Reviewer #2 (Public Review):

The paper by Pal et al. examines the role of Nrp1 in organ-specific permeability response to VEGF. The subject is certainly interesting, but there are a number of significant methodological problems that make data evaluation rather problematic. In particular, lung endothelial cells are used to assess the effectiveness of Nrp1 knockout when experiments focus on different organs; small number of data points (as small as 2 or 3) are used to claim statistically significant differences; obvious data scatter is not commented on and seems ignored; key reagents (anti-Nrp1 Ab) are not well characterized, a proposed model is not verified in vitro, etc. Some of these issues are outlined in detail below, but the list of problems is much longer than this.

(1) Intradermal injection of anti-Nrp1 Ab: I am puzzled by this experiment: Will Ab presence be limited locally or is there a systemic distribution? This needs to be verified.

(2) What does anti-Nrp1 Ab actually do? Does it block VEGF binding? Induces Nrp1 and VEGFR2 endocytosis?

(3) How does i.v. injection of anti-Nrp1 Ab affect permeability in different organs?

(4) Effect of endothelial Nrp1KO: Since the authors examine organ-specific effects of Nrp1, it seems illogical to assess its expression in the lung as a measure of KO as KO efficiency may differ organ by organ. Immunocytochemistry is not particularly quantitative and prone to selection bias. I'd suggest using EC bulk RNAseq from different organs to confirm the magnitude of the knockout in different beds.

(5) Figures 1B and 2B show profoundly different levels of Nrp1 KO in lung ECs. Were different mouse strains used in Figure 1 and Figure 2 experiments? This may well explain the differences the authors have observed.

(6) Supplementary Figure 2: why is there no leakage of 10kD dextran in the heart in response to VEGF when there is an increase in the 70kD dextran leakage? That does not seem possible. Further, the authors observed no significant increase in 70kD dextran leakage after VEGF in the skeletal muscle. That also seems very unlikely and flies against experience of many labs in the field.

(7) Since the authors think that peri-vascular cell Nrp1 expression accounts for organ-specific Nrp1 effects, this should be studied and examined in an in vitro co-culture model.

(8) Quantification: a lot of quantifications- of Nrp1 expression level, VE-cadherin Y685 phosphorylation, etc. are done on the basis of immunocytochemistry. This really is not a quantitative technique and is prone to numerous artifacts. The data should be at least confirmed by whole-tissue Westerns. I am also puzzled by small numbers of samples. If each dot on a graph represents an individual data point, how do authors get a p<0.5 value with an N of 3? (for example Figure 5B, but there are other examples). Also, in Figure 4F data scatter is quite enormous. This is either an experimental problem or, more likely, there is a biological message here - the tissue is not uniform. In any case, I do not see how one gets a significant result here. Figures 5B and 5C have a similar problem while Figure 5D seems to be based on only two data points?

Reviewer #3 (Public Review):

Summary:

Pal et al. provide valuable evidence supporting distinct vascular bed-specific VEGF-A mediated vascular permeability function of Neuropilin-1 (NRP1) in adult mice. Using a suite of genetic mice models and state-of-the-art vascular permeability assays the authors demonstrate that ear skin vasculature of EC-specific NRP1 adult knockout mice is hypersensitive to VEGF-A mediated high-molecular weight dye leakage from venules, as opposed to back skin and tracheal vasculature where EC-specific NRP1 loss had a more classical negative effect on permeability. Interestingly, both whole organism KO of NRP1 and a blocking antibody treatment, attenuated VEGF-A mediated permeability in ear skin and had the usual attenuation of permeability phenotype in back skin and tracheal vasculature. Using a pericyte promoter specific reporter mice line, the authors characterize NRP1 expression in the vascular beds of the ear dermis and back skin and conclude that NRP1 expression is higher in perivascular cells in the ear dermis as opposed to back skin vasculature, thus indicating a juxtracrine NRP1-VEGFR2 signaling model in adult mice. Further, they use a Vegfr2 phosphosite mutant homozygous mice model in the background of NRP1 iECKO to find the hypersensitivity to VEGF-A stimulation in ear skin is abrogated and therefore, prove the juxtracrine NRP1 control of VEGFR2 mediated downstream signaling leading to vascular permeability. Further, they successfully show distinctive vascular bed-specific results as above using a well-characterized VE-Cadherin Y685 antibody staining which corresponds to vascular leakage downstream of VEGF-A/VEGFR2 signaling in ear dermis and back skin vascular beds.

Strengths:

The question of the in vivo role of NRP1 in VEGF-A-induced hyper-permeability is an unresolved one and the elegant use of genetic mice models to demonstrate the phenotypes is valuable to the field. The organotypic differences observed in vascular permeability upon VEGF-A treatment in ear skin versus back skin and tracheal vasculature are solid. The subsequent investigation to validate heightened VEGFR2 signaling in ear dermis downstream of VEGF-A stimulation using Vegfr2 Y949F mice, VEC Y685 antibody, and pPLCγ antibody is also very convincing.

Weaknesses:

The mechanism proposed by the authors by which EC-specific loss of NRP1 caused hypersensitivity to VEGF-A in ear dermis is through elevated juxtracrine signaling of NRP1 expressed in pericytes in trans binding and retaining VEGFR2 on the cell surface of ECs to sustain downstream signaling for longer time, in corroboration to earlier findings in Koch et al., 2014, where NRP1 was studied in the context of tumor angiogenesis. To support their claim, the authors stain the ear dermis and back skin vasculature of Pdgfrb-GFP reporter mice, with NRP1 and CD31 antibodies and find out that ear skin vasculature has higher perivascular cells as opposed to back skin vasculature. While this is a good experiment to prove the above point, there are no functional experiments to support this model.

Overall, although the paper presents very useful findings in the field of NRP1-VEGFR2 biology, and most of the conclusions are well supported by the data, there are a few points if addressed can significantly substantiate the model of juxtracrine signaling proposed by the authors. They are:

(1) It will be important to know if the perivascular to vascular NRP1 expression (such as in Figure 3B) increases further in ear skin vasculatures of NRP1 iECKO mice compared to otherwise WT mice.

(2) Does knocking out NRP1 in pericytes attenuate the VEGF-A mediated hyperpermeability observed in ear skin of NRP1 iECKO mice (similar to experiments in 1C, 2C)?

(3) What is the status of VEGFR2 expression in ECs of ear skin and back skin of NRP1 iECKO and NRP1 iKO mice? This experiment is a proof-of-concept and is not essential to prove the point of juxtracrine NRP1 signaling since downstream readouts - pPLCγ and VEC Y685 staining have already been shown to correlate in the ear dermis.

Reviewer #1 (Public Review):

Summary:

In this article, Kremser et al set off to explore how local interactions between cells can drive pattern formation by focusing on the French flag problem whereby an initially homogeneous system breaks axial symmetry to form three distinct regions of different cell fates. The authors use a cellular automata model together with evolution searches on possible rules that determine cell state and tissue level patterning. It is assumed that three cell states are possible and that at each time iteration each cell updates its fate according to the current state of itself and its neighbours. The authors use a computational procedure based on evolution algorithms to identify "fit" update rules that can successfully drive patterning into three distinct domains and go on to provide insights with regards to the function of these rules as well as their properties such as robustness and patterning dynamics. The article is generally well-written, the results seem solid, and the analysis and methods are thorough and generally well-explained. A main concern is the lack of connection between the biology that motivated the analysis and the results, this could be improved in the discussion by making the methods somewhat more concise to allow space to make links back to potential biological mechanisms when the results are presented. We raise some general points and some more specific questions and suggestions for clarification below that we hope will help improve the MS and make it more accessible to a wider audience.

General points:

• Although the authors motivate their work on the premise that biological patterns at the tissue level often are driven by local cell-cell interactions, by the end of the analysis any possible connection to the underlying biology is lost. For example, it would have been useful to discuss how the rules that evolved to dominate the patterning process in the results section could be implemented by cells. Is there a connection that could be made back to Notch signalling and its multiple ligands or to morphogens that diffuse only locally? Would the large number of rules possible in the cellular automata context reflect transcriptional feedback? This is an important point to bring the work "home". At the moment, it feels like a nice computational analysis of cellular automata but the links to the systems that motivate the work are lost in the process.

• When growth is considered (p.14-15) a discussion of timescales seems pertinent. Often patterning takes place at a timescale faster than cell division so the system could be allowed to reach a steady state before a new division event takes place. What are the time scales of updating the phenotype compared with the time scales of division in the model and in relevant biological systems? How would different limiting cases impact conclusions, e.g. new cells added and pattern allowed to reach steady state before more growth versus cells added while patterning dynamics are still updating?

• An interesting question is whether certain elements of rules (out of the 27 possible elements for the system with 3 states) are more or less likely to appear together in an evolved final rule. This may give a mechanistic understanding of what combinations of elements are likely to drive the optimal pattern and which combinations are avoided altogether.

Reviewer #2 (Public Review):

Summary:

In this paper, the authors seek to identify strategies that can be used to generate robust one-dimensional large-scale patterns through the sequential application of only local, unchanging, space-independent rules. This is an important general question in developmental biology.

Strengths:

The authors do a nice job of laying out the problem, which they explore through cellular automaton (CA) modeling. The modeling framework is well described, as are the methods used for computational identification of effective (most "fit") strategies. As many biologists are unfamiliar with CA models, the clarity of description offered by these authors is especially important, as is the attention that was paid to useful visualization of results.

Ultimately, the authors use their approach to converge on certain generic strategies for achieving robust patterns. In the case when there are only three states (no hidden or transient states) available to cells, they rationalize the consensus strategy that emerges to involve a combination of "sorting" and "bulldozer" modules, which are relatively easy to rationalize. In cases involving a fourth state, a more complicated set of strategies arise and are considered.

As a pure modeling paper, I find the work to be very well done, and the conclusions are well supported by the data and analyses. In terms of the long-term importance of this approach to biologists studying pattern formation, I see this paper as primarily laying a foundation for taking the next step, which is moving into two (or three dimensions). Clearly, the complexity of rules becomes much greater, but one may expect some big qualitative differences to show up in higher dimensions, where simple strategies like sorting and bulldozing cannot work quite as simply. It will be interesting to see where this leads.

Weaknesses:

Ultimately, the relevance of this work to biology rests with its ability to provide insight into important biological problems. In terms of explaining the challenging nature of generating long-range patterns using short-range rules, I think the authors do a good job. However, they could do a better job of relating the results of the work back to biology. For example, are there examples of "sorting module" and "bulldozer module" behavior in biology? Could they be involved in explaining actual biological patterns?

It also would have been helpful for the authors to generalize more about the way in which their CA rules achieve global patterns with other patterning mechanisms. For example, in a Wolpert positional information model, patterning information is distributed over space in a steady-state gradient. In the CA model, no information spreads more than one cell at any one time point, but over time information still spreads, so in a sense a stationary spatial gradient has been traded for a moving spatial discontinuity. Because the discontinuity moves without decrement, any stationary state ends up being determined by the boundaries of the system, which goes a long way to explaining the robustness they observe, as well as why the result is quite sensitive to growth (which keeps changing the boundary).

Reviewer #2 (Public Review):

In this study, Torcq and colleagues make carefull observations of the cellular morphology of haemogenic endothelium undergoing endothelial to haematopoietic transition (EHT) to become stem cells, using the zebrafish model. To achieve this, the used an extensive array of transgenic lines driving fluorescent markers, markers of apico-basal polarity (podocalixin-FP fusions) or tight junction markers (jamb-FP fusions). The use of the runx truncation to block native Runx1 only in endothelial cells is an elegant tool to achieve something akin to tissue-specific deletion of Runx1. Overall, the imaging data is of excellent quality. They demonstrate that differences in apico-basal polarity are strongly associated with different cellular morphologies of cells undergoing EHT from HE (EHT pol- and EHT pol+) which raises the exciting possibility that these morphological differences reflect heterogeneity of HE (and potentially HSCs, but this is not addressed in this manuscript) at a very early stage. They then overexpress a truncated form of Runx1 (just the runt domain) to block Runx1 function and show that more HE cells abort EHT and remain associated with the embryonic dorsal aorta. The revised version identifies pard3ab as differentially distributed in dtRunx mutants and correlates that distribution with a potential regulatory role on cell polarity. No direct evidence for their role in EHT is presented.

The manuscript has now been streamlined and reference to figures made much clearer. It provides for a clearer reading, and clearly a well thought out discussion of HE, polarity and the regulation of the EHT process. The evidence for the different cellular morphologies of cells undergoing EHT is strong, and the main claim that tuning apico-basal polarity and junctional recycling underlie morphological complexity of EHT (rather than of HSCs) is well supported by the data.

Reviewer #1 (Public Review):

Summary

The authors investigated the antigenic diversity of recent (2009-2017) A/H3N2 influenza neuraminidases (NAs), the second major antigenic protein after haemagglutinin. They used 27 viruses and 43 ferret sera and performed NA inhibition. This work was supported by a subset of mouse sera. Clustering analysis determined 4 antigenic clusters, mostly in concordance with the genetic groupings. Association analysis was used to estimate important amino acid positions, which were shown to be more likely close to the catalytic site. Antigenic distances were calculated and a random forest model used to determine potential important sites.

This revision has addressed many of my concerns of inconsistencies in the methods, results and presentation. There are still some remaining weaknesses in the computational work.

Strengths

(1) The data cover recent NA evolution and a substantial number (43) of ferret (and mouse) sera were generated and titrated against 27 viruses. This is laborious experimental work and is the largest publicly available neuraminidase inhibition dataset that I am aware of. As such, it will prove a useful resource for the influenza community.

(2) A variety of computational methods were used to analyse the data, which give a rounded picture of the antigenic and genetic relationships and link between sequence, structure and phenotype.

(3) Issues raised in the previous review have been thoroughly addressed.

Weaknesses:

Some concerns regarding the robustness of the machine learning model and potential overfitting remain.

Reviewer #2 (Public Review):

Summary:

The authors characterized the antigenicity of N2 protein of 43 selected A(H3N2) influenza A viruses isolated from 2009-2017 using ferret and mice immune sera. Four antigenic groups were identified, which the authors claimed to be correlated with their respective phylogenic/ genetic groups. Among 102 amino acids differed by the 44 selected N2 proteins, the authors identified residues that differentiate the antigenicity of the four groups and constructed a machine-learning model that provides antigenic distance estimation. Three recent A(H3N2) vaccine strains were tested in the model but there was no experimental data to confirm the model prediction results.

Strengths:

This study used N2 protein of 44 selected A(H3N2) influenza A viruses isolated from 2009-2017 and generated corresponding panels of ferret and mouse sera to react with the selected strains. The amount of experimental data for N2 antigenicity characterization is large enough for model building.

Weaknesses:

One weakness is the use of double-immune ferret sera (post-infection plus immunization with recombinant NA protein) or mouse sera (immunized twice with recombinant NA protein) to characterize the antigenicity of the selected A(H3N2) viruses. Conventionally, NA antigenicity is characterized using ferret sera after a single infection. Repeated influenza exposure in ferrets has been shown to enhance antibody binding affinity and may affect the cross-reactivity to heterologous strains (PMID: 29672713). The increased cross-reactivity is supported by the NAI titers shown in Table S3, as many of the double immune ferret sera showed the highest reactivity not against its own homologous virus but to heterologous strains. In response to the reviewer's comment, the authors agreed the use of double-immune ferret sera may be a limitation of the study.

Another weakness is that the authors used the newly constructed a model to predict antigenic distance of three recent A(H3N2) viruses but there is no experimental data to validate their prediction (eg. if these viruses are indeed antigenically deviating from group 2 strains as concluded by the authors). Leaving out data from some strains for testing is a useful check, but due to phylogenetic correlations in the data the generalizability of the machine learning is not guaranteed.

Reviewer #3 (Public Review):

Summary:

This paper by Portela Catani et al examines the antigenic relationships (measured using monotypic ferret and mouse sera) across a panel of N2 genes from the past 14 years, along with the underlying sequence differences and phylogenetic relationships. This is a highly significant topic given the recent increased appreciation of the importance of NA as a vaccine target, and the relative lack of information about NA antigenic evolution compared with what is known about HA. Thus, these data will be of interest to those studying the antigenic evolution of influenza viruses. The methods used are generally quite sound, though there are a few addressable concerns that limit the confidence with which conclusions can be drawn from the data/analyses.

Strengths:

-The significance of the work, and the (general) soundness of the methods.<br /> -Explicit comparison of results obtained with mouse and ferret sera

Weaknesses:

- Machine learning analyses neither experimentally validated nor shown to be better than simple, phylogenetic-based inference.

Reviewer #2 (Public Review):

Summary:

The manuscript by Kandoi et al. describes a new 3D retinal organoid model of a mono-allelic copy number variant of the rhodopsin gene that in a patient led to autosomal dominant retinitis pigmentosa. The evidence provided here is relatively strong that the rod photoreceptor phenotype observed in an adult patient with RP in vivo is similar to that phenotype observed in human stem cell-derived retinal organoids. Increases in RHO expression were detected by qPCR, RNA-seq, and IHC support this phenotype. Importantly, the amelioration of photoreceptor rhodopsin mislocalization and related defects using the small molecule drug photoregulin demonstrates an important potential clinical application.

Strengths:<br /> - Retinal organoids derived from patient with adRP.<br /> - RHO mislocalization could explain the phenotype in patients.

Weaknesses:

- Organoids at 300 days do not show PR loss.

Additional minor weaknesses

- Bulk RNAseq methods require greater detail, particularly with respect to how total or mRNA was purified, how was it quantified for concentration and integrity (i.e. Nanodrop, Tape station, Bioanalyzer), what reagents were used for library preparation and how many reads were analyzed per sample.

- Fig. 4. The levels of RHO visualized in tissue sections (panels A-C) does not seem to match the general levels shown for the western blots (panel D) which appear to be far higher in RM western blot samples than in the IHC images. Please clarify why there is such a difference.

- Line 186: by what criteria are the authors able to state that " there were no clear visible anatomical changes in apical-basal retinal cell type distribution (data not shown)". Was this based on histological staining with antibodies, nuclear counter-staining or some other evaluation?

Reviewer #3 (Public Review):

This manuscript reports a novel pedigree with four intact copies of RHO on a single chromosome which appears to lead to overexpression of rhodopsin and a corresponding autosomal dominant form of RP. The authors generate retinal organoids from patient- and control-derived cells, characterize the phenotypes of the organoids, and then attempt to 'treat' aberrant rhodopsin expression/mislocalization in the patient organoids using a small molecule called photoregulin 3 (PR3). While this novel genetic mechanism for adRP is interesting, the organoid work is not compelling. There are multiple problems related to the technical approaches, the presentation of the results, and the interpretations of the data. I will present my concerns roughly in the order in which they appear in the manuscript and will separate them into 'major' and 'minor' categories:

Major concerns:<br /> (1) Individual human retinal organoids in culture can show a wide range of differentiation phenotypes with respect to the expression of specific markers, percentages of given cell types, etc. For this reason, it can be very difficult to make rigorous, quantitative comparisons between 'wild-type' and 'mutant' organoids. Despite this difficulty, the author of the present manuscript frequently present results in an impressionistic manner without quantitation. Furthermore, there is no indication that the investigator who performed the phenotypic analyses was blind with respect to the genotype. In my opinion, such blinding is essential for the analysis of phenotypes in retinal organoids.

To give an example, in lines 193-194 the authors write "we observed that while the patient organoids developing connecting cilium and the inner segments similar to control organoids, they failed to extend outer segments". Outer segments almost never form normally in human retinal organoids, even when derived from 'wild-type' cells. Thus, I consider it wholly inadequate to simply state that outer segment formation 'failed' without a rigorous, quantitative, and blinded comparison of patient and control organoids.

(2) The presentation of qPCR results in Fig. 3A in very confusing. First, the authors normalize expression to that of CRX, but they don't really explain why. In lines 210-211 they write "CRX, a ubiquitously expressing photoreceptor gene maintained from development to adulthood." Several parts of this sentence are misleading or incomplete. First, CRX is not 'ubiquitously expressed' (which usually means 'in all cell types') nor is it photoreceptor-specific: CRX is expressed in rods, cones, and bipolar cells. Furthermore, CRX expression levels are not constant in photoreceptors throughout development/adulthood. So, for these reasons alone, CRX is a poor choice for normalization of photoreceptor gene expression.

Second, the authors' interpretation of the qPCR results (lines 216-218) is very confusing. The authors appear to be saying that there is a statistically significant increase in RHO levels between D120 and D300. However, the same change is observed in both control and patient organoids and is not unexpected, since the organoids are more mature at D300. The key comparison is between control and patient organoids at D300. At this time point, there appears to be no difference control and patient. The authors don't even point this out in the main text.

Third, the variability in number of photoreceptor cells in individual organoids makes a whole-organoid comparison by qPCR fraught with difficulty. It seems to me that what is needed here is a comparison of RHO transcript levels in isolated rod photoreceptors.

(3) I cannot understand what the authors are comparing in the bulk RNA-seq analysis presented in the paragraph starting with line 222 and in the paragraph starting with line 306. They write "we performed bulk-RNA sequencing on 300-days-old retinal organoids (n=3 independent biological replicates). Patient retinal organoids demonstrated upregulated transcriptomic levels of RHO... comparable to the qRT-PCR data." From the wording, it suggests that they are comparing bulk RNA-seq of patient and control organoids at D300. However, this is not stated anywhere in the main text, the figure legend, or the Methods. Yet, the subsequent line "comparable to the qRT-PCR data" makes no sense, because the qPCR comparison was between patient samples at two different time points, D120 and D300, not between patient and control. Thus, the reader is left with no clear idea of what is even being compared by RNA-seq analysis.

Remarkably, the exact same lack of clarity as to what is being compared plagues the second RNA-seq analysis presented in the paragraph starting with line 306. Here the authors write "We further carried out bulk RNA-sequencing analysis to comprehensively characterize three different groups of organoids, 0.25 μM PR3-treated and vehicle-treated patient organoids and control (RC) organoids from three independent differentiation experiments. Consistent with the qRT-PCR gene expression analysis, the results showed a significant downregulation in RHO and other rod phototransduction genes." Here, the authors make it clear that they have performed RNA-seq on three types of sample: PR3-treated patient organoids, vehicle-treated patient organoids, and control organoids (presumably not treated). Yet, in the next sentence they state "the results showed a significant downregulation in RHO", but they don't state what two of the three conditions are being compared! Although I can assume that the comparison presented in Fig. 6A is between patient vehicle-treated and PR3-treated organoids, this is nowhere explicitly stated in the manuscript.

(4) There are multiple flaws in the analysis and interpretation of the PR3 treatment results. The authors wrote (lines 289-2945) "We treated long-term cultured 300-days-old, RHO-CNV patient retinal organoids with varying concentrations of PR3 (0.1, 0.25 and 0.5 μM) for one week and assessed the effects on RHO mRNA expression and protein localization. Immunofluorescence staining of PR3-treated organoids displayed a partial rescue of RHO localization with optimal trafficking observed in the 0.25 μM PR3-treated organoids (Figure 5B). None of the organoids showed any evidence of toxicity post-treatment."

There are multiple problems. First, the results are impressionistic and not quantitative. Second, it's not clear that the investigator was blinded with respect to treatment condition. Third, in the sections presented, the organoids look much more disorganized in the PR3-treated conditions than in the control. In particular, the ONL looks much more poorly formed. Overall, I'd say the organoids looked considerably worse in the 0.25 and 0.5 microM conditions than in the control, but I don't know whether or not the images are representative. Without rigorously quantitative and blinded analysis, it is impossible to draw solid conclusions here. Lastly, the authors state that "none of the organoids showed any evidence of toxicity post-treatment," but do not explain what criteria were used to determine that there was no toxicity.

(5) qPCR-based quantitation of rod gene expression changes in response to PR3 treatment is not well-designed. In lines 294-297 the authors wrote "PR3 drove a significant downregulation of RHO in a dose-dependent manner. Following qRT-PCR analysis, we observed a 2-to-5 log2FC decrease in RHO expression, along with smaller decreases in other rod-specific genes including NR2E3, GNAT1 and PDE6B." I assume these analyses were performed on cDNA derived from whole organoids. There are two problems with this analysis/interpretation. First, a decrease in rod gene expression can be caused by a decrease in the number of rods in the treated organoids (e.g., by cell death) or by a decrease in the expression of rod genes within individual rods. The authors do not distinguish between these two possibilities. Second, as stated above, the percentage of cells that are rods in a given organoid can vary from organoid to organoid. So, to determine whether there is downregulation of rod gene expression, one should ideally perform the qPCR analysis on purified rods.

(6) In Fig. 4B 'RM' panels, the authors show RHO staining around the somata of 'rods' but the inset images suggest that several of these cells lack both NRL and OTX2 staining in their nuclei. All rods should be positive for NRL. Conversely, the same image shows a layer of cells sclerad to the cells with putative RHO somal staining which do not show somal staining, and yet they do appear to be positive for NRL and OTX2. What is going on here? The authors need to provide interpretations for these findings.

Minor concerns:

(1) The writing is poor in many places. Problems include: poor word choice (e.g., 'semi-occasional' is used three times where 'occasional' or 'infrequent' would be better); superfluous use of the definite article in many places (e.g., lines 189-190 "by the light microscopy" should be "by light microscopy"); awkward sentence structures (e.g., lines 208-209: "To equilibrate the data to equivalent the number of photoreceptors in organoids"), opaque expressions (e.g., line 217 "there was a significant ~3 log2 fold change (log2FC)"; why not just say "an ~8-fold change"?); poor proof-reading (Abstract says that 40% of adRP cases are due to mutation in RHO, then the Introduction says the figure is 25%) etc.

(2) The figures are not numbered, which makes it painful for the reviewer to correlate main text call-outs, figure legends, and actual figures. I had to repeatedly count down the list of figures to determine which figure I should be looking at.

(3) In the abstract, the authors suggest that the patient's disease "develops from a dominant negative gain of function" mechanism. I don't agree with this interpretation. Typically 'dominant-negative' refers to an aberrant protein which directly interferes with the function of the normal protein, for example by forming non-functional heterodimers. In the present patient, the disease can be explained by a simple overexpression mechanism, as it has been previously demonstrated in mice that even minimal overexpression of rhodopsin (e.g., ~25% more than normal levels) can led to progressive rod degeneration: PMID: 11222515.

(4) In line 85 the word 'Morphologically' is superfluous and can be deleted.

(5) In the Introduction the authors should more clearly articulate the rationale for using PR3 to treat this patient: because it leads to downregulation of multiple rod genes including RHO. This isn't clearly explained until the Discussion.

(6) The authors mention in several places that PR3 may act via inhibition of NR2E3. Although this was the conclusion of the original publication, the evidence that PR3 acts via Nr2e3 in mice is not solid. The original study (PMID: 29148976) showed that the main effect of PR3 application on mouse retinas is downregulation of numerous rod genes. However, knockout of Nr2e3 in mouse has been shown to have very little effect on rod gene expression, and Nr2e3 mutant rods have largely preserved rod function as demonstrated by scotopic ERGs PMIDs: 15634773, 16110338, 15689355). The primary gene expression defect in Nr2e3 mutant mouse rods is upregulation of a subset of cone genes, a change not observed upon application of PR3 to mouse retinas. For these reasons, I am skeptical that PR3 acts via inhibition of Nr2e3 activity, and I would suggest that the present authors qualify that interpretation.

(7) This mechanistic speculation presented in lines 274-278 is not warranted. Ectopic localization of opsin to the cytoplasmic membrane occurs in a wide range of genetic forms of rod degeneration.

Reviewer #1 (Public Review):

Valk and Engert et al. examined the potential relations between three different mental training modules, hippocampal structure and functional connectivity, and cortisol levels (stress) over a 9-month period. They found that among the three types of mental training: Presence (attention and introspective awareness), Affect (socio-emotional - compassion and prosocial motivation), and Perspective (socio-cognitive - metacognition and perspective taking) modules; Affect training most robustly related to changes in hippocampal structure and function - specifically, CA1-3 subfields of the hippocampus. Moreover, change in intrinsic functional connectivity related to changes in diurnal cortisol release and long-term cortisol exposure. These changes are proposed to result from a combination of factors, which is supported by multivariate analyses showing changes across subfields and training content relate to cortisol changes.

The authors demonstrate that mindfulness training programs are a potential avenue for stress interventions that impact hippocampal structure and cortisol, providing a promising approach to improve health. The data contribute to the literature on plasticity of hippocampal subfields during adulthood, the impact of mental training interventions on the brain, and the link between CA1-3 and both short- and long-term stress changes.

The authors thoughtfully approached the study of hippocampal subfields, utilizing a method designed for T1w images that outperformed Freesurfer 5.3 and that produced comparable results to an earlier version of ASHS. The authors note the limitations of their approaches and provide detailed information on the data used and analyses conducted. The results provide a strong basis from which future studies can expand using computational approaches or more fine-grained investigations of the impact of mindfulness training on cortisol levels and the hippocampus.

Reviewer #1 (Public Review):

The manuscript considers a hierarchical network of neurons, of the type that can be found in sensory cortex, and assumes that they aim to constantly predict sensory inputs that may change in time. The paper describes the dynamics of neurons and rules of synaptic plasticity that minimize the integral of prediction errors over time.

The manuscript describes and analyses the model in great detail, and presents multiple and diverse simulations illustrating the model's functioning. However, the manuscript could be made more accessible and easier to read. The paper may help to understand the organization of cortical neurons, their properties, as well as the function of its particular components (such as apical dendrites).

Reviewer #2 (Public Review):

Neuroscientists often state that we have no theory of the brain. The example of theoretical physics is often cited, where numerous and quite complex phenomena are explained by a compact mathematical description. Lagrangian and Hamiltonian pictures provide such powerful 'single equation'. These frameworks are referred to as 'energy', an elegant way to turn numerous differential equations into a single compact relationship between observable quantities (state variables like position and speed) and scaling constants (like the gravity constant or the Planck constant). Such energy pictures have been used in theoretical neuroscience since the 1980s.

The manuscript "neuronal least-action principle for real-time learning in cortical circuits" by Walter Senn and collaborators describes a theoretical framework to link predictive coding, error-based learning, and neuronal dynamics. The central concept is that an energy function combining self-supervised and supervised objectives is optimized by realistic neuronal dynamics and learning rules when considering the state of a neuron as a mixture of the current membrane potential and its rate of change. As compared with previous energy functions in theoretical neuroscience, this theory captures a more extensive range of observations while satisfying normative constraints. Particularly, no theory had to my knowledge related adaptive dynamics widely observed in the brain (referred to as prospective coding in the text, but is sometimes referred to as adaptive coding or redundancy reduction) with the dynamics of learning rules.

The manuscript first exposes the theory of two previously published papers by the same group on somato-dendritic error with apical and basal dendrites. These dynamics are then related to an energy function, whose optimum recovers the dynamics. The rest of the manuscript illustrates how features of this model fits either normative or observational constraints. Learning follows a combination of self-supervised learning (learning to predict the next step) and supervised learning (learning to predict an external signal). The credit assignment problem is solved by an apical-compartment projecting set of interneurons with learning rules whose role is to align many weight matrices to avoid having to do multiplexing. An extensive method section and supplementary material expand on mathematical proofs and make more explicit the mathematical relationship between different frameworks.

Experts would say that much of the article agglomerates previous theoretical papers by the same authors that have been published recently either in archival servers or in conference proceedings. A number of adaptations to previous theoretical results were necessary, so the present article is not easily reduced to a compendium of previous pre-prints. However, the manuscript is by no means easy to read. Also, there remain a few thorny assumptions (unobserved details of the learning rules or soma-dendrites interactions), but the theory is likely going to be regarded as an important step towards a comprehensive theory of the brain.

Reviewer #1 (Public Review):

The authors start from the premise that neural circuits exhibit "representational drift" -- i.e., slow and spontaneous changes in neural tuning despite constant network performance. While the extent to which biological systems exhibit drift is an active area of study and debate (as the authors acknowledge), there is enough interest in this topic to justify the development of theoretical models of drift.

The contribution of this paper is to claim that drift can reflect a mixture of "directed random motion" as well as "steady state null drift." Thus far, most work within the computational neuroscience literature has focused on the latter. That is, drift is often viewed to be a harmless byproduct of continual learning under noise. In this view, drift does not affect the performance of the circuit nor does it change the nature of the network's solution or representation of the environment. The authors aim to challenge the latter viewpoint by showing that the statistics of neural representations can change (e.g. increase in sparsity) during early stages of drift. Further, they interpret this directed form of drift as "implicit regularization" on the network.

The evidence presented in favor of these claims is concise, but on balance I find their evidence persuasive, at least in artificial network models. This paper includes a brief analysis of four independent experiments in Figure 3, which corroborates the main claims of the paper. Future work should dig deeper into the experimental data to provide a finer grained characterization. For example, in addition to quantifying the overall number of active units, it would be interesting to track changes in the signal-to-noise ratio of each place field, the widths of the place fields, et cetera.

To establish the possibility of implicit regularization in artificial networks, the authors cite convincing work from the machine learning community (Blanc et al. 2020, Li et al., 2021). Here the authors make an important contribution by translating these findings into more biologically plausible models and showing that their core assumptions remain plausible. The authors also develop helpful intuition in Figure 5 by showing a minimal model that captures the essence of their result.

Reviewer #3 (Public Review):

Summary:

Single unit neural activity tuned to environmental or behavioral variables gradually changes over time. This phenomenon, called representational drift, occurs even when all external variables remain constant, and challenges the idea that stable neural activity supports the performance of well-learned behaviors. While a number of studies have described representational drift across multiple brain regions, our understanding of the underlying mechanism driving drift is limited. Ratzon et al. propose that implicit regularization - which occurs when machine learning networks continue to reconfigure after reaching an optimal solution - could provide insights into why and how drift occurs in neurons. To test this theory, Ratzon et al. trained a recurrent neural network (RNN) trained to perform the oft-utilized linear track behavioral paradigm and compare the changes in hidden layer units to those observed in hippocampal place cells recorded in awake, behaving animals.

Ratzon et al. clearly demonstrate that hidden layer units in their model undergo consistent changes even after the task is well-learned, mirroring representational drift observed in real hippocampal neurons. They show that the drift occurs across three separate measures: the active proportion of units (referred to as sparsification), spatial information of units, and correlation of spatial activity. They continue to address the conditions and parameters under which drift occurs in their model to assess the generalizability of their findings to non-spatial tasks. Last, they investigate the mechanism through which sparsification occurs, showing that flatness of the manifold near the solution can influence how the network reconfigures. The authors suggest that their findings indicate a three stage learning process: 1) fast initial learning followed by 2) directed motion along a manifold which transitions to 3) undirected motion along a manifold.

Overall, the authors' results support the main conclusion that implicit regularization in machine learning networks mirrors representational drift observed in hippocampal place cells. Their findings promise to open new fields of inquiry into the connection between machine learning and representational drift in other, non-spatial learning paradigms, and to generate testable predictions for neural data.

Strengths:

(1) Ratzon et al. make an insightful connection between well-known phenomena in two separate fields: implicit regularization in machine learning and representational drift in the brain. They demonstrate that changes in a recurrent neural network mirror those observed in the brain, which opens a number of interesting questions for future investigation.

(2) The authors do an admirable job of writing to a large audience and make efforts to provide examples to make machine learning ideas accessible to a neuroscience audience and vice versa. This is no small feat and aids in broadening the impact of their work.

(3) This paper promises to generate testable hypotheses to examine in real neural data, e.g., that drift rate should plateau over long timescales (now testable with the ability to track single-unit neural activity across long time scales with calcium imaging and flexible silicon probes). Additionally, it provides another set of tools for the neuroscience community at large to use when analyzing the increasingly high-dimensional data sets collected today.

Weaknesses:

The revised manuscript addresses all the weaknesses outlined in my initial review. However, there is one remaining (minor) weakness regarding how "sparseness" is used and defined.

Sparseness can mean different things to different fields. For example, for engram studies, sparseness could be measured at the population level by the proportion of active cells, whereas for a physiology study, sparseness might be measured at the neuron level by the change in peak firing rate of each cell as an animal enters that cell's place field. In this manuscript, the idea of "sparseness" is introduced indirectly in the last paragraph of the introduction as "...changes in activity statistics (sparseness)...", but it is unclear from the preceding text if the referenced "activity statistics" used to define sparseness are the "fraction of active units," or their "tuning specificity," or both. While sparseness is clearly defined in the Methods section for the RNN, there is no mention of how it is defined for neural data, and spatial information is not mentioned at all. For clarity, I suggest explicitly defining sparseness for both the RNN and real neural data early in the main text, e.g. "Here, we measure sparseness in neural data by A and B, and by the analogous metric(s) of X and Y in our RNN..." This is a small but important nuance that will enhance the ease of reading for a broad neuroscience audience.

Reviewer #1 (Public Review):

In this manuscript, Nagel et al. sought to characterize the composition of urinary compounds, some of which are putative chemosignals. They used urines from adult males and females in three different strains, including one wild-derived strain. By performing mass spectrometry of two classes of compounds: volatile organic compounds and proteins, they found that urines from inbred strains are qualitatively similar to those of a wild strain. This finding is significant because there is a high degree of diversity in different inbred strains and wild mice, with respect to the polymorphisms of chemosensory receptor genes and expression of vomeronasal ligands previously identified. Notably, their study did not characterize steroids, which represent a major class of urinary chemosignals activating vomeronasal neurons. Therefore, important future studies should address the strain dependence of steroid composition in urines.

In the second part of this work, the authors used calcium imaging to monitor the pattern of vomeronasal neuron responses to these urines. By performing pairwise comparisons, the authors found a large degree of strain-specific response and a relatively minor response to sex-specific urinary stimuli. This is a finding generally in agreement with previous calcium imaging work by Ron Yu and colleagues in 2008. The authors extend the previous work by using urines from wild mice. They further report that the concentration diversity of urinary compounds in different urine batches is largely uncorrelated with the activity profiles of these urines. In addition, the authors found that the patterns of vomeronasal neuron response to urinary cues are not identical when measured using different recipient strains.

The pitfalls of this study are the omission of steroids for the mass spectrometry experiments and the indirect (correlational) nature of their mass spectrometry data and activity data. Whether the urinary compounds identified in this study activate vomeronasal neurons were not tested.

Nevertheless, the major contribution of this work is the identification of specific molecules in mouse urines. This work is likely to be of significant interest to researchers in chemosensory signaling in mammals and could provide a systematic avenue to exhaustively identify additional pheromones in mice.

Reviewer #2 (Public Review):

This manuscript by Nagel et al provides a comprehensive examination of the chemical composition of mouse urine (an important source of semiochemicals) across strain and sex, and correlates these differences with functional responses of vomeronasal sensory neurons (an important sensory population for detecting chemical social cues). The strength of the work lies in the careful and comprehensive imaging and chemical analyses, the rigor of quantification of functional responses, and the insight into the relevance of olfactory work on lab-derived vs wild-derived mice.

With regards to the chemical analysis, the reader should keep in mind (and the authors acknowledge) that a difference in the concentration of a chemical across strain or sex does not necessarily mean that that chemical is used for chemical communication. In the most extreme case, the animals may be completely insensitive to the chemical. Thus, the fact that the repertoire of proteins and volatiles could potentially allow sex and/or strain discrimination, it is unclear to what degree both are used in different situations.

Reviewer #3 (Public Review):

Summary:

The manuscript by Nagel, et al. describes studies of mouse vomeronasal sensory neuron (VSN) tuning to mouse urine samples across different sexes and strains, including wild mice, alongside mass spectrometry analysis of the same samples. The authors performed live Ca2+ imaging (CAL520 dye) of VSNs in acute vomeronasal organ (VNO) slices to determine how VSNs are tuned to pairs of stimuli that differ in their origin (e.g. male C57BL/6 versus male BALB/c urine, male C57BL/6 versus female C57BL/6, etc.). For each pair of tested odorants, the results measure the proportion of VSNs that respond to both stimuli ("generalists") or just one of the two ("specialists"), as well as metrics of tuning preference and response reliability. The authors find in most cases that generalists make up a larger proportion of responsive VSNs than specialists, but several pairwise comparisons showed a high degree of strain selectivity. Notably, the authors evaluated VSN tuning in both male C57BL/6 and male BALB/c VNOs, finding strain-dependent differences in the representation of mouse urine. Alongside these measurements of VSN tuning, the authors report results of mass spectrometry analyses of volatiles and proteins in the same urine samples. These analyses indicated a number of molecules in each category that vary across sex and strain, and therefore represent candidate vomeronasal ligands. However, this study did not directly test whether any of these candidate molecules drives VSN activity. Overall, this work provides solid information related to mouse vomeronasal chemosensation.

Strengths:

A strength of the current study is its focus on characterizing the neural responses of the VNO to urine derived from wild mice. The majority of existing vomeronasal system research has relied on the use of inbred strains for both neural response recordings and investigations of candidate vomeronasal system ligands. Inbreeding in laboratory environments may alter the chemical composition of bodily secretions, thereby potentially changing the information they contain. Moreover, the more homogeneous nature of inbred strains could be critical when studying the AOS mediated social aspects. If there exist noticeable differences in the chemical composition of secretions from wild animals compared to inbred strains, this would suggest that future research must consider natural sources of candidate ligands outside of inbred strains. This work identifies some intriguing differences, worthy of further exploration, between the urine composition of wild mice versus inbred mice, as well as disparities in how the VNO responds to urine from these different sources. However, the molecular composition and VNO responsiveness to wild mouse urine was found to be highly overlapping with inbred mouse urine, supporting the continued investigation of candidate ligands found in inbred mouse urine.

Another positive aspect of this work is its use of the same set of stimuli as a previous study by the same authors (Bansal et al., 2021) in the downstream accessory olfactory bulb. The consistency in stimulus selection facilitates a comparison of information processing of sex and strain information from the sensory periphery to the brain. Although comparisons between the two connected regions are not a focus of this work, and methodological differences (e.g., Ca2+ imaging versus electrophysiology) may introduce caveats into comparisons, the support of "apples to apples" comparisons across connected circuits is critical to progress in the field.

Finally, this study directly measured VSN tuning in both male C57BL/6 and male BALB/c VNOs, finding subtle but important differences in the representation of mouse urine in these two recipient strains. Given that there is a long history of behavioral research into strain-specific differences in social behavior, this research paves the way for future studies into how different mouse strains detect and process social chemosignals.

Weaknesses:

One of the primary objectives in this study is to ascertain the extent to which the response profiles of VSNs are specific to sex and strain. The design of these Ca2+ imaging experiments uses a simple stimulus design, using two interleaved bouts of stimulation with pairs of urine (e.g., male versus female C57BL/6, male C57BL/6 versus male BALB/c) at a single dilution factor (1:100). This introduces two significant limitations: (1) the "generalist" versus "specialist" descriptors pertain only to the specific pairwise comparisons made and (2) there is no information about the sensitivity/concentration-dependence of the responses.

The functional measurements of VSN tuning to various pairs of urine stimuli are presented alongside mass spectrometry-based comparisons. However, the mass spectrometry-based analysis was performed separately from VSN tuning experiments/analysis. The juxtaposition of these measurements may give some readers the impression that VSN tuning measurements were integrated with molecular profiling (i.e., that the molecular diversity was causally related physiological responses). This is a hypothesis raised by the parallel studies, but not a supported conclusion of the current work.

The impact of mass spectrometry findings is acknowledged to be limited to nonvolatile organic compounds and proteins/peptides, and that it is possible that few of these candidate molecules are active in the VNO. Moreover, it remains possible that the VSN responses are driven mostly by small nonvolatiles (e.g., polar steroids), a class of strong VSN ligands that were excluded from molecular analysis.

Reviewer #1 (Public Review):

Summary:

This important study investigated the role of the PHOX2B transcription factor in neurons in the key brainstem chemosensory structure, the retrotrapezoid nucleus (RTN), for maintaining proper CO2 chemoreflex responses of breathing in the adult rat in vivo. PHOX2B has an important transcriptional role in neuronal survival and/or function, and mutations of PHOX2B severely impair the development and function of the autonomic nervous system and RTN, resulting in the developmental genetic disease congenital central hypoventilation syndrome (CCHS) in neonates, where the RTN may not form and is functionally impaired. The function of the wild-type PHOX2B protein in adult RTN neurons that continue to express PHOX2B is unknown. By utilizing a viral PHOX2B-shRNA approach for the knockdown of PHOX2B specifically in RTN neurons, the authors' solid results show impaired ventilatory responses to elevated inspired CO2, measured by whole-body plethysmography in freely behaving adult rats, that develop progressively over a four-week period in vivo, indicating effects on RTN neuron transcriptional activity and associated blunting of the CO2 ventilatory response. The RTN neuronal mRNA expression data presented suggests the impaired hypercapnic ventilatory response is possibly due to the decreased expression of key proton sensors in the RTN. This study will be of interest to neuroscientists studying respiratory neurobiology as well as the neurodevelopmental control of motor behavior.

Strengths:

(1) The authors used a shRNA viral approach to progressively knock down the PHOX2B protein, specifically in RTN neurons, to determine whether PHOX2B is necessary for the survival and/or chemosensory function of adult RTN neurons in vivo.

(2) To determine the extent of PHOX2B knockdown in RTN neurons, the authors combined RNAScope® and immunohistochemistry assays to quantify the subpopulation of RTN neurons expressing PHOX2B and Neuromedin B (Nmb), which has been proposed to be key chemosensory neurons in the RTN.

(3) The authors demonstrate that knockdown efficiency is time-dependent, with a progressive decrease in the number of Nmb-expressing RTN neurons that co-express PHOX2B over a four-week period.

(4) Their results convincingly show hypoventilation, particularly in 7.2% CO2 only, for PHOX2B-shRNA RTN-injected rats after four weeks compared to naïve and non-PHOX2B-shRNA targeted (NT-shRNA) RTN-injected rats, suggesting a specific impairment of chemosensitive properties in RTN neurons with PHOX2B knockdown.

(5) Analysis of the association between PHOX2B knockdown in RTN neurons and the<br /> attenuation of the hypercapnic ventilatory response (HCVR), by evaluating the correlation between the number of Nmb+/PHOX2B+ or Nmb+/PHOX2B- cells in the RTN and the resulting HCVR, showed a significant correlation between HCVR and number of Nmb+/PHOX2B+ and Nmb+/PHOX2B- cells, suggesting that the number of PHOX2B-expressing cells in the RTN is a predictor of the chemoreflex response and the reduction of PHOX2B protein impairs the CO2-chemoreflex.

(6) The data presented indicate that PHOX2B knockdown reduces the HCVR and the expression of Gpr4 and Task2 mRNAs. This suggests that PHOX2B knockdown affects RTN neurons' transcriptional activity and decreases the CO2 response, possibly by reducing the expression of key proton sensors in the RTN.

(7) This study's results show that independent of its role during development, PHOX2B is still required to maintain proper CO2 chemoreflex responses in the adult brain, and its reduction in CCHS may contribute to the respiratory impairment in this disorder.

Weaknesses:

(1) The authors found a significant decrease in the total number of Nmb+ RTN neurons (i.e., Nmb+/PHOX2B+ plus Nmb+/ PHOX2B-) in NT-shRNA rats at two weeks post viral injection, and also at the four-week period where the impairment of the chemosensory function of the RTN became significant, suggesting some inherent cell death possibly due to off-target toxic effects associated with shRNA procedures.

(2) The tissue sampling procedures for quantifying numbers of cells expressing proteins/mRNAs throughout the extended RTN region bilaterally have not been completely validated to accurately represent the full expression patterns in the RTN under the experimental conditions.

(3) The inferences about RTN neuronal expression of NMB, GPR4, or TASK2 are based on changes in mRNA levels, so it remains speculation that the observed reduction in Gpr4 and Task2 mRNA translates to a reduction in the protein levels and associated reduction of RTN neuronal chemosensitive properties.

Reviewer #3 (Public Review):

A brain region called the retrotrapezoid nucleus (RTN) regulates breathing in response to changes in CO2/H+, a process termed central chemoreception. A transcription factor called PHOX2B is important for RTN development and mutations in the PHOX2B gene result in a severe type of sleep apnea called Congenital Central Hypoventilation Syndrome. PHOX2B is also expressed throughout life, but its postmitotic functions remain unknown. This study shows that knockdown of PHOX2B in the RTN region in adult rats decreased expression of Task2 and Gpr4 in Nmb-expressing RTN chemoreceptors and this corresponded with a diminished ventilatory response to CO2 but did not impact baseline breathing or the hypoxic ventilatory response. These results provide novel insight regarding postmitotic functions of PHOX2B in RTN neurons.

I have two main concerns and several points of clarification.

Main issues:<br /> (1) The experimental approach was not targeted to Nmb+ neurons and since other cells in the area also express Phox2b, conclusions should be tempered to focus on Phox2b expressing parafacial neurons NOT specifically RTN neurons

(2) It's not clear whether PHOX2B is important for transcription of pH sensing machinery, cell health or both. If knockdown of PHOX2B knockdown results in loss of RTN neurons this is also expected to decrease Task2 and Gpr4 levels, albeit by a transcription-independent mechanism.

Other points:

(3) All individual data points should be visible in floating bar graphs in Figs 1 and 4. For example, I don't see any dots for naïve animals in any of the panels in Fig. 1.

(4) the C1 and facial partly overlap with the RTN at this level of the medulla and these cells should appear as Phox2b+/Nmb- cells so it is not clear to me why these cells are not evident in the control tissue in figs 2B and 3B. Also, some of the bregma levels shown in Fig. 5A overlap with Figs 2-3 so again it's not clear to me how this non-cell type specific viral approach was targeted to Nmb cells but not near by TH+ cells. Please clarify.

(5) How do you get a loss of Nmb+ neurons (Figs 2-3) with no change in Nmb fluorescence (Fig. 5B)? In the absence of representative images these results are not compelling and should be substantiated by more readily quantifiable approaches like qPCR.

Reviewer #1 (Public Review):

Summary:

The aim of the study described in this paper was to test whether visual stimuli that pulse synchronously with the systole phase of the cardiac cycle are suppressed compared with stimuli that pulse in the diastole phase. To this end, the authors employed a binocular rivalry task and used the duration of the perceived image as the metric of interest. The authors predicted that if there was global suppression of the visual stimulus during systole then the durations of the stimulus that were pulsing synchronously with systole should be of shorter duration than those pulsing in diastole. However, the results observed were the opposite of those predicted. The authors speculate on what this facilitation effect might mean for the baroreceptor suppression hypothesis.

Strengths:

This is an interesting and timely study that uses a clever paradigm to test the baroreceptor suppression hypothesis in vision. This is a refreshingly focussed paper with interesting and seemingly counterintuitive results.

Weaknesses:

The paper could benefit from a clearer explanation of the predicted results. For those not experts in binocular rivalry, it would be useful to explain the predicted results. Does pulsing stimuli in this way change durations in such a task? If there is global suppression of visual stimuli why would this lead to shorter/longer durations in the systole compared to the diastole conditions? In addition, the duration lengths in both conditions seem to be longer than one cardiac cycle. If the cardiac cycle modulates duration it would be interesting to discuss why this occurs on some cycles but not on others. If there is a facilitation effect why does it only occur on some cycles?

Reviewer #2 (Public Review):

Summary:

This is a binocular rivalry study that uses electrocardiogram events to modulate visual stimuli in real-time, relative to participants' heartbeats. The main finding is that modulations during the period around when the heart has contracted (systole) increase rivalry dominance durations. This is a really neat result, that demonstrates the link between interoception and vision. I thought the Bayesian mixture modelling was a really smart way to identify cardiac non-perceivers, and the finding that the main result is preserved in this group is compelling. Overall, the study has been conducted to a high standard, is appropriately powered, and reported clearly. I have one suggestion about interpretation, which concerns the explanation of increased dominance durations with reference to contemporary models of binocular rivalry, and a few minor queries. However, I think this paper is a worthwhile addition to the literature.

Reviewer #3 (Public Review):

Summary:

The manuscript addresses a question inspired by the Baroceptor Hypothesis and its links to visual awareness and interoception. Specifically, the reported study aimed to determine if the effects of cardiac contraction (systole) on binocular rivalry (BR) are facilitatory or suppressive. The main experiment - relying on a technically challenging procedure of presenting stimuli synchronised with the heartbeats of participants - has been conducted with great care, and numerous manipulation checks the authors report convincingly show that the methods they used work as intended. Moreover, the control experiment allows for excluding alternative explanations related to participants being aware of their heartbeats. Therefore, the study convincingly shows the effect of cardiac activity on BR - and this is an important finding. The results, however, do not allow for unambiguously determining if this effect is facilitatory or suppressive (see details below), which renders the study not as informative as it could be.

While the authors strongly focus on interoception and awareness, this study will be of interest to researchers studying BR as such. Moreover, the code and the data the authors share can facilitate the adoption of their methods in other labs.

Strengths:

(1) The study required a complex technical setup and the manuscript both describes it well and demonstrates that it was free from potential technical issues (e.g. in section 3.3. Manipulation check).

(2) The sophisticated statistical methods the authors used, at least for a non-statistician like me, appear to be well-suited for their purpose. For example, they take into account the characteristics of BR (gamma distributions of dominance durations). Moreover, the authors demonstrate that at least in one case their approach is more conservative than a more basic one (Binomial test) would be.

(3) Finally, the control experiment, and the analysis it enabled, allow for excluding a multitude of alternative explanations of the main results.

(4) The authors share all their data and materials, even the code for the experiment.

(5) The manuscript is well-written. In particular, it introduces the problem and methods in a way that should be easy to understand for readers coming from different research fields.

Weaknesses:

(1) The interpretation of the main result in the context of the Baroceptor hypothesis is not clear. The manuscript states: The Baroreceptor Hypothesis would predict that the stimulus entrained to systole would spend more time suppressed and, conversely, less time dominant, as cortical activity would be suppressed each time that stimulus pulses. The manuscript does not specify why this should be the case, and the term 'entrained' is not too helpful here (does it refer to neural entrainment? or to 'being in phase with'?). The answer to this question is provided by the manuscript only implicitly, and, to explain my concern, I try to spell it out here in a slightly simplified form.

During systole (cardiac contraction), the visual system is less sensitive to external information, so it 'ignores' periods when the systole-synchronised stimulus is at the peak of its pulse. Conversely, the system is more sensitive during diastole, so the stimulus that is at the peak of its pulse then should dominate for longer, because its peaks are synchronised with the periods of the highest sensitivity of the visual system when the information used to resolve the rivalry is sampled from the environment. This idea, while indeed being a clever test of the hypothesis in question, rests on one critical assumption: that the peak of the stimulus pulse (as defined in the manuscript) is the time when the stimulus is the strongest for the visual system. The notion of 'stimulus strength' is widely used in the BR literature (see Brascamp et al., 2015 for a review). It refers to the stimulus property that, simply speaking, determines its tendency to dominate in the BR. The strength of a stimulus is underpinned by its low-level visual properties, such as contrast and spatial frequency content. Coming back to the manuscript, the pulsing of the stimuli affected at least spatial frequency (and likely other low-level properties), and it is unknown if it was in phase with the pulsing of the stimulus strength, or not. If my understanding of the premise of the study is correct, the conclusions drawn by the authors stand only if it was.

In other words, most likely the strength of one of the stimuli was pulsating in sync with the systole, but is it not clear which stimulus it was. It is possible that, for the visual system, the stimulus meant to pulse in sync with the systole was pulsing strength-wise in phase with the diastole (and the one intended to pulse with in sync with the diastole strength-wise pulsed with the systole). If this is the case, the predictions of the Baroceptor Hypothesis hold, which would change the conclusion of the manuscript.

(2) Using anaglyph goggles necessitates presenting stimuli of a different colour to each eye. The way in which different colours are presented can impact stimulus strength (e.g. consider that different anaglyph foils can attenuate the light they let through to different degrees). To deal with such effects, at least some studies on BR employed procedures of adjusting the colours for each participant individually (see Papathomas et al., 2004; Patel et al., 2015 and works cited there). While I think that counterbalancing applied in the study excludes the possibility that colour-related effects influenced the results, the effects of interest still could be stronger for one of the coloured foils.

(3) Several aspects of the methods (e.g. the stimuli), are not described at the level of detail some readers might be accustomed to. The most important issue here is the task the participants performed. The manuscript says that they pressed a button whenever they experienced a switch in perception, but it is only implied that there were different buttons for each stimulus.

Brascamp, J. W., Klink, P. C., & Levelt, W. J. M. (2015). The 'laws' of binocular rivalry: 50 years of Levelt's propositions. Vision Research, 109, 20-37. https://doi.org/10.1016/j.visres.2015.02.019<br /> Papathomas, T. V., Kovács, I., & Conway, T. (2004). Interocular grouping in binocular rivalry: Basic attributes and combinations. In D. Alais & R. Blake (Eds.), Binocular Rivalry (pp. 155-168). MIT Press<br /> Patel, V., Stuit, S., & Blake, R. (2015). Individual differences in the temporal dynamics of binocular rivalry and stimulus rivalry. Psychonomic Bulletin and Review, 22(2), 476-482. https://doi.org/10.3758/s13423-014-0695-1

Reviewer #1 (Public Review):

Summary:

Pham and colleagues provide an illuminating investigation of aquaporin-4 water flux in the brain utilizing ex vivo and in vivo techniques. The authors first show in acute brain slices, and in vivo with fiber photometry, SRB-loaded astrocytes swell after inhibition of AQP4 with TGN-020, indicative of tonic water efflux from astrocytes in physiological conditions. Excitingly, they find that TGN-020 increases the ADC in DW-MRI in a region-specific manner, potentially due to AQP4 density. The resolution of the DW-MRI cannot distinguish between intracellular or extracellular compartments, but the data point to an overall accumulation of water in the brain with AQP4 inhibition. These results provide further clarity on water movement through AQP4 in health and disease.

Overall, the data support the main conclusions of the article, with some room for more detailed treatment of the data to extend the findings.

Strengths:

The authors have a thorough investigation of AQP4 inhibition in acute brain slices. The demonstration of tonic water efflux through AQP4 at baseline is novel and important in and of itself. Their further testing of TGN-020 in hyper- and hypo-osmotic solutions shows the expected reduction of swelling/shrinking with AQP4 blockade.

Their experiment with cortical spreading depression further highlights the importance of water efflux from astrocytes via AQP4 and transient water fluxes as a result of osmotic gradients. Inhibition of AQP4 increases the speed of tissue swelling, pointing to a role in the efflux of water from the brain.

The use of DW-MRI provides a non-invasive measure of water flux after TGN-020 treatment.

Weaknesses:

The authors specifically use GCaMP6 and light sheet microscopy to image their brain sections in order to identify astrocytic microdomains. However, their presentation of the data neglects a more detailed treatment of the calcium signaling. It would be quite interesting to see whether these calcium events are differentially affected by AQP4 inhibition based on their cellular localization (ie. processes vs. soma vs. vascular end feet which all have different AQP4 expressions).

The authors show the inhibition of AQP4 with TGN-020 shortens the onset time of the swelling associated with cortical spreading depression in brain slices. However, they do not show quantification for many of the other features of CSD swelling, (ie. the duration of swelling, speed of swelling, recovery from swelling).

Significance:

AQP4 is a bidirectional water channel that is constitutively open, thus water flux through it is always regulated by local osmotic gradients. Still, characterizing this water flux has been challenging, as the AQP4 channel is incredibly water-selective. The authors here present important data showing that the application of TGN-020 alone causes astrocytic swelling, indicating that there is constant efflux of water from astrocytes via AQP4 in basal conditions. This has been suggested before, as the authors rightfully highlight in their discussion, but the evidence had previously come from electron microscopy data from genetic knockout mice.

AQP4 expression has been linked with the glymphatic circulation of cerebrospinal fluid through perivascular spaces since its rediscovery in 2012 [1]. Further studies of aging[2], genetic models[3], and physiological circadian variation[4] have revealed it is not simply AQP4 expression but AQP4 polarization to astrocytic vascular endfeet that is imperative for facilitating glymphatic flow. Still, a lingering question in the field is how AQP4 facilitates fluid circulation. This study represents an important step in our understanding of AQP4's function, as the basal efflux of water via AQP4 might promote clearance of interstitial fluid to allow an influx of cerebrospinal fluid into the brain. Beyond glymphatic fluid circulation, clearly, AQP4-dependent volume changes will differentially alter astrocytic calcium signaling and, in turn, neuronal activity.

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.<br /> (2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.<br /> (3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.<br /> (4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature Communications, 2020. 11(1).

Reviewer #2 (Public Review):

Summary:

The paper investigates the role of astrocyte-specific aquaporin-4 (AQP4) water channel in mediating water transport within the mouse brain and the impact of the channel on astrocyte and neuron signaling. Throughout various experiments including epifluorescence and light sheet microscopy in mouse brain slices, and fiber photometry or diffusion-weighted MRI in vivo, the researchers observe that acute inhibition of AQP4 leads to intracellular water accumulation and swelling in astrocytes. This swelling alters astrocyte calcium signaling and affects neighboring neuron populations. Furthermore, the study demonstrates that AQP4 regulates astrocyte volume, influencing mainly the dynamics of water efflux in response to osmotic challenges or associated with cortical spreading depolarization. The findings suggest that AQP4-mediated water efflux plays a crucial role in maintaining brain homeostasis, and indicates the main role of AQP4 in this mechanism. However authors highlight that the report sheds light on the mechanisms by which astrocyte aquaporin contributes to the water environment in the brain parenchyma, the mechanism underlying these effects remains unclear and not investigated. The manuscript requires revision.

Strengths:

The paper elucidates the role of the astrocytic aquaporin-4 (AQP4) channel in brain water transport, its impact on water homeostasis, and signaling in the brain parenchyma. In its idea, the paper follows a set of complimentary experiments combining various ex vivo and in vivo techniques from microscopy to magnetic resonance imaging. The research is valuable, confirms previous findings, and provides novel insights into the effect of acute blockage of the AQP4 channel using TGN-020.

Weaknesses:

Despite the employed interdisciplinary approach, the quality of the manuscript provides doubts regarding the significance of the findings and hinders the novelty claimed by the authors. The paper lacks a comprehensive exploration or mention of the underlying molecular mechanisms driving the observed effects of astrocytic aquaporin-4 (AQP4) channel inhibition on brain water transport and brain signaling dynamics. The scientific background is not very well prepared in the introduction and discussion sections. The important or latest reports from the field are missing or incompletely cited and missconcluded. There are several citations to original works missing, which would clarify certain conclusions. This especially refers to the basis of the glymphatic system concept and recently published reports of similar content. The usage of TGN-020, instead of i.e. available AER-270(271) AQP4 blocker, is not explained. While employing various experimental techniques adds depth to the findings, some reasoning behind the employed techniques - especially regarding MRI - is not clear or seemingly inaccurate. Most of the time the number of subjects examined is lacking or mentioned only roughly within the figure captions, and there are lacking or wrongly applied statistical tests, that limit assessment and reproducibility of the results. In some cases, it seems that two different statistical tests were used for the same or linked type of data, so the results are contradictory even though appear as not likely - based on the figures. Addressing these limitations could strengthen the paper's impact and utility within the field of neuroscience, however, it also seems that supplementary experiments are required to improve the report.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors propose that astrocytic water channel AQP4 represents the dominant pathway for tonic water efflux without which astrocytes undergo cell swelling. The authors measure changes in astrocytic sulforhodamine fluorescence as the proxy for cell volume dynamics. Using this approach, they perform a technically elegant series of ex vivo and in vivo experiments exploring changes in astrocytic volume in response to AQP4 inhibitor TGN-020 and/or neuronal stimulation. The key finding is that TGN-020 produces an apparent swelling of astrocytes and modifies astrocytic cell volume regulation after spreading depolarizations. Additionally, systemic application of TGN-020 produced changes in diffusion-weighted MRI signal, which the authors interpret as cellular swelling. This study is perceived as potentially significant. However, several technical caveats should be strongly considered and perhaps addressed through additional experiments.

Strengths:

(1) This is a technically elegant study, in which the authors employed a number of complementary ex vivo and in vivo techniques to explore functional outcomes of aquaporin inhibition. The presented data are potentially highly significant (but see below for caveats and questions related to data interpretation).

(2) The authors go beyond measuring cell volume homeostasis and probe for the functional significance of AQP4 inhibition by monitoring Ca2+ signaling in neurons and astrocytes (GCaMP6 assay).

(3) Spreading depolarizations represent a physiologically relevant model of cellular swelling. The authors use ChR2 optogenetics to trigger spreading depolarizations. This is a highly appropriate and much-appreciated approach.

Weaknesses:

(1) The main weakness of this study is that all major conclusions are based on the use of one pharmacological compound. In the opinion of this reviewer, the effects of TGN-020 are not consistent with the current knowledge on water permeability in astrocytes and the relative contribution of AQP4 to this process.

Specifically: Genetic deletion of AQP4 in astrocytes reduces plasmalemmal water permeability by ~two-three-fold (when measured a 37oC, Solenov et al., AJP-Cell, 2004). This is a significant difference, but it is thought to have limited/no impact on water distribution. Astrocytic volume and the degree of anisosmotic swelling/shrinkage are unchanged because the water permeability of the AQP4-null astrocytes remains high. This has been discussed at length in many publications (e.g., MacAulay et al., Neuroscience, 2004; MacAulay, Nat Rev Neurosci, 2021) and is acknowledged by Solenov and Verkman (2004).

Keeping this limitation in mind, it is important to validate astrocytic cell volume changes using an independent method of cell volume reconstruction (diameter of sulforhodamine-labeled cell bodies? 3D reconstruction of EGFP-tagged cells? Else?)

(2) TGN-020 produces many effects on the brain, with some but not all of the observed phenomena sensitive to the genetic deletion of AQP4. In the context of this work, it is important to note that TGN-020 does not completely inhibit AQP4 (70% maximal inhibition in the original oocyte study by Huber et al., Bioorg Med Chem, 2009). Thus, besides not knowing TGN-020 levels inside the brain, even "maximal" AQP4 inhibition would not be expected to dramatically affect water permeability in astrocytes.

This caveat may be addressed through experiments using local delivery of structurally unrelated AQP4 blockers, or, preferably, AQP4 KO mice.

(3) This reviewer thinks that the ADC signal changes in Figure 5 may be unrelated to cellular swelling. Instead, they may be a result of the previously reported TGN-020-induced hyphemia (e.g., H. Igarashi et al., NeuroReport, 2013) and/or changes in water fluxes across pia matter which is highly enriched in AQP4. To amplify this concern, AQP4 KO brains have increased water mobility due to enlarged interstitial spaces, rather than swollen astrocytes (RS Gomolka, eLife, 2023). Overall, the caveats of interpreting DW-MRI signal deserve strong consideration.

Reviewer #1 (Public Review):

Yun et al. examined the molecular and neuronal underpinnings of changes in Drosophila female reproductive behaviors in response to social cues. Specifically, the authors measure the ejaculate-holding period, which is the amount of time females retain male ejaculate after mating (typically 90 min in flies). They find that female fruit flies, Drosophila melanogaster, display shorter holding periods in the presence of a native male or male-associated cues, including 2-Methyltetracosane (2MC) and 7-Tricosene (7-T). They further show that 2MC functions through Or47b olfactory receptor neurons (ORNs) and the Or47b channel, while 7-T functions through ppk23 expressing neurons. Interestingly, their data also indicates that two other olfactory ligands for Or47b (methyl laurate and palmitoleic acid) do not have the same effects on the ejaculate-holding period. By performing a series of behavioral and imaging experiments, the authors reveal that an increase in cAMP activity in pC1 neurons is required for this shortening of the ejaculate-holding period and may be involved in the likelihood of remating. This work lays the foundation for future studies on sexual plasticity in female Drosophila.

The conclusions of this paper are mostly supported by the data, but aspects of the lines used for individual pC1 subtypes and visual contributions as well as the statistical analysis need to be clarified.

(1) The pC1 subtypes (a - e) are delineated based on their morphology and connectivity. While the morphology of these neurons is distinct, they do share a resemblance that can be difficult to discern depending on the imaging performed. Additionally, genetic lines attempting to label individual neurons can easily be contaminated by low-level expression in off-target neurons in the brain or ventral nerve cord (VNC), which could contribute to behavioral changes following optogenetic manipulations. In Figures 5C - D, the authors generated and used new lines for labeling pC1a and pC1b+c. The line for pC1b+c was imaged as part of another recent study (https://doi.org/10.1073/pnas.2310841121). However, similar additional images of the pC1a line (i.e. 40x magnification and VNC expression) would be helpful in order to validate its specificity.

(2) The author's experiments examining olfactory and gustatory contributions to the holding period were well controlled and described. However, the experiments in Figure 1D examining visual contributions were not sufficiently convincing as the line used (w1118) has previously been shown to be visually impaired (Wehner et al., 1969; Kalmus 1948). Using another wild-type line would have improved the authors' claims.

(3) When comparisons between more than 2 groups are shown as in Figures 1E, 3D, and 5E, the comparisons being made were not clear. Adding in the results of a nonparametric multiple comparisons test would help for the interpretation of these results.

Reviewer #2 (Public Review):

The work by Yun et al. explores an important question related to post-copulatory sexual selection and sperm competition: Can females actively influence the outcome of insemination by a particular male by modulating the storage and ejection of transferred sperm in response to contextual sensory stimuli? The present work is exemplary for how the Drosophila model can give detailed insight into the basic mechanism of sexual plasticity, addressing the underlying neuronal circuits on a genetic, molecular, and cellular level.

Using the Drosophila model, the authors show that the presence of other males or mated females after mating shortens the ejaculate-holding period (EHP) of a female, i.e. the time she takes until she ejects the mating plug and unstored sperm. Through a series of thorough and systematic experiments involving the manipulation of olfactory and chemo-gustatory neurons and genes in combination with exposure to defined pheromones, they uncover two pheromones and their sensory cells for this behavior. Exposure to the male-specific pheromone 2MC shortens EHP via female Or47b olfactory neurons, and the contact pheromone 7-T, present in males and on mated females, does so via ppk23 expressing gustatory foreleg neurons. Both compounds increase cAMP levels in a specific subset of central brain receptivity circuit neurons, the pC1b,c neurons. By employing an optogenetically controlled adenyl cyclase, the authors show that increased cAMP levels in pC1b and c neurons increase their excitability upon male pheromone exposure, decrease female EHP, and increase the remating rate. This provides convincing evidence for the role of pC1b,c neurons in integrating information about the social environment and mediating not only virgin but also mated female post-copulatory mate choice.

Understanding context and state-dependent sexual behavior is of fundamental interest. Mate behavior is highly context-dependent. In animals subjected to sperm competition, the complexities of optimal mate choice have attracted a long history of sophisticated modelling in the framework of game theory. These models are in stark contrast to how little we understand so far about the biological and neurophysiological mechanisms of how females implement post-copulatory or so-called "cryptic" mate choice and bias sperm usage when mating multiple times.

The strength of the paper is decrypting "cryptic" mate choice, i.e. the clear identification of physiological mechanisms and proximal causes for female post-copulatory mate choice. The discovery of peripheral chemosensory nodes and neurophysiological mechanisms in central circuit nodes will provide a fruitful starting point to fully map the circuits for female receptivity and mate choice during the whole gamut of female life history.

Reviewer #1 (Public Review):

Summary:

Olfactory sensory neurons (OSNs) in the olfactory epithelium detect myriads of environmental odors that signal essential cues for survival. OSNs are born throughout life and thus represent one of the few neurons that undergo life-long neurogenesis. Until recently, it was assumed that OSN neurogenesis is strictly stochastic with respect to subtype (i.e. the receptor the OSN chooses to express).

However, a recent study showed that olfactory deprivation via naris occlusion selectively reduced birthrates of only a fraction of OSN subtypes and indicated that these subtypes appear to have a special capacity to undergo changes in birthrates in accordance with the level of olfactory stimulation. These previous findings raised the interesting question of what type of stimulation influences neurogenesis, since naris occlusion does not only reduce the exposure to potentially thousands of odors but also to more generalized mechanical stimuli via preventing airflow.

In this study, the authors set out to identify the stimuli that are required to promote the neurogenesis of specific OSN subtypes. Specifically, they aim to test the hypothesis that discrete odorants selectively stimulate the same OSN subtypes whose birthrates are affected. This would imply a highly specific mechanism in which exposure to certain odors can "amplify" OSN subtypes responsive to those odors suggesting that OE neurogenesis serves, in part, an adaptive function.

To address this question, the authors focused on a family of OSN subtypes that had previously been identified to respond to musk-related odors and that exhibit higher transcript levels in the olfactory epithelium of mice exposed to males compared to mice isolated from males. First, the authors confirm via a previously established cell birth dating assay in unilateral naris occluded mice that this increase in transcript levels actually reflects a stimulus-dependent birthrate acceleration of this OSN subtype family. In a series of experiments using the same assay, they show that one specific subtype of this OSN family exhibits increased birthrates in response to juvenile male exposure while a different subtype shows increased birthrates to adult mouse exposure. In the core experiment of the study, they finally exposed naris occluded mice to a discrete odor (muscone) to test if this odor specifically accelerates the birth rates of OSN types that are responsive to this odor. This experiment reveals a complex relationship between birth rate acceleration and odor concentrations showing that some muscone concentrations affect birth rates of some members of this family and do not affect two unrelated OSN subtypes.

Strengths:

The scientific question is valid and opens an interesting direction. The previously established cell birth dating assay in naris occluded mice is well performed and accompanied by several control experiments addressing potential other interpretations of the data.

Weaknesses:

(1) The main research question of this study was to test if discrete odors specifically accelerate the birth rate of OSN subtypes they stimulate, i.e. does muscone only accelerate the birth rate of OSNs that express muscone-responsive ORs, or vice versa is the birthrate of muscone-responsive OSNs only accelerated by odors they respond to?

This question is only addressed in Figure 5 of the manuscript and the results only partially support the above claim. The authors test one specific odor (muscone) and find that this odor (only at certain concentrations) accelerates the birth rate of some musk-responsive OSN subtypes, but not two other unrelated control OSN subtypes. This does not at all show that musk-responsive OSN subtypes are only affected by odors that stimulate them and that muscone only affects the birthrate of musk-responsive OSNs, since first, only the odor muscone was tested and second, only two other OSN subtypes were tested as controls, that, importantly, are shown to be generally stimulus-independent OSN subtypes (see Figure 2 and S2).

As a minimum the authors should have a) tested if additional odors that do not activate the three musk-responsive subtypes affect their birthrate b) choose 2-3 additional control subtypes that are known to be stimulus-dependent (from their own 2020 study) and test if muscone affects their birthrates.

(2) The finding that Olfr1440 expressing OSNs do not show any increase in UNO effect size under any muscone concentration (Figure 5D, no significance in line graph for UNO effect sizes, middle) seems to contradict the main claim of this study that certain odors specifically increase birthrates of OSN subtypes they stimulate. It was shown in several studies that olfr1440 is seemingly the most sensitive OR for muscone, yet, in this study, muscone does not further increase birthrates of OSNs expressing olfr1440. The effect size on birthrate under muscone exposure is the same as without muscone exposure (0%).

In contrast, the supposedly second most sensitive muscone-responsive OR olfr235 shows a significant increase in UNO effect size between no muscone exposure (0%) and 0.1% as well as 1% muscone.

(3) The authors introduce their choice to study this particular family of OSN subtypes with first, the previous finding that transcripts for one of these musk-responsive subtypes (olfr235) are downregulated in mice that are deprived of male odors. Second, musk-related odors are found in the urine of different species. This gives the misleading impression that it is known that musk-related odors are indeed excreted into male mouse urine at certain concentrations. This should be stated more clearly in the introduction (or cited, if indeed data exist that show musk-related odors in male mouse urine) because this would be a very important point from an ethological and mechanistic point of view.

In addition, this would also be important information to assess if the chosen muscone concentrations fall at all into the natural range.

Related: If these are male-specific cues, it is interesting that changes in OR transcripts (Figure 1) can already be seen at the age of P28 where other male-specific cues are just starting to get expressed. This should be discussed.

(4) Figure 5: Under muscone exposure the number of newborn neurons on the closed sides fluctuates considerably. This doesn't seem to be the case in other experiments and raises some concerns about how reliable the naris occlusion works for strong exposure to monomolecular odors or what other potential mechanisms are at play.

(5) In contrast to all other musk-responsive OSN types, the number of newborn OSNs expressing olfr1437 increases on the closed side of the OE relative to the open in UNO-treated male mice (Figure 1). This seems to contradict the presented theory and also does not align with the bulk RNAseq data (Figure S1).

(6) The authors hypothesize in relation to the accelerated birthrate of musk-responsive OSN subtypes that "the acceleration of the birthrates of specific OSN subtypes could selectively enhance sensitivity to odors detected by those subtypes by increasing their representation within the OE". However, for two other OSN subtypes that detect male-specific odors, they hypothesize the opposite "By contrast, Olfr912 (Or8b48) and Olfr1295 (Or4k45), which detect the male-specific non-musk odors 2-sec-butyl-4,5-dihydrothiazole (SBT) and (methylthio)methanethiol (MTMT), respectively, exhibited lower representation and/or transcript levels in mice exposed to male odors, possibly reflecting reduced survival due to overstimulation."

Without any further explanation, it is hard to comprehend why exposure to male-derived odors should, on one hand, accelerate birthrates in some OSN subtypes to potentially increase sensitivity to male odors, but on the other hand, lower transcript levels and does not accelerate birth rates of other OSN subtypes due to overstimulation.

Reviewer #2 (Public Review):

In their paper entitled "In mice, discrete odors can selectively promote the neurogenesis of sensory neuron subtypes that they stimulate" Hossain et al. address lifelong neurogenesis in the mouse main olfactory epithelium. The authors hypothesize that specific odorants act as neurogenic stimuli that selectively promote biased OR gene choice (and thus olfactory sensory neuron (OSN) identity). Hossain et al. employ RNA-seq and scRNA-seq analyses for subtype-specific OSN birthdating. The authors find that exposure to male and musk odors accelerates the birthrates of the respective responsive OSNs. Therefore, Hossain et al. suggest that odor experience promotes selective neurogenesis and, accordingly, OSN neurogenesis may act as a mechanism for long-term olfactory adaptation.

The authors follow a clear experimental logic, based on sensory deprivation by unilateral naris occlusion, EdU labeling of newborn neurons, and histological analysis via OR-specific RNA-FISH. The results reveal robust effects of deprivation on newborn OSN identity. However, the major weakness of the approach is that the results could, in (possibly large) parts, depend on "downregulation" of OR subtype-specific neurogenesis, rather than (only) "upregulation" based on odor exposure. While, in Figure 6, the authors show that the observed effects are, in part, mediated by odor stimulation, it remains unclear whether deprivation plays an "active" role as well. Moreover, as shown in Figure 1C, unilateral naris occlusion has both positive and negative effects in a random subtype sample.

Another weakness is that the authors build their model (Figure 8), specifically the concept of selectivity, on a receptor-ligand pair (Olfr912 that has been shown to respond, among other odors, to the male-specific non-musk odors 2-sec-butyl-4,5-dihydrothiazole (SBT)) that would require at least some independent experimental corroboration. At least, a control experiment that uses SBT instead of muscone exposure should be performed. In this context, it is somewhat concerning that some results, which appear counterintuitive (e.g., lower representation and/or transcript levels of Olfr912 and Olfr1295 in mice exposed to male odors) are brushed off as "reflecting reduced survival due to overstimulation." The notion of "reduced survival" could be tested by, for example, a caspase3 assay.<br /> Important analyses that need to be done to better be able to interpret the findings are to present (i) the OR+/EdU+ population of olfactory sensory neurons not just as a count per hemisection, but rather as the ratio of OR+/EdU+ cells among all EdU+ cells; and (ii) to the ratio of EdU+ cells among all nuclei (UNO versus open naris). This way, data would be normalized to (i) the overall rate of neurogenesis and (ii) any broad deprivation-dependent epithelial degeneration.

Finally, the paper will benefit from improved data presentation and adequate statistical testing. Images in Figures 2 - 7, showing both EdU labeling of newborn neurons and OR-specific RNA-FISH, are hard to interpret. Moreover, t-tests should not be employed when data is not normally distributed (as is the case for most of their samples).

Reviewer #3 (Public Review):

Summary:

Neurogenesis in the mammalian olfactory epithelium persists throughout the life of the animal. The process replaces damaged or dying olfactory sensory neurons. It has been tacitly that replacement of the OR subtypes is stochastic, although anecdotal evidence has suggested that this may not be the case. In this study, Santoro and colleagues systematically test this hypothesis by answering three questions: is there enrichment of specific OR subtypes associated with neurogenesis? Is the enrichment dependent on sensory stimulus? Is the enrichment the result of differential generation of the OR type or from differential cell death regulated by neural activity? The authors provide some solid evidence indicating that musk odor stimulus selectively promotes the OR types expressing the musk receptors. The evidence argues against a random selection of ORs in the regenerating neurons.

Strengths:

The strength of the study is a thorough and systematic investigation of the expression of multiple musk receptors with unilateral naris occlusion or under different stimulus conditions. The controls are properly performed. This study is the first to formulate the selective promotion hypothesis and the first systematic investigation to test it. The bulk of the study uses in situ hybridization and immunofluorescent staining to estimate the number of OR types. These results convincingly demonstrate the increased expression of musk receptors in response to male odor or muscone stimulation.

Weaknesses:

A major weakness of the current study is the single-cell RNASeq result. The authors use this piece of data as a broad survey of receptor expression in response to unilateral nasal occlusion. However, several issues with this data raise serious concerns about the quality of the experiment and the conclusions. First, the proportion of OSNs, including both the immature and mature types, constitutes only a small fraction of the total cells. In previous studies of the OSNs using the scRNASeq approach, OSNs constitute the largest cell population. It is curious why this is the case. Second, the authors did not annotate the cell types, making it difficult to assess the potential cause of this discrepancy. Third, given the small number of OSNs, it is surprising to have multiple musk receptors detected in the open side of the olfactory epithelium whereas almost none in the closed side. Since each OR type only constitutes ~0.1% of OSNs on average, the number of detected musk receptors is too high to be consistent with our current understanding and the rest of the data in the manuscript. Finally, unlike the other experiments, the authors did not describe any method details, nor was there any description of quality controls associated with the experiment. The concerns over the scRNASeq data do not diminish the value of the data presented in the bulk of the study but could be used for further analysis.

A weakness of the experiment assessing musk receptor expression is that the authors do not distinguish immature from mature OSNs. Immature OSNs express multiple receptor types before they commit to the expression of a single type. The experiments do not reveal whether mature OSNs maintain an elevated expression level of musk receptors.

There are also two conceptual issues that are of concern. The first is the concept of selective neurogenesis. The data show an increased expression of musk receptors in response to male odor stimulation. The authors argue that this indicates selective neurogenesis of the musk receptor types. However, it is not clear what the distinction is between elevated receptor expression and a commitment to a specific fate at an early stage of development. As immature OSNs express multiple receptors, a likely scenario is that some newly differentiated immature OSNs have elevated expression of not only the musk receptors but also other receptors. The current experiments do not distinguish the two alternatives. Moreover, as pointed out above, it is not clear whether mature OSNs maintain the increased expression. Although a scRNASeq experiment can clarify it, the authors, unfortunately, did not perform an in-depth analysis to determine at which point of neurogenesis the cells commit to a specific musk receptor type. The quality of the scRNASeq data unfortunately also does not lend confidence for this type of analysis.

A second conceptual issue, the idea of homeostasis in regeneration, which the authors presented in the Introduction, needs clarification. In its current form, it is confusing. It could mean that a maintenance of the distribution of receptor types, or it could mean the proper replacement of a specific OR type upon the loss of this type. The authors seem to refer to the latter and should define it properly.

Reviewer #1 (Public Review):

Summary:

In this study, the author aimed to develop a method for estimating neuronal-type connectivity from transcriptomic gene expression data. They sought to develop an interpretable model that could be used to characterize the underlying genetic mechanisms of circuit assembly and connectivity in various neuronal systems.

Strengths:

Many of the proposed suggestions were addressed by the author from the initial review. In general the claims made by the author are more strongly supported by the data and better situated in the literature. A major improvement includes the application of the model to the C. elegans gap junction neuronal system. Despite several key differences in the dataset as compared to the mouse retina data, the proposed model performs comparably to the SCM model currently considered state of the art in the literature (the author should remain cautious about claiming better performance given extremely marginal differences). In section 7.2, the author clearly outlines additional advantages of the proposed model including superior time and space complexity. The overall model performance remains modest, but it learns the same rules as the SCM model as well as other candidate patterns.

As in the initial submission, the bilinear model recapitulates key connectivity motifs for the mouse dataset. The algorithm is shown to converge across several runs affirming its stability/replicability. The model is also extended to predict connectivity on unknown RGC-BC cell type pairs. Without ground truth, the author posits how it should perform based on known functional properties of the RGC type. The hypotheses are confirmed for 8/10 neuronal types with unknown connectivity. The author more clearly describes how this model can be used experimentally for hypothesis testing and presents a more comprehensive future roadmap regarding validation, avenues for improving the model, and incorporation of growing datasets.

Weaknesses:

While the C Elegans dataset is useful because it enables benchmarking to existing models, the dataset is quite different. The gene expression dimensionality is 18 genes as opposed to over 3000 genes in the mouse dataset. It is a strength that the model still works as intended, but a weakness that the bilinear model could not be tested on a similar mouse dataset. This distinction matters because it remains an open question if the PCA methodology would hold up in a dataset with varied distributions of gene expression. Variations of the PCA methodology could be evaluated further with the present dataset to make the generalizability of the model more convincing.

The Gene Ontology analysis requires more methodological explanation. The author claims, "(the linear nature of the model) enables the direct interpretation of gene expressions by examining their associated weights in the model. These weights signify the importance of each gene in determining the connectivity motifs between the BC and RGC types." If I am correctly understanding the methods, the model weights in each dimension are indexing the importance of a gene expression feature as opposed to the importance of a single gene alone, "the gene expression of the BCs in X and the RGCs in Y were featurized by their respective PCs, resulting in matrices of dimensions 22453 × 11323 and 3779 × 3142, respectively." It would be helpful to explain how gene weights are extracted from a gene expression feature once highlighted.

There could be a more rigorous analysis of the predictive capacity of the model even with the current data. The model recapitulates connectivity patterns from the full dataset and a prediction is demonstrated for unknown data. The model is thus championed as a useful tool for predicting how genetic modifications will influence connectivity, but this is not empirically evaluated.

Appraisal of whether the author achieved their aims, and whether results support their conclusions:

In line with the aims of the paper, the author proposed an interpretable bilinear model to learn a shared latent feature space derived from gene expression profiles to predict synaptic connectivity between various neuron types. The model was shown to generalize to two distinct neuronal systems with varying levels of genomic and cellular resolution. While the performance remains modest, the model performs comparably to the existing state of the art despite improved computational complexity.

Discussion of likely impact of the work on the field, and utility of methods and data to the community:

The author has elaborated substantially on the impact of this work, particularly how it could be leveraged in experimental settings. The clear methodology could be implemented by other researchers to test the model on new datasets and for benchmarking novel methods.

Reviewer #1 (Public Review):

The work by Debashish U. Menon, Noel Murcia, and Terry Magnuson brings important knowledge about histone H3.3 dynamics involved in meiotic sex chromosome inactivation (MSCI). MSCI is unique to gametes and failure during this process can lead to infertility. Classically, MSCI has been studied in the context of DNA Damage repair pathways and little is known about the epigenetic mechanisms behind maintenance of the sex body as a silencing platform during meiosis. One of the major strengths of this work is the evidence provided on the role of ARID1A, a BAF subunit, in MSCI through the regulation of H3.3 occupancy in specific genic regions.

Using RNA seq and CUT&RUN and ATAC-seq, the authors show that ARID1A regulates chromatin accessibility of the sex chromosomes and XY gene expression. Loss of ARID1A increases promoter accessibility of XY linked genes with concomitant influx of RNA pol II to the sex body and up regulation of XY-linked genes. This work suggests that ARID1A regulates chromatin composition of the sex body since in the absence of ARID1A, spermatocytes show less enrichment of H3.3 in the sex chromosomes and stable levels of the canonical histones H3.1/3.2. By overlapping CUT&RUN and ATAC-seq data, authors show that changes in chromatin accessibility in the absence of ARID1A are given by redistribution of occupancy of H3.3. Gained open chromatin in mutants corresponds to up regulation of H3.3 occupancy at transcription start sites of genes mediated by ARID1A.

Interestingly, ARID1A loss caused increased promoter occupancy by H3.3 in regions usually occupied by PRDM9. PRDM9 catalyzes histone H3 lysine 4 trimethylation during meiotic prophase I, and positions double strand break (DSB) hotspots. Lack of ARID1A causes reduction in occupancy of DMC1, a recombinase involved in DSB repair, in non-homologous sex regions. These data suggest that ARID1A might indirectly influence DNA DSB repair on the sex chromosomes by regulating the localization of H3.3. This is very interesting given the recently suggested role for ARID1A in genome instability in cancer cells. It raises the question of whether this role is also involved in meiotic DSB repair in autosomes and/or how this mechanism differs in sex chromosomes compared to autosomes.

The fact that there are Arid1a transcripts that escape the Cre system in the Arid1a KO mouse model might difficult the interpretation of the data. The phenotype of the Arid1a knockout is probably masked by the fact that many of the sequencing techniques used here are done on a heterogeneous population of knockout and wild type spermatocytes. In relation to this, I think that the use of the term "pachytene arrest" might be overstated, since this is not the phenotype truly observed. Nonetheless, the authors provide evidence showing that the spermatids observed in cKO testes that progress in spermatogenesis are the ones expressing Arid1a. This work presents enough evidence to include the BAF complex as part of the MSCI process, which increases our knowledge on specific regulation of the sex chromatin during meiosis.

Reviewer #2 (Public Review):

The authors tried to characterize the function of the SWI/SNF remodeler family, BAF, in spermatogenesis. The authors focused on ARID1A, a BAF-specific putative DNA binding subunit, based on gene expression profiles.

The authors disagreed with my previous assessments. I disagree with their response.

Reviewer #3 (Public Review):

In this manuscript, Magnuson and colleagues investigate the meiotic functions of ARID1A, a putative DNA binding subunit of the SWI/SNF chromatin remodeler BAF. The authors develop a germ cell specific conditional knockout (cKO) mouse model using Stra8-cre and observe that ARID1A-deficient cells fail to progress beyond pachytene, although due to inefficiency of the Stra8-cre system the mice retain ARID1A-expressing cells that yield sperm and allow fertility. Because ARID1A was found to accumulate at the XY body late in Prophase I, the authors suspected a potential role in meiotic silencing and by RNAseq observe significant misexpression of sex-linked genes that typically are silenced at pachytene. They go on to show that ARID1A is required for exclusion of RNA PolII from the sex body and for limiting promoter accessibility at sex-linked genes, consistent with a meiotic sex chromosome inactivation (MSCI) defect in cKO mice. The authors proceed to investigate the impacts of ARID1A on H3.3 deposition genome-wide. H3.3 is known be regulated by ARID1A and is linked to silencing, and here the authors find that upon loss of ARID1A, overall H3.3 enrichment at the sex body as measured by IF failed to occur, but H3.3 was enriched specifically at transcriptional start sites of sex-linked genes that are normally regulated by ARID1A. The results suggest that ARID1A normally prevents H3.3 accumulation at target promoters on sex chromosomes and based on additional data, restricts H3.3 to intergenic sites. Finally, the authors present data implicating ARID1A and H3.3 occupancy in DSB repair, finding that ARID1A cKO leads to a reduction in focus formation by DMC1, a key repair protein. Overall the paper provides new insights into the process of MSCI from the perspective of chromatin composition and structure, and raises interesting new questions about the interplay between chromatin structure, meiotic silencing and DNA repair.

In general the data are convincing. The conditional KO mouse model has some inherent limitations due to incomplete recombination and the existence of 'escaper' cells that express ARID1A and progress through meiosis normally. This reviewer feels that the authors have addressed this point thoroughly and have demonstrated clear and specific phenotypes using the best available animal model. The data demonstrate that the mutant cells fail to progress past pachytene, although it is unclear whether this specifically reflects pachytene arrest, as accumulation in other stages of Prophase also is suggested by the data in Table 1.

The revised manuscript more appropriately describes the relationship between ARID1A and DNA damage response (DDR) signaling. The authors don't see defects in a few DDR markers in ARID1A CKO cells (including a low resolution assessment of ATR), suggesting that ARID1A may not be required for meiotic DDR signaling. However, as previously noted the data do not rule out the possibility that ARID1A is downstream of DDR signaling, and the authors note the possibility of a role for DDR signaling upstream of ARID1A.

A final comment relates to the impacts of ARID1A loss on DMC1 focus formation and the interesting observation of reduced sex chromosome association by DMC1. The authors additionally assess the related recombinase RAD51 and suggest that it is unaffected by ARID1A loss. However, only a single image of RAD51 staining in the cKO is provided (Fig. S11) and there are no associated quantitative data provided. The data are suggestive and conclusions about the impacts of ARID1A loss on RAD51 must be considered as preliminary until more rigorously assessed.

Reviewer #1 (Public Review):

Summary:

Asymptomatic malaria infections are frequent during the dry season and have been associated with lower cytoadherence of P. falciparum parasites and lower expression of variant surface antigens. The mechanisms underlying parasite adaptation during the low transmission season remain poorly understood. The authors previously established that members of the non-coding RNA RUF6 gene family, transcribed by RNA pol III, are required for expression of the main variant surface antigens in P. falciparum, PfEMP1, which drive parasite cytoadherence and pathogenicity. In this study, the authors investigated the contribution of RNA pol III transcription in the regulation of PfEMP1 expression in different clinical states, either symptomatic malaria cases during the wet season or asymptomatic infections during the dry season.

By reanalyzing RNAseq data from a previous study in Mali, complemented with RT-qPCR on new samples collected in The Gambia, the authors first report the down-regulation of RNA pol III genes (tRNAs, RUF6) in P. falciparum isolates collected from asymptomatic individuals during the dry season, as compared to isolates from symptomatic (wet season) individuals. They also confirm the down-regulation of var (DBLalpha) gene expression in asymptomatic infection as compared to symptomatic malaria. Plasma analysis in the two groups in the Gambian study reveals higher Magnesium levels in dry season as compared to wet season samples, pointing at a possible role of external factors. The authors tested the effect of MgCl2 supplementation on cultured parasites, as well as three other stimuli (temperature, low glucose, Ile deprivation), and show that Ile deprivation and MgCl2 both induce down-regulation of RNA pol III transcription but not pol I or pol II (except the active var gene). Using RNAseq, they show that MgCl2 supplementation predominantly inhibits RNA pol III-transcribed genes, including the entire RUF6 family. Conditional depletion of Maf1 leads to the up-regulation of RNA pol III gene transcription, confirming that Maf1 is a RNA pol III inhibitor in P. falciparum, as described in other organisms. Quantitative mass spectrometry shows that Maf1 interacts with RNA pol III complex in the nucleus, and with distinct proteins including two phosphatases in the cytoplasm. Using the Maf1 cKD parasites, the authors document that down-regulation of RNA pol III by MgCl2 is dependent on Maf1. Finally, they show that MgCl2 results in decreased cytoadherence of infected erythrocytes, associated with reduced PfEMP1 expression.

Strengths:

-The work is very well performed and presented.<br /> -The study uncovers a novel regulatory mechanism relying on RNA pol III-dependent regulation of variant surface antigens in response to external signals, which could contribute to parasite adaptation during the low transmission season.<br /> -Potential regulators of Maf1 were identified by mass spectrometry, including phosphatases, paving the way for future mechanistic studies.

Weaknesses:

-The signaling pathway upstream of Maf1 remains unknown. In eukaryotes, Maf1 is a negative regulator of RNA pol III and is regulated by external signals via the TORC pathway. Since TORC components are absent in the apicomplexan lineage, one central question that remains open is how Maf1 is regulated in P. falciparum. Magnesium is probably not the sole stimulus involved, as suggested by the observation that Ile deprivation also down-regulates RNA pol III activity.<br /> -The study does not address why MgCl2 levels vary depending on the clinical state. It is unclear whether plasma magnesium is increased during asymptomatic malaria or decreased during symptomatic infection, as the study does not include control groups with non-infected individuals. Along the same line, MgCl2 supplementation in parasite cultures was done at 3mM, which is higher than the highest concentrations observed in clinical samples.<br /> -Although the study provides biochemical evidence of Maf1 accumulation in the parasite nuclear fraction upon magnesium addition, this is not fully supported by the immunofluorescence experiments.

Reviewer #2 (Public Review):

The study by Diffendall et al. set out to establish a link between the activity of RNA polymerase III (Pol III) and its inhibitor Maf1 and the virulence of Plasmodium falciparum in vivo. Having previously found that knockdown of the ncRNA ruf6 gene family reduces var gene expression in vitro, they now present experimental evidence for the regulation of ruf6 and subsequently, var gene expression by Pol III using a commercially available inhibitor. They confirm their findings with samples from a previously published Gambian cohort study using asymptomatic dry season and mildly symptomatic wet season samples, showing that higher levels of Pol III-dependent transcripts and var transcripts as well as lower MgCl2 plasma concentrations are present in wet season samples. From this, they hypothesize that the external stimuli heat, reduced glucose and essential amino acid supply, and increased MgCl2 levels are sensed by the parasite through the only known Pol III inhibitor Maf1 and result in lower Pol III activity and fewer ruf6 transcripts, which in turn reduces var gene expression, leading to reduced cytoadherence and virulence of P. falciparum. In their in vitro experiments they focus on investigating higher MgCl2 levels and their impact on Pol III and Maf1 activity as well as var gene expression and parasites adherence to purified CD36, thereby successfully confirming their hypothesis for MgCl2. Nicely, MgCl2-induced down-regulation of Pol III activity was shown to be dependent on Maf1 using a knock-down cell line. Additionally, they show that the Maf1-KD cell line displays a slower growth rate with fewer merozoites per schizonts and Maf1 interacts with RNA pol III subunits and some kinases/phosphatases.

Comments on latest version:

It is understandable that the RNA samples from the Gambian cohort were limited, but for all in vitro analyses a larger panel of qPCR primers or RNAseq would have been feasible. I also understand the rationale for using the general var primer pair (DBLa) for field isolates, but since the authors were working with a clonal parasite line (3D7) in vitro, qPCR with specific 3D7/NF54 primer pairs or RNAseq, which would also allow inferences about ruf6 regulation of specific (neighboring?) var genes and other Pol III-regulated genes, would have been a far better option.

As far as I could see from the resubmitted manuscript, the authors did not correct the statistical analyses. For example, they continue to apply a t-test to fold-change values (which must be transformed to log2), many t-test based analyses rely on only 2-3 replicates (a non-parametric test would be more appropriate), they have not corrected for multiple testing, and it is unclear how the authors handle technical and biological replicates in their plots. Therefore, I still suspect that more appropriate statistical analyses might have an impact on the significance of their results.

I agree that CD36 binding is associated with mild malaria, but since the authors only make a link between Pol III and CD36 binding in vitro, I think it is an overstatement to claim something like "Our study reveals a regulatory mechanism in P. falciparum involving RNA Polymerase III, which plays a pivotal role in the parasite's virulence."

Finally, if the authors have checked all the relevant literature on MgCl2, it should be easy for them to give a brief explanation why they included only one study and ignored all the other contradictory results.

Reviewer #3 (Public Review):

Summary:

This work describes a new pathway by which malaria parasites, P. falciparum, may regulate their growth and virulence (i.e. their expression of virulence-linked cytoadhesins). This is a topic of considerable interest in the field - does this important parasite sense factor(s) in its host bloodstream and regulate itself accordingly? Several fragments of evidence have come out on this topic in the past decade, showing, for example, reduced parasite growth under calorie restriction (in mice); parasite dormancy in response to amino acid starvation (in culture and in mice), and also reduced virulence in dry-season, low-parasitaemia infections in humans. The molecular mechanisms that may underlie this interesting biology remain only poorly understood.

Here, the authors show that dry-season P. falciparum parasites have reduced expression of Pol3-transcribed tRNAs and ncRNAs that positively regulate virulence gene expression. They link the level of Pol3 activity to PfMaf1, a remnant of the largely-absent nutrient-sensing TOR pathway in this parasite. They propose that in the dry season, human hosts may be calorie-restricted, leading to Maf1 moving to the nucleus and suppressing Pol3, thus downregulated growth and virulence of parasites. The evidence is intriguing and the idea is conceptually elegant.

Strengths:

The use of dry/wet-season field samples from The Gambia is a strength, showing potential real-world relevance. The generation of an inducible knockdown of Maf1 in lab-cultured parasites is also a strength, allowing this pathway to be studied somewhat in isolation.

Weaknesses:

(1) The signals upstream of Maf1 remain rather a black box. 4 are tested - heatshock and low-glucose, which seem to suppress ALL transcription; low-Isoleucine and high magnesium, which suppress Pol3. Therefore the authors use Mg supplementation throughout as a 'starvation type' stimulus. They do not discuss why they didn't use amino acid limitation, which could be more easily rationalised physiologically. It may for experimental simplicity (no need for dropout media) but this should be discussed, and ideally sample experiments with low-IsoLeu should be done too, to see if the responses (e.g. cytoadhesion) are all the same.

(2) The proteomics, conducted to seek partners of Maf1, is probably the weakest part. From Fig S4 it is clear that the proteins highlighted in the text are highly selected (as ones that might be relevant, e.g. phosphatases), but many others are more enriched. It would be good to see a) the top hits from the whole list provided as a short table within the main proteomics figure, along with the GO terms that actually came top in enrichment; b) the whole list provided as a supp. spreadsheet for easy re-analysis, rather than a PDF which cannot be easily re-used.

(3) Fig 3 shows the Maf1-low line has very poor growth after only 5 days but it is stated that no dead parasites are seen even after 8 cycles and the merozoites number is down only ~18 to 15... is this too small to account for such poor growth (~5-fold reduced in a single cycle, day 3-5)? It would additionally be interesting to see a cell-cycle length assessment and invasion assay, to see of Maf1-low parasite have further defects in growth.

Other weaknesses, which are more restricted but were not addressed in revision, are highlighted below:

Fig S1B - The downregulation of RNAPol3 transcripts caused by a commercial Pol3 inhibitor is pretty weak - mostly non-significant. The authors might comment on why they think this is, when interfering with PfMaf1 evidently has a greater effect.

Fig 2D: the legend states ' Expressed transcripts from three replicates between control and addition of MgCl2 that are significantly up-regulated are highlighted in red while significantly down-regulated RNA Pol III genes are highlighted in blue (FDR corrected p-value of <0.05) and a FC {greater than or equal to}{plus minus} 1.95) with examples listed as text'. This isn't very clear. The authors could clarify whether they took ALL (Pol3 or not) upregulated genes to show in red, but only putative Pol3-regulated genes to show in blue? If so, why? Or did they take all significantly downregulated genes, and found they were all annotated as pol3 transcribed? (I cannot see any dots that are not blue. If there are some, a clearer figure is needed?)

Line 227: 'PfMaf1 levels were shown to decrease by approximately 57% in total extracts after one cycle' - the provenance of this very precise percentage isn't clear (it does not appear on the figure). Is it densitometry of a western blot? And if so, is it an average of the 3 replicates that are stated in the legend (but not shown), or from the single example blot shown in Figure 3?

Fig 4A: the western blot, as shown, lacks controls, both for loading and for completeness of cyto/nuclear fractionation. To avoid confusion, these should be shown in the main figure, as is standard in the field, rather than separately in a supp figure. Ideally, 3 repeats should be done, with densitiometry quantification.

Reviewer #1 (Public Review):

Bian et al showed that biomarker-informed PhenoAgeAccel was consistently related to an increased risk of site-specific cancer and overall cancer within and across genetic risk groups. The results showed that PhenoAgeAccel and genetic liability of a bunch of cancers serve as productive tools to facilitate the identification of cancer-susceptible individuals under an additive model. People with a high genetic risk for cancer may benefit from PhenoAgeAccel-imformed interventions.

As the authors pointed out, the large sample size, the prospective design UK Biobank study, and the effective application of PhenoAgeAccel in predicting the risk of overall cancer are the major strengths of the study. Meanwhile, the CPRS seems to be a solid and comprehensive score based on incidence-weighted site-specific polygenic risk scores across 20 well-powered GWAS for cancers.

Reviewer #2 (Public Review):

Bian et al. calculated Phenotypic Age Acceleration (PhenoAgeAccel) via a linear model regressing Phenotypic Age on chronological age. They examined the associations between PhenoAgeAccel and cancer incidence using 374,463 individuals from the UK Biobank and found that older PhenoAge was consistently related to an increased risk of incident cancer, even among each risk group defined by genetics.

The study is well-designed, and uses a large sample size from the UK biobank.

Comments on revised version:

The authors have addressed all my concerns.

Reviewer #1 (Public Review):

Summary:

This manuscript is an extension of previous studies by this group looking at the new drug spectinamide 1599. The authors directly compare therapy with BPaL (bedaquiline, pretomanid, linezolid) to a therapy that substitutes spectinamide for linezolid (BPaS). The Spectinamide is given by aerosol exposure and the BPaS therapy is shown to be as effective as BPaL without adverse effects. The work is rigorously performed and analyses of the immune responses are consistent with curative therapy.

Strengths:

(1) This group uses 2 different mouse models to show the effectiveness of the BPaS treatment.

(2) Impressively the group demonstrates immunological correlates associated with Mtb cure with the BPaS therapy.

(3) Linezolid is known to inhibit ribsomes and mitochondria whereas spectinaminde does not. The authors clearly demonstrate the lack of adverse effects of BPaS compared to BPaL.

Weaknesses:

(1) Although this is not a weakness of this paper, a sentence describing how the spectinamide would be administered by aerosolization in humans would be welcomed.

Reviewer #1 (Public Review):

Summary:

In this work, Huang et al used SMRT sequencing to identify methylated nucleotides (6mA, 4mC, and 5mC) in Pseudomonas syringae genome. They show that the most abundant modification is 6mA and they identify the enzymes required for this modification as when they mutate HsdMSR they observe a decrease of 6mA. Interestingly, the mutant also displays phenotypes of change in pathogenicity, biofilm formation, and translation activity due to a change in gene expression likely linked to the loss of 6mA.

Overall, the paper represents an interesting set of new data that can bring forward the field of DNA modification in bacteria.

Major Concerns:

• Most of the authors' data concern Psph pathovar. I am not sure that the authors' conclusions are supported by the two other pathovars they used in the initial 2 figures. If the authors want to broaden their conclusions to Pseudomonas synringae and not restrict it to Psph, the authors should have stronger methylation data using replicates. Additionally, they should discuss why Pss is so different than Pst and Psph. Could they do a blot to confirm it is really the case and not a sequencing artefact? Is the change of methylation during bacterial growth conserved between the pathovar? The authors should obtain mutants in the other pathovar to see if they have the same phenotype. The authors have a nice set of data concerning Psph but the broadening of the results to other pathovar requires further investigation.

• The authors should include proper statistical analysis of their data. A lot of terms are descriptive but not supported by a deeper analysis to sustain the conclusions. For example, in Figure 4E, we do not know if the overlap is significant or not. Are DEGs more overlapping to 6mA sites than non-DEGs? Here is a non-exhaustive list of terms that need to be supported by statistics: different level (L145), greater conservation (L162), significant conservation (L165), considerable similarity (L175), credible motifs (L189), Less strong (L277) and several "lower" and "higher" throughout the text.

• The authors performed SMRT sequencing of the delta hsdMSR showing a reduction of 6mA. Could they include a description of their results similar to Figures 1-2. How reduced is the 6mA level? Is it everywhere in the genome? Does it affect other methylation marks? This analysis would strengthen their conclusions.

• In Figure 6E to conclude that methylation is required on both strands, the authors are missing the control CAGCN6CGC construct otherwise the effect could be linked to the A on the complementary strand.

Reviewer #2 (Public Review):

In the present manuscript, Huang et.al. revealed the significant roles of the DNA methylome in regulating virulence and metabolism within Pseudomonas syringae, with a particular focus on the HsdMSR system in this model strain. The authors used SMRT-seq to profile the DNA methylation patterns (6mA, 5mC, and 4mC) in three P. syringae strains (Psph, Pss, and Psa) and displayed the conservation among them. They further identified the type I restriction-modification system (HsdMSR) in P. syringae, including its specific motif sequence. The HsdMAR participated in the process of metabolism and virulence (T3SS & Biofilm formation), as demonstrated through RNA-seq analyses. Additionally, the authors revealed the mechanisms of the transcriptional regulation by 6mA. Strictly from the point of view of the interest of the question and the work carried out, this is a worthy and timely study that uses third-generation sequencing technology to characterize the DNA methylation in P. syringae. The experimental approaches were solid, and the results obtained were interesting and provided new information on how epigenetics influences the transcription in P. syringae. The conclusions of this paper are mostly well supported by data, but some aspects of data analysis and discussion need to be clarified and extended.

Reviewer #1 (Public Review):

Summary:

The manuscript presents a compelling model to explain the impact of mosaicism in preimplantation genetic testing for aneuploidies.

Strengths:

A new view of mosaicism is presented with a computational model, that brings new insights into an "old" debate in our field. It is a very well-written manuscript.

Weaknesses:

Although the manuscript is very well written, this is in a way that assumes that the reader has existing knowledge about specific terms and topics. This was apparent through a lack of definitions and minimal background/context to the aims and conclusions for some of the author's findings.

There is a need for some examples to connect real evidence and scenarios from clinical reports with the model.

Reviewer #2 (Public Review):

Summary:

Although an oversimplification of the biological complexities, this modeling work does add, in a limited way, to the current knowledge on the theoretical difficulties of detecting mosaicism in human blastocysts from a single trophectoderm biopsy in PGT. However, many of the premises that the modeling was built on are theoretical and based on unproven biological and clinical assumptions that could yet lead to be untrue. Therefore, the work should be considered only as a simplified model that could assist in further understanding of the complexities of preimplantation embryo mosaicism, but assumptions of real-world application are, at this stage, premature and should not be considered as evidence in favour of any clinical strategies.

Strengths:

The work has presented an intriguing theoretical model for elaborating on the interpretation of complex and still unclear biological phenomena such as chromosomal mosaicism in preimplantation embryos.

Weaknesses:

Lines 134-138: The spatial modeling of mitotic errors in the embryo was oversimplified in this manuscript. There is only limited (and non-comprehensive) evidence that meiotic errors leading to chromosome mosaicism arise from chromosome loss or gain only (e.g. anaphase lag). This work did not take into account the (more recognised) possibility of mitotic nondisjunction where following the event there would be clones of cells with either one more or one less of the same chromosome. Although addressed in the discussion (lines 572-574), not including this in the most basic of modeling is a significant oversight that, based on the simple likelihood, could significantly affect results.

General comment: the premise of the manuscript is that an embryologist (embryology laboratory) is aware of and can accurately quantify the number of cells in a blastocyst or TE biopsy. The reality is that it is not possible to accurately do this without the destruction of the sample which is obviously not clinically applicable. Based on many assumptions the findings show that taking small biopsies poorly classifies mosaic embryos, which is not disputed. However, extrapolating this to the clinic and making suggestions to biopsy a certain amount of cells (lines 539-540) is careless and potentially harmful by suggesting the introduction of potential change in clinical practice without validation. Additionally, no embryologist in the field can tell how many cells are present in a clinical TE biopsy, making this suggestion even more impractical.

On a more general clinical consideration, the authors should acknowledge that when reporting findings of unproven clinical utility and unknown predictive values this inevitably results in negative consequences for infertile couples undergoing IVF. It is proven and established that when couples face the decision on how to manage a putative mosaicism finding, the vast majority decide on embryo disposal. It was recently reported in an ESHRE survey that about 75% of practitioners in the field consider discarding or donating to research embryos with reported mosaicism. A prospective clinical trial showed that about 30% live birth rate reduction can be expected if mosaic embryos are not considered (Capalbo et al., AJHG 2021). The real-world experience is that when mosaicism is reported, embryos with almost normal reproductive potential are discarded. The authors should be more careful with the clinical interpretation and translation of these theoretical findings.

There is a robust consensus within the field of clinical genetics and genomics regarding the necessity to exclusively report findings that possess well-established clinical validity and utility. This consensus is grounded in the imperative to mitigate misinterpretation and ineffective actions in patient care. However, the clinical framework delineated in this manuscript diverges from the prevailing consensus in clinical genetics. Clinical genetics and genomics prioritize the dissemination of findings that have undergone rigorous validation processes and have demonstrated clear clinical relevance and utility. This emphasis is crucial for ensuring accurate diagnosis, prognosis, and therapeutic decision-making in patient care. By adhering to established standards of evidence and clinical utility, healthcare providers can minimize the potential for misinterpretation and inappropriate interventions. The framework proposed in this manuscript appears to deviate from the established principles guiding clinical genetics practice. It is imperative for clinical frameworks to align closely with the consensus guidelines and recommendations set forth by professional organizations and regulatory bodies in the field. This alignment not only upholds the integrity and reliability of genetic testing and interpretation but also safeguards patient well-being and clinical outcomes.

References:<br /> ACMG Board of Directors. (2015). Clinical utility of genetic and genomic services: a position statement of the American College of Medical Genetics and Genomics. Genetics in Medicine, 17(6), 505-507. https://doi.org/10.1038/gim.2014.194.<br /> Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., ... ACMG Laboratory Quality Assurance Committee. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine, 17(5), 405-424. https://doi.org/10.1038/gim.2015.30

Line 61: "Self correction" - This terminology is unfortunately indiscriminately used in the field for PGT when referring to mosaicism and implies that the embryo can actively correct itself from a state of inherent abnormality. Apart from there being no evidence to suggest that there is an active process by which the embryo itself can correct chromosomal errors, most presumed euploid/aneuploid mosaic embryos will have been euploid zygotes and therefore "self-harm" may be a better explanation. True self-correction in the form of meiotic trisomy/monosomy rescue is of course theoretically possible but not at all clinically significant. The concept being conveyed in this part of the manuscript is not disputed but it is strongly suggested that the term "self correction" is not used in this context, nor in the rest of the manuscript, to prevent the perpetuation of misinformation in the field and instead use a better description.

Lines 69-73: The ability to quantify aneuploidy in known admixtures of aneuploid cells is indeed well established. However, the authors claim that the translation of this to embryo biopsy samples is inferred with some confidence and that if a biopsy shows an intermediate chromosome copy number (ICN), that the biopsy and the embryo are mosaic. There are no references provided here and indeed the only evidence in the literature relating to this is to the contrary. Multifocal biopsy studies have shown that an ICN result in a single biopsy is often not seen in other biopsies from the same embryo (Capalbo et al 2021; Kim et al., 2022; Girardi et al., 2023; Marin, Xu, and Treff 2021). Multifocal biopsies showing reciprocal gain and loss which would provide stronger validation for the presence of true mosaicism are also rare. In this work, the entire manuscript is based on the accuracy of ICN in a biopsy being reflective of mosaicism in the embryo. The evidence however points to a large proportion of ICN detected in embryo biopsy potentially being technical artifacts (misdiagnosing both constitutionally normal and abnormal (meiotic aneuploid) embryos as mosaic. Therefore, although results from the modelling provide insight into theoretical results, these can not be used to inform clinical decision-making at all.

Lines 87-89: The authors make the claim that emerging evidence is suggestive that the majority of embryos are mosaic to some degree. If in fact, mosaicism is the norm, the clinical importance may be limited.

Line 102-103: The statement that data shows that the live birth rate per ET is generally lower in mosaic embryos than euploid embryos is from retrospective cohort studies that suffer from significant selection bias. The authors have ignored non-selection study results (Capalbo et al, ajhg 2021) that suggest that putative mosaicism has limited predictive value when assessed prospectively and blinded.

Lines 94-98: The authors have misrepresented the works they have presented as evidence for biopsy result accuracy (Kim et al., 2023; Victor et al 2019; Capalbo et al., 2021; Girardi et al., 2023, and any others). These studies show that a mosaic biopsy is not representative of the whole embryo and can actually be from embryos where the remainder of the embryo shows no evidence of mosaicism. There is also a missing key reference of Capalbo et al, AJHG 2021, and Girardi et al., HR 2023 where multifocal biopsies were taken.

Lines 371-372: "Selecting the embryo with the lowest number of aneuploid cells in the biopsy for transfer is still the most sensible decision". Where is the evidence for this other than the modeling which is affected by oversimplification and unproven assumptions? Although the statement seems logical at face value, there is no concrete evidence that the proportion of aneuploid cells within a biopsy is valuable for clinical outcomes, especially when co-evaluated with other more relevant clinical information.

Lines 431-463: In this section, the authors discuss clinical outcome data from the transfer of putative mosaic embryos and make conclusions about the relationship between ICN level in biopsy and successful pregnancy outcomes. The retrospective and selective nature of the data used in forming the results has the potential to lead to incorrect conclusions when applied to prospective unselected data.

Reviewer #3 (Public Review):

Unfortunately, this study fails to incorporate the most important variable impacting the ability to predict mosaicism, the accuracy of the test. The fact is that most embryos diagnosed as mosaic are not mosaic. There may be 4 cases out of thousands and thousands of transfers where a confirmation was made. Mosaicism has become a category of diagnosis in which embryos with noisy NGS profiles are placed. With VeriSeq NGS it is not possible to routinely distinguish true mosaicism from noise. An analysis of NGS noise levels (MAPD) versus the rate of mosaics by clinic using the registry will likely demonstrate this is the case. Without accounting for the considerable inaccuracy of the method of testing the proposed modeling is meaningless.

Recent data using more accurate methods of identifying mosaicism indicate that the prevalence of true preimplantation embryonic mosaicism is only 2%, which is also consistent with findings made post-implantation. This model fails to account for the possibility that, because so few embryos are actually mosaic, there is actually no relevance to clinical care whatsoever. In fact, differences in clinical outcomes of embryos designated as mosaic could be entirely attributed to poor embryo quality resulting in noise levels that make NGS results fall into the "mosaic" category.

Additional comments:

Indeed, as more data emerges, it appears that the majority of embryos from both healthy and infertile couples are mosaic to some degree (Coticchio et al., 2021; Griffin et al., 2022).

This statement should be softened as all embryos will be considered mosaic when a method with a 10% false positive rate is applied to 10 more parts of the same embryo. The distinction between artifact and true mosaicism cannot be made with nearly all current methods of testing. When virtually no embryos display uniform aneuploidy in a rebiopsy study, there should be great concern over the accuracy of the testing used. The vast majority of aneuploidy is meiotic in origin.

Experimental data provides strong evidence that, for the most part, the biopsy result obtained accurately represents the chromosome constitution of the rest of the embryo (Kim 96 et al., 2022; Navratil et al., 2020; Victor et al., 2019).

This statement is incorrect given published systematic review of the literature indicates a 10% false positive rate based on rebiopsy results.

This shows that accurately classifying a mosaic embryo based on a single biopsy is not robust.

This is exactly why the practice of designating embryo mosaics with intermediate copy numbers should not exist.

Reviewer #1 (Public Review):

Summary:

This study by Park and colleagues uses longitudinal saliva viral load data from two cohorts (one in the US and one in Japan from a clinical trial) in the pre-vaccine era to subset viral shedding kinetics and then use machine learning to attempt to identify clinical correlates of different shedding patterns. The stratification method identifies three separate shedding patterns discriminated by peak viral load, shedding duration, and clearance slope. The authors also assess micro-RNAs as potential biomarkers of severity but do not identify any clear relationships with viral kinetics.

Strengths:

The cohorts are well developed, the mathematical model appears to capture shedding kinetics fairly well, the clustering seems generally appropriate, and the machine learning analysis is a sensible, albeit exploratory approach. The micro-RNA analysis is interesting and novel.

Weaknesses:

The conclusions of the paper are somewhat supported by the data but there are certain limitations that are notable and make the study's findings of only limited relevance to current COVID-19 epidemiology and clinical conditions.

(1) The study only included previously uninfected, unvaccinated individuals without the omicron variant. It has been well documented that vaccination and prior infection both predict shorter duration shedding. Therefore, the study results are no longer relevant to current COVID-19 conditions. This is not at all the authors' fault but rather a difficult reality of much retrospective COVID research.

(2) The target cell model, which appears to fit the data fairly well, has clear mechanistic limitations. Specifically, if such a high proportion of cells were to get infected, then the disease would be extremely severe in all cases. The authors could specify that this model was selected for ease of use and to allow clustering, rather than to provide mechanistic insight. It would be useful to list the AIC scores of this model when compared to the model by Ke.

(3) Line 104: I don't follow why including both datasets would allow one model to work better than the other. This requires more explanation. I am also not convinced that non-linear mixed effects approaches can really be used to infer early model kinetics in individuals from one cohort by using late viral load kinetics in another (and vice versa). The approach seems better for making population-level estimates when there is such a high amount of missing data.

(4) Along these lines, the three clusters appear to show uniform expansion slopes whereas the NBA cohort, a much larger cohort that captured early and late viral loads in most individuals, shows substantial variability in viral expansion slopes. In Figure 2D: the upslope seems extraordinarily rapid relative to other cohorts. I calculate a viral doubling time of roughly 1.5 hours. It would be helpful to understand how reliable of an estimate this is and also how much variability was observed among individuals.

(5) A key issue is that a lack of heterogeneity in the cohort may be driving a lack of differences between the groups. Table 1 shows that Sp02 values and lab values that all look normal. All infections were mild. This may make identifying biomarkers quite challenging.

(6) Figure 3A: many of the clinical variables such as basophil count, Cl, and protein have very low pre-test probability of correlating with virologic outcome.

(7) A key omission appears to be micoRNA from pre and early-infection time points. It would be helpful to understand whether microRNA levels at least differed between the two collection timepoints and whether certain microRNAs are dynamic during infection.

(8) The discussion could use a more thorough description of how viral kinetics differ in saliva versus nasal swabs and how this work complements other modeling studies in the field.

(9) The most predictive potential variables of shedding heterogeneity which pertain to the innate and adaptive immune responses (virus-specific antibody and T cell levels) are not measured or modeled.

(10) I am curious whether the models infer different peak viral loads, duration, expansion, and clearance slopes between the 2 cohorts based on fitting to different infection stage data.

Reviewer #2 (Public Review):

Summary:

This study argues it has found that it has stratified viral kinetics for saliva specimens into three groups by the duration of "viral shedding"; the authors could not identify clinical data or microRNAs that correlate with these three groups.

Strengths:

The question of whether there is a stratification of viral kinetics is interesting.

Weaknesses:

The data underlying this work are not treated rigorously. The work in this manuscript is based on PCR data from two studies, with most of the data coming from a trial of nelfinavir (NFV) that showed no effect on the duration of SARS-CoV-2 PCR positivity. This study had no PCR data before symptom onset, and thus exclusively evaluated viral kinetics at or after peak viral loads. The second study is from the University of Illinois; this data set had sampling prior to infection, so has some ability to report the rate of "upswing." Problems in the analysis here include:

-- The PCR Ct data from each study is treated as equivalent and referred to as viral load, without any reports of calibration of platforms or across platforms. Can the authors provide calibration data and justify the direct comparison as well as the use of "viral load" rather than "Ct value"? Can the authors also explain on what basis they treat Ct values in the two studies as identical?

-- The limit of detection for the NFV PCR data was unclear, so the authors assumed it was the same as the University of Illinois study. This seems a big assumption, as PCR platforms can differ substantially. Could the authors do sensitivity analyses around this assumption?

-- The authors refer to PCR positivity as viral shedding, but it is viral RNA detection (very different from shedding live/culturable virus, as shown in the Ke et al. paper). I suggest updating the language throughout the manuscript to be precise on this point.

-- Eyeballing extended data in Figure 1, a number of the putative long-duration infections appear to be likely cases of viral RNA rebound (for examples, see S01-16 and S01-27). What happens if all the samples that look like rebound are reanalyzed to exclude the late PCR detectable time points that appear after negative PCRs?

-- There's no report of uncertainty in the model fits. Given the paucity of data for the upslope, there must be large uncertainty in the up-slope and likely in the peak, too, for the NFV data. This uncertainty is ignored in the subsequent analyses. This calls into question the efforts to stratify by the components of the viral kinetics. Could the authors please include analyses of uncertainty in their model fits and propagate this uncertainty through their analyses?

-- The clinical data are reported as a mean across the course of an infection; presumably vital signs and blood test results vary substantially, too, over this duration, so taking a mean without considering the timing of the tests or the dynamics of their results is perplexing. I'm not sure what to recommend here, as the timing and variation in the acquisition of these clinical data are not clear, and I do not have a strong understanding of the basis for the hypothesis the authors are testing.

It's unclear why microRNAs matter. It would be helpful if the authors could provide more support for their claims that (1) microRNAs play such a substantial role in determining the kinetics of other viruses and (2) they play such an important role in modulating COVID-19 that it's worth exploring the impact of microRNAs on SARS-CoV-2 kinetics. A link to a single review paper seems insufficient justification. What strong experimental evidence is there to support this line of research?

Reviewer #3 (Public Review):

The article presents a comprehensive study on the stratification of viral shedding patterns in saliva among COVID-19 patients. The authors analyze longitudinal viral load data from 144 mildly symptomatic patients using a mathematical model, identifying three distinct groups based on the duration of viral shedding. Despite analyzing a wide range of clinical data and micro-RNA expression levels, the study could not find significant predictors for the stratified shedding patterns, highlighting the complexity of SARS-CoV-2 dynamics in saliva. The research underscores the need for identifying biomarkers to improve public health interventions and acknowledges several limitations, including the lack of consideration of recent variants, the sparsity of information before symptom onset, and the focus on symptomatic infections.

The manuscript is well-written, with the potential for enhanced clarity in explaining statistical methodologies. This work could inform public health strategies and diagnostic testing approaches. However, there is a thorough development of new statistical analysis needed, with major revisions to address the following points:

(1) Patient characterization & selection: Patient immunological status at inclusion (and if it was accessible at the time of infection) may be the strongest predictor for viral shedding in saliva. The authors state that the patients were not previously infected by SARS-COV-2. Was Anti-N antibody testing performed? Were other humoral measurements performed or did everything rely on declaration? From Figure 1A, I do not understand the rationale for excluding asymptomatic patients. Moreover, the mechanistic model can handle patients with only three observations, why are they not included? Finally, the 54 patients without clinical data can be used for the viral dynamics fitting and then discarded for the descriptive analysis. Excluding them can create a bias. All the discarded patients can help the virus dynamics analysis as it is a population approach. Please clarify. In Table 1 the absence of sex covariate is surprising.

(2) Exact study timReviewer #3 (Public Review):eline for explanatory covariates: I understand the idea of finding « early predictors » of long-lasting viral shedding. I believe it is key and a great question. However, some samples (Figure 4A) seem to be taken at the end of the viral shedding. I am not sure it is really easier to micro-RNA saliva samples than a PCR. So I need to be better convinced of the impact of the possible findings. Generally, the timeline of explanatory covariate is not described in a satisfactory manner in the actual manuscript. Also, the evaluation and inclusion of the daily symptoms in the analysis are unclear to me.

(3) Early Trajectory Differentiation: The model struggles to differentiate between patients' viral load trajectories in the early phase, with overlapping slopes and indistinguishable viral load peaks observed in Figures 2B, 2C, and 2D. The question arises whether this issue stems from the data, the nature of Covid-19, or the model itself. The authors discuss the scarcity of pre-symptom data, primarily relying on Illinois patients who underwent testing before symptom onset. This contrasts earlier statements on pages 5-6 & 23, where they claim the data captures the full infection dynamics, suggesting sufficient early data for pre-symptom kinetics estimation. The authors need to provide detailed information on the number or timing of patient sample collections during each period.

(4) Conditioning on the future: Conditioning on the future in statistics refers to the problematic situation where an analysis inadvertently relies on information that would not have been available at the time decisions were made or data were collected. This seems to be the case when the authors create micro-RNA data (Figure 4A). First, when the sampling times are is something that needs to be clarified by the authors (for clinical outcomes as well). Second, proper causal inference relies on the assumption that the cause precedes the effect. This conditioning on the future may result in overestimating the model's accuracy. This happens because the model has been exposed to the outcome it's supposed to predict. This could question the - already weak - relation with mir-1846 level.

(5) Mathematical Model Choice Justification and Performance: The paper lacks mention of the practical identifiability of the model (especially for tau regarding the lack of early data information). Moreover, it is expected that the immune effector model will be more useful at the beginning of the infection (for which data are the more parsimonious). Please provide AIC for comparison, saying that they have "equal performance" is not enough. Can you provide at least in a point-by-point response the VPC & convergence assessments?

(6) Selected features of viral shedding: I wonder to what extent the viral shedding area under the curve (AUC) and normalized AUC should be added as selected features.

(7) Two-step nature of the analysis: First you fit a mechanistic model, then you use the predictions of this model to perform clustering and prediction of groups (unsupervised then supervised). Thus you do not propagate the uncertainty intrinsic to your first estimation through the second step, ie. all the viral load selected features actually have a confidence bound which is ignored. Did you consider a one-step analysis in which your covariates of interest play a direct role in the parameters of the mechanistic model as covariates? To pursue this type of analysis SCM (Johnson et al. Pharm. Res. 1998), COSSAC (Ayral et al. 2021 CPT PsP), or SAMBA ( Prague et al. CPT PsP 2021) methods can be used. Did you consider sampling on the posterior distribution rather than using EBE to avoid shrinkage?

(8) Need for advanced statistical methods: The analysis is characterized by a lack of power. This can indeed come from the sample size that is characterized by the number of data available in the study. However, I believe the power could be increased using more advanced statistical methods. At least it is worth a try. First considering the unsupervised clustering, summarizing the viral shedding trajectories with features collapses longitudinal information. I wonder if the R package « LongituRF » (and associated method) could help, see Capitaine et al. 2020 SMMR. Another interesting tool to investigate could be latent class models R package « lcmm » (and associated method), see Proust-Lima et al. 2017 J. Stat. Softwares. But the latter may be more far-reached.

(9) Study intrinsic limitation: All the results cannot be extended to asymptomatic patients and patients infected with recent VOCs. It definitively limits the impact of results and their applicability to public health. However, for me, the novelty of the data analysis techniques used should also be taken into consideration.

Strengths are:<br /> - Unique data and comprehensive analysis.<br /> - Novel results on viral shedding.

Weaknesses are:<br /> - Limitation of study design.<br /> - The need for advanced statistical methodology.

Reviewer #3 (Public Review):

Summary:

This manuscript by Liu et al. presents a case that CAPSL mutations are a cause of familial exudative vitreoretinopathy (FEVR). Attention was initially focused on the CAPSL gene from whole exome sequence analysis of two small families. The follow-up analyses included studies in which CAPSL was manipulated in endothelial cells of mice and multiple iterations of molecular and cellular analyses. Together, the data show that CAPSL influences endothelial cell proliferation and migration. Molecularly, transcriptomic and proteomic analyses suggest that CAPSL influences many genes/proteins that are also downstream targets of MYC and may be important to the mechanisms.

Strengths:

This multi-pronged approach found a previously unknown function for CAPSLs in endothelial cells and pointed at MYC pathways as high-quality candidates in the mechanism.

Weaknesses:

Two issues shape the overall impact for me. First, the unreported population frequency of the variants in the manuscript makes it unclear if CAPSL should be considered an interesting candidate possibly contributing to FEVR, or possibly a cause. Second, it is unclear if the identified variants act dominantly, as indicated in the pedigrees. The studies in mice utilized homozygotes for an endothelial cell-specific knockout, leaving uncertainty about what phenotypes might be observed if mice heterozygous for a ubiquitous knockout had instead been studied.

In my opinion, the following scientific issues are specific weaknesses that should be addressed:

(1) Please state in the manuscript the number of FEVR families that were studied by WES. Please also describe if the families had been selected for the absence of known mutations, and/or what percentage lack known pathogenic variants.

(2) A better clinical description of family 3104 would enhance the manuscript, especially the father. It is unclear what "manifested with FEVR symptoms, according to the medical records" means. Was the father diagnosed with FEVR? If the father has some iteration of a mild case, please describe it in more detail. If the lack of clinical images in the figure is indicative of a lack of medical documentation, please note this in the manuscript.

(3) The TGA stop codon can in some instances also influence splicing (PMID: 38012313). Please add a bioinformatic assessment of splicing prediction to the assays and report its output in the manuscript.

(4) More details regarding utilizing a "loxp-flanked allele of CAPSL" are needed. Is this an existing allele, if so, what is the allele and citation? If new (as suggested by S1), the newly generated CAPSL mutant mouse strain needs to be entered into the MGI database and assigned an official allele name - which should then be utilized in the manuscript and who generated the strain (presumably a core or company?) must be described.

(5) The statement in the methods "All mice used in the study were on a C57BL/6J genetic background," should be better defined. Was the new allele generated on a pure C57BL/6J genetic background, or bred to be some level of congenic? If congenic, to what generation? If unknown, please either test and report the homogeneity of the background, or consult with nomenclature experts (such as available through MGI) to adopt the appropriate F?+NX type designation. This also pertains to the Pdgfb-iCreER mice, which reference 43 describes as having been generated in an F2 population of C57BL/6 X CBA and did not designate the sub-strain of C57BL/6 mice. It is important because one of the explanations for missing heritability in FEVR may be a high level of dependence on genetic background. From the information in the current description, it is also not inherently obvious that the mice studied did not harbor confounding mutations such as rd1 or rd8.

(6) In my opinion, more experimental detail is needed regarding Figures 2 and 3. How many fields, of how many retinas and mice were analyzed in Figure 2? How many mice were assessed in Figure 3?

(7) I suggest adding into the methods whether P-values were corrected for multiple tests.

Reviewer #1 (Public Review):

Summary:

The author presents the discovery and characterization of CAPSL as a potential gene linked to Familial Exudative Vitreoretinopathy (FEVR), identifying one nonsense and one missense mutation within CAPSL in two distinct patient families afflicted by FEVR. Cell transfection assays suggest that the missense mutation adversely affects protein levels when overexpressed in cell cultures. Furthermore, conditionally knocking out CAPSL in vascular endothelial cells leads to compromised vascular development. The suppression of CAPSL in human retinal microvascular endothelial cells results in hindered tube formation, a decrease in cell proliferation, and disrupted cell polarity. Additionally, transcriptomic and proteomic profiling of these cells indicates alterations in the MYC pathway.

Strengths:

The study is nicely designed with a combination of in vivo and in vitro approaches, and the experimental results are good quality.

Weaknesses:

My reservations lie with the main assertion that CAPSL is associated with FEVR, as the genetic evidence from human studies appears relatively weak. Further careful examination of human genetics evidence in both patient cohorts and the general population will help to clarify. In light of human genetics, more caution needs to be exercised when interpreting results from mice and cell models and how is it related to the human patient phenotype.

Reviewer #2 (Public Review):

Summary:

This work identifies two variants in CAPSL in two-generation familial exudative vitreoretinopathy (FEVR) pedigrees, and using a knockout mouse model, they link CAPSL to retinal vascular development and endothelial proliferation. Together, these findings suggest that the identified variants may be causative and that CAPSL is a new FEVR-associated gene.

Strengths:

The authors' data provides compelling evidence that loss of the poorly understood protein CAPSL can lead to reduced endothelial proliferation in mouse retina and suppression of MYC signaling in vitro, consistent with the disease seen in FEVR patients. The study is important, providing new potential targets and mechanisms for this poorly understood disease. The paper is clearly written, and the data generally support the author's hypotheses.

Weaknesses:

(1) Both pedigrees described appear to suggest that heterozygosity is sufficient to cause disease, but authors have not explored the phenotype of Capsl heterozygous mice. Do these animals have reduced angiogenesis similar to KOs? Furthermore, while the p.R30X variant protein does not appear to be expressed in vitro, a substantial amount of p.L83F was detectable by western blot and appeared to be at the normal molecular weight. Given that the full knockout mouse phenotype is comparatively mild, it is unclear whether this modest reduction in protein expression would be sufficient to cause FEVR - especially as the affected individuals still have one healthy copy of the gene. Additional studies are needed to determine if these variants alter protein trafficking or localization in addition to expression, and if they can act in a dominant negative fashion.

(2) The manuscript nicely shows that loss of CAPSL leads to suppressed MYC signaling in vitro. However, given that endothelial MYC is regulated by numerous pathways and proteins, including FOXO1, VEGFR2, ERK, and Notch, and reduced MYC signaling is generally associated with reduced endothelial proliferation, this finding provides little insight into the mechanism of CAPSL in regulating endothelial proliferation. It would be helpful to explore the status of these other pathways in knockdown cells but as the authors provide only GSEA results and not the underlying data behind their RNA seq results, it is difficult for the reader to understand the full phenotype. Volcano plots or similar representations of the underlying expression data in Figures 6 and 7 as well as supplemental datasets showing the differentially regulated genes should be included. In addition, while the paper beautifully characterizes the delayed retinal angiogenesis phenotype in CAPSL knockout mice, the authors do not return to that model to confirm their in vitro findings.

(3) In Figure S2D, the result of this vascular leak experiment is unconvincing as no dye can be seen in the vessels. What are the kinetics for biocytin tracers to enter the bloodstream after IP injection? Why did the authors choose the IP instead of the IV route for this experiment? Differences in the uptake of the eye after IP injection could confound the results, especially in the context of a model with vascular dysfunction as here.

(4) In Figure 5, it is unclear how filipodia and tip cells were identified and selected for quantification. The panels do not include nuclear or tip cell-specific markers that would allow quantification of individual tip cells, and in Figure 5C it appears that some filipodia are not highlighted in the mutant panel.

Reviewer #2 (Public Review):

Summary:

An article with lots of interesting ideas and questions regarding the evolution of timing of dormancy, emphasizing mammalian hibernation but also including ectotherms. The authors compare selective forces of constraints due to energy availability versus predator avoidance and requirements and consequences of reproduction in a review of between and within species (sex) differences in the seasonal timing of entry and exit from dormancy.

Strengths:

The multispecies approach including endotherms and ectotherms is ambitious. This review is rich with ideas if not in convincing conclusions. Limitations are discussed yet are impactful, namely that differences among and within species are contrast only for ecological hibernation (the duration of remaining sequestered) and not for "heterothermic hibernation" the period between first and last torpor. Differences between the two can have significant energetic consequences, especially for mammals returning to euthermic levels of body temperature whilst remaining in their cold burrows before emerging, eg. reproductively developing males in spring.

Weaknesses:

The differences between physiological requirements for gameatogenesis between sexes that affect the timing of heterothermy and need for euthermy during mammalian hibernator are significant issues that underlie, but are under discussed, in this contrast of selective pressures that determine seasonal timing of dormancy. Some additional discussion of the effects of rapid rapid climate change on between and within species phenologies of dormancy would have been interesting.

Reviewer #2 (Public Review):

Summary:

This work by Cloarec-Ung et al. sets out to uncover strategies that would allow for the efficient and precision editing of primitive human hematopoietic stem and progenitor cells (HSPCs). Such effective editing of HSPCs via homology directed repair has implications for the development of tractable gene therapy approaches for monogenic hematopoietic disorders as well as precise engineering of these cells for clinical regenerative and/or cell therapy strategies. In the setting of experimental hematology, precision introduction of disease relevant mutations would also open the door to more robust disease modeling approaches. It has been recognized that to encourage HDR, NHEJ as the dominant mode of repair in quiescent HSPCs must be inhibited. Testing editing of human cord blood HSPCs the authors first incorporate a prestimulation phase then identify optimal RNP amounts and donor types/amounts using standard editing culture conditions identifying optimal concentrations of AAV and short single-stranded oligonucleotide donors (ssODNs) that yield minimal impacts to cell viability while still enabling heightened integration efficiency. They then demonstrate the superiority of AZD7648, an inhibitor of NHEJ-promoting DNA-PK, in allowing for much increased HDR with toxicities imparted by this compound reduced substantially by siRNAs against p53 (mean targeting efficiencies at 57 and 80% for two different loci). Although AAV offered the highest HDR frequencies, differing from ssODN by a factor by ~2-fold, the authors show that spacer breaking sequence mutations introduced into the ssODN to better mimic the disruption of the spacer sequence provided by the synthetic intron in the AAV backbone yielded ssODN HDR frequencies equal to that attained by AAV. By examining editing efficiency across specific immunophenotypically identified subpopulations they further suggest that editing efficiency with their improved strategy is consistent across stem and early progenitors and use colony assays to quantify an approximate 4-fold drop in total colony numbers but no skewing in the potentiality of progenitors in the edited HSPC pool. Finally, the authors provide a strategy using mutation-introducing AAV mixed with different ratios of silent ssODN repair templates to enable tuning of zygosity in edited CD34+ cells.

Strengths:

The methods are clearly described and the experiments for the most part also appropriately powered. In addition to using state-of-the-art approaches, the authors also provided useful insights into optimizing the practicalities of the experimental procedures that will aid bench scientists in effectively carrying out these editing approaches, for example avoiding longer handling times inherent when scaling up to editing over multiple conditions.

The sum of the adjustments to the editing procedure have yielded important advances towards minimizing editing toxicity while maximizing editing efficiency in HSPCs. In particular, the significant increase in HDR facilitated by the authors' described application of AZD7648 and the preservation of a pool of targeted progenitors is encouraging that functionally valuable cell types can be effectively edited.

The discovery of the effectiveness of spacer breaking changes in ssODNs allowing for substantially increased targeting efficiency is a promising advance towards democratizing these editing strategies given the ease of designing and synthesizing ssODNs relative to the production of viral donors.

The ability to zygosity tune was convincingly presented and provides a valuable strategy to modify this HDR procedure towards more accurate disease modelling.

Weaknesses:

Despite providing convincing evidence that functional progenitors can be successfully edited by their procedure, as the authors acknowledge it remains to be verified to what degree the survival/self-renewal capacity and in vivo regenerative potential of the more primitive fractions is maintained with their strategy. That said the inclusion of LTC-IC assays that verify the lack of effect on these quite primitive cells is encouraging that functionality of stem cells will be similarly spared.

Reviewer #1 (Public Review):

Summary: The type I ABC importer OpuA from Lactococcus lactis is the best studied transporter involved in osmoprotection. In contrast to most ABC import systems, the substrate binding protein is fused via a short linker to the transmembrane domain of the transporter. Consequently, this moiety is called the substrate binding domain (SBD). OpuA has been studied in the past in great detail and we have a very detailed knowledge about function, mechanisms of activation and deactivation as well as structure.

Strengths: Application of smFRET to unravel transient interactions of the SBDs. The method is applied at a superb quality and the data evaluation is excellent.

Weaknesses: The proposed model is not directly supported by experimental data. Rather alternative models are excluded as they do not fit to the obtained data. However, this is now clearly stated in the manuscript

Reviewer #2 (Public Review):

Summary:<br /> In this report the authors used solution-based single-molecule FRET and low resolution cryo-EM to investigate the interactions between the substrate-binding domains of the ABC-importer OpuA from Lactococcus lactis. Based on their results, the authors suggest that the SBDs interact in an ionic strength-dependent manner.

Strengths:<br /> The strength of this manuscript is the uniqueness and importance of the scientific question, the adequacy of the experimental system (OpuA), and the combination of two very powerful and demanding experimental approaches.

Weaknesses:<br /> A demonstration that the SBDs physically interact with one another, and that this interaction is important for the transport mechanism will greatly strengthen the claims of the authors. The relation to cooperativity is also unclear.

Reviewer #1 (Public Review):

Building on previous work from the Tansey lab, here Howard et al. characterize transcriptional and translational changes upon WIN site inhibition of WDR5 in MLL-rearranged cancer cells. They first analyze whether C16, a newer generation compound, has the same cellular effects as C6, an early generation compound. Both compounds reduce the expression of WDR5-bound RPGs in addition to the unbound RPG RPL22L1. They then investigate differential translation by ribo-seq and observe that WIN site inhibition reduces the translational RPGs and other proteins related to biomass accumulation (spliceosome, proteasome, mitochondrial ribosome). Interestingly, this reduction adds to the transcriptional changes and is not limited to RPGs whose promoters are bound by WDR5. Quantitative proteomics at two time points confirmed the downregulation of RPGs. Interestingly, the overall effects are modest, but RPL22LA is strongly affected. Unexpectedly, most differentially abundant proteins seem to be upregulated 24 h after C6 (see below). A genetic screen showed that loss of p53 rescues the effect of C6 and C16 and helped the authors to identify pathways that can be targeted by compounds together with WIN site inhibitors in a synergistic way. Finally, the authors elucidated the underlying mechanisms and analyzed the functional relevance of the RPL22, RPL22L1, p53 and MDM4 axis.

Comments on revised version:

The authors have answered my points satisfactorily and the manuscript has become clearer and more meaningful as a result. In particular, the measurement of global translation rate is important and validates the upregulation of a number of proteins following WDR5 inhibitor treatment.

Reviewer #2 (Public Review):

Summary:

The manuscript by Howard et al reports the development of high affinity WDR5-interaction site inhibitors (WINi) that engage the protein to block the arginine-dependent engagement with its partners. Treatment of MLL-rearranged leukemia cells with high-affinity WINi (C16) decreases the expression of genes encoding most ribosomal proteins and other proteins required for translation. Notably, although these targets are enriched for WDR5-ChIP-seq peaks, such peaks are not universally present in the target genes. High concordance was founded between the alterations in gene expression due to C16 treatment and the changes resulting from treatment with an earlier, lower affinity WINi (C6). Besides protein synthesis, genes involved in DNA replication or MYC responses are downregulated, while p53 targets and apoptosis genes are upregulated. Ribosome profiling reveals a global decrease in translational efficiency due to WINi with overall ribosome occupancies of mRNAs ~50% of control samples. The magnitude in the decrements of translation for most individual mRNAs exceeds the respective changes in mRNA levels genome-wide. From these results and other considerations, the authors hypothesize that WINi results in ribosome depletion. Quantitative mass spec documents the decrement in ribosomal proteins following WINi treatment along with increases in p53 targets and proteins involved in apoptosis occurring over 3 days. Notably RPL22L1 is essentially completely lost upon WINi treatment. The investigators next conduct a CRISPR screen to find moderators and cooperators with WINi. They identify components of p53 and DNA repair pathways as mediators of WINi inflicted cell death (so gRNAs against these genes permit cell survival). Next, WINi are tested in combination with a variety of other agents to explore synergistic killing to improve their expected therapeutic efficacy. The authors document loss of the p53 antagonist MDM4 (in combination with splicing alterations of RPL22L1), an observation that supports the notion that WINi killing is p53-mediated.

This is a scientifically very strong and well-written manuscript that applies a variety of state-of-the art molecular approaches to interrogate the role of the WDR5 interaction site and WINi. They reveal that the effects of WINi seem to be focused on the overall synthesis of protein components of the translation apparatus, especially ribosomal proteins-even those that do not bind WDR5 by ChIP (a question left unanswered is how such the WDR5-less genes are nevertheless WINi targeted). They convincingly show that disruption of the synthesis of these proteins occurs upon activation of p53 dependent apoptosis, likely driven by unbalanced ribosomal protein synthesis leading to MDM2 inhibition. This apoptosis is subsequently followed, as expected by ɣH2AX-activation. Pathways of possible WINi resistance and synergies with other anti-neoplastic approaches are explored. These experiments are all well-executed and strongly invite more extensive pre-clinical and translational studies of WINi in animal studies. The studies also may anticipate the use of WINi as probes of nucleolar function and ribosome synthesis though this was not really explored in the current manuscript. The current version of the manuscript documents ribosomal stress revealed by leakage of NPM1 into the nucleoplasm while nucleolar integrity is preserved. A progressive loss of rRNA synthesis occurs upon drug treatment that is presumably secondary to the decrement in ribosomal protein production.

Comments on revised version:

(1) The authors to my mind, have quite nicely and professionally addressed the comments of the reviewers and are to be congratulated on an important contribution to the elucidation of WDR5 biology and pathology.

Reviewer #1 (Public Review):

Summary:

The authors use truncations, fragments, and HCN2/4 chimeras to narrow down the interaction and regulatory domains for LRMP inhibition of cAMP-dependent shifts in the voltage dependence of activation of HCN4 channels. They identify the N-terminal domain of HCN4 as a binding domain for LRMP, and highlight two residues in the C-linker as critical for the regulatory effect. Notably, whereas HCN2 is normally insensitive to LRMP, putting the N-terminus and 5 additional C-linker and S5 residues from HCN4 into HCN2 confers LRMP regulation in HCN2.

Strengths:

The work is excellent, the paper well written, and the data convincingly support the conclusions which shed new light on the interaction and mechanism for LRMP regulation of HCN4, as well as identifying critical differences that explain why LRMP does not regulate other isoforms such as HCN2.

Reviewer #2 (Public Review):

Summary:

HCN-4 isoform is found primarily in sino-atrial node where it contributes to the pacemaking activity. LRMP is an accessory subunit which prevents cAMP-dependent potentiation of HCN4 isoform but does not have any effect on HCN2 regulation. In this study, the authors combine electrophysiology, FRET with standard molecular genetics to determine the molecular mechanism of LRMP action on HCN4 activity. Their study shows parts of N- and C-termini along with specific residues in C-linker and S5 of HCN4 are crucial for mediating LRMP action on these channels. Furthermore, they show that the initial 224 residues of LRMP are sufficient to account for most of the activity. In my view, the highlight of this study is Fig. 7 which recapitulates LRMP modulation on HCN2-HCN4 chimera. Overall, this study is an excellent example of using time-tested methods to probe the molecular mechanisms of regulation of channel function by an accessory subunit.

The authors adequately addressed my earlier concerns.

Reviewer #3 (Public Review):

Summary:

Using patch clamp electrophysiology and Förster resonance energy transfer (FRET), Peters and co-workers showed that the disordered N-terminus of both LRMP and HCN4 are necessary for LRMP to interact with HCN4 and inhibit the cAMP-dependent potentiation of channel opening. Strikingly, they identified two HCN4-specific residues, P545 and T547 in the C-linker of HCN4, that are close in proximity to the cAMP transduction centre (elbow Clinker, S4/S5-linker, HCND) and account for the LRMP effect.

Strengths:

Based on these data, the Authors propose a mechanism in which LRMP specifically binds to HCN4 via its isotype-specific Nterminal sequence and thus prevents the cAMP transduction mechanism by acting at the interface between the elbow Clinker, the S4S5-linker, the HCND.

Weaknesses:

Although the work is interesting, there are some discrepancies between data that need to be addressed.

- I suggest inserting in Table 1 and in the text, the Δ shift values (+cAMP; + LRMP; +cAMP/LRMP). This will help readers.

- Figure 1 is not clear, the distribution of values is anomalously high. For instance, in 1B the distribution of values of V1/2 in the presence of cAMP goes from - 85 to -115. I agree that in the absence of cAMP, HCN4 in HEK293 cells shows some variability in V1/2 values, that nonetheless cannot be so wide (here the variability spans sometimes even 30 mV) and usually disappears with cAMP (here not).<br /> This problem is spread throughout the ms, and the measured mean effects indeed always at the limit of statistical significance. Why so? Is this a problem with the analysis, or with the recordings?<br /> There are several other problems with Figure 1 and in all figures of the ms: the Y scale is very narrow while the mean values are marked with large square boxes. Moreover, the exemplary activation curve of Fig 1A is not representative of the mean values reported in Figure 1B, and the values of 1B are different from those reported in Table 1.<br /> On this ground it is difficult to judge the conclusions and it would also greatly help if exemplary current traces would also be shown.

- "....HCN4-P545A/T547F was insensitive to LRMP (Figs. 6B and 6C; Table 1), indicating that the unique HCN4 C-linker is necessary for regulation by LRMP. Thus, LRMP appears to regulate HCN4 by altering the interactions between the C-linker, S4-S5 linker, and N-terminus at the cAMP transduction centre."

Although this is an interesting theory, there are no data supporting it. Indeed, P545 and T547 at the tip of the C-linker elbow (fig 6A) are crucial for LRMP effect, but these two residues are not involved in the cAMP transduction centre (interface between HCND, S4S5 linker and Clinker elbow), at least for the data accumulated till now in the literature. Indeed, the hypothesis that LRMP somehow inhibits the cAMP transduction mechanism of HCN4 given the fact that the two necessary residues P545 and T547 are close to the cAMP transduction centre, awaits to be proven.

Moreover, I suggest analysing the putative role of P545 and T547 in the light of the available HCN4 structures. In particular, T547 (elbow) point towards the underlying shoulder of the adjacent subunit and, therefore, it is in a key position for the cAMP transduction mechanism. The presence of bulky hydrophobic residues (very different nature compared to T) in the equivalent position of HCN1 and HCN2 is also favouring this hypothesis. In this light, it will also be interesting to see whether single T547F mutation is sufficient to prevent LRMP effect.

Reviewer #1 (Public Review):

Summary:

In this study, Pan DY et al. discovered that the clearance of senescent osteoclasts can lead to a reduction in sensory nerve innervation. This reduction is achieved through the attenuation of Netrin-1 and NGF levels, as well as the regulation of H-type vessels, resulting in a decrease in pain-related behavior. The experiments are well-designed. The results are clearly presented, and the legends are also clear and informative. Their findings represent a potential treatment for spine pain utilizing senolytic drugs.

Strengths:

Rigorous data, well-designed experiments as well as significant innovation make this manuscript stand out.

Weaknesses:

All my concerns have been well addressed, no further comments.

Reviewer #2 (Public Review):

Summary:

This manuscript examined the underlying mechanisms between senescent osteoclasts (SnOCs) and lumbar spine instability (LSI) or aging. They first showed that greater numbers of SnOCs are observed in mouse models of LSI or aging, and these SnOCs are associated with induced sensory nerve innervation, as well as the growth of H-type vessels, in the porous endplate. Then, the deletion of senescent cells by administration of the senolytic drug Navitoclax (ABT263) results in significantly less spinal hypersensitivity, spinal degeneration, porosity of the endplate, sensory nerve innervation, and H-type vessel growth in the endplate. Finally, they also found that there is greater SnOC-mediated secretion of Netrin-1 and NGF, two well-established sensory nerve growth factors, compared to non-senescent OCs. The study is well conducted and data strongly support the idea.

Reviewer #3 (Public Review):

Summary:

This research article reports that a greater number of senescent osteoclasts (SnOCs), which produce Netrin-1 and NGF, are responsible for innervation in the LSI and aging animal models.

Strengths:

The research is based on previous findings in the authors' lab and the fact that the IVD structure was restored by treatment with ABT263. The logic is clear and clarifies the pathological role of SnOCs, suggesting the potential utilization of senolytic drugs for the treatment of LBP. Generally, the study is of good quality and the data is convincing.

Weaknesses:

All my concerns have been well addressed, no further comments.

Reviewer #1 (Public Review):

Here, using an organoid system, Wong et al generated a new model of hereditary diffuse leukoencephalopathy with axonal spheroids, with which they investigated how CSF1R-mutaions affect the phenotypes of microglia/macrophages, and revealed metabolic changes in microglia/macrophages associated with a proinflammatory phenotype.

In general, this paper is interesting and well-written, and tackles important issues to be addressed.

This study suffers from several major concerns and limitations that dampen the value of the study. As the authors also mentioned, models that perfectly recapitulate the complexity of the HDLS brain the models would be required to better understand the molecular mechanisms of the disease. In this regard, it is unclear how nicely the organoid system in this study can recapitulate the condition in patients with HDLS (e.g. reduced microglia density, downregulated expression of P2YR12, pathological alterations). In addition, the authors used two different models with distinct mutations that could produce different readouts in CSF1R-mediated cellular responses.

Although the reviewer does understand the importance of providing several options/tools to study rare diseases like HDLS and the difficulty of generating stable organoids with less variation, it is unclear if the different outcomes between HD1 and HD2 are generated through different mutations or simply due to different differentiation efficiency from iMacs (e.g. Figure 2B), which needs to be confirmed. Lastly, there is an over-interpretation regarding the results in Figure 6A. There is no difference between isoHD1 iMac control and HD1 Mut iMac.

Reviewer #2 (Public Review):

Summary:

This paper investigates a rare and severe brain disease called Hereditary Diffuse Leukoencephalopathy with Axonal Spheroids (HDLS). The authors aimed to understand how mutations in the gene CSF-1R affect microglia, the resident immune cells in the brain, and which alterations and factors lead to the specific pathophysiology. To model the human brain with the pathophysiology of HDLS, they used the human-specific model system of induced pluripotent stem cell (iPSC)-derived forebrain organoids with integrated iPSC-derived microglia (iMicro) from patients with the HDLS-causing mutation and an isogenic cell line with the corrected genome. They found that iPSC-derived macrophages (iMac) with HDLS mutations showed changes in their response, including increased inflammation and altered metabolism. Additionally, they studied these iMacs in forebrain organoids, where they differentiate into iMicro, and showed transcriptional differences in isolated iMicro when carrying the HDLS mutation. In addition, the authors described the influence of the mutation within iMicro on the transcriptional level of neurons and neural progenitor cells (NPCs) in the organoid. They observed that the one mutation showed implications for impaired development of neurons, possibly contributing to the progression of the disease.

Overall, this study provides valuable insights into the mechanisms underlying HDLS and emphasizes the importance of studying diseases like these with a suitable model system. These findings, while promising, represent only an initial step towards understanding HDLS and similar neurodegenerative diseases, and thus, their direct translation into new treatment options remains uncertain.

Strengths:

The strength of the work lies in the successful reprogramming of two HDLS patient-derived induced pluripotent stem cells (iPSCs) with different mutations, which is crucial for the study of HDLS using human forebrain organoid models. The use of corrected isogenic iPSC lines as controls increases the validity of the mutation-specific observations. In addition, the model effectively mimics HDLS, particularly concerning deficits in the frontal lobe, mirroring observations in the human brain. Obtaining iPSCs from patients with different CSF1R mutations is particularly valuable given the limitations of rodent and zebrafish models when studying adult-onset neurodegenerative diseases. The study also highlights significant metabolic changes associated with the CSF1R mutation, particularly in the HD2 mutant line, which is confirmed by the HD1 line. In addition, the work shows transcriptional upregulation of the proinflammatory cytokine IL-1beta in cells carrying the mutation, particularly when they phagocytose apoptotic cells, providing further insight into disease mechanisms.

Weaknesses:

The authors have not elucidated the significance of the increased CSF1 dosage in Figure 2F, aside from its effect on cell viability, lacking a thorough discussion of this result. Additionally, while transcriptomic and metabolic alterations related to the mutation were demonstrated in iMac models, similar investigations in iMicros are absent, necessitating further experiments to validate the findings across cell models. The conclusion drawn regarding cytokine levels lacks robust support from the data, particularly considering the varied responses observed in different mutant lines. Further analysis of the secretome (e.g. via ELISA) could provide additional insights. Moreover, the characterization of iMicros is incomplete, with limited protein-level analysis (e.g. validate RNA-seq via flow cytometry). Additionally, the claim of microglial-like morphology lacks adequate evidence, as the provided image is insufficient for such an assessment. RNA-seq experiments should be represented better, it is not possible to read the legends or gene names in the figures. Maybe the data sets can be combined into PCAone and one overall analysis, e.g. via WGCNA-like analyses? This would make it easier for the reader to compare the two cell lines side by side. Furthermore, inaccuracies and omissions in the figure legends compromise the clarity of data representation. Statistical test information is missing. Finally, inconsistent terminology usage throughout the paper may confuse readers (iMac versus iMicros).

Reviewer #2 (Public Review):

Summary:

Fertilization is a crucial event in sexual reproduction, but the molecular mechanisms underlying egg-sperm fusion remain elusive. Elofsson A et al. used AlphaFold to explore possible synapse-like assemblies between sperm and egg membrane proteins during fertilization. Using a systematic search of protein-protein interactions, the authors proposed a pentameric complex of three sperm (IZUMO1, SPACA6, and TMEM81) and two egg (JUNO and CD9) proteins, providing a new structural model to be used in future structure-function studies.

Strengths:

(1) The study uses the AlphaFold algorithm to predict higher-order assemblies. This approach could offer insights into a highly transient protein complex, which are challenging to detect experimentally.<br /> (2) The article predicts a pentameric complex between proteins involved in fertilization, shedding light on the architectural aspects of the egg-sperm fusion synapse.

Weaknesses:

The proposed model, which is a prediction from a modeling algorithm, lacks experimental validation of the identity of the components and the predicted contacts.

It is noteworthy that in an independent study, Deneke et al. provides experimental evidence of the interaction between IZUMO1/SPACA6/TMEM81 in zebrafish. This is an important element that supports the findings presented in this manuscript

Regarding the authors response on the question of a global search:<br /> I understand that a global search might be difficult to interpret because a large number of putative false positives. But it is this type of information that is needed to assess the validity of the model and the scoring power in the absence of any experimental validation. At minimum, the search should include a negative control set of proteins known to be unrelated to sperm fertilization or homologous egg-sperm fusion complexes from incompatible species to account for species-specific interactions.

I acknowledge that experimentally validating highly transient complexes presents technical hurdles. However, a high-confidence structural model could enable the design of point mutations specifically disrupting the predicted interactions. Subsequent rescue experiments could then validate the directionality of these interactions. Ultimately, such experiments are crucial for robust model validation.

Reviewer #3 (Public Review):

Summary:

Sperm-egg fusion is a critical step in successful fertilization. Although several proteins have been identified in mammals that are required for sperm-egg adhesion and fusion, it is still unclear whether there are other proteins involved in this process and how the reported proteins complex co-operate to complete the fusion process. In this study, the authors first identified TMEM81 as a structural homologue of IZUMO1 and SPACA6, and predicted the interactions with a pool of human proteins associated with gamete fusion, using AlphaFold-Multimer, a recent advance in protein complex structure prediction. The prediction is compelling and well discussed, and the experimental evidence to verify this interaction is lacking in this study but supported by a complementary and independent study by another group.

Strengths:

The authors present a pentameric complex formation of four previously reported proteins involved in egg/sperm interaction together with TMEM181 using a deep learning tool, AlphaFold-Multimer.

Weaknesses:

It is intriguing to see that some of the proteins involved in sperm-egg interaction are successfully predicted to be assembled into a single multimeric structure by AlphaFold-Multimer. The experimental validation of the interactions is not directly supported in this study. As there are more candidate proteins in the process, testing other possible protein interactions more comprehensively will provide more rationale for the current 3D multi-protein modeling.

Reviewer #1 (Public Review):

Summary:

In this study, the authors generated a novel transgenic mouse line OpalinP2A-Flpo-T2A-tTA2 to specifically label mature oligodendrocytes, and at the same time their embryonic origins by crossing with a progenitor cre mouse line. With this clever approach, they found that LGE/CGE-derived OLs make minimum contributions to the neocortex, whereas MGE/POA-derived OLs make a small but lasting contribution to the cortex. These findings are contradictory to the current belief that LGE/CGE-derived OPCs make a sustained contribution to cortical OLs, whereas MGE/POA-derived OPCs are completely eliminated. Thus, this study provides a revised and more comprehensive view on the embryonic origins of cortical oligodendrocytes. To specifically label mature oligodendrocytes, and at the same time their embryonic origins by crossing with a progenitor cre mouse line. With this clever approach, they found that LGE/CGE-derived OLs make minimum contributions to the neocortex, whereas MGE/POA-derived OLs make a small-but-lasting contribution to to cortex. These findings are contradictory to the current belief that LGE/CGE-derived OPCs make a sustained contribution to cortical OLs, whereas MGE/POA-derived OPCs are completely eliminated. Thus, this study has provided a revised and updated view on the embryonic origins of cortical oligodendrocytes.

Strengths:

The authors have generated a novel transgenic mouse line to specifically label mature differentiated oligodendrocytes, which is very useful for tracing the final destiny of mature myelinating oligodendrocytes. Also, the authors carefully compared the distribution of three progenitor cre mouse lines and suggested that Gsh-cre also labeled dorsal OLs, contrary to the previous suggestion that it only marks LGE-derived OPCs. In addition, the author also analyzed the relative contributions of OLs derived from three distinct progenitor domains in other forebrain regions (e.g. Pir, ac). Finally, the new transgenic mouse lines and established multiple combinatorial genetic models will facilitate future investigations of the developmental origins of distinct OL populations and their functional and molecular heterogeneity.

Weaknesses:

Since OpalinP2A-Flpo-T2A-tTA2 only labels mature oligodendrocytes but not OPCs, the authors can not suggest that the lack of LGE/CGE-derived-OLs in the neocortex is less likely caused by competitive postnatal elimination, but more likely due to limited production and/or allocation (line 118-9). It remains possible that LGE/CGE-derived OPCs migrate into the cortex but are later eliminated.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Cai et al use a combination of mouse transgenic lines to re-examine the question of the embryonic origin of telencephalic oligodendrocytes (OLs). Their tools include a novel Flp mouse for labelling mature oligodendrocytes and a number of pre-existing lines (some previously generated by the last author in Josh Huang's lab) that allowed combinatorial or subtractive labelling of oligodendrocytes with different origins. The conclusion is that cortically-derived OLs are the predominant OL population in the motor and somatosensory cortex and underlying corpus callosum, while the LGE/CGE generates OLs for the piriform cortex and anterior commissure rather than the cerebral cortex. Small numbers of MGE-derived OLs persist long-term in the motor, somatosensory and piriform cortex.

Strengths:

The strength and novelty of the manuscript lies in the elegant tools generated and used and which have the potential to elegantly and accurately resolve the issue of the contribution of different progenitor zones to telencephalic regions.

Weaknesses:

(1) Throughout the manuscript (with one exception, lines 76-78), the authors quantified OL densities instead of contributions to the total OL population (as a % of ASPA for example). This means that the reader is left with only a rough estimation of the different contributions.

(2) All images and quantifications have been confined to one level of the cortex and the potential of the MGE and the LGE/CGE to produce oligodendrocytes for more anterior and more posterior cortical regions remains unexplored.

(3) Hence, the statement that "In summary, our findings significantly revised the canonical model of forebrain OL origins (Figure 4A) and provided a new and more comprehensive view (Figure 4B )." (lines 111, 112) is not really accurate as the findings are neither new nor comprehensive. Published manuscripts have already shown that (a) cortical OLs are mostly generated from the cortex [Tripathi et al 2011 (https://doi.org/10.1523/JNEUROSCI.6474-10.2011), Winker et al 2018 (https://doi.org/10.1523/JNEUROSCI.3392-17.2018) and Li et al (https://doi.org/10.1101/2023.12.01.569674)] and (b) MGE-derived OLs persist in the cortex [Orduz et al 2019 (https://doi.org/10.1038/s41467-019-11904-4) and Li et al 2024 (https://doi.org/10.1101/2023.12.01.569674)]. Extending the current study to different rostro-caudal regions of the cortex would greatly improve the manuscript.

Reviewer #3 (Public Review):

In the manuscript entitled "Embryonic Origins of Forebrain Oligodendrocytes Revisited by Combinatorial Genetic Fate Mapping," Cai et al. used an intersectional/subtractional strategy to genetically fate-map the oligodendrocyte populations (OLs) generated from medial ganglionic eminence (NKX2.1+), lateral ganglionic eminences, and dorsal progenitor cells (EMX1+). Specifically, they generated an OL-expressing reporter mouse line OpalinP2A-Flpo-T2A-tTA2 and bred with region-specific neural progenitor-expressing Cre lines EMX1-Cre for dOL and NKX2.1-Cre for MPOL. They used a subtractional strategy in the OpalinFlp::Emx1Cre::Nkx2.1Cre::RC::FLTG mouse line to predict the origins of OLs from lateral/caudal ganglionic eminences (LC). With their genetic tools, the authors concluded that neocortical OLs primarily consist of dOLs. Although the populations of OLs (dOLs or MP-OLs) from Emx1+ or Nkx2.1+ progenitors are largely consistent with previous findings, they observed that MP-OLs contribute minimally but persist into adulthood without elimination as in the previous report (PMID: 16388308).

Intriguingly, by using an indirect subtraction approach, they hypothesize that both Emx1-negative and Nkx2.1-negative cells represent the progenitors from lateral/caudal ganglionic eminences (LC), and conclude that neocortical OLs are not derived from the LC region. This is in contrast to the previous observation for the contribution of LC-expressing progenitors (marked by Gsx2-Cre) to neocortical OLs (PMID: 16388308). The authors claim that Gsh2 is not exclusive to progenitor cells in the LC region (PMID: 32234482). However, Gsh2 exhibits high enrichment in the LC during early embryonic development. The presence of a small population of Gsh2-positive cells in the late embryonic cortex could originate/migrate from Gsh2-positive cells in the LC at earlier stages (PMID: 32234482). Consequently, the possibility that cortical OLs derived from Gsh2+ progenitors in LC could not be conclusively ruled out. Notably, a population of OLs migrating from the ventral to the dorsal cortical region was detected after eliminating dorsal progenitor-derived OLs (PMID: 16436615).

The indirect subtraction data for LC progenitors drawn from the OpalinFlp-tdTOM reporter in Emx1-negative and Nkx2.1-negative cells in the OpalinFlp::Emx1Cre::Nkx2.1Cre::RC::FLTG mouse line present some caveats that could influence their conclusion. The extent of activity from the two Cre lines in the OpalinFlp::Emx1Cre::Nkx2.1Cre::RC::FLTG mice remains uncertain. The OpalinFlp-tdTOM expression could occur in the presence of either Emx1Cre or Nkx2.1Cre, raising questions about the contribution of the individual Cre lines. To clarify, the authors should compare the tdTOM expression from each individual Cre line, OpalinFlp::Emx1Cre::RC::FLTG or OpalinFlp::Nkx2.1Cre::RC::FLTG, with the combined OpalinFlp::Emx1Cre::Nkx2.1Cre::RC::FLTG mouse line. This comparison is crucial as the results from the combined Cre lines could appear similar to only one Cre line active.

Overall, the authors provided intriguing findings regarding the origin and fate of oligodendrocytes from different progenitor cells in embryonic brain regions. However, further analysis is necessary to substantiate their conclusion about the fate of LC-derived OLs convincingly.

Reviewer #1 (Public Review):

This is an interesting, informative, and well-designed study that combines theoretical and experimental methodologies to tackle the phenomenon of higher-resolution structures/substructures in model biomolecular condensates. However, there is significant room for improvement in the presentation and interpretation of the results. As it stands, the precise definition of "frustration," which is a main theme of this manuscript (as emphasized in the title), is not sufficiently well articulated. This situation should be rectified to avoid "frustration" becoming a "catch-all" term without a clear perimeter of applicability rather than a precise, informative description of the physical state of affairs. There are also a few other concerns, e.g., regarding interpretation of correlation of phase-separation critical temperature and transfer free energy of amino acid residues as well as the difference between critical temperature and onset temperature, and the way the simulated configurations are similar to that of gyroids. Accordingly, the manuscript should be revised to address the following:

(1) It is accurately pointed out on p.4 that elastin-like polypeptides (ELPs) undergo heat-induced phase separation and therefore exhibit lower critical solution temperatures (LCSTs). But it is not entirely clear how this feature is reproduced by the authors' simulation. A relationship between simulated surface tension and "transition temperature" is provided in Fig.1C; but is the "transition temperature" (authors cited ref.41 by Urry) the same as critical temperature? Apparently, Urry's Tt is "critical onset temperature", the temperature when phase separation happens at a given polymer concentration. This is different from the (global) critical temperature LCST - though the two may be correlated-or not-depending on the shape of the phase boundary. Moreover, is the MOFF coarse-grained forcefield (first step in the multi-scale simulation), by itself, capable of reproducing heat-induced phase separation in a way similar to the forcefield of Dignon et al., ACS Cent Sci 5, 821-230 (2019)? Or, is this temperature-dependent effect appearing only subsequently, after the implementation of the MARTINI and/or all-atom steps? Clarification is needed. To afford a more informative context for the authors' introductory discussion, the aforementioned Dignon et al. work and the review by Cinar et al. [Chem Eur J 25, 13049-13069 (2019)], both touching upon the physical underpinning of the LCST feature of elastin, should also be cited along with refs.41-43.

(2) "Frustration" and "frustrated" are used prominently in the manuscript to characterize certain observed molecular configurations (11 times total, in both the title and in the abstract). Apparently, it is the most significant conceptual pronouncement of this work, hence its precise meaning is of central importance to the authors' thesis. Whereas one should recognize that the theoretical and experimental observations are striking without invocation of the "frustration" terminology, usage of the term can be useful if it offers a unifying conceptual framework. However, as it stands, a clear definition of the term "frustration" is lacking, leaving readers to wonder what molecular configurations are considered "frustrated" and what are not (i.e.,is the claim of observation of frustration falsifiable?). For instance, "frustrated microphase separation" appears in both the title and abstract. A logical question one may ask is: "Are all microphase separations frustrated"? If the answer is in the affirmative, does invocation of the term "frustration" add anything to our physical insight? If the answer is not in the affirmative, then how does one distinguish between microphase separations that are frustrated from those that are not frustrated? Presumably all simulated and experimental molecular configurations in the present study are those of lowest free energy for the given temperature. In other words, they are what they are. In the discussion about frustrated phase separation on p.13, for example, the authors appear to refer to the fact that chain connectivity is preventing hydrophobic residues to come together in a way to achieve the most favorable interactions as if there were no chain connectivity (one may imagine in that case all the hydrophobic residues will form a large cluster without microphase separation). Is this what the authors mean by "frustration"? If that's true, isn't that merely stating the obvious, at least for the observed microphase separation? In general, does "frustration" always mean deviation of actual, physical molecular configurations from certain imagined/hypothetical/reference molecular configurations, and therefore dependent upon the choice of the imagined reference configuration? If this is how the authors apply the term "frustration" in the present work, what is the zero-frustration reference state/configuration for microphase separation? And, similarly, what is the zero-frustration reference state/configuration when frustrated EPS-water interactions are discussed (~p.14-p.15, Fig.5)? How do non-frustrated water-protein interactions look like? Is the classic clathrate-like organization of water hydrogen bonds around small nonpolar solute "frustrated"?

(3) In the discussion about the correlation of various transfer free energy scales for amino acids and Urry's critical onset temperature (ref.41) on p.11 and Fig.4, is there any theoretical relationship to be expected between the interactions among amino acids of ELPs and their critical onset temperatures? While a certain correlation may be intuitively expected if the free energy scale "is working", is there any theoretical insight into the mathematical form of this relationship? A clarifying discussion is needed because it bears logically on whether the observed correlation or lack thereof for different transfer energy scales is a good indication of the adequacy of the energy scales in describing the actual physical interactions at play. This question requires some prior knowledge of the expected mathematical relationship between interaction parameters and onset temperature.

(4) To provide a more comprehensive context for the present study, it is useful to compare the microphase separation seen in the authors' simulation with the micelle-like structures observed in recent simulated condensed/aggregated states of hydrophobic-polar (HP) model sequences in Statt et al., J Chem Phys 152, 075101 (2020) [see esp. Fig.6] and Wessén et al., J Phys Chem B 126, 9222-9245 (2022) [see, e.g., Fig.10].

(5) "Gyroid-like morphology" is mentioned several times in the manuscript (p.4, p.8, p.17, Fig.S3). This is apparently an interesting observation but a clear explanation is lacking. A more detailed and specific discussion, perhaps with additional graphical presentations, should be provided to demonstrate why the simulated condensed-phase ELP configurations are similar to the classical description of gyroid as in, e.g., Terrones & Mackay, Chem Phys Lett 207, 45-50 (1993) and Lambert et al., Phil Trans R Soc A 354, 2009-2023 (1996).

Comments on the revised manuscript:

The authors have adequately addressed my previous concerns.

Reviewer #2 (Public Review):

Summary:

Latham A.P. et al. apply simulations and FLIM to analyse several di-block elastin-like polypetides and connect their sequence to the micro-structure of coacervates resulting from their phase-separation.

Strengths:

Understanding the molecular grammar of phase separating proteins and the connection with mesoscale properties of the coacervates is highly relevant. This work provides insights into micro-structures of coacervates resulting from di-block polypetides.

Weaknesses:

The results apply to a very specific architecture (di-block polypetides) with specific sequences.

Reviewer #1 (Public Review):

Most amino acids are stereoisomers in the L-enantiomer, but natural D-serine has also been detected in mammals and its levels shown to be connected to a number of different pathologies. Here, the authors convincingly show that D-serine is transported in the kidney by the neutral amino acid transporter ASCT2 and as a non-canonical substrate for the sodium-coupled monocarboxylate transporter SMCTs. Although both transport D-serine, this important study further shows in a mouse model for acute kidney injury that ASCT2 has the dominant role.

Strengths:

The paper combines proteomics, animal models, ex vivo transport analyses and in vitro transport assays using purified components. The exhaustive methods employed provide compelling evidence that both transporters can translocate D-serine in the kidney.

Weakness:

In the model for acute kidney injury the SMCTs proteins were not showing a significant change in expression levels and were rather analysed based on other, circumstantial evidence. Although its clear SMCTs can transport D-serine its physiological role is less obvious compared to ASCT2.

Reviewer #2 (Public Review):

Summary:

The manuscript "A multi-hierarchical approach reveals D-1 serine as a hidden substrate of sodium-coupled monocarboxylate transporters" by Wiriyasermkul et al. is a resubmission of a manuscript, which focused first on the proteomic analysis of apical membrane isolated from mouse kidney with early ischemia- reperfusion injury (IRI), a well-known acute kidney injury (AKI) model. In a second part, the transport of D-serine by Asct2, Smct1, and Smct2 has been characterized in detail in different model systems, such as transfected cells and proteoliposomes.

Strengths:

A major problem with the first submission was the explanation of the link between the two parts of the manuscript: it was not very clear why the focus on Asct2, Smct1 and Smct2 was a consequence of the proteomic analysis. In the present version of the manuscript, the authors have focused on the expression of membrane transporters in the proteome analysis, thus making the reason for studying Asct2, Smct1 and Smct2 transporters more clear. In addition, the authors used 2D-HPLC to measure plasma and urinary enantiomers of 20 amino acids in plasma and urine samples from sham and ischaemia-reperfusion injury (IRI) mice. The results of this analysis demonstrated the value of D-serine as a potential marker of renal injury. These changes have greatly improved the manuscript and made it more convincing.

Weaknesses:

More than weakness I would speak of discussion points: I have a few suggestions that may help to make the paper more accessible to a general audience.<br /> (1) In the Introduction, when the authors introduce the term "micromolecules", it would be beneficial to provide a precise definition or clarification of what they mean by this term. Adding a brief explanation may help the reader to better understand the context.<br /> (2) In line 91, I suggest specifying that this is a renal IRI model.<br /> (3) Lines 167-168 state that Asct2 is localised to the apical side of the renal proximal tubules. Is there any expression of Asct2 in other nephron segments?<br /> (4) Lines 225-226: Have the authors expressed the candidate genes in HEK293 cells with ASCT2 knockdown?<br /> (5) lines 254-255: why was D-serine transport enhanced by ASCT2 knockdown in FlpInTR-SMCT1 or 2 cells?<br /> (6) line 265: The low affinity of SMCT1 for D-serine alone makes it an unlikely transporter for urinary D-serine.<br /> (7) line 316: The authors state that there is a high tubular D-serine reabsorption in IRI and in line 424 that there is an inactivation of DAAO during the pathology. This suggests that there is a reabsorption of D-serine mediated by a transport system in the basolateral membrane domain of proximal tubular cells. Do the authors have any information about this transporter?<br /> (8) in lines 462-463, the authors state: "It is suggested that PAT1 is less active at the apical membrane where the luminal pH is neutral". However, the pH of urine in the proximal tubules is normally acidic due to the high activity of NH3. I suggest rewording this sentence.

Reviewer #3 (Public Review):

Summary:

The main objective of this work has been to delve into the mechanisms underlying the increment of D-serine in serum, as a marker of renal injury.

Strengths:

With a multi-hierarchical approach, the work shows that Ischemia reperfusion injury in kidney causes a specific increment in renal reabsorption of D-serine that, at least in part, is due to the increased expression of the apical transporter ASCT2. In the way, the authors revealed that SMCT1 also transports D-serine.

The manuscript also supports that increased expression of ASCT2, even together with the parallel decreased expression of SMCT1, in renal proximal tubules underlies the increased reabsorption of D-serine responsible of the increment of this enantiomer in serum in a murine model of ischemia reperfusion injury.

Weaknesses:

Remains to be clarified whether ASCT2 has substantial stereospecificity in favor of D- versus L-serine to sustain a ~10-fold decreased in the ratio D-serine/L-serine in the urine of mouse under ischemia reperfusion injury (IRI).<br /> It is not clear how the increment in the expression of ASCT2, in parallel with the decreased expression of SMCT1, results in increased renal reabsorption of D-serine in IRI.

I am satisfied with the changes the authors have introduced in the text of the revised version of their manuscript.

Reviewer #1 (Public Review):

Summary:

LRRK2 protein is familially linked to Parkinson's disease by the presence of several gene variants that all confer a gain-of-function effect on LRRK2 kinase activity.

The authors examine the effects of BDNF stimulation in immortalized neuron-like cells, cultured mouse primary neurons, hIPSC-derived neurons, and synaptosome preparations from the brain. They examine an LRRK2 regulatory phosphorylation residue, LRRK2 binding relationships, and measures of synaptic structure and function.

Strengths:

The study addresses an important research question: how does a PD-linked protein interact with other proteins, and contribute to responses to a well-characterized neuronal signalling pathway involved in the regulation of synaptic function and cell health?

They employ a range of good models and techniques to fairly convincingly demonstrate that BDNF stimulation alters LRRK2 phosphorylation and binding to many proteins. Some effects of BDNF stimulation appear impaired in (some of the) LRRK2 knock-out scenarios (but not all). A phosphoproteomic analysis of PD mutant Knock-in mouse brain synaptosomes is included.

Weaknesses:

The data sets are disjointed, conclusions are sweeping, and not always in line with what the data is showing. Validation of 'omics' data is very light. Some inconsistencies with the major conclusions are ignored. Several of the assays employed (western blotting especially) are likely underpowered, findings key to their interpretation are addressed in only one or other of the several models employed, and supporting observations are lacking.

As examples to aid reader interpretation:

(a) pS935 LRRK2 seems to go up at 5 minutes but goes down below pre-stimulation levels after (at times when BDNF-induced phosphorylation of other known targets remains very high). This is ignored in favour of discussion/investigation of initial increases, and the fact that BDNF does many things (which might indirectly contribute to initial but unsustained changes to pLRRK2) is not addressed.

(b) Drebrin coIP itself looks like a very strong result, as does the increase after BDNF, but this was only demonstrated with a GFP over-expression construct despite several mouse and neuron models being employed elsewhere and available for copIP of endogenous LRRK2. Also, the coIP is only demonstrated in one direction. Similarly, the decrease in drebrin levels in mice is not assessed in the other model systems, coIP wasn't done, and mRNA transcripts are not quantified (even though others were). Drebrin phosphorylation state is not examined.

(c) The large differences in the CRISPR KO cells in terms of BDNF responses are not seen in the primary neurons of KO mice, suggesting that other differences between the two might be responsible, rather than the lack of LRRK2 protein.

(d) No validation of hits in the G2019S mutant phosphoproteomics, and no other assays related to the rest of the paper/conclusions. Drebrin phosphorylation is different but unvalidated, or related to previous data sets beyond some discussion. The fact that LRRK2 binding occurs, and increases with BDNF stimulation, should be compared to its phosphorylation status and the effects of the G2019S mutation.

Reviewer #2 (Public Review):

Taken as a whole, the data in the manuscript show that BDNF can regulate PD-associated kinase LRRK2 and that LRRK2 modifies the BDNF response. The chief strength is that the data provide a potential focal point for multiple observations across many labs. Since LRRK2 has emerged as a protein that is likely to be part of the pathology in both sporadic and LRRK2 PD, the findings will be of broad interest. At the same time, the data used to imply a causal throughline from BDNF to LRRK2 to synaptic function and actin cytoskeleton (as in the title) are mostly correlative and the presentation often extends beyond the data. This introduces unnecessary confusion. There are also many methodological details that are lacking or difficult to find. These issues can be addressed.

(1) The writing/interpretation gets ahead of the data in places and this was confusing. For example, the abstract highlights prior work showing that Ser935 LRRK2 phosphorylation changes LRRK2 localization, and Figure 1 shows that BDNF rapidly increases LRRK2 phosphorylation at this site. Subsequent figures highlight effects at synapses or with synaptic proteins. So is the assumption that LRRK2 is recruited to (or away from) synapses in response to BDNF? Figure 2H shows that LRRK2-drebrin interactions are enhanced in response to BDNF in retinoic acid-treated SH-SY5Y cells, but are synapses generated in these preps? How similar are these preps to the mouse and human cortical or mouse striatal neurons discussed in other parts of the paper (would it be anticipated that BDNF act similarly?) and how valid are SH-SY5Y cells as a model for identifying synaptic proteins? Is drebrin localization to synapses (or its presence in synaptosomes) modified by BDNF treatment +/- LRRK2? Or do LRRK2 levels in synaptosomes change in response to BDNF? The presentation requires re-writing to stay within the constraints of the data or additional data should be added to more completely back up the logic.

(2) The experiments make use of multiple different kinds of preps. This makes it difficult at times to follow and interpret some of the experiments, and it would be of great benefit to more assertively insert "mouse" or "human" and cell type (cortical, glutamatergic, striatal, gabaergic) etc.

(3) Although BDNF induces quantitatively lower levels of ERK or Akt phosphorylation in LRRK2KO preps based on the graphs (Figure 4B, D), the western blot data in Figure 4C make clear that BDNF does not need LRRK2 to mediate either ERK or Akt activation in mouse cortical neurons and in 4A, ERK in SH-SY5Y cells. The presentation of the data in the results (and echoed in the discussion) writes of a "remarkably weaker response". The data in the blots demand more nuance. It seems that LRRK2 may potentiate a response to BDNF that in neurons is independent of LRRK2 kinase activity (as noted). This is more of a point of interpretation, but the words do not match the images.

(4) Figure 4F/G shows an increase in PSD95 puncta per unit length in response to BDNF in mouse cortical neurons. The data do not show spine induction/dendritic spine density/or spine morphogenesis as suggested in the accompanying text (page 8). Since the neurons are filled/express gfp, spine density could be added or spines having PSD95 puncta. However, the data as reported would be expected to reflect spine and shaft PSDs and could also include some nonsynaptic sites.

(5) Experimental details are missing that are needed to fully interpret the data. There are no electron microscopy methods outside of the figure legend. And for this and most other microscopy-based data, there are few to no descriptions of what cells/sites were sampled, how many sites were sampled, and how regions/cells were chosen. For some experiments (like Figure 5D), some detail is provided in the legend (20 segments from each mouse), but it is not clear how many neurons this represents, where in the striatum these neurons reside, etc. For confocal z-stacks, how thick are the optical sections and how thick is the stack? The methods suggest that data were analyzed as collapsed projections, but they cite Imaris, which usually uses volumes, so this is confusing. The guide (sgRNA) sequences that were used should be included. There is no mention of sex as a biological variable.

(6) For Figures 1F, G, and E, how many experimental replicates are represented by blots that are shown? Graphs/statistics could be added to the supplement. For 1C and 1I, the ANOVA p-value should be added in the legend (in addition to the post hoc value provided).

(7) Why choose 15 minutes of BDNF exposure for the mass spec experiments when the kinetics in Figure 1 show a peak at 5 mins?

(8) The schematic in Figure 6A suggests that iPSCs were plated, differentiated, and cultured until about day 70 when they were used for recordings. But the methods suggest they were differentiated and then cryopreserved at day 30, and then replated and cultured for 40 more days. Please clarify if day 70 reflects time after re-plating (30+70) or total time in culture (70). If the latter, please add some notes about re-differentiation, etc.

(9) When Figures 6B and 6C are compared it appears that mEPSC frequency may increase earlier in the LRRK2KO preps than in the WT preps since the values appear to be similar to WT + BDNF. In this light, BDNF treatment may have reached a ceiling in the LRRK2KO neurons.

(10) Schematic data in Figures 5A and C and Figures 5B and E are too small to read/see the data.

Reviewer #1 (Public Review):

Amason et al. investigated the formation of granulomas in response to Chromobacterium violaceum infection, aiming to uncover the cellular mechanisms governing the granuloma response. They identify spatiotemporal gene expression of chemokines and receptors associated with the formation and clearance of granulomas, with a specific focus on those involved in immune trafficking. By analyzing the presence or absence of chemokine/receptor RNA expression, they infer the importance of immune cells in resolving infection. Despite observing increased expression of neutrophil-recruiting chemokines, treatment with reparixin (an inhibitor of CXCR1 and CXCR2) did not inhibit neutrophil recruitment during infection. Focusing on monocyte trafficking, they found that CCR2 knockout mice infected with C. violaceum were unable to form granulomas, ultimately succumbing to infection.

The spatial transcriptomics data presented in the figures could be considered a valuable resource if shared, with the potential for improved and clarified analyses. The primary conclusion of the paper, that C. violaceum infection in the liver cannot be contained without macrophages, would benefit from clarification.

While the spatial transcriptomic data generated in the figures are interesting and valuable, they could benefit from additional information. The manual selection of regions of granulomas for analysis could use additional context - was the rest of the liver not sequenced, or excluded for other reasons? Including a healthy liver in the analysis could serve as a control for any lasting effects at the final time point of 21 days. Providing more context for the scalebars throughout the spatial analyses, such as whether the data are raw counts or normalized based on the number of reads per spatial spot, would be helpful for interpretation, as changes in expression could signal changes in the numbers of cells or changes in the gene expression of cells.

In Figure 4, qualitative measurements are valuable, but having an idea of the raw data for a few of the pursued chemokines/receptors would aid interpretation. It would also be beneficial to clarify whether the reported values are across all clusters and consider focusing on clusters with the greatest change in expression. Figures 5E and F would benefit from clarification regarding the x-axis units and whether the expression levels are summed across all clusters for each time point. Additionally, information on the sequencing depth of the samples would be helpful, particularly as shallow sequencing of RNA can result in poor capture of low-expression transcripts.

Regarding the conclusion of the essentiality of macrophages in granuloma formation, it may be prudent to further investigate the role of macrophages versus CCR2. Analyzing total cell counts in the liver after infection could provide insight into whether the decrease in the fraction of macrophages is due to decreased numbers or infiltration of other cell types. Consideration of experiments deleting macrophages directly, instead of CCR2, could provide more definitive evidence of the necessity of macrophage migration in containing infections.

Reviewer #2 (Public Review):

Summary:

In this study, Amason et al employ spatial transcriptomics and intervention studies to probe the spatial and temporal dynamics of chemokines and their receptors and their influence on cellular dynamics in C. violaceum granulomas. As a result of their spatial transcriptomic analysis, the authors narrow in on the contribution of neutrophil- and monocyte-recruiting pathways to host response. This results in the observation that monocyte recruitment is critical for granuloma formation and infection control, while neutrophil recruitment via CXCR2 may be dispensable.

Strengths:

Since C. violaceum is a self-limiting granulomatous infection, it makes an excellent case study for 'successful' granulomatous inflammation. This stands in contrast to chronic, unproductive granulomas that can occur during M. tuberculosis infection, sarcoidosis, and other granulomatous conditions, infectious or otherwise. Given the short duration of C. violaceum infection, this study specifically highlights the importance of innate immune responses in granulomas.

Another strength of this study is the temporal analysis. This proves to be important when considering the spatial distribution and timing of cellular recruitment. For example, the authors observe that the intensity and distribution of neutrophil- and monocyte-recruiting chemokines vary substantially across infection time and correlate well with their previous study of cellular dynamics in C. violaceum granulomas.

The intervention studies done in the last part of the paper bolster the relevance of the authors' focus on chemokines. The authors provide important negative data demonstrating the null effect of CXCR1/2 inhibition on neutrophil recruitment during C. violaceum infection. That said, the authors' difficulty with solubilizing reparixin in PBS is an important technical consideration given the negative result. On the other hand, monocyte recruitment via CCR2 proves to be indispensable for granuloma formation and infection control. I would hesitate to agree with the authors' interpretation that their data proves macrophages are serving as a physical barrier from the uninvolved liver. It is possible and likely that they are contributing to bacterial control through direct immunological activity and not simply as a structural barrier.

Weaknesses:

There are several shortcomings that limit the impact of this study. The first is that the cohort size is very limited. While the transcriptomic data is rich, the authors analyze just one tissue from one animal per time point. This assumes that the selected individual will have a representative lesion and prevents any analysis of inter-individual variability. Granulomas in other infectious diseases, such as schistosomiasis and tuberculosis, are very heterogeneous, both between and within individuals. It will be difficult to assert how broadly generalizable the transcriptomic features are to other C. violaceum granulomas. Furthermore, this undermines any opportunity for statistical testing of features between time points, limiting the potential value of the temporal data.

Another caveat to these data is the limited or incompletely informative data analysis. The authors use Visium in a more targeted manner to interrogate certain chemokines and cytokines. While this is a great biological avenue, it would be beneficial to see more general analyses considering Visum captures the entire transcriptome. Some important questions that are left unanswered from this study are:

What major genes defined each spatial cluster?

What were the top differentially expressed genes across time points of infection?

Did the authors choose to focus on chemokines/receptors purely from a hypothesis perspective or did chemokines represent a major signature in the transcriptomic differences across time points?

In addition to the absence of deep characterization of the spatial transcriptomic data, the study lacks sufficient quantitative analysis to back up the authors' qualitative assessments. Furthermore, the authors are underutilizing the spatial information provided by Visium with no spatial analysis conducted to quantify the patterning of expression patterns or spatial correlation between factors.

Impact:

The author's analysis helps highlight the chemokine profiles of protective, yet host protective granulomas. As the authors comment on in their discussion, these findings have important similarities and differences with other notable granulomatous conditions, such as tuberculosis. Beyond the relevance to C. violaceum infection, these data can help inform studies of other types of granulomas and hone candidate strategies for host-directed therapy strategies.

Reviewer #1 (Public Review):

Pineda et al investigate the association of the hypothesis that Dux4, an embryonic transcription factor, expression in tumor cells is associated with immune evasion and resistance to immunotherapy. They analyze existing cohorts of bulk RNAseq sequenced tumors across cancer types to identify Dux4 expression and association with survival. They find that Dux4 expression is detected in a higher proportion of metastatic tumors compared to primary tumors, is associated with decreased immune infiltrate and a variety of immune metrics and previously nominated immune signatures, and do an in depth evaluation of a cohort of metastatic urothelial cell carcinoma, finding that Dux4 expression is associated with a more immunodeficient tumor microenvironment (desert or excluded microenvironment) and worse survival in this aPDL1 treated cohort. They then find that Dux4 expression is a major independent predictor of survival in this cohort using different types of survival analyses (KM, Cox PH, and random survival forests). With prior existing biological data supporting the hypothesis (in prior work, the senior author has demonstrated Dux4 expression causally suppresses MHC-I expression in interferon-gamma treated cell lines), the current work links Dux4 expression with less immune activity in clinical tumor samples and with survival in ICI treated urothelial carcinomas, and demonstrates that Dux4 expression provides independent information towards survival including other molecular and clinical characteristics (TMB, ECOG PS as the other strongest markers), and provides interesting resolution on landmark analyses with TMB and Dux4 expression providing greater informativeness at later survival landmarks (e.g. 1 year and later), while ECOG PS has strong informativeness already at earlier time points. This work provides impetus towards more mechanistic and functional dissection of the mechanism of Dux4-associated changes with the tumor microenvironment (e.g. in vivo mouse studies) as well as potential interventional studies (e.g. Dux4 as a target in combination therapies). What the work does not provide is additional resolution on the mechanism of how Dux4 may be associated with a more immunodeficient microenvironment.

The conclusions are generally well supported, but there are issues that would benefit from clarification and extension:

- The finding that Dux4 expression is detected in a higher proportion of metastatic tumors and at higher levels compared to TCGA samples (Fig 1BC) is striking. However, at least for one tumor type (melanoma), the TCGA cohort is comprised of mostly locoregional metastatic (n=81 primary and 367 metastatic tumors in the PanCan Atlas). Since there are annotations for primary and (locoregional) metastatic samples in TCGA, an analysis of the primary vs. locoregional metastasis vs distant metastatic samples seems reasonable and likely informative. The analysis of tumors with matched FFPE and flash frozen samples with hybrid probe capture and polyA sequencing, respectively is a nice validation to show that the difference in Dux4 expression is not due to differences in preservation of starting material/sequencing in the metastatic samples vs TCGA samples (S1BC).<br /> - The findings that Dux4 expression in the metastatic urothelial carcinoma setting is associated with a more immunodeficient microenvironment (Figure 2) is clear and unambiguous using multiple lines of data and analyses (bulk RNAseq, DUX4-positive vs DUX4-negative tumors, different immune cell and cytokine signatures; IHC showing an association with immune deserts and immune excluded phenotypes). However, this is an association and does not demonstrate causality.<br /> - The survival analyses (Fig 3,4,5) show fairly convincingly that Dux4 provide independent predictive information beyond clinical variables and TMB towards survival in the aPDL1 treated metastatic urothelial carcinoma cohort. However, the choice to split the cohort into Dux4 negative (defined as < 0.25 TPM) and Dux4 positive (> 1 TPM) while excluding a large number of patients (n=126 pts) that fall in between has significant impact on the rigor of conclusions. This would benefit from showing all the data (e.g. including the 3rd group of in-betweens in the survival analyses as a separate group).<br /> - The authors demonstrate that adding Dux4 to clinical markers and TMB results in an improved predictive model for survival, but there are a few questions regarding this model as a clinical biomarker<br /> o Is Dux4 expression better than other correlated immune signatures/markers (e.g. interferon gamma, T effector signature, overall immune infiltrate) in providing additional information?<br /> - The use of random survival forests to quantify the (predictive) marginal effect of Dux4+ vs Dux4- expression on survival in a non-parametric model as well as shed light on association with survival at different landmark times using Shapley values is quite interesting and well conducted.

Reviewer #2 (Public Review):

Summary:

This article takes an expansive look at the potential role of DUX4 in cancer treatment and prognosis, including its correlation with other key biomarkers, the potential for cancer to be resistant to treatment, and risk prediction.

Strengths:

The primary strength of this work is the breadth of the analyses. The authors have linked DUX4 to not just one but multiple points in the trajectory of cancer, which increases the face validity of their conclusion that DUX4 is meaningfully related to the course of a cancer as well as the prognosis for a patient.

Statistically, the authors have taken care to properly validate their findings using appropriate bootstrapping and testing strategies.

Weaknesses:

Several weaknesses are noted. First, there is little-to-no description of the underlying sample population. It is only stated that "several large cohorts of patients with different metastatic cancers" were analyzed, and that a cohort of patients with advanced urothelial cancer was used for estimating associations with clinical outcomes. Lacking is information on the sampling mechanism, inclusion/exclusion criteria, treatment modalities, the definition of 'time = 0', the number of events observed, or even the sample size. Knowledge about the underlying study design would help explain some counterintuitive results, e.g. that the hazard of death among patients with Stage IV cancer is half that of those with Stage I cancer (Table 1); presumably this is not because Stage IV is actually protective but rather an artifact of the sampling scheme for these data. Second, the definition of negative versus positive DUX4 expression varies throughout the paper. In Figure 2B, Figure 3A, and Figure 3C, it is defined as >1 TPM vs. <= 1 TPM; in Figure 4A and Figure 5A, it is defined as >1 TPM vs. < 0.25 TPM; in Figure S1C it is partitioned into four groups, with boundaries defined at 0.25 TPM, 1 TPM, and 5 TPM. If categorization is needed, a rationale should be provided (ideally prospectively and not based upon the observed data, so as to avoid the perception of forking paths analyses), and it should be consistently applied. Third and finally, data seem to be occasionally excluded without rationale. For example, as mentioned above, the Cox model presented in Figure 4A seems to exclude all patients with DUX4 TPM between 0.25 and 1. Figure 3C excludes patients with TMB in the lowest quartile (although the decision was ostensibly to control for TMB confounding, there are more appropriate ways to do so that don't result in loss of data, e.g. a stratified KM plot). Excluding patients based upon a particular region of the covariate space makes interpreting the resulting model awkward.

Reviewer #1 (Public Review):

The authors have made a novel and important effort to distinguish and include different sources of active deformations for fitting C elegans embryo development: cyclic muscle contractions and actomyosion circumferential stresses. The combination and synchronisation of both contributions are, according to the model, responsible for different elongation rates, and can induce bending and torsion deformations, which are a priori not expected from purely contractile forces. The model can be applied to other growth processes in initially cylindrical shapes.

The tilt of the fibers is an important assumption of the model. However, fiber direction in Figure 3B is not fully clear for explaining the tilting. The fiber in 3B has not very much in common with the fibers in the color part of the figure. Also, is vector m supposed to be tangent to the fiber? In the figure does not seem to be so. It should be expected that alpha is a consequence of the deformation, not as an input parameter, as it seems in the tests of Figure 6A. How is the value of alpha chosen? According to Figure 6, torsion is expected for alpha>0, but for beta=pi/2 and alpha>0 no torsion may be obtained. In fact, it seems that torsion should appear when cos(beta)*sin(alpha)>0. As a consequence, value of beta should be given in Figure 6. Can the amount of torsion be tested as a function of alpha and beta?

The transfer of energy and deformation is a very interesting aspect of the paper, and also crucial for the model and predicting elongation. However, the modelling of this transfer remains very obscure and only explained in the Appendix. Some more details on how the transfer is selected should be given in the main text. Can the transfer of energy interpreted as a change of the relaxed reference configuration? Once a ratio of the energy transferred is fixed, the assumption on elongation distribution should be stated. (Uniformly? ) The authors should also define in the main text the factor g_a1, and explain how this value is computed from condition W_c=W_r .

Given the convoluted shape of the embryo in the egg, contact may be a crucial mechanism for determining growth and torsion. The model does not include this contact, and this limitation should be reflected in the article.

Minor comment:<br /> -Line 300: "we determine the optimal values for the activation parameters". the optimal with respect to which objective? Norm of difference between experimental and computational displacements? How this is quantified needs to be specified.

Reviewer #2 (Public Review):

Summary:

During C. elegans development, embryos undergo elongation of their body axis in absence of cell proliferation or growth. This process relies in an essential way on periodic contractions of two pairs muscles that extend along the embryo's main axis. How contraction can lead to extension along the same direction is unknown.

To address this question, the authors use a continuum description of a multicomponent elastic solid. The various components are the interior of the animal, the muscles, and the epidermis. The different components form separate compartments and are described as hyperelastic solids with different shear moduli. For simplicity, a cylindrical geometry is adopted. The authors consider first the early elongation phase, which is driven by contraction of the epidermis, and then late elongation, where contraction of the muscles injects elastic energy into the system, which is then transferred into elongation. The authors get elongation that can be successfully fitted to the elongation dynamics of wild type worms and two mutant strains.

Strengths:

The work proposes a physical mechanism underlying a puzzling biological phenomenon. The framework developed by the authors could be used to explain phenomena in other organisms and could be exploited in the design of soft robots.

Weaknesses:

(1) The manuscript is hard to read without being very familiar with continuum descriptions of elastic media. This might make the work difficult to access for biologists. This is a real pity because the findings are potentially of great interest to developmental biologists and engineers alike.

(2) The discussion of the worm's mechanical properties could go deeper. The authors hardly justify their assumptions.

Reviewer #1 (Public Review):

Summary:

This study presents careful biochemical experiments to understand the relationship between LRRK2 GTP hydrolysis parameters and LRRK2 kinase activity. The authors report that incubation of LRRK2 with ATP increases the KM for GTP and decreases the kcat. From this they suppose an autophosphorylation process is responsible for enzyme inhibition. LRRK2 T1343A showed no change, consistent with it needing to be phosphorylated to explain the changes in G-domain properties. The authors propose that phosphorylation of T1343 inhibits kinase activity and influences monomer-dimer transitions.

Strengths:

Strengths of the work are the very careful biochemical analyses and interesting result for wild type LRRK2.

Weaknesses:

The conclusions related to involvement of a monomer-dimer transition are to this reviewer, premature and an independent method needs to be utilized to bolster this aspect of the story.

Reviewer #2 (Public Review):

As discussed in the original review, this manuscript is an important contribution to a mechanistic understanding of LRRK2 kinase. Kinetic parameters for the GTPase activity of the ROC domain have been determined in the absence/presence of kinase activity. A feedback mechanism from the kinase domain to GTP/GDP hydrolysis by the ROC domain is convincingly demonstrated through these kinetic analyses. However, a regulatory mechanism directly linking the T1343 phospho-site and a monomer/dimer equilibrium is not fully supported. The T1343A mutant has reduced catalytic activity and can form similar levels of dimer as WT. The revised manuscript does point out that other regulatory mechanisms can also play a role in kinase activity and GTP/GDP hydrolysis (Discussion section). The environmental context in cells cannot be captured from the kinetic assays performed in this manuscript, and the introduction contains some citations regarding these regulatory factors. This is not a criticism, the detailed kinetics here are rigorous, but it is simply a limitation of the approach. Caveats concerning effects of membrane localization, Rab/14-3-3 proteins, WD40 domain oligomers, etc... should be given more prominence than a brief (and vague) allusion to 'allosteric targeting' near the end of the Discussion.

Specific comments

(1) The revised version is better organized with respect to the significance of monomer/dimer equilibrium and the relevance of the GTP-binding region of ROC domain that encompasses the T1343 phospho-site. The relevance of monomers/dimers of LRRK2 from previous studies is better articulated and readers are able to follow the reasoning for the various mutations.

(2) As a suggestion I would change the following on page 6 to clarify for readers:<br /> "...would show no change in kcat and KM values upon in vitro ATP treatment" to:<br /> "...would show no change in kcat and KM values for GTP hydrolysis upon in vitro ATP treatment"

(3) The levels of dimer in WT (+ATP) and T1343A (+/- ATP) are the same, about 40-45%. These data are cited when the authors state that ATP-induced monomerization is 'abolished' (page 6). My suggestion is to re-phrase this conclusion for consistency with data (Fig 5). For example, one can state that 'ATP incubation does not affect the percentage of dimer for the T1343A variant of LRRK2'. This would be similar to the authors' description of these data on page 8 - 'no difference in dimer formation upon ATP treatment'.

Reviewer #1 (Public Review):

The author studies a family of models for heritable epigenetic information, with a focus on enumerating and classifying different possible architectures. The key aspects of the paper are:

- Enumerate all 'heritable' architectures for up-to 4 constituents.<br /> - A study of whether permanent ("genetic") or transient ("epigenetic") perturbations lead to heritable changes<br /> - Enumerated the connectivity of the "sequence space" formed by these heritable architectures<br /> - Incorporating stochasticity, the authors explore stability to noise (transient perturbations)<br /> - A connection is made with experimental results on C elegans.

The study is timely, as there is a renewed interest in the last decade in non-genetic, heritable heterogeneity (e.g., from single-cell transcriptomics). Consequently, there is a need for a theoretical understanding of the constraints on such systems. There are some excellent aspects of this study: for instance, the attention paid to how one architecture "mutates" into another. Unfortunately, the manuscript as a whole does not succeed in formalising nor addressing any particular open questions in the field. Aside from issues in presentation and modelling choices (detailed below), it would benefit greatly from a more systematic approach rather than the vignettes presented.

## Terminology

The author introduces a terminology for networks of interacting species in terms of "entities" and "sensors" -- the former being nodes of a graph, and the latter being those nodes that receive inputs from other nodes. In the language of directed graphs, "entities" would seem to correspond to vertices, and "sensors" those vertices with positive indegree and outdegree. Unfortunately, the added benefit of redefining accepted terminology from the study of graphs and networks is not clear.

## Model

The model seems to suddenly change from Figure 4 onwards. While the results presented here have at least some attempt at classification or statistical rigour (i.e. Fig 4 D), there are suddenly three values associated with each entity ("property step, active fraction, and number"). Furthermore, the system suddenly appears to be stochastic. The reader is left unsure what has happened, especially after having made the effort to deduce the model as it was in Figs 1 through 3. No respite is to be found in the SI, either, where this new stochastic model should have been described in sufficient detail to allow one to reproduce the simulation.

## Perturbations

Inspired especially by experimental manipulations such as RNAi or mutagenesis, the author studies whether such perturbations can lead to a heritable change in network output. While this is naturally the case for permanent changes (such as mutagenesis), the author gives convincing examples of cases in which transient perturbations lead to heritable changes. Presumably, this is due the the underlying multistability of many networks, in which a perturbation can pop the system from one attractor to another.

Unfortunately, there appears to be no attempt at a systematic study of outcomes, nor a classification of when a particular behaviour is to be expected. Instead, there is a long and difficult-to-read description of numerical results that appear to have been sampled at random (in terms of both the architecture and parameter regime chosen). The main result here appears to be that "genetic" (permanent) and "epigenetic" (transient) perturbations can differ from each other -- and that architectures that share a response to genetic perturbation need not behave the same under an epigenetic one. This is neither surprising (in which case even illustrative evidence would have sufficed) nor is it explored with statistical or combinatorial rigour (e.g. how easy is it to mistake one architecture for another? What fraction share a response to a particular perturbation?)

As an additional comment, many of the results here are presented as depending on the topology of the network. However, each network is specified by many kinetic constants, and there is no attempt to consider the robustness of results to changes in parameters.

## DNA analogy

At two points, the author makes a comparison between genetic information (i.e. DNA) and epigenetic information as determined by these heritable regulatory architectures. The two claims the author makes are that (i) heritable architectures are capable of transmitting "more heritable information" than genetic sequences, and (ii) that, unlike DNA, the connectivity (in the sense of mutations) between heritable architectures is sparse and uneven (i.e. some architectures are better connected than others).

In both cases, the claim is somewhat tenuous -- in essence, it seems an unfair comparison to consider the basic epigenetic unit to be an "entity" (e.g., an entire transcription factor gene product, or an organelle), while the basic genetic unit is taken to be a single base-pair. The situation is somewhat different if the relevant comparison was the typical size of a gene (e.g., 1 kb).

Reviewer #1 (Public Review):

Summary:

This describes the molecular identity of the intermediate status of cranial neural crest cells (NCCs) during the initial delamination process. Taking advantage of single-cell RNA seq, the authors identify new populations of cells during EMT characterized by a specific set of gene expressions, including Dlc1. Promigratory cranial NCCs differentiate through different trajectories depending on their cell cycle phases but converge into a common progenitor, then differentiate into mesenchymal cells expressing region-specific genes.

Strengths:

Single-cell RNA seq data convincingly support what the authors claim. This is the first time to identify intermediate states between premigratory and migratory cranial NCCs. Silencing one of the marker genes, Dlc1, reduces the migratory activity of cranial NCCs. These findings deepen our understanding of the mechanism of EMT in general.

Comments on revised version:

Weaknesses:

None after substantial revision.

Reviewer #2 (Public Review):

Zhao et al., focus on mechanisms through which cells convert from epithelium to mesenchyme and become migratory. This phenomenon of epithelial-to-mesenchymal transition (EMT) occurs during both embryonic development and cancer progression. During cancer progression, EMT seemingly includes cells at intermediate states as defined by the combinatorial expression of epithelial and mesenchymal markers. But the importance of these markers and the role of these intermediate states remains unclear. Moreover, whether EMT during development also involves equivalent intermediate cell states is not known. To address this gap in knowledge, the authors devise a strategy to identify and characterize changes that an embryonic population of cells called the cranial neural crest undergo as they delaminate from the neuroepithelium and become a highly migratory population of mesenchymal cells that ultimately give rise to a broad range of derivatives.

To isolate and study the neural crest, the authors use embryos collected at E8.5 from two transgenic mouse lines. Wnt1-Cre;RosaeYFP labels Wnt1-positive neuroepithelial cells in the dorsolateral neural plate, which includes pre-migratory neural crest that reside in the dorsal neuroectoderm and neural plate border before induction (as well as some other lineages). Mef2c-F10N-LacZ leverages a neural crest cell-specific enhancer of Mef2c to control LacZ expression in predominantly migratory neural crest. This dual genetic approach that allows the authors to distinguish and compare pre-migratory and migratory neural crest cells is a strength of the work.

To assay for the differential expression of genes involved in the EMT and migration of cranial neural crest, the authors perform single cell RNA sequencing (scRNA-seq) using current methods. A strength is a large sample size per mouse line, and relatively high numbers of single cells analyzed. The authors identify six major cell/tissue types present in mouse E8.5 cranial tissues using known markers, which they then segregate into a cranial neural crest cluster using a well-reasoned bioinformatic strategy. The cranial neural crest cluster contains pre-migratory and migratory cells that they partition further into five subclusters and then characterize using the differential expression and combinatorial patterns of neural crest specifier genes, markers of pre-migratory neural crest, markers of early versus late migratory neural crest, markers of undifferentiated versus differentiated neural crest, tissue-specific markers, and region-specific markers. One weakness is that there is little attempt to map potential novel genes and/or pathways that also distinguish these clusters.

The authors then go on to subdivide the five cranial neural crest subclusters into almost two dozen smaller subclusters, again using the combinatorial expression of known markers (e.g., neural crest genes, cell junction genes, and cell cycle genes). A weakness is that the marker analysis and accompanying interpretation of the results relies heavily on the purported roles of different genes as described in the published work of others, which potentially introduces some untested assumptions and a bit of hand-waving into the study. Moreover, the limited correlation between mRNA and protein abundance for cell cycle markers is well documented in the literature but the authors rely heavily on gene expression to determine cell cycle status. Even though the authors add a compelling Edu/pHH3 double-labeling experiment and cell cycle inhibition studies, the work would be strengthened by including some analysis of protein expression to see if the cell cycle correlations hold up. Nonetheless, the subcluster and cell cycle analyses lead the authors to conclude that there are a series of intermediate cell states between neural crest EMT and delamination, and that cell cycle regulation is a defining feature and necessary component of those states. These novel findings are generally well supported by the data.

To test if there are spatiotemporal differences in the localization of neural crest cells during EMT in vivo, the authors apply a cutting-edge technique called signal amplification by exchange reaction for multiplexed fluorescent in situ hybridization (SABER-FISH), which they validate using standard in situ hybridization. The authors select specific marker genes that seem justified based on their scRNA-seq dataset, and they generate a series of convincing images and quantitative data that add valuable depth to the story.

As a functional test of their hypothesis that one of the genes indicative of an EMT intermediate stage (i.e., Dlc1) is essential for neural crest migration, the authors use a lentivirus-mediated knockdown strategy. A strength is that the authors include appropriate scramble and cell death controls as part of their experimental design.

The authors use Sox10 as a marker to count neural crest cells, but Sox10 may only label a subset of neural crest cells and thus some unaffected lineages may not have been counted. Although the data are persuasive, a second marker for counting neural crest cells following knockdown would make the analysis more robust.

Overall, this is a first-rate study with many more strengths than weaknesses. The authors generate high quality data, and their interpretations are reasonable and balanced. Another strength is the writing, which is clear and well organized, and the figures (including supplemental), which are excellent and provide unambiguous visualization of some very complex data sets. The methods are state-of the art and are effectively executed, and they will be useful to the broader cell and developmental biology community. The work contains well-substantiated findings and supports the conclusion that EMT is a highly dynamic, multi-step process, which was previously thought to be more-or-less binary. Such findings will alter the way the field thinks about EMT in neural crest and the work will likely serve as an important example alongside cancer metastasis.

Reviewer #3 (Public Review):

Summary:

Zhao et al. address the question of whether intermediate states of the epithelial-to-mesenchymal transition (EMT) exist in a natural developmental context as well as in cancer cells. This is important not only for our understanding of these developmental systems but also for their development as resources for new anti-cancer approaches. Guided by single-cell RNA sequencing analysis of delaminating mouse cranial neural crest cells, they identify two distinct populations with transcriptional signatures intermediate between neuroepithelial progenitors and migrating crest. Both clusters are also spatially intermediate and are actively cycling, with one in S-phase and one in G2/M. They show that blocking progression through S phase prior to the onset of delamination and knockdown of intermediate state marker Dlc1 both reduce the number of migratory cells that have completed EMT. Overall, the work provides a modern take and new insights into the classical developmental process of neural crest delamination.

Strengths:

• Deep analysis of the scRNAseq dataset revealed previously unappreciated cell populations intermediate between premigratory and migratory crest.<br /> • The observation that delaminating/intermediate neural crest cells appear to be in S or G2/M phase is interesting and worth reporting, though the ultimate significance remains unclear, given that they do not make distinct derivatives depending on their cycle state.<br /> • The authors employ new methods for multiplex spatial imaging to more accurately define their populations of interest and their relative positions.<br /> • The authors present evidence that intermediate state gene Dlc1 (a Rho GAP) is not just a marker but functionally required for neural crest delamination in mouse, as previously shown in chicken.

Weaknesses:

• Similar experiments involving blockade of cell cycle progression and Dlc1 dose manipulation were previously performed in chick models, as noted in the discussion. The newly-defined intermediate states give added context to the results, but they are not entirely novel.

Reviewer #1 (Public Review):

In this manuscript, the authors report a molecular mechanism for recruiting syntaxin 17 (Syn17) to the closed autophagosomes through the charge interaction between enriched PI4P and the C-terminal region of Syn17. How to precisely control the location and conformation of proteins is critical for maintaining autophagic flux. Particularly, the recruitment of Syn17 to autophagosomes remains unclear. In this paper, the author describes a simple lipid-protein interaction model beyond previous studies focusing on protein-protein interactions. This represents conceptual advances.

Reviewer #2 (Public Review):

Summary:

Syntaxin17 (STX17) is a SNARE protein that is recruited to mature (i.e., closed) autophagosomes, but not to immature (i.e., unclosed) ones, and mediates the autophagosome-lysosome fusion. How STX17 recognizes the mature autophagosome is an unresolved interesting question in the autophagy field. Shinoda and colleagues set out to answer this question by focusing on the C-terminal domain of STX17 and found that PI4P is a strong candidate that causes the STX17 recruitment to the autophagosome.

Strengths:

The main findings are: 1) Rich positive charges in the C-terminal domain of STX17 are sufficient for the recruitment to the mature autophagosome; 2) Fluorescence charge sensors of different strengths suggest that autophagic membranes have negative charges and the charge increases as they mature; 3) Among a battery of fluorescence biosensors, only PI4P-binding biosensors distribute to the mature autophagosome; 4) STX17 bound to isolated autophagosomes is released by treatment with Sac1 phosphatase; 5) By dynamic molecular simulation, STX17 TM is shown to be inserted to a membrane containing PI4P but not to a membrane without it. These results indicate that PI4P is a strong candidate that STX17 binds to in the autophagosome.

Weaknesses:

• It was not answered whether PI4P is crucial for the STX17 recruitment in cells because manipulation of the PI4P content in autophagic membranes was not successful for unknown reasons.<br /> • The question that the authors posed in the beginning, i.e., why is STX17 recruited to the mature (closed) autophagosome but not to immature autophagic membranes, was not answered. The authors speculate that the seemingly gradual increase of negative charges in autophagic membranes is caused by an increase in PI4P. However, this was not supported by the PI4P fluorescence biosensor experiment that showed their distribution to the mature autophagosome only. Here, there are at least two possibilities: 1) The increase of negative charges in immature autophagic membranes is derived from PI4P. However the fluorescence biosensors do not bind there for some reason; for example, they are not sensitive enough to recognize PI4P until it reaches a certain level, or simply, their binding does not occur in a quantitative manner. 2) The negative charge in immature membranes is not derived from PI4P, and PI4P is generated abundantly only after autophagosomes are closed. In either case, it is not easy to explain why STX17 is recruited to the mature autophagosome only. For the first scenario, it is not clear how the PI4P synthesis is regulated so that it reaches a sufficient level only after the membrane closure. In the second case, the mechanism that produces PI4P only after the autophagosome closure needs to be elucidated (so, in this case, the question of the temporal regulation issue remains the same).

Reviewer #3 (Public Review):

Summary:

In this study, the authors set out to address the question of how the SNARE protein Syntaxin 17 senses autophagosome maturation by being recruited to autophagosomal membranes only once autophagosome formation and sealing is complete. The authors discover that the C-terminal region of Syntaxin 17 is essential for its sensing mechanism that involves two transmembrane domains and a positively charged region. The authors discover that the lipid PI4P is highly enriched in mature autophagosomes and that electrostatic interaction with Syntaxin 17's positively charged region with PI4P drives recruitment specifically to mature autophagosomes. The temporal basis for PI4P enrichment and Syntaxin 17 recruitment to ensure that unsealed autophagosomes do not fuse with lysosomes is a very interesting and important discovery. Overall, the data are clear and convincing, with the study providing important mechanistic insights that will be of broad interest to the autophagy field, and also to cell biologists interested in phosphoinositide lipid biology. The author's discovery also provides an opportunity for future research in which Syntaxin 17's c-terminal region could be used to target factors of interest to mature autophagosomes.

Strengths:

The study combines clear and convincing cell biology data with in vitro approaches to show how Syntaxin 17 is recruited to mature autophagosomes. The authors take a methodical approach to narrow down the critical regions within Syntaxin 17 required for recruitment and use a variety of biosensors to show that PI4P is enriched on mature autophagosomes.

Weaknesses:

There are no major weaknesses, overall the work is highly convincing. It would have been beneficial if the authors could have shown whether altering PI4P levels would affect Syntaxin 17 recruitment. However, this is understandably a challenging experiment to undertake and the authors outlined their various attempts to tackle this question.

Reviewer #1 (Public Review):

The authors investigated how global brain activity varied during reward-based motor learning. During early learning, they found increased covariance between the sensorimotor and dorsal attention networks, coupled with reduced covariance between the sensorimotor and default mode networks; during late learning, they found the opposite pattern. Individual learning performance varied only with changes in the dorsal attention network. The authors certainly used a wide variety of valuable, state-of-the-art techniques to interrogate whole-brain networks and extract the key components of learning behavior. However, the findings are incomplete, tempered by potential confounds in the experimental design. As such, the underlying claim regarding how these networks jointly support reward-based motor learning is unclear.

Reviewer #2 (Public Review):

This useful investigation of learning-driven dynamics of cortical and some subcortical structures combines a novel in-scanner learning paradigm with interesting analysis approaches. The new task for reward-based motor learning is highly compelling and goes beyond the current state-of-the-art, but it is incomplete with respect to examining different signatures of learning, clarifying probed learning processes, and investigating changes in all relevant subcortical structures is incomplete and would benefit from more rigorous approaches. With the rationale and data presentation strengthened this paper would be of interest to neuroscientists working on motor control and reward-based learning.

Reviewer #3 (Public Review):

The manuscript of Nick and colleagues addresses the intriguing question of how brain connectivity evolves during reward-based motor learning. The concept of quantifying connectivity through changes in extraction and contraction across lower-dimensional manifolds is both novel and interesting and the presented results are clear and well-presented. Overall, the manuscript is a valuable addition to the field. The evidence supporting the presented findings is strong, though at times lacking rigorous statistical quantification. Nevertheless, there are several issues that require attention and clarification.

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 JNK-MMP1 signalling pathway downstream of PVR. They also show that blocking endocytosis prolongs the involvement of this pathway in the TBI response.

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.

For this revised version the authors have removed all the unsupported claims. This renders their remaining claims more solid. However, it has resulted in the loss of important cellular aspects of the response to TBI, limiting the scope and value of the work.

The main weakness is that novelty and insight are both rather limited. 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). The authors claim that the novelty was investigating injury responses in astrocytes in Drosophila. However, others had investigated injury responses by astrocytes in Drosophila before. It had been previously shown that astrocytes - defined as the Prospero+ neuropile glia, and also sharing evolutionary features with mammalian NG2 glia - respond to injury both in larval ventral nerve cords and in adult brains, where they proliferate regenerating glia and induce a neurogenic response (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 did not investigate what consequences the up-regulation of MMP1 after injury has in repair or regeneration processes.

The statistical analysis is incorrect in places, and this could affect the validity of some claims.

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 #3 (Public Review):

In this study, authors used the Drosophila model to characterize molecular details underlying traumatic brain injury (TBI). 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, the connection suggested by the authors between Pvr and AP-1, potentially mediated through the JNK pathway, lacks strong experimental support in my view. It's important to recognize that AP-1 activity is influenced by multiple upstream signaling pathways, not just the JNK pathway, which is the most well-characterized among them. Therefore, assuming that AP-1 transcriptional activity solely reflects the activity of the JNK pathway without additional direct evidence is unwarranted. To strengthen their argument, the study could benefit from direct evidence implicating the JNK pathway in linking Pvr to AP-1. This could be achieved through genetic studies involving mutants or transgenes targeting key components of the JNK pathway, such as Bsk and Hep, the Drosophila homologues of JNK and JNKK, respectively. Alternatively, employing p-JNK antibody-based techniques like Western blotting, while considering the potential challenges associated with p-JNK immunohistochemistry, could provide further validation. This important criticism regarding the molecular link between Pvr and AP-1 has been overlooked.

Reviewer #1 (Public Review):

Dasguta et al. have dissected the role of Sema7a in fine tuning of a sensory microcircuit in the posterior lateral line organ of zebrafish. They attempt to also outline the different roles of a secreted verses membrane-bound form of Sema7a in this process. Using genetic perturbations and axonal network analysis, the authors show that loss of both Sema7a isoforms causes abnormal axon terminal structure with more bare terminals and fewer loops in contact with presynaptic sensory hair cells. Further, they show that loss of Sema7a causes decreased number and size of both the pre- and post-synapse. Finally, they show that overexpression of the secreted form of Sema7a specifically can elicit axon terminal outgrowth to an ectopic Sema7a expressing cell. Together, the analysis of Sema7a loss of function and overexpression on axon arbor structure is fairly thorough and revealed a novel role for Sema7a in axon terminal structure. However, the connection between different isoforms of Sema7a and the axon arborization needs to be substantiated. Furthermore, the effect of loss of Sema7a on the presynaptic cell is not ruled out as a contributing factor to the synaptic and axon structure phenotypes. These issues weaken the claims made by the authors including the statement that they have identified dual roles for the GPI-anchored verses secreted forms of Sema7a on synapse formation and as a chemoattractant for axon arborization respectively.

Reviewer #2 (Public Review):

In this work, Dasgupta et al. investigates the role of Sema7a in the formation of peripheral sensory circuit in the lateral line system of zebrafish. They show that Sema7a protein is present during neuromast maturation and localized, in part, to the base of hair cells (HCs). This would be consistent with pre-synaptic Sema7a mediating formation and/or stabilization of the synapse. They use sema7a loss-of-function strain to show that lateral line sensory terminals display abnormal arborization. They provide highly quantitative analysis of the lateral line terminal arborization to show that a number of specific topological parameters are affected in mutants. Next, they ectopically express a secreted form of Sema7a to show that lateral line terminals can be ectopically attracted to the source. Finally, they also demonstrate that the synaptic assembly is impaired in the sema7a mutant. Overall, the data are of high quality and properly controlled. The availability of Sema7a antibody is a big plus, as it allows to address the endogenous protein localization as well to show the signal absence in the sema7a mutant. The quantification of the arbor topology should be useful to people in the field who are looking at the lateral line as well as other axonal terminals. I think some results are overinterpreted though. The authors state: "Our findings demonstrate that Sema7A functions both as a juxtracrine and as a secreted cue to pattern neural circuitry during sensory organ development." However, they have not actually demonstrated which isoform functions in HCs (also see comments below). In addition, they have to be careful in interpreting their topology analysis, as they cannot separate individual axons. Thus, such analysis can generate artifacts. They can perform additional experiments to address these issues or adjust their interpretations.

Reviewer #3 (Public Review):

The data reported here demonstrate that Sema7a defines the local behavior of growing axons in the developing zebrafish lateral line. The analysis is sophisticated and convincingly demonstrates effects on axon growth and synapse architecture. Collectively, the findings point to the idea that the diffusible form of sema7a may influence how axons grow within the neuromast and that the GPI-linked form of sema7a may subsequently impact how synapses form, though additional work is needed to strongly link each form to its' proposed effect on circuit assembly.

Comments on revised submission:

The revised manuscript is significantly improved. The authors comprehensively and appropriately addressed most of the reviewers' concerns. In particular, they added evidence that hair cells express both Sema7A isoforms, showed that membrane bound Sema7A does not have long range effects on guidance, demonstrated how axons behave close to ectopic Sema7A, and analyzed other features of the hair cells that revealed no strong phenotypes. The authors also softened the language in many, but not all places. Overall, I am satisfied with the study as a whole.

Reviewer #4 (Public Review):

This study provides direct evidence showing that Sema7a plays a role in the axon growth during the formation of peripheral sensory circuits in the lateral-line system of zebrafish. This is a valuable finding because the molecules for axon growth in hair-cell sensory systems are not well understood. The majority of the experimental evidence is convincing, and the analysis is rigorous. The evidence supporting Sema7a's juxtracrine vs. secreted role and involvement in synapse formation in hair cells is less conclusive. The study will be of interest to cell, molecular and developmental biologists, and sensory neuroscientists.

Reviewer #1 (Public Review):

Summary:

Through an unbiased genomewide KO screen, the authors identified loss of DBT to suppress MG132-mediated death of cultured RPE cells. Further analyses suggested that DBT reduces ubiquitinated proteins by promoting autophagy. Mechanistic studies indicated that DBT loss promotes autophagy via AMPK and its downstream ULK and mTOR signaling. Furthermore, loss of DBT suppresses polyglutamine- or TDP-43-mediated cytotoxicity and/or neurodegeneration in fly models. Finally, the authors showed that DBT proteins are increased in ALS patient tissues, compared to non-neurological controls.

Strengths:

The idea is novel, the evidence is convincing, and the data are clean. The findings have implications for human diseases.

Weaknesses:

None.

Reviewer #2 (Public Review):

Summary:

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

Strengths:

This is a mechanistic and well-designed paper that identifies DBT as a novel regulator of proteotoxicity via activating autophagy in the setting of proteasome inhibition. Major strengths include careful delineation of a mechanistic pathway to define how DBT is protective. These conclusions are well-justified.

Weaknesses:

None

Reviewer #1 (Public Review):

Summary:

In this study, the authors distinguished afferent inputs to different cell populations in the VTA using dimensionality reduction approaches and found significantly distinct patterns between normal and drug treatment conditions. They also demonstrated negative correlations of the inputs induced by drugs with gene expression of ion channels or proteins involved in synaptic transmission and demonstrated the knockdown of one of the voltage-gated calcium ion channels caused decreased inputs.

Weaknesses:

(1) For quantifications of brain regions in this study, boundaries were based on the Franklin-Paxinos (FP) atlas according to previous studies (Beier KT et al 2015, Beier KT et al 2019). It has been reported significant discrepancies exist between the anatomical labels on the FP atlas and the Allen Brain Atlas (ref: Chon U et al., Nat Commun 2019). Although a summary of conversion is provided as a sheet, the authors need to describe how consistent or different the brain boundaries they defined in the manuscript with Allen Brain Atlas by adding histology images. Also, I wonder how reliable the annotations were for over a hundred of animals with manual quantification. The authors should briefly explain it rather than citing previous studies in the Material and Methods Section.

(2) Regarding the ellipsoids in the PC, although it's written in the manuscript that "Ellipsoids were centered at the average coordinate of a condition and stretched one standard deviation along the primary and secondary axes", it's intuitively hard to understand in some figures such as Figure 2O, P and Figure S1. The authors need to make their data analysis methods more accessible by providing source code to the public.

(3) In histology images (Figure 1B and 3K), the authors need to add dashed lines or arrows to guide the reader's attention.

(4) In Figure 2A and G, apparently there are significant differences in other brain regions such as NAcMed or PBN. If they are also statistically significant, the authors should note them as well and draw asterisks(*).

(5) In Figure 2N about the spatial distribution of starter cells, the authors need to add histology images for each experimental condition (i.e. saline, fluoxetine, cocaine, methamphetamine, amphetamine, nicotine, and morphine) as supplement figures.

(6) In the manuscript, it is necessary to explain why Cacna1e was selected among other calcium ion channels.

Reviewer #2 (Public Review):

The application of rabies virus (RabV)-mediated transsynaptic tracing has been widely utilized for mapping cell-type-specific neural connectivities and examining potential modifications in response to biological phenomena or pharmacological interventions. Despite the predominant focus of studies on quantifying and analyzing labeling patterns within individual brain regions based on labeling abundance, such an approach may inadvertently overlook systemic alterations. There exists a considerable opportunity to integrate RabV tracing data with the global connectivity patterns and the transcriptomic signatures of labeled brain regions. In the present study, the authors take an important step towards achieving these objectives.

Specifically, the authors conducted an intensive reanalysis of a previously generated large dataset of RabV tracing to the ventral tegmental area (VTA) using dimension reduction methods such as PCA and UMPA. This reaffirmed the authors's earlier conclusion that different cell types in the VTA, namely dopamine neurons (DA) and GABAergic neurons, exhibit quantitatively distinct input patterns, and a single dose of addictive drugs, such as cocaine and morphine, induced altered labeling patterns. Additionally, the authors illustrate that distinct axes of PCA can discriminate experimental variations, such as minor differences in the injection site of viral tracers, from bona fide alternations in labeling patterns caused by drugs of abuse. While the specific mechanisms underlying altered labeling in most brain regions remain unclear, whether involving synaptic strength, synaptic numbers, pre-synaptic activities, or other factors, the present study underscores the efficacy of an informatics approach in extracting more comprehensive information from the RabV-based circuit mapping data.

Moreover, the authors showcased the utility of their previously devised bulk gene expression patterns inferred by the Allen Gene Expression Atlas (AGEA) and "projection portrait" derived from bulk axon mapping data sourced from the Allen Mouse Brain Connectivity Atlas. The utilization of such bulk data rests upon several limitations. For instance, the collection of axon mapping data involves an arbitrary selection of both cell type-specific and non-specific data, which might overlook crucial presynaptic partners, and often includes contamination from neighboring undesired brain regions. Concerns arise regarding the quantitativeness of AGEA, which may also include the potential oversight of key presynaptic partners. Nevertheless, the authors conscientiously acknowledged these potential limitations associated with the dataset.

Notably, building on the observation of a positive correlation between the basal expression levels of Ca2+ channels and the extent of drug-induced changes in RabV labeling patterns, the authors conducted a CRISPRi-based knockdown of a single Ca2+ channel gene. This intervention resulted in a reduction of RabV labeling, supporting that the observed gene expression patterns have causality in RabV labeling efficiency. While a more nuanced discussion is necessary for interpreting this result (see below), overall I commend the authors for their efforts to leverage the existing dataset in a more meaningful way. This endeavor has the potential to contribute significantly to our understanding of the mechanisms underlying alterations in RabV labeling induced by drugs of abuse.

Finally, drawing upon the aforementioned reanalysis of previous data, the authors underscored that a single administration of ketamine/xylazine anesthesia could induce enduring modifications in RabV labeling patterns for VTA DA neurons, specifically those projecting to the nucleus accumbens and amygdala. Given the potential impact of such alterations on motivational behaviors at a broader level, I fully agree that prudent consideration is warranted when employing ketamine/xylazine for the investigation of motivational behaviors in mice.

Specific Points:

(1) Beyond advancements in bioinformatics, readers may find it insightful to explore whether the PCA/UMPA-based approach yields novel biological insights. For example, the authors are encouraged to discuss more functional implications of PBN and LH in the context of drugs of abuse, as their labeling abundance could elucidate the PC2 axis in Fig. 2M.

2) While I appreciate the experimental data on Cacna1e knockdown, I am unclear about the rationale behind specifically focusing on Cacna1e. The logic behind the statement, "This means that expression of this gene is not inhibitory towards RABV transmission," is also unclear. Loss-of-function experiments only signify the necessity or permissive functions of a gene. In this context, Cacna1e expression levels are required for efficient RabV labeling, but this neither supports nor excludes the possibility that this gene expression instructively suppresses RabV labeling/transmission, which could be assessed through gain-of-function experiments.

Reviewer #3 (Public Review):

Summary:

Authors mapped monosynaptic inputs to dopamine, GABA, and glutamate neurons in VTA under different anesthesia methods, and under drugs (cocaine, morphine, methamphetamine, amphetamine, nicotine, fluoxetine). They found that input patterns under different conditions are separated, and identified some key brain areas to contribute to such separation. They also searched a database for gene expression patterns that are common across input brain areas with some changes by anesthesia or drug administration.

Strengths:

The whole-brain approach to address drug effects is appealing and their conclusion is clear. The methodology and motivation are clearly explained.

Weaknesses:

While gene expression analyses may not be related to their findings on the anatomical effects of drugs, this will be a nice starting point for follow-up studies.

Reviewer #2 (Public Review):

The authors analysed functional MRI recordings of brain activity at rest, using state-of-the-art methods that reveal the diverse ways in which the information can be integrated in the brain. In this way, they found brain areas that act as (synergistic) gateways for the 'global workspace', where conscious access to information or cognition would occur, and brain areas that serve as (redundant) broadcasters from the global workspace to the rest of the brain. The results are compelling and consisting with the already assumed role of several networks and areas within the Global Neuronal Workspace framework. Thus, in a way, this work comes to stress the role of synergy and redundancy as complementary information processing modes, which fulfill different roles in the big context of information integration.<br /> In addition, to prove that the identified high-order interactions are relevant to the phenomenon of consciousness, the same analysis was performed in subjects under anesthesia or with disorders of consciousness (DOC), showing that indeed the loss of consciousness is associated with a deficient integration of information within the gateway regions.

However, there is still a standing issue that could be the basis for an improved analysis: the concepts of gateways and broadcasters allude to a directionality in the information flow. In fact, Figure 1 depicts Stage (i) and Stage (iii) as one-way processes. However, the identification of gateway and broadcaster regions relies on matrices that are symmetrical, i.e. they are not directed. Would it be possible to assess the gateway or broadcaster nature of a region taking into account the directionality of the information flow? In other words, if region X is a gateway, I would expect a synergistic relationship between the past of X,Y and present of Y (Y not being a gateway) towards the present of X; but not necessarily the other way around (i.e. the present of Y being less dependent on the past/present of X). A similar reasoning can be made for broadcasters.

Although regional differences in haemodynamics complicate attempts to map directed information flow from fMRI recordings, perhaps the IID framework could be leveraged to extract directed data (i.e., there are many atoms that are explicitly directed). As an avenue for future research, it would be interesting to discuss the feasibility or limitations of such analysis.

Also, there is something confusing in Figure 4B-C and its description. Awake should be similar to recovery (they are both awake, aren't they? Not much info is given, anyway); thus it seems counterintuitive that anesthesia minus awake looks so different than anesthesia minus recovery. The first is mostly blue-ish and the second is mostly red. Is it possible that Figure 4C is actually recovery minus anesthesia? That would make much more sense, also for Figure 4D. Please correct me if I am wrong.

Reviewer #3 (Public Review):

The work proposes a model of neural information processing based on a 'synergistic global workspace,' which processes information in three principal steps: a gatekeeping step (information gathering), an information integration step, and finally, a broadcasting step. They provided an interpretation of the reduced human consciousness states in terms of the proposed model of brain information processing, which could be helpful to be implemented in other states of consciousness. The manuscript is well-organized, and the results are important and could be interesting for a broad range of literature, suggesting interesting new ideas for the field to explore.

Comments on revised version:

The authors have addressed all my comments made in the previous revision.

Reviewer #3 (Public Review):

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 #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 discovered 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 /> -Evidence that the defects they see in pumping can be specifically attributed to md-C is based on severing the labellum and allowing md-L neurons to degrade.<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 /> -MN11/MN12 LexA lines are not included in the manuscript and their expression patterns (shared with the reviewers in the author response) do not appear to contain any motor neurons. Double labeling with previously described MN11 and MN12 motor neuron Gal4 lines is needed to support the claim that these LexA lines in fact label MN11 and 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<br /> -Example traces of md-C calcium imaging during ingestion in vivo are not included, and evidence that md-C neurons respond to mechanical force is lacking<br /> -A positive control (perhaps demonstrating that sugar sensory neurons respond to sucrose in this preparation) is needed to assess whether the lack of response to sucrose ex vivo in Figure 4K is informative<br /> -Proximity between md-C neurons and muscles is not evidence that they sense stretch<br /> -Reporting of posthoc tests needs to be improved throughout the manuscript, as it is not clear which comparisons are noted with asterisks in the figures.

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 #3 (Public Review):

Summary:

The authors aimed to develop an automated tool to easily collect, process, and annotate the biomedical literature for higher efficiency and better reproducibility.

Strengths:

Two charms coming with the efforts made by the team are Pubget (for efficient and reliable grabbing articles from PubMed) and labelbuddy (for annotating text). They make text-mining of the biomedical literature more accessible, effective, and reproducible for streamlined text-mining and meta-science projects. The data were collected and analyzed using solid and validated methodology and demonstrated a very promising direction for meta-science studies.

Weaknesses:

More developments are needed for different resources of literature and strengths of AI-powered functions.

Reviewer #1 (Public Review):

Summary:

In this paper, the authors present new tools to collect and process information from the biomedical literature that could be typically used in a meta-analytic framework. The tools have been specifically developed for the neuroimaging literature. However, many of their functions could be used in other fields. The tools mainly enable to downloading of batches of paper from the literature, extracting relevant information along with meta-data, and annotating the data. The tools are implemented in an open ecosystem that can be used from the command line or Python.

Strengths:

The tools developed here are really valuable for the future of large-scale analyses of the biomedical literature. This is a very well-written paper. The presentation of the use of the tools through several examples corresponding to different scientific questions really helps the readers to foresee the potential application of these tools.

Weaknesses:

The tools are command-based and store outcomes locally. So users who prefer to work only with GUI and web-based apps may have some difficulties. Furthermore, the outcomes of the tools are constrained by inherent limitations in the scientific literature, in particular, here the fact that only a small portion of the publications have full text openly available.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors described the litmining ecosystem that can flexibly combine automatic and manual annotation for meta-research.

Strengths:

Software development is crucial for cumulative science and of great value to the community. However, such works are often greatly under-valued in the current publish-or-perish research culture. Thus, I applaud the authors' efforts devoted to this project. All the tools and repositories are public and can be accessed or installed without difficulty. The results reported in the manuscript are also compelling that the ecosystem is relatively mature.

Weaknesses:

First and foremost, the logic flow of the current manuscript is difficult to follow.

The second issue is the results from the litmining ecosystem were not validated and the efficiency of using litmining was not quantified. To validate the results, it would be better to directly compare the results of litmining with recognized ground truth in each of the examples. To prove the efficiency of the current ecosystem, it would be better to use quantitative indices for comparing the litmining and the other two approaches (in terms of time and/or other costs in a typical meta-research).

The third family of issues is about the functionality of the litmining ecosystem. As the authors mentioned, the ecosystem can be used for multiple purposes, however, the description here is not sufficient for researchers to incorporate the litmining ecosystem into their meta-research project. Imagine that a group of researchers are interested in using the litmining ecosystem to facilitate their meta-analyses, how should they incorporate litmining into their workflow? I have this question because, in a complete meta-analysis, researchers are required to (1) search in more than one database to ensure the completeness of their literature search; (2) screen the articles from the searched articles, which requires inspection of the abstract and the pdf; (3) search all possible pdf file of included articles instead of only relying on the open-access pdf files on PMC database. That said, if researchers are interested in using litmining in a meta-analysis that follows reporting standards such as PRISMA, the following functionalities are crucial:<br /> (a) How to incorporate the literature search results from different databases;<br /> (b) After downloading the meta-data of articles from databases, how to identify whose pdf files can be downloaded from PMC and whose pdf files need to be searched from other resources;<br /> (c) Is it possible to also annotate pdf files that were not downloaded by pubget?<br /> (d) How to maintain and update the meta-data and intermediate data for a meta-analysis by using litmining? For example, after searching in a database using a specific command and conducting their meta-analysis, researchers may need to update the search results and include items after a certain period.

Reviewer #1 (Public Review):

Original Review:

Bischoff et al present a carefully prepared study on a very interesting and relevant topic: the role of ion channels (here a Ca2+-activated K+ channel BK) in regulating mitochondrial metabolism in breast cancer cells. The potential impact of these and similar observations made in other tumor entities has only begun to be appreciated. That being said, the authors pursue in my view an innovative approach to understanding breast cancer cell metabolism.

Considering the following points would further strengthen the manuscript:

Methods:

(1) The authors use an extracellular Ca2+ concentration (2 mM) in their Ringer's solutions that is almost twice as high as the physiologically free Ca2+ concentration (ln 473). Moreover, the free Ca2+ concentration of their pipette solution is not indicated (ln 487).

(2) Ca2+I measurements: The authors use ATP to elicit intracellular Ca2+ signals. Is this then physiological stimulus for Ca2+ signaling in breast cancer? What is the rationale for using ATP? Moreover, it would be nice to see calibrated baseline values of Ca2+i

(3) Membrane potential measurements: It would be nice to see a calibration of the potential measurements; this would allow to correlate IV relationship with membrane potential. Without calibration it is hard to compare unless the identical uptake of the dye is shown.<br /> Do paxilline or IbTx also induce a depolarization?

(4) mito-potential measurements: Why did the authors use such a long time course and preincubated cells mit channel blockers overnight? Why did they not perform paired experiments and record the immediate effect of the BK channel blockers in the mito potential?

(5) MTT assay are also based on mitochondrial function - since modulation of mito function is at the core of this manuscript, an alternative method should be used.

Results:

(1) Fig. 5G: The number of BK "positive" mitoplasts is surprisingly low - how does this affect the interpretation? Did the authors attempt to record mitoBK current in the "whole-mitoplast" mode? How does the mitoBK current density compare with that of the plasma membrane? Is it possible to theoretically predict the number of mitoBK channels per mitochondrium to elicit the observed effects? Can these results be correlated with immuno-localization of mitoBK channels?

(2) There are also reports about other mitoK channels (e.g. Kv1.3, KCa3.1, KATP) playing an important role in mitochondrial function. Did the authors observe them, too? Can the authors speculate on the relative importance of the different channels? Is it known whether they are expressed organ-/tumor-specifically?

Comments on revised version:

The authors responded to all of my comments - except for one - in a satisfactory way so that I have no further concerns. The authors have prepared a very interesting piece of work that advances the field.

However, I disagree with respect to their interpretation of statistics. Individually analyzed cells are not the best biological replicate per se. In my view a true replicate requires the use of an independent batch of cells derived from a new passage. The statistical analysis can only based on the total number of n cells, if each replicate contributes the same number of cells. If this is not the case, the authors will have to calculate the average of each replicate first so that they are equally weighted.

Reviewer #2 (Public Review):

Summary:

The large-conductance Ca2+ activated K+ channel (BK) has been reported to promote breast cancer progression, but it is not clear how. The present study, carried out in breast cancer cell lines, concludes that BK located in mitochondria reprograms cells towards the Warburg phenotype, one of the metabolic hallmarks of cancer.

Strengths:

The use of a wide array of modern complementary techniques, including metabolic imaging, respirometry, metabolomics and electrophysiology. On the whole experiments are astute and well designed, and appear carefully done. The use of a BK knock out cells to control for the specificity of the pharmacological tools is a major strength. The manuscript is clearly written. There are many interesting original observations that may give birth to new studies.

Weaknesses: The main conclusion regarding the role of a BK channel located in mitochondria appears is not sufficiently supported. Other perfectible aspects are the interpretation of co-localization experiments and the calibration of Ca2+ dyes. These points are discussed in more detail in the following paragraphs:

(1) May the metabolic effects be ascribed to a BK located in mitochondria? Unfortunately not, at least with the available evidence. While it is clear these cells have a BK in mitochondria (characteristic K+ currents detected in mitoplasts) and it is also well substantiated that the metabolic effects in intact cells are explained by an intracellular BK (paxilline effects absent in the BK KO), it does not follow that both observations are linked. Given that ectopic BK-DEC appeared at the surface, a confounding factor is the likely expression of BK in other intracellular locations such as ER, Golgi, endosomes, etc. To their credit authors acknowledge this limitation several times throughout the text ("...presumably mitoBK...") but not in other important places, particularly in title and abstract.

(2) mitoBK subcellular location. Pearson correlations of 0.6 and about zero were obtained between the locations of mitoGREEN on one side, and mRFP or RFP-GPI on the other (Figs. 1G and S1E). These are nice positive and negative controls. For BK-DECRFP however the Pearson correlation was about 0.2. What is the Z resolution of apotome imaging? Assuming an optimum optical section of 600 nm, as obtained a 1.4 NA objective with a confocal, that mitochondria are typically 100 nm in diameter and that BK-DECRFP appears to stain more structures that mitoGREEN, the positive correlation of 0.2 may not reflect colocalization. For instance, it could be that BK-DECRFP in not just in mitochondria but in a close underlying organelle e.g. the ER. Along the same line, why did BK-RFP also give a positive Pearson? Isn´t that unexpected? Considering that BK-DEC was found by patch clamping at the plasma membrane, the subcellular targeting of the channel is suspect. Could it be that the endogenous BK-DEC does actually reside exclusively in mitochondria (a true mitoBK), but overflows to other membranes upon overexpression? Regarding immunodetection of BK in the mitochondrial Percoll preparation (Fig. S5), absence of NKA demonstrates absence of plasma membrane contamination, but does not inform about contamination by other intracellular membranes.

(3) Calibration of fluorescent probes. The conclusion that BK blockers or BK expression affects resting Ca2+ levels should be better supported. Fluorescent sensors and dyes provide signals or ratios that need be calibrated if comparisons between different cell types or experimental conditions are to be made. This is implicitly acknowledged here when monitoring ER Ca2+, with an elaborate protocol to deplete the organelle in order to achieve a reading at zero Ca2+.

(4) Line 203. "...solely by the expression of BKCa-DECRFP in MCF-7 cells". Granted, the effect of BKCa-DECRFP on the basal FRET ratio appears stronger than that of BK-RFP, but it appears that the latter had some effect. Please provide the statistics of the latter against the control group (after calibration, see above).

The revised version of the manuscript has incorporated my suggestions to a very reasonable degree, in several cases with new experiments. The details of these improvements can be found in the correspondence.

Reviewer #3 (Public Review):

The original research article, titled "mitoBKCa is functionally expressed in murine and human breast cancer cells and promotes metabolic reprogramming" by Bischof et al, has demonstrated the underlying molecular mechanisms of alterations in the function of Ca2+ activated K+ channel of large conductance (BKCa) in the development and progression of breast cancer. The authors also proposed that targeting mitoBKCa in combination with established anti-cancer approaches, could be considered as a novel treatment strategy in breast cancer treatment.

The paper is modified according to the reviewer's comments. Most of the queries raised by this reviewer were answered. However, the preclinical implication of this study can also be manifested in combinatorial treatment with known chemotherapeutic drugs which is lacking in this manuscript. Hopefully, the authors will consider this in their future study.

Joint Public Review:

Neuropeptide signaling is an important component of nervous systems, where neuropeptides typically act via G-protein coupled receptors (GPCRs) to regulate many physiological and behavioral processes. Neuropeptides and their cognate GPCRs have been extensively characterized in bilaterian animals, revealing that a core set of neuropeptide signaling systems originated in common ancestors of extant Bilateria. Neuropeptides have also been identified in cnidarians, which are a sister group to the Bilateria. However, the GPCRs that mediate the effects of neuropeptides in cnidarians have not been identified.

In this paper the authors perform a phylogenetic analysis of GPCRs in metazoans and report that the orthologs of bilaterian neuropeptide receptors are not found in cnidarians. This indicates that neuropeptide signaling systems have largely evolved independently in cnidarians and bilaterians. To accomplish this, they generated a library of putative and known neuropeptides computationally identified in the genome of the cnidarian sea anemone Nematostella vectensis. These peptides were systematically screened for their ability to activate any of the 161 putative Nematostella GPCRs.

This work identified 31 validated GPCRs. These, together with GPCRs that cluster with them, were then used to demonstrate the independent expansion of GPCRs in cnidarian and bilaterian lineages. The authors then mapped validated receptors and ligands to the Nematostella single cell data to provide an overview of the cell types expressing these signaling genes. In addition, the authors have begun to analyze neuropeptide signaling networks in N. vectensis by showing potential signaling connections between cell types expressing neuropeptides and cell types expressing cognate receptors.

This work is the most extensive pharmacological characterization of neuropeptide GPCRs in a cnidarian to date and thus represents an important accomplishment, and is one that will improve our understanding of how peptidergic signaling evolved in animals and its impact on evolution of nervous systems. In addition, this impressive work transforms our knowledge of neuropeptide signaling systems in cnidarians and provides the foundations for extensive functional characterization neuropeptide systems in the context of nervous systems that exhibit radial symmetry, contrasting with the bilaterally symmetrical architecture of the majority of bilaterian nervous systems.

The reviewers did not detect any weaknesses in the work but asked that the authors comment on the following points, which they have done in the revised version.

(1) Clearly, other neuropeptide signaling systems in cnidarians remain to be discovered but this paper represents a huge step forward.

(2) There are limitations in what can be interpreted from single cell transcriptomic data but the data nevertheless provide the foundations for future studies involving i). detailed anatomical analysis of neuropeptide and neuropeptide receptor expression in N. vectensis using mRNA in situ hybridization and/or immunohistochemical methods and ii). functional analysis of the physiological/behavioral roles of neuropeptide signaling systems in N. vectensis.

Reviewer #1 (Public Review):

Summary:

In the resubmitted manuscript by Chen et al. entitled, "Retinal metabolism: Evidence for uncoupling of glycolysis and oxidative phosphorylation via Cori-, Cahill-, and mini-Krebs-cycle", the authors look to provide insight on retinal metabolism and substrate utilization but using a murine explant model with various pharmacological treatments in conjunction with metabolomics. The authors conclude that photoreceptors, a specific cell within the explant, which also includes retinal pigment epithelium (RPE) and many other types of cells, are able to uncouple glycolytic and Krebs-cycle metabolism via three different pathways: 1) the mini-Krebs-cycle, fueled by glutamine and branched-chain amino acids; 2) the alanine-generating Cahill-cycle; and 3) the lactate-releasing Cori-cycle. While the authors have toned down some of their bold conclusions made in the original manuscript, they did very little in the way of providing additional well-controlled experiments, including cell-specific treatments, genetic knockouts, or stable isotope tracing to support their conclusions. Rather, the authors proceed to speculate more without additional data. The major issues raised by this reviewer were not adequately addressed. As such, the conclusions continue to be highly speculative and not well supported with evidence.

Strengths of resubmission:

The resubmission toned down some of its bold statements.

Weaknesses of resubmission:

Major weaknesses of this study persist including lack of in vivo supporting data. Also, retinal explant culture metabolomics are done in neuroretina with RPE attached, which are metabolically active and can be altered by the treatments investigated herein, further confounding the claims made regarding the neuroretina. While including the RPE in the explant model is commended, it needs to be separated from the retina prior to metabolomics to get a better sense of each tissues' metabolism. Also, melanin within RPE will hinder immunofluorescence signal, so one cannot state that RPE do not express certain enzymes based solely on immunofluorescence. Pharmacologic treatments are not cell-specific as the enzymes are expressed in numerous cells within the retina and RPE, and/or the treatments have significant off-target effects (such as shikonin). So, it is difficult to ascertain that the metabolic changes are secondary to the effects on photoreceptors alone, which the authors claim. Additionally, the explants are taken at a very early age when photoreceptors are known to still be maturing. No mention or data is presented on how these metabolic changes are altered in retinal explants after photoreceptors have fully matured. Likewise, significant assumptions are made based on a single metabolomics experiment with no stable isotope tracing to support the pathways suggested. In vivo, stable-isotope retinal metabolomics are being done and have been done, so stating this technology is beyond our field is false. Therefore, the conclusions reached in this manuscript are still not supported.

Reviewer #2 (Public Review):

Summary:

The authors aim to learn about retinal cell specific metabolic pathways, which could substantially improve the way retinal diseases are understood and treated. They culture ex vivo mouse retinas for 6 days with 2 - 4 days of various drug treatments targeting different metabolic pathways or by removing the RPE/choroid tissue from the neural retina. They then look at photoreceptor survival, stain for various metabolic enzymes/transporters and quantify a broad panel of metabolites. While this is an important question to address, the results are not sufficient to support the conclusions.

Strengths:

The questions the authors are exploring at extremely valuable and I commend the authors and working to learn more about retina metabolism. The different sensitivity of the cones to various drugs is interesting and may suggest key differences between rod and cones. The authors also provide a thoughtful discussion of various metabolic pathways in the context of previous publications.

Weaknesses:

As the authors point out, ex vivo culture models allow for control over multiple aspects of the environment (such as drug delivery) not available in vivo. Ex vivo cultures can provide good hints as to what pathways are available between interacting tissues. However, there are many limitations to ex vivo cultures, including shifting to a very artificial culture media condition that is extremely different than the native environment of the retina. It is well appreciated that cells have flexible metabolism and will adapt to conditions provided. Therefore, observations of metabolic responses obtained under culture conditions need to be interpreted with caution, they indicate what the tissue is doing under those specific conditions (which include cells adapting and dying).

Chen et al use pharmacological interventions are to the impact of various metabolic pathways on photoreceptor survival and "long term" metabolic changes. The dose and timing of these drug treatments are not examined though. It is also hard to know how these drugs penetrate the tissue and it needs to be validated that they intended targets are being accurately hit. These relatively long term treatments should be causing numerous downstream changes to metabolism, cell function and survival, which makes looking at a snap shot of metabolite levels hard to interpret. It would be more valuable to look at multiple time points after drug treatment, especially easy time points (closer to 1 hr). the authors use metabolite ratios to make conclusions about pathway activity. It would be more valuable to directly measure pathway activity by looking a metabolite production rates in the media and/or with metabolic tracers again in time scales closer to minutes and hours instead of days.

While the data is interesting and may give insights into some rod and cone specific metabolic susceptibility, more work is needed to validate these conclusions. Given the limitations of the model the authors have over interpreted their findings and the conclusions are not supported by the results. They need to either dramatically limit the scope of their conclusions or validate these hypotheses with additional models and tools.

Reviewer #3 (Public Review):

Summary:

The neural retina is one of the most energetically active tissues in the body and research into retinal metabolism has a rich history. Prevailing dogma in the field is that the photoreceptors of the neural retina (rods and cones) are heavily reliant on glycolysis, and as oxygen tension at the level of photoreceptors is very low, these specialized sensory neurons carry out aerobic glycolysis, akin to the Warburg effect in cancer cells. It has been found that this unique metabolism changes in many retinal diseases, and targeting disease-altered retinal metabolism may be a viable treatment strategy. The neural retina is composed of 11 different cell types, and many research groups over the past century have contributed to our current understanding of cell-specific metabolism of retinal cells. More recently, it has been shown in mouse models and co-culture of the mouse neural retina with human RPE cultures that photoreceptors are reliant on the underlying retinal pigment epithelium for supplying nutrients. Chen and colleagues add to this body of work by studying an ex vivo culture of the developing mouse retina that maintained contact with the retinal pigment epithelium. They exposed such ex vivo cultures to small molecule inhibitors of specific metabolic pathways, performing targeted metabolomics on the tissue and staining tissue with key metabolic enzymes to lay the groundwork for what metabolic pathways may be active in particular cell types of the retina. The authors conclude that rod and cone photoreceptors are reliant on different metabolic pathways to maintain their cell viability - in particular, that rods rely on oxidative phosphorylation and cones rely on glycolysis. Further, their data suggest multiple mechanisms whereby glycolysis may occur simultaneously with anapleurosis to provide abundant energy to photoreceptors. The data from metabolomics revealed several novel findings in retinal metabolism, including the use of glutamine to fuel the mini-Krebs cycle, the utilization of the Cahill cycle in photoreceptors, and a taurine/hypotaurine shuttle between the underlying retinal pigment epithelium and photoreceptors to transfer reducing equivalents from the RPE to photoreceptors. In addition, this study provides quantitative metabolomics datasets that can be compared across experiments and groups. The use of this platform will allow for rapid testing of novel hypotheses regarding the metabolic ecosystem in the neural retina.

Strengths:

The data on differences in susceptibility of rods and cones to mitochondrial dysfunction versus glycolysis provides novel hypothesis-generating conjectures that can be tested in animal models. The multiple mechanisms that allow anapleurosis and glycolysis to run side-by-side add significant novelty to the field of retinal metabolism, setting the stage for further testing of these hypotheses as well.

Weaknesses:

Almost all of the conclusions from the paper are preliminary, based on data showing enzymes necessary for a metabolic process are present and the metabolites for that process are also present. However, to truly prove whether these processes are happening (rather than speculation of the possibility they are happening), further experiments are necessary. As it currently stands, results from this study contradict results from other studies - in particular that cones, not rods, are most reliant of glycolysis. The authors attempt to address these contradictions, but without further experimentation, logical arguments carry only so much weight. At a minimum, the authors have argued that the small molecules they use are exquisitely specific for their intended targets, but validating results with a second small molecule that hits the same target but is structurally different would bolster their claims. Genetically knocking down the intended targets with interfering RNA technology would also be possible, as would explant cultures from knock-out animals. Without these studies to confirm target specificity, combined with the fact that conclusions from this study contradict existing studies in the literature, the results have to be categorized as speculative and hypothesis-generating rather than conclusive.

Joint Public Review:

Bolivar et al. set out to explore whether four distinct neuronal subtypes within the peripheral nervous system exhibit varying potentials for axon regeneration following nerve injury. To investigate this question, they harnessed the power of four distinct reporter mouse models featuring fluorescent labeling of these neuronal subtypes. Their findings reveal that axons of nociceptor neurons exhibit faster regeneration than those of motor neurons, with mechanoreceptors, and proprioceptors displaying the slowest regeneration rate.

To delve into the molecular mechanisms underlying this divergence in regeneration potential, the authors employed the Ribotag technique in mice. This approach enabled them to dissect the differential translatomes of these four neuronal populations after nerve injury, comparing them to uninjured neurons. Their comprehensive expression profiling data uncovers a remarkably heterogeneous response among these neuron subtypes to axon injury.

To focus on one identified target with a mechanistic experiment as a proof of concept, their analysis highlights a striking upregulation of MED12 in proprioceptors, leading to the hypothesis that this molecule may play an inhibitory role, contributing to the comparatively slower regeneration of proprioceptor axons when compared to other neuronal subtypes. This hypothesis gains support from their in vitro model, where siRNA-mediated downregulation of MED12 results in a significant increase in neurite outgrowth in proprioceptive neurons after plating in cell culture dishes.

Overall, this is an interesting study, and in conjunction with similar work from others will be highly valuable for neurobiologists studying how to modulate the regeneration of axons from distinct neuronal subtypes. The quality of data presentation appears to be very good in general, and the manuscript is appropriately written.

Comments on revised version:

Because there are multiple explanations for the differential regeneration responses, the authors have provided further discussion about how regeneration may be regulated in vitro and in vivo. The detection of a gene, Med12, which is unregulated in proprioceptive neurons, but not nociceptive and mechanoceptors, gives support to the existence of specific programs of responses in the peripheral nervous system after injury. Further investigation is needed to define this responsiveness in detail.

Another response is the role of neurotrophins and their receptors. The authors have considered outcomes as a result of different Trk receptor signaling and also the effect of TGFbeta and IL6 as cytokine modulators. Add to this list is the possibility that axon guidance molecules and downstream substates may also play a role.

The original title was considered to be too broad and did not explain all the mechanistic aspects of this study. Therefore a revised title "Neuron-specific RNA-sequencing reveals different response in peripheral neurons after nerve injury" was used. It is appropriately suitable for the results reported in this manuscript.

Reviewer #1 (Public Review):

Summary:

The authors are interested in the developmental origin of the neurons of the cerebellar nuclei. They identify a population of neurons with a specific complement of markers that originate in a distinct location from where cerebellar nuclear precursor cells have been thought to originate that show distinct developmental properties. The cerebellar nuclei have been well studied in recent years both to understand their development and through an evolutionary lens, which supports the importance of this study. The discovery of a new germinal zone giving rise to a new population of CN neurons is an exciting finding, and it enriches our understanding of cerebellar development, which has previously been quite straightforward, where cerebellar inhibitory cells arise from the ventricular zone and the excitatory cells arise from the rhombic lip.

Strengths:

One of the strengths of the manuscript is that the authors use a wide range of technical approaches, including transgenic mice that allow them to disentangle the influence of distinct developmental organizers such as ATOH.<br /> Their finding of a novel germinal zone and a novel population of CN neurons is important for developmental neuroscientists, and cerebellar neuroscientists.

Weaknesses:

One important question raised by this work is what these newly identified cells eventually become in the adult cerebellum. Are they excitatory or inhibitory? Do they correspond to a novel cell type or perhaps one of the cell classes that have been recently identified in the cerebellum (e.g. Fujita et al., eLife, 2020)? Understanding this would significantly bolster the impact of this manuscript.

The major weakness of the manuscript is that it is written for a very specialized reader who has a strong background in cerebellar development, making it hard to read for a general audience. It's challenging to follow the logic of some of the experiments as well as to contextualize these findings in the field of cerebellar development.

Reviewer #2 (Public Review):

Summary:

Canonically cerebellar neurons are derived from 2 primary germinal zones within the anterior hindbrain (dorsal rhombomere 1). This manuscript identifies an important, previously underappreciated origin for a subset of early cerebellar nuclei neurons - the dorsal mesencephalon. This is an exciting finding. While the conclusions are generally supported, several of the figure panels are of inferior quality and do not readily convey the results the authors assert.

Strengths:

The authors have identified a novel early population of cerebellar neurons with likely novel origin in the midbrain. They have used multiple assays to support their conclusions, including immunohistochemistry and in situ analyses of a number of markers of this population which appear to stream from the midbrain into the dorsal anterior cerebellar anlage.

The inclusion of Otx2-GFP short-term lineage analyses and analysis of Atoh1 -/- animals also provide considerable support for the midbrain origin of these neurons as streams of cells seem to emanate from the midbrain. However, without live imaging, there remains the possibility that these streams of cells are not actually migrating, and rather, gene expression is changing in static cells. Hence the authors have conducted midbrain diI labelling experiments of short-term and long-term cultured embryos showing di-labelled cells in the developing cerebellum. These studies confirm the migration of cells from the midbrain into the early cerebellum.

Reviewer #1 (Public Review):

Summary:

This interesting study explores the mechanism behind an increased susceptibility of daf-18/PTEN mutant nematodes to paralyzing drugs that exacerbate cholinergic transmission. The authors use state-of-the-art genetics and neurogenetics coupled with locomotor behavior monitoring and neuroanatomical observations using gene expression reporters to show that the susceptibility occurs due to low levels of DAF-18/PTEN in developing inhibitory GABAergic neurons early during larval development (specifically, during the larval L1 stage). DAF-18/PTEN is convincingly shown to act cell-autonomously in these cells upstream of the PI3K-PDK-1-AKT-DAF-16/FOXO pathway, consistent with its well-known role as an antagonist of this conserved signaling pathway. The authors exclude a role for the TOR pathway in this process and present evidence implicating selectivity towards developing GABAergic neurons. Finally, the authors show that a diet supplemented with a ketogenic body, β-hydroxybutyrate, which also counteracts the PI3K-PDK-1-AKT pathway, promoting DAF-16/FOXO activity, partially rescues the proper development (morphology and function) of GABAergic neurons in daf-18/PTEN mutants, but only if the diet is provided early during larval development. This strongly suggests that the critical function of DAF-18/PTEN in developing inhibitory GABAergic neurons is to prevent excessive PI3K-PDK-1-AKT activity during this critical and particularly sensitive period of their development in juvenile L1 stage worms. Whether or not the sensitivity of GABAergic neurons to DAF-18/PTEN function is a defining and widespread characteristic of this class of neurons in C. elegans and other animals, or rather a particularity of the unique early-stage GABAergic neurons investigated remains to be determined.

Strengths:

The study reports interesting and important findings, advancing the knowledge of how daf-18/PTEN and the PI3K-PDK-1-AKT pathway can influence neurodevelopment, and providing a valuable paradigm to study the selectivity of gene activities towards certain neurons. It also defines a solid paradigm to study the potential of dietary interventions (such as ketogenic diets) or other drug treatments to counteract (prevent or revert?) neurodevelopment defects and stimulate DAF-16/FOXO activity.

Weaknesses:

(1 )Insufficiently detailed methods and some inconsistencies between Figure 4 and the text undermine the full understanding of the work and its implications.

The incomplete methods presented, the imprecise display of Figure 4, and the inconsistency between this figure and the text, make it presently unclear what are the precise timings of observations and treatments around the L1 stage. What exactly do E-L1 and L1-L2 mean in the figure? The timing information is critical for the understanding of the implications of the findings because important changes take place with the whole inhibitory GABAergic neuronal system during the L1 stage into the L2 stage. The precise timing of the events such as neuronal births and remodelling events are well-described (e.g., Figure 2 in Hallam and Jin, Nature 1998; Fig 7 in Mulcahy et al., Curr Biol, 2022). Likewise, for proper interpretation of the implication of the findings, it is important to describe the nature of the defects observed in L1 larvae reported in Figure 1E - at present, a representative figure is shown of a branched commissure. What other types of defects, if any, are observed in early L1 larvae? The nature of the defects will be informative. Are they similar or not to the defects observed in older larvae?

(2) The claim of proof of concept for a reversal of neurodevelopment defects is not fully substantiated by data.

The authors state that the work "constitutes a proof of concept of the ability to revert a neurodevelopmental defect with a dietary intervention" (Abstract, Line 56), however, the authors do not present sufficient evidence to distinguish between a "reversal" or prevention of the neurodevelopment defect by the dietary intervention. This clarification is critical for therapeutic purposes and claims of proof-of-concept. From the best of my understanding, reversal formally means the defect was present at the time of therapy, which is then reverted to a "normal" state with the therapy. On the other hand, prevention would imply an intervention that does not allow the defect to develop to begin with, i.e., the altered or defective state never arises. In the context of this study, the authors do not convincingly show reversal. This would require showing "embryonic" GABAergic neuron defects or showing convincing data in newly hatched L1 (0-1h), which is unclear if they do so or not, as I have failed to find this information in the manuscript. Again, the method description needs to be improved and the implications can be very different if the data presented in Figure 2D-E regard newly born L1 animals (0-1h) or L1 animals at say 5-7h after hatching. This is critical because the development of the embryonically-born GABAergic DD neurons, for instance, is not finalized embryonically. Their neurites still undergo outgrowth (albeit limited) upon L1 birth (see DataS2 in Mulcahy et al., Curr Biol 2022), hence they are susceptible to both committing developmental errors and to responding to nutritional interventions to prevent them. In contrast to embryonic GABAergic neurons, embryonic cholinergic neurons (DA/DB) do not undergo neurite outgrowth post-embryonically (Mulcahy et al., Curr Biol 2022), a fact which could provide some mechanistic insight considering the data presented. However, neurites from other post-embryonically-born neurons also undergo outgrowth post-embryonically, but mostly during the second half of the L1 stage following their birth up to mid-L2, with significant growth occurring during the L1-L2 transition. These are the cholinergic (VA/VB and AS neurons) and GABAergic (VD) neurons. The fact that AS neurons undergo a similar amount of outgrowth as VD neurons is informative if VD neurons are or are not susceptible to daf-18/PTEN activity. Independently, DD neurons are still quite unique on other aspects (see below), which could also bring insight into their selective response.

Finally, even adjusting the claim to "constitutes a proof-of-concept of the ability of preventing a neurodevelpmental defect with a dietary intervention" would not be completely precise, because it is unclear how much this work "constitutes a proof of concept". This is because, unless I misunderstood something, dietary interventions are already applied to prevent neurodevelopment defects, such as when folic acid supplementation is recommended to pregnant women to prevent neural tube defects in newborns.

(3) The data presented do not warrant the dismissal of DD remodeling as a contributing factor to the daf-18/PTEN defects.

Inhibitory GABAergic DD neurons are quite unique cells. They are well-known for their very particular property of remodeling their synaptic polarity (DD neurons switch the nature of their pre- and post-synaptic targets without changing their wiring). This process is called DD remodeling. It starts in the second half of the L1 stage and finishes during the L2 stage. Unfortunately, the fact that the authors find a specific defect in early GABAergic neurons (which are very likely these unique DD neurons) is not explored in sufficient detail and depth. The facts that these neurons are not fully developed at L1, that they still undergo limited neurite growth, and that they are poised for striking synaptic plasticity in a few hours set them apart from the other explored neurons, such as early cholinergic neurons, which show a more stable dynamics and connectivity at L1 (see Mulcahy et al., Curr Biol 2022).

The authors use their observation that daf-18/PTEN mutants present morphological defects in GABAergic neurons prior to DD remodeling to dismiss the possibility that the DAF-18/PTEN-dependent effects are "not a consequence of deficient rearrangement during the early larval stages". However, DD remodeling is just another cell-fate-determined process and as such, its timing, for instance, can be affected by mutations in genes that affect cell fates and developmental decisions, such as daf-18 and daf-16, which affect developmental fates such as those related with the dauer fate. Specifically, the authors do not exclude the possibility that the defects observed in the absence of either gene could be explained by precocious DD remodeling. Precocious DD remodeling can occur when certain pathways, such as the lin-14 heterochronic pathway, are affected. Interestingly, lin-14 has been linked with daf-16/FOXO in at least two ways: during lifespan determination (Boehm and Slack, Science 2005) and in the L1/L2 stages via the direct negative regulation of an insulin-like peptide gene ins-33 (Hristova et al., Mol Cell Bio 2005). It is likely that the prevention of DD dysfunction requires keeping insulin signaling in check (downregulated) in DD neurons in early larval stages, which seems to coincide with the critical timing and function of daf-18/PTEN. Hence, it will be interesting to test the involvement of these genes in the daf-18/daf-16 effects observed by the authors.

Discussion on the impact of the work on the field and beyond:

The authors significantly advance the field by bringing insight into how DAF-18/PTEN affects neurodevelopment, but fall short of understanding the mechanism of selectivity towards GABAergic neurons, and most importantly, of properly contextualizing their findings within the state-of-the-art C. elegans biology.

For instance, the authors do not pinpoint which type of GABAergic neuron is affected, despite the fact that there are two very well-described populations of ventral nerve cord inhibitory GABAergic neurons with clear temporal and cell fate differences: the embryonically-born DD neurons and the post-embryonically-born VD neurons. The time point of the critical period apparently defined by the authors (pending clarifications of methods, presentation of all data, and confirmation of inconsistencies between the text and figures in the submitted manuscript) could suggest that DAF-18/PTEN is required in either or both populations, which would have important and different implications. An effect on DD neurons seems more likely because an image is presented (Figure 2D) of a defect in an L1 daf-18/PTEN mutant larva with 6 neurons (which means the larva was processed at a time when VD neurons were not yet born or expressing pUnc-47, so supposedly it is an image of a larva in the first half of the L1 stage (0-~7h?)). DD neurons are also likely the critical cells here because the neurodevelopment errors are partially suppressed when the ketogenic diet is provided at an "early" L1 stage, but not later (e.g., from L2-L3, according to the text, L2-L4 according to the figure? ).

This study brings important contributions to the understanding of GABAergic neuron development in C. elegans, but unfortunately, it is justified and contextualized mostly in distantly-related fields - where the study has a dubious impact at this stage rather than in the central field of the work (post-embryonic development of C. elegans inhibitory circuits) where the study has stronger impact. This study is fundamentally about a cell fate determination event that occurs in a nutritionally-sensitive developmental stage (post-embryonic L1 larval stage) yet the introduction and discussion are focused on more distantly related problems such as excitatory/inhibitory (E/I) balance, pathophysiology of human diseases, and treatments for them. Whereas speculation is warranted in the discussion, the reduced in-depth consideration of the known biology of these neurons and organisms weakens the impact of the study as redacted. For instance, the critical role of DAF-18/PTEN seems to occur at the early L1 larval stage, a stage that is particularly sensitive to nutritional conditions. The developmental progression of L1 larvae is well-known to be sensitive to nutrition - eg, L1 larvae arrest development in the absence of food, something that is explored in nematode labs to synchronize animals at the L1 stage by allowing embryos to hatch into starvation conditions (water). Development resumes when they are exposed to food. Hence, the extensive postembryonic developmental trajectory that GABAergic neurons need to complete is expected to be highly susceptible to nutrition. Is it? The sensitivity towards the ketogenic diet intervention seems to favor this. In this sense, the attribution of the findings to issues with the nutrition-sensitive insulin-like signaling pathway seems quite plausible, yet this possibility seems insufficiently considered and discussed.

Finally, the fact that imbalances in excitatory/inhibitory (E/I) inputs are linked to Autism Spectrum Disorders (ASD) is used to justify the relevance of the study and its findings. Maybe at this stage, the speculation would be more appropriate if restricted to the discussion. In order to be relevant to ASD, for instance, the selectivity of PTEN towards inhibitory neurons should occur in humans too. However, at present, the E/I balance alteration caused by the absence of daf-18/PTEN in C. elegans could simply be a coincidence due to the uniqueness of the post-embryonic developmental program of GABAergic neurons in C. elegans. To be relevant, human GABAergic neurons should also pass through a unique developmental stage that is critically susceptible to the PI3K-PDK1-AKT pathway in order for DAF-18/PTEN to have any role in determining their function. Is this the case? Hence, even in the discussion, where the authors state that "this study provides universally relevant information on.... the mechanisms underlying the positive effects of ketogenic diets on neuronal disorders characterized by GABA dysfunction and altered E/I ratios", this claim seems unsubstantiated as written particularly without acknowledging/mentioning the criteria that would have to be fulfilled and demonstrated for this claim to be true.

Reviewer #2 (Public Review):

Summary:

Disruption of the excitatory/inhibitory (E/I) balance has been reported in Autism Spectrum Disorders (ASD), with which PTEN mutations have been associated. Giunti et al choose to explore the impact of PTEN mutations on the balance between E/I signaling using as a platform the C. elegans neuromuscular system where both cholinergic (E) and GABAergic (I) motor neurons regulate muscle contraction and relaxation. Mutations in daf-18/PTEN specifically affect morphologically and functionally the GABAergic (I) system, while leaving the cholinergic (E) system unaffected. The study further reveals that the observed defects in the GABAergic system in daf-18/PTEN mutants are attributed to reduced activity of DAF-16/FOXO during development.

Moreover, ketogenic diets (KGDs), known for their effectiveness in disorders associated with E/I imbalances such as epilepsy and ASD, are found to induce DAF-16/FOXO during early development. Supplementation with β-hydroxybutyrate in the nematode at early developmental stages proves to be both necessary and sufficient to correct the effects on GABAergic signaling in daf-18/PTEN mutants.

Strengths:

The authors combined pharmacological, behavioral, and optogenetic experiments to show the GABAergic signaling impairment at the C. elegans neuromuscular junction in DAF-18/PTEN and DAF-16/FOXO mutants. Moreover, by studying the neuron morphology, they point towards neurodevelopmental defects in the GABAergic motoneurons involved in locomotion. Using the same set of experiments, they demonstrate that a ketogenic diet can rescue the inhibitory defect in the daf-18/PTEN mutant at an early stage.

Weaknesses:

The morphological experiments hint towards a pre-synaptic defect to explain the GABAergic signaling impairment, but it would have also been interesting to check the post-synaptic part of the inhibitory neuromuscular junctions such as the GABA receptor clusters to assess if the impairment is only presynaptic or both post and presynaptic.

Moreover, all observations done at the L4 stage and /or adult stage don't discriminate between the different GABAergic neurons of the ventral nerve cord, ie the DDs which are born embryonically and undergo remodeling at the late L1 stage, and VDs which are born post-embryonically at the end of the L1 stage. Those additional elements would provide information on the mechanism of action of the FOXO pathway and the ketone bodies.

Conclusion:

Giunti et al provide fundamental insights into the connection between PTEN mutations and neurodevelopmental defects through DAF-16/FOXO and shed light on the mechanisms through which ketogenic diets positively impact neuronal disorders characterized by E/I imbalances.

Reviewer #3 (Public Review):

Summary:

This is a conceptually appealing study by Giunti et al in which the authors identify a role for PTEN/daf-18 and daf-16/FOXO in the development of inhibitory GABA neurons, and then demonstrate that a diet rich in ketone body β-hydroxybutyrate partially suppresses the PTEN mutant phenotypes. The authors use three assays to assess their phenotypes: (1) pharmacological assays (with levamisole and aldicarb); (2) locomotory assays and (3) cell morphological assays. These assays are carefully performed and the article is clearly written. While neurodevelopmental phenotypes had been previously demonstrated for PTEN/daf-18 and daf-16/FOXO (in other neurons), and while KB β-hydroxybutyrate had been previously shown to increase daf-16/FOXO activity (in the context of aging), this study is significant because it demonstrates the importance of KB β-hydroxybutyrate and DAF-16 in the context of neurodevelopment. Conceptually, and to my knowledge, this is the first evidence I have seen of a rescue of a developmental defect with dietary metabolic intervention, linking, in an elegant way, the underpinning genetic mechanisms with novel metabolic pathways that could be used to circumvent the defects.

Strengths:

What their data clearly demonstrate, is conceptually appealing, and in my opinion, the biggest contribution of the study is the ability of reverting a neurodevelopmental defect with a dietary intervention that acts upstream or in parallel to DAF-16/FOXO.

Weaknesses:

The model shows AKT-1 as an inhibitor of DAF-16, yet their studies show no differences from wildtype in akt-1 and akt-2 mutants. AKT is not a major protein studied in this paper, and it can be removed from the model to avoid confusion, or the result can be discussed in the context of the model to clarify interpretation.

When testing additional genes in the DAF-18/FOXO pathway, there were no significant differences from wild type in most cases. This should be discussed. Could there be an alternate pathway via DAF-18/DAF-16, excluding the PI3K pathway or are there variations in activity of PI3K genes during a ketogenic diet that are hard to detect with current assays?

The consequence of SOD-3 expression in the broader context of GABA neurons was not discussed. SOD-3 was also measured in the pharynx but measuring it in neurons would bolster the claims.

If they want to include AKT-1, seeing its effect on SOD-3 expression could be meaningful to the model.

Reviewer #1 (Public Review):

This study presents valuable observations of white matter organisation from diffusion MRI and two types of synchrotron imaging in both monkeys and mice. Cross-modality comparisons are interesting as the different methods are able to probe anatomical structures at different length scales, from single axons in high-resolution synchrotron (ESRF) imaging, to clusters of axons in lower-resolution synchrotron (DEXY) data, to axon populations at the mm-scale in diffusion MRI. By acquiring all modalities in monkey and mouse ex vivo samples, the authors can observe principles of fibre organisation, and characterise how fibre characteristics, such as tortuosity and micro-dispersion, vary across select brain regions and in healthy tissue versus a demyelination model. The results are solid, though some statements (in the abstract/discussion) do not appear to be fully supported, and statistical tests would help confirm whether tissue characteristics are similar/different between different conditions.

One very interesting result is the observation of apparent laminar organisation of fibres in ex vivo monkey white matter samples. DESY data from the corpus callosum shows fibres with two dominant orientations (one L-R, one slightly inclined), clustered in laminar structures within this major fibre bundle. Thanks to the authors providing open data, I was able to look through the raw DESY volume and observe regions with different "textures" (different orientations) in the described laminar arrangement. That this organisation can be observed by eye, as well as by structure tensor, is fairly convincing. As not all readers will download the data themselves, the manuscript could benefit from additional figures/videos to demonstrate (1) the quality of the DESY data and (2) a more 3D visualisation of the laminar structures (where the coronal plane shows convincing columnar structure or stripes). Similarly in Figure 5A, though this nicely depicts two populations with different orientations, it is somewhat difficult to see the laminar structure in the current image.

ESRF data of the centrum semiovale (CS) contributes evidence for similar laminar structures in a crossing fibre region, where primarily AP fibres are shown to cluster in 3 laminar structures. As above, further visualisations of the ESRF volume in the CS (as shown in Figure 4E) would be of value (e.g. showing consistency across the 4 volumes, 2D images showing stripey/columnar patterns along different axes, etc).

A key limitation of this result is that, though the DESY data from the CC seems convincing, the same structures were not observed in high-resolution synchrotron (ESRF) data of the same tissue sample in the corpus callosum. This seems surprising and the manuscript does not provide a convincing explanation for this inconsistency. The authors argue that this is due to the limited FOV of the ESRF data (~200x200x800 microns). However, the observed laminar structures in DESY are ~40 microns thick, and ERSF data from the CST suggests laminar thicknesses in the range of 5-40 microns with a similar FOV. This suggests the ERSF FOV would be sufficient to capture at least a partial description of the laminar organisation. Further, the DESY data from the CC shows columnar variations along the LR axis, which we might expect to be observed along the long axis of the ESFR volume of the same sample. Additional analyses or explanations to reconcile these apparently conflicting observations would be of value. For example, the authors could consider down-sampling the ESRF data in an appropriate manner to make it more similar to the DESY data, and running the same analysis, to see if the observed differences are related to resolution (i.e. the thinner laminar structures cluster in ways that they look like a thicker laminar structure at lower resolution), or crop the DESY data to the size of the ESRF volume, to test whether the observed differences can be explained by differences in FOV.

Laminar structures were not observed in mouse data, though it is unclear if this is due to anatomical differences or somewhat related to differences in data quality across species.

The authors further quantify various other characteristics of the white matter, such as micro-dispersion, tortuosity, and maximum displacement. Notably, the microscopic FA calculated via structure tensor is fairly consistent across regions, though not modalities. When fibre orientations are combined across the sample, they are shown to produce similar FODs to dMRI acquired in the same tissue, which is reassuring. As noted in the text, the estimates of tortuosity and max displacement are dependent on the FOV over which they are calculated. Calculating these metrics over the same FOV, or making them otherwise invariant to FOV, could facilitate more meaningful comparisons across samples and/or modalities.

Though the results seem solid, some statements, particularly in the abstract and discussion, do not seem to be fully supported by the data. For example, the abstract states "Our findings revealed common principles of fibre organisation in the two species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways.", though the results show "no strong indication within the mouse CC of the axonal laminar organisation observed in the monkey". Similarly, the introduction states: "By these means, we demonstrated a new organisational principle of white matter that persists across anatomical length scales and species, which governs the arrangement of axons and axonal fasciculi into sheet-like laminar structures." Further comments on the text are provided below.

One observation not notably discussed in the paper is that the spherical histograms of Figure 3E/H appear to have an anisotropic spread of the white points about 0,0. It would be interesting if the authors could comment on whether this could be interpreted as the FOD having asymmetric dispersion and if so, whether the axis of dispersion relates to the fibre orientations of the laminar structures.

A limitation of the study is that it considers only small ex vivo tissue samples from two locations in a single postmortem monkey brain and slightly larger regions of mouse brain tissue. Consequently, further evidence from additional brain regions and subjects would be required to support more generalised statements about white matter organisation across the brain.

Given the monkey results, the mouse study (section 2.5 onwards) lacks some motivation. In particular, it is unclear why a demyelination model was studied and if/how this would link to the laminar structure observed in the monkey data. Further, it is unclear how comparable tortuosity/max deviation values are across species, considering the differences in data quality and relative resolution, given that the presented results show these values are very modality-dependent.

The paper introduces a new method of "scale-space" parameters for structure tensors. Since, to my understanding, this is the first description of the method, some simple validation of the method would be welcomed. Further, the same scale parameters are not used across monkeys and mice, with a larger kernel used in mice (Table 2) which is surprising given their smaller brain size. Some explanation would be helpful.

Reviewer #2 (Public Review):

Summary:<br /> In this work, the authors combine diffusion MRI and high-resolution x-ray synchrotron phase-contrast imaging in monkey and mouse brains to investigate the 3D organization of brain white matter across different scales and species. The work is at the forefront of the anatomical investigation of the human connectome and aligns with several current efforts to bridge the resolution gap between what we can see in vivo at the millimeter scale and the complexity of the human brain at the sub-micron scale. The authors compare the 3D white matter organization across modalities within 2 small regions in one monkey brain (body of the corpus callosum, centrum semiovale) and within one region (splenium of the corpus callosum) in healthy mice and in one murine model of focal demyelination. The study compares measures of tissue anisotropy and fiber orientations across modalities, performs a qualitative comparison of fasciculi trajectories across brain regions and tissue conditions using streamlined tractography based on the structure tensor, and attempts to quantify the shape of fasciculi trajectories by measuring the tortuosity index and the maximum deviation for each reconstructed streamline. Results show measures of anisotropy and fiber orientations largely agree across modalities, especially for larger FOV data. The high-resolution data allows us to explore the fiber trajectories in relation to tissue complexity and pathology. The authors claim the study reveals new common organization principles of white matter fibers across species and scales, for which axonal fasciculi arrange into sheet-like laminar structures.

Strengths:

The aim of the study is of central importance within present efforts to bridge the gap between macroscopic structures observable in vivo in humans using conventional diffusion MRI and the microscopic organization of white matter tissue. Results obtained from this type of study are important to interpret data obtained in vivo, inform the development of novel methodologies, and expand our knowledge of the structural and thus functional organization of brain circuits.

Multi-scale data acquired across modalities within the same sample constitute extremely valuable data that is often hard to acquire and represent a precious resource for validation of both diffusion MRI tractography and microstructure methods.

The inclusion of multi-species data adds value to the study, allowing the exploration of common organization principles across species.

The addition of data from a murine cuprizone model of focal demyelination adds interesting opportunities to study the underlying biological changes that follow demyelination and how these impact tissue anisotropy and fiber trajectories. These data can inform the interpretation and development of diffusion MRI microstructure models.

Weaknesses:

The main claim of a newly discovered laminar organization principle that is consistent across scales and species is not supported strongly enough by the data. The main evidence in support of the claim comes from the larger FOV data obtained from the body of the corpus callosum in the monkey brain. A laminar organization principle is partially shown in the centrum semiovale in the monkey brain and it is not shown in mice data. Additionally, the methods lack details to help the correct interpretation of these findings (e.g., how were these fasciculi defined?; how well do they represent different axonal populations?; what is the effect of blood vessels on the structure tensor reconstruction?; how was laminar separation quantified?) and the discussion does not provide a biological background for this organization. The corpus callosum sample suggests axons within a bundle of fibers are organized in a sheet-like fashion, while data from the centrum semiovale suggest fibers belonging to different fiber bundles are organized in a sheet-like arrangement. While I acknowledge the challenges in acquiring such high-resolution data, additional samples from different regions in the same animals and from different animals would help strengthen this claim.

The main goal of the study is to bridge the organization of white matter across anatomical length scales and species. However, given the substantial difference in FOVs between the two imaging modalities used, and the absence of intermediate-resolution data, it remains difficult to effectively understand how these results can be used to inform conventional diffusion MRI. In this sense, the introduction does not do a good enough job of building a strong motivation for the scientific questions the authors are trying to answer with these experiments and for the specific methodology used.

The cuprizone data represent a unique opportunity to explore the effect of demyelination on white matter tissue. However, this specific part of the study is not well motivated in the introduction and seems to represent a missed opportunity for further exploration of the qualitative and quantitative relationship between diffusion MRI and sub-micron tissue information (although unfortunately not within the same brain sample). This is especially true considering the diffusion MRI protocol for mice would allow extrapolation of advanced measures from different tissue compartments.

Reviewer #1 (Public Review):

Summary:

The work in the manuscript titled " Altered firing output of VIP interneurons and early dysfunctions in CA1 hippocampal circuits in the 3xTg mouse model of Alzheimer's disease" utilized patch-clamp techniques to explore the electrophysiological characteristics of VIP interneurons in the early stages of AD using the 3xTg mouse model. The study revealed that VIP interneurons exhibited prolonged action potentials and reduced firing rates. These changes could not be attributed to modifications in input signals or morphological transformations. The authors attributed aberrant VIP activity to the accumulation of beta-amyloid in those interneurons.

The decreased frequency of VIP inhibitory events was associated with no observed changes in excitatory drive to these interneurons. Consequently, heightened activity in the general population of CA1 interneurons was observed during a decision-making task and an object recognition test. In light of these findings, the authors concluded that the altered firing patterns of VIP interneurons may initiate early-stage dysfunction in hippocampal CA1 circuits, potentially influencing the progression of AD pathology.

Strengths:

Overall the work is novel and moves the field of Alzheimer's disease forward in a significant way. The manuscript reports a novel concept of aberrant activity in VIP interneurons during the early stages of AD thus contributing to dysfunctions of the CA1 microcircuit. This results in the enhancement of the inhibitory tone on the primary cells of CA1. Thus, the disinhibition by VIP interneurons of Principal Cells is dampened. The manuscript was skillfully composed, and the study was of strong scientific rigor featuring well-designed experiments. Necessary controls were present. Both sexes were included.

Limitations:

(1) The authors attributed aberrant circuit activity to the accumulation of "Abeta intracellularly" inside IS-3 cells. That is problematic. 6E10 antibody recognizes amyloid plaques in addition to Amyloid Precursor Protein (APP) as well as the C99 fragment. There are no plaques at the ages 3xTg mice were examined. Thus, the staining shown in Figure 1a is of APP/C99 inside neurons, not abeta accumulations in neurons. At the ages of 3-6 months, 3xTg starts producing abeta oligomers and potentially tau oligomers as well (Takeda et al., 2013 PMID: 23640054; Takeda et al., 2015 PMID: 26458742 and others). Emerging literature suggests that abeta and tau oligomers disrupt circuit function. Thus, a more likely explanation of abeta and tau oligomers disrupting the activity of VIP neurons is plausible.

(2) Authors suggest that their animals do not exhibit loss of synaptic connections and show Figure 3d in support of that suggestion. However, imaging with confocal microscopy of 70-micron thick sections would not allow the resolution of pre- and post-synaptic terminals. More sensitive measures such as electron microscopy or array tomography are the appropriate techniques to pursue. It is important for the authors to either remove that data from the manuscript or address the limitations of their technique in the discussion section. There is a possibility of loss of synaptic connections in their mouse model at the ages examined.

Reviewer #2 (Public Review):

Summary:

The submitted manuscript by Michaud and Francavilla et al., is a very interesting study describing early disruptions in the disinhibitory modulation exerted by VIP+ interneurons in CA1, in a triple transgenic model of Alzheimer's disease. They provide a comprehensive analysis at the cellular, synaptic, network, and behavioral level on how these changes correlate and might be related to behavioral impairments during these early stages of the disease.

Main findings:

- 3xTg mice show early Aß accumulation in VIP-positive interneurons.

- 3xTg mice show deficits in a spatially modified version of the novel object recognition test.

- 3xTg mice VIP cells present slower action potentials and diminished firing frequency upon current injection.

- 3xTg mice show diminished spontaneous IPSC frequency with slower kinetics in Oriens / Alveus interneurons.

- 3xTg mice show increased O/A interneuron activity during specific behavioral conditions.

- 3xTg mice show decreased pyramidal cell activity during specific behavioral conditions.

Strengths:

This study is very important for understanding the pathophysiology of Alzheimer´s disease and the crucial role of interneurons in the hippocampus in healthy and pathological conditions.

Weaknesses:

Although results nicely suggest that deficits in VIP physiological properties are related to the differences in network activity, there is no demonstration of causality.

Reviewer #1 (Public Review):

Summary:

The paper of Mao et al. expands the genetic toolset that was previously developed by the Rao lab (Denfg et al 2019) to introduce the conditional KO or downregulation of neurotransmission components in Drosophila. The authors then use these tools to investigate neurotransmission in the the clock neurons of the Drosophila brain. They first test some known components and then analyze the contribution of the CNMa neuropeptide and its receptor to the circadian behavior. The results indicate that CNMA acts from a subset of DN1ps (dorsal clock neurons) to set the phase of the morning peak of locomotor activity in light:dark cycles, with an advanced morning activity in the absence of the neuropeptide. Interestingly, the receptor for the PDF neuropeptide appears to be acting in some of the CNMa neurons to control morning activity.

Strengths/weaknesses:

This is clearly a very useful new set of tools to restrict the manipulation of these components to specific neuronal populations, and overall (see specific points below), the paper is convincing to show that the tools indeed allow to efficiently and specifically eliminate neuropeptides/receptors from subsets of neurons. The analysis of the CNMa function in the clock network reveals a new and interesting function for CNMa in the control of morning anticipation in LD conditions. This function appears to depend on CNMA_expressing DN1ps.

Comment on revised version:

I believe that the authors properly addressed the main points that were raised in my comment on version 1.

Reviewer #2 (Public Review):

Original Review:

In this study Mao and co-workers deliver a substantial suite of genetic tools in support of the senior author's recent proposal to create a "chemoconnectomic" tool kit for the expression mapping and conditional disruption of specific neurotransmitter systems with fly neurons of interest. Specifically, they describe the creation of two toolsets for recombination-based and CRISPR/Cas9-based conditional knockouts of genes supporting neurotransmitter and neuromodulator function and Flp-Out and Split-LexA toolkit for the examination of gene expression within defined subsets of neurons. The authors report the creation of conditional genetic tools for the disruption/mapping of approximately 200 chemoconnectomic gene products, an examination of the general effectiveness of these tools in the fly brain and apply them to the circadian clock network in an attempt to reveal new information regarding the transmitter/modulator systems involved in daily behavioral timing. The authors provide clear evidence of the effectiveness of the new methods along with a transparent assessment of the variability of the tools. In addition, they present evidence that the neuro peptide CNMa influences the morning peak of daily activity in the fly by regulating the timing of activity increases in anticipation of dawn.

A major strength of the study is the transparent assessment of the effectiveness and variability of the conditional genetic approaches developed by the authors. The authors have largely achieved their aims and the study therefore represents a major delivery on the promise of chemoconnectomics made by the senior author in 2019 (Neuron, Vol. 101, p. 876). Though there are some concerns about the variability of knockout effectiveness, off target effects of the knockout strategies, and (especially) the accuracy of the gene expression approach, the tools created for this study will almost certainly be useful for the field and support a great deal of future work.

Comments on revised version:

The authors have responded to each of my concerns. Most importantly, they have made the discrepancies within the study and between the study and previously published work clearer to the reader. they have also corrected statements that are not consistent with the current state of the field. The issue regarding opposing effects of PDF signaling and CNMa, which was also raised by Reviewer One still stands, notwithstanding the edits made to the text.

Reviewer #3 (Public Review):

Summary:

Mao and colleagues generated powerful reagents to genetically analyse chemical communication (CCT) in the brain, and in the process uncovered a function for the CNMa neuropeptide expressed in a subset of DN1p neurons that contributes to the temporal organization of locomotor activity, i.e., the timing of morning anticipation.

Strengths:

The strength of the manuscript relies in the generation/characterization of new tools for conditional targeting a well-defined set of CCT genes along with the design and testing of improved versions of Cas9 for efficient knock out. Such invaluable resources will be of interest to the whole community. The authors employed these tools and intersectional genetics to provide an alternative profiling of clock neurons, which is complementary to the ones already published. Furthermore, they uncovered a role for CNMamide, expressed in two DN1ps, in the timing of morning anticipation.

Weaknesses:

All prior concerns have been addressed.

Reviewer #1 (Public Review):

Summary:

In this work, Xie, Prescott and colleagues have reevaluated the role of Nav1.7 in nociceptive sensory neurons excitability. They find that nociceptors can make use of different sodium channel subtypes to reach equivalent excitability. The existence of this degeneracy is critical to understanding the neuronal physiology under normal and pathological conditions and could explain why Nav subtype-selective drugs have failed in clinical trials. More concretely, nociceptor repetitive spiking relies on Nav1.8 at DIV0 (and probably under normal conditions in vivo), but on Nav1.7 and Nav1.3 at DIV4-7 (and after inflammation in vivo).

The conclusions of this paper are mostly well supported by data, and these findings should be of broad interest to scientists working on pain, drug development, neuronal excitability and ion channels.

Strengths:

The authors have employed elegant electrophysiology experiments (including specific pharmacology and dynamic clamp) and computational simulations to study the excitability of a subpopulation of DRGs that would very likely match with nociceptors (they take advantage of using transgenic mice to detect Nav1.8-expressing neurons). They make a strong point showing the degeneracy that occurs at the ion channel expression level in nociceptors, adding this new data to previous observations in other neuronal types. They also demonstrate that the different Nav subtypes functionally overlap and are able to interchange their "typical" roles in action potential generation. As Xie, Prescott and colleagues argue, the functional implications of the degenerate character of nociceptive sensory neurons excitability need to be seriously taken into account regarding drug development and clinical trials with Nav subtype-selective inhibitors.

In this revised version, the quality of the manuscript has been visibly improved. In my opinion, the questions and concerns raised by reviewers have been addressed clearly. After a detailed reading of this version and the comments to the reviewers, I have no additional comments or criticisms.

Reviewer #2 (Public Review):

Summary:

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

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

Strengths:

The results are clearly and impressively supported by the experiments and data shown. During the revision, the manuscript was consistently improved and the concerns of the first reviews were resolved. All methods are described in detail, and presumably, allow good reproducibility and were suitable to address the scientific question.

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

In summary, the authors achieved their overall aim to enlighten the role of the NaV1.7 in nociceptor excitability and the effects on drug efficacy. The data support the conclusions and clinical implications are highlighted as far as is currently justifiable due to the still limited experience in translation. This appears well-considered, not too speculative, and ultimately appropriate.

The main weaknesses of the first version were fixed during the revision:

(i) After revising the manuscript, the initial weakness that the computer model was described superficially has been fixed. Important information was added to the main text and additional information, including the full code and equations and values are deposited on ModelDB or are given in the Supplementary information (Suppl. Table 5 & 6).

(ii) The authors now comment that corresponding studies on protein levels or e.g. neuroinflammatory changes could support the characterization of the time course of membrane expression and cellular changes, but this should be addressed in future studies, as these analyses would also raise new questions, such as about membrane trafficking, post-translational modifications, etc. This is plausible and has now been appropriately addressed in the text.

(iii) During the initial review the authors were asked to discuss the promising role of NaV1.7 in the light of clinical results. In their response, the authors confidently state that they „wish to avoid speculating on which particular clinical results are better explained because our study was not designed for that." They, however, emphasize their take-home message, which is well supported "Instead, our take-home message (which is well supported; see Discussion on lines 309-321) is that NaV1.7-selective drugs may have a variable clinical effect because nociceptors' reliance on NaV1.7 is itself variable - much more than past studies would have readers believe. ... The challenge (as highlighted in the Abstract, lines 21-22) is that identifying the dominant Nav subtype to predict drug efficacy is difficult."

Against the background of this argumentation, it must be admitted that the decision not to present as yet unproven speculations is probably appropriate from a scientific point of view and that this ultimately proves the critical assessment of one's own data and the limitations of the study. This is undoubtedly acceptable and - in retrospect - probably the right way to go.

Reviewer #3 (Public Review):

Summary:

In this study the authors used patch-clamp to characterize the implication of various voltage-gated Na+ channels in the firing properties of mouse nociceptive sensory neurons. They claim that depending on the culture conditions NaV1.3, NaV1.7, and NaV1.8 have distinct contributions to action potential firing and that similar firing patterns can result from distinct relative roles of these channels.

Strengths:

The paper addresses the important issue of understanding the lack of success of therapeutic strategies targeting NaV channels in the context of pain. Specifically, the authors test the hypothesis that different NaV channels contribute in a plastic manner to action potential firing, which may be the reason why it is difficult to target pain by inhibiting these channels.

Weaknesses:

(1) - The main claim of this paper is that "nociceptors can achieve equivalent excitability using different combinations of NaV1.3, NaV1.7, and NaV1.8". From this, they allude to the manifestation of "degeneracy", a concept implying that a biological process can occur via distinct sets of underlying components.<br /> In my opinion, the analyses of the data is biased towards the author's interpretation.<br /> - First, when comparing the excitability across neurons one should relate the response (in this case mean firing frequency) to the absolute size of the stimulus, not to the size of the stimulus normalized to the rheobase (see e.g., Figs. 1A). From this particular figure the authors conclude that the excitability is similar in the culture stages DIV0 and DIV4-7, but these data were not directly compared.<br /> - Second, the authors reach their conclusion from the comparison of the (average) firing rate determined over 1 s current stimulation in distinct conditions. However, this is not the only parameter that determines how sensory neurons might convey information. For instance, the time dependence of the instantaneous frequency, the actual firing pattern, maybe also important.<br /> - Third, the use of 1 s of current stimulation might not be sufficient to characterize the firing pattern if one wants to obtain conclusions that could translate to clinical settings (i.e., sustained pain).<br /> - Fourth, out of principle, the gating properties of NaV1.7 and NaV1.8 channels are not identical, and therefore their contributions to excitability should not be the same. A neuron in which NaV1.7 is the main contributor is expected to have a damping firing pattern due to cumulative channel inactivation, whereas another depending mainly on NaV1.8 is expected to display more sustained firing. This is actually seen in the results of the modelling.

(2) - The quality of some recordings is dubious. The currents shown as TTX-sensitive in Fig. 1D look very strange (not like the ones at Baseline DIV4-7). These traces show abnormally fast inactivation and even transient deflections above zero current line. These are obvious artifacts of the subtraction procedure, probably due to unstable current amplitudes along the recording time. Similar odd-looking traces are shown in Fig. 3A.

(3) - I would like to point out that the main Significance Statement of the manuscript reads "The analgesic efficacy of subtype-selective drugs hinges on which subtype controls excitability". I would like to point out that, in addition of being extremely obvious for anyone knowing a bit about pain signaling, the authors did not test the analgesic efficacy of any drug in this study.

(4) - A critical issue in the manuscript is the unnecessary use of phrases that imply that biological entities have some sort of willpower, flirting with anthropomorphism and teleological language.<br /> Sentences such as "Nociceptive sensory neurons convey pain signals to the CNS using action potentials" (see the Abstract) should be avoided. Neurons do not really "use" action potentials, they have no will to do so. Action potentials are not tools or means to be "used" by neurons. There are many other examples of misuse of the verb "use" in many other sentences. These were pointed out during the revision phase, but unfortunately the authors refused to correct them.

Reviewer #1 (Public Review):

Summary:

In Ryu et al., the authors use a cortical mouse astrocyte culture system to address the functional contribution of astrocytes to circadian rhythms in the brain. The authors' starting point is transcriptional output from serum-shocked culture, comparative informatics with existing tools and existing datasets. After fairly routine pathway analyses, they focus on the calcium homeostasis machinery and one gene, Herp, in particular. They argue that Herp is rhythmic at both mRNA and protein levels in astrocytes. They then use a calcium reporter targeted to the ER, mitochondria, or cytosol and show that Herp modulates calcium signaling as a function of circadian time. They argue that this occurs through the regulation of inositol receptors. They claim that the signaling pathway is clock-controlled by a limited examination of Bmal1 knockout astrocytes. Finally, they switch to calcium-mediated phosphorylation of the gap junction protein Connexin 43 but do not directly connect HERP-mediated circadian signaling to these observations. While these experiments address very important questions related to the critical role of astrocytes in regulating circadian signaling, the mechanistic arguments for HERP function, its role in circadian signaling through inositol receptors, the connection to gap junctions, and ultimately, the functional relevance of these findings is only partially substantiated by experimental evidence.

Strengths:

- The paper provides useful datasets of astrocyte gene expression in circadian time.

- Identifies HERP as a rhythmic output of the circadian clock.

- Demonstrates the circadian-specific sensitivity of ATP -> calcium signaling.

- Identifies possible rhythms in both Connexin 43 phosphorylation and rhythmic movement of calcium between cells.

Weaknesses:

- It is not immediately clear why the authors chose to focus on Ca2+ homeostasis or Herp from their initial screens as neither were the "most rhythmic" pathways in their primary analyses.

- It would have been interesting (and potentially important) to know whether various methods of cellular synchronization would also render HERP rhythmic (e.g., temperature, forskolin, etc). If Herp is indeed relatively astrocyte-specific and rhythmic, it should be easy to assess its rhythmicity in vivo.

- The authors show that Herp suppression reduces ATP-mediated suppression of calcium whereas it initially increases Ca2+ in the cytosol and mitochondria and then suppresses it. The dynamics of the mitochondrial and cytosolic responses are not discussed in any detail and it is unclear what their direct relationship is to Herp-mediated ER signaling. What is the explanation for Herp (which is thought to be ER-specific) to calcium signaling in other organelles?

- What is the functional significance of promoting ATP-mediated suppression of calcium in ER?

- The authors then nicely show that the effect of ATP is dependent on intrinsic circadian timing but do not explain why these effects are antiphase in cytosol or mitochondria. Moreover, the ∆F/F for calcium in mitochondria and cytosol both rise, cross the abscissa, and then diminish - strongly suggesting a biphasic signaling event. Therefore, one wonders whether measuring the area under the curve is the most functionally relevant measurement of the change.

- Why are mitochondrial and cytosolic calcium not also demonstrated for Bmal1 KO astrocytes?

- The authors claim that Herp acts by regulating the degradation of ITPRs but this hypothesis - rather central to the mechanisms proposed in this study - is not experimentally substantiated.

- There is no clear demonstration of the functional relevance of the circadian rhythms of ATP-mediated calcium signaling.

Reviewer #2 (Public Review):

Summary:

The article entitled "Circadian regulation of endoplasmic reticulum calcium response in mouse cultured astrocytes" submitted by Ryu and colleagues describes the circadian control of astrocytic intracellular calcium levels in vitro.

Strengths:

The authors used a variety of technical approaches that are appropriate

Weaknesses:

Statistical analysis is poor and could lead to a misinterpretation of the data

Several conceptual issues have been identified.

Overinterpretation of the data should be avoided. This is a mechanistic paper done completely in vitro, all references to the in vivo situation are speculative and should be avoided.

Reviewer #3 (Public Review):

Astrocyte biology is an active area of research and this study is timely and adds to a growing body of literature in the field. The RNA-seq, Herp expression, and Ca2+ release data across wild-type, Bmal1 knockout, and Herp knockdown cellular models are robust and lend considerable support to the study's conclusions, highlighting their importance. Despite these strengths, the manuscript presents a gap in elucidating the dynamics of HERP and the involvement of ITPR1/2 in modulating Ca2+ release patterns and their circadian variations, which remains insufficiently supported and characterized. While the Connexin data underscore the importance of rhythmic Ca2+ release triggered by ATP, the relationship here appears correlational and the role of HERP and ITPR in Cx function remains to be characterized. Moreover, enhancing the manuscript's clarity and readability could significantly benefit the presentation and comprehension of the findings.

Reviewer #1 (Public Review):

This is an interesting and well-written paper reporting on a novel approach to studying cerebellar function based on the idea of selective recruitment using fMRI. The study is well-designed and executed. Analyses are sound and results are properly discussed. The paper makes a significant contribution to broadening our understanding of the role of the cerebellum in human behavior.

- While the authors provide a compelling case for the link between BOLD and the cerebellar cortical input layer, there remains considerable unexplained variance. Perhaps the authors could elaborate a bit more on the assumption that BOLD signals mainly reflect the input side of the cerebellum (see for example King et al., elife. 2023 Apr 21;12:e81511).

- The current approach does not appear to take the non-linear relationships between BOLD and neural activity into account.

- The authors may want to address a bit more the issue of closed loops as well as the underlying neuroanatomy including the deep cerebellar nuclei and pontine nuclei in the context of their current cerebello-cortical correlational approach. But also the contribution of other brain areas such as the basal ganglia and hippocampus.

- What about the direct projections of mossy fibers to the DCN that actually bypasses the cerebellar cortex?

Reviewer #2 (Public Review):

Summary:

Shahshahani and colleagues used a combination of statistical modelling and whole-brain fMRI data in an attempt to separate the contributions of cortical and cerebellar regions in different cognitive contexts.

Strengths:

* The manuscript uses a sophisticated integration of statistical methods, cognitive neuroscience, and systems neurobiology.

* The authors use multiple statistical approaches to ensure robustness in their conclusions.

* The consideration of the cerebellum as not a purely 'motor' structure is excellent and important.

Weaknesses:

* Two of the foundation assumptions of the model - that cerebellar BOLD signals reflect granule cells > purkinje neurons and that corticocerebellar connections are relatively invariant - are still open topics of investigation. It might be helpful for the reader if these ideas could be presented in a more nuanced light.

* The assumption that cortical BOLD responses in cognitive tasks should be matched irrespective of cerebellar involvement does not cohere with the idea of 'forcing functions' introduced by Houk and Wise.

Reviewer #1 (Public Review):

In this work, the authors set out to ask whether the MYRF family of transcription factors, represented by myrf-1 and myrf-2 in C. elegans, have a role in the temporally controlled expression of the miRNA lin-4. The precisely timed onset of lin-4 expression in the late L1 stage is known to be a critical step in the developmental timing ("heterochronic") pathway, allowing worms to move from the L1 to the L2 stage of development. Despite the importance of this step of the pathway, the mechanisms that control the onset of lin-4 expression are not well understood.

Overall, the paper provides convincing evidence that MYRF factors have a key role in promoting lin-4 expression in young larvae. Using state-of-the-art techniques (knock-in reporters and conditional alleles), the authors show that MYRF factors are essential for lin-4 activation and act cell-autonomously. Results using some unusual gain-of-function alleles are supported by consistent results using other approaches. The authors also provide evidence supporting the idea that MYRF factors activate lin-4 by directly activating its promoter. Because these results are indirect test of this, further experiments will be necessary to conclusively determine whether lin-4 is indeed a direct target of MYRF factors. myrf-1 and myrf-2 likely function redundantly to activate lin-4; potential complex interactions between these two genes will be an interesting area for future work.

Overall, the paper's results are convincing. The important findings on miRNA regulation and the control of developmental timing will make this work of interest to a broad range of developmental biologists.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors examine how temporal expression of the lin-4 microRNA is transcriptionally regulated.

Comments on revised version:

In the revised manuscript, the authors have suitably addressed my original concerns.

Aims achieved: The aims of the work are now achieved.

Impact: This study shows that a single transcription factor (MYRF-1) is important for the regulation of multiple microRNAs that are expressed early in development to control developmental timing.

Reviewer #2 (Public Review):

Summary:

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

Strengths:

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

Weaknesses:

There are many dangers about analysing RNA-seq data at the subfamily level. Outputs may be greatly confounded by pervasive transcription, DNA contamination, and overlap of TEs with highly expressed genes. Whether TE transcripts are independent units or part of a gene also has important implications for the conclusions drawn. The authors have tried to mitigate against some of these issues, but they have not been completely ruled out.

Reviewer #1 (Public Review):

Summary:

Transposable Elements (TEs) are exogenously acquired DNA regions that have played important roles in the evolutional acquisition of various biological functions. TEs may have been important in the evolution of the immune system, but their role in thymocytes has not been fully clarified.

Using the human thymus scRNA dataset, the authors suggest the existence of cell type-specific TE functions in the thymus. In particular, it is interesting to show that there is a unique pattern in the type and expression level of TEs in thymic antigen-presenting cells, such as mTECs and pDCs, and that they are associated with transcription factor activities. Furthermore, the authors suggested that TEs may be non-redundantly regulated in expression by Aire, Fezf2, and Chd4, and that some TE-derived products are translated and present as proteins in thymic antigen-presenting cells. These findings provide important insights into the evolution of the acquired immune system and the process by which the thymus acquires its function as a primary lymphoid tissue.

Strengths:

(1) By performing single-cell level analysis using scRNA-seq datasets, the authors extracted essential information on heterogeneity within the cell population. It is noteworthy that this revealed the diversity of expression not only of known autoantigens but also of TEs in thymic antigen-presenting cells.

(2) The attempt to use mass spectrometry to confirm the existence of TE-derived peptides is worthwhile, even if the authors did not obtain data on as many transcripts as expected.

(3) The use of public data sets and the clearly stated methods of analysis improved the transparency of the results.

Weaknesses:

(1) The authors sometimes made overstatements largely due to the lack or shortage of experimental evidence.

For example in Figure 4, the authors concluded that thymic pDCs produced higher copies of TE-derived RNAs to support the constitutive expression of type-I interferons in thymic pDCs, unlike peripheral pDCs. However, the data was showing only the correlation between the distinct TE expression pattern in pDCs and the abundance of dsRNAs. We are compelled to say that the evidence is totally too weak to mention the function of TEs in the production of interferon. Even if pDCs express a distinct type and amount of TE-derived transcripts, it may be a negligible amount compared to the total cellular RNAs. How many TE-derived RNAs potentially form the dsRNAs? Are they over-expressed in pDCs?<br /> The data interpretation requires more caution to connect the distinct results of transcriptome data to the biological significance.

We contend that our manuscript combines the attributes of a research article (novel concepts) and a resource article (datasets of TEs implicated in various aspects of thymus function). The critical strength of our work is that it opens entirely novel research perspectives. We are unaware of previous studies on the role of TEs in the human thymus. The drawback is that, as with all novel multi-omic systems biology studies, our work provides a roadmap for a multitude of future mechanistic studies that could not be realized at this stage. Indeed, we performed wet lab experiments to validate some but not all conclusions: i) presentation of TE-derived MAPs by TECs and ii) formation of dsRNAs in thymic pDCs. In response to Reviewer #1, we performed supplementary analyses to increase the robustness of our conclusions. Also, we indicated when conclusions relied strictly on correlative evidence and clarified the hypotheses drawn from our observations. Regarding the Reviewer's questions about TE-derived dsRNAs, LINE, LTR, and SINE elements all have the potential to generate dsRNAs, given their highly repetitive nature and bi-directional transcription (1). As ~32% of TE subfamilies are overexpressed in pDCs, we hypothesized that these TE sequences might form dsRNA structures in these cells. To address the Reviewer's concerns regarding the amount of TE-derived RNAs among total cellular RNAs, we also computed the percentage of reads assigned to TEs in the different subsets of thymic APCs (see Reviewer 1 comment #4).<br /> ------

I appreciate the authors' efforts to improve the quality of this valuable paper. The additional data proposed by the authors enhanced the possibility that the non-negligible amount of RNAs in pDCs is derived from TE elements. Their biological roles and significance will be demonstrated in future research.

(2) Lack of generality of specific examples. This manuscript discusses the whole genomic picture of TE expression. In addition, one good way is to focus on the specific example to clearly discuss the biological significance of the acquisition of TEs for the thymic APC functions and the thymic selection.

In Figure 2, the authors focused on ETS-1 and its potential target genes ZNF26 and MTMR3, however, the significance of these genes in NK cell function or development is unclear. The authors should examine and discuss whether the distinct features of TEs can be found among the genomic loci that link to the fundamental function of the thymus, e.g., antigen processing/presentation.

We thank the Reviewer for this highly relevant comment. We investigated the genomic loci associated with NK cell biology to determine if ETS1 peaks would overlap with TE sequences in protein-coding genes' promoter region. Figure 2h illustrates two examples of ETS1 significant peaks overlapping TE sequences upstream of PRF1 and KLRD1. PRF1 is a protein implicated in NK cell cytotoxicity, whereas KLRD1 (CD94) dimerizes with NKG2 and regulates NK cell activation via interaction with the nonclassical MHC-I molecule HLA-E (2, 3). Thus, we modified the section of the manuscript addressing these results to include these new analyses: "Finally, we analyzed publicly available ChIP-seq data of ETS1, an important TF for NK cell development (4), to confirm its ability to bind TE sequences. Indeed, 19% of ETS1 peaks overlap with TE sequences (Figure 2g). Notably, ETS1 peaks overlapped with TE sequences (Figure 2h, in red) in the promoter regions of PRF1 and KLRD1, two genes important for NK cells' effector functions (2, 3)."<br /> ------

I am convinced by the authors' explanation that TE elements may contribute to the functions of NK cells.<br /> However, since I have understood that the main topic of this paper is about the thymus and thymic antigen-presenting cells, the mention of NK cells seems abrupt and unconnected to me. NK cells are a type of innate lymphocyte that arise in the bone marrow, and thymus is dispensable for their development and function. The readers might expect to find something more fundamental regarding the function of the thymus and immunological tolerance.

(3) Since the deep analysis of the dataset yielded many intriguing suggestions, why not add a discussion of the biological reasons and significance? For example, in Figure 1, why is TE expression negatively correlated with proliferation? cTEC-TE is mostly postnatal, while mTEC-TE is more embryonic. What does this mean?

We thank the Reviewer for this comment. To our knowledge, the relationship between cell division and transcriptional activity of TEs has not been extensively studied in the literature. However, a recent study has shown that L1 expression is induced in senescent cells. We therefore added the following sentences to our Discussion: "The negative correlation between TE expression and cell cycle scores in the thymus is coherent with recent data showing that transcriptional activity of L1s is increased in senescent cells (5). A potential rationale for this could be to prevent deleterious transposition events during DNA replication and cell division." We also added several discussion points regarding the regulation of TEs by KZFPs to answer concerns raised by Reviewer 2 (see Reviewer 2 comment #1).<br /> ------

I agree on the possibility suggested by the authors.

(4) To consolidate the experimental evidence about pDCs and TE-derived dsRNAs, one option is to show the amount of TE-derived RNA copies among total RNAs. The immunohistochemistry analysis in Figure 4 requires additional data to demonstrate that overlapped staining was not caused by technical biases (e.g. uneven fixation may cause the non-specifically stained regions/cells). To show this, authors should have confirmed not only the positive stainings but also the negative staining (e.g. CD3, etc.). Another possible staining control was showing that non-pDC (CD303- cell fractions in this case) cells were less stained by the ds-RNA probe.

We thank the Reviewer for this suggestion. We computed the proportion of reads in each cell assigned to two groups of sequences known to generate dsRNAs: TEs and mitochondrial genes (1). These analyses showed that the proportion of reads assigned to TEs is higher in pDCs than other thymic APCs by several orders of magnitude (~20% of all reads). In contrast, reads derived from mitochondrial genes had a lower abundance in pDCs. We included these results in Figure 4 - figure supplement 2 and included the following text in the Results section "To evaluate if these dsRNAs arise from TE sequences, we analyzed in thymic APC subsets the proportion of the transcriptome assigned to two groups of genomic sequences known as important sources of dsRNAs, TEs and mitochondrial genes (1). Strikingly, whereas the percentage of reads from mitochondrial genes was typically lower in pDCs than in other thymic APCs, the proportion of the transcriptome originating from TEs was higher in pDCs (~22%) by several orders of magnitude (Figure 4 - figure supplement 2)." As a negative control for the immunofluorescence experiments, we used CD123- cells. Indeed, flow cytometry analysis of the magnetically enriched CD303+ fraction was around 90% pure, as revealed by double staining with CD123 and CD304 (two additional markers of pDCs): CD123- cells were also CD304-/lo, showing that these cells are non- pDCs. Thus, we decided to compare the dsRNA signal between CD123+ cells (pDCs) and CD123- cells (non-pDCs). The difference between CD123+ and CD123- cells was striking (Figure 4d).<br /> ------

Although the technical concerns about immunostaining were not resolved, it is understandable that it would be difficult to rerun the experiment since the authors used the precious human thymi as the experimental material. Immunostaining co-staining requires careful interpretation so that careful experimental setup is needed.

Reviewer #2 (Public Review):

Summary:

Shotgun data have been analysed to obtain fungal and bacterial organisms abundance. Through their metabolic functions and through co-occurrence networks, a functional relationship between the two types of organisms can be inferred. By means of metabolomics, function-related metabolites are studied in order to deepen the fungus-bacteria synergy.

Strengths:

Data obtained in bacteria correlate with data from other authors.<br /> The study of metabolic "interactions" between fungi and bacteria is quite new.<br /> The inclusion of metabolomics data to support the results is a great contribution.

Weaknesses:

All my concerns have been clarified

Reviewer #1 (Public Review):

The manuscript by Mullen et al. investigated the gene expression changes in cancer cells treated with the DHODH inhibitor brequinar (BQ), to explore the therapeutic vulnerabilities induced by DHODH inhibition. The study found that BQ treatment causes upregulation of antigen presentation pathway (APP) genes and cell surface MHC class I expression, mechanistically which is mediated by the CDK9/PTEFb pathway triggered by pyrimidine nucleotide depletion. The combination of BQ and immune checkpoint therapy demonstrated a synergistic (or additive) anti-cancer effect against xenografted melanoma, suggesting the potential use of BQ and immune checkpoint blockade as a combination therapy in clinical therapeutics.

The interesting findings in the present study include demonstrating a novel cellular response in cancer cells induced by DHODH inhibition. However, whether the increased antigen presentation by DHODH inhibition actually contributed to the potentiation of the efficacy of immune-check blockade (ICB) is not directly examined is the limitation of the study. Moreover, the mechanism of the increased antigen presentation pathway by pyrimidine depletion mediated by CDK9/PTEFb was not validated by genetic KD or KO targeting by CDK9/PTEFb pathways. Finally, high concentrations of BQ have been reported to show off-target effects, sensitizing cancer cells to ferroptosis, and the authors should discuss whether the dose used in the in vivo study reached the ferroptotic sensitizing dose or not.

Comment on the revised version:

In their response letter, the authors appropriately addressed the reviewer's comments.

However, it is unfortunate that these comments are not reflected in the main text. Consequently, readers may encounter the same questions. Therefore, the reviewer recommends mentioning them in the discussion or limitations of the study, even if briefly, to address readers' concerns. Especially, addressing the comments such as the dosage of BQ being lower than the reported pro-ferroptotic dose (PMID 37407687), and the lack of examining potential impact of immune cell depletion on the efficacy of BQ treatment would be necessary for considering the proposed mechanism. The latter limitation is also raised by the other reviewer.

Reviewer #2 (Public Review):

In their manuscript entitled "DHODH inhibition enhances the efficacy of immune checkpoint blockade by increasing cancer cell antigen presentation", Mullen et al. describe an interesting mechanism of inducing antigen presentation. The manuscript includes a series of experiments that demonstrate that blockade of pyrimidine synthesis with DHODH inhibitors (i.e. brequinar (BQ)) stimulates the expression of genes involved in antigen presentation. The authors provide evidence that BQ mediated induction of MHC is independent of interferon signaling. A subsequent targeted chemical screen yielded evidence that CDK9 is the critical downstream mediator that induces RNA Pol II pause release on antigen presentation genes to increase expression. Finally, the authors demonstrate that BQ elicits strong anti-tumor activity in vivo in syngeneic models, and that combination of BQ with immune checkpoint blockade (ICB) results in significant lifespan extension in the B16-F10 melanoma model. Overall, the manuscript uncovers an interesting and unexpected mechanism that influences antigen presentation and provides an avenue for pharmacological manipulation of MHC genes, which is therapeutically relevant in many cancers. However, a few key experiments are needed to ensure that the proposed mechanism is indeed functional in vivo.

Major Points:

(1) According to the proposed model, BQ mediated induction of antigen presentation is a contributing factor to the efficacy of this therapeutic strategy. If this is true, then depletion of immune cells should reduce the therapeutic efficacy of BQ in vivo. The authors should perform the B16-F10 transplant experiments in either Rag null mice (if available) or with CD8/CD4 depletion. The expectation would be that T cell depletion (or MHC loss with genetic manipulation) should reduce the efficacy of BQ treatment. Absent this critical experiment, it is difficult to confidently conclude that induction of antigen presentation is a fundamental component of the in vivo response to DHODH inhibition.

(2) Does BQ treatment induce antigen presentation in non-malignant cells? APCs? If the induction of antigen presentation is not cancer specific and related to a pyrimidine depletion stress response, then there is a possibility that healthy tissues will also exhibit a similar phenotype, raising concerns about the specificity of a de novo immune response. The authors should examine antigen presentation genes in healthy tissues treated with BQ.

(3) In the title, the authors claim that DHODH enhances the efficacy of ICB. However, the experiment shown in Figure 5D does not demonstrate this. The Kaplan Meier curves reflect more of an additive response versus a synergistic combination. Furthermore, the concurrent treatment of BQ and ICB seems to inhibit the efficacy of ICB due to BQ toxicity in immune cells. When concurrently administered, the survival of the mice is the same as with brequinar alone, suggesting that the efficacy of ICB was diminished. However, if ICB is administered following an initial dose of BQ, there is an added survival benefit of a magnitude that is similar to ICB alone. This result seems to contradict the title. Furthermore, the authors should show the longitudinal growth curves of these tumors.

(4) Related to Point 3, the temporal separation of BQ and ICB raises the question of whether the induction of antigen presentation with BQ is persistent during the course of delayed ICB treatment. One explanation for the results is that BQ treatment reduces tumor burden, and then a subsequent course of ICB also reduces tumor burden but not that the two therapies are functioning in synergy. To address this, the authors should measure the duration of BQ mediated induction of antigen presentation after stopping treatment.

(5) In Figure 1, the authors show that DHODH inhibition induces expression of both MHC-I and MHC-II genes at the RNA level. However, they only validate MHC-I by flow cytometry. A simple experiment to evaluate the effect of BQ treatment on MHC-II surface expression would provide important additional mechanistic insight into the immunomodulatory effects of DHODH inhibition, especially given recent literature reinforcing the importance of MHC-II expression on epithelial cancers, including melanoma (Oliveira et al. Nature 2022).

Minor Points:

(1) The authors show ChIP-seq tracks from Tan et al. for HLA-B. However, given the pervasive effect of Ter treatment across many HLA genes, the authors should either show tracks at additional loci, or provide a heatmap of read density across more loci. This would substantiate the mechanistic claim that RNA Pol II occupancy and activity across antigen presentation genes is the major driver of response to DHODH inhibition as opposed to mRNA stabilization/increased translation.

(2) A compelling way to demonstrate a change in antigen presentation is through mass spectrometry based immunopeptidomics. Performing immunopeptidomic analysis of BQ treated cell lines would provide substantial mechanistic insight into the outcome of BQ treatment. While this approach may be outside the scope of the current work, the authors should speculate on how this treatment may specifically alter the antigenic landscape where future directions would include empirical immunopeptidomics measurements.

(3) While the signaling through CDK9 seems convincing, it still does not provide a mechanistic link between depleted pyrimidines and CDK9 activity. The authors should speculate on the mechanism that signals to CDK9.

(4) Related to minor point 2, the authors should consider a genetic approach to confirm the importance of CDK9. While the pharmacological approach, including multiple mechanistically distinct CDK9 inhibitors provides strong evidence, an additional experiment with genetic depletion of CDK9 (CRISPR KO, shRNA, etc) would provide compelling mechanistic confirmation.

(5) The authors should comment in the discussion on how this strategy may be particularly useful in patients harboring genetic or epigenetic loss of interferon signaling, a known mechanism of ICB resistance. Perhaps DHODH inhibition could rescue MHC expression in cells that are deficient in interferon sensing.

Overall, the paper is clearly written and presented. With the additional experiments described above, especially in vivo, this manuscript would provide a strong contribution to the field of antigen presentation in cancer. The distinct mechanisms by which DHODH inhibition induces antigen presentation will also set the stage for future exploration into alternative methods of antigen induction.

Comments on latest version:

The authors address the majority of the points raised in my previous review. However, no additional in vivo experiments were performed, which seems necessary for the major conclusions of the paper.

I disagree with the authors' assessment of Major Point 3 in my review. I have updated the text of Major Point 3 in my public review to further clarify my position.

My final assessment is that if the authors want to claim that DHODH inhibition potentiates immune checkpoint blockade, as is stated in the title, then further in vivo experimentation is needed.

Reviewer #3 (Public Review):

Mullen et al present an important study describing how DHODH inhibition enhances efficacy of immune checkpoint blockade by increasing cell surface expression of MHC I in cancer cells. DHODH inhibitors have been used in the clinic for many years to treat patients with rheumatoid arthritis and there has been a growing interest in repurposing these inhibitors as anti-cancer drugs. In this manuscript, the Singh group builds on their previous work defining combinatorial strategies with DHODH inhibitors to improve efficacy. The authors identify an increased expression of genes in the antigen presentation pathway and MHC I after BQ treatment which is mediated strictly by pyrimidine depletion and CDK9/P-TEFb. The authors rationalize that increased MHC I expression induced by DHODH inhibition might favor efficacy of dual immune checkpoint blockade. In fact, this combinatorial treatment prolonged survival in an immunocompetent B16F10 melanoma model.

Previous studies have shown that DHODH inhibitors can increase expression of innate immunity-related genes but the role of DHODH and pyrimidine nucleotides in antigen presentation has not been previously reported. A strength of the manuscript is the solid in vitro mechanistic data supported by analysis in multiple cell lines. The in vivo data show compelling additive effects of DHODH inhibitors and ICB. However, more controls and experiments would be required to define the nature of these effects and to confirm that the mechanistic in vitro data is conserved in vivo.

This is a relevant manuscript proposing a mechanistic link between pyrimidine depletion and MHC I expression and a novel therapeutic approach combining DHODH inhibitors with dual checkpoint blockade. These results might be relevant for the clinical development of DHODH inhibitors in the treatment of solid tumors, a setting where these have not shown optimal efficacy yet.

Comments on revised version:

The authors have addressed my questions regarding validation of gene expression in other cell lines. They have also provided an explanation about why in vivo evaluations could not be performed for the experiment in Figure 5E.

Reviewer #1 (Public Review):

Summary:

This manuscript by Warfvinge et al. reports the results of CITE-seq to generate single-cell multi-omics maps from BM CD34+ and CD34+CD38- cells from nine CML patients at diagnosis. Patients were retrospectively stratified by molecular response after 12 months of TKI therapy using European Leukemia Net (ELN) recommendations. They demonstrate heterogeneity of stem and progenitor cell composition at diagnosis, and show that compared to optimal responders, patients with treatment failure after 12 months of therapy demonstrate increased frequency of molecularly defined primitive cells at diagnosis. These results were validated by deconvolution of an independent previously published dataset of bulk transcriptomes from 59 CML patients. They further applied a BCR-ABL-associated gene signature to classify primitive Lin-CD34+CD38- stem cells as BCR:ABL+ and BCR:ABL-. They identified variability in the ratio of leukemic to non-leukemic primitive cells between patients, showed differences in expression of cell surface markers and determined that a combination of CD26 and CD35 cell surface markers could be used to prospectively isolate the two populations. The relative proportion of CD26-CD35+ (BCR:ABL-) primitive stem cells was higher in optimal responders compared to treatment failures, both at diagnosis and following 3 months of TKI therapy.

Strengths:

The studies are carefully conducted and the results are very clearly presented. The data generated will be a valuable resource for further studies. The strengths of this study are the application of single-cell multi-omics using CITE-Seq to study individual variations in stem and progenitor clusters at diagnosis that are associated with good versus poor outcomes in response to TKI treatment. These results were confirmed by deconvolution of a historical bulk RNAseq data set. Moreover, they are also consistent with a recent report from Krishnan et al. and are a useful confirmation of those results. The major new contribution of this study is the use of gene expression profiles to distinguish BCR-ABL+ and BCR-ABL- populations within CML primitive stem cell clusters and then applying antibody-derived tag (ADT) data to define molecularly identified BCR:ABL+ and BCR-ABL- primitive cells by expression of surface markers. This approach allowed them to show an association between the ratio of BCR-ABL+ vs BCR-ABL- primitive cells and TKI response and study dynamic changes in these populations following short-term TKI treatment.

Weaknesses:

The number of samples studied by CITE-Seq is limited. However, the authors have confirmed their key observations in additional samples. The BCR-ABL+ versus BCR-ABL- status of cells was not confirmed by direct sequencing for BCR-ABL. However, we recognize that the methodologies to perform these analyses on single cells is still evolving and the authors have shown that CD26 and CD35 expression can consistently identify BCR-ABL+ versus BCR-ABL- cells. It will be of interest to learn whether the GEP and surface markers identified here can distinguish BCR-ABL+ primitive stem cells later in the course of TKI treatment.

Reviewer #3 (Public Review):

Summary:

In this study, Warfvinge and colleagues use CITE-seq to interrogate how CML stem cells change between diagnosis and after one year of TKI therapy. This provides important insight into why some CML patients are "optimal responders" to TKI therapy while others experience treatment failure. CITE-seq in CML patients revealed several important findings. First, substantial cellular heterogeneity was observed at diagnosis, suggesting that this is a hallmark of CML. Further, patients who experienced treatment failure demonstrated increased numbers of primitive cells at diagnosis compared to optimal responders. This finding was validated in a bulk gene expression dataset from 59 CML patients, in which it was shown that the proportion of primitive cells versus lineage-primed cells correlates to treatment outcome. Even more importantly, because CITE-seq quantifies cell surface protein in addition to gene expression data, the authors were able to identify the BCR/ABL+ and BCR/ABL- CML stem cells express distinct cell surface markers (CD26+/CD35- and CD26-/CD35+, respectively). In optimal responders, BCR/ABL- CD26-/CD35+ CML stem cells were predominant, while the opposite was true in patients with treatment failure. Together, these findings represent a critical step forward for the CML field and may allow more informed development of CML therapies, as well as the ability to predict patient outcomes prior to treatment.

Strengths:

This is an important, beautifully written, well-referenced study that represents a fundamental advance in the CML field. The data are clean and compelling, demonstrating convincingly that optimal responders and patients with treatment failure display significant differences in the proportion of primitive cells at diagnosis, and the ratio of BCR-ABL+ versus negative LSCs. The finding that BCR/ABL+ versus negative LSCs display distinct surface markers is also key and will allow for more detailed interrogation of these cell populations at a molecular level.

Weaknesses:

CITE-seq was performed in only 9 CML patient samples and 2 healthy donors. Additional samples would greatly strengthen the very interesting and notable findings.

Reviewer #1 (Public Review):

This is an important manuscript that links gene expression to genetic variants and regions of open chromatin. The mechanisms of genetic gene regulation are essential to understanding how standing genetic variation translates to function and phenotype. This data set has the ability to add substantial insight into the field. In particular, the authors show how the relationships between variants, chromatin, and genes are spatially constrained by topologically associated domains.

Reviewer #2 (Public Review):

The experiments described in the manuscript are well designed and executed. Most of the data presented are of high quality, convincing, and in general support the conclusions made in the manuscript. This manuscript should be of great interest to the field of mammalian gene regulation and the approaches used here can have broader applications in studying genetic and epigenetic regulations of gene expression. The key finding reported here, the importance of 3D chromatin structure in controlling gene expression, although not unexpected, offers a better understanding of the physiological roles of TADs.

Comments on revised version:

I think the authors have substantially addressed reviewers' concerns. I have no further comments to add.

Reviewer #1 (Public Review):

Cell type deconvolution is one of the early and critical steps in the analysis and integration of spatial omic and single cell gene expression datasets, and there are already many approaches proposed for the analysis. Sang-aram et al. provide an up-to-date benchmark of computational methods for cell type deconvolution.

In doing so, they provide some (perhaps subtle) additional elements that I would say are above the average for a benchmarking study: i) a full Nextflow pipeline to reproduce their analyses; ii) methods implemented in Docker containers (which can be used by others to run their datasets); iii) a fairly decent assessment of their simulator compared to other spatial omics simulators. A key aspect of their results is that they are generally very concordant between real and synthetic datasets. And, it is important that they authors include an appropriate "simpler" baseline method to compare against and surprisingly, several methods performed below this baseline. Overall, this study also has the potential to also set the standard of benchmarks higher, because of these mentioned elements.

The only weakness of this study that I can readily see is that this is a very active area of research and we may see other types of data start to dominate (CosMx, Xenium) and new computational approaches will surely arrive. The Nextflow pipeline will make the the prospect of including new reference datasets and new computational methods easier.

Reviewer #2 (Public Review):

In this manuscript Sangaram et al provide a systematic methodology and pipeline for benchmarking cell type deconvolution algorithms for spatial transcriptomic data analysis in a reproducible manner. They developed a tissue pattern simulator that starts from single-cell RNA-seq data to create silver standards and used spatial aggregation strategies from real in situ-based spatial technologies to obtain gold standards. By using several established metrics combined with different deconvolution challenges they systematically scored and ranked 12 different methods and assessed both functional and usability criteria. Altogether, they present a reusable and extendable platform and reach very similar conclusions to other deconvolution benchmarking paper, including that RCTD, SpatialDWLS and Cell2location typically provide the best results. Major strengths of the simulation engine include the ability to downsample and recapitulate several cell and tissue organization patterns.

More specifically, the authors of this study sought to construct a methodology for benchmarking cell type deconvolution algorithms for spatial transcriptomic data analysis in a reproducible manner. The authors leveraged publicly available scRNA-seq, seqFISH, and STARMap datasets to create synthetic spatial datasets modeled after that of the Visium platform. It should be noted that the underlying experimental techniques of seqFISH and STARMap (in situ hybridization) do not parallel that of Visium (sequencing), which could potentially bias simulated data. Furthermore, to generate the ground truth datasets cells and their corresponding count matrix are represented by simple centroids. Although this simplifies the analysis it might not necessarily accurately reflect Visium spots where cells could lie on a boundary and affect deconvolution results.

The authors thoroughly and rigorously compare methods while addressing situational discrepancies in model performance, indicative of a strong analysis. The authors make a point to address both inter- and intra- dataset reference handling, which has a significant impact on performance, as the authors note in the text and conclusions. Indeed, supplying optimal reference data is - potentially most - important to achieve the best performance and hence it's important to understand that experimental design or sample matching is at least equally important to selecting the ideal deconvolution tool.

Similarly, the authors conclude that many methods are still outperformed by bulk deconvolution methods (e.g. Music or NNLS), however, it needs to be noted that these 'bulk' methods are also among the most sensitive when using an external (inter) dataset (S10), which likely resembles the more realistic scenario for most labs.

As the authors also discuss it's important to realize that deconvolution approaches are typically part of larger exploratory data analysis (EDA) efforts and require users to change parameters and input data multiple times. Thus, running time, computing needs, and scalability are probably key factors that researchers would like to consider when looking to deconvolve their datasets.

The authors achieve their aim to benchmark different deconvolution methods and the results from their thorough analysis support the conclusions that creating cell type deconvolution algorithms that can handle both cell abundance and rarity throughout a given tissue sample are challenging.

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

Reviewer #1 (Public Review):

The authors primary objective in this study was to identify differences between patients with preeclampsia and normal patients with respect to the placental syncytiotrophoblast extracellular vesicle proteome.

A strength of this study is that the authors identified novel STB-EV protein markers that are more abundant in the placenta of patients with preeclampsia compared with normal controls. This contributes a little more to what is already known about STB-EV markers and preeclampsia. If these markers can be shown to be more abundant in maternal plasma of preeclampsia patients, it would be very useful for identifying patients who are at high risk for developing early-onset preeclampsia.

Weaknesses include:<br /> (1) The small sample size. There were only 6 patients in the study group and 6 normal controls. However, this can be considered as a pilot study.<br /> (2) The normal controls were not matched with the study patients and the authors did not state how the controls were selected.<br /> (3) The authors state that the placenta samples were obtained at the time of elective cesarean section. However, it is likely that all the preeclampsia patients were delivered for clinical indications rather than electively. This should be clarified.

Reviewer #2 (Public Review):

Summary:

Preeclampsia is a disorder of pregnancy that affects 4-5% of pregnancies worldwide. Identifying this condition early is clinically relevant as it will help clinicians to make management decisions to prevent adverse outcomes. The placenta holds a key to many pregnancy-related pathologies including preeclampsia and studies have shown many differences in the placenta of women with preeclampsia as compared to controls. However as the placenta cannot be collected directly during pregnancy, the exosomes secreted by it are considered a good alternative to tissue biopsy. In this study, the authors have compared the proteins in different sizes of exosomes from the placenta of women with and without preeclampsia. The idea is to eventually use these as biomarkers for early detection of preeclampsia.

Strengths:

The novelty factor of this study is the use of two different-sized exosomes which has not been achieved earlier.

Weaknesses:

The study measured the proteins at only a single time point after the disease has already occurred. However, the placenta is an ever-changing tissue throughout pregnancy and different proteins can come up at different times in pregnancy. Thus serial measurements are necessary and a single time point measurement. The has not validated the identified biomarkers in plasma or circulating placental exosomes from women with and without preeclampsia. Thus the utility of these findings in real-life situations can not be judged from this work.

Reviewer #1 (Public Review):

The authors set out to use structural biology (cryo-em), SPR and complement convertase assays to understand the mechanism(s) by which ISG65 dampens the cytotoxicity/cellular clearance to/of trypanosmes opsonised with C3b by the innate immune system.

The cryo-EM structure adds significantly the the author's previous crystallographic data because the latter was limited to the C3d sub-domain of C3b. Further, the in vitro convertase assay adds an additional functional dimension to this study.

The authors have achieved their aims and the results support their conclusions.

The role of complement in immunity to T. brucei (or lack thereof) has been a significant question in molecular parasitology for over 30 years. The identification of ISG65 as the C3 receptor and now this study providing mechanistic insights represents a major advance in the field.

The authors have appropriately put their results into perspective with other recent reports on the role of ISG65.

Reviewer #3 (Public Review):

The authors investigate the mechanisms by which ISG65 and C3 recognize and interact with each other. The major strength is the identification of exo-site by determining the cryoEM structure of the complex, which suggests new intervention strategies. This is a solid body of work that has an important impact in parasitology, immunology, and structural biology.

Comments on revised version:

The authors have addressed all the previous concerns.

Reviewer #1 (Public Review):

The goal of this study is to understand the allosteric mechanism of overall activity regulation in an anaerobic ribonucleotide reductase (RNR) that contains an ATP-cone domain. Through cryo-EM structural analysis of various nucleotide-bound states of the RNR, the mechanism of dATP inhibition is found to involve order-disorder transitions in the active site. These effects appear to prevent binding of substrate and a radical transfer needed to initiate the reaction.

Strengths of the manuscript include the comprehensive nature of the work - including both numerous structures of different forms of the RNR and detailed characterization of enzyme activity to establish the parameters of dATP inhibition. The manuscript has been improved in a revision by performing additional experiments to help corroborate certain aspects of the study. But these new experiments do not address all of the open questions about the structural basis for mechanism. Additionally, some questions about the strength of biochemical data and fit of binding or kinetic curves to data that were raised by other referees still remain. Some experimental observations are not consistent with the proposed model. For example, why does dATP enhance Gly radical formation when the proposed mechanism of dATP inhibition involves disorder in the Gly radical domain?

The work is impactful because it reports initial observations about a potentially new mode of allosteric inhibition in this enzyme class. It also sets the stage for future work to understand the molecular basis for this phenomenon in more detail.

Reviewer #3 (Public Review):

The manuscript by Bimai et al describes a structural and functional characterization of an anaerobic ribonucleotide reductase (RNR) enzyme from the human microbe, P. copri. More specifically, the authors aimed to characterize the mechanism by how (d)ATP modulates nucleotide reduction in this anaerobic RNR, using a combination of enzyme kinetics, binding thermodynamics, and cryo-EM structural determination, complemented by hydrogen-deuterium exchange (HDX). One of the principal findings of this paper is the ordering of a NxN 'flap' in the presence of ATP that promotes RNR catalysis and the disordering (or increased protein dynamics) of both this flap and the glycyl radical domain (GRD) when the inhibitory effector, dATP, binds. The latter is correlated with a loss of substrate binding, which is the likely mechanism for dATP inhibition. It is important to note that the GRD is remote (>30 Ang) from the binding site of the dATP molecule, suggesting long-range communication of the structural (dis)ordering. The authors also present evidence for a shift in oligomerization in the presence of dATP. The work does provide evidence for new insights/views into the subtle differences of nucleotide modulation (allostery) of RNR, in a class III system, through long-range interactions.

The strengths of the work are the impressive, in-depth structural analysis of the various regulated forms of PcRNR by (d)ATP using cryo-EM. The authors present seven different models in total, with striking differences in oligomerization and (dis)ordering of select structural features, including the GRD that is integral to catalysis. The authors present several, complementary biochemical experiments (ITC, MST, EPR, kinetics) aimed at resolving the binding and regulatory mechanism of the enzyme by various nucleotides. The authors present a good breadth of the literature in which the focus of allosteric regulation of RNRs has been on the aerobic orthologues.

The addition of hydrogen-deuterium exchange mass spectrometry (HDX-MS) complements the results originating from cryo-EM data. Most notably, is the observation of the enhanced exchange (albeit quite subtle) of the GRD domain in the presence of dATP that matches the loss of structural information in this region in the cryo-EM data. The most pronounced and compelling HDX results are seen in the form of dATP-induced protection of peptides immediately adjacent to the b-hairpin at the s-site, where dATP is expected to bind based on cryo-EM. It is clear that the presence of dATP increases the rigidity of this region.

Weaknesses: The discussion of the change in peptide mobility in the N-terminal region is complicated by the presence of bimodal mass spectral features and this may prevent detailed interpretation of the data, especially for select peptide region that shows opposite trends upon nucleotide association. Further, the HDX data in the NxN flap is unchanged upon nucleotide binding (ATP, dATP, or CTP), despite changes observed in the cryo-EM data.

Reviewer #3 (Public Review):

In the revised manuscript by Maio et al, the authors examined the bioenergetic mechanisms involved in the delayed migration of DC's during Mtb infection. The authors performed a series of in vitro infection experiments including bioenergetic experiments using the Agilent Seahorse XF, and glucose uptake and lactate production experiments. Also, data from SCENITH is included in the revised manuscript as well as some clinical data. This is a well written manuscript and addresses an important question in the TB field. A remaining weakness is the use of dead (irradiated) Mtb in several of the new experiments and claims where iMtb data were used to support live Mtb data. Another notable weakness lies in the author's insistence on asserting that lactate is the ultimate product of glycolysis, rather than acknowledging a large body of historical data in support of pyruvate's role in the process. This raises a perplexing issue highlighted by the authors: if Mtb indeed upregulates glycolysis, one would expect that inhibiting glycolysis would effectively control TB. However, the reality contradicts this expectation. Lastly, the examination of the bioenergetics of cells isolated from TB patients undergoing drug therapy, rather than studying them at their baseline state is a weakness.

Reviewer #1 (Public Review):

Summary:

The authors use insights into the dynamics of the PKA kinase domain, obtained by NMR experiments, to inform MD simulations that generate an energy landscape of PKA kinase domain conformational dynamics.

Strengths:

The authors integrate strong experimental data through the use of state-of-the-art MD studies and derive detailed insights into allosteric communication in PKA kinase. Comparison of wt kinase with a mutant (F100A) shows clear differences in the allosteric regulation of the two proteins. These differences can be rationalized by NMR and MD results. During the revision process, the authors have addressed the reviewers' comments adequately and have improved the accessibility of the manuscript to a wider audience.

Reviewer #3 (Public Review):

Summary:

Combining several MD simulation techniques (NMR-constrained replica-exchange metadynamics, Markov State Model, and unbiased MD) the authors identified the aC-beta4 loop of PKA kinase as a switch crucially involved in PKA nucleotide/substrate binding cooperatively. They identified a previously unreported excited conformational state of PKA (ES2), this switch controls and characterized ES2 energetics with respect to the ground state. Based on translating the simulations into chemical shits and NMR characterizing of PKA WT and an aC-beta4 mutant, the author made a convincing case in arguing that the simulation-suggested excited state is indeed an excited state observed by NMR, thus giving the excited state conformational details.

Strengths:

This work incorporates extensive simulation works, new NMR data, and in vitro biochemical analysis. It stands out in its comprehensiveness, and I think it made a great case.

Weaknesses:

The manuscript is somewhat difficult to read even for kinase experts, and even harder for the layman. The difficulty partially arises from mixing the technical description of the simulations with the structural interpretation of the results, which is more intuitive, and partially arises from the assumption that readers are familiar with kinase architecture and its key elements (the aC helix, the APE motif, etc).

Reviewer #1 (Public Review):

Summary:

The authors have implemented Optimal Transport algorithm in GromovMatcher for comparing LC/MS features from different datasets. This paper gains significance in the proteomics field for performing meta-analysis of LC/MS data.

Strengths:

The main strength is that GromovMatcher acheives significant performance metrics compared to other existing methods. The authors have done extensive comparisons to claim that GromovMatcher performs well.

Weaknesses:

The authors might need to add the limitation of datasets and thus have tested/validated their tool using simulated data in the abstract as well.

Reviewer #2 (Public Review):

Summary

The goal of untargeted metabolomics is to identify differences between metabolomes of different biological samples.

Untargeted metabolomics identifies features with specific mass-to-charge-ratio (m/z) and retention time (RT). Matching those to specific metabolites based on the model compounds from databases is laborious and not always possible, which is why methods for comparing samples on the level of unmatched features are crucial.<br /> The main purpose of the GromovMatcher method presented here is to merge and compare untargeted metabolomes from different experiments. These larger datasets could then be used to advance biological analyses, for example, for identification of metabolic disease markers.

The main problem that complicates merging different experiments is that m/z and RT vary slightly for the same feature (metabolite).

The main idea behind the GromovMatcher is built on the assumption that if two features match between two datasets (that feature i from dataset 1 matches feature j from dataset 2, and feature k from dataset 1 matches feature l from dataset 2), then the correlations or distances between the two features within each of the datasets (i and k, and j and l) will be similar. The authors then use the Gromov-Wasserstein method to find the best matches matrix from these data.

The variation in m/z between the same features in different experiments is a user-defined value and it is initially set to 0.01 ppm. There is no clear limit for RT deviations, so the method estimates a non-linear deviation (drift) of RT between two studies. GromovMatcher estimates the drift between two studies, and then discards the matching pairs where the drift would deviate significantly from the estimate. It learns the drift from a weighted spline regression.