiles such as estimated for CA1 and CA3?<br /> 2. How does the number of excited cells that could potentially contribute to an engram influence the representational drift and the decoding quality?<br /> 3. How does the rate of the drift influence the quality of readout from the readout-out neuron?

Reviewer #3 (Public Review):

The authors explore an important question concerning the underlying mechanism of representational drift, which despite intense recent interest remains obscure. The paper explores the intriguing hypothesis that drift may reflect changes in the intrinsic excitability of neurons. The authors set out to provide theoretical insight into this potential mechanism.

They construct a rate model with all-to-all recurrent connectivity, in which recurrent synapses are governed by a standard Hebbian plasticity rule. This network receives a global input, constant across all neurons, which can be varied with time. Each neuron also is driven by an "intrinsic excitability" bias term, which does vary across cells. The authors study how activity in the network evolves as this intrinsic excitability term is changed.

They find that after initial stimulation of the network, those neurons where the excitability term is set high become more strongly connected and are in turn more responsive to the input. Each day the subset of neurons with high intrinsic excitability is changed, and the network's recurrent synaptic connectivity and responsiveness gradually shift, such that the new high intrinsic excitability subset becomes both more strongly activated by the global input and also more strongly recurrently connected. These changes result in drift, reflected by a gradual decrease across time in the correlation of the neuronal population vector response to the stimulus.

The authors are able to build a classifier that decodes the "day" (i.e. which subset of neurons had high intrinsic excitability) with perfect accuracy. This is despite the fact that the excitability bias during decoding is set to 0 for all neurons, and so the decoder is really detecting those neurons with strong recurrent connectivity, and in turn strong responses to the input. The authors show that it is also possible to decode the order in which different subsets of neurons were given high intrinsic excitability on previous "days". This second result depends on the extent by which intrinsic excitability was increased: if the increase in intrinsic excitability was either too high or too low, it was not possible to read out any information about past ordering of excitability changes.

Finally, using another Hebbian learning rule, the authors show that an output neuron, whose activity is a weighted sum of the activity of all neurons in the network, is able to read out the activity of the network. What this means specifically, is that although the set of neurons most active in the network changes, the output neuron always maintains a higher firing rate than a neuron with randomly shuffled synaptic weights, because the output neuron continuously updates its weights to sample from the highly active population at any given moment. Thus, the output neuron can readout a stable memory despite drift.

Strengths:<br /> The authors are clear in their description of the network they construct and in their results. They convincingly show that when they change their "intrinsic excitability term", upon stimulation, the Hebbian synapses in their network gradually evolve, and the combined synaptic connectivity and altered excitability result in drifting patterns of activity in response to an unchanging input (Fig. 1, Fig. 2a). Furthermore, their classification analyses (Fig. 2) show that information is preserved in the network, and their readout neuron successfully tracks the active cells (Fig. 3). Finally, the observation that only a specific range of excitability bias values permits decoding of the temporal structure of the history of intrinsic excitabililty (Fig. 2f and Figure S1) is interesting, and as the authors point out, not trivial.

Weaknesses:<br /> 1) The way the network is constructed, there is no formal difference between what the authors call "input", Δ(t), and what they call "intrinsic excitability" Ɛ_i(t) (see Equation 3). These are two separate terms that are summed (Eq. 3) to define the rate dynamics of the network. The authors could have switched the names of these terms: Δ(t) could have been considered a global "intrinsic excitability term" that varied with time and Ɛ_i(t) could have been the external input received by each neuron i in the network. In that case, the paper would have considered the consequence of "slow fluctuations of external input" rather than "slow fluctuations of intrinsic excitability", but the results would have been the same. The difference is therefore semantic. The consequence is that this paper is not necessarily about "intrinsic excitability", rather it considers how a Hebbian network responds to changes in excitatory drive, regardless of whether those drives are labeled "input" or "intrinsic excitability".

2) Given how the learning rule that defines input to the readout neuron is constructed, it is trivial that this unit responds to the most active neurons in the network, more so than a neuron assigned random weights. What would happen if the network included more than one "memory"? Would it be possible to construct a readout neuron that could classify two distinct patterns? Along these lines, what if there were multiple, distinct stimuli used to drive this network, rather than the global input the authors employ here? Does the system, as constructed, have the capacity to provide two distinct patterns of activity in response to two distinct inputs?

Impact:<br /> Defining the potential role of changes in intrinsic excitability in drift is fundamental. Thus, this paper represents a potentially important contribution. Unfortunately, given the way the network employed here is constructed, it is difficult to tease apart the specific contribution of changing excitability from changing input. This limits the interpretability and applicability of the results.

Reviewer #1 (Public Review):

Qin et al. set out to investigate the role of mechanosensory feedback during swallowing and identify neural circuits that generate ingestion rhythms. They use Drosophila melanogaster swallowing as a model system, focusing their study on the neural mechanisms that control cibarium filling and emptying in vivo. They find that pump frequency is decreased in mutants of three mechanotransduction genes (nompC, piezo, and Tmc), and conclude that mechanosensation mainly contributes to the emptying phase of swallowing. Furthermore, they find that double mutants of nompC and Tmc have more pronounced cibarium pumping defects than either single mutants or Tmc/piezo double mutants. They discover that the expression patterns of nompC and Tmc overlap in two classes of neurons, md-C and md-L neurons. The dendrites of md-C neurons warp the cibarium and project their axons to the subesophageal zone of the brain. Silencing neurons that express both nompC and Tmc leads to severe ingestion defects, with decreased cibarium emptying. Optogenetic activation of the same population of neurons inhibited filling of the cibarium and accelerated cibarium emptying. In the brain, the axons of nompC∩Tmc cell types respond during ingestion of sugar but do not respond when the entire fly head is passively exposed to sucrose. Finally, the authors show that nompC∩Tmc cell types arborize close to the dendrites of motor neurons that are required for swallowing, and that swallowing motor neurons respond to the activation of the entire Tmc-GAL4 pattern.

Strengths:<br /> -The authors rigorously quantify ingestion behavior to convincingly demonstrate the importance of mechanosensory genes in the control of swallowing rhythms and cibarium filling and emptying<br /> -The authors demonstrate that a small population of neurons that express both nompC and Tmc oppositely regulate cibarium emptying and filling when inhibited or activated, respectively<br /> -They provide evidence that the action of multiple mechanotransduction genes may converge in common cell types

Weaknesses:<br /> -A major weakness of the paper is that the authors use reagents that are expressed in both md-C and md-L but describe the results as though only md-C is manipulated<br /> -Severing the labellum will not prevent optogenetic activation of md-L from triggering neural responses downstream of md-L. Optogenetic activation is strong enough to trigger action potentials in the remaining axons. Therefore, Qin et al. do not present convincing evidence that the defects they see in pumping can be specifically attributed to md-C.<br /> -GRASP is known to be non-specific and prone to false positives when neurons are in close proximity but not synaptically connected. A positive GRASP signal supports but does not confirm direct synaptic connectivity between md-C/md-L axons and MN11/MN12.<br /> -As seen in Figure Supplement 2, the expression pattern of Tmc-GAL4 is broader than md-C alone. Therefore, the functional connectivity the authors observe between Tmc expressing neurons and MN11 and 12 cannot be traced to md-C alone

Overall, this work convincingly shows that swallowing and swallowing rhythms are dependent on several mechanosensory genes. Qin et al. also characterize a candidate neuron, md-C, that is likely to provide mechanosensory feedback to pumping motor neurons, but the results they present here are not sufficient to assign this function to md-C alone. This work will have a positive impact on the field by demonstrating the importance of mechanosensory feedback to swallowing rhythms and providing a potential entry point for future investigation of the identity and mechanisms of swallowing central pattern generators.

Reviewer #2 (Public Review):

In this manuscript, the authors describe the role of cibarial mechanosensory neurons in fly ingestion. They demonstrate that pumping of the cibarium is subtly disrupted in mutants for piezo, TMC, and nomp-C. Evidence is presented that these three genes are co-expressed in a set of cibarial mechanosensory neurons named md-C. Silencing of md-C neurons results in disrupted cibarial emptying, while activation promotes faster pumping and/or difficulty filling. GRASP and chemogenetic activation of the md-C neurons is used to argue that they may be directly connected to motor neurons that control cibarial emptying.

The manuscript makes several convincing and useful contributions. First, identifying the md-C neurons and demonstrating their essential role for cibarium emptying provides reagents for further studying this circuit and also demonstrates the important of mechanosensation in driving pumping rhythms in the pharynx. Second, the suggestion that these mechanosensory neurons are directly connected to motor neurons controlling pumping stands in contrast to other sensory circuits identified in fly feeding and is an interesting idea that can be more rigorously tested in the future.

At the same time, there are several shortcomings that limit the scope of the paper and the confidence in some claims. These include:

a) the MN-LexA lines used for GRASP experiments are not characterized in any other way to demonstrate specificity. These were generated for this study using Phack methods, and their expression should be shown to be specific for MN11 and MN12 in order to interpret the GRASP experiments.

b) There is also insufficient detail for the P2X2 experiment to evaluate its results. Is this an in vivo or ex vivo prep? Is ATP added to the brain, or ingested? If it is ingested, how is ATP coming into contact with md-C neuron if it is not a chemosensory neuron and therefore not exposed to the contents of the cibarium?

c) In Figure 3C, the authors claim that ablating the labellum will remove the optogenetic stimulation of the md-L neuron (mechanosensory neuron of the labellum), but this manipulation would presumably leave an intact md-L axon that would still be capable of being optogenetically activated by Chrimson.

d) Average GCaMP traces are not shown for md-C during ingestion, and therefore it is impossible to gauge the dynamics of md-C neuron activation during swallowing. Seeing activation with a similar frequency to pumping would support the suggested role for these neurons, although GCaMP6s may be too slow for these purposes.

e) The negative result in Figure 4K that is meant to rule out taste stimulation of md-C is not useful without a positive control for pharyngeal taste neuron activation in this same preparation.

In addition to the experimental limitations described above, the manuscript could be organized in a way that is easier to read (for example, not jumping back and forth in figure order).

Reviewer #3 (Public Review):

Swallowing is an essential daily activity for survival, and pharyngo-laryngeal sensory function is critical for safe swallowing. In Drosophila, it has been reported that the mechanical property of food (e.g. Viscosity) can modulate swallowing. However, how mechanical expansion of the pharynx or fluid content sense and control swallowing was elusive. Qin et al. showed that a group of pharyngeal mechanosensory neurons, as well as mechanosensory channels (nompC, Tmc, and Piezo), respond to these mechanical forces for regulation of swallowing in Drosophila melanogaster.

Strengths:<br /> There are many reports on the effect of chemical properties of foods on feeding in fruit flies, but only limited studies reported how physical properties of food affect feeding especially pharyngeal mechanosensory neurons. First, they found that mechanosensory mutants, including nompC, Tmc, and Piezo, showed impaired swallowing, mainly the emptying process. Next, they identified cibarium multidendritic mechanosensory neurons (md-C) are responsible for controlling swallowing by regulating motor neuron (MN) 12 and 11, which control filling and emptying, respectively.

Weaknesses:<br /> While the involvement of md-C and mechanosensory channels in controlling swallowing is convincing, it is not yet clear which stimuli activate md-C. Can it be an expansion of cibarium or food viscosity, or both? In addition, if rhythmic and coordinated contraction of muscles 11 and 12 is essential for swallowing, how can simultaneous activation of MN 11 and 12 by md-C achieve this? Finally, previous reports showed that food viscosity mainly affects the filling rather than the emptying process, which seems different from their finding.

Reviewer #4 (Public Review):

A combination of optogenetic behavioral experiments and functional imaging are employed to identify the role of mechanosensory neurons in food swallowing in adult Drosophila. While some of the findings are intriguing and the overall goal of mapping a sensory to motor circuit for this rhythmic movement are admirable, the data presented could be improved.

The circuit proposed (and supported by GRASP contact data) shows these multi-dendritic neurons connecting to pharyngeal motor neurons. This is pretty direct - there is no evidence that they affect the hypothetical central pattern generator - just the execution of its rhythm. The optogenetic activation and inhibition experiments are constitutive, not patterned light, and they seem to disrupt the timing of pumping, not impose a new one. A slight slowing of the rhythm is not consistent with the proposed function.

The mechanosensory channel mutants nompC, piezo, and TMC have a range of defects. The role of these channels in swallowing may not be sufficiently specific to support the interpretation presented. Their other defects are not described here and their overall locomotor function is not measured. If the flies have trouble consuming sufficient food throughout their development, how healthy are they at the time of assay? The level of starvation or water deprivation can affect different properties of feeding - meal size and frequency. There is no description of how starvation state was standardized or measured in these experiments.

The brain is likely to move considerably during swallow, so the GCaMP signal change may be a motion artifact. Sometimes this can be calculated by comparing GCaMP signal to that of a co-expressed fluorescent protein, but there is no mention that this is done here. Therefore, the GAaMP data cannot be interpreted.

Reviewer #1 (Public Review):

In this preprint, Zhang et al. describe a new tool for mapping the connectivity of mouse neurons. Essentially, the tool leverages the known peculiar infection capabilities of Rabies virus: once injected into a specific site in the brain, this virus has the capability to "walk upstream" the neural circuits, both within cells and across cells: on one hand, the virus can enter from a nerve terminal and infect retrogradely the cell body of the same cell (retrograde transport). On the other hand, the virus can also spread to the presynaptic partners of the initial target cells, via retrograde viral transmission.

Similarly to previously published approaches with other viruses, the authors engineer a complex library of viral variants, each carrying a unique sequence ('barcode'), so they can uniquely label and distinguish independent infection events and their specific presynaptic connections, and show that it is possible to read these barcodes in-situ, producing spatial connectivity maps. They also show that it is possible to read these barcodes together with endogenous mRNAs, and that this allows spatial mapping of cell types together with anatomical connectivity.

The main novelty of this work lies in the combined use of rabies virus for retrograde labeling together with barcoding and in-situ readout. Previous studies had used rabies virus for retrograde labeling, albeit with low multiplexing capabilities, so only a handful of circuits could be traced at the same time. Other studies had instead used barcoded viral libraries for connectivity mapping, but mostly focused on the use of different viruses for labeling individual projections (anterograde tracing) and never used a retrograde-infective virus.

The authors creatively merge these two bits of technology into a powerful genetic tool, and extensively and convincingly validate its performance against known anatomical knowledge. The authors also do a very good job at highlighting and discussing potential points of failure in the methods.

Unresolved questions, which more broadly affect also other viral-labeling methods, are for example how to deal with uneven tropism (ie. if the virus is unable or inefficient in infecting some specific parts of the brain), or how to prevent the cytotoxicity induced by the high levels of viral replication and expression, which will tend to produce "no source networks", neural circuits whose initial cell can't be identified because it's dead. This last point is particularly relevant for in-situ based approaches: while high expression levels are desirable for the particular barcode detection chemistry the authors chose to use (gap-filling), they are also potentially detrimental for cell survival, and risk producing extensive cell death (which indeed the authors single out as a detectable pitfall in their analysis). This is likely to be one of the major optimisation challenges for future implementations of these types of barcoding approaches.

Overall the paper is well balanced, the data are well presented and the conclusions are strongly supported by the data. Impact-wise, the method is definitely going to be useful for the neurobiology research community.

Reviewer #2 (Public Review):

Although the trans-synaptic tracing method mediated by the rabies virus (RV) has been widely utilized to infer input connectivity across the brain to a genetically defined population in mice, the analysis of labeled pre-synaptic neurons in terms of cell-type has been primarily reliant on classical low-throughput histochemical techniques. In this study, the authors made a significant advance toward high-throughput transcriptomic (TC) cell typing by both dissociated single-cell RNAseq and the spatial TC method known as BARseq to decode a vast array of molecularly-labeled ("barcoded") RV vector library. First, they demonstrated that a barcoded-RV vector can be employed as a simple retrograde tracer akin to AAVretro. Second, they provided a theoretical classification of neural networks at the single-cell resolution that can be attained through barcoded-RV and concluded that the identification of the vast majority (ideally 100%) of starter cells (the origin of RV-based trans-synaptic tracing) is essential for the inference of single-cell resolution neural connectivity. Taking this into consideration, the authors opted for the BARseq-based spatial TC that could, in principle, capture all the starter cells. Finally, they demonstrated the proof-of-concept in the somatosensory cortex, including infrared connectivity from 381 putative pre-synaptic partners to 31 uniquely barcoded-starter cells, as well as many insightful estimations of input convergence at the cell-type resolution in vivo. While the manuscript encompasses significant technical and theoretical advances, it may be challenging for the general readers of eLife to comprehend. The following comments are offered to enhance the manuscript's clarity and readability.

Major points:<br /> 1. I find it difficult to comprehend the rationale behind labeling inhibitory neurons in the VISp through long-distance retrograde labeling from the VISal or Thalamus (Fig. 2F, I and Fig. S3) since long-distance projectors in the cortex are nearly 100% excitatory neurons. It is also unclear why such a large number of inhibitory neurons was labeled at a long distance through RV vector injections into the RSP/SC or VISal (Fig. 3K). Furthermore, a significant number of inhibitory starter cells in the somatosensory cortex was generated based on their projection to the striatum (Fig. 5H), which is unexpected given our current understanding of the cortico-striatum projections.

2. It is unclear as to why the authors did not perform an analysis of the barcodes in Fig. 2. Given that the primary objective of this manuscript is to evaluate the effectiveness of multiplexing barcoded technology in RV vectors, I would strongly recommend that the authors provide a detailed description of the barcode data here, including any technical difficulties or limitations encountered, which will be of great value in the future design of RV-barcode technologies. In case the barcode data are not included in Fig. 2, I would suggest that the authors consider excluding Fig. 2 and Fig. S1-S3 in their entirety from the manuscript to enhance its readability for general readers.

3. Regarding the trans-synaptic tracing utilizing a barcoded RV vector in conjunction with BARseq decoding (Fig. 5), which is the core of this manuscript, I have a few specific questions/comments. First, the rationale behind defining cells with only two rolonies counts of rabies glycoprotein (RG) as starter cells is unclear. Why did the authors not analyze the sample based on the colocalization of GFP (from the AAV) and mCherry (from the RV) proteins, which is a conventional method to define starter cells? If this approach is technically difficult, the authors could provide an independent histochemical assessment of the detection stringency of GFP positive cells based on two or more colonies of RG. Second, it is difficult to interpret the proportion of the 2,914 barcoded cells that were linked to barcoded starter cells (single-source, double-labeled, or connected-source) and those that remained orphan (no-source or lost-source). A simple table or bar graph representation would be helpful. The abundance of the no-source network (resulting from Cre-independent initial infection of the RV vector) can be estimated in independent negative control experiments that omit either Cre injection or AAV-RG injection. The latter, if combined with BARseq decoding, can provide an experimental prediction of the frequency of double-labeled events since connected-source networks are not labeled in the absence of RG. Third, I would appreciate more quantitative data on the putative single-source network (Fig. 5I and S6) in terms of the distribution of pre- and post-synaptic TC cell types. The majority of labeling appeared to occur locally, with only two thalamic neurons observed in sample 25311842 (Fig. S6). How many instances of long-distance labeling (for example, > 500 microns away from the injection site) were observed in total? Is this low efficiency of long-distance labeling expected based on the utilized combinations of AAVs and RV vectors? A simple independent RV tracing solely detecting mCherry would be useful for evaluating the labeling efficiency of the method. I have experienced similar "less jump" RV tracing when RV particles were prepared in a single step, as this study did, rather than multiple rounds of amplification in traditional protocols, such as Osakada F et al Nat Protocol 2013.

Reviewer #3 (Public Review):

The manuscript by Zhang and colleagues attempts to combine genetically barcoded rabies viruses with spatial transcriptomics in order to genetically identify connected pairs. The major shortcoming with the application of a barcoded rabies virus, as reported by 2 groups prior, is that with the high dropout rate inherent in single cell procedures, it is difficult to definitively identify connected pairs. By combining the two methods, they are able to establish a platform for doing that, and provide insight into connectivity, as well as pros and cons of their method, which is well thought out and balanced.

Overall the manuscript is well-done, but I have a few minor considerations about tone and accuracy of statements, as well as some limitations in how experiments were done. First, the idea of using rabies to obtain broader tropism than AAVs isn't really accurate - each virus has its own set of tropisms, and it isn't clear that rabies is broader (or can be made to be broader). Second, rabies does not label all neurons that project to a target site - it labels some fraction of them. Third, the high rate of rabies virus mutation should be considered - if it is, or is not a problem in detecting barcodes with high fidelity, this should be noted. Fourth, there are a number of implicit assumptions in this manuscript, not all of which are equally backed up by data. For example, it is not clear that all rabies virus transmission is synaptic-specific; in fact, quite a few studies argue that it is not (e.g., detection of rabies transcripts in glial cells). Thus, arguments about lost-source networks and the idea that if a cell is lost from the network, that will stop synaptic transmission, is not clear. There is also the very real propensity that, the sicker a starter cell gets, the more non-specific spread of virus (e.g., via necrosis) occurs. Fifth, in the experiments performed in Figure 5, the authors used a FLEx-TVA expressed via a retrograde Cre, and followed this by injection of their rabies virus library. The issue here is that there will be many (potentially thousands) of local infection events near the injection site that TVA-mediated but are Cre-dependent (=off-target expression of TVA in the absence of Cre). This is a major confound in interpreting the labeling of these cells. They may express very low levels of TVA, but still have infection be mediated by TVA. The authors did not clearly explore how expression of TVA related to rabies virus infection of cells near the rabies injection site. A modified version of TVA, such as 66T, should have been used to mitigate this issue. Otherwise, it is impossible to determine connectivity locally. The authors do not go to great lengths to interpret the findings of these observations, so I am not sure this is a critical issue, but it should be pointed out by the authors as a caveat to their dataset. Sixth, the authors are making estimates of rabies spread by comparison to a set of experiments that was performed quite differently. In the two studies cited (Liu et al., done the standard way, and Wertz et al., tracing from a single cell), the authors were likely infecting with a rabies virus using a high multiplicity of infection, which likely yields higher rates of viral expression in these starter cells and higher levels of input labeling. However, in these experiments, the authors need to infect with a low MOI, and explicitly exclude cells with >1 barcode. Having only a single virion trigger infection of starter cells will likely reduce the #s of inputs relative to starter neurons. Thus, the stringent criteria for excluding small networks may not be entirely warranted. If the authors wish to only explore larger networks, this caveat should be explicitly noted.

Overall, if the caveats above are noted and more nuance is added to some of the interpretation and discussion of results, this would greatly help the manuscript, as readers will be looking to the authors as the authority on how to use this technology.

Reviewer #1 (Public Review):

The manuscript describes an interesting experiment in which an animal had to judge a duration of an interval and press one of two levers depending on the duration. The Authors recorded activity of neurons in key areas of the basal ganglia (SNr and striatum), and noticed that they can be divided into 4 types.

The data presented in the manuscript is very rich and interesting, however, I am not convinced by the interpretation of these data proposed in the paper. The Authors focus on neurons of types 1 & 2 and propose that their difference encodes the choice the animal makes. However, I would like to offer an alternative interpretation of the data. Looking at the description of task and animal movements seen in Figure 1, it seems to me that there are 4 main "actions" the animals may do in the task: press right lever, press left lever, move left, and move right. It seems to me that the 4 neurons authors observed may correspond to these actions, i.e. Figure 1 shows that Type 1 neurons decrease when right level becomes more likely to be correct, so their decrease may correspond to preparation of pressing right lever - they may be releasing this action from inhibition (analogously Type 2 neurons may be related to pressing left lever). Furthermore, comparing animal movements and timing of activity of neurons of type 3 and 4, it seems to me that type 3 neurons decrease when the animal moves left, while type 4 when the animal moves right.

I suggest Authors analyse if this interpretation is valid, and if so, revise the interpretation in the paper and the model accordingly.

Reviewer #2 (Public Review):

In this valuable manuscript Li & Jin record from the substantial nigra and dorsal striatum to identify subpopulations of neurons with activity that reflects different dynamics during action selection, and then use optogenetics in transgenic mice to selectively inhibit or excite D1- and D2- expressing spiny projection neurons in the striatum, demonstrating a causal role for each in action selection in an opposing manner. They argue that their findings cannot be explained by current models and propose a new 'triple control' model instead, with one direct and two indirect pathways. These findings will be of broad interest to neuroscientists, but lacks some direct evidence for the proposal of the new model.

Overall there are many strengths to this manuscript including the fact that the empirical data in this manuscript is thorough and the experiments are well-designed. The model is well thought through, but I do have some remaining questions and issues with it.

Weaknesses:<br /> 1. The nature of 'action selection' as described in this manuscript is a bit ambiguous and implies a level of cognition or choice which I'm not sure is there. It's not integral to the understanding of the paper really, but I would have liked to know whether the actions are under goal-directed/habitual or even Pavlovian control. This is not really possible to differentiate with this task as there are a number of Pavlovian cues (e.g. lever retraction interval, house light offset) that could be used to guide behavior.<br /> 2. In a similar manner, the part of the striatum that is being targeted (e.g. Figures 4E,I, and N) is dorsal, but is central with regards to the mediolateral extent. We know that the function of different striatal compartments is highly heterogeneous with regards to action selection (e.g. PMID: 16045504, 16153716, 11312310) so it would have been nice to have some data showing how specific these findings are to this particular part of dorsal striatum.<br /> 3. I'm not sure how I feel about the diagrams in Figure 4S. In particular, the co-activation model is shown with D2-SPNs represented as a + sign (which is described as "having a facilitatory effect to selection" in the caption), but the co-activation model still suggests that D2-SPNs are largely inhibitory - just of competing actions rather than directly inhibiting actions. Moreover, I am not sure about these diagrams because they appear to show that D2-SPNs far outnumbers D1-SPNs and we know that this isn't the case. I realize the diagrams are not proportionate, but it still looks a bit misrepresented to me.<br /> 4. There are a number of grammatical and syntax errors that made the manuscript difficult to understand in places.<br /> 5. I wondered if the authors had read PMID: 32001651 and 33215609 which propose a quite different interpretation of direct/indirect pathway neurons in striatum in action selection. I wonder if the authors considered how their findings might fit within this framework.<br /> 6. There is no direct evidence of two indirect pathways, although perhaps this is beyond the scope of the current manuscript and is a prediction for future studies to test.

Joint Public Review:

In this work, Jain and colleagues have created two libraries of the AAV2 rep gene - either expressed separately from a strong heterologous promoter or embedded in the viral wild-type context - containing all possible single codon mutations. The libraries were cleverly made through a cloning process that ensured each mutant was attached to an exactly known 20-nt barcode included in each mutagenic oligo. This allowed the authors to confidently observe nearly all rep variants in their experiments, resulting in a comprehensive map between Rep protein variants and AAV production. Interrogation of these libraries identified several variants that improved AAV production, including mutations not observed in natural AAV isolates thus far, as independently verified through a conventional AAV vector production protocol. These benefits were also conserved across multiple natural AAV capsid variants including the heterologous AAV5 serotype.

While many other groups have previously created and interrogated individual point mutants of the AAV rep gene/protein or domain swapping mutants, this study is distinguished and excels by its degree of comprehensiveness and the complexity of the two complementary libraries. This reflects the next step in the field's efforts to better understand the natural biology of AAV and, as a result, to improve the production of recombinant AAV gene transfer vectors. Considering the rapidly increasing momentum of these vectors in the clinics and as approved drugs on the gene therapy market, and considering that the individual validation experiments reported in this work support the conclusions, this work including the reported resources and technologies is likely to have a critical impact on current and future research on AAV biology and vector development.

However, there are a few areas in which the study could be expanded for even greater impact. For instance, the authors may consider testing the selected rep variants in the context of a self-complementary AAV genome, which has different biology compared to the single-stranded genomes used in this study, and which is widely used granted its compatibility with the transgene of choice (which should be <2.5 kb). Likewise, it would be important to study the functionality of the selected rep variants with at least one AAV genome of regular size, considering that the two tested here seem rather unusual in length (2.9 kb, which is very small, or 5.0 kb, which is borderline large). Last but not least, despite the fact that the AAV2 ITRs are by far most commonly used in the field, it will also be interesting to test these rep variants in combination with ITRs derived from other AAV serotypes, considering that numerous groups have previously cloned and analyzed them, and that they can provide several benefits over the AAV2 ITRs.

Furthermore, in interpreting the results of this study, the reader should bear in mind that what has been measured and validated in this work is the production of intact genome-containing AAVs. Production is a precondition to functional AAVs that can transduce cells but is not equivalent to it. While the two are likely well correlated, further studies are needed to determine how well the effects of Rep protein variants on AAV production translate to their ability to then transduce cells. The more relevant property for gene therapy is the efficiency by which an AAV preparation transduces cells. For example, might production-enhancing Rep protein variants change the ratio of empty capsids to genome-containing capsids in a way that influences transduction efficiency of the corresponding AAV preparations? Does this influence reduce or enhance the production benefit? This particular scenario of empty capsid ratios influencing transduction represents a population effect that is not possible to capture in the multiplex assay, but it seems like a good idea to at least test transduction of some individual variants because transduction is the important function of AAV for gene therapy.

One additional aspect that may warrant further consideration is the assumption, as mentioned in Figure 2's legend, that synonymous mutations are neutral and can serve as controls for normalizing the production rate. However, Figures S5-6 and Figures S11-12 suggest that synonymous mutations are not necessarily neutral, as their distribution is similar to that of nonsynonymous mutations. Thus, a deeper examination of the impacts of synonymous mutations on the genotype-phenotype landscape could provide more nuanced insights into AAV2 rep gene function.

Reviewer #1 (Public Review):

In this study, the authors identify an insect salivary protein participating viral initiate infection in plant host. They found a salivary LssaCA promoting RSV infection by interacting with OsTLP that could degrade callose in plants. Furthermore, RSV NP bond to LssaCA in salivary glands to form a complex, which then bond to OsTLP to promote degradation of callose.

The story focus on tripartite virus-insect vector-plant interaction, and is interesting. However, the study is too simple and poor-conducted. The conclusion is also overstated due to unsolid findings.

Major comments:<br /> 1. The key problem is that how long the LssCA functioned for in rice plant. Author declared that LssCA had no effect on viral initial infection, but on infection after viral inoculation. It is unreasonable to conclude that LssCA promoted viral infection based on the data that insect inoculated plant just for 2 days, but viral titer could be increased at 14 day post-feeding. How could saliva proteins, which reached phloem 12-14 days before, induce enough TLP to degrade callose to promote virus infection? It was unbelievable.

2. Lines 110-116 and Fig. 1, the results of viruliferous insect feeding and microinjection with purified virus could not conclude the saliva factor necessary of RSV infection, because these two tests are not in parallel and comparable. Microinjection with salivary proteins combined with purified virus is comparable with microinjection with purified virus.

The second problem is how many days post viruliferous insect feeding and microinjection with purified virus did author detect viral titers? in Method section, authors declared that viral titers was detected at 7-14 days post microinjection. Please demonstrate the days exactly.

The last problem is that how author made sure that the viral titers in salivary glands of insects between two experiments was equal, causing different phenotype of rice plant. If not, different viral titers in salivary glands of insects between two experiments of course caused different phenotype of rice plant.

3. The callose deposition in phloem can be induced by insect feeding. In Fig. 5H, why was the callose deposition increased in the whole vascular bundle, but not phloem? Could the transgenic rice plant directional express protein in the phloem? In Fig.5, why was callose deposition detected at 24 h after insect feeding? In Fig. 6A, why was callose deposition decreased in the phloem, but not all the cells of the of TLP OE plant? Also in Fig.6A and B, expression of callose synthase genes was required.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants. Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

While this is an interesting work, there are, in my opinion, some weak points. The microinjection experiments result in much lower virus accumulation in rice than infection by vector inoculation, so their interpretation is difficult. Also, the effect of injected recombinant CA protein might fade over time because of degradation or dilution. The authors claim that enzymatic activity of CA is not required for its proviral activity. However, this is difficult to assess because all CA mutants used for the corresponding experiments possess residual activity. It remains also unclear whether viral infection deregulates CA expression in planthoppers and TLP expression in plants. However, increased CA and TLP levels could alone contribute to reduced callose deposition.

Reviewer #1 (Public Review):

Bacteria can adapt to extremely diverse environments via extensive gene reprogramming at transcriptional and post-transcriptional levels. Small RNAs are key regulators of gene expression that participate in this adaptive response in bacteria, and often act as post-transcriptional regulators via pairing to multiple mRNA-targets.

In this study, Melamed et al. identify four E. coli small RNAs whose expression is dependent on sigma 28 (FliA), involved in the regulation of flagellar gene expression. Even though they are all under the control of FliA, expression of these 4 sRNAs peaks under slightly different growth conditions and each has different effects on flagella synthesis/number and motility. Combining RILseq data, structural probing, northern-blots and reporter assays, the authors show that 3 of these sRNAs control fliC expression (negatively for FliX, positively for MotR and UhpU) and two of them regulate r-protein genes from the S10 operon (again positively for MotR, and negatively for FliX). UhpU also directly represses synthesis of the LrhA transcriptional regulator, that in turn regulates flhDC (at the top of flagella regulation cascade). Based on RILseq data, the fourth sRNA (FlgO) has very few targets and may act via a mechanism other than base-pairing.

As r-protein S10 is also implicated in anti-termination via the NusB-S10 complex, the authors further hypothesize that the up-regulation of S10 gene expression by MotR promotes expression of the long flagellar operons through anti-termination. Consistent with this possible connection between ribosome and flagella synthesis, they show that MotR overexpression leads to an increase in flagella number and in the mRNA levels of two long flagellar operons, and that both effects are dependent on the NusB protein. Lastly, they provide data supporting a more general activating and repressing role for MotR and FliX, respectively, in flagellar genes expression and motility.

This study brings a lot of new information on the regulation of flagellar genes, from the identification of novel sigma 28-dependent sRNAs to their effects on flagella production and motility. It represents a considerable amount of work; the experimental data are clear and solid and support the conclusions of the paper. Even though mechanistic details underlying the observed regulations by MotR or FliX sRNAs are lacking, the effect of these sRNAs on fliC, several rps/rpl genes, and flagellar genes and motility is convincing.<br /> The connection between r-protein genes regulation and flagellar operons is exciting and raises a few questions. First, from the RILseq data, chimeric reads with mRNA for r-proteins (including rpsJ) are not restricted to the sigma 28-dependent sRNAs (e.g. rpsJ-sucD3'UTR, rpsF-DicF, rplN-DicF, rplK-ChiX, rplU-CyaR, rpsT-CyaR, rpsK-CyaR, rpsF-MicA...), suggesting that regulation of r-protein synthesis by sRNAs is not necessarily related to flagella/motility. Second, it would be interesting to know if the flagellar operons are more sensitive than other long operons to antitermination following MotR overexpression? In other words, does pMotR similarly affect antitermination in rrn or other long operons?

The general effect of pMotR or pFliX on the expression of multiple middle and late flagellar genes is also interesting even though the mechanism is not clear. While it may be difficult to fully address it, testing whether some of these regulatory events depend on the control of fliC and/or the S10 operon could be relevant (by analyzing the effects in strains deleted for fliC or nusB for instance).

Reviewer #2 (Public Review):

This manuscript discusses the posttranscriptional regulation of flagella synthesis in Escherichia coli. The bacterial flagellum is a complex structure that consists of three major domains, and its synthesis is an energy-intensive process that requires extensive use of ribosomes. The flagellar regulon encompasses more than 50 genes, and the genes are activated in a sequential manner to ensure that flagellar components are made in the order in which they are needed. Transcription of the genes is regulated by various factors in response to environmental signals. However, little is known about the posttranscriptional regulation of flagella synthesis. The manuscript describes four UTR-derived sRNAs (UhpU, MotR, FliX, and FlgO) that are controlled by the flagella sigma factor σ28 (fliA) in Escherichia coli. The sRNAs have varied effects on flagellin protein levels, flagella number, and cell motility, and they regulate different aspects of flagella synthesis.<br /> UhpU corresponds to the 3´ UTR of uhpT.

UhpU is transcribed from its own promoter inside the coding sequence of uhpT.

MotR originates from the 5´ UTR of motA. The promoter for motR is within the flhC CDS and is also the promoter of the downstream motAB-cheAW operon.

FliX originates from the 3´ UTR of fliC. Probably processed from parental mRNA.

FlgO originates from the 3´ UTR of flgL. Probably processed from parental mRNA.

This is a very interesting study that shows how sRNA-mediated regulation can create a complex network regulating flagella synthesis. The information is new and gives a fresh outlook at cellular mechanisms of flagellar synthesis. The presented work could benefit from additional experiments to confirm the effect of endogenous sRNAs expressed at natural level.

Reviewer #3 (Public Review):

Flagella are crucial for bacterial motility and virulence of pathogens. They represent large molecular machines that require strict hierarchical expression control of their components. So far, mainly transcriptional control mechanisms have been described to control flagella biogenesis. While several sRNAs have been reported that are environmentally controlled and regulate motility mainly via control of flagella master regulators, less is known about sRNAs that are co-regulated with flagella genes and control later steps of flagella biogenesis.

In this carefully designed and well-written study, the authors explore the role of four E. coli σ28-dependent 3' or 5' sUTR-derived sRNA in regulating flagella biogenesis. UhpU and MotR sRNAs are generated from their own σ28(FliA)-dependent promoter, while FliX and FlgO sRNAs are processed from the 3'UTRs of flagella genes under control of FliA. The authors provide an impressive amount of data and different experiments, including phenotypic analyses, genomics approaches as well as in-vitro and in-vivo target identification and validation methods, to demonstrate varied effects of three of these sRNAs (UhpU, FliX and MotR) on flagella biogenesis and motility. For example, they show different and for some sRNAs opposing phenotypes upon overexpression: While UhpU sRNA increases flagella number and motility, FliX has the opposite effect. MotR sRNA also increases the number of flagella, with minor effects on motility.

While the mechanisms and functions of the fourth sRNA, FlgO, remain elusive, the authors provide convincing experiments demonstrating that the three sRNAs directly act on different targets (identified through the analysis of previous RIL-seq datasets), with a variety of mechanisms. The authors demonstrate, UhpU sRNA, which derives from the 3´UTR of a metabolic gene, downregulates LrhA, a transcriptional repressor of the flhDC operon encoding the early genes that activate the flagellar cascade. According to their RIL-seq data analyses, UhpU has hundreds of additional potential targets, including multiple genes involved in carbon metabolism. Due to the focus on flagellar biogenesis, these are not further investigated in this study and the authors further characterize the two other flagella-associated sRNAs, FliX and MotR. Interestingly, they found that these sRNAs seem to target coding sequences rather than acting via canonical targeting of ribosome binding sites. The authors show FliX sRNA represses flagellin expression by interacting with the CDS of the fliC mRNA. Both FliX and MotR sRNA turn out to modulate the levels of ribosomal proteins of the S10 operon with opposite effects. MotR, which is expressed earlier, interacts with the leader and the CDS of rpsJ mRNA, leading to increased S10 protein levels and S10-NusB complex mediated anti-termination, promoting readthrough of long flagellar operons. FliX interacts with the CDSs of rplC, rpsQ, rpsS-rplV, repressing the production of the encoded ribosomal proteins. The authors also uncover MotR and FliX affect transcription selected representative flagellar genes, with an unknown mechanism.

Overall, this comprehensive study expands the repertoire of characterized UTR derived sRNAs and integrate new layers of post-transcriptional regulation into the highly complex flagellar regulatory cascade. Moreover, these new flagella regulators (MotR, FliX) act non-canonically, and impact protein expression of their target genes by base-pairing with the CDS of the transcripts. Their findings directly connect flagella biosynthesis and motility, highly energy consuming processes, to ribosome production (MotR and FliX) and possibly to carbon metabolism (UhpU).

Specific points to be considered:

- The authors use a crl- hyper-motile strain as WT strain for the study and sometimes also a crl+ strain is used. Can the authors comment on potential reasons why some phenotypes (e.g., UhpU and MotR effects on motility) are only detectable in the crl+ strain or vice versa? Is σS regulation important for the function of these sRNAs?

- In several experiments, a variant of MotR sRNA, MotR* that harbors a 3 nt mutation upstream of the seed sequence is used and seems to mediate stronger phenotypes (impact on flagellar number) upon overexpression compared to WT or phenotypes not retrieved for WT MotR (increased flagellin expression). It would be helpful to have some more clarification throughout the text, why this variant was used, even when OE of WT MotR already has impact on the target and how these three mutated nucleotides impact target regulation. For example, does MotR* show increased RNA stability or Hfq binding compared to MotR? Does the mutation in MotR* impact MotR structure (e.g., based on secondary structure predictions) or increase the complementarity with selected targets at potential secondary binding sites (e.g., based on target predictions)? For example, Fig. S7 shows additional regions of interaction between MotR and fliC mRNA beside the seed sequence. It is also suggested that MotR might have multiple interaction sites on rpsJ mRNA. Additional structure probing or biocomputational predictions could clarify these points.

- It is suggested that UphU impacts on motility via regulation of LrhA, which represses transcription of flhDC, and therefore the flagellar cascade. While LhrA-mediated regulation by UphU is validated based on reporter genes, the effect of UhpU OE on FlhDC levels is not directly examined (Fig. 3). Furthermore, as deletion of LrhA de-represses the flagellar cascade and UhpU was also shown to increase motility, the conclusions could be further strengthened by examining flhDC levels and/or the effect of ∆UhpU (if the sRNA part can be deleted) on motility (reduction) due to relieved down-regulation of LrhA.

-This study provides many opportunities for future follow-work. Now that the four sRNAs and some of their targets and opposing effects on flagella biogenesis have been identified, it will be interesting to see how the sRNAs themselves are temporally regulated throughout the flagella biogenesis cascade and which other targets are regulated by them. Future studies could also provide insights into the mechanism and function of FlgO sRNA, which seems to act via a different mechanism than base-pairing to target RNAs, as well as the global effects of regulation of ribosomal genes via FliX and MotR.

Reviewer #1 (Public Review):

The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

One concern is that the abstract highlights "Unpredictable var gene switching....." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites......". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

Reviewer #2 (Public Review):

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

Reviewer #3 (Public Review):

This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately there is no attempt to describe the rationale for the different approaches and how they might inform one another.

It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

Reviewer #2 (Public Review):

In this work Ushio et al. combine environmental DNA metabarcoding with novel statistical approaches to demonstrate how fish communities respond to changing sea temperatures over a seasonal cycle. These findings are important due to the need for new techniques that can better measure community stability under climate change. The eDNA metabarcoding dataset of 550 water samples over two years is, I feel, of sufficient scale to provide power to detect fine-scale ecological interactions, the experiments are well controlled, and the statistical analysis is thorough.

The major strengths of the manuscript are: (1) the magnitude of the dataset, which provides densely replicated sampling that can overcome some of the noise associated with eDNA metabarcoding data and scale up the number of data points to make unique inferences; (2) the novel method of transforming the metabarcode reads using endogenous qPCR "spike-in" data from a common reference species to obtain estimates of DNA concentration across other species; and (3) the statistical analysis of time-series and network data and translating it into interaction strengths between species provides a cross-disciplinary dimension to the work.

I feel like this kind of study showcases the power of eDNA metabarcoding to answer some really interesting questions that were previously unobtainable due to the complexities and cost of such an exercise. Notwithstanding the problems associated with PCR primer bias and PCR stochasticity, the qPCR "spike-in" method is easy to implement and will likely become a standardised technique in the field. Further studies will examine and improve on it.

Overall I found the manuscript to be clear and easy to follow for the most part. I did not identify any serious weaknesses or concerns with the study, although I am not able to comment on the more complex statistical procedures such as the "unified information-theoretic causality" method devised by the authors. The section on limitations of the study is important and acknowledges some issues with interpretation that need to be explained. The methods, while brief in parts, are clear. The code used to generate the results has been made available via a GitHub repository. The figures are clear and attractive.

Consensus Public Review:

Ottenheimer et al., present an interesting study looking at the neural representation of value in mice performing a pavlovian association task. The task is repeated in the same animals using two odor sets, allowing a distinction between odor identity coding and value coding. The authors use state-of-the-art electrophysiological techniques to record thousands of neurons from 11 frontal cortical regions to conclude that 1) licking is represented more strongly in dorsal frontal regions, 2) odor cues are represented more strongly in ventral frontal regions, 3) cue values are evenly distributed across regions. They separately perform a calcium imaging study to track coding across days and conclude that the representation of task features increments with learning and remains stable thereafter.Overall, these conclusions are interesting and well supported by the data.

The authors use reduced-rank kernel regression to characterize the 5332 recorded neurons on a cell-by-cell basis in terms of their responses to cues, licks, and reward, with a cell characterized as encoding one of these parameters if it accounts for at least 2% of the observed variance (while at first this seemed overly lenient, the authors present analyses demonstrating low false-positives at this threshold and that the results are robust to different cutoffs).

Having identified lick, reward, and cue cells, the authors next select the 24% of "cue-only" neurons and look for cells that specifically encode cue value. Because the animal's perception of stimulus value can't be measured directly, the authors created a linear model that predicts the amount of anticipatory licking in the interval between odor cue and reward presentations. The session-average-predicted lick rate by this model is used as an estimate of cue value and is used in the regression analysis that identified value cells. (Hence, the authors' definition of value is dependent on the average amount of anticipatory behavior ahead of a reward, which indicates that compared to the CS+, mice licked around 70% as much to the CS50 and 10% as much to the CS-.) The claim that this is an encoding of value is strengthened by the fact that cells show similar scaling of responses to two odor sets tested. Whereas the authors found more "lick" cells in motor regions and more "cue" cells in sensory regions, they find a consistent percentage of "value" cells (that is, cells found to be cue-only in the initial round of analysis that is subsequently found to encode anticipatory lick rate) across all 11 recorded regions, leading to their claim of a distributed code of value.

In subsequent sections, the authors expand their model of anticipatory-licking-as-value by incorporating trial and stimulus history terms into the model, allowing them to predict the anticipatory lick rate on individual trials within a session. They also use 2-photon imaging in PFC to demonstrate that neural coding of cue and lick are stable across three days of imaging, supported by two lines of evidence. First, they show that the correlation between cell responses on all periods except for the start of day 1 is more correlated with day 3 responses than expected by chance (although the correlation is low, the authors attribute this to inherent limitations of the data), and that response for a given neuron is substantially better correlated with its own activity across time than random neurons. Second, they show that cue identity is able to capture the highest unique fraction of variance (around 8%) in day 3 cue cells across three days of imaging, and similarly for lick behavior in lick cells and cue+lick in cue+lick cells. Nonetheless, their sample rasters for all imaged cells also indicate that representations are not perfectly stable, and it will be interesting to see what *does* change across the three days of imaging.

Reviewer #1 (Public Review):

This work describes a novel high-throughput approach to diverse transgenesis which the authors have named TARDIS for Transgenic Arrays Resulting in Diversity of Integrated Sequences. The authors describe the general approach: the generation of a synthetic 'landing pad' for transgene insertion (as previously reported by this group) that has a split selection hygromycin resistance gene, meaning that only perfect integration with the insert confers resistance to the otherwise lethal hygromycin drug. The authors then demonstrate two possible applications of the technology: individually barcoded lineages for lineage tracing and transcriptional reporter lines generated by inserting multiple promoters. In both cases, the authors did a limited 'proof of concept' study including many important controls, showcasing the potential of the method. The conclusions for applications of this method in C. elegans are supported by the data and the authors discuss important observations and considerations. In the discussion, the discuss the potential application of the method beyond C. elegans, although this remains speculative at this point given that a nontrivial aspect of the success of the method in worms is the self-assembly of DNA into heritable extrachromosomal arrays (a feature that is absent in most other systems).

Reviewer #2 (Public Review):

This paper explores the possibility of integrating diverse and multiple DNA fragments in the genome taking advantage of plasmids in arrays, and CRISPR. Since the efficiency of integration in the genome is low, they, as others in the field, use selection markers to identify successful events of integration. The use of these selection markers is common and diverse, but they use a couple of distinct strategies of selection to:

- Introduce bar codes in the genome of individuals at one specific genomic site (gene for Hygromycin resistance with bar code in an intron with homology arms to complete a functional gene);

- Introduce promoters at two specific genomic landing pads downstream of fluorescent reporters.

The strengths of the study are the clever design of the selection markers, which enrich the collection of this type of markers. While the work is not methodologically novel - it adds to other recent studies, e.g. from Nonet, Mouridi et al., and Malaiwong et al, that use the integration of single and multiple/diverse DNA sequences in the C. elegans genome - it provides a protocol for doing so and tool to make it practical. A limited number of experiments using the method are presented here, and the real test of this method will be its use to address biological questions.

Reviewer #1 (Public Review):

The authors investigated state-dependent changes in evoked brain activity, using electrical stimulation combined with multisite neural activity across wakefulness and anesthesia. The approach is novel, and the results are compelling. The study benefits from in depth sophisticated analysis of neural signals. The effects of behavioral state on brain responses to stimulation are generally convincing.

It is possible that the authors' use of "an average reference montage that removed signals common to all EEG electrodes" could also remove useful components of the signal, which are common across EEG electrodes, especially during deep anesthesia. For example, it is possible (in fact from my experience I would be surprised if it is not the case) that under isoflurane anesthesia, electrical stimulation induces a generalized slow wave or a burst of activity across the brain. Subtracting the average signal will simply remove that from all channels. This does not only result in signals under anesthesia being affected more by the referencing procedure than during waking, but also will have different effects on different channels, e.g. depending on how strong the response is in a specific channel.

Reviewer #2 (Public Review):

This study reports a novel role of thalamic activity in the late components of a cortical event related potential (ERP). To show this association, the authors used high-density EEG together with multiple deep electrophysiological recordings combined with electrical stimulation of superficial and deep cortical layers. Stimulation of deep layers elicits a late ERP component that is closely related to bursts of thalamic activity during quiet wakefulness. This relationship is quite noticeable when deep layers of the cortex are stimulated, and it does depend on arousal state, being maximal during quiet wakefulness, diminished during active wakefulness, and absent during anesthesia.

The study is very well performed, with a high number of subjects and appropriate methodology. Performing simultaneous recording of EEG and several neuropixels probes together with cortical microstimulation is no small feat considering the size of the mouse head and the fact that mice are freely behaving in many of the experiments. It is also noticeable how the authors use a seemingly outdated technique (electrical microstimulation) to produce compelling and significant research. The conclusions regarding the thalamic contributions to the ERP components are strongly supported by the data.

The spatiotemporal complexity is almost a side point compared to what seems to me the most important point of the paper: showing the contribution of thalamic activity to some components of the cortical ERP. Scalp ERP's have long been regarded as purely cortical phenomena, just like most of EEG, and this study shows convincing evidence to the contrary.

The data presented seemingly contradicts the results presented in Histed et al. (2009), who asserts that cortical microstimulation only affects passing fibers near the tip of the electrodes, and results in distant, sparse, and somewhat random neural activation. In this study, it is clear that the maximum effect happens near the electrodes, decays with distance, and it is not sparse at all, suggesting that not only passing fibers are activated but that also neuronal elements might be activated by antidromic propagation from the axonal hillock. This appears to offer proof that microstimulation might be much more effective than it was thought after the publication of Histed 2009, as the uber-successful use of DBS to treat Parkinson disease has also shown.

Reviewer #1 (Public Review):

The manuscript, "A versatile high-throughput assay based on 3D ring-shaped cardiac tissues generated from human induced pluripotent stem cell-derived cardiomyocytes" developed a unique culture platform with PEG hydrogel that facilitates the in-situ measurement of contractile dynamics of the engineered cardiac rings. The authors optimized the tissue seeding conditions, demonstrated tissue morphology with expressions of cardiac and fibroblast markers, mathematically modeled the equation to derive contractile forces and other parameters based on imaging analysis, and ended by testing several compounds with known cardiac responses.

To strengthen the paper, the following comments should be considered:

1. This paper provided an intriguing platform that creates miniature cardiac rings with merely thousands of CMs per tissue in a 96-well plate format. The shape of the ring and the squeezing motion can recapitulate the contraction of the cardiac chamber to a certain degree. However, Thavandiran et al (PNAS 2013) created a larger version of the cardiac ring and found the electrical propagation revealed spontaneous infinite loop-like cycles of activation propagation traversing the ring. This model was used to mimic a reentrant wave during arrhythmia. Therefore, it presents great concerns if a large number of cardiac tissues experience arrhythmia by geometry-induced re-entry current and cannot be used as a healthy tissue model. It would be interesting to see the impulse propagation/calcium transient on these miniature cardiac rings and evaluate the % of arrhythmia occurrence.

2. The platform can produce 21 cardiac rings per well in 96-well plates. The throughput has been the highest among competing platforms. The resulting tissues have good sarcomere striation due to the strain from the pillars. Now the emerging questions are culture longevity and reproducibility among tissues. According to Figure 1E, there was uneven ring formation around the pillar, which leads to the tissue thinning and breaking off. There is only 50% survival after 20 days of culture in the optimized seeding group. Is there any way to improve it? The tissues had two compartments, cardiac and fibroblast-rich regions, where fibroblasts are responsible for maintaining the attachment to the glass slides. Do the cardiac rings detach from the glass slides and roll up? The SD of the force measurement is a quarter of the value, which is not ideal with such a high replicate number. As the platform utilizes imaging analysis to derive contractile dynamics, calibration should be done based on the angle and the distance of the camera lens to the individual tissues to reduce the error. On the other hand, how reproducible of the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within the wells to understand the variance.

3. Does the platform allow the observation of non-synchronized beating when testing with compounds? This can be extremely important as the intended applications of this platform are drug testing and cardiac disease modeling. The author should elaborate on the method in the manuscript and explain the obtained results in detail.

4. The results of drug testing are interesting. Isoproterenol is typically causing positive chronotropic and positive inotropic responses, where inotropic responses are difficult to obtain due to low tissue maturity. It is inconsistent with other reported results that cardiac rings do not exhibit increased beating frequency, but slightly increased forces only. Zhao et al were using electrical pacing at a defined rate during force measurement, whereas the ring constructs are not.

Overall, the manuscript is well written and the designed platform presented the unique advantages of high throughput cardiac tissue culture. Besides the contractile dynamics and IHC images, the paper lacks other cardiac functional evaluations, such as calcium handling, impulse propagation, and/or electrophysiology. The culture reproducibility (high SD) and longevity (<20 days) still remain unsolved.

Reviewer #2 (Public Review):

The authors should be commended for developing a high throughput platform for the formation and study of human cardiac tissues, and for discussing its potential, advantages and limitations. The study is addressing some of the key needs in the use of engineered cardiac tissues for pharmacological studies: ease of use, reproducible preparation of tissues, and high throughput.

There are also some areas where the manuscript should be improved. The design of the platform and the experimental design should be described in more detail.

It would be of interest to comprehensively document the progression of tissue formation. To this end, it would be helpful to show the changes in tissue structure through a series of images that would correspond to the progression of contractile properties shown in Figure 3.

The very interesting tissue morphology (separation into the two regions) that was observed in this study is inviting more discussion.

Finally, the reader would benefit from more specific comparisons of the contractile function of cardiac tissues measured in this study with data reported for other cardiac tissue models.

Reviewer #1 (Public Review):

Kou and Kang et al. investigated the role of Notch-RBP-J signaling in regulating monocyte homeostasis. Specifically, they examined how a conditional knockout of Rbpj expression in monocytes through a Rbpjfl/fl Lyz2cre/cre mouse affects the homeostasis of Ly6Chi versus Ly6Clo monocytes. They discovered that Rbpj deficiency did not affect the percentage of Ly6Chi monocytes but instead, led to an accumulation of Ly6Clo monocytes in the peripheral blood. Using a comprehensive number of in vivo techniques to investigate the origin of this increase, the authors revealed that the accumulation of Rbpj deficient Ly6Clo monocytes was not due to an increase in bone marrow egress and that this defect was cell intrinsic. However, EdU-labelling and apoptosis assays revealed that this defect was not due to an increase in proliferation nor conversion of Ly6Chi to Ly6Clo monocytes. Interestingly, it was revealed that Rbpj deficient Ly6Clo monocytes had increased expression of CCR2 and ablation of CCR2 expression reversed the accumulation of these cells in the periphery. Lastly, they discovered that Rbpj deficiency also led to downstream effects such as an accumulation of Ly6Clo monocytes in the lung tissue and increased CD16.2+ interstitial macrophages, presumably due to dysregulated monocyte differentiation and function.

Their findings are interesting and highlight a previously unexplored mechanistic link between Notch-RBP-J signaling and CCR2 expression in monocyte homeostasis, providing further insight into the distinct pathways that regulate Ly6Chi vs Ly6Clo monocyte subsets individually.

The conclusions of this paper are mostly well substantiated from the experimental data. The strengths of this paper include the use of multiple conditional genetic knock out mouse models to explore their hypothesis and the use of sophisticated in vivo techniques to study the major mechanisms involved in monocyte homeostasis.

Reviewer #2 (Public Review):

The authors provide compelling data to demonstrate that the Notch-related transcription factor RBP-J can influence the number of circulating and recruited monocytes. The authors first delete the Rbpj gene in the myeloid lineage (Lyz2) and show that, as a proportion, only Ly6Clo monocytes are increased in the blood. The authors then attempted to identify why these cells were increased but ruled out proliferation or reduced apoptosis. Next, they investigated the gene signature of Rbpj null monocytes using RNA-sequencing and identified elevated Ccr2 as a defining feature. Crossing the Rbpj null mice to Ccr2 null mice showed reduced numbers of Ly6Clo monocytes compared with Rbpj null alone. Finally, the authors identify that an increased burden of blood Ly6Clo monocytes is correlated with increased lung recruitment and expansion of lung interstitial macrophages.

The main conclusion of the authors, that there is a 'cell intrinsic requirement of RBP-J for controlling blood Ly6CloCCR2hi monocytes' is strongly supported by the data. However, other claims and aspects of the study require clarification and further analysis of the data generated.

Strengths<br /> The paper is well written and structured logically. The major strength of this study is the multiple technically challenging methods used to reinforce the main finding (e.g. parabiosis, adoptive transfer). The finding reinforces the fact that we still know little about how immune cell subsets are maintained in situ, and this study opens the way for novel future work. Importantly, the authors have generated an RNA-sequencing dataset that will prove invaluable for identifying the mechanism - they have promised public access to this data via GEO.

Weaknesses - The main weakness of the study, is that although the main result is solidly supported, as written it is mostly descriptive in nature. For instance, there is no given mechanism by which RBP-J increases Ly6Clo monocytes. The authors conclude this is dependent on CCR2, however CCR2 deletion has a global effect on monocyte numbers and importantly in this study, it does not remove the Ly6Clo bias of cell proportions, if anything it seems to enhance the difference between the ly6C low and high populations in Rbpj null mice (figure 5C). This oversight in data interpretation likely occurred because this experiment is missing a potentially important control (Lyz2cre/cre Ccr2RFP/RFP or RBP-J variations). In general, there seemed to be a focus on the Ly6C low cells, where the mechanism may be more identifiable in their precursors - likely the Ly6C high monocytes.

Other specific weaknesses were identified:<br /> 1) The confirmation of knockout in supplemental figure 1A shows only a two third knockdown when this should be almost totally gone. Perhaps poor primer design, cell sorting error or low Cre penetrance is to blame, but this is below the standard one would expect from a knockout.<br /> 2) Many figures (e.g. 1A) only show proportional data (%) when the addition of cell numbers would also be informative<br /> 3) Many figures only have an n of 1 or 2 (e.g. 2B, 2C)<br /> 4) Sometimes strong statements were based on the lack of statistical significance, when more n number could have changed the interpretation (e.g. 2G, 3E)<br /> 5) There is incomplete analysis (e.g. Network analysis) and interpretation of RNA-sequencing results (figure 4), the difference between the genotypes in both monocyte subsets would provide a more complete picture and potentially reveal mechanisms<br /> 6) The experiments in Figures 5 and 7 are missing a control (Lyz2cre/cre Ccr2RFP/RFP or the Rbpj+/+ versions) and may have been misinterpreted. For example if the control (RBP-J WT, CCR2 KO) was used then it would almost certainly show falling Ly6C low numbers compared to RBP-J WT CCR2 WT, but RBP-J KO CCR2 KO would still have more Ly6c low monocytes than RBP-J WT, CCR2 KO - meaning that the RBP-J function is independent of CCR2. I.e. Ly6c low numbers are mostly dependent on CCR2 but this is irrespective of RBP-J.<br /> 7) Figure 6 was difficult to interpret because of the lack of shown gating strategy. This reviewer assumes that alveolar macrophages were gated out of analysis<br /> 8) The statements around Figure 7 are not completely supported by the evidence, i) a significant proportion of CD16.2+ cells were CCR2 independent and therefore potentially not all recently derived from monocytes, and ii) there is nothing to suggest that the source was not Ly6C high monocytes that differentiated - the manuscript in general seems to miss the point that the source of the Ly6C low cells is almost certainly the Ly6C high monocytes - which further emphasises the importance of both cells in the sequencing analysis<br /> 9) The authors did not refer to or cite a similar 2020 study that also investigated myeloid deletion of Rbpj (Qin et al. 2020 - https://doi.org/10.1096/fj.201903086RR). Qin et al identified that Ly6Clo alveolar macrophages were decreased in this model - it is intriguing to synthesise these two studies and hypothesise that the ly6c low monocytes steal the lung niche, but this was not discussed

Reviewer #3 (Public Review):

In this study, the authors investigate the role of the Notch signalling regulator RBP-J on Ly6Clow monocyte biology starting with the observation that RBP-J-deficient mice have increased circulating Ly6low monocytes. Using myeloid specific conditional mouse models, the authors investigate how RBP-J deficiency effects circulating monocytes and lung interstitial macrophages.

A major strength of this study is that it describes RBP-J as a novel, critical factor regulating Ly6Clow monocyte cell frequency in the blood. The authors demonstrate that RBP-J deficiency leads to increased Ly6Clow monocytes in the blood and lung and CD16.2+ interstitial macrophages in steady state. The authors use a number of different techniques to confirm this finding including bone marrow transplantation experiments and parabiosis.

There are several critical weaknesses that need to be assessed to improve the manuscript, in summary the data presented in the current manuscript are highly descriptive and without mechanistic insight. The inclusion of more mechanistic insight would greatly improve the manuscript.

The authors begin to explore the potential mechanism underlying why Ly6Clow monocytes are increased in the absence of RBP-J - is it through increased survival, increased conversion from Ly6C+ monocytes, increased proliferation or increased egress from the bone marrow. The majority of the data they present here is negative. Whilst I applaud the authors for including negative data, I think that their exploration into how RBP-J leads to increased monocytes does not go far enough and it is critical to understand the mechanism by which RBP-J increases circulating monocytes. Low n-numbers in multiple figures mean that the claims made are not fully supported.

The current title of the paper "RBP-J regulates homeostasis and function of circulating Ly6Clo monocytes" does not fully reflect the manuscript in its current form - there is no exploration of Ly6Clow monocyte functionality in the paper as it stands.<br /> Given that targeting monocytes and macrophages in a range of inflammatory diseases is an attractive yet elusive therapeutic option, understanding the underlying biology that regulates monocyte biology are critically important. This manuscript has the potential to add to our current knowledge of how Ly6Clow monocyte biology is regulated and potentially opens novel avenues for preferentially enhancing Ly6Clow monocytes without influencing Ly6C+ monocytes. This is an attractive proposition for many inflammatory conditions however, considerably more in-depth analysis is required to understand the role of RBP-J in monocyte biology.

Reviewer #1 (Public Review):

The manuscript entitled, "Loss of PTPMT1 limits mitochondrial utilization of carbohydrates and leads to muscle atrophy and heart failure," by Zheng, et al., is focused on assessing the role of deletion of PTPMT1, a mitochondria-based phosphatase, in mitochondrial fuel selection. Authors show that the utilization of pyruvate, a key mitochondrial substrate derived from glucose, is inhibited, whereas fatty acid utilization is enhanced. Importantly, while the deletion of PTPMT1 does not impact development of skeletal muscle or heart, the metabolic inflexibility leads to muscular atrophy, heart failure, and sudden death. Mechanistically, authors claim that the prolonged substrate shift from carbohydrates to lipids causes oxidative stress and mitochondrial dysfunction, leading to accumulation of lipids and muscle cell and CM damage in the KO. Interestingly, PTPMT1 deletion from the liver or adipose tissue does not generate any local or systemic defects. Authors conclude that PTPMT1 plays an important role in maintaining mitochondrial flexibility and that the balanced utilization of carbohydrates and lipids is essential for skeletal muscle and heart. While interesting and authors did a ton of experiments for this project, several major concerns exist. First, because both the CKMM- and the MYHC-Cre express early, during development , it seems the effects of the deletion of PTPMT1 are more likely be specific to defects in muscle and cardiac development rather than postnatal, especially since loss of PTPMT1 affects mTOR activity; indeed, previous reports have shown that selective deletion of mTOR or raptor in skeletal muscle during embryonic development leads to a reduction in postnatal growth and the development of late-onset myopathy and premature death around 6 to 8 months of age. The effects of the deletion in muscle seem eerily similar and therefore likely mechanistically function the same -embryonically, as has been previously suggested. This is also true for cardiac abnormalities, where developmental defects can manifest in mice as they age after at least 3-4 months and decreased mTOR activity can lead to significant cardiac dysfunction and failure (similarly to the effects observed here by the authors). To prove one way or another, authors should show developmental data providing evidence that the effects are not occurring at this stage. It is a lot of work, but the right way to differentiate pre- vs post- development functions of PTPMT1 in the muscle and heart, otherwise cannot verify mechanistically what the precise cause for the phenotype may be. Authors could consider generating mice that have inducible Cre drivers. In addition, how is it that the effects of loss of PTPMT1 are similar between muscle and heart given the differences in energy usage and utilization between these two tissues? Increases in AMPK are usually associated with better metabolic outcomes, particularly in the heart. Increased AMPK activation has also been shown to help reduce fat storage, increase insulin sensitivity, reduce cholesterol/triglyceride production, and suppress chronic inflammation. In addition, increases in carnitines are associated with enhanced metabolic function. Carnitines facilitate transport of long-chain fatty acids into the mitochondrial matrix, triggering cardioprotective effects through reduced oxidative stress, inflammation and necrosis of cardiac myocytes. All of these factors are positive, so how do authors explain this discrepancy in their findings which suggest opposing outcomes- as above, I suggest the explanation is that it is due to developmental effects of deletion of PTPMT1.

Authors attribute much of the pathology in the muscle and heart due to increased lipid accumulation in these tissues; but how do authors explain how hearts and muscle have more fat when the mice are smaller than wt? Is there a difference in energy expenditure in the mice? What about changes in white fat, core temperature or browning of fat? Authors do not mechanistically prove that lipid accumulation is the cause of death in these animals. Rescue experiments should be considered.

Reviewer #2 (Public Review):

Zheng et al. have investigated the effects of PTPMT1 Knock-out on cellular metabolic flexibility. Using several types of appropriate tissue-specific mouse models, the authors have generated data that are both reasonable and broadly significant. While the central mechanism driving the metabolic fuel preference and flexibility remains elusive as the author mentioned in the main text, the finding that the absence of PTPMT1 inhibits glucose (pyruvate) utilization and promotes FAO, resulting in cellular stress and damage, particularly in skeletal and cardiac muscle cells, is intriguing and has practical implications for further research. However, some quantitative data are lacking and certain explanations may be misleading, warranting revisions.

Public Review:

In this manuscript, Karl et al. explore mechanisms underlying the activation of the receptor tyrosine kinase FGFR1 and stimulation of intracellular signaling pathways in response to FGF4, FGF8, or FGF9 binding to the extracellular domain of FGFR1. Quantitative binding experiments presented in the manuscript demonstrate that FGF4, FGF8, and FGF9 exhibit distinct binding affinities towards FGFRs. It is also proposed that FGF8 exhibits "biased ligand" characteristics that is manifested via binding and activation FGFR1 mediated by "structural differences in the FGF- FGFR1 dimers, which impact the interactions of the FGFR1 trans membrane helices, leading to differential recruitment and activation of the downstream signaling adapter FRS2".

Major points:

1. Previous studies have demonstrated that the variability of signal transduction stimulated by different FGF family members originates from their preferential activation of different members of the FGFR family (Ornitz et al., 1996). For example, it was previously shown that members of the FGF8 subfamily preferentially activate FGFR3c, whereas members of the FGF4 subfamily activate FGFR1c more potently than other FGFs. Moreover, it was shown that FGF18, a member of the FGF8 subfamily, preferentially binds to and activates the FGFR3c isoform. Indeed, this can be seen in the data shown in Figure 3 in this manuscript, where maximum levels of FGFR1 pY653/4 and pFRS2 are reached at different concentrations when stimulated with increasing concentrations of each ligand in HEK293T cells. In order to be sure that the 'biased agonist' described in this manuscript for FGF8 binding is not caused by binding preference towards different FGFR members, the authors should present data comparing cell signaling via FGFR3c stimulated by FGF4, FGF8, and FGF9.

2. It is well-established that FGFR signaling by canonical FGF family members including FGF4, FGF8, and FGF9 is dependent on interactions of heparin or heparan sulfate proteoglycans (HSPG) to the ligand the receptors. Differential contributions of heparin to cell signaling mediated by FGF4, FGF8, and FGF9 binding and activation of different FGFRs expressed in RCS cells as this cell express endogenous HSPG molecules. This question should be addressed by comparing cell signaling via FGFRs ectopically expressed in BAF/3 cells (which do not possess endogenous FGFRs and HSPG) stimulated by FGF4, FGF8, and FGF9 in the absence or presence of different heparin concentrations. This approach has been applied many times in the past to explore and establish the role of heparin in control of ligand induced FGFR activation. It is impossible to interpret the FGFR binding characteristics and cellular activates of FGF4, FGF8, and FGF9 in the absence of information about the role of heparin in their binding and activation.

3. It is not clear how some of the experimental data were analyzed. Blots in Figures 3A and 3B should include controls (total FGFR1 for pY653/4 and total FRS for pFRS2). How are the data shown in Figure 3C normalized? It does look like the level of phosphorylation was all normalized against the strongest signals irrespective of which ligand was used. Each data representing each ligand should be separately normalized.

4. In page 6, authors used the plot shown in Figure 3 for 'FGFR downregulation' to conclude that "the effect of FGF4 on FGFR1 downregulation is smaller when compared to the effects of FGF8 and FGF9. However, it is unclear how the data shown in the plot was normalized - none of the data seem to reach "1.0". Moreover, the plot seems to suggest that FGF4 can strongly downregulate FGFR as it can downregulate FGFR with higher potency.

5. The structural basis of FGFR1 ligand bias and the different dimeric configurations and interactions between the kinase domain of FGFR1 dimers are not warranted (Figure 6). In the absence of any structural experimental data of different forms of FGFR dimers stimulated by FGF ligands the model presents in the manuscript is speculative and misleading.

Reviewer #1 (Public Review):

The authors generated detailed anatomical descriptions and images of the coronary vasculature of mice, quails, zebrafish, Japanese tree frogs, Japanese fire belly newt, African clawed frogs, salmon sharks, Japanese sleeper rays and bird-beak dogfish. Using this data, they are able to show anatomical similarities in the origination points of evolutionary distant vertebrates from the third to fourth brachial arch. Additionally, the authors highlight the additional presence of a coronary vascular plexuses as a unique amniote trait, since it is seen in quail and mice but not xenopus frogs. Based on the presence of the possible homologies, the authors propose that the early developmental amniotic coronary artery is a derived from the ancestral hypobrachial artery. The methods for labeling and imaging the cardiac vessels are robust and congruent with previous studies describing these structures in mice and zebrafish. The study also presents an intriguing hypothesis; however, it could benefit from a more expansive survey of vertebrate coronary diversity using an increased number of species and developmental time points. A more exhaustive surveying of vertebrate diversity is required to demonstrate that the coronary vasculature anatomy observed is from common ancestral states or novel adaptations. The author's claim that a primitive vascular plexus represents a novel amniote phenotype, is reasonable, but could benefit from further confirmation using additional species.

Reviewer #2 (Public Review):

In this manuscript, Mizukami et al. investigate the differences in coronary vasculature morphology across several diverse species to investigate the transition of extrinsic coronary arteries existing on the outflow track in non-amniotes to arteries presenting on the ventricle surface itself in amniotes. They use various visualization techniques, including resin-filling, tissue staining, and fluorescence microscopy to compare the gross morphology and orifice locations of the aortic subepicardial vessels (ASVs) between several amniotes and non-amniotes. Intriguingly, the authors show that the embryonic amniotes rely on a similar ASV structure to adult non-amniotes, but this primitive structure is lost during development in favor of the formation of true coronary arteries on the ventricle surface. While these data intend to show that the difference in coronary artery structure exists between amniotes and non-amniotes, the authors only investigated mice and quail as amniote representatives. Without the inclusion of an ectothermic reptile species as an additional amniote representative, it is entirely possible that the difference in coronary artery structure may instead exist across the endotherm-ectotherm axis as opposed to amniotes and non-amniotes. Despite these concerns, Mizukami et al. show intriguing evolutionary differences between coronary artery structure that draw parallels to changes observed during amniote development.

Reviewer #3 (Public Review):

Mizukami et al. compare the structure of the coronary arteries in multiple species of amniotes, amphibians, and fish. By selecting species from each of these taxa, the authors were able to evaluate modifications to the coronary arteries during key evolutionary transitions. In mice and quail, they show two populations of vessels that are visible on the developing heart-true coronary arteries on the ventricle and a second population of vessels on the outflow tract known as the ASV., They found that in amphibians, outflow tract vessels were present but ventricular coronary arteries were completely absent. In zebrafish (a more ancestral species) an arterial branch off the rostral section of the hypobranchial artery was shown to have similar anatomical features to outflow tract vessels found in higher organisms. These zebrafish outflow tract arteries also appeared conserved in several chondriichthyes specimens. The authors conclude that rearrangement of the outflow tract vasculature or hypobranchial arteries in fish during evolution, could be homologous to the ASV population of coronary arteries in amphibians and amniotes. These data give new insight into the evolutionary origins of the coronary vasculature.

Major Points

1. The manuscript presents important data on the coronary vascular structure of several different species. However, these data alone do not conclusively demonstrate whether the developmental origins of ASV like vessels are homologous. Therefore, care should be taken when concluding that the outflow tract vessels found in all different species are conserved features. While this is a reasonable hypothesis and should be presented, the manuscript could be improved by also discussing alternate explanations. For example, ASVs in mice originate during embryonic development, while in fish and amphibians outflow tract vessels are formed only in mature animals.

2. Figure 3 A-D: The authors state that "the ASV ran through the outflow tract, then entered the aortic root before reaching the ventricle to form a secondary orifice". Do the authors have serial sections to conclude that the vessel branching off the carotid runs the length of the aorta and is continuous with an orifice at the aortic root? The endothelial projection off the aorta in panel C could reasonably be an independent projection. For example, Chen et al., described similar looking projections in the base of the aorta that were not attached to external vessels. A whole mount approach would be the most convincing to show the attachments of the ASV vessel.

3. Figure 3E: Similar as above, how is it concluded that the orifice is continuous with the ASV and that this projection is not the coronary artery stem?

4. The discussion section could be improved by making some statements more consistent, using more precise or appropriate terminology accepted in the field, and being more cognizant of how the authors' findings fit within the history of the field. For example, when referring to coronary arteries, please clarify whether this refers to ASV/ outflow tract coronary arteries, or true ventricular coronary arteries. In addition, the first sentence of the discussion makes it seem like the origins of coronary arteries were unknown prior to this study, however, their origins have been described in multiple papers previously. The authors could revise their statement to acknowledge these previous findings.

Reviewer #1 (Public Review):<br /> <br /> Lobanov et al. investigated the effects of spatial structure in microbial communities that interact via secreted metabolites. The work builds up on a previous theoretical model by the authors that considered well-mixed populations in which different bacterial species secrete and consume different sets of metabolites, and metabolites in turn modify the growth rates of species. The model considers communities that are periodically exposed to dilutions, and the authors focus on the regime in which bacterial densities do not reach saturation before the next dilution. Analyzing the stable outcome of these dynamics through comparison with well-mixed scenarios, the authors found that space can favor species richness, especially in the case of communities with prevalent facilitative interactions. This positive effect on species coexistence is also more pronounced in situations in which species produce more kinds of metabolites than they consume. On the other hand, the positive effects on coexistence can be reversed when bacterial dispersal becomes relevant over the timescale of the simulations, as well as in cases in which the diffusion of metabolites is too slow - which could even result in less coexistence than in well-mixed scenarios. These results add to an ongoing discussion on the different ways in which spatial effects can impact microbial community dynamics and species richness.

The conclusions of this paper are mostly well supported by the data, but some aspects of the methodology and analysis need to be clarified and extended.

1) This is a model with many parameters and the manuscript should be clearer about how these parameters were used in different scenarios. It is probably a matter of rewriting the text, but I found it hard to understand which parameter values remained the same in scenarios with or without space, as well as how the strength of interactions was assigned, among a few other examples. In other cases, additional analysis (e.g. on how the spatial impact on coexistence depends on the average strength of interactions) would make the work more comprehensive.<br /> 2) To assess stable coexistence and richness, the authors use a criterium in which species have to be almost equally abundant (above 90% of the abundance of the fastest-growing species). It is not clear if the results would change significantly if potentially less abundant species would be classified as coexisting ones.<br /> 3) The majority of the results consider scenarios in which bacteria cannot disperse very effectively so bacterial dynamics is mostly driven by the growth of the initial populations at each region. Expanding on the analysis of higher dispersal rates would be valuable in order to analyze additional realistic scenarios of how bacteria grow and disperse in space.

Reviewer #2 (Public Review):

In this work, the authors extend a mathematical model that they previously developed. Their original paper (Niehaus..Momeni, Nature Comm., 2019) models species interactions using mediators (i.e. metabolites) that species produce and that can affect other species' growth rates. Here, they extend the original model, which was well-mixed, to study communities in space. To do this, here they assume that species grow on a 1D grid, that species can possibly overlap in the same grid spot, and that species and mediators can diffuse in space. They find that spatial structure promotes the coexistence of species when interactions are more facilitating than inhibiting, and when species dispersal is low. Both of these features separately allow for species to self-organize in a way that allows them to be closer in space to partners that facilitate their growth. Properties of the metabolic interactions, such as the amount of metabolites produced and consumed, consumption and production rates, and metabolite diffusion also have effects on species coexistence.

Strengths: The authors extend their previously published model (Niehaus..Momeni, Nature Comm., 2019) to study the role of space in maintaining species diversity. The authors have the goal of modeling realistic bacterial communities; they in fact claim that the model's motivation is to "capture situations in which microbes can disperse inside a matrix", such as the mucosal layer of the digestive or intestinal tract, yogurt or cheese. To do this, the authors add relevant spatial aspects to their previous well-mixed model: species grow on a grid (even though 1D), where they can possibly overlap in the same grid spot, and species and mediators can diffuse in space. The advantage of the model they develop here is that it is simple enough for it to be used to explore general features of systems for which the assumptions of the model are justified. The authors perform a thorough investigation of the effect of spatial structure on the diversity that is maintained in the system. Their investigation includes the role of different types of interactions (facilitation and inhibition), species dispersal, and a range of properties of the metabolic interactions (number of mediators consumed and produced, consumption and production rates, mediator diffusion). Every scenario is compared to the well-mixed scenario to highlight the role of space.

Weaknesses: We are not convinced about some assumptions the authors make when extending their model from well-mixed (Niehaus..Momeni, Nature Comm., 2019) to spatial (this manuscript). The authors want to model a spatially structured system, with a framework that resembles the metacommunity framework, to which they add specific biophysical processes, such as the diffusion of metabolites. However, when adding these specific biophysical processes, the authors use parameters that seem to be unrealistic. One example is the packing of cells: 10^9, which implies a ratio between cells and the environment of 1:1000 volume-wise. Another example is the diffusion of molecules, which is 10 times slower than stated in the literature. With these parameters, the authors aim at describing physical processes in their model, but overall the parameters seem to be far from real values. Thus we suggest either changing these parameters to realistic values, discussing why the chosen parameters are meaningful or reframing the model as an heuristic model.

Overall, we think that the contribution of the paper is to extend a previously published work (Niehaus..Momeni, Nature Comm., 2019) to model spatial communities. It is thus fundamental that the assumptions made by the authors to model the spatial dynamics are well justified. Several physical parameters are chosen to values that do not represent realistic values for spatially structured communities. The authors should discuss if the results hold also for more realistic values.

Reviewer #3 (Public Review):

The authors develop and analyze a novel model of microbial communities that considers both space and chemical mediator dynamics explicitly, with the goal of understanding the impact of spatial structure on coexistence. The authors' primary method for assessing the impact of space is to compare numerical simulations of their spatial model to simulations of an equivalent well-mixed model. They explore how spatial structure changes coexistence over a wide range of parameter space, varying parameters such as the ratio of facilitative to inhibitory interactions and the degree of mediator diffusion. They find that spatial structure can have variable effects on richness (the number of cell types within a community), in contrast to existing intuition in the field that spatial structure increases diversity.

Overall, I think the approach that the authors have taken is sound. A very interesting aspect of this model is that the diffusion of mediators and microbes can occur at different rates. In other spatial systems, such as the classic Turing model of pattern formation, differences in diffusion timescales are the key ingredient needed for interesting spatial dynamics. However, while the authors have thoroughly characterized the impact of model parameters on ecological richness, their focus on this single metric provides a somewhat limited view of coexistence in their models. For example, richness considers neither the population composition nor the spatial patterns of coexistence emerging from the model. I also have some concerns about the implementation of the carrying capacity in the model, which in its current form may lead to non-physical outcomes in a small part of the phase space.

Reviewer #1 (Public Review):

In this manuscript, Castrillon et al. analyze the heterogeneity of B cells exiting spontaneous germinal center reactions by scRNA-seq in a new mouse model of autoimmunity. In this model, they track the fate of wild-type Aid-Cre ERT2-EYFP B cells in the presence of 564 lgi B cells harboring a BCR specific for RNP. Throughout the manuscript, the authors compared the results obtained in the autoimmune model with those obtained after acute immunization with NP/OVA in Alum. They found extensive clonal overlap among dark/light zone germinal centers, memory B cells, and antibody-secreting cells (ASC). Within the ASC compartment, they found seven clusters. Through pseudotime analysis, they conclude the presence of two early ASC clusters, three intermediate ASC clusters, and two terminal ASC clusters. The two late ASCs have different patterns of gene expression (CD28, Itga4 among them), isotype expression (ASC_Late_1 mostly class-switched while ASC_Late_2 mostly IgM), and potentially different antibody-secreting capacity and metabolic program based on Ig counts and OXPHOS signature. Regarding memory B cells, they found four clusters of memory B cells with similar isotype expression (except for MemB2 which expresses more IgM) but different gene expression patterns (CD83, Fcrl5, Vim, Fcer2a). Finally, the authors found that FCRL5+ and CD23+ memory B cells are located in different areas of the spleen based on confocal microscopy analysis and their accessibility to blood after anti-CD45 iv administration. The data provided by the authors are very attractive and interesting. Yet, I found that the manuscript over relies on scRNA-seq. It will be important that authors back up some of their conclusions made from the scRNA-seq analysis with functional experiments, like measuring the differential antibody-secreting capacity of both terminal ASC subsets or profiling their metabolic status through one of the many metabolic techniques available.

Reviewer #2 (Public Review):

This paper uses single-cell RNA sequencing to assess the B cell response in a mouse model of autoimmunity. The authors find that the B cell response is transcriptionally similar to the response induced by protein immunization. They further determine that the memory B cell response is composed of transcriptionally distinct subsets that may have distinct spatial distributions.

A major strength of this manuscript is the author's use of an elegant model of autoimmunity in which self-reactive B cells can escape negative selection to become activated and participate in the germinal center response. This system allows the author's a system to study the development of B cells in an autoimmune setting without restricting the repertoire of those cells though the use of BCR transgenes. This single-cell data generated in this study is also likely to be useful to individuals interested in understanding the differences in the B cell response between autoimmune and protein immunization settings.

One weakness of this study is that its main findings do not seem to represent a major conceptual advancement. There are already many published single-cell RNA-seq data sets that show that heterogeneity exists within B cell subsets. Therefore, the author's data primarily extends these findings to indicate that heterogeneity also exists in their model of autoimmunity.

Another major weakness of this study is that the authors only analyze about 13K cells in their single cell RNA-seq experiment with only 3.3K coming from the immunized mice. This low number of cells likely prevents the authors from identifying differences between specific B cell subsets between the two disease settings because there are likely very few cells in many of the clusters in the immunized group.

Finally, the author's data in which they seek to validate their use of Fcrl5 and CD23 to identify memory B cell subsets is not convincing. The flow cytometry gating used to distinguish the memory B cell subsets seem somewhat arbitrary with there not being a clear separation between the four populations shown using the author's gating strategy. This strategy also causes many CD23+ cells to not be analyzed in Fig. 6G.

The imaging data is also not clear as it is not apparent whether the S1pr2-expressing cells indicated by the authors express Fcrl5 since Fcrl5 does not encircle the indicated cell. The authors also do not quantify their images. While the authors do see a difference between the populations following in vivo labeling, it is not clear why the CD45+ population among the Fcrl5+ cells have a higher staining intensity than the Cd23+ cells. It is expected that cells that are exposed to circulation would have a similar staining intensity. Therefore, it is possible that there may be a technical issue with this data. Finally, it is not clear whether the results in figure 6 were repeated with several of the plots only having three mice per group limiting the conclusions that can be drawn from this data.

Reviewer #1 (Public Review):

This manuscript made use of a biologically realistic neuronal network model of cortico-basal ganglia-thalamic (CBGT) circuits and a cognitive drift-diffusion model (DDM) to account for both behavioural and functional neuroimaging (fMRI) data and to understand how change in reward contingency in the environment can affect different decision dynamics. They found that the rate of evidence accumulation was most affected, allowing explorative behaviour with a lower drift rate during likely contingency change and exploitative behaviour with a higher drift rate when contingency was likely similar. The multi-pronged approach presented in the manuscript is commendable. The biophysical model was sufficiently realistic with varying ramping firing rates of spiny projection neurons linked to the varying drift rates in the DDM. However, there are a few concerns regarding this work.

The model's cortical neurons had no contralateral encoding, unlike their neuroimaging data. Another concern with this work is that it was unclear why the spiking neuronal network model with so many model parameters was used to account for coarse-scale fMRI data - a simple firing-rate neural population model would perhaps do the work. Moreover, the activity dynamics of the fMRI were not shown. It would have been more rigorous to show the fMRI (BOLD) signals in different (particularly CBGT) brain regions and compare that with the CBGT model simulations.

The association between classier uncertainty and drift rate (by participants) was an order of magnitude difference between the simulated and actual participants (compare Figure 2E with Figure 4B). There was also a weak effect on human reaction times (Supp. Fig. 2).

There were only 4 human participants that performed the experiment - the results would perhaps be better with more human participants.

For such a complex biophysical computational model, there could perhaps have been more model predictions provided.

Overall, this work is interesting and could potentially be a good contribution in the area of computational modelling and neuroscience of adaptive choice behaviour.

Reviewer #2 (Public Review):

In this paper, Bond et al. build on previous behavioral modelling of a reversal-learning task. They replicate some features of human behavior with a spiking neural network model of cortical basal ganglia thalamic circuits, and they link some of these same behavioral patterns to corresponding areas with BOLD fMRI. I applaud the authors for sharing this work as a preprint, and for publicly sharing the data and code.

While the spiking neural network model offers a helpful tool to complement behavior and neuroimaging, it is not very clear which predictions are specific to this model (and thus dissociate it from, or go beyond, previous work). Thus, the main strength of this work (combining behavior, brain, and in silico experiments) is not fully fleshed out and could be stronger in the conclusions we can draw from them.

It would be helpful to know more about which features of the spiking NN model are crucial in precisely replicating the behavioral patterns of interest (and to be more precise in which behaviors are replicated from previous work with the same task, vs. which ones are newly acquired because the task has changed - or the spiking CBGT model has afforded new predictions for behavior). Throughout, I am wondering if the authors can compare their results to a reasonable 'null model' which can then be falsified (e.g. Palminteri et al. 2017, TICS); this would give more intuition about what it is about this new CBGT model that helps us predict behavior.

The same question about model comparison holds for the behavior: beyond relying on DIC score differences, what features of behavior can and cannot be explained by the family of DDMs?

Reviewer #1 (Public Review):

In this manuscript, Modi et al present a novel method to analyze brain oscillations. Traditional approaches are typically based on analyzing spectral features on individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). The authors take a different, multivariate, approach to simultaneously analyze interactions between multiple oscillations. This is a better way to study dynamics interactions in a complex system than the more traditional 'reductionist' approach and, so far, few methods exist that allow such multivariate analysis of neural oscillations. The method is well demonstrated in the paper, including several application cases. Several aspects of the results need to be better characterized, a clear discussion of the caveats and limitations of the method is lacking and the advantages over existing methods need to be outlined more clearly. Provided these issues are corrected I believe this would be an important contribution to the field that may have multiple applications.

Reviewer #2 (Public Review):

Modi and colleagues describe a multivariate framework to analyze local field potentials, which is specifically applied to CA1 data in this work. Multivariate approaches are welcome in the field and the effort of the authors should be appreciated. However, I found the analyses presented here are too superficial and do not seem to bring new insights into hippocampal dynamics. Further, some surrogate methods used are not necessarily controlling for confounding variables. These concerns are further detailed below.

1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not recorded from hippocampus LFPs. Thus, the fact that you filter the signal in the same frequency range matching cortical spindles does not mean you are analyzing spindles. The terminology, therefore, is misleading. I would recommend the authors to change spindles to "beta", which at least has been reported in the hippocampus, although in very particular behavioral circumstances. However, one must note that the presence of power in such bands does not guarantee one is recording from these oscillations. For example, the "fast gamma" band might be related to what is defined as fast gamma nested in theta, but it might also be related to ripples in sleep recordings. The increase of "spindle" power in sleep here is probably related to 1/f components arising from the large irregular activity of slow wave sleep local field potentials. The authors should avoid these conceptual confusions in the manuscript, or show that these band power time courses are in fact matching the oscillations they refer to (for example, their spindle band is in fact reflecting increased spindle occurrence).

2. The shuffling procedure to control for the occupancy difference between awake and sleep does not seem to be sufficient. From what I understand, this shuffling is not controlling for the autocorrelation of each band which would be the main source of bias to be accounted for in this instance. Thus, time shifts for each band would be more appropriate. Further, the controls for trial durations should be created using consecutive windows. If you randomly sample sleep bins from distant time points you are not effectively controlling for the difference in duration between trial types. Finally, it is not clear from the text if the UMAP is recomputed for each duration-matched control. This would be a rigorous control as it would remove the potential bias arising from the unbalance between awake and sleep data points, which could bias the subspace to be more detailed for the LFP sleep features. It is very likely the results will hold after these controls, given it is not surprising that sleep is a more diverse state than awake, but it would be good practice to have more rigorous controls to formalize these conclusions.

3. Lots of the observations made from the state space approach presented in this manuscript lack any physiological interpretation. For example, Figure 4F suggests a shift in the state space from Sleep1 to Sleep2. The authors comment there is a change in density but they do not make an effort to explain what the change means in terms of brain dynamics. It seems that the spectral patterns are shifting away from the Delta X Spindle region (concluding this by looking at Fig4B) which could be potentially interesting if analyzed in depth. What is the state space revealing about the brain here? It would be important to interpret the changes revealed by this method otherwise what are we learning about the brain from these analyses? This is similar to the results presented in Figure 5, which are merely descriptions of what is seen in the correlation matrix space. It seems potentially interesting that non-REM seems to be split into two clusters in the UMAP space. What does it mean for REM that delta band power in pyramidal and lm layers is anti-correlated to the power within the mid to fast gamma range? What do the transition probabilities shown in Figures 6B and C suggest about hippocampal functioning? The authors just state there are "changes" but they don't characterize these systematically in terms of biology. Overall, the abstract multivariate representation of the neural data shown here could potentially reveal novel dynamics across the awake-sleep cycle, but in the current form of this manuscript, the observations never leave the abstract level.

Reviewer #3 (Public Review):

Modi et al. developed a novel data-driven computational framework to investigate interactions between multiple brain oscillations and validated this approach in hippocampal CA1 utilizing well-studied changes in oscillations across CA1 layers. This approach provides a new way to investigate complex interactions between diverse neural oscillations during different behaviors. In contrast to standard approaches that classify LFP recordings into a few different oscillatory states which simplify patterns in the LFP, this approach maps a complex state space. The essential idea behind the method is novel and interesting with the potential to expand to other studies of other brain regions or interactions between regions. The authors provide a comprehensive analysis showing how this state space relates to traditional oscillatory states (like delta, theta, and gamma). Among the reported results, it is sometimes unclear what is a validation of their approach versus a novel scientific finding (in the context of the larger literature) and the significance of the finding. Although the overall results seem convincing, the paper is a lacking a demonstration that shows why this approach is of high physiological significance. Furthermore, more evidence showing the specific advantages of using this method in LFP data from a single CA1 layer would make this approach more readily adoptable for the community.

Major concerns:<br /> 1. My primary concern is to provide clear evidence that this approach will provide key insights of high physiological significance, especially for readers who may think the traditional approaches are advantageous (for example due to their simplicity). I think the authors' findings of distinct sleep state signatures or altered organization of the NLG3-KO mouse could serve this purpose. However, right now the physiological significance of these results is unclear. For example, do these sleep state signatures predict later behavior performance, or is altered organization related to other functional impairments in the disease model? Do neurons with distinct sleep state signatures form distinct ensembles and code for related information?<br /> 2. For cells with different mean firing rates during exploration: is that because they are putative fast-spiking interneurons and pyramidal cells? From the reported mean firing rates, I think some of these cells are interneurons. Since mean firing rates are well known to vary with cell type, this should be addressed. For example, the sleep state signatures may be distinct for different putative pyramidal cells and interneurons. This would be somewhat expected considering prior work that has shown different cell types have different oscillatory coupling characteristics. I think it would be more interesting to determine if pyramidal cells had distinct sleep state signatures and, if so, whether pyramidal cells from the same sleep state signature have similar properties like they code for similar things or commonly fire together in an ensemble. It seems the number of cells in Fig. 8 may be limited for this analysis. The authors could use the hc-11 data in addition, which was also tested in this work.<br /> 3. Example traces are needed to show how LFPs change over the state-space. Example traces should be included for key parts of the state-space in Figures 2 and 3.<br /> 4. What is the primary rationale for 200ms time bins? Is this time scale sufficient to capture the slow dynamics of delta rhythm (1-5Hz) with a maximum of 1s duration?<br /> 5. Since oscillatory frequency and power are highly associated with running speed, how does speed vary over the state space. Is the relationship between speed and state-space similar to the results of previous studies for theta (Slawinska and Kasicki, Brain Res 1998; Maurer et al, Hippocampus 2005) and gamma oscillations (Ahmed and Mehta J. Neurosci 2012; Kemere et al PLOS ONE 2013), or does it provide novel insights?<br /> 6. The separation of 9 states (Fig. 6ABC) seems arbitrary, where state 1 (bin 1) is never visited. I suggest plotting the density distribution of the data in Fig. 2A or Fig. 6A to better determine how many states are there within the state space. For example, five peaks in such a density plot might suggest five states. Alternately, clustering methods could be useful to determine how the number of states.<br /> 7. The results in Fig. 4G are very interesting and suggest more variation of sub-states during nonREM periods in sleep1 than in sleep2. What might explain this difference? Was it associated with more frequent ripple events occurring in sleep2?<br /> 8. The state transition results in Fig. 6 are confusing because they include two fundamentally different timescales: fast transitions between oscillatory states and slow dynamics of sleep states. I recommend clarifying the description in the results and the figure caption. Furthermore, how can an animal transition between the same sleep state (Fig. 6EF)? Would they both be in a single sleep state?

Reviewer #1 (Public Review):

This manuscript by Bohannon et al. continues a line of work from the Larsson laboratory with fundamental contributions describing the effects of polyunsaturated fatty acids (PUFAs) on the cardiac delayed rectifier potassium channel (IKs) formed by Kv7.1 and KCNE1 heteromers. Although the activating effect of PUFAs on these specific channels has been previously described, the authors now present a novel finding related to PUFAs containing large aromatic tyrosine head groups, showing significant activation effects on IKs, larger than other PUFAs previously studied. A combination of site-directed mutagenesis, electrophysiological and pharmacological approaches are used to dissect the different molecular mechanisms and sites involved in the functional interactions. The main conclusions are: 1) PUFA analogues with Tyr head groups are strong activators of the cardiac IKs channel by action on two previously described mechanisms: left-shift of the voltage-activation curve (by interaction with the voltage-sensor region of Kv7.1); and increased Gmax (by interacting with the pore region). 2) the underlying molecular interactions between PUFA and Kv7.1 are not cation-pi, as shown by the lack of effect of different chemical variations that disrupt the electrostatic surface potential. 3) the presence of electronegative groups on the aromatic ring favors increases in the maximal conductance. 4) the generation of a hydrogen bond with the -OH on the Tyr group seems to selectively impact on IKs voltage dependence of activation. 4) Kv7.1 sites involved in interactions with aromatic PUFAs are similar to the ones previously described for non-aromatic PUFAS, that is: R231 in S4 and K326 in S6. 5) residue T224 is newly identified as a potential site forming a hydrogen bond between the Tyr in the aromatic PUFA and Kv7.1.

The manuscript is very well written and structured. The experiments are solid and lead to mostly well-grounded conclusions. There are some aspects that would benefit from some clarification, which are mainly related to the different effects of the aromatic PUFA variants on IKs voltage dependence and/or conductance.

Reviewer #2 (Public Review):

In the present study, Briana M. Bohannon et al. expand on the study of the effect of Polyunsaturated fatty acids (PUFAS) on Iks (KV7.1 + KCNE1), a delayed rectifier potassium channel of critical relevance in cardiac physiology. PUFAs are amphipathic molecules that activate IKs channels by interacting with positively charged residues on the voltage sensor domain and in the channel's pore. The authors aim to characterize the molecular mechanisms behind the Iks activation by PUFA analogs that contains a tyrosine head group instead of the carboxyl or sulfonyl group present in other PUFAs.

The authors present a well-written manuscript with clear data and well-presented figures. The authors describe the effects of various tyrosine-PUFA analogs and unveil the mechanistic nature of their interactions with the channel. The focus is the N -(alpha-linolenoyl) Tyrosine (NALT), a potent activator by shifting the channel G-V by more than 50mV facilitating the opening of the channel, although the authors tested other tyrosine-PUFA analogs. Remarkably, the hydroxyl group in the tyrosine head is essential to shift the voltage-dependence of activation due to an H-bond with a threonine from the S3-S4 linker that helps coordinate the PUFA together with an electrostatic interaction with arginine in the S4. Furthermore, to test whether the aromatic ring from the tyrosine had a role in the interaction, the authors took a fascinating and exciting approach by modifying it and making the ring more electronegative by adding negatively charged atoms. Interestingly, they discovered that an electronegative-modified aromatic PUFA could increase the channel's conductance, an effect mediated by a specific interaction with a Lysine at the top of the S6 helix.

Although the question addressed in the manuscript is fascinating due to the possible use of these tyrosine-PUFA analogs as IKs modulators, the presented work is very mechanistic and specialized. While the effect of tyrosine-PUFA analogs is robust, the authors could improve the story by highlighting their interest in them and discussing whether they have potential therapeutic uses.

Due to the relevance of IKs currents in cardiac physiology and Long QT syndrome, the discovery and characterization of activators are highly relevant. The present manuscript presents a group of potent IKs channel activators that have the potential to impact the cardiac physiology field dramatically if they can perform under pathophysiological conditions or in the presence of disease-causing mutations.

Reviewer #3 (Public Review):

Bohannon and colleagues demonstrate that aromatic PUFA analogues positively modulate delayed rectifier potassium channel (Iks) currents, identifying new compounds that could be useful for the treatment of long QT syndrome. The data suggest that aromatic PUFA analogues have two modulatory effects that occur by distinct mechanisms involving hydrogen bonds and ionic interactions. However, the exact determinants of these molecular interactions remain unclear.

Strengths of the study include the following:<br /> 1) By examining a large panel of aromatic PUFA analogues, the study provides a thorough understanding of the relationship between the structure of these analogues and the modulatory effect. Of note, these aromatic PUFA analogues are more efficacious than previously characterized PUFAs such as DHA and N-AT. This knowledge will be important for the design of PUFA analogues for the modulation of IKs current, which could be a strategy for the treatment of long QT syndrome.<br /> 2) By examining the effect of mutations previously shown to disrupt two mechanisms of PUFA modulation, the results suggest that aromatic PUFAs act through the same mechanisms. Furthermore, the effects of the different analogues shed light on the determinants of these binding sites such as the presence of additional hydrogen bonds and electrostatic interactions between the aromatic PUFAs and ion channels.

One limitation of the study is that the structure-activity relationships and effects of the mutations do not provide a complete molecular understanding of how the aromatic PUFA analogues are interacting with the channel. This understanding will require additional studies to examine PUFA analogue binding combined with more extensive mutagenesis. Specifically, the model in Figure 5 suggests that the effect of aromatic PUFAs on the voltage dependence of activation depends on an electrostatic interaction with R231 and a hydrogen bond interaction possibly with T224. Similarly, the effect on channel conductance depends on an electrostatic interaction with K326 through the carboxylate anion of the aromatic PUFA as well as an additional electrostatic interaction with some other part of the protein. It is unclear what residues mediate these interactions. Additionally, the authors propose that T224 is forming a hydrogen bond interaction with the hydroxyl group of NALT, but there appears to be a relatively similar effect of the T224V mutation on NAL-phe, only that the spread in the data makes this effect statistically insignificant. Therefore, the conclusion that T224 mediates NALT action by forming a hydrogen bond with the hydroxyl group (a chemical moiety that is absent in NAL-phe) is not fully supported by the data. A structural model to indicate that T224 is well-positioned to form a hydrogen bond with NALT when it is also interacting with R231 would strengthen this model.

Reviewer #1 (Public Review):

In this study the authors investigate whether a presumably allosteric P2RX7 activating compound that they previously discovered reduces fibrosis in a bleomycin mouse model. They chose this particular model as publicly available mRNA data indicate that the P2XR7 pathway is downregulated in idiopathic pulmonary fibrosis patients compared to control individuals. The authors first demonstrate that two proxies of lung damage, Ashcroft score and collagen fibers, are significantly reduced in the bleomycin model on HEI3090 treatment. Additional data implicate specific immune cell infiltrates and cytokines, namely inflammatory macrophages and damped release of IL-17A, as potential mechanistic links between their compound and reduced fibrosis. Finally, the researchers transplant splenocytes from WT, NLRP3-KO, and IL-18-KO mice into animals lacking the P2XR7 receptor to specifically ascertain how the transplanted splenocytes, which are WT for P2XR7 receptor, respond to HEI3090 (a P2XR7 agonist). Based on these results, the authors conclude that HEI3090 enhanced IL-18 production through the P2XR7-NLRP3 inflammasome axis to dampen fibrosis.

These findings could be interesting to the field, as there are conflicting results as to whether NLRP3 activation contributes to fibrosis and if so, at what stage(s) (e.g., acute damage phase versus progression). However, major weaknesses of the study include inconsistent and small effect sizes in key outcomes used to measure fibrosis, small animal cohorts that do not empower results, and lack of key experimental controls. For example, damage indicators for the vehicle-treated mice transplanted with splenocytes of various genetic background are markedly different, and there are no statistical tests of these effects because the data are presented as separate graphs. Moreover, the fundamental assumption that HEI3090 acts specifically through the P2XR7 pathway in this model is questionable, as P2XR7 knockout mice are not included in the initial key experiments. These issues must be addressed as stimulating an inflammasome response might lead to pathogenic inflammation, which could counterproductively exacerbate fibrosis in the clinic and harm people.

Experimental concerns:

1. Ashcroft method quantification throughout is outdated and prone to bias. The methods describing quantification are lacking, and only include a citation: there should be mention of researcher blinding, etc. In general, please re-quantify using an automated classifier, and consider staining for additional markers of lung damage that are appropriate in the field.

2. For Figure 2, P2XR7 knockout mice, and an additional P2XR7 activator, should be included (e.g, A74003, AZ10606120, others), to support the hypothesis that HEI3090 acts through this pathway to alleviate fibrosis. Moreover, these data are especially important as the author's conclusions are directly opposed to a previous study demonstrating that the P2XR7 receptor is required for inflammation/fibrosis in this model system (PMID: 20522787). Two-way ANOVA or similar statistical tests on all groups should be examined to see whether genetic knockout of this DAMP receptor alone is protective or exacerbates fibrosis (e.g., comparing the vehicle-alone groups), and whether compounds exert a specific effect through this receptor.

3. Fig. 3A: Please show the individual IFN/IL-17A plots in the supplement, as a ratiometric result might mask variance. Moreover, please conduct a statistical test for the outlier in the HEI3090 condition (to potentially remove it), as this sole data point might skew the entire mean, causing the observed statistical difference between means despite a very modest change. If the results are still significant, please comment on effect size.

4. Fig. 3: How is IL-17A measured and what is the abbreviation GMFI?

5. Fig. 3E: It's unclear how the left and right figures align-it looks like the gates are 45.8 % and 25 %, respectively, but the means on the right are between 2-3%. Also, is this effect size (2 versus 3 %) significant biologically?

6. For Figure 4B-G, the Ashcroft scores for the vehicle mice treated with HEI3090 are at entirely different starting points following adoptive transfer of cells with different genetic background. In Fig. 1, WT mice have starting scores of around 3 following the induction of fibrosis, with a modest decrease of about 0.8 following HEI3090 treatment. Here, there is a much greater effect of the genetic background itself rather than the treatment, with the IL-18 knockout mice having a much lower baseline "vehicle" score (~1) compared to Fig. 1F (both of which are 14 day treatments). In fact, adoptive transfer of WT splenocytes start at a baseline of 1.8 here, which is much lower than Fig. 1F, and NLRP3-KO splenocytes score nearly the same as Fig. 1F following BLM treatment, with a modest reduction following treatment with HEI3090. Please analyze all of these groups together with appropriate multiple hypothesis testing to examine the effect of the genetic background, and please comment on why IL-18-knockout splenocytes might be protective at vehicle baseline while NLRP3-knockout splenocytes might exacerbate the phenotype at vehicle baseline.

7. The variance on Supplemental Figure 5C is quite large. These data have a decrease in mean Ashcroft score between untreated and HEI3090 treatment of around 0.8, which is similar to the WT mice in Figure 1. This is very concerning, as the underlying assumption is that KO of the protein required for HEI3090's on-target effect would completely ablate response, and this would be required for the subsequent adoptive transfer experiments in Figure 4. Please conduct power analysis, comment, and provide additional evidence (other than Ashcroft score).

8. Figure 4: Should quantify collagen fibers or have an additional quantitative metric for lung damage, as in Fig. 2C/J.

9. Figure 4: Should group the quantification of C/E/G and perform a 2-way Anova to assess effects of genetic background versus treatment.

10. Fig. 4H, Supplemental Fig. 6D: Is it reasonable to expect differences in IL-1beta and IL-18 in sera compared to in lung tissue itself?