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Referee #3

Evidence, reproducibility and clarity

The authors analyze three datasets of Single cell RNA velocity measured during phenotypic transition. They infer the gene regulatory network in each case and characterize the transition between the initial and final expression states (in which different sets of genes are expressed). Their motivating question was to find whether during such transitions first genes characterizing the initial state are no longer expressed and only then the genes associated with the final state start expressing or alternatively there is gradual transition through an intermediate state in which subsets of both initial and final state genes are transiently expressed.

They define a measure of regulatory frustration representing the mismatch between regulatory signals a gene receives and its current expression state. They conclude that phenotypic transitions involve transient interactions between otherwise non-interacting gene modules and a temporary increase of gene frustration, which is relaxed once the final expression state is reached.

The study uses of advanced inference and machine learning methods.

I find the question studied in this manuscript interesting, opening avenue to further questions and studies and relevant to different scientific communities. Personally I think that the focus of the paper should be the exposition of the methods used this manuscript would benefit from a longer format, but that depends of course on the journal they are aiming at.

Statistical analysis is missing. Especially since the authors mention the potential of over-fitting due to large number of genes (on the order of the number of cells) - I think the authors should provide a sensitivity analysis testing how sensitive are the conclusions to the choice of cells or genes by applying the methods to subsets of the cells / genes.

What is the meaning of the distribution in the frustration plots?

In general, the conclusions are well-justified, but I think some statements in the discussion are inaccurate: "intercommunity interactions of a GRN are indeed minimized' - are they minimal or are they only lower at the stable states? There are two stable states - for which of them is intercommunity interaction lower?

It is written in the discussion that 'for all three datasets frustration decreases with differentiation', but then Fig. 1g shows the opposite (final state is more frustrated than initial state). It is interesting to discuss the differences between the datasets analyzed in that respect and what could cause transition to a more frustrated state. I suggest that the authors also refer in the discussion to related questions and possible follow-up studies, such as: what determines the duration of the phenotypic transition? A relevant number is the switching time of a single gene.

The authors mention at the end that the networks can often reach multiple final states from a common initial states. Do such transitions share some of their path (and in particular the intermediate frustrated state)? Given the intermediate connected state, it would be interesting to characterize the network stability to perturbations. While interesting, the manuscript itself is unfortunately hard to read and would benefit from major editing, including better exposition of the science and language editing.

Methods: Description of PCA and 'revised finite temperature string method' are missing in the Methods section.

Some examples:

Figure captions are very short and often non-informative. Some variables are not defined (or only defined later on) and the reader then needs to guess their meaning: it took me a while to understand what is 'r' in Fig. 1f and what 'r=10' (p. 4) means.

p. 4: what are 'f' (as opposed to F) and 's_ij' and 's_j' (expression states?) Or is fs_ij one variable? What does a Hamiltonian of a cell mean (p. 4, bottom)?

P. 4: how are 'E and M genes' defined?

What does 'network heterogeneity' (p. 5) mean?

Fig. 1 is too tiny and hard to read and details are missing.

A glossary for all the acronyms used would be very helpful.

Language (some examples):

p. 5 bottom: Another system is on development... invitro -> in vitro

p. 6: 'measure on developmental potential' -> measure of...

Significance

This study presents a methodological advance in demonstrating the application of data analysis methods to study developmental phenotypic transitions. High throughput measurements and computation power available today enable putting to test theoretical conjectures, as made by Waddington. I think this is a promising line of research, which could be used to further develop the computational methods as well as to further our understanding of developmental transitions and potentially develop associated mathematical modeling frameworks.

This study should be of interest to a diverse readership composed of developmental biologists as well as to quantitative biologists and CS researchers applying optimization techniques and data analysis methods to high-throughput biological data.

I am not an expert on the computational methods applied in this manuscript and hence cannot assess their correct use and statistical analysis.

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Referee #2

Evidence, reproducibility and clarity

Understanding the cellular and molecular basis of cell type or cell state transitions occurring during development or reprogramming is a fundamental challenge. scRNA-seq has provided a window into gene expression programs across thousands of cells undergoing such transitions. Wang and colleagues leverage scRNA-seq and develop an approach to reverse engineer gene regulatory network underlying cells along a path from one cell type/state to another, and characterize community-level properties of this network associated with various stages of the cell phenotype transition. The study is innovative and rigorous, and their results point to how intercommunity interactions increase and then decrease, indicating a concerted regulatory rewiring that orchestrates transitions. Application of their approach to three different datasets also shows that this trend is consistent across three different transitions and maybe a general trend. However, there are some major and minor concerns that need to be addressed.

Major comments and questions

1. The analogy to SN1 and SN2 mechanisms of chemical bond formation is very nice.
2. What is the basis for the two statements made in paragraph 3 of Introduction (beginning with "A question arises ...") about transitions being sequential or concurrent? Please

2.1. Provide references to previous experimental and computational studies that have investigated developmental and reprogramming gene expression programs.

2.2. Describe specific examples of findings that support the two possible transitions highlighted here. Why couldn't transitions happen through an entirely gradual process involving changes to overlapping subsets of genes.

1. Please make plots of the number of effective intra-community edges vs. number of active genes to support the statement that these two numbers are correlated.
2. A bunch of notations are not clear:

4.1. What is the "r" in "strongest intercommunity interactions at r = 10 (Fig. 1F)"? Is it the same as the "r" mentioned in the Methods section?

4.2. What is "s_i" in "cell-specific effective matrix, Fbar_ij = (2s_i - 1)F_ij"? Also, that description of F_ij, f_ij, and H should be moved to the Methods section, and a more high-level, intuitive description should instead be included in this Results paragraph.

1. How were the h_f and h_m thresholds chosen?
2. What is the "density of each single cell" ("⍴_t")? The formulation of the penalty of the distance between cells i and j (the expression with -logP_ij...) is unclear. What is the intuition behind it? What is r? How were the values of r (0.5 and 0.8) chosen?
3. One of the reasons the authors state to justify the choice of PLSR is "In the scRNA dataset, the number of genes is often comparable to or larger than the number of cells." This is not true most of the time. In nearly all recent studies, the number of cells is way larger than the number of genes measured.
4. There is a fleeting reference to a nice previous finding that supports their observations: "several lines of evidence support that EMT proceeds through a concerted mechanism. Indeed, both in vivo and in vitro studies have identified intermediate states of EMT that have co-expressed epithelial and mesenchymal genes (Pastushenko et al, 2018; Zhang et al, 2014)". The authors should thoroughly survey the literature related to EMT transition, development of pancreatic endocrine cells, and development of the granule cell lineage in dentate gyrus, to find more previously identified molecular/cellular features relevant to cell state/type transitions, compared and contrasted with findings from this study.
5. What is the "dynamo" package, which is supposed to contain a Python notebook? As of now, the code and data have not been made available. Both need to be released along with thorough documentation on how to run the code to reproduce the analyses described here.

Minor comments and questions

1. Replace "confliction" throughout the manuscript with "conflict" or "conflicting" as appropriate.
2. Paragraph two of the Introduction (beginning with "Another example of transitions ...") is missing multiple references, esp. for the last four sentences.
3. There are direct quotes from previous papers like "predicts the future state of individual cells on a timescale of hours". The authors are highly encouraged to check for usage of exact phrasing using available text software such as iThenticate.
4. "Each community contains both E and M genes": what does this mean?
5. Reference to Qui 2021 is missing in the "Path analysis" subsection under Methods.
6. Fix: "transition between the cells that their sample time points are successive" in Methods.
7. In Methods, under "Network inference", it is "partial least square regression" (not least s square).
8. Figure 1: The cyan, magenta, and lime in 1C are very hard to see and, perhaps, the grey of the points can be made lighter. Also, change the red and green colors for the arrows in 1I to something else. These colors are not colorblind-friendly.
9. Periods and commas are missing at several places.

Significance

The study uses RNA-velocity calculated from scRNA-seq data in an inventive way to characterize paths that reflect cell phenotype transitions. Then, a sparse gene regulatory network is reverse engineered from the data and the community structure within this network is examined at various stages along the transition to make observations about inter- and intra-community regulation and network "frustration". However, the study lacks the context of existing literature in terms of previous work studying cell transitions both experimentally and computationally. Adding this context (as suggested in the comments) will considerably improve the utility and significance of the findings. Overall, this study will be of broad interest to researchers interested in development and reprogramming as well as computational scientists developing and applying methods for scRNA-seq data analysis, trajectory inference, and network reconstruction. All the comments and questions raised here are based on my background and expertise in omics data (including scRNA-seq) analysis and network biology.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript by Wang and colleagues, the authors analyse single-cell RNA-seq (scRNAseq) data by applying transition path theory to infer gene regulatory network (GRN) changes along the transition (reaction coordinate, trajectory) between free energy stable states (i.e. cell types). The work aims to understand how stable cell types, and their regulatory programs (combination of active and repressed genes) switches during differentiation/reprogramming/response (i.e. cell phenotypic transition/CPT). The premise of the work is to assess whether genes within GRNs undergo step-wise repression, state-change and activation (& vice-versa; analogous to SN1) or concurrently regulate gene expression (analogous to SN2). The GRNs are inferred based on highly variable genes and their expression dynamics from RNA velocity over CPT, across 3 scRNA-seq datasets.

The authors first analyse public scRNA-seq dataset of 3003 human A549 adenocarcinomic basal epithelial cells treated with TGF- for 0hrs, 8hrs, 1 day and 3 days (4 timepoints). The authors select two stable states (Day0-untreated; Epithelial and Day 3-treatment; Mesenchymal) using local kernel densities and set transition paths using Dijkstra shortest path, dividing state space into Voronoi cells (i.e. reaction coordinate value), and constructed single-cell GRNs based on RNA velocity differences (n=500 genes) and a linear model (from Qiu et al). This GRN is based on expression and velocity estimates, and does not distinguish direct from indirect regulation. Calculating interaction frequency (edges) across two stable states over 4 louvain clusters, the authors find global increase in effective edges that correlates with increased active genes; but with variable trend within inter-cluster edges. To quantify the concerted GRN changes between clusters, the authors utilise a "frustration" score (from Tripathi et al 2020). The average frustration score increases and peaks at day 1 treatment, followed by a decline over terminal stable state (day 3-treatment); similar to interaction frequency trends. The author also separately measure network heterogeneity and repeat analysis using alternative transition matrix. The authors conclude that EMT proceeds through concerted regulation of multiple genes first with an increase in inter-cluster edges, frustration and heterogeneity followed by a decrease into final stable state. The authors apply the analysis to scRNA-seq data from (i) pancreatic endocrine differentiation where Ngn3-low progenitors give rise to Ngn3-high, then Fev-high and into glucagon producing -endocrine cells; (ii) dendate gyrus; radial glial cell differentiation into nIPCs, neuroblast, immature granule and mature granule cells. In both cases, the authors observe concerted regulation with initial increase in inter-community edges, heterogeneity during differentiation followed by decrease towards final stable state.

The study and ideas in the manuscript are interesting and the methods would be potentially be useful. However, there are a few specific and general comments stated below, which the authors should try to address.

• P4: "RC increases first and reaches a peak when cells were treated with TGF-β for about one day, then decreases (Fig. 1G)". It would be better to label the figure with the treatment information. • Fig. 1G and EV1D: Why are the trends different? • How is the appropriate community/cluster/Louvain resolution selected? This can have a major impact on number of cell states, types and transition path from initial to final state. • What effect does the Louvain resolution have on e.g. frustration scores? • The authors match resolution to samples/timepoints/known prior cell types i.e. 3-4 communities. However it is unclear whether this is enough to describe entire differentiation/transition process. • Gene selection: The selection based on minimum 20 counts as highly expressed genes is arbitrary and dependent on sequencing depth. Perhaps the authors could show distribution of gene counts for the datasets and have a data-driven filtering criteria • The choice of 500 variable genes (for human A549 cells) is also quite arbitrary. Perhaps, the authors could compare how additional genes (all highly variable genes) affects their analysis and interpretation. • How are other factors (sequencing depth, genes detected, #of cell types, multiple branches) affects the connectivity between communities at different phases of transition/development? • Are the velocity graph, transition matrix and further shortest path estimation derived in a reduced latent space, and if so, how much (nPCs) and what impact does it have. Presumably, the density estimation is not performed in expression space.

• The figure legends and labels were hard to read. These should be improved for better readability.
• A suggestion would be move the initial results section to methods and highlight the biological interpretation. The authors could highly which GRN and representative genes/edge pairs are highest ranked within inter-community and to overall final stable states.
• How does the GRN inference compare to current state-of-the-art GRN inference scRNA-seq methods?
• How do extremely noisy/stochastic genes vary in metrics between final stable states? How are the metrics affected by number of cells and stochasticity of expression within a given cluster/community.
• Given that the author's approach includes both direct and indirect genes effects, the authors could further prune genes based on existing TF databases or protein-protein validated networks.
• It is unclear which GRNs are already known and which ones are novel and biologically relevant
• It would be good for authors to comment when there are multiple bifurcations instead of A-B transitions. Particularly in datasets with multiple discrete stable states.
• Another suggestion would be to highlight gene expression of selected markers based on f-regression and mi over the trajectory
• If possible, a proof of principle could be re-analysis of a perturbation scRNA-seq dataset (e.g. where one path/transition path is stalled)

Significance

Nature and significance of advance: The study and ideas in the manuscript are interesting and the methods would be potentially be useful to community.

Compare to existing published knowledge: -

Audience: Predominantly computational audience

Your Expertise: PI with background in experimental, computational biology and expertise in single-cell genomic tools and developmental biology

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Reply to the reviewers

We are very grateful to the three referees for their constructive comments and suggestions which have helped improve the quality of our manuscript.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In the publication HAT-field: a very cheap, robust and quantitative point-of-care serological test for Covid-19 by Joly and Ribes the authors describe an adaption and an improved protocol to their previously published haemagglutination based test to detect antibodies to SARS-CoV-2 in patient blood (Towsend et al., 2021). In detail, they analyzed the effect of several adaptions including buffer optimization, plate coating, usage of patient whole blood instead of washed RBCs and plasma. Additionally they tested different temperatures and stability of the reagents, namely the nanobody-RBD construct IH4-RBD. For validation they compared their optimized HAT-field assay with Jurkat-S&R as a FACS-based assay.

Major comments:

Introduction: This section is rather short and could benefit from a broader overview of currently established methods and assays to detect appropriate immune responses against SARS-CoV-2. The author are advised to summarize the current literature in the field more comprehensively and not only focus on their own work.

Response: Hundreds of different tests to monitor immune responses against SARS-CoV-2 have been described to date, and the literature on these various tests is vast, with new articles coming out almost on a daily basis. We would not feel either that the introduction of our rather technical paper would benefit from being lengthened by such a review of the current literature, or even competent to carry out such a summary. Following the referee’s suggestion, we have, however, introduced a new sentence and given three references providing relatively recent overviews on the subject of immune-monitoring.

Cross-reactivity with IH4-RBD. In Figure 6, the authors highlight the samples in red and orange that showed cross-reactivity with IH4-RBD. In their discussion, however, the authors state that only 2 of 60 (3%) were cross-reactive. In making this statement, they ignore the proportion of cross-reactive samples that were also positive in the Jurkat S&R assay. Therefore, the authors should acknowledge in the discussion that the actual number of cross-reactive samples was higher.

Response*: The statement in the discussion about 2 cross reactive samples out of 60 concerns the results obtained after an incubation of one hour under normal gravity, and not the two red dots in each of the three graphs of figure 6, which correspond to the two negative samples which gave false-positive results in HAT plasma titrations after spinning (Figure 6C), for which we correctly state in the discussion that 12 samples showed cross-reactivity on IH4 alone. The data presented in Figure 6B corresponds to HAT-field after spinning, for which we correctly state in the discussion that 5 out of 60 showed cross-reactivity (4 orange dots + 1 red dot, the second red dot having a score of 0, in accordance with the fact that this sample showed no cross reaction on IH4 alone in HAT-field after spinning). *

*To try to prevent this possible confusion, we have now clarified what data we are referring to at the start of that paragraph in the discussion. *

Quantitative Assay. Since the HAT assay does not allow determination of the absolute number of antibodies reactive to SARS-CoV-2 in the blood samples, the authors should refrain from claiming that the HAT-field is a quantitative assay.

Response*: Since immune sera are inherently polyclonal, they contain a multitude of different types of antibodies of different affinities and avidities, and we are not aware of any technique that allows to determine the “absolute number” of antibodies directed against a given antigen in such samples. *

*For many serological tests, including ELISA and the initial protocol of HAT, serum or plasma titrations are used as a means to obtain what is widely considered as a quantitative evaluation of the amounts of antibodies in blood samples. Even FACS-based assays such as the Jurkat-S&R-flow test we have used, are commonly considered as quantitative but those only provide relative results and not absolute numbers. *

We perceive that the close correlations we find between the results of the HAT-field protocol and those of the Jurkat-S&R-flow test as well as with serum titrations using the standard HAT protocol warrants considering the results of HAT-field as being as quantitative as those obtained with all those other tests.

Morphological read out For field application, the morphological description of the observed deposits ("teardrop" vs. "button") could be problematic and might lead to bias depending on the user. Thus, the authors should provide a clearer description for phenotype classification.

Response: We have now introduced a specific paragraph detailing how to score HAT assays in the Methods section, as well as a new figure providing a graphic description of positive, partial and negative RBCs deposits.

Minor comments: Title: the authors should remove "very"

Response*: We have now removed the word ‘very’ from the title, and thank the referee for this helpful suggestion. *

By the way: What are the costs of IH4-RBD for a 96 well plate? Who will produce this reagent? Is the sequence of the IH4 fully disclosed?

Response*: As specified in our original paper (see Townsend et al. 2021), the plasmid coding for the IH4-RBD is available upon request from Alain Townsend (Oxford, UK). Furthermore, his laboratory funded the production of 1 gram of the IH4-RBD reagent by a commercial company, and professor Townsend has been graciously sending aliquots of 1 mg of this reagent, which suffice for several thousand tests, to all the laboratories that have requested it from him. *

*In its initial format, HAT only required 100 ng of IH4-RBD per well, corresponding to a cost of 0.0027 £ per well. For the HAT-field protocol, 5 times more reagent is needed, thus bringing the cost of the reagent to 1.5 cts per test, to which one would have to add a similar cost for the IH4-reagent alone. This would thus bring the cost of the two reagents to approximately 3 cts, which is still lower than the price of any of the cheap disposable plasticware necessary for the test (lancet, pipet, plastic tube and portion of a plate). *

The sequence of the IH4 nanobody is indeed fully disclosed (see figure 1 of Townsend et al. 2021), and has actually been protected by a patent ( US9879090B2 ). Whilst IH4 can be used freely for research purposes, licensing rights would have to be taken into consideration by any health authority wishing to use the technique broadly, or for any commercial distribution.

The usage of the CR3022 as positive control for neutralizing antibodies should be reconsidered since this antibody does not confer viral neutralization. Other well describe antibodies blocking the ACE2:RBD interface might be better suited.

Response*: CR3022 was the one that we had at our disposal, but other mAbs can certainly be used instead of as positive controls, and this is actually indicated in the detailed HAT-field protocol provided. Since the use of a positive control is only to ensure that the IH4-RBD has not been degraded and works as well as expected, and that any negative samples are not due to a very rare glycophorin mutation that could prevent IH4 from binding to it at the surface of RBCs, we are not sure why using a mAb with neutralizing activity would necessarily be better than the CR3022 mAb. *

Figure 2: Please state the concentration of IH4-RBD used. As stated in the figure legends for Figure 2 B, the authors should show the result all 4 replicates (incl. SD)

Response: The concentration of IH4-RBD was 1 m*g/ml, i.e. the normal concentration for standard HAT tests. This was already indicated in the Methods section, but has now been added to the legend of Figure 2. *

Whilst 4 experiments were indeed carried out, which all gave similar results, i.e. showed that using PBS-N3 or PBN did not hinder HAT performance, but could instead result in a slight increase in HAT sensitivity, those various experiments were not all exact replicates of the experiment shown on figure 2. Furthermore, performing of those various experiments was spread over a period of over a year, using different reagents, thus precluding numerical comparisons between the various results. We have clarified this issue by rewording the final statement to “Comparable results were obtained in four similar experiments.”

Figure 3: Although the authors showed stability of IH4-RBD at 2 µg/ml they do not provide data for the stabilities at higher dilutions. As the authors suggest to predistribute the IH4-RBD in plates they should at least discuss this issue.

We thank the referee for raising this valid point, which has now been discussed in the paragraph entitled “Practical considerations for performing HAT assays” in the Methods section: “One aspect that will have to be considered for the design and use of such individual strips of wells will be to ensure that, upon storage, the various dilutions of IH4-RBD are as stable in such strips as the working stocks of IH4-RBD (2 mg/ml) tested in Figure 3.”

Figure 6/Supplementary Figure 1 and 3 The presentation of the data is not accurate, as many of the points (samples) are obviously identically positioned in the graph. The authors should choose a different representation of their data. E.g. they could adjust the size of the points to the number of overlapping samples.

Response: We thank the referee for raising this issue, which was also pointed to by referee #2. This apparent inaccuracy is due to the fact that, on these plots, the scales for both x and Y axes used discrete values, which indeed results in multiple points overlapping on top of one another. This was resolved by adding numbers next to the positions where several dots overlapped

Wording / text length In the current manuscript the text is very long. Thus, the authors should shorten it to report the essential findings more appropriately. Additionally they should check for correct English wording.

Response*: We thank the referee for this remark, which helped us realize that the excessive length of the manuscript was mostly due to an extensive discussion of highly technical and practical points. The corresponding paragraphs were indeed out of place in the general discussion, and have not been deleted but have been moved to the Methods section since we feel that they contain very important information for people who would actually start to performing HAT assays. *

Reviewer #1 (Significance (Required)):

In summary, the authors describe the HAT-field test as a simple PoC test for the detection of SARS-CoV-2 antibodies in patients. Because of its ease of use and robustness, the test appears to be particularly well suited for use in countries with underdeveloped health care or limited testing facilities, as also reported previously. The value of this manuscript lies mainly in the detailed description of the protocol and its validation. In this context, the adaptations described are certainly useful and helpful from a practical point of view, but do not provide significant new scientific insights. In light of these considerations, we recommend that this work be submitted to an appropriate journal specializing in the publication of such methods

Expertise The reviewers have established and published different serological assays to monitor immune responses against SARS-CoV-2

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this paper, the authors developed a feasible protocol for an affordable point-of-care serological test for SARS-CoV-2. This method was adapted from the HAT plasma titration test that the authors previously published. Specifically, the test utilizes a 96-well plate pre-coated with the RBD of SARS-CoV-2 spike glycoprotein fusing to a red blood cell targeting nanobody (IH4). By adding microliters amount of the blood or plasma samples to the plate, it allows the detection of antibodies against RBD by measuring the level of hemagglutination. In the current upgraded protocol (so called HAT-field), the authors made major modifications including optimizations of buffer and experimental protocol and the use of pre-titrated IH4-RBD on the plate, which collectively helped to lower the sample consumptions, improved the stability and the sensitivity of detection, and made the test more user-friendly under non-clinical settings.

Major comments: My major concerns are related to the robustness and quantitative capability of this approach. Specifically: It seems that multiple variables may impact the results. These include volume of droplets, the presence/absence of serum IH4 or BSA cross-reactive antibodies, and the amount (%) of red blood cells which may vary substantially among samples. Could you find a way to normalize the results (e.g., the discrepancy shown in Figure 6) instead of only leaving them as false-positives or false-negative?

Response*: Regarding the volume of the droplets, in other words, the amount of blood collected and used in an assay, two sentences in the manuscript underline the fact that this is not a critical variable: *

In the Results section “the precise volume of blood collected is not critical; it may vary by as much as 30% with no detectable influence on the results.”

In the discussion: “On this subject, we have found that increasing the amount of whole blood per well (in other words using blood that is less dilute) has very little influence over the HAT-field results, and, if anything, adding more blood can sometimes reduce the sensitivity, albeit never by more than 1 dilution.”

Consequently the % of RBCs in samples seem unlikely to influence the HAT-field scores significantly. This is supported by the fact that, although men tend to have higher hematocrits than women, we have not noticed any detectable difference between men and women in the correlation of the HAT-field scores with those of the Jurkat-S&R-flow test.

We are not sure that we fully understand what discrepancy shown in Figure 6 the referee is pointing to, but if it is about the increase in the number of samples found to be cross reacting on IH4 alone when the sensitivity increases, in the discussion, we propose to perform tests using titrations of the IH4 nanobody alone simultaneously to using the IH4-RBD reagent, so as to minimize the number of samples that would be identified as false positives if only one concentration of IH4 alone was used as negative control. Comparing the titers obtained with IH4-RBD and IH4 alone will then provide some level of normalization for the samples cross reacting on IH4. As for the hypothetical presence of antibodies cross reacting on BSA alluded to by the referee, since such antibodies would not bind to RBCs, we do not think they would affect the HAT results.

Second, the score of the HAT-field ranges from 0 - 8. However, based on the current manuscript, it is not clear how the scoring and scaling works. How is the noise (non-specific antibody signal) defined here?

Response: We have now introduced a specific paragraph and a new figure detailing how to score HAT assays in the Methods section.

In addition, it is unclear how to translate the HAT-field score into a meaningful measure of protection by serum antibodies.

Response*: Documenting the correlation between HAT-field scores and levels of protection against SARS-CoV-2 infections and/or Covid-19 severity would indeed be extremely interesting. This would, however, require setting up a large scale clinical trial carried out over several months. This type of work could only be carried out by a large consortium including clinicians or even preferably a national health agency. This was, however, far beyond the reach of this initial project, which was based on the work of a single person on a shoestring budget. *

Can you provide more evidence to demonstrate that the test is quantitative? For example, performing additional orthogonal experiments to better validate the scoring and generate a correlation function?

Response*: Inasmuch as it would have been very interesting to perform additional serological tests from commercial sources on the samples of our cohort, such tests are all very expensive (e.g. ca. 500 € for one ELISA plate). This was in fact the main reason for developing the Jurkat-S&R-flow test in the first place, since it is much cheaper, more modular, and at least as sensitive as ELISA (see Maurel Ribes et al. 2021). The funds for this whole project came from a single 15 k€ grant obtained from the ANR, and we simply did not have access to the funds, or to the human resources to carry out such experiments based on commercial serological tests. *

Minor comments: Figure 6: are all results included? To me, it does not seem that all 60 samples data were included in the plot.

Response: We thank the referee for raising this issue, which was also pointed to by referee #1. This apparent inaccuracy is due to the fact that the scales for both x and Y axes used discrete values, which results in multiple points overlapping on top of one another. This was resolved by adding numbers next to the positions where several dots overlapped.

There are several redundant statements in the discussion and results section. Please make the text more concise.

Response: The discussion has now been shortened considerably, mostly by moving the paragraphs pertaining to technical considerations to the Methods section.

Reviewer #2 (Significance (Required)):

The current paper is built upon the improvement of previous published work. In addition, there are similar approaches that have been published. It was unclear if the current method is superior to other works.

Response: Whilst we have made no statement regarding whether the method we describe is superior to other methods, we are pretty confident that very few alternatives will be as frugal and simple as the HAT-field protocol described here. As alluded to in the final paragraph of the discussion, two recent reports have described that HAT could be performed on cards rather than in V-shaped wells, with semi-quantitative results being obtained in minutes. If such card-based approaches turn out to provide sensitivity and reliability comparable to those of the HAT-field protocol, they will certainly represent very interesting alternatives. As stated in our manuscript, we would be very interested if the comparative evaluation of the two approaches could be carried out by one or several independent third party.

My research involves the development of antiviral antibody therapeutics. This method may be used as a point-of-care tool for the measurement of serologic response to RBD in less developed countries. However, due to the high vaccination rate and large infected populations, the overall needs for such detection drastically decrease. The significance of the work and utilities of the test may expand with more experiments related to the variants.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This paper describes a low-cost robust and quantitative serological test based on haemgglutination, which could be used in resource limited settings for evaluating population-based and vaccine induced immunity. Neutralising antibodies to the receptor binding domain (RBD) on the SARS CoV-2 spike protein are an immunological correlate of protection. The HAT has a single reagent the RBD domain of SARS CoV-2 linked to a monomeric anti-erythrocyte single domain nanobody. When human polyclonal serum antibodies bind to the RBD they cross-link and agglutinate human red blood cells, resulting in haemagglutination which can be read visually.

This paper thoroughly evaluate the stability of the HAT reagents used to measure human and monoclonal antibodies examining the robustness of the HAT reagent. It provides a comprehensive protocol for conducting field based HAT with limited reagents. The test can evaluate is subjects have been infected using a simple finger prick to detect RBD specific antibodies. The field HAT can also be used to define people that can be susceptible to reinfection or in need of vaccination, With the use of RBDs from the variants of concern the test can be rapidly adapted to evaluate antibodies as new variants arise to evaluate surrogate correlates of protection to allow timely evaluation of vaccine effectiveness and predict the need for vaccine booster doses. The data are very comprehensively presented with good figures demonstrating the most appropriate buffer to store the IH4-RBD reagent and the robustness of the HAT over time at different temperatures. No additional experiments are needed and suitable numbers of replicates are included. All data, methods and reagents are comprehensively described.

Minor comments: The paper is well written but rather long in places and may have benefited from being more succinct.

Response: The excessive length of the manuscript was mostly due to an extensive discussion of highly technical and practical points. The discussion has now been shortened considerably, mostly by moving the paragraphs pertaining to technical considerations to the Methods section.

Panels in figures could be labelled as A, B, C etc to help in identifying the correct panel..

Response: We thank the referee for this helpful suggestion, which we have followed.

I would avoid the use of experiment and project and refer to next we confirmed... or in this paper or our results show Please make sure all abbreviation are defined upon first use. Perhaps include early in the paper that most of the work was conducted with the Wuhan RBD

Response: We thank the referee for these helpful suggestions, which we have followed to the best of our abilities. The abstract now contains a mention of the fact the work on optimizing the protocol was carried out with the IH4-RBD carrying the Wuhan version.

Figure 2: I would suggest placing either a solid line between the two halves of the plates to make it easier for the reader to differentiate between the two antibodies. It also would have been easier to read if the bottom PBS, PBS-N3 and PBN were at 45 degree angle. In B include the serum name (e.g. serum 197).

Response: We thank the referee for these helpful suggestions, which we have followed.

Legend to figure 4: please include the serum numbers after covid-19 patients. Perhaps include arrows to demonstrate the dilutions of serum and IH4-RBD in the figure.

Page 6 it might be easiest to use the same times as in figure 6 and use for example more than one year in the discussion

Response: We thank the referee for these helpful suggestions, which we have all followed.

Legend figure 6 perhaps replace dots with circles page 10 include the R values from figure 6 in the description of results.

Response: We are grateful to the referee for these helpful suggestions, but have not followed them since we do not feel that these changes would be real improvements.

Page 12 of note perhaps this can be moved to the methods ?

Response: This, and several other paragraphs of the Discussion, have now been moved to the Methods section.

Supplementary figure 2 A can be seen, is something missing here?

Response: An s was indeed missing : “A can be seen” corrected to “As can be seen “

*

Reviewer #3 (Significance (Required)):

This paper describes a simple rapid field test for evaluating antibodies to the receptor binding domain of the spike of SARS CoV-2 using the Wuhan and delta variant. Whilst high income countries can provide booster doses and extensive testing (either lateral flow or RT-PCR based) and contact racing to control the waves of the pandemic, low income countries have had limited access to Covid vaccine and the extent of previous waves of the pandemic in the populations are unknown.

This paper describes a robust and simple test for investigating human antibodies to SARS-CoV-2 which could be performed in resource limited settings providing a very useful tool for monitoring infection in the community and potentially for prioritising this scarce COVID-19 vaccines available.

This study builds upon the work conducted on the HAT and has extensively studied and optimised the test so that it could be used globally. This paper provides a comprehensive protocol and has simplified the test to ensure it could be used in LMICs.

This paper would be of great interest to a wide scientific audience who are interested in a rapid low-cost test to evaluate population based and vaccine induced immunity.

Reviewer: serological assays for use in virology and vaccinology. Suitable competence to review the whole paper *

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Referee #3

Evidence, reproducibility and clarity

This paper describes a low-cost robust and quantitative serological test based on haemgglutination, which could be used in resource limited settings for evaluating population-based and vaccine induced immunity. Neutralising antibodies to the receptor binding domain (RBD) on the SARS CoV-2 spike protein are an immunological correlate of protection. The HAT has a single reagent the RBD domain of SARS CoV-2 linked to a monomeric anti-erythrocyte single domain nanobody. When human polyclonal serum antibodies bind to the RBD they cross-link and agglutinate human red blood cells, resulting in haemagglutination which can be read visually.

This paper thoroughly evaluate the stability of the HAT reagents used to measure human and monoclonal antibodies examining the robustness of the HAT reagent. It provides a comprehensive protocol for conducting field based HAT with limited reagents. The test can evaluate is subjects have been infected using a simple finger prick to detect RBD specific antibodies. The field HAT can also be used to define people that can be susceptible to reinfection or in need of vaccination, With the use of RBDsfrom the variants of concern the test can be rapidly adapted to evaluate antibodies as new variants arise to evaluate surrogate correlates of protection to allow timely evaluation of vaccine effectiveness and predict the need for vaccine booster doses. The data are very comprehensively presented with good figures demonstrating the most appropriate buffer to store the IH4-RBD reagent and the robustness of the HAT over time at different temperatures. No additional experiments are needed and suitable numbers of replicates are included. All data, methods and reagents are comprehensively described.

Minor comments:

The paper is well written but rather long in places and may have benefited from being more succinct.

Panels in figures could be labelled as A, B, C etc to help in identifying the correct panel..

I would avoid the use of experiment and project and refer to next we confirmed... or in this paper or our results show

Please make sure all abbreviation are defined upon first use.

Perhaps include early in the paper that most of the work was conducted with the Wuhan RBD

Figure 2: I would suggest placing either a solid line between the two halves of the plates to make it easier for the reader to differentiate between the two antibodies. It also would have been easier to read if the bottom PBS, PBS-N3 and PBN were at 45 degree angle. In B include the serum name (e.g. serum 197).

Legend to figure 4: please include the serum numbers after covid-19 patients. Perhaps include arrows to demonstrate the dilutions of serum and IH4-RBD in the figure.

Page 6 it might be easiest to use the same times as in figure 6 and use for example more than one year in the discussion Legend figure 6 perhaps replace dots with circles page 10 include the R values from figure 6 in the description of results.

Page 12 of note preps this can be moved to the methods Supplementary figure 2 A can be seen, is something missing here?

Significance

This paper describes a simple rapid field test for evaluating antibodies to the receptor binding domain of the spike of SARS CoV-2 using the Wuhan and delta variant. Whilst high income countries can provide booster doses and extensive testing (either lateral flow or RT-PCR based) and contact racing to control the waves of the pandemic, low income countries have had limited access to Covid vaccine and the extent of previous waves of the pandemic in the populations are unknown.

This paper describes a robust and simple test for investigating human antibodies to SARS-CoV-2 which could be performed in resource limited settings providing a very useful tool for monitoring infection in the community and potentially for prioritising this scarce COVID-19 vaccines available.

This study builds upon the work conducted on the HAT and has extensively studied and optimised the test so that it could be used globally. This paper provides a comprehensive protocol and has simplified the test to ensure it could be used in LMICs.

This paper would be of great interest to a wide scientific audience who are interested in a rapid low-cost test to evaluate population based and vaccine induced immunity.

Reviewer: serological assays for use in virology and vaccinology. Suitable competence to review the whole paper

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Referee #2

Evidence, reproducibility and clarity

In this paper, the authors developed a feasible protocol for an affordable point-of-care serological test for SARS-CoV-2. This method was adapted from the HAT plasma titration test that the authors previously published. Specifically, the test utilizes a 96-well plate pre-coated with the RBD of SARS-CoV-2 spike glycoprotein fusing to a red blood cell targeting nanobody (IH4). By adding microliters amount of the blood or plasma samples to the plate, it allows the detection of antibodies against RBD by measuring the level of hemagglutination. In the current upgraded protocol (so called HAT-field), the authors made major modifications including optimizations of buffer and experimental protocol and the use of pre-titrated IH4-RBD on the plate, which collectively helped to lower the sample consumptions, improved the stability and the sensitivity of detection, and made the test more user-friendly under non-clinical settings.

Major comments:

My major concerns are related to the robustness and quantitative capability of this approach.

Specifically:

It seems that multiple variables may impact the results. These include volume of droplets, the presence/absence of serum IH4 or BSA cross-reactive antibodies, and the amount (%) of red blood cells which may vary substantially among samples. Could you find a way to normalize the results (e.g., the discrepancy shown in Figure 6) instead of only leaving them as false-positives or false-negative? Second, the score of the HAT-field ranges from 0 - 8. However, based on the current manuscript, it is not clear how the scoring and scaling works. How is the noise (non-specific antibody singal) defined here? In addition, it is unclear how to translate the HAT-field score into a meaningful measure of protection by serum antibodies. Can you provide more evidence to demonstrate that the test is quantitative? For example, performing additional orthogonal experiments to better validate the scoring and generate a correlation function?

Minor comments:

Figure 6: are all results included? To me, it does not seem that all 60 samples data were included in the plot.

There are several redundant statements in the discussion and results section. Please make the text more concise.

Significance

The current paper is built upon the improvement of previous published work. In addition, there are similar approaches that have been published. It was unclear if the current method is superior to other works. My research involves the development of antiviral antibody therapeutics. This method may be used as a point-of-care tool for the measurement of serologic response to RBD in less developed countries. However, due to the high vaccination rate and large infected populations, the overall needs for such detection drastically decrease. The significance of the work and utilities of the test may expand with more experiments related to the variants.

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Referee #1

Evidence, reproducibility and clarity

In the publication HAT-field: a very cheap, robust and quantitative point-of-care serological test for Covid-19 by Joly and Ribes the authors describe an adaption and an improved protocol to their previously published haemagglutination based test to detect antibodies to SARS-CoV-2 in patient blood (Towsend et al., 2021). In detail, they analyzed the effect of several adaptions including buffer optimization, plate coating, usage of patient whole blood instead of washed RBCs and plasma. Additionally they tested different temperatures and stability of the reagents, namely the nanobody-RBD construct IH4-RBD. For validation they compared their optimized HAT-field assay with Jurkat-S&R as a FACS-based assay.

Major comments:

Introduction: This section is rather short and could benefit from a broader overview of currently established methods and assays to detect appropriate immune responses against SARS-CoV-2. The author are advised to summarize the current literature in the field more comprehensively and not only focus on their own work.

Cross-reactivity with IH4-RBD. In Figure 6, the authors highlight the samples in red and orange that showed cross-reactivity with IH4-RBD. In their discussion, however, the authors state that only 2 of 60 (3%) were cross-reactive. In making this statement, they ignore the proportion of cross-reactive samples that were also positive in the Jurkat S&R assay. Therefore, the authors should acknowledge in the discussion that the actual number of cross-reactive samples was higher.

Quantitative Assay. Since the HAT assay does not allow determination of the absolute number of antibodies reactive to SARS-CoV-2 in the blood samples, the authors should refrain from claiming that the HAT-field is a quantitative assay.

Morphological read out For field application, the morphological description of the observed deposits ("teardrop" vs. "button") could be problematic and might lead to bias depending on the user. Thus, the authors should provide a clearer description for phenotype classification.

Minor comments:

Title: the authors should remove "very" By the way: What are the costs of IH4-RBD for a 96 well plate? Who will produce this reagent? Is the sequence of the IH4 fully disclosed?

The usage of the CR3022 as positive control for neutralizing antibodies should be reconsidered since this antibody does not confer viral neutralization. Other well describe antibodies blocking the ACE2:RBD interface might be better suited.

Figure 2: Please state the concentration of IH4-RBD used. As stated in the figure legends for Figure 2 B, the authors should show the result all 4 replicates (incl. SD)

Figure 3: Although the authors showed stability of IH4-RBD at 2 µg/ml they do not provide data for the stabilities at higher dilutions. As the authors suggest to predistribute the IH4-RBD in plates they should at discuss this issue.

Figure 6/Supplementary Figure 1 and 3 The presentation of the data is not accurate, as many of the points (samples) are obviously identically positioned in the graph. The authors should choose a different representation of their data. E.g. they could adjust the size of the points to the number of overlapping samples.

Wording / text length In the current manuscript the text is very long. Thus, the authors should shorten it to report the essential findings more appropriately. Additionally they should check for correct English wording.

Significance

In summary, the authors describe the HAT-field test as a simple PoC test for the detection of SARS-CoV-2 antibodies in patients. Because of its ease of use and robustness, the test appears to be particularly well suited for use in countries with underdeveloped health care or limited testing facilities, as also reported previously. The value of this manuscript lies mainly in the detailed description of the protocol and its validation. In this context, the adaptations described are certainly useful and helpful from a practical point of view, but do not provide significant new scientific insights. In light of these considerations, we recommend that this work be submitted to an appropriate journal specializing in the publication of such methods

Expertise The reviewers have established and published different serological assays to monitor immune responses against SARS-CoV-2

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Referee #3

Evidence, reproducibility and clarity

Summary:

Estrach and colleagues seek to identify the ECM components that are key to regulating hair follicle stem cell (HFSC) activation using the highly-characterized mouse hair follicle as a model. They first use a targeted approach to examine key ECM components expressed by HFSC and find that Fibronectin (FN) is highly expressed. Further, wholemount analysis of the hair follicle reveals a meshwork of FN enveloping the hair follicle. They hypothesize that FN is a fundamental regulator of hair follicle (HF) cycling and then proceed to carry out longterm studies required to examine hair follicle cycling and knockout FN with two different HFSC Cre lines (Lrig1 and Krt19), as well as integrin coreceptor SLC3A2. They clearly show that absence of Fibronectin (FN) and SLC3A2 is detrimental to hair follicle stem cell activation and cycling (FN) and hair follicle identity (SLC3A2).

Overall comments:

The authors use the tail hair follicles as a model similarly to the highly-characterized, synchronous back skin hair follicles. However, the tail hair follicles are asynchronous (Braun et al. 2003, PMID: 12954714), thus reporting the age of the mouse from which the tail whole mounts came from is not sufficient to claim a HF cycle disorder - HF should be imaged in an unbiased manner and subsequently quantified for phase. The manuscript would greatly benefit from including more information in the figure legends, such as age of mice, number of mice and HF quantified, as well as what the error bars represent. Further, in samples where many HF were counted per mouse, these should be averaged and then the average per mouse displayed; super plots would be great to use here.

Major comments:

1. In Figure 1, the use of tail whole mount images indeed provides striking display of the fibronectin meshwork that envelops the hair follicle. However, addition of a marker of the regenerative phase (e.g. proliferation) and resting phase would provide more convincing evidence that this is the particular phase of the hair cycle that you have captured, especially given my overall comment regarding the asynchronous nature of the tail HF cycle.
2. The authors show that FN is expressed in early-mid anagen and conclude that FN is a regenerative signal. This claim should be substantiated with FN staining on more time points across the HF cycle to substantiate the argument that it is a regeneration-specific signal, found only in the telogen-anagen transition.
3. Lrig1-cre and K19-cre-mediated FN knockout result in HF that are thinner at D158 - this is not immediately apparent from histological sections. Can you use your thick sections to give better perspective?
4. The authors measure the width of the infundibulum from lightsheet microscope images. It is a bit difficult to position whole tissues using this technique, and the images that are shown are not from the same perspective, and thus measurement of the width is not accurate from these images. I suggest either removing this analysis or using more comparable images. Further, if this is a true phenotype, can you speculate on what the thickened infundibulum might mean?
5. The authors then show mislocalization of Lrig1+ cells to the infundibulum in absence of FN. Are other stem markers localized to the infundibulum or outside of the bulge? Further, what might the mislocalization of Lrig1+ cells might mean?
6. Please explain your conclusion after Figure 3i and at the end of the manuscript that states that FN is required for stem cell anchorage. I think that a very plausible explanation is that FN is required for stem cell function and identity, but anchorage of the SC lacks sufficient evidence. Further, your only evidence to support the anchoring theory only comes from expression of Lrig1 in FN knockout and no other markers. Are they also mislocalized? Please either tone down this conclusion on SC anchorage or provide stainings for more SC markers to show mislocalization in absence of FN.
7. In Figure 3l-o, you examine proliferation on the control vs the conditional deletion of FN in D30 and D158. However, in D30, these tissues are not at all directly comparable since one is obviously in anagen and the knockout in telogen. You must compare the anagen knockout sample, although this occurs a bit later than the control. Further, how was the infundibulum distinguished from the bulb in these control images?
8. In Figure 3P, you carry out RT-qPCR on whole skin to detect HFSC markers. This should have been carried out on sorted epithelial cells as isolation of whole back skin introduces bias to the system in that the number of stem cells may artificially look different in skin that is in anagen vs skin that is in telogen as the anagen skin has a different proportion of SC to progenitor cells to dermal cells. This concern is also similar to point 9 - the control and FN knockout at D30 are not comparable given that they are in different phases of the hair cycle.
9. Figure 4a these images need to be of the whole mouse - it is not possible to determine what we are looking at or where - there is not even a scale bar.
10. After Figure 4, you argue that because fibronectin expression resolves from healing dermis is the reason that hair follicles do not form, and site Dekonick and Blanpain (PMID: 30602767) - however this review makes no mention of the dynamics of fibronectin in wound healing. Further, evidence from Driskell et al (2013, PMID: 30602767) would suggest that it is the fibroblast population that responds to the wound that determines whether HF regenerate. And further, very large wounds do regenerate HF (Ito et al PMID: 30602767). In addition, this would all be fibroblast-derived FN, as opposed to the current study which examines keratinocyte-derived FN. Please reconsider this argument.
11. The authors knockout SLC3A2, an integrin coreceptor that is localized to the plasma membrane. They show a very similar, yet more severe phenotype to the Lrig1- and K19- mediated knockout of FN. Given the bidirectional communication that SLC3A2 is responsible for, can you reconcile whether the defects in the HF cycle and the HFSC are a result of outside-in or inside-out signaling? Further, is it possible that integrin function regulated by SLC3A2 is necessary for more than FN assembly? This could be especially relevant given that your targeted screen also identified Col17A1, which is well known to be required for HFSC function (Matsumura et al., PMID: 26912707)
12. It is intriguing that in the absence of HFSC-derived SLC3A2 that no FN network forms. Is FN expressed or is the assembly perturbed in the absence of properly functioning integrins? The authors conclude that the signaling cascade flows from fibronectin to integrin to SLC3A2, but do not test where the FN phenotype arises in the SLC3A2 knockout - is it due to aberrant assembly of the FN meshwork or a change in transcriptional or translational levels?
13. In the grafting assay in Supplemental Figure 3, keratinocytes undergo a de novo hair follicle morphogenesis - is Lrig1 expression maintained in order to carry out cre-mediated deletion? Further, the fibroblasts in this assay may adopt a wound-like phenotype, expressing FN, which you earlier claim to be required for hair follicle production in wounds. Yet in the absence of epithelial FN, no HF form. Can the authors reconcile this?

Minor comments:

1. In Figure 1a, the two populations are Lgr5+ and basal; please define what the basal population is in this experiment.
2. Significative is not a word.
3. In Figure 4 figure legend, there is reference to a grafting experiment but no experiment shown.
4. The authors delete FN in Lrig1+ or K19+ cells starting D19 and harvest at D30, and conclude that the hair follicles do not enter anagen after the second telogen, can you please include the data supporting the statement that mutant HF did not reenter the hair cycle after D65.

Significance

The authors show for the first time that fibronectin is expressed during cutaneous homeostasis and that it is required for normal function of the hair follicle stem cells. This is significant conceptual advance for the field of skin biology because fibronectin is thought to only be present in wounds: derived first from infiltrating serum and second from fibroblasts to act as provisional dermal ECM to support epithelialization during wound-response, which is ultimately resolved upon the conclusion of wound healing (reviewed in: Singer and Clark, PMID: 10471461). Further, FN has also been characterized as an EMT marker during cancerous progression (Lamouille et al, PMID: 24556840). Estrach and colleagues show that fibronectin is actually expressed by hair follicle stem cell keratinocytes and then is assembled into a meshwork that envelops the hair follicle and is in fact necessary for hair follicle stem cell homeostasis. This work would be broadly interesting to the field of stem cell biology as well as those working on extra cellular matrix signaling. My field is epithelial stem cells and more specifically hair follicle development and cycling.

Referee Cross-commenting

I have no disagreement with any of the points raised by the other reviewers. In fact, we seem to agree on the majority of the concerns. This includes the use of the tail wholemount model, the use of Lrig1-cre, selection of timepoint vs phase of the hair cycle, the appropriateness of the link between Fibronectin and SLC3A2, and further significant issues related to display of data and their reproducibility. Further, all of the major comments raised need to be addressed in order to properly evaluate the conclusions that the authors make. In my opinion, none of the comments raised here are unreasonable.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Estrach et al., investigated whether extracellular matrix component fibronectin function in hair follicle regeneration, using a range of approaches including FACS, RT-qPCR, immunofluorescent staining, and mouse genetics. They proposed that fibronectin in Lrig1+ cells was necessary for hair follicle stem cell maintenance and activation, and the fibronectin expression and assembly relayed on the integrin co-receptor SLC3A2.

Significance

Major points:

1. In Figure 1a, the author used Lrig1+GFP and a6 to isolate Lrig1+ cells in the infundibulum junction zone above the sebaceous gland at Day 28. However, in Figure 1f-h, the GFP expression was not only in infundibulum above SG, but also in some inner root sheath cells. Since the Lrig1+ cells do not include the hair follicle stem cells (CD34+ bulge cells), result in Figure 1a does not support fibronectin expression in HFSCs.
2. In Figure 1b-e, the author detected fibronectin expression by IF staining with tail skin whole mount and back skin section. The fibronectin is mainly detected in differentiated cells in the inner root sheath (IRS) in anagen (Figure 1b and 1d), upper IRS in catagen (Figure 1c), hair germ in telogen (Figure 1e), but not in the bulge region (Figure 2i-l). Again, these results do not support fibronectin enrichment in HFSCs either.
3. In Figure 2a-c, the author knocked out fibronectin in Lrig1+ cells with Lrig1-CreERT2, FN fl/fl mice, and then validated the knockout efficiency by IF staining. However, result shows the fibronectin expression was not only depleted in the GFP+ Lrig1+ cells, but also depleted in GFP- inner root sheath and matrix. Similarly in Figure 4n, fibronectin was only knocked out in Lrig1+ cells, however, result showed the fibronectin cannot be detected in any cell types in skin. The author should explain why fibronectin depletion in Lrig1+ cell lead to completely fibronectin depletion.
4. In Figure 2r, by Flow cytometry, the author found a significative reduction of a6+CD34+ SC population when fibronectin is conditional knocked out in Lrig1+ cells. As the Lrig1+ cells and a6+CD34+ HFSCs are two distinct cell populations, the author needs to explain how fibronectin depletion in Lrig1+ cells affect the number and activation of HFSCs population.
5. In Figure 3b-c and 2g-h, the author reported the HF thinning in Lrig1-CreERT cKO mice by back skin HE staining. However, by tail skin wholemount staining, the HF thinning was not observed in those cKO mice (Figure 3f-g; Figure 2k-l). The author needs to explain the discrepency. In addition to this, the low quality of HE staining and poor orientation of HF (Figure 2h and 3b), coupled with lack of quantification, made these results and conclusion unconvincing.

Mini Points:

1. Color information in each IF staining panel is only completely presented in Figure 4. It is incomplete or lost in other 4 Figures.
2. In Figure 1a, FACS profile and representative figures are needed to demonstrate the author isolated the correct population at correct hair follicle stage.

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Referee #1

Evidence, reproducibility and clarity

Summary

In the manuscript, Estrach et al addressed the role of skin epithelial stem cell-derived extracellular matrix in hair follicle regeneration. They found that Lrig1+ epithelial stem cells highly express fibronectin gene compared to other basal epithelial cells. Conditional deletion of fibronectin gene using Lrig1CreERt2-GFP or K19CreER (the latter is expressed in the bulge stem cells) resulted in hair follicle regeneration blockade, change in the expression pattern of Lrig1-GFP. Injection of fibronectin protein into the dermis of the conditional fibronectin mutants (Lrig1CreERt2-GFP) rescued the hair regeneration blockade phenotype. The authors also conditionally deleted SLC3A2, an integrin coreceptor, using the same CreER lines and found a decrease in fibronectin deposition and CD34+ bulge stem cell number. With the results from these mouse genetics and phenotype analysis, they conclude that fibronectin-SLC3A2 cascade finely tunes hair follicle stem cell fate and their tissue regenerative capacity.

Major comments

1 Immunostaining results of fibronectin in the tail epidermal wholemounts are not convincing enough and would require improvements. First, the tail epidermal wholemounts lack the mesenchymal matrix and the basement membrane (Fig. 1b, c, f-h; Fig. 2b, c; Fig. 5d-j; Fig. S1b, c) (Braun et al., 2003 Development (PMID: 12954714)). Fibronectin is localized mainly in the mesenchymal matrix and the basement membrane in the skin and other organs (Stenman and Vaheri, 1978 JEM (PMID: 650151); Couchman et al., 1979 Archives of Dermatological Research (PMID: 393184); Jahoda et al., 1992 J. Anat (PMID: 1294570)), thus this sample preparation method is not appropriate to assess fibronectin tissue distribution. The authors use thick back skin sections, which contain entire skin tissues, thus I would recommend this method. Furthermore, fibronectin antibody signals in the tail epidermal wholemounts are detected in the inner part of the hair follicle epithelium, where there is no expected ECM structure (see Couchman et al. and Jahoda et al. above). Consistently, fibronectin signals are localized inside the Lrig1-GFP+ epithelial basal cells (Fig. 1f-h). Thus the specificity of the fibronectin staining needs to be confirmed. The reviewer understands that the authors provide an image showing the great reduction of fibronectin staining in a D30 tail epidermal wholemount of 4-OHT-treated Lrig1CreERt2GFP,FNfl/fl mice (Fig. 2c). However, as the D65 tail epidermal wholemount from wildtype mice also show many hair follicles without fibronectin signals (Fig. 1c), rigorous assessments would be required.

2 Lrig1+ stem cells have been reported to maintain the upper pilosebaceuos unit, containing the infundibulum and sebaceous gland, but contribute to neither the hair follicle nor the interfollicular epidermis under normal homeostatic condition (Page et al., 2013 Cell Stem Cell (PMID: 23954751)). However, only 11 days after the first 4-OHT treatment on Lrig1CreERt2GFP;FNfl/fl mice, Estrach et al found the defects in hair cycle blockade, reduced cell proliferation in the hair bulb, and significant reduction in fibronectin deposition in entire hair follicle structure. Please explain how the deletion of fibronectin gene in Lrig1+ stem cells, which do not contribute to hair follicle lineages, lead to significant hair regeneration defects in a short period of time. Current data do not well explain a causal relationship between the genetic perturbation and the observed phenotypes.

3 In some experiments (listed below), description about the methods, replication and statistics is not adequate, raising concerns about reproducibility. 3.1 Fig. 1a: data variation for basal cells should be presented. Biological replicate number should also be indicated in the figure legend. 3.2 Fig. 2g, h: hair follicle thinning is described here, but only one HE staining image with only one hair follicle is not enough to support this important claim. 3.3 Fig. 2r, 3i: flow cytometric data should be presented. 3.4 Fig. 4: No biological replicate and reproducibility information are provided. 3.5 Fig. 5j: how many biological replicates and hair follicles were analysed? The authors should also perform statistical tests. 3.6 Fig. S3g, h: information for biological replicates should be described. Statistical tests should be applied to Fig. S3h. 3.7 Fig. 5k-n: only one HE staining panel from each mouse line cannot provide rigorous evidence of the defects, which are not obvious from the HE staining.

4 In Fig. 3j, k, n, hair follicles in the control and 4-OHT treated skin are in different hair cycle phases. Therefore there is a possibility that the difference in their PCNA pattern simply reflects the difference in the cell proliferative activity between different hair cycle phases, but not indicates direct effects from the deletion of fibronectin gene in Lrig1+ cells.

5 To assess the expression levels of signaling-related genes (Fig. 3p, S2), the authors used mRNA extracted from whole skin tissues, which contain all epithelial and mesenchymal cell populations in different hair cycle phases. Thus, the time and spatial resolution of the analysis is low and it also cannot eliminate confounding factors derived from the difference in hair cycle phases between control and cKO.

6 In order to provide the characteristics and purity of the FACS isolated cell populations at D28 (Fig. 1a), their flow cytometry data and some marker gene expression data should be presented (see Page et al., 2013 Cell Stem Cell). This assessment is particularly important for the skin compared to other static organs, as it exhibits dynamic gene expression and tissue structural changes during the hair cycle. It is also important to check whether fibronectin protein accumulates around Lrig1+ stem cells in D28 dorsal skin, where upregulation of fibronectin gene expression was detected. The authors should not use tail epidermal wholemounts for the reason described above.

Minor comments

7 The increase in stem cell marker expressions shown in Fig. 3p contradicts to the reduction in the number of bulge stem cells shown in Fig. 2r and 3i. Please provide an explanation for this apparent discrepancy.

8 Although Fig. 5s-v show reduction of a6+CD34+ bulge cell population, the bulge tissue structure can be observed in Fig. 5p. Please explain how to interpret this apparent discrepancy. They just lost the expression of CD34?

9 Connectivity of the data in the fibronectin cKO with that of SLC3A2 cKO is weak. For example, it could be strengthened if the authors show colocalization of fibronectin and SLC3A2 in vivo.

10 Although the format of the manuscript is free in Review Commons, the Introduction and Discussion of this manuscript are too brief for us to understand the background and significance of this study. So I would recommend the authors to provide more detailed background information and discussion.

11 The authors use the term 'HFSC', but it is unclear which stem cell populations they mention; bulge, Lrig1+ or other stem cell populations?

12 Please provide details of fibronectin protein and antibody used in this study.

13 Due to the short for experimental information for Fig S3a, b, d, e, I cannot evaluate the data, thus several questions are raised. As the SLC3A2 level was significantly reduced in most cells in the plot, I assumed that Lrig1-GFP+ cells were gated before examining the expression level of SLC3A2. However, no information on the procedures for 4OHT treatment, isolation of cells and flow gating strategy is described. In the case of K19CreER mice (Fig. S3d, e), if the authors gated GFP+ cells before analysis, what GFP means in this case?

14 The manuscript wants to be checked for copyediting.

Significance

These findings might provide a conceptual advance into the role of epithelial stem cell-derived extracellular matrix in regulating stem cell behaviour and tissue regeneration. As fibronectin is upregulated in development, wound healing and cancers in many other organs, their findings may point to the importance of fibronectin in activating tissue progenitors and stem cells in these processes. Thus, this manuscript is likely to be of interest to a wide range of readers, not only in skin biology, but also in stem cell, regenerative and matrix biology. The contributions of this paper could be enhanced if the documentation were to be made stronger and more rigorous in a revised manuscript.

Referee Cross-commenting

I totally agree with Reviewer #3's comments in this consultation session. I have no disagreement with any of the points raised by other reviewers.

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Reply to the reviewers

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We are grateful to the reviewers for their honest opinion regarding this work and plan to address the majority of the comments in a revised version either through new analysis or revision to the text, as we believe these will improve the manuscript by making some of the details clearer. There were few suggestions that will lead to substantiative changes to the findings. Here, we address the most salient critiques, the primary one being related to novelty.

We respectfully disagree, as our detailed analysis of the DNA methylome in Octopus bimaculoides represents a significant advance to understanding how the epigenome is patterned in non-model invertebrates in general, and cephalopods in particular. We acknowledge that the previous report that the octopus methylome resembles the few other invertebrates where low DNA methylation has been found, the finding was part of a multi-organism study last year (de Mendoza et al., 2021), which lacked any detailed investigation. Our study provides the first in depth analysis on methylation patterning, the relationship with transposons and gene expression, and reports the finding of other key epigenetic marks in O. bimaculoides, and in other cephalopods.

In short, we believe our study to be highly novel and that it represent the first analysis of this kind in cephalopods and one of the few existing in non-model invertebrate organisms. In addition, we identify the conservation of the histone code in cephalopods. While this may be expected, this is the first experimental evidence in this class and represents an important step forward to understand the epigenetic regulation of genes and transposons in invertebrates. Finally, we plan to provide an updated transcriptome annotation for O. bimaculoides that will be available for the scientific community as a new valuable resource. We believe these features will make this study highly cited.

We believe that findings like ours will complement several recent studies that extend the epigenetics field out of the current narrow focus on model organisms to understand how epigenetic mechanisms function in diverse animals. This provides new insights regarding the epigenetic mechanism of gene regulation in an emerging invertebrate model.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

Reviewer 1 raised the following points that we are planning to address:

*- It is unclear why the authors did not use the original gene models of O. bimaculoides or tried to improve them. By only relying on adult tissue (but the relatively late hatchling stage), they would have omitted most developmentally expressed genes, that are incidentally also the ones that are subjected to extensive spatiotemporal gene regulation (which is also a problem to assess the role of methylation). I think more comparisons with existing gene models and how the newly generated stringtie models should be provided. *

We agree that using as many tissues and developmental stages as possible will expand the octopus transcriptome.

We plan to:

• Add RNA-seq data from stage 15 embryos to improve this.
• Compare the gene model used in the original version of the manuscript (Stringtie model to use in Trinotate for improving the annotation of the genes) to the existing annotation model and report on which has superior performance for annotating the * bimaculoides* transcriptome.
• Extend the annotation of the transcriptome which we undertook in a focused fashion in the first iteration of this manuscript. Reviewer 2 raised the following points that we are planning to address:

*- It is not exactly clear to me why the authors look for expression clusters in the first part of the manuscript? This information, while interesting, does not seem to be used in the methylation analysis. It is also somewhat contradictory because the authors first claim that, based on their GO-term enrichment analysis, that different expression clusters are associated with "complex regulatory mechanisms, potentially based in the epigenome". Yet at the end they conclude that, due to the global and tissue-overarching nature of methylation, this "argues against this epigenetic modification as a player in the dynamic regulation of gene expression". *

We thank the reviewer for pointing out this issue and we plan to clarify the point through changing the text and additional analysis. Since we found that the methylation pattern was stable across tissues, and that it corresponded to gene expression levels regardless of tissues, we concluded that the methylation pattern is not likely relevant for the tissue-specific gene expression pattern reported in Figure 1.

We plan to:

• Ask whether there is a correlation between the gene clusters generated in Figure 1 and the DNA methylation patterns identified in Figure 4. *- At least for the trees that are shown in the main figures it would be great to show support values. *

We thank the reviewer for this request.

We plan to:

• Add full Supplementary information regarding the support values in Supplemental Files for all the trees present in the main Figures. Reviewer 3 raised the following points that we are planning to address:

*- It would be great to see more data on cephalopod TET and MBD structure. For example, it would be interesting to know whether octopus TETs have a CxxC domain or whether MBD proteins harbor functional 5mC - binding domains. *

We agree that it would be of interest to examine the conservation of TET genes to expand upon the initial analysis by Planques et al 2021 showing that O. bimaculoides have one TET homolog, one MBD4 homolog and one MBD1/2/3 homolog. Detailed analysis of MBD4 protein has been already performed in de Mendoza et al. 2021 by using the protein sequence of O. vulgaris, as the MBD4 gene in the O. bimaculoides genome appears truncated.

We plan to:

• add the PFAM domain analysis for TET proteins This will be added as a new figure panel.
• Update the text to include the reference to the identification of MBD4/MECP2 as the invertebrate homologs of vertebrate MBD4. *- Even though RRBS provides limited insight into DNA methylation patterns, the authors could have done more to explore read-level 5mC information. For example, by studying single reads, the authors could deduce the numbers of fully methylated, unmethylated or partially methylated reads. Such analyses might provide valuable insight into potentially different modes of epigenetic inheritance in different tissues i.e are there tissues that favor fully methylated or unmethylated stretches of DNA vs tissues that favor partial methylation? *

We think this is a really interesting point. This has been partially addressed in a previous work (de Mendoza et al., 2021) which found limited to no partially methylated reads in whole-genome bisulfite sequencing from O. bimaculoides brain.

We plan to:

• Use Proportional Discordant Reads on the RRBS data we generated from 30 dpf hatchlings to assess the presence of discordant methylated reads across different tissues to assess possible different modes of epigenetic inheritance. We will use “WSHPackage” in R: https://academic.oup.com/nar/article/48/8/e46/5760751?login=true https://github.com/MPIIComputationalEpigenetics/WSHPackage/blob/master/vignettes/WSH.md#x1-50003.1 This will be added as a new figure panel.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

Reviewer 1 raised the following points that we have already addressed:

We addressed all the comments raised by this Reviewer by revising the text, fixing references, typos and improving clarity.

Reviewer 2 raised the following points that we have already addressed:

We addressed all the minor Comments raised by this Reviewer regarding spelling errors and Supplementary Figures.

- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome.

We have now provided this data and new Supplemental Table 1 (refereed in the text as Table S1).

*- It is very exciting to see methylation of gene bodies and some correlation to their expression levels, but the authors may need to include a disclaimer that the methylation of TEs may go undetected due to the gapness of the genome. In fact, the authors may try to map their data onto a somewhat closely related Octopus sinensis genome sequenced with long reads available at NCBI to confirm overall pattern. It is likely though that due the evolutionary distance only gene bodies will have mapping. *

The thank the reviewer for this suggestion and we included a sentence in the Result session indicating that methylation of TEs may go undetected due to the poor annotation of the octopus genome.

*- The statistical reasoning (and methodology) behind how clusters in Figures 1 and 4 were defined is unclear. In particular, in Figure 4, it seems that the authors had asked the program to give four clusters in total - why was this number chosen? It seems that using the same generic clustering approach as in Figure 1 may benefit or confirm the results in Figure 4. *

We clarified the rationale in the Material and Methods session to describe the bioinformatic analysis. We will put the full code used in the manuscript in our GitHub page (https://github.com/SadlerEdepli-NYUAD/) to have a more comprehensive understanding of the Method used.

Reviewer 3 raised the following points that we have already addressed:

We addressed all the minor comments in the text and figures raised by this reviewer regarding typos and clarity.

*- There is little info on the generated 5mC data. To bolster its value as a resource, the manuscript should have a link to the table describing RRBS metrics. This should include: non-conversion rates, numbers of sequenced and mapped reads, read length and other info that the authors deem useful. *

We have now provided this data in a new Supplemental Table 1 (refereed in the text as Table S1).

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

Reviewer 1 raised the following points that we are not planning to address:

*- The newly sequence RNA-seq samples are using a ribodepletion protocol (RiboZero) while the other ones are using a polyA selection. This might be a slight problem to compare them quantitatively. Actually in the Figure 1, all 4 newly generated samples group together in the hierarchical clustering. *

We acknowledge the reviewer’s point here and agree that heterogeneity in library prep and batch is a common issue when comparing public available with newly generated datasets. This could account for the clustering of the Ribosomal RNA depleted (i.e. RiboZero) from polyA selected RNA libraries. While this could potentially introduce bias, we do not believe that it substantially alters any of the main findings or the interpretations of this data. Our purpose for carrying out the cluster analysis of transcriptomic data from multiple tissues was to identify distinct gene patterns that defined different tissue types. This was accomplished regardless of the potential confounding variable introduced by different library preparations. In addition, we used TMP which seems to help in the comparison across different samples when used for qualitative analysis such as PCA and cluster analysis (Zhao et al. 2020; DOI: http://www.rnajournal.org/cgi/doi/10.1261/rna.074922.120). Therefore, even if not ideal we think that this approach is still valuable.

*- I am not so sure about the way the authors used z-score normalized logTPMs and applied hierarchical clusters, this most likely would not fully alleviate the impact of expression level on the outcome compared to more advanced form of normalization and clustering. *

We agree with the reviewer that applying z-score or a logTPMs normalization would not fully resolve the technical variance in the direct comparison of libraries generataed with different RNA selection methods. We did not apply z-score on logTPMs but these 2 methods were applied separately: z-score on TPMs in Figure 1B to define the gene clusters and log2(TPM+1) in Figure 4E. We have clarified the text to reflect this.

*- I am not convinced that differences in western blot for histone modification could really provide a clear insight into their regulatory role. *

We agree with the reviewer that Western blotting for histone modifications does not provide deep insight into their regulatory role. However, this is the first description of these marks in any cephalopod, and we believe that reporting a finding from experimental evidence is important, even if the result is aligned with the existing paradigm. Moreover, the marked difference in levels of distinct histone marks across tissues supports the hypothesis that they play a regulatory role. We observed this in mice where difference abundance in western blot correspond to different abundance and enrichment also by ChIP-seq (Zhang et al., 2021 DOI: https://doi.org/10.1038/s41467-021-24466-1). Considering the limited tools available in this species, we still consider this an important finding.

Reviewer 2 raised the following points that we are not planning to address:

*- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome. I also wonder how much the gap-richness of the genome may affect the overall methylation estimate. If assembly permits, would it make sense to limit the sampled sites to areas where no flanking gaps are present (and sufficient scaffold length is available, maybe excluding very short scaffolds)? *

We added all the statistical values regarding the RRBS in a NEW Supplemental Table 1. We used a single base pair analysis approach (not tiling windows), so the data we extracted is not biased by the length of the scaffolds. This is confirmed by the fact that the DNA methylation value obtained in our RRBS data matches the findings observed in Whole Genome Bisulfite Sequencing (WGBS). Moreover, global DNA methylation values assessed by Slot blot analysis as a technique independent from genome assembly confirmed what observed with RRBS.

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Macchi et al describes the epigenome and the transcriptome of Octopus bimaculoides. While the manuscript itself is well written and the data are properly analyzed, it is fair to say that the work itself offers little biological novelty. Nevertheless, I still believe that the datasets and some of the analyses could be useful to researchers studying invertebrate epigenomes and gene regulation.

1. It would be great to see more data on cephalopod TET and MBD structure. For example, it would be interesting to know whether octopus TETs have a CxxC domain or whether MBD proteins harbor functional 5mC - binding domains.
2. There is little info on the generated 5mC data. To bolster its value as a resource, the manuscript should have a link to the table describing RRBS metrics. This should include: non-conversion rates, numbers of sequenced and mapped reads, read length and other info that the authors deem useful.
3. Even though RRBS provides limited insight into DNA methylation patterns, the authors could have done more to explore read-level 5mC information. For example, by studying single reads, the authors could deduce the numbers of fully methylated, unmethylated or partially methylated reads. Such analyses might provide valuable insight into potentially different modes of epigenetic inheritance in different tissues i.e are there tissues that favor fully methylated or unmethylated stretches of DNA vs tissues that favor partial methylation?

Minor comments:

There are a few spelling errors throughout the manuscript. Please check for those: Figure 4F ("Trascrips" instead of transcripts), Schmedtea instead of Schmidtea. There are likely other errors as well.

Page 3 - "intergenome"sounds a bit weird.

The authors might consider citing Planques et al, 2021 (BMC Biol) alongside Mendoza et al when discussing unusually high 5mC levels in the sponge.

Significance

The main points of the paper are: i) a somewhat improved transcriptome, ii) DNA methylation data generated by RRBS that follows a canonical invertebrate pattern (low 5mCG levels present in GBs and absent from repeats), and iii) evolutionary analyses of epigenetic machinery components. While lacking biological novelty, the presented data have a resource value and could likely serve as a decent starting point for further exploration of cephalopod gene regulation. I therefore believe that with some revision the manuscript will merit publication in one of the Review Commons - associated journals.

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Referee #2

Evidence, reproducibility and clarity

The paper by Macchi et al studies DNA methylation patterns in Octopus bimaculoides, describing overall conservation of DNA methylation machinery and genome-wide methylation patterns and their effect on gene expression across broad tissue sampling. As such, the paper comrpises a key advancement in the emerging field of cephalopod (epi)genomics and gene regulation. Despite the difficulties relating to the genome assembly of O. bimaculoides, the authors have done a solid analysis of methylation patterns and the results look generally sound. I have a few points that may help the authors improve their manuscript:

• The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome. I also wonder how much the gap-richness of the genome may affect the overall methylation estimate. If assembly permits, would it make sense to limit the sampled sites to areas where no flanking gaps are present (and sufficient scaffold length is available, maybe excluding very short scaffolds)?
• It is not exactly clear to me why the authors look for expression clusters in the first part of the manuscript? This information, while interesting, does not seem to be used in the methylation analysis. It is also somewhat contradictory because the authors first claim that, based on their GO-term enrichment analysis, that different expression clusters are associated with "complex regulatory mechanisms, potentially based in the epigenome". Yet at the end they conclude that, due to the global and tissue-overarching nature of methylation, this "argues against this epigenetic modification as a player in the dynamic regulation of gene expression".
• It is very exciting to see methylation of gene bodies and some correlation to their expression levels, but the authors may need to include a disclaimer that the methylation of TEs may go undetected due to the gapness of the genome. In fact, the authors may try to map their data onto a somewhat closely related Octopus sinensis genome sequenced with long reads available at NCBI to confirm overall pattern. It is likely though that due the evolutionary distance only gene bodies will have mapping.
• At least for the trees that are shown in the main figures it would be great to show support values.
• The statistical reasoning (and methodology) behind how clusters in Figures 1 and 4 were defined is unclear. In particular, in Figure 4, it seems that the authors had asked the program to give four clusters in total - why was this number chosen? It seems that using the same generic clustering approach as in Figure 1 may benefit or confirm the results in Figure 4.
• In the discussion Scmidtea is misspelled.
• Some supplementary figures have to be exported as spell checker highlights are still present (e.g., in Suppl Fig 4).

Significance

This manuscript is an important step towards understanding the workings of gene regulation at the epi-genomic level in octopus and cephalopods in general

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Referee #1

Evidence, reproducibility and clarity

This manuscript focuses on the role of DNA methylation and histone modification in the gene regulation of cephalopods. It complements recently published RNA-seq and MethylSeq datasets with a few extra samples and generally confirms previous findings that DNA methylation does not play an active role in tissue or stage-specific regulation of gene expression in cephalopods (which is the general rule for most non-vertebrates). I don't see any methodological issue serious enough to preclude publication but some details should be strengthened.

• the newly sequence RNA-seq samples are using a ribodepletion protocol (RiboZero) while the other ones are using a polyA selection. This might be a slight problem to compare them quantitatively. Actually in the Figure 1, all 4 newly generated samples group together in the hierarchical clustering.
• It is unclear why the authors did not use the original gene models of O. bimaculoides or tried to improve them. By only relying on adult tissue (but the relatively late hatchling stage), they would have omitted most developmentally expressed genes, that are incidentally also the ones that are subjected to extensive spatiotemporal gene regulation (which is also a problem to assess the role of methylation). I think more comparisons with existing gene models and how the newly generated stringtie models should be provided.
• I am not so sure about the way the authors used z-score normalised logTPMs and applied hierarchical clusters, this most likely would not fully alleviate the impact of expression level on the outcome compared to more advanced form of normalisation and clustering.
• I am not convinced that differences in western blot for histone modification could really provide a clear insight into their regulatory role

Significance

This manuscript reports confirmatory results, partly reanalysing and confirming previous work. I would also like to stress that the methylation results have already been reported and discussed in a previous paper (de Mendoza et al. 2021). I don't have a fundamental problem with this but I also find the paper slightly overambitious and unspecific in its goals. I think it should benefit from being made slightly more concise. I find the part of histone marks is quite overstated. These marks are quite universal in eukaryotes and generally demonstrated to play a regulatory role, the fact that they can be detected in cephalopods by western blot is therefore not really a result.

Comments on the text (difficult without line numbers):

• Intro, first section: it would be good to have a few more references
• "While this has been extremely fruitful in elucidating detailed mechanisms of epigenome patterning, regulation and function, they do not provide a comprehensive understanding of the multiple and varied ways that the epigenome functions." -> sentence is quite confusing and without very clear meaning
• "In contrast, the most common invertebrate model organisms - Caenorhabditis elegans and Drosophila melanogaster - lack DNA methylation entirely. " -> could sound like this is the case for more invertebrates.
• P4 "This is the case in many animals, " -> give examples, it is unclear which examples of TE control by methylation outside vertebrates have been corroborated by data. The paper cited do not deal with methylation in squid
• Evolution has selected for variations in the canonical patterns of methylation -> such explanation could also be consistent with neutralistic explanations
• p19: Schmidtea (type) "such as the planarian Schmedtea mediterranea"

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Referee #2

Evidence, reproducibility and clarity

Summary:

The manuscript entitled "Lhx2 is a progenitor-intrinsic modulator of Sonic Hedgehog signaling during early retinal neurogenesis" by Li et al is a very interesting study in which the importance of Lhx2 is studied in conditional knock-out background to decipher the importance during retinal neurogenesis of developing embryo. The study reveal importance of co-receptors essential to Shh signalling. Data presented are clean and would add depth of knowledge to the literature. The study/manuscript do have some lacunae which are listed below which would be good to address before it is published

Major comments

1. Authors do not seem to have performed a rescue experiment with Lhx2 CKO. If this is absolutely not possible, a conditional overexpression could have given confirmatory clues on the Lhx CKO phenotype discussed. In any case some sort of rescue experiments are essential with respect to Lhx2 as done with Cdon and Gas1
2. Also, overexpression studies with the following genes, Cdon, and Gas1 is interesting in the CKO background. What about their over expression phenotype in Wild-type, Ptch1-CKO, and purmorphamine/Shh-N treated conditions. Alternatively, an ex-vivo or in vitro approach using cultured cells may also prove worthy to prove this point.
3. A logical explanation of Tamoxifen administration at a window E11.5 - E15.5 is good to have in the results section.
4. Authors say ".........that Lhx2-deficient RPCs can respond to recombinant Shh-N at more physiologically relevant concentrations, but their response is still attenuated compared to Lhx2-expressing RPCs." If the Shh-N dose is increased above physiological concentrations in Lhx-CKO conditions does the Gli1 read-out will restore to normalcy ? This would give insight on to the roles of co-receptors such as Cdon, Gas1.

Minor comments

1. In the 'Introduction' the authors write "Pathway activation is not achieved, however, by simple ligand binding to Patched, but also requires one of three co-receptors: Cell Adhesion, Oncogene Regulated (Cdon), Brother of Cdon (Boc), or Growth Arrest Specific 1 (Gas1)." Please give a citation of appropriate literature.
2. In the introduction authors write "In this case, an interaction with Notch signaling is partly responsible, through Lhx2-dependent expression of ligand (Notch1), receptors (Dll1, Dll3), and downstream transcriptional effectors (Hes1, Hes5)" I feel Dll1 and Dll3 are ligand and Notch is receptor. Please check.
3. Figure 1c: Western blots tubulin is less in CKO alongside Gli1. It would be better to do quantification for showing any significant changes.
4. Figure 2A and Result 2: Why harvesting at E14.5 specifically for qPCR/ChIP sequencing, while for RNA sequencing and in situ , it was done E15.5.
5. Figure 5D, E: It is not clear why there were more mCitrine+ cells in CKO explants at 96 hours.
6. Figure 6 C,D,E,F: Does Lhx2 CKO, cause cell death as the levels of vsx2 are low in vehicle in CKO (D,F) as compared to ctrl (C,E).
7. The levels of Gli1 are higher in CKO vehicle (D,F) than the control panel (C,E). This difference in Gli1 expression is more evident D versus C. Does it mean that CKO increases Gli1 expression in explants, which seems to be opposite of what shown in the first results (Figure 1D, E).
8. It is not clear why the increase in Gli1 expression with Shh-N (E,F) is not that much evident than with purmorphamine (C,D) in both control and CKO explants.
9. Figure 7 7-F: The changes in the levels of Cyclin D1 and Hes1 in Ctrl/Lhx2 CKO/ dCKO are not very clear from the data in present form. It would be better to show the changes in their mRNA levels by qPCR analysis. Further, it is not clear how the changes in CyclidD1 and Hes1 levels are proving that Lhx2 acts downstream of Shh signaling.
10. Supplementary table 4: In the page 3 have typo error "centrations at 72 hr"
11. Supplementary Figure 8 is labeled as Supplementary Figure 7, so there are two figures labeled as Supplementary Figure 7.

Significance

Study is significant, and adds more depth to existing knowledge in this science field. Developmental and cell biologists would benefit from this study.

Retina regeneration, Cellular signalling, Epigenetics, One knock outs/knockdowns, transgenics, RNAseq, Microarray, ChIPseq, Cell sorting

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Li et al., entitled: "Lhx2 is a progenitor-intrinsic modulator of Sonic Hedgehog signaling during early retinal neurogenesis," focuses on an important topic in developmental biology-the regulatory interaction between transcription factors (TFs) and signaling pathways, namely how the TF confers cells' competence to respond to extrinsic cues. The study focuses specifically on Lhx2 regulation of Sonic Hedgehog (HH)-pathway genes in retinal development. The authors approach this complex topic through global transcriptomic analyses combined with elegant functional in-vivo studies, including a systematic examination of the pathway genes through the use of inhibitors and ligand on cKO retinal explants. The results reveal complex regulation by Lhx2 of several HH-pathway genes in the developing mouse retina, including Ptch, Gli1 and the co receptors Cdon and Gas1. The finding that a single TF controls several components of the signaling pathway is interesting. Nevertheless, probably due to the complex activity of Lhx2, which functions on additional targets, it remains unclear how regulation of HH-pathway genes by Lhx2 impacts the eventual phenotype of the Lhx2 cKO retinal progenitor cells (RPCs). In the following, I list the main findings and several comments that need to be addressed:

Fig. 1 presents the experimental system using inducible Hes1-CreERT to mutate Lhx2 on E11.5 and examine the expression of Gli1 and Sonic Hedgehog in the control and mutant.

• The authors should present the distribution of the Lhx2 protein in the control vs. mutant. Considering that the deletion is of only part of the gene (as shown in Fig. 2), it is important to present the loss of the protein as well as the efficiency of Cre activity. • On the figure, add a characterization of the cellular phenotype of the cKO retinas on E15.5 by presenting the expression of markers for ganglion cells and RPCs. Figs. 2 & 3 present the bulk RNA-seq analysis of the Lhx2 cKO retinas, including experimental design, validation and results. The integration of previously published ATAC-seq and ChIP-seq data for Lhx2 point to the direct targets and bound regulatory regions.<br /> • "4 biological repeats per genotype" - Specify if four eyes were sampled from two embryos or from four different embryos. Were the embryos from different litters? • Add GSEA analysis for the HH-pathway genes. Fig. 4 presents a published approach to quantifying the response to HH using a cellular reporter assay (Li et al., 2018), whereas in Fig. 5, availability of HH ligand is evaluated by elegantly implementing the cellular reporter assay. The results suggest that Lhx2 does not regulate ligand availability. • Fig. 4 presents a published approach and thus can be included in Fig. 5.<br /> Fig. 6 presents evidence that in the Lhx2 cKO, the Shh pathway is functional downstream of Smo, because the expression of Gli1 increases in cKO cells following Smo activation (with purmorphamine). Furthermore, the response to Shh-N is shown to be partly attenuated in the Lhx2 cKO retina. <br /> Figure 7 examines whether Ptch deletion can rescue aspects of the Lhx2 phenotype. This was done by comparing the phenotypes of cKOs of Lhx2, Ptch, or both Ptch and Lhx2. The results revealed partial rescue, in the Ptch and Lhx2 cKO, of the expression of Ptch1 and Gli1, but not of the proliferation and premature differentiation phenotypes based on expression of Cyclin D1, EDU, PCNA and Hes1. • Add images of the control to Fig. 7B,C. • Explain how the deletion of Ptch1 was examined. They next investigated regulation of the Ptch co - receptors Cdon and Gas1 by Lhx2 (Figs. 8, 9). Fig. 8 presents the developmental expression pattern of Cdon and Gas1 in the control, and their downregulation in the Lhx2 cKO (although Cdon is maintained in the dorsal optic cup). The results show that Cdon is the co-receptor that is normally expressed in RPCs. GAS1 seems to play a role in the peripheral progenitors destined to ciliary body and iris.<br /> Electroporation of both receptors into the Lhx2 cKO retinas resulted in increased pathway activity (based on Gli1 reporter). • Both Cdon and Gas1 were electroporated into the Lhx2 cKO retina, although Gas1 is not expressed in control RPCs (based on the analysis in previous panels). Explain why both were co-electroporated and the outcome of electroporating only Cdon. • The outcome of electroporation of the co-receptors into control retina should be presented. • It is important to include staining for Lhx2; it is possible that the cells that respond to the co-receptors are those that were not mutated (escapers). Presenting the loss of Lhx2 (or Cre activity through the use of a reporter) and comparing it to the outcome of electroporation into the control retina are therefore required.

Finally, the authors present evidence that Lhx2 cKO, on E13.5 when Cdon is no longer expressed in the RPCs, continues to compromise the HH - pathway genes. This further supports continued regulation of several HH-pathway genes in early and late RPCs.

• The finding that a Lhx2 controls several components of HH pathway could be relevant to Lhx2 activity in patterning of the cortex - I suggest to discuss the possible relevance of the findings to other organs.

Additional comments:

• Fig. 4E: Add explanation of the quantitative analysis. • Fig. 5: Explain how results were normalized based on retinal size (which is significantly smaller in cKO retinas). How many independent experiments were run here? How many different retinas were tested? Were retinas taken from the same mouse considered 'independent'?<br /> • Fig. 8B: Indicate the genotype of the presented tissue. • Fig. 8 A,B should be presented in one panel, in the same orientation. • Fig. 8D: Present the different channels, in addition to the merge image.

Significance

The study focuses on an important topic in developmental biology-the regulatory interaction between transcription factors (TFs) and signaling pathways, namely how the TF confers cells' competence to respond to extrinsic cues. The study focuses specifically on Lhx2 regulation of Sonic Hedgehog (HH)-pathway genes in retinal development. The results reveal complex regulation by Lhx2 of several HH-pathway genes in the developing mouse retina, including Ptch, Gli1 and the co receptors Cdon and Gas1. The finding that a single TF controls several components of the signaling pathway is interesting. Nevertheless, probably due to the complex activity of Lhx2, which functions on additional targets, it remains unclear how regulation of HH-pathway genes by Lhx2 impacts the eventual phenotype of the Lhx2 cKO retinal progenitor cells (RPCs).

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Reply to the reviewers

We plan to address the minor comments from both reviewers as here described:

Reviewer 1 – comments____:

1. Figure 1a + 1b: The pattern of H3K9me3 at SVA elements appears to be quite different between the two cell types tested - in iPSCs it appears to be more strongly centered on the SVA with some "spreading" occurring upstream of the element, however in NCCITs it appears to mark downstream of the element and is not clearly seen to occur on the SVA. Is this due to sequencing data from different labs or due to cell - type specific epigenetic marks? The authors should consider showing H3K9me3 marks at a comparable region for example repressed genes or another family of repressed TEs as an internal control as well as showing profile plots to more clearly show the spread of epigenetic marks over the SVAs. As recommended by the Reviewer, in Figure 1 we will show several examples of H3K9me3 marked SVAs in both cell types. We will propose explanations for discrepancies if needed, but acknowledge the possibility of a sequencing artifact, as suggested by the Reviewer.

Figure 1: Given that the data in this paper is mostly from NCCIT cells it would be advisable for conclusions in hiPSCs to be tempered accordingly.

We will temper the conclusions as recommended.

Figure 1c: Which SVAs have neither H3K9me3 nor H3K27ac, what might these represent? Do they have any other notable features, or are they of a specific age?

The (very few) SVAs that have neither of those markers are almost certainly an artifact of mappability issues. We will point this out in the manuscript, including both the text and the figure legend.

Line 128-129: Is the assumption that SVAs are enriched in these region as opposed to dictating the epigenetic landscape as a consequence of their own sequence, is this correct?

This is correct, and we will clarify this in the text.

Figure 2c: Are there any enriched motifs in the repressed SVAs?

We performed this analysis, and several motifs are enriched in the repressed SVAs. We will add this data in the revised manuscript and in Fig. 2C.

Figure 2c: How many of the de-repressed SVAs contain this motif? It would be interesting to know if the YY1/OCT4 binding sites exist within any of the repressed SVAs and then, later, whether they accordingly lack actual binding of the TFs due to the presence of H3K9me3.

We will perform this straightforward analysis using the MEME-suit or HOMER, and include it in the revised version.

Figure 3d: It would be interesting to assess how close the differentially expressed genes are to an LTR5H element, or however many of the SVA-proximal and differentially expressed genes are also LTR5H-proximal?

We will perform this straightforward analysis and include it in the revised version.

Figure 3e: Are genes close only to de-repressed SVAs considered here? Worth specifying in the text. Also, what are the 20% of genes which are upregulated?

Yes, the reviewer is correct, only genes close to de-repressed SVAs are considered. We will specify that in the manuscript, and also add a results paragraph on the 20% of genes that are upregulated.

Figure 4a: Are the binding motifs for YY1 present at both binding sites? Are they the same, is the surrounding sequence the same? Are either of the binding sites present in repressed SVAs/is there any detectable binding of SVA/OCT4 in repressed SVAs? Are the YY1/OCT4 bound SVAs also those marked with H3K27ac in the Wysocka Lab dataset?

The YY1-OCT4 motif is present only where both of the factors bind together. We will specify this in the manuscript (results section). As for the second question: yes these correspond to the regions marked by H3K27ac in the Wysocka dataset. We will clarify this in the manuscript.

Figure 4c: Example used is a SVA-D element, are the YY1/OCT4-bound SVAs within the de-repressed group of a specific age?

No, they are found in all SVA groups (SVA through F). We will specify this in the manuscript (results section).

Figure 4d: are there GO enrichment terms for the genes bound by either YY1 and / or OCT4 different?

We will perform this straightforward analysis using the Ingenuity Pathway Analysis toolkit, and include it in the revised version in the results section.

Figure 5: Concluding figure should address how the SVAs subset in terms of binding, H3K9me3, gene expression changes and TFBS/TF binding - there are a lot of parameters which are assessed within the de-repressed subclass and it would be useful to show somewhere graphically where and when they co-occur or not.

We will edit the model figure to show the mechanism highlighted by the reviewer, as requested.

Finally, it would be helpful to see a discussion about what dictates the absence of H3K9me3 / presence of H3K27ac? Is this due to the TFBS sequence within the element? Further, a discussion on how the TFBS is gained in newer elements / lost in older elements is lacking. While the authors begin by stating that they are going to address what dictates whether an element is co-opted and conclude that it is due to sequence and location, I would suggest that as no conclusion is drawn on how the sequence changes to permit co-option and how the location dictates co-option, it may be worth tempering down the introduction on this point.

We will edit the introduction and discussion, as requested.

Reviewer 2 – comments____:

1) Given the CRISPR sgRNAs also target LTR5Hs, which are also bound by OCT4 in NCCIT cells (PMID: 25896322), it would be helpful to rule out more specifically that the observed effects on gene regulation associated with SVAs are actually due to nearby LTR5Hs copies being similarly repressed by CRISPRi. Depending on those results, it may also be fair to further note in the Discussion that this aspect of sgRNA selection is a potential caveat.

We will edit the discussion as recommended by the Reviewer.

2) The approaches taken here provide surprisingly good locus-specific resolution of histone modifications and TF binding to SVAs using only uniquely mapping reads. An example of this, SVA_D_r153 (a heavily 5' truncated SVA) is provided. It could be really useful to convey the central theme of the study by providing in a main figure another SVA example. Except, show a longer SVA (to demonstrate mappability and perhaps enrichment of reads on specific SVA features) near a protein-coding gene, where the SVA becomes repressed in the CRISPRi approach and the gene is differentially expressed as a result. An IGV-style figure perhaps demonstrating each key component of the work in one figure.

As recommended, we will update figure 4 by adding a full length SVA.

3) Literature. Line 58 - would suggest adding PMID: 27197217 and PMID: 33186547. Line 60 - would add PMID: 33722937. Line 67 - would add PMID: 22053090.

We will add these citations in the manuscript as recommended by the Reviewer.

4) Clarifications: Line 108 - the same 751 SVAs came up in both iPSCs and NCCITs? Line 117 - how many (if any) of the SVAs called as repressed by H3K9me3 were also called as de-repressed by H3K27ac? i.e. are the two histone marks giving completely concordant results for calling SVAs as repressed / de-repressed. Line 215 - no evidence for OCT4 or YY1 binding to any SVAs after CRISPRi at all? We will provide the exact numbers in the manuscript to answer these questions.

5) Finally, a point perhaps best left to the Discussion. Was there any cellular phenotype identified subsequent to the CRISPRi?

We did not identify any obvious cellular phenotype and we will mention this in the discussion, as recommended.

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Referee #2

Evidence, reproducibility and clarity

Barnada et al. explore the regulatory impact of SVA retrotransposons on gene regulation, focusing on NCCIT cells as a workhorse model of pluripotency. They use published and new H3K9me3 and H3K27ac ChIP-seq datasets to identify repressed and de-repressed SVAs, noting that many are in close proximity to protein coding genes. De-repressed SVAs are enriched for YY1/OCT4 binding sites. The authors then use RNA-seq and ChIP-seq to demonstrate YY1 and OCT4 binding are abrogated by SVA CRISPRi, disrupting gene regulation enacted by the SVAs. These findings highlight an important mechanism by which SVA retrotransposons can regulate genes in pluripotent cells.

This work is well executed. I appreciated the consideration paid to ChIP-seq read mappability and the implementation of CRISPRi followed by additional ChIP-seq. The following comments are intended to clarify the findings, which appear to have been obtained from robust experimental approaches.

Minor issues:

1. Given the CRISPR sgRNAs also target LTR5Hs, which are also bound by OCT4 in NCCIT cells (PMID: 25896322), it would be helpful to rule out more specifically that the observed effects on gene regulation associated with SVAs are actually due to nearby LTR5Hs copies being similarly repressed by CRISPRi. Depending on those results, it may also be fair to further note in the Discussion that this aspect of sgRNA selection is a potential caveat.
2. The approaches taken here provide surprisingly good locus-specific resolution of histone modifications and TF binding to SVAs using only uniquely mapping reads. An example of this, SVA_D_r153 (a heavily 5' truncated SVA) is provided. It could be really useful to convey the central theme of the study by providing in a main figure another SVA example. Except, show a longer SVA (to demonstrate mappability and perhaps enrichment of reads on specific SVA features) near a protein-coding gene, where the SVA becomes repressed in the CRISPRi approach and the gene is differentially expressed as a result. An IGV-style figure perhaps demonstrating each key component of the work in one figure.
3. Literature. Line 58 - would suggest adding PMID: 27197217 and PMID: 33186547. Line 60 - would add PMID: 33722937. Line 67 - would add PMID: 22053090.
4. Clarifications: Line 108 - the same 751 SVAs came up in both iPSCs and NCCITs? Line 117 - how many (if any) of the SVAs called as repressed by H3K9me3 were also called as de-repressed by H3K27ac? i.e. are the two histone marks giving completely concordant results for calling SVAs as repressed / de-repressed. Line 215 - no evidence for OCT4 or YY1 binding to any SVAs after CRISPRi at all?
5. Finally, a point perhaps best left to the Discussion. Was there any cellular phenotype identified subsequent to the CRISPRi?

Significance

This interesting study systematically demonstrates the importance of SVA-mediated gene regulation, mediated by YY1 and OCT4, in a cellular model of pluripotency. It would be of broad interest, particularly to those interested in gene regulation, pluripotency and retrotransposons. I would say most of the findings are new to the literature and that the closest publications I can think of in terms of scope either deal differently with SVA regulation (e.g. PMID: 33722937) or upon different retrotransposons (e.g. PMID: 30070637). The significant advance is therefore both technical and conceptual.

Geoff Faulkner (University of Queensland)

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Referee #1

Evidence, reproducibility and clarity

Genomic features underlie the co-option of SVA transposons as cis-regulatory elements in human pluripotent stem cells Barnada et al.

Barnada et al., set out to address the question of which factors dictate when and how certain TEs become co-opted to regulate host cellular functions while others do not, citing a lack of global understanding of the mechanisms underpinning this widely occurring phenomenon. To do so they use human SVA elements as a study, interrogating the epigenetic regulation of these elements in NCCITs as a proxy for pluripotent cells to reveal that a subset of younger, human-specific SVAs lack canonical H3K9me3 repressive marks while being enriched for H3K27ac active enhancer marks. The authors show that these SVAs, termed de-repressed SVAs, contain adjacent YY1 and OCT4 binding motifs, are closer to genes and TFBSs than the repressed SVAs and as such propose that they may function as active enhancers. To demonstrate this they generate a CRISPRi NCCIT cell line to epigenetically silence de-repressed SVAs using two gRNAs targeting SVAs genome-wide. Upon activation of dCas9 in this system differential expression of >3000 genes occurs, notably the broad repression of de-repressed SVA-proximal genes which are enriched for GO terms and TFBSs related to gametogenesis. The authors then demonstrate that silencing of naturally de-repressed SVAs disrupts YY1/OCT4 binding which is seen in wildtype NCCITs to occur adjacently at one site in the SVA element with solo-YY1 binding also occurring in a subset of SVAs at a second site. Disruption of YY1/OCT4 binding by targeting H3K9me3 to derepressed SVAs leads to dysregulation of proximal genes providing further evidence that SVAs act as enhancers via YY1/OCT4 binding to regulate nearby cellular genes.

Overall, the manuscript is clearly written and the computational and experimental approaches thorough; the findings are compelling and novel and make a helpful contribution to our current understanding of transposon co-option by host genomes. The demonstration of TFBS located within a subset of newer SVA elements is particularly interesting, with binding disrupted upon epigenetic silencing of these elements by CRISPRi. I have some minor comments and queries about further discussion points:

Minor comments:

1. Figure 1a + 1b: The pattern of H3K9me3 at SVA elements appears to be quite different between the two cell types tested - in iPSCs it appears to be more strongly centred on the SVA with some "spreading" occurring upstream of the element, however in NCCITs it appears to mark downstream of the element and is not clearly seen to occur on the SVA. Is this due to sequencing data from different labs or due to cell - type specific epigenetic marks? The authors should consider showing H3K9me3 marks at a comparable region for example repressed genes or another family of repressed TEs as an internal control as well as showing profile plots to more clearly show the spread of epigenetic marks over the SVAs.
2. Figure 1: Given that the data in this paper is mostly from NCCIT cells it would be advisable for conclusions in hiPSCs to be tempered accordingly.
3. Figure 1c: Which SVAs have neither H3K9me3 nor H3K27ac, what might these represent? Do they have any other notable features, or are they of a specific age?
4. Line 128-129: Is the assumption that SVAs are enriched in these region as opposed to dictating the epigenetic landscape as a consequence of their own sequence, is this correct?
5. Figure 2c: Are there any enriched motifs in the repressed SVAs?
6. Figure 2c: How many of the de-repressed SVAs contain this motif? It would be interesting to know if the YY1/OCT4 binding sites exist within any of the repressed SVAs and then, later, whether they accordingly lack actual binding of the TFs due to the presence of H3K9me3.
7. Figure 3d: It would be interesting to assess how close the differentially expressed genes are to an LTR5H element, or however many of the SVA-proximal and differentially expressed genes are also LTR5H-proximal?
8. Figure 3e: Are genes close only to de-repressed SVAs considered here? Worth specifying in the text. Also, what are the 20% of genes which are upregulated?
9. Figure 4a: Are the binding motifs for YY1 present at both binding sites? Are they the same, is the surrounding sequence the same? Are either of the binding sites present in repressed SVAs/is there any detectable binding of SVA/OCT4 in repressed SVAs? Are the YY1/OCT4 bound SVAs also those marked with H3K27ac in the Wysocka Lab dataset?
10. Figure 4c: Example used is a SVA-D element, are the YY1/OCT4-bound SVAs within the de-repressed group of a specific age?
11. Figure 4d: are the GO enrichment terms for the genes bound by either YY1 and / or OCT4 different?
12. Figure 5: Concluding figure should address how the SVAs subset in terms of binding, H3K9me3, gene expression changes and TFBS/TF binding - there are a lot of parameters which are assessed within the de-repressed subclass and it would be useful to show somewhere graphically where and when they co-occur or not.
13. Finally, it would be helpful to see a discussion about what dictates the absence of H3K9me3 / presence of H3K27ac? Is this due to the TFBS sequence within the element? Further, a discussion on how the TFBS is gained in newer elements / lost in older elements is lacking. While the authors begin by stating that they are going to address what dictates whether an element is co-opted and conclude that it is due to sequence and location, I would suggest that as no conclusion is drawn on how the sequence changes to permit co-option and how the location dictates co-option, it may be worth tempering down the introduction on this point.

Significance

This work represents a novel, comprehensive and significant contribution to the understanding of co-option of transposons into host gene networks and factors underlying these processes.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

The article by Skokan and coworkers studies the regulation of macropinocytosis in the Hydra. They design a clever assay to image the formation of macropinosomes in the ectodermal cells of the Hydra body, by amputating the head and the foot of the animal and then helving it onto a thin glass rod, allowing them to study the dynamics of actin rings formation, associated with uptake of external fluid phase. They also observe the cyclic formation of macropinosomes during the oscillatory contractions of spheroids formed from amputated animals during regeneration. By using agonist and antagonist drugs targeting mechano-sensitive calcium channels, they show that the formation of macropinosomes correlates with the reduction of cell tension. Overall, the article is succint, but clear and convincing. However, in my opinion, two major points should be clarified, if not solved before considering publication.

Major points:

1. the function of macropinocytosis in the Hydra is not known. The author postulates that it could be linked to a regulation of membrane area during animal contractions. However, one may wonder if the membrane cell surface really changes during contractions. I wonder if another explanation is possible: most of the organisms leaving in fresh water require an efficient mechanism to remove excess water that comes in the cells through osmosis. The hydra regular contractile movement are part of this, and I am wondering the macropinocytosis could be linked to this mechanism. Would the author be able to apply osmotic shocks, in particular hypertonic shocks, and see how it changes the formation rate and the dynamics of macropinosomes? On the reverse, in paralyzed animal, I am wondering if macropinosomes are still formed? Results from these experiments may give a clue about the function of macropinocytosis in the Hydra.
2. Because of the role of Piezo and other mechano-sensitive calcium channels, the author conclude that the factor that limits macropinocytosis is membrane tension. However, unless I am mistaken, actin cytoskeleton has also been involved in mechano-sensing channels, it could be that cortical tension, rather than membrane tension is playing a regulatory role. A direct proof of membrane tension (by measuring it) changes would be required to conclude as the authors do. The role of membrane tension versus macropinocytosis could be directly assessed using membrane tension probes such as FliptR or flipper probes. Otherwise, a less clearly defined term, that combines both cortical tension and membrane tension, such as cell surface tension or cell tension would be preferable.
3. Number of macropinocytic cups(actin rings) per cell is used as a readout for rate of macropinocytosis. Yet in addition to the number of cups parameters the diameter increases in certain conditions such as GdCi3. It would ideally be interesting to show the changes in diameter of cups and how this varies per in different conditions. For example, in videos of Jedi1 treated body columns the cups seem bigger in size. Supporting experiments of monitoring macropinosomes via dextran uptake assays needs to be performed for quantifications a rate of change in macropinocytosis is proposed. Alternatively, dextran beads of different molecular sizes with different fluorophores could also be used to assess the differences in rate and volume of uptake via macropinocytosis in various conditions of this study.
4. If membrane tension is altered upon dissecting Hydra fragments, would it make sense to study potential changes in macropinocytosis within the regenerating body column? Such as differences in actin ring formation in cells close to wound edges versus equatorial regions of regenerating body columns and spheroids?
5. Reasoning for selection of Piezo as molecular target over other stretch activated channels has not been provided. Piezo activators have been used, on the contrary depletion of Piezo via RNAi could be performed in intact animals to assess increased macropinocytosis. Furthermore, rate of macropinocytosis could be assessed in body columns generated from Piezo depleted animals. This would further support the direct role of Piezo in the process.

Minor points:

1. The authors report differences in macropinocytosis based on different parts of the animal (Fig.S1), upon treating intact animals with GdCI3 (Fig.2C) how does this vary? Do the differences still persist in spite of increased macropinocytosis?
2. Hydra are animals with an elongated body column. Dissecting body columns of different lengths could give rise to spheroids of different volume, these could then be inflated to establish a comparative volume study with different volumes and macropinocytosis.
3. For all graphical representation it would also be ideal to state the p-values for each significant comparison to better appreciate differences instead of stars.
4. For better understanding of figures, highlight in graph legends and figure panels which tissue sample has been used i.e intact animal, body column or spheroid.
5. A graphical representation could be given for the comparison of macropinocytic cups between intact hydra versus body column samples with statistical analysis. To appreciate the claims made by the authors regarding the trend of more cups being observed in body columns versus intact animals, as only the mean values are stated in the text.
6. In Fig.1F, there exist streaks of dextran distinctly outlining apical membranes of cell sets in the hydra epithelia, what are these suggestive of?
7. Fig.S1 No p-values

Significance

Overall, the work is of interest for several research communities. The significance could be increase by providing a few more experiments about the physiological role of macropinocytosis in the Hydra.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, Skokan et al. develop a platform of cnidarian Hydra vulgaris, a powerful model for cellular self-assembly and organismal regeneration, to enable visualization of macropinocytosis in living tissue. Utilizing this system and small molecule perturbation, authors discover that macropinocytosis occurs constitutively at the ectoderm across the entire body axis of Hydra, and is constrained by membrane tension through stretch-activated channels and the downstream calcium influx.

Major Comments:

The manuscript is clearly written and logically organized, and the imaging results are properly quantified. With the logical interpretation, adequate biological repeats and statistical analysis, the method and data in this manuscript are clear and compelling. The major concern is the missing physiological significance of macropinocytosis induced by membrane relaxation in Hydra, if any.

Suggested experiments:

1. In Fig 2, the importance of SAC and Ca2+ for macropinocytosis are addressed. However, only one SAC inhibitor was used, whereas Ca2+ concentration in Ionomycin treated Hydro remained high even after 60 min when macropinocytic cup density had recovered (Fig 2E and G). As the authors mentioned in the DISCUSSION, other SAC transported cations may be involved in and thus need to be tested. Simply, the medium depleted of specific cation or water containing specific cation could be used to monitor the requirement of each cation on Jedi2 treated Hydra.
2. In Fig 3, the authors demonstrate that increased membrane tension leads to higher Ca2+ concentration and less macropinocytic cups in Hydras. The SAC inhibitors and EDTA (or calcium free buffer) used in Fig2 should be applied in the inflated regenerative spheroids to confirm that membrane tension inhibits macropinocytosis via SAC and Ca2+.
3. The authors observed an increase of macropinocytic cups in both amputated Hydra and regenerative spheroids than intact animal (0.186 and ~0.3 compared to 0.015 cups per cell, Fig S1, 2E, 3C). Would the inflation or inhibition of macropinocytosis perturb spheroid regeneration or polarization/sorting? Authors have discussed several potential biological functions of macropinocytosis in Hydra, including tension homeostasis and surface remodeling that are important during spheroid regeneration. It will be worthy to examine if mild membrane tension increase or SAC activation would delay the sorting process of regenerating Hydra tissues.

Minor points:

1. Fig 2C is the quantification results of 2B but include three sets of data (labeled as 1, 2, and 3) without explanation.
2. Would amputation of one tentacle lead to local or global Ca2+ reduction and macropinocytosis in a Hydra?

Significance

Macropinocytosis is an evolutionary conserved, from amoeba to human, and versatile endocytic route critical for mammalian immune and cancer cells for antigen surveillance and nutrient uptake. Despite ample understanding of macropinocytosis in cultured cells has been made, the function and mechanism of macropinocytosis at the organ or organismal level remains poorly studied. Therefore, this work is intriguing and timely to support the physiological occurence of macropinocytosis from the tissue and evolutionary aspects.

Macropinocytosis is critical process for membrane trafficking, cell signaling, immune surveliance and cancer cell growth, and Hydra vulgaris is a powerful model organism for regeneration biology, neuro biology and marine biology. Therefore, audiences from these fields will be interested and influenced by this report studying developing a new method for visualizing macropinocytosis in living Hydra.

I am a cell biologist studying the regulation of membrane remodeling and trafficking upon mechanical or biochemical stimuli. Due to my unfamiliar with Hydra as a model organism, the details of suggested experiments may need to be adjusted.

Referees cross-commenting

I agree with other reviewers and think their comments important and valid. This manuscript will be more clear and compelling after addressing these questions.

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Referee #1

Evidence, reproducibility and clarity

In the manuscript by Skokan et al, the authors demonstrate a constitutive and robust program of macropinocytosis in the outer epithelial layer of the cnidarian Hydra vulgaris. While the model system is less tractable than others including mammalian cell types from a genetic stand-point, the authors have devised a neat approach to visualizing the planar epithelium in live organisms and provide clear evidence for macropinocytosis by a tissue monolayer in vivo. This model also supports the ancient conservation of macropinocytosis, supporting the studies in Dictyostelium, and may represent early modes of nutrient acquisition in complex fluid environments. Using probes for the cytoskeleton, fluid phase indicators, and mechanical and pharmacological interventions, the authors describe how stretch-activated calcium channels inhibit micropinocytosis. In general, while the manuscript is concisely written, and the available data are compelling, much more rigorous experimentation is required to make such a conclusion. In addition, the physiological importance for mechanical stretch in orchestrating the arrest of macropinocytosis remains unclear. Conceivably, this may be involved in the regulation of membrane tension since macropinocytosis (high membrane turnover) would demand that cells have a high rate of membrane recycling to compensate. Below, I have outlined some approaches that the authors could take to improve the study without demanding them to utilize additional model systems, which I think would be outside the scope of the work.

Major comments:

Most importantly, the role of Ca2+ entry via stretch-activated channels and how this would inhibit macropinocytosis remains unclear. In fact, the findings are somewhat counterintuitive since stretch applied to the monolayer would increase membrane tension while Ca2+ influx would support membrane delivery and exocytosis, thereby restoring tensional homeostasis.

In Fig 3, the authors demonstrate that applied stretch to the epithelium increases cytosolic Ca2+ and decreases membrane tension as expected. But whether the Ca2+ influx is required for the loss of macropinocytosis is not clear. This can be tested by either chelating Ca2+ transients in the cytosol or depleting the cells of Ca2+ by inhibiting ER-resident Ca2+ pumps and removing Ca2+ from the medium. In fact, if the authors think that extracellular Ca2+ is the only issue to arresting macropinocytosis, substituting Ca2+ for another divalent cation (or removing all divalent cations from the medium, should the epithelium be amenable to it for short periods of time) could be employed.

The connection between [Ca2+]cyto and macropinocytosis is established by Jedi and ionomycin. In the case of ionomycin, the large and sustained increase [Ca2+]cyto, well beyond what could be expected in physiological conditions, leads to the loss of plasma membrane PIP2, PIP3, and membrane associated F-actin. Jedi1/2 are certainly more targeted, but it is difficult to attribute their effects to Piezo in this system. More worryingly, the Ca2+ influx in response to Jedi2 and especially Jedi1 occurs maximally after 10 min of exposure. Yet, the authors show the complete loss of macropinocytic cups after 10 min (Fig 2E). It's difficult to reconcile that the Ca2+ is the issue.

The authors do not quantify macropinocytosis beyond Figure 1. Instead, they use "macropinocytic cups" as their surrogate for bona fide, sealed macropinosomes. Macropinocytosis can occur at different scales and different rates, so the authors should instead use the 70 kDa dextran as the gold standard in Figure 2. And as part of gold standard approaches, the authors would appease the macropinocytosis field if they tested the requirement for PI3K and Na H+ exchangers in Figure 1.

The appearance of the GCAMP6s in Figure 2F before given Jedi2 is interesting. Aside from the Ca2+ signal that appears where the Hydra has been severed, the Ca2+ through the epithelium appears very heterogeneous. Does this Ca2+ signal oscillate in the cells and/or across the epithelium? Since the authors are able to image the cytoskeleton and Ca2+ in this system, it would be interesting to determine any correlations in their kinetics.

Minor comments:

At this point, minor comments may be less useful to the authors since some of the more major suggestions are likely to impact the overall breadth of the work.

Significance

The work represents a technical advance and new system to consider macropinocytosis, albeit with limited mechanistic insights owed to some intrinsic challenges and remaining experimentation.

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Reply to the reviewers

We would like to thank all the reviewers for their time and for their positive and constructive review of our study. We are happy that they all regard this as a highly significant piece of work. We have addressed some of their suggestions in our updated preprint and indicate below where we are planning further revisions.

Reply to Reviewer 1 Point 1

The reviewer pointed out a possibility that the Golgi polarisation leads to local/centre-most regional E-cadherin junction “maturation”, then contribute to AMIS seeding. To address this suggestion, we did fluorescence recovery after photobleaching (FRAP) using a mESC line that expresses E-cadherin-GFP in the updated manuscript. We compared the recovery speed and rate in the centre-most region and side regions to discuss whether E-cadherin junctions have different stability at these regions. What we found is that though the E-cadherin and E-cadherin-GFP protein level is at the same level at the two regions in mESC doublets (Figure S3), the mobile fraction of E-cadherin-GFP is lower in the centre-most region than the side regions (Figure 3 I, J). This implies that E-cadherin junctions in the centre-most region are more stable. We have included corresponding description of this data in Results, Methods and Discussion. We will also include equivalent data from non-mitomycin c treated control cells in the final manuscript.

Still, we do not know whether the more stable E-cadherin junctions were due to the Golgi polarization, but we have included the possibility of Golgi polarisation leads to local E-cadherin maturation in our Discussion in the transferred manuscript as follows:

“In addition, a recent study of chick neural tube polarisation (where N-Cadherin is the dominant Cadherin) has demonstrated that the interaction of β-catenin with pro-N-cadherin in the Golgi apparatus is necessary for the maturation of N-Cadherin, which is in turn important for apicobasal polarity establishment (Herrera et al, 2021). This provides the possibility that the polarised Golgi apparatus that we observe in the mESC clusters might be directionally delivering mature E-cadherin to the central-most region of cell-cell contact.”

Reply to Reviewer 1 Point 2

The reviewer suggests it would be interesting to know whether there is a role for the proteins JAM-1 or Nectin in AMIS formation and in polarising the Golgi and centrosomes towards the cell-cell contact. Like E-Cadherin, these are transmembrane junctional proteins that are present at the initiation of spot adhesions in epithelial 2D monolayers and are known to be part of a complex network of interactions between PAR-complex, junctional molecules, MAGUK scaffolding proteins and the actin cytoskeleton. Whilst we don’t propose to untangle this network here, we agree that it would be interesting to know more about the potential role of JAM-1 and Nectin in initiating polarity in mESC 3D cultures. However, it is important to note that, regardless of whether JAM-1 and Nectin also play a role in polarisation and AMIS formation, our results already demonstrate that E-cadherin-based adhesions are sufficient to initiate AMIS localisation. For example, our results from figure 4C-E demonstrate that, in a reductionist system of a single cell plated on E-Cadherin covered glass, a centrally located AMIS still forms. Precisely unravelling the mechanisms by which this happens would be better for a future study (which we have now stated in the Discussion).

Nevertheless, we now have new FRAP data (Figure 3I and J), which demonstrates that E-Cadherin is relatively more stable at the central-most point of contact between two adhering cells. This suggests that E-Cadherin is more stably bound via its downstream partners to the internal actin cytoskeleton at this point and may provide at least a partial explanation for why AMIS localisation occurs precisely at this region. We therefore suggest that the most relevant information to our study would be to determine whether either JAM or Nectin proteins are specifically localised at the AMIS, alongside PAR-3 and ZO-1, and might therefore be somehow enabling this stabilisation of E-Cadherin. We therefore plan to carry out IHC stains for JAM-A (new name for JAM-1), which has been found to be present in the mouse inner cell mass, to determine where it is localised within the mESC cell clusters with/without cell division and in WT/Cdh1 KO cells. We will update the supplementary results and discussion accordingly in the final manuscript.

Depending on these results, we might also try to knock down the function of JAM-A, using siRNA. If successful knock down were achieved, we would carry out FRAP to determine whether E-cadherin junctional stability had been altered and would also stain for AMIS markers such as PAR-3 and determine whether Golgi and centrosomes were polarised. However, it is important to note that, although we were able to achieve E-cadherin RNAi to a certain degree, it is not always possible to achieve sufficient knock down of protein by the 24-hour AMIS timepoint. Since the results of these experiments would not alter the impact of our pre-existing data, we do not propose to create new knock out cell lines in the current study. Also, possible redundancy between different paralogs may affect the interpretation of this experiment so we would only include these results if they allowed for clear interpretation.

A previous study (Gao L ,et al. Development. 2017) has already shown that knocking out Afadin (which would therefore disable Nectin junctions) in MDCK cell 3D cultures did not affect initial AMIS formation or localisation, although later cell division orientation and therefore lumen positioning was affected. Afadin was also not localised to the AMIS. Therefore, it is less likely that Nectin is involved in AMIS localisation and while we will stain for its localisation by IHC, we don’t propose to try to knock down its function.

Reply to Reviewer 2 Point 1

The reviewer pointed out using a different mitosis blocker beside Mitomycin C. a) In the updated manuscript, we included one additional drug treatment: Aphidicolin. The results showed the AMIS could form in the centre of cell-cell contacts in Aphidicolin treated, division-blocked cells. AMIS (PAR3, ZO1) and the Golgi network was also polarised towards this point (Figure S1 G-I). In the final manuscript, we will include a full data set with N=3 independent experiments. Though­ the same as Mitomycin C, Aphidicolin is a DNA replication blocker, it confirmed that the AMIS formation upon treatments is not a Mitomycin-only artefact. b) As the reviewer suggested to block mitosis at the M phase, we are testing using microtubule polymerization inhibitors, Nocodazole and Taxol and will include these results if appropriate. However, these treatments will also affect the cytoskeleton, significantly affecting the cell shape and potentially interrupting the cell-cell contact interface. Therefore, it may not be possible to include these experiments.

Reply to Reviewer 2 Point 2

The reviewer suggested to include more examples of movies showing 2 and 4 cell cluster formation in division blocked conditions. We will be happy to provide more examples of the movies included in Figure 2 and Movie 2 in the final submission. The puncta in submitted Movie 2 was not as clear as the in Figure 2D as the reviewer pointed out. This was largely due to the reduce-sized movie in the original submission. We will provide full-resolution movies in the final submission. We do often see the ‘perfect’ 4-cell shape in division-blocked cells (e.g. the last frame of movie 2, shown at timepoint 19:00 in figure 2D). The shape of the clusters appears largely dependent on how many cells fuse together.

Reply to Reviewer 2 Points 3 & 5 and Reviewer 3 Point 2

We appreciate the comments from the reviewers regarding qualifying some of the discussion of our results.

Reviewer 2 points out that E-cadherin is not providing a ‘Symmetry breaking’ step, since cells are eventually able to polarise in the absence of E-cadherin (even though they can’t make an AMIS). We have therefore modified our discussion of this point to read: “Our results therefore suggest that Cadherin-mediated cell-cell adhesion may provide the spatial cue required for AMIS localisation during de novo polarisation.”. The last paragraph of the manuscript now reads: “In summary, our work suggests that Cadherin-mediated cell-cell adhesion is necessary for localising the AMIS during de novo polarisation of epithelial tubes and cavities.”

Reviewer 3 points out that the E-Cadherin molecule by itself is not sufficient to recruit the AMIS proteins to the centre-most region of the cell-cell contacts since E-cadherin is localised all along the cell-cell contact. We have now included a FRAP analysis demonstrating that E-cadherin is more stable in the centre-most region of cell-cell contacts (Figure 3I,J), which supports the role of E-Cadherin in directing AMIS localisation to this centre-most region. Nevertheless, we accept the reviewer’s point that we still do not know the downstream mechanism by which the AMIS is precisely localised to the central region of cell-cell contacts, and we have extended our discussion of this point in the updated manuscript. To clarity the language, we have also altered our results heading and other references to this point to read: “E-Cadherin adhesions are sufficient to initiate AMIS localisation, independent of ECM signalling and cell division”. We believe our experiments with two methods support this claim that the formation of E-cadherin-based adhesions without cell divisions and ECM signals are sufficient to initiate AMIS localisation; in particular Figure 4C-E, in which a centrally located AMIS formed even in a reductionist system of only 1 cell plated on E-cadherin covered glass.

Reply to Reviewer 2 Point 4

The reviewer reasoned that the WT and Cdh1 KO mESC were from different genetic backgrounds. The WT (ES-E14) mESCs were generated from 129P2/Ola mice and the Cdh1 KO mESCs were generated from 129S6/SvEvTacArc mice. To confirm the results acquired based on the two cells lines, we are doing two approaches: 1) As the reviewer suggested, we are using siRNA knock-down of E-cadherin in the Wild-type mESCs (ES-E14) to confirm the results we had of the AMIS absence in the E-cadherin knock-out mESC cultures. As Figure S2C,D now shows, the concentrated PAR3 between two mESCs was largely reduced after E-cadherin knock-down. We will also include Mitomycin-treated conditions in this experiment for the final publication. 2) As an alternative approach, not dependent on RNAi functionality, we have acquired a 129S6/SvEvTacArc background mESC (the W4 line) as the wild-type mESC line that has the same background as the Cdh1 KO mESC line. We are using this line to perform the control experiments of Figure 3A-C to confirm the previous results, which so far are comparable in both the ES-14 and W4 mESC cell lines. Our preliminary data below show the same results as we had with the ES-E14 cells in the current Figure 3A. We will finish the full data set of N = 3 experiments and replace the current Figure 3A-C, S2A data with that from the W4 mESC cell line. In the meanwhile, we have labelled the type of wide type mESC used for each experiment in the manuscript.

Reply to Reviewer 3 Point 1

The reviewer pointed out we should include three independent experiments for our data in Figure 4E. We agree with the reviewer. We are very happy to do the suggested experiments and data analysis and will be able to provide the data of N=3 independent experiments in the final manuscript.

Reply to Review 3 Point 3

We agree with the reviewer. Our current data set of live imaging at day 3 are used to confirm the idea from the fixed images that a wrapping process does happen for lumenogenesis during the Cdh1 KO cyst formation. The current dataset could not exclude the possibility that the hollowing might co-exist. The reviewer therefore suggests including a live movie depicting early stages (before 78:00) of E-Cadherin knock-out cluster development. We did try to collect this data before we first submitted the manuscript but encountered significant technical problems due to the high sensitivity of early stage Cdh1 KO cells to phototoxicity. This meant that we could not image with less than one hour interval nor over longer than 24 hour and were therefore unable to analyse how the cell clusters behave before forming the cup-shaped cavity. We will attempt these experiments again (e.g. imaging from 12-24 hours and 24-36 hours). However, there is a high likelihood that the experiments will not be technically possible, which is why we list them in section 4 of our review plan. Instead, we include the following sentence in our discussion: “We were unable to live-image earlier stages of Cdh1 KO cluster development due to the sensitivity of these cells to phototoxicity so we can’t exclude the possibility that hollowing lumenogenesis occurs in parallel, although our IHC analysis does not indicate that this is the case.”

Reply to reviewers’ minor points

We have revised our texts, made the nomenclature of protein PAR3 consistent, and included the information of antibody suppliers, as the reviewers pointed out. Specific response to Reviewer 2; in p2 and p7, the texts were referring to zebrafish studies, where PAR3 is referred to as Pard3. We have marked it with “Pard3 (PAR-3)” now. We have increased the size of images in figure 5B and inverted the colour to make it more visible. Since this made the figure too big, we moved the ZO1 images to Figure S5A. We will provide a co-staining of mCherry (to label mCherry-PAR6B), Phalloidin and PAR-3 in a more updated manuscript to replace Figure 2A.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Xuan Liang and collaborators shed light on how the precise localisation of the apical membrane initiation site (AMIS), necessary for organised lumen formation, is directed at the single-cell level. By characterising de novo polarising mouse embryonic stem cells (mESCs) cultured in 3D, the authors have uncovered a division-independent mechanism of de novo polarisation and AMIS localisation based on adhesion molecules. More precisely, they suggest that E-CADHERIN-mediated cell-cell adhesion may provide the symmetry-breaking step required for AMIS localisation during de novo polarisation since this molecule alone is sufficient and necessary to drive correct AMIS localisation. Interestingly, a high proportion of E-Cadherin knock-out (Cdh1 KO) mESC cell clusters do not hollow but instead generate lumen-like cavities via a closure mechanism. Despite not knowing the mechanism involved in the closure of these lumen-like cavities, the role of E-CADHERIN in de novo polarisation would be associated with initial steps in lumen formation (AMIS formation and localisation) but not in later steps where E-Cadherin knock-out mESC cell clusters can still make an apical membrane but do so more slowly than in WT cells and without going through a centralised AMIS stage.

Altogether, this study supports their previously published zebrafish neuroepithelial cell in vivo analysis, which demonstrated the division-independent localisation of Pard3 and ZO-1 at the neural rod primordial midline (Buckley et al., 2013). The authors have provided a novel mechanism of de novo polarisation and AMIS formation that occurs in vivo and in vitro. For this reason, this is a work with great significance that will undoubtedly be of general interest to the readers of Review commons. Nonetheless, several issues should be addressed before the publication of this manuscript.

1. In figure 4, the authors tried to demonstrate that E-CADHERIN is sufficient for AMIS localisation, independent of ECM signalling and cell division. To this end, they cultured individual division-blocked mESCs onto either E-CADHERIN-FC recombinant protein or FIBRONECTIN pre-coated glass and carried out IHC for PAR-3 after 24 hours in culture. They then performed heatmaps and analysed PAR3 intensity (Fig. 4 D, E). Although the data presented are fascinating and show the effects the authors describe, the authors should improve their sample number and repeat this experimental procedure and analysis at least two more times for their results to be consistent (only 15 cells in one experiment have been used to carry out the statistical analysis described previously).
2. The expression of E-cad is necessary for the proteins that define the apical membrane (Par3, Par6, aPKC) to be located in the AMIS. The results are clear and robust. Even so, I do not think this is sufficient, as the authors claim (headline of page 5, Figure 4). It seems clear that something more than Ecad is needed for the localisation of Par3 in the AMIS because, as the authors indicate in the discussion, Ecad is located along the entire cell-cell junction, while par3 is focused on AMIS. There must be something else that is necessary for the location of Par3. Therefore, the experiment in figure 4 does not prove that E-cad is sufficient but confirms that it is necessary for that location. Another series of experiments would have to be carried out to prove that it is sufficient. This must be clearly stated in the final version of the manuscript.
3. It is clear that E-Cadherin knock-out mESC cell clusters open cup-shaped cavities before generating a lumen-like structure. Fig. 5 presents compelling data about this in vivo lumen formation mechanism without hollowing, though they briefly describe this process. Whilst I am conscious that they do not know the mechanism by which such 'closure' occurs and that this would be suitable for another manuscript, I would strongly suggest including a live movie depicting early stages (before 78:00) of E-Cadherin knock-out cluster development. Many queries arise with this piece of data, as it seems that a small lumen could be forming prior to the cup-shaped cavity.

Minor points

1. Fig. 2A: Actin staining could be included to better visualise the spheroids.
2. Fig. 5B is very small, I would recommend them to present it bigger.
3. I would encourage the authors to revise the figures as some have displaced text. (see Fig. 5, 72 hours, Cdh1 KO).

Significance

Altogether, this study supports their previously published zebrafish neuroepithelial cell in vivo analysis, which demonstrated the division-independent localisation of Pard3 and ZO-1 at the neural rod primordial midline (Buckley et al., 2013). The authors have provided a novel mechanism of de novo polarisation and AMIS formation that occurs in vivo and in vitro. For this reason, this is a work with great significance that will undoubtedly be of general interest to the readers of Review commons. Nonetheless, several issues should be addressed before the publication of this manuscript. My lab work in lumen formation in 3D organotypic cultures and organoids

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Referee #2

Evidence, reproducibility and clarity

Summary:

The importance of cell division and the post-mitotic midbody in the establishment of the apical membrane initiation site (AMIS) is quite well established. However, there are observations hinting to a cell division-independent mechanism of the AMIS formation. The authors hypothesized that cell adhesion involving E-cadherin could direct the site for AMIS localisation during de novo polarisation. As model system the authors used mouse embryo stem cell (mESC) culture in Matrigel, which has been used as an in vitro model for the de novo polarisation of the mouse epiblast. The slow lumen formation in culture allows for a relatively clear separation of the stages of de novo polarisation. This enables to study the initiation of apico-basal polarity of embryonic cells alongside the first cell-cell contacts between isolated cells and small cell clusters. Here, the goal was to determine the role of cell adhesion, and in particular E-cadherin, in mESC AMIS localisation.

Major comments:

1. Mitomycin C is commonly used to block cell division, however, what it does is it blocks DNA replication, and the blockage of cell division is a consequence. It could have other effects than only blocking cell division. What about using a mitosis blocker? It would be good to have a second way in addition to mitomycin C treatment of confirming that the results support the conclusion that cell division is dispensable for AMIS localization, as the full work builds up on that first observation and this experimental setup is carried on through the manuscript.
2. Many clusters at higher cell stage that are shown (e.g. Fig 2C, 3A), look like they are in the perfect 4-cell stage after cell division. Movie 1 does not show that, only 2 cells are clustering. Movie 2 shows how two 2-cell clusters form a 4-cell cluster, however, that does not look as "perfect" as the 4-cell stages shown in the figures, which look as said more like 4-cell stages resulting from cell division. Maybe the authors could provide more movies that show the 2-cell cluster doublets (4-cell clusters)? Also, the puncta relocalization to the cell-cell contacts in the 2-cell cluster doublet is not so very clear in the movie 2. Maybe the authors have more movies for 2-cell cluster doublets?
3. On page 4 the authors observe: "Whilst homogenous control doublets localised PAR-3 to the central region of the cell-cell interface, heterogeneous chimeric doublets did not localise PAR-3 centrally (Figure 3D,E). Golgi and centrosome localisation towards the cell-cell interface suggested that the overall axis of polarity was maintained, even in the absence of both cell division and E-CADHERIN (Figure 3F-H & S2C)." This means that the axis of polarity is established without E-cadherin and without the AMIS. So, the cell-cell contact site itself is able to establish polarity, but not localize the AMIS. In line with this, the authors state: "The results also demonstrate that ECM in the absence of E-CADHERIN is insufficient for AMIS localisation." So, without E-cadherin, there is no AMIS, the ECM cannot establish it, but polarity is still established. On p. 6, it is then concluded that E-cadherin and centralised AMIS localisation are not required for apical membrane formation, but that they promote its formation earlier and more efficiently in development. In the discussion, however, the authors then state in a rather contrary manner "Our results therefore suggest that CADHERIN-mediated cell-cell adhesion may provide the symmetry-breaking step required for AMIS localisation during de novo polarisation." However, cells are polarized and have an apical membrane (Figure 3F-H & S2C) without cadherin and the AMIS, so how can this be the symmetry-breaking step for de novo polarization? Later on, in line with the earlier statement that E-cadherin and the AMIS location are not required for apical membrane formation, the authors then refine the previous statement: "the role of E-CADHERIN in de novo polarisation is specifically to localise the AMIS, which enables the integration of individual cell apical domains to a centralised region preceding lumen hollowing." This seems to be more likely to me than the CADHERIN-mediated cell-cell adhesion as the symmetry-breaking step. I therefore disagree in this point with their overall summary on p. 8, and find this a bit confusing.
4. As Wild-type mESCs (ES-E14) were purchased from Cambridge Stem Cell Institute and Cdh1 KO mESCs were gifted from a lab, it would be good to (genetically) characterize the cell lines because, apparently, they do not have the same origin and the KO cells were not derived from the parental mESCs. Alternatively, a control experiment with knockdown of Cdh1 in the purchased mESCs could be done, even if that would not lead to a complete knockdown, to make sure that the observed effects are the same as with the Cdh1 KO cell line.
5. The existing data is carefully analyzed with appropriate statistics. Replicates are sufficient. The conclusions are not yet fully justified, as discussed.

Minor comments:

• Fig S1D: It is labeled "Pard3". While also correct, it should be consistent, i.e. PAR3.
• P. 5 remove (slightly)
• P. 2, p. 7 "Pard3", replace with PAR3
• The antibody lists already contain catalog numbers in case of the primary antibodies, but no suppliers. They should be added, and also specified for the secondary antibodies for better reproducibility. In general, the text and figures are well prepared and of high quality. The citations appear appropriate.

Significance

The work describes the conceptual novelty of cell adhesion as alternative mechanism to cell division for AMIS localization, and in particular E-Cadherin as being required for AMIS positioning. It is still unclear why the AMIS is centered and the localization of cadherin is equal along cell-cell contacts (Fig 2C, S1E). How do Cadherin localization dynamics look like during the clustering of two cells? During cell division in a MDCK cyst (which is where my expertise lies), cell adhesion has to be partially removed during cytokinesis and abscission, and then be installed again, basically like a new cell-cell contact. Thus, could E-cadherin focus ("trap") the "AMIS initiation seed", rather than direct binding of PAR3 /PAR6 to cadherin as discussed by the authors, since E-cadherin is localized along the whole contact site and not centred? Could the unknown "apical seed" (which in cell division is the midbody) be trapped by cell adhesion? Could this be a common mechanism between cell division- and cell adhesion-driven AMIS localization? This finding could therefore have an even broader impact. What are the author's thoughts? While my speculation might be wrong, it might be worth hypothesizing on the connection between the role of E-cadherin in the two ways of AMIS localization.

Another novelty is the observation that polarity and cavities form later on in development independently of E-cadherin and an AMIS. This type of mechanism should be discussed further and put more into perspective with the literature.

The work describes a new mechanism which could be of broad importance in developmental biology. I therefore think that this work is highly significant.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Formation of tubes in a developing organism may arise from the closure of a pre-existing polarized epithelium or from de novo polarization and cavity formation in group of dividing cells. The concept of apical membrane initiation site (AMIS) refers to the fact that polarity proteins as PAR3 accumulate at a point where the apical membrane will be created. This accumulation occurs as early as the two cell stage. Previous reports have demonstrated the importance of the division process in defining this AMIS, however, in the present work the authors in vitro 3D cultures of mESC to report a mitosis independent mechanism that creates an AMIS, induces the polarization of groups of two or more cells, and permits the formation of a central cavity. The report shows that the mechanism is fully dependent on the polarized accumulation of E-cadherin at the cell membrane in contact with the other cells. Moreover, the mechanism does not require mitosis or interaction with the extracellular matrix.

Major comments:

The main objective of the work is to demonstrate that AMIS creation and cavity formation can be mitosis independent and that it is dependent on the accumulation of E-cadherin at the midline between two cells in contact. To demonstrate these objectives, the authors perform 3D cultures of mESC. To rule out the requirement of mitosis the authors perform cultures that are treated with mitomycin C and the purify single cells that are cultured again. The authors show time-laps experiments demonstrating that individual cells that do not dived create an AMIS when they contact one to each other. With this cultures they demonstrate that the process does not require an interaction with ECM (provided by the matrigel) but requires E-cadherin, to demonstrate, that they use E-cadherin KO cells (the same line where E-cadherin has been deleted). The work is well written and the objectives very clear. The technology used and the experiments done are adequate and sufficient to accomplish the proposed objectives and the results obtained clearly support the conclusions reached. The methods are well explained and transparent to be reproduced elsewhere and the number of replicas and the statistical methods applied seem corrects to me, although I am just a biologist, not a mathematician. Although the objectives of the work, that are: to demonstrate that AMIS formation can be independent of mitosis and that AMIS requires E-cadherin, there are parts of the results that could be farther studied or at least discussed more thoroughly. Firstly, the authors show that in non-dividing cells an AMIS is formed at the first contact site between the two cells, they also show that in the absence of E-cadherin the cell maintains the polarization of centrioles and Golgi apparatus, in spite that no AMIS is formed, this indicates that the deposition of E-cadherin at the midline membrane is part of a more global polarization event that most likely is initiated by the a directional activity of the Golgi apparatus that may direct the delivery of mature E-cadherin in that particular direction, initiating or maintaining the basis for an AMIS, since recent work (already cited in the manuscript) has demonstrated the importance of cadherin maturation for polarity establishment and maintenance (Herrera et al, 2021), the actual results should be farther discussed in this context. Secondly, it was previously shown that in different epithelia, upon cell-cell contact, the aPKC complex (that includes Par3 and Par6) is recruited early to the contact site where with the participation of Cdc42, aPKC is activated generating an initial spot-like adherent junction (AJs) (Suzuki et al., 2002). In that case it is thought to be mediated by a direct interaction between the first PDZ domain of PAR-3 and the C-terminal PDZdomain-binding sequences of immunoglobulin-like cell adhesion molecules: JAM-1 and nectin-1/3 (Fig. 3) (Ebnet et al., 2001; Itoh et al., 2001; Takekuni et al., 2003). Thus it wold be interesting to know if AMIS formation in absence of cell division depends on JAM-1 or nectin and whether JAM-/Nectin signalling is sufficient to initiate the Golgi and centriole polarization and which is the mechanism governing it.

Minor comments:

As I mentioned before, the paper is well presented and very clear, yes it is simple, but simple is always better, no complicated graphics or letterings, thank you. Although in my opinion the work is very well written, I have to admit that I am not qualified to evaluate the literary style of the work since English is not my mother tongue, also I have not reviewed typographical errors since I think that is the work of the editorial, not of scientific reviewers. Please include the full reference of all the antibodies used, including the company and not just the catalog number

Quoted references:

Ebnet, K., Suzuki, A., Horikoshi, Y., Hirose, T., Meyer Zu Brickwedde, M. K., Ohno, S. and Vestweber, D. (2001). The cell polarity protein ASIP/PAR-3 directly associates with junctional adhesion molecule (JAM). EMBO J. 20, 3738-3748.

Itoh, M., Sasaki, H., Furuse, M., Ozaki, H., Kita, T. and Tsukita, S. (2001). Junctional adhesion molecule (JAM) binds to PAR-3: a possible mechanism for the recruitment of PAR-3 to tight junctions. J. Cell Biol. 154, 491-497.

Takekuni, K., Ikeda, W., Fujito, T., Morimoto, K., Takeuchi, M., Monden, M. and Takai, Y. (2003). Direct binding of cell polarity protein PAR-3 to cell-cell adhesion molecule nectin at neuroepithelial cells of developing mouse. J. Biol. Chem. 278, 5497-5500

Suzuki, A., Ishiyama, C., Hashiba, K., Shimizu, M., Ebnet, K. and Ohno, S. (2002). aPKC kinase activity is required for the asymmetric differentiation of the premature junctional complex during epithelial cell polarization. J. Cell Sci. 115, 3565-3573.

Significance

The paper describes for the first time that contrary to what was previously believed an AMIS can be generated without a cell division. This is very important because it opens the possibility that the mechanisms that originate the biologic cavities are in fact not really how we believed. The work is of interest of all cell biology scientists, specially working in developmental biology, cancer research.

My particular field of expertise is cell biology and signaling, always applied to particular events as nervous system development or cancer, in particular I am interested in Wnt/b-catenin and Sonic Hedgehog pathways.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Here, authors confirm that glycolysis is important macrophage defense against mycobacterial infection and describe a central role of pyruvate in linking glycolysis and antimycobacterial mtROS production to control the intracellular burden. Alike previous authors who have demonstrated that the non-pathogenic Bacillus Calmette-Guerin and heat-killed M. tb increase glycolysis, they show that human primary macrophages infected with M. avium increase glycolysis to facilitate mycobacterial control. Rost and coll. show evidence that the killing mechanism act through the production of mtROS by the complex I of the electron transport chain via the engagement of RET. This mechanism acts in parallel to other immunometabolic defense pathways activated in M. avium infected macrophages, such as the production/induction of itaconate via the IRF-IRG1 pathways (Alexandre Gidon 2021). * They give evidence that IL-6 and TNFa are not involved in regulating the pyruvate-mtROS and show chemical evidence that mitochondrial import of pyruvate through MPC activity is necessary to generate a high membrane potential and the subsequent production mtROS. However, the data presented here don’t explain how pyruvate is driving RET and mtROS; if pyruvate targets the electron transport chain directly or is converted (via TCA) to another metabolite that initiates RET and mtROS. Above all, this work brings attention to the possibility of using compounds that specifically engage mtROS production for therapeutic perspectives*

Reviewer #1 (Significance (Required)):

While the data presented here don t explain how pyruvate is driving RET and mtROS; if pyruvate targets the electron transport chain directly or is converted (via TCA) to another metabolite that initiates RET and mtROS, this work merits to be deeply evaluated for potential publication in a RC journal. However, the language must be improved and polished before submission.

We thank the reviewer for appreciating the importance of our findings. We are sorry for any inconveniences the language may have caused and have carefully revised the manuscript with the intention of improving it.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Overall evaluation

This study addresses an interesting aspect of host-pathogen relationship, namely how the metabolism of the host impacts directly or indirectly on the metabolism and/or fitness of the pathogen. For example, the generation of ROS in a way independent of NADPH-oxidases has been suggested to play a role in a number of infections. In particular, whether and how such ROS might be part of the cell-autonomous defence against an intracellular bacterial pathogen, in the present case M. avium, is of relevance. Despite these positive points, the study and manuscript suffer from a high number of serious problems both in form and content. The authors are strongly advised to revise the experimental evidence presented, including by performing additional experiments and re-interpreting some of the ones documented, as well as extensively rewrite/reformat the manuscript.

Major comments:

1- Normally, I would list the following criticisms in minor comments, but their accumulation makes them a major point:

1. • The reference style is cumbersome, because they are listed in alphabetical order of the FIRST NAMES of the first authors, which renders it difficult to identify what is cited and when *
2. • Similarly, the citations in the text include the author first names, whoch is unusual and heavy. * We thank the reviewer for bringing this to our attention. It was a mistake and we have now changed the reference style accordingly by replacing first names with initials and listing citations alphabetically from the first author’s last name.

In addition, the authors almost systematically introduce each of the articles cited with a sentence such as "Mills and colleagues did this and that ...". This is sometimes used in articles, but should not be the norm. Usually, this is used to emphasise that a given group has contributed not only substantially, but also on a regular basis to a field for years. One would write Palade and colleagues ..., or Rothman and colleagues ... . But in the present manuscript, this is mentioning first authors and their colleagues. Mills et al is a contribution from the O'Neils laboratory, which speaks to me.

We see the point and have changed some of these sentences accordingly to either write “O’Neill and colleagues …(ref)”, “First-author et al. showed that…. (ref)”, or “Others have shown …(ref)”. Editing was made page 3, lines 49, 51 and 54-56; page 4, lines 74; page 6, line 111; page 9, lines 178, 182, 193 and 197.

2- Page 4, the authors report that the positive control used to shift macrophage metabolism towards glycolysis did not work. This places doubt on the other experiments and conditions.

We thank the reviewer for bringing this point of confusion to our attention. In an Agilent Seahorse assay, which is commonly used to report glycolytic flux in scientific publications, the extracellular acidification rate (ECAR) is used as an indicator of glycolysis. Extracellular acidification occurs as protons are exported from the cell alongside lactate. In figure 1B we show quantitatively (ng/cell/24h) that lactate export is significantly increased in MDMs after LPS challenge, which translates to an increased ECAR on the traditional Seahorse assay. We also show further evidence that LPS treatment does switch the metabolism to glycolysis: the two first intermediates of glycolysis, G6P and F6P, are consumed (Fig. 1D), though between-donor variation leaves the LPS-induced increase in glucose consumption not significant. Overall, we are confident that our NMR and mass spectrometric metabolic profilings, which have been tested in several publications listed in the methods section are reliable and recapitulate previous knowledge. We have rephrased the paragraph on page 4 line 76 to clarify this point.

*3- Page 5. I am not sure to really understand the reasoning behind : "Quantification by targeted mass spectrometry did not reveal a significant accumulation in the intracellular level of pyruvate in macrophages infected with M. avium or treated with LPS when compared to untreated controls (Fig. 2A), suggesting that pyruvate is rapidly metabolized." *

The rationale for performing mass spectrometric quantification of pyruvate was to confirm experimentally that pyruvate is consumed - which we already know indirectly as its reduced product, lactate, is produced and excreted by the infected cells (Figure 1B). The hypothesis is that a proportion of the pyruvate could also enter the mitochondria and TCA cycle as shown by Mills et al.

We have tried clarifying this in the revised manuscript by replacing the original text by “Quantification by targeted mass spectrometry did not reveal a significant accumulation in the intracellular level of pyruvate in macrophages infected with M. avium or treated with LPS when compared to untreated controls (Fig. 2A), confirming that pyruvate is metabolized. Mills et al have demonstrated that during LPS activation, mouse macrophages switch to aerobic glycolysis while repurposing the TCA cycle activity to generate specific immunomodulatory metabolites (Mills EL 2016), which implies that a fraction of the pyruvate formed by glycolysis enters mitochondria.”(page 5 line 90).

4- The metabolomics experiments seem to be performed on a global population of infected and uninfected cells, without any clear mention of the fraction of infected cells, which is potentially low (Fig 1 appears to indicate less than 50%), and very likely variable between experiments. This is a serious confounding factor and likely precludes interpretation of the results?! The percentage of infected cells, at time "zero" and at each time point post-infection has to be quantified in each experiment.

The reviewer is right that the analysis was carried out on a mixed population. However, even with an infection level of 50% this should be sufficient to pick up significant changes in metabolite levels resulting from infection, which is also not seen with LPS treatment that you would assume activates all cells. We have tested another protocol of infection (MOI 10 for 120 min) that yields almost 100% infection with similar results. These data are included as supplementary figure 1 in the revised manuscript (page 6, line 124).

It would also be useful to analyse and graph the total fluorescence (coming from M. avium) per cell and the average fluorescence per cell.

Intracellular growth was quantified by measuring M. avium fluorescence intensity per cell (n>500 cells per donor and per condition) as mentioned in the figure legends. The bar charts represent the average intensity obtained from at least 500 cells per donor and conditions, each point representing an individual donor. We have successfully used this method to analyze and quantify M. avium growth in human primary macrophages (Gidon et al, PLoS Pathogens, 2017; Gidon et al, mBio 2021).

5- Page 6. How can the authors conclude "Overall, this set of data reveals that no major perturbations of the TCA cycle are induced by the infection, excluding a potential antimicrobial property of these TCA intermediates" from their data? Their experiment do not test the potential antimicrobial activity of the metabolites!

We agree with the reviewer that our data cannot preclude any anti-microbial effects of TCA intermediates. We agree that the phrasing is confusing and not as intended and have replaced it with the following sentence “Since we and others have previously found that altered intracellular levels of the TCA cycle-derived metabolite itaconate following an infection was indicative of an anti-microbial function (Gidon et al, mBio 2021; Chen et al, Science, 2020), we conclude that none of the TCA cycle intermediates warranted further investigation to explain the anti-microbial effect of glycolysis.”. (page 6, line 115).

*6- The effect of the chemical inhibitors used has to be evaluated on the growth of bacteria in broth to exclude the possibility that they directly impact them. *

We agree with the reviewer that this is important to control for. We have performed the suggested experiments and the results, showing that none of the different drugs influence M. avium in vitro growth, are included in a supplementary figure 2 in the revised manuscript (page 8, line 158).

*7- Figures. None of the graphs present error bars. In addition, for example for Fig 1A, the number of points correspond each to one donor. But there is mention neither of the number of biological replicates nor of technical replicates. This is absolutely required. *

The number of donors used for each experiment are included in the figure legend. All the experiments were done independently and are therefore biological replicates. Each point represents the value obtained for one independent donor with no technical replicates. Since we show all individual measurements (donors) an error-bar, to our opinion, is not needed. We have now changed the text in the legend to better reflect this information (page 17, lines 353, 357; page 18, line 361; page 20, lines 371, 375, 378, 383, 383; page 23, lines 410, 413, 417, 422).

8- It is unclear whether the effects documented have been measured in the whole population or only in the infected cells. And when they are measured in infected cells and uninfected cells, are these cells from a population in the same well, or from a well containing only uninfected cells?

By nature, antimicrobial effects can only be detected in infected cells therefore all the experiments measuring the effect on intracellular growth, the mitochondrial potential and the production of mitochondrial ROS were measured on infected cells. Control refers to a well containing only uninfected cells.

*9- In Figure 3A, the localisation of M. avium has to be shown. *

We have edited the Figure 3 that now includes images from the M. avium-CFP channel to help identify the infected cells.

*10- The mechanism proposed at the end of the abstract "...this work stresses out that compounds specifically inducing mitochondrial reactive oxygen species could present themself as valuable adjunct treatments." should be tested to close the loop and validate the data and hypothesis. *

We agree with the reviewer, and we are currently finalizing another manuscript on metformin, which is known to induce mitoROS, as a possible Host Directed Therapeutic agent in a mouse model of M. avium infection.

Minor comments:

1- The manuscript does not show any numbering, neither of pages nor of lines, which renders the writing of the review difficult.

We are sorry for the inconvenience. This is now included in the revised manuscript.

*2- The authors write "undirect" instead of indirect. *

We have corrected the mistake in the revised manuscript.

*3- They also use "if" instead of whether quite frequently. *

We thank the reviewer for bringing that detail to our attention. We have changed the manuscript according to the comment.

*4- Page 5, second line "... a 40% increase in cells treated ..." An increase of what? *

Treatment of infected cells with 2-DG increases the fluorescence coming from M. avium, reflecting the increase of the intracellular burden. We have changed the manuscript to make this point clearer (page 4, line 82).

*5- Page 5. The second paragraph belongs to the introduction or the discussion. *

We don’t agree on this point. We feel that it is important to inform the reader on how pyruvate can be used within the cell before showing the results, but we feel it does not fit with the broader introduction on glycolysis. However, if the editor/reviewers disagree with us, we will move this paragraph in introduction.

*6- Page 6. The authors mention that AMP, ADP etc... are nucleosides. But they are nucleotides. *

We changed the manuscript according to the suggestion (page 6, line 120; page 13, line 279; page 20 line 381).

Reviewer #2 (Significance (Required)):

The study explores an interesting question, but in its present state, the conclusions are not sustained by the evidence.

We thank the reviewer for acknowledging the importance of our work. We believe that we have addressed the concerns expressed in the comments.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

*This manuscript reported that macrophages rely on glycolysis and RET to control M. avium infection and provide molecular evidence linking pyruvate, the end-product of glycolysis, to anti-mycobacterial mtROS production. The advantages of this paper are the clear thinking, from phenomenon to molecular mechanism, strong logic. However, there are also many shortcomings: *

Major comments:

*1.The main shortcoming of this paper is that authors only found macrophages control M. avium infection through glycolysis and RET in vitro. Although they use primary macrophages from healthy donners, not the cells lines, is it consistent in vivo? Authors should use mouse model that challenged with M. avium. Moreover, authors can isolate primary macrophages from patients that infected with M. avium, and compared it with primary macrophages from healthy donners. *

We agree with the reviewer opinion, and we are finalizing another manuscript using Metformin, a drug known to induce mitochondrial ROS, as a Host Directed Therapy in a mouse model. However, dissection of mechanisms involved such as pyruvate import to mitochondria and RET is not possible in vivo. We are not sure of the meaning of the suggested experiment: comparing primary macrophages from mav-infected patients vs healthy donors.

*2.This paper found that macrophages control M. avium infection by producing mitochondrial reactive oxygen species. This is a very interesting observation. How does mitochondrial reactive oxygen species resist mycobacterial infection? *

We thank the reviewer for appreciating our work. Yet, we are not sure what does the reviewer mean by “How does mitochondrial reactive oxygen species resist mycobacterial infection?”. It has been shown in many studies that cellular ROS causes oxidative damage and can be toxic to pathogens (and cells), including mycobacteria (Fang FC, Nature Reviews in Microbiology, 2004; Dryden M, Int. J. Antimicrob. Agents, 2018; Kim et al, J. Microbiology, 2019; Herb and Schramm, Antioxidants, 2021). However, the role of RET-induced mitochondrial ROS is a relatively new concept, that, to the best of our knowledge, has never been demonstrated to be involved in the control of mycobacterial infection nor in human primary macrophages. Conversely, bacteria have evolved defense mechanisms to protect and counteract the production of antimicrobial ROS (Kim et al, J. Microbiology, 2019).

*3.To make this data solid, whether giving pyruvate supplements to patients with mycobacterium infection can alleviate their disease? or it can be tested in mouse model. *

Initiating a clinical study is beyond the scope of this study. Furthermore, even if we could supplement infected mice with pyruvate, there is no guarantee it will get into the cells and further imported into mitochondria to induce the anti-mycobacterial effects shown in the present study. We rather believe that the key for future treatment would be to induce mitochondrial ROS through the use of other, known agonists to strengthen this cell-intrinsic defense mechanism. As stated above, we are finalizing another manuscript using a compound known to induce mitochondrial ROS as Host Directed Therapy in a mouse model.

*4.This work demonstrated that IL-6 and TNF-α could control the intracellular burden of M. avium. Many cytokines are produced by macrophage during infection. Are there other pro-inflammatory cytokines that play a role? *

We agree with the reviewer view that many cytokines influence host defenses to mycobacterial infections in addition to TNF-a and IL-6, e.g., IL-1, IL-10 and interferons. However, some of these are not induced in Mav infected macrophages (IL-1, interferons), and our previous works have shown that TNF-a and IL-6 are consistently induced by the infection (Gidon et al, PLoS Pathogens, 2017) and that they are involved in the control of the intracellular burden (Gidon et al, mBio, 2021). We therefore chose to focus on these.

*5.In Figure 1C, authors did not observe an increase of glutamine consumption in LPS-activated human macrophage which is in contrary to previous published study. How author explain this contrary result? *

We thank the reviewer for bringing this point to our attention. We have previously published the glutamine consumption of multiple myeloma cell lines quantified by the NMR based method described herein, proving it is sensitive enough to detect differences between cell lines at cell densities comparable to those of the seeded MDMs (Abdollahi et al, The FASEB journal, 2021). Hence, we are confident that the applied methodology would detect significant differences in glutamine consumption, given that the cells in question rely on glutamine. Previous observations of glutamine uptake were made using mouse macrophages and it is referenced that human and mouse macrophages do not share the exact same metabolism (Thomas et al, Frontiers in Immunology, 2014; Vijayan et al, Redox Biology, 2019). It’s worth noting that the species-specificity also extend to how macrophages respond to TLRs ligands (Sun et al, Science Signaling, 2016). As this result does not contribute significantly to the mechanism described in our paper, we do not feel the need to discuss it extensively.

Minor comment:

The authors do not provide sufficient information in the Materials and Methods, and figure legends, such as how many times the experiments were repeated? How to measure the concentration of citrate, isocitrate, succinate......

The number of donors used for each experiment are included in the figure legends. All the experiments were done independently and are therefore biological replicates. Each point represents the value obtained for one independent donor with no technical replicates. The concentrations of citrate, isocitrate, succinate and the other TCA cycle intermediates were measured by capillary ion chromatography tandem mass spectrometry, as described in the legend of Figure 2 and in detail in the Materials and Methods section on page 13-14. All metabolite measurements by targeted mass spectrometry are based on validated and published methods from our laboratory (Kvitvang et al, 2014; Stafnes et al, 2018; Røst et al, 2020). We have included more details to the methods section describing mass spectrometric metabolic profiling (page 13-14).

Reviewer #3 (Significance (Required)):

Mycobacteria avium infection is a common and serious kind of inflammation, in which macrophages has been reported to play an important role. Recently metabolic reprogramming of macrophages is proved in many diseases. By using LPS stimulation, the metabolic reprogramming of macrophages has been reported and have been confirmed to play a role during infection. Therefore, it is not so exciting to see this role of metabolic reprogramming in controlling M. avium infection.

We are sorry that our findings did not excite the reviewer, but we strongly disagree that our study does not report any novel findings. Both the significance of mitochondrial ROS in mycobacterial defense and the discovery that pyruvate can induce mitochondrial ROS via RET, are novel findings not shown before to our knowledge. And – as a note – a phenomenon described for LPS and/or in mouse macrophages does not necessarily reflect what happens during any bacterial or viral infections, nor in humans.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

This manuscript reported that macrophages rely on glycolysis and RET to control M. avium infection and provide molecular evidence linking pyruvate, the end-product of glycolysis, to anti-mycobacterial mtROS production. The advantages of this paper are the clear thinking, from phenomenon to molecular mechanism, strong logic. However, there are also many shortcomings:

Major comments:

1. The main shortcoming of this paper is that authors only found macrophages control M. avium infection through glycolysis and RET in vitro. Although they use primary macrophages from healthy donners, not the cells lines, is it consistent in vivo? Authors should use mouse model that challenged with M. avium. Moreover, authors can isolate primary macrophages from patients that infected with M. avium, and compared it with primary macrophages from healthy donners.
2. This paper found that macrophages control M. avium infection by producing mitochondrial reactive oxygen species. This is a very interesting observation. How does mitochondrial reactive oxygen species resist mycobacterial infection?
3. To make this data solid, whether giving pyruvate supplements to patients with mycobacterium infection can alleviate their disease? or it can be tested in mouse model.
4. This work demonstrated that IL-6 and TNF-α could control the intracellular burden of M. avium. Many cytokines are produced by macrophage during infection. Are there other pro-inflammatory cytokines that play a role?
5. In Figure 1C, authors did not observe an increase of glutamine consumption in LPS-activated human macrophage which is in contrary to previous published study. How author explain this contrary result?

Minor comment:

The authors do not provide sufficient information in the Materials and Methods, and figure legends, such as how many times the experiments were repeated? How to measure the concentration of citrate, isocitrate, succinate......

Significance

Mycobacteria avium infection is a common and serious kind of inflammation, in which macrophages has been reported to play an important role. Recently metabolic reprogramming of macrophages is proved in many diseases. By using LPS stimulation, the metabolic reprogramming of macrophages has been reported and have been confirmed to play a role during infection. Therefore, it is not so exciting to see this role of metabolic reprogramming in controlling M. avium infection.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Overall evaluation

This study addresses an interesting aspect of host-pathogen relationship, namely how the metabolism of the host impacts directly or indirectly on the metabolism and/or fitness of the pathogen. For example, the generation of ROS in a way independent of NADPH-oxidases has been suggested to play a role in a number of infections. In particular, whether and how such ROS might be part of the cell-autonomous defence against an intracellular bacterial pathogen, in the present case M. avium, is of relevance. Despite these positive points, the study and manuscript suffer from a high number of serious problems both in form and content. The authors are strongly advised to revise the experimental evidence presented, including by performing additional experiments and re-interpreting some of the ones documented, as well as extensively rewrite/reformat the manuscript.

Major comments:

1. Normally, I would list the following criticisms in minor comments, but their accumulation makes them a major point:
   • a. The reference style is cumbersome, because they are listed in alphabetical order of the FIRST NAMES of the first authors, which renders it difficult to identify what is cited and when
   • b. Similarly, the citations in the text include the author first names, whoch is unusual and heavy.
   • c. In addition, the authors almost systematically introduce each of the articles cited with a sentence such as "Mills and colleagues did this and that ...". This is sometimes used in articles, but should not be the norm. Usually, this is used to emphasise that a given group has contributed not only substantially, but also on a regular basis to a field for years. One would write Palade and colleagues ..., or Rothman and colleagues ... . But in the present manuscript, this is mentioning first authors and their colleagues. Mills et al is a contribution from the O'Neils laboratory, which speaks to me.
2. Page 4, the authors report that the positive control used to shift macrophage metabolism towards glycolysis did not work. This places doubt on the other experiments and conditions.
3. Page 5. I am not sure to really understand the reasoning behind : "Quantification by targeted mass spectrometry did not reveal a significant accumulation in the intracellular level of pyruvate in macrophages infected with M. avium or treated with LPS when compared to untreated controls (Fig. 2A), suggesting that pyruvate is rapidly metabolized."
4. The metabolomics experiments seem to be performed on a global population of infected and uninfected cells, without any clear mention of the fraction of infected cells, which is potentially low (Fig 1 appears to indicate less than 50%), and very likely variable between experiments. This is a serious confounding factor and likely precludes interpretation of the results?! The percentage of infected cells, at time "zero" and at each time point post-infection has to be quantified in each experiment. It would also be useful to analyse and graph the total fluorescence (coming from M. avium) per cell and the average fluorescence per cell.
5. Page 6. How can the authors conclude "Overall, this set of data reveals that no major perturbations of the TCA cycle are induced by the infection, excluding a potential antimicrobial property of these TCA intermediates" from their data? Their experiment do not test the potential antimicrobial activity of the metabolites!
6. The effect of the chemical inhibitors used has to be evaluated on the growth of bacteria in broth to exclude the possibility that they directly impact them.
7. Figures. None of the graphs present error bars. In addition, for example for Fig 1A, the number of points correspond each to one donor. But there is mention neither of the number of biological replicates nor of technical replicates. This is absolutely required.
8. It is unclear whether the effects documented have been measured in the whole population or only in the infected cells. And when they are measured in infected cells and uninfected cells, are these cells from a population in the same well, or from a well containing only uninfected cells?
9. In Figure 3A, the localisation of M. avium has to be shown.
10. The mechanism proposed at the end of the abstract "...this work stresses out that compounds specifically inducing mitochondrial reactive oxygen species could present themself as valuable adjunct treatments." should be tested to close the loop and validate the data and hypothesis.

Minor comments:

1. The manuscript does not show any numbering, neither of pages nor of lines, which renders the writing of the review difficult.
2. The authors write "undirect" instead of indirect.
3. They also use "if" instead of whether quite frequently.
4. Page 5, second line "... a 40% increase in cells treated ..." An increase of what?
5. Page 5. The second paragraph belongs to the introduction or the discussion.
6. Page 6. The authors mention that AMP, ADP etc... are nucleosides. But they are nucleotides.

Significance

The study explores an interesting question, but in its present state, the conclusions are not sustained by the evidence.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Here, authors confirm that glycolysis is important macrophage defense against mycobacterial infection and describe a central role of pyruvate in linking glycolysis and antimycobacterial mtROS production to control the intracellular burden. Alike previous authors who have demonstrated that the non-pathogenic Bacillus Calmette-Guerin and heat-killed M. tb increase glycolysis, they show that human primary macrophages infected with M. avium increase glycolysis to facilitate mycobacterial control. Rost and coll. show evidence that the killing mechanism act through the production of mtROS by the complex I of the electron transport chain via the engagement of RET. This mechanism acts in parallel to other immunometabolic defense pathways activated in M. avium infected macrophages, such as the production/induction of itaconate via the IRF-IRG1 pathways (Alexandre Gidon 2021).

They give evidence that IL-6 and TNFa are not involved in regulating the pyruvate-mtROS and show chemical evidence that mitochondrial import of pyruvate through MPC activity is necessary to generate a high membrane potential and the subsequent production mtROS.

However, the data presented here don t explain how pyruvate is driving RET and mtROS; if pyruvate targets the electron transport chain directly or is converted (via TCA) to another metabolite that initiates RET and mtROS. Above all, this work brings attention to the possibility of using compounds that specifically engage mtROS production for therapeutic perspectives

Significance

While the data presented here don t explain how pyruvate is driving RET and mtROS; if pyruvate targets the electron transport chain directly or is converted (via TCA) to another metabolite that initiates RET and mtROS, this work merits to be deeply evaluated for potential publication in a RC journal. However, the language must be improved and polished before submission.

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The paper tackles an important problem regarding the effect of demographic dependent vaccination protocols on the reduction in the number of deaths with respect to the situation of no vaccination (say J). A compartmental SIRD model with reinfection Y is proposed, stratified in two (age dependent) groups, based on a binary reduction of a given contact map, and given infection fatality risk (IFR). Several countries are then analyzed.

As far as I understand we have a control variable v, parameters of the stratified model (i=1,2) tuned to match IFRi, and a control objective, i.e. minimization of J over one year.

The paper is well written. The final message and some theoretical passages are not completely clear, at least to me. I have the following observations that the authors may want to consider.

We thank the referee for the revision and are very glad that the overall evaluation is positive. Comments and suggestions have been thoroughly addressed, as we discuss in the following.

1) The study of stability of infection free and endemic equilibria should be better developed. The 5 equations can be reduced to 4 (neglecting D) and the characteristic of the reduced Jacobian used to characterize the local asymptotic stability of equilibria, instability, bifurcation points etc... Alternatively, one can use a co-positive Lyapunov function (LF). For instance, if we take the LF V=S+I+Y+R, we get $\dot V=-\mu_I I-\mu_Y Y \le 0$. If $\mu_I$ and $\mu_y$ are strictly positive all equilibria are characterized by (S*,0 0,R*) and D=1-S*-R*. So, I don't understand the phrase after (7,8), notice that Y cannot be zero in finite time. For $\mu_y=0$ then Y* can be nonzero. I guess that closed-form computation of S* and R* is possible as function of the parameters at least in the case v=0. The stability result should be cast in function of the current reproduction number (not explicitated) wrt to S and R.

The authors are invited to have a look at

1.1) Pagliara et al, "Bistability and Resurgent Epidemics in Reinfection Models", IEEE CSLetters, 2018,

for a theoretical analysis of stability on a similar (just a little bit simpler) model.

We appreciate the suggestions of the referee for improvement of this material. We have carried out an in-depth revision of the stability analysis and significantly extended it. The major addition has been, as suggested, a section relating the current reproductive number at equilibrium (we call it the asymptotic reproductive number in the text) to the fixed points of the dynamics for three different scenarios: general model, no vaccination, and zero mortality of reinfected individuals. As Pagliara et al. show in their paper, the connection between the fixed points and the reproductive number is not trivial, but it is possible to derive it through the next-generation matrix technique, as we now do. Additional references regarding this technique have been added. We have included a Table summarizing the stability analysis (page 2 in SI 3) at the end of this new section.

Other modifications include the reduction of 5 equations to 4 for the stability analysis and a clarification of possible equilibria (page 1 of SI 3), rephrasing and correcting our sentence after eqs. (7) and (8). We also attempted to obtain a closed-form computation of S* and R* but, to the best of our knowledge, concluded that it is not possible. We would be happy to pursue any insight in this respect the referee may have.

What said before should be also extended to the stratified model, where a "network" Rt could be defined, see for instance

1.2) L. Stella et al, "The Role of Asymptomatic Infections in the COVID-19 Epidemic via Complex Networks and Stability Analysis", SIAM J Cont. Opt., 2021, (arxiv.org/pdf/2009.03649.pdf)

We thank the referee for pointing out this reference. Following the analysis in Stella et al., we have carried out a stability analysis for the stratified model as well. The results are included in a new section (pages 7-10 in the SI 3).

2) It is not clear whether the free contagion parameters of the model have been fitted on real data (identification from infection and reinfection data). Notice that the interplay between vaccination strategies and NPI is important, see e.g.

*2.1) Giordano et al, Modeling vaccination rollouts, SARS-CoV-2 variants and the requirement for non-pharmaceutical interventions in Italy", Nature Medicine 2021, *

where progressive vaccination in reverse age order is considered together with different enforced NPI countermeasures.

In the first part of our study, parameters are intendedly left free because we aim at describing the generic behavior of the model. Still, we derive several inequalities and relationships between parameter ratios that seem to be sensible attending to what the different classes in the model stand for. This is as described in sections regarding model parameters when the two generic models (SIYRD and S2IYRD) are introduced. The aim is to represent both the generic dependence with some variables and a broad class of contagious diseases, so parameters are mostly free. In agreement with this approach, parameters can be also freely varied in the companion webpage.

In the second part of our study, the model is applied to COVID-19. In that case, we have used parameter values in agreement with observations, as (admittedly poorly) explained in pages 9-10 of the main text. Indeed, not enough information on parameter estimation was provided in the main text, and the SI 2 also needed some additional information. This has been amended. Let us explicitly mention that we have not fitted the dynamics of the model to any actual data set to fix specific values, as Giordano et al. do. In our case, we have first used different demographic data sets to evaluate contact rates and IFRs of the two population groups (these are parameters Mij and Ni in eqs. (7-10)). Secondly, recovery and death rates are estimated through the IFRi values for each age group i and the infectious period of COVID-19, that we fix at dI=13 days. Third, infection rate βSI=R0/dI has been estimated fixing R0=1, since the reproductive number of COVID-19 all over the world fluctuates around this value (Arroyo-Marioli et al. (2020) Tracking R of COVID-19: A new real-time estimation using the Kalman filter, PLoS ONE 16(1):e0244474). The reinfection rate is defined through its relationship with the infection rate, βRI= α1 βSI, where α1 was in the range 0-0.011 at early COVID-19 stages (Murchu et al. (2022), Quantifying the risk of SARS‐CoV‐2 reinfection over time, Rev Med Virol 32:e2260) and seems to be about 3-4 fold larger for the omicron variant (Pulliam et al., Increased risk of SARS-CoV-2 reinfection associated with emergence of the Omicron variant in South Africa, www.medrxiv.org/content/10.1101/2021.11.11.21266068v2). Given the relationships derived among parameters, our only free parameter was α2RY= α2 βRI, and we fixed it to α2=0.5 (i.e., reinfected individuals recover twice as fast as individuals infected for the first time).

Once more, it was not our goal to precisely recover specific trajectories of COVID-19 or to point at possible future scenarios, but to illustrate the dependence of major trends with model parameters. Also, the appearance of new variants requires the reevaluation of parameters. For example, omicron has different IFR (therefore different mortality and recovery rates), a different infectious period, and higher infection and reinfection rates. In this context, the interactive webpage (where we will update demographic profiles and IFR data as they become available) is a useful resource to simulate any situation different from current or past ones.

3) In the model the immunity waning is not explicitly considered (flux from R to S or better from a vaccinated compartment to S). It is clear that this complicates the model. Please discuss why the indirect way the waning is considered here is justified.

3.1) Batistela et al, "SIRSi compartmental model for COVID-19 pandemic with immunity loss", Chaos Soliton and fractals, 2021.

3.2) McMahon et al, "Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected,Recovered) Modeling Using Empirical Infection Data", JMIR Public health and surveillance, 2020.

Though the model does not consider an incoming flux of individuals to compartment S, the existence of a "backward" flux from R to Y yields a transient phenomenology analogous to models with increases in the S class. Indeed, it is these fluxes that cause persistent endemic states; otherwise, the S class is monotonously depleted until infection extinction.

In Batistela's et al. work, the possibility that individuals become reinfected is effectively implemented through a flux between the R and S classes, since only one class of infected individuals is considered and recovered individuals cannot be infected again. In our case, feeding back to S would mean that previous immunity is completely lost or that vaccines are not effective at all for some individuals. This is neither what McMahon et al. conclude when evaluating real data nor what more recent surveys indicate (see for instance the Science Brief published in October 2021 by the CDC, SARS-CoV-2 Infection-induced and Vaccine-induced Immunity, https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/vaccine-induced-immunity.html).

This nonetheless, complete immunity waning (feedback to the S class) and reinfections (feedback to a partly immune class experiencing overall lower severity of the disease) are equivalent to a large extent: the trend of COVID-19 seems to indicate that our Y class will be the "new S", and that fully naive individuals would arrive mostly due to demographic dynamics (birth and death processes, as also implemented by Batistela et al.). Summarizing, complete immunity waning is rare in the time scales considered in our simulations, while partial immunity that decreases the severity of the disease (after infection or vaccination) is the rule, in agreement with our choices.

4) Reduction of deaths wrt no vaccination is of course important, but also reduction of stress in hospitals. This is particularly important now with the advent in Europe of the omicron variant. Please discuss on the real message you want to convey to policy makers in the actual scenario of the pandemic.

The model in this work is deliberately simple. Our main goal was to explore the qualitative effects of demographic structure and disease parameters in protocols for vaccine administration. This was the reason to consider a mean-field model in a population structured into two groups. The main conclusion is that optimal vaccination protocols are demography- and disease-dependent. If this is so in our streamlined model, the more it will be in more realistic models, where one should include a finer stratification and, in all likelihood, heterogeneity in contagions. Our main message, therefore, is that there is no unique protocol for vaccine roll-out, valid for all populations and diseases. The abstract has been modified to highlight this conclusion.

Some qualitative considerations also allow us to draw preliminary conclusions on the reduction of stress in hospitals. Since the number of hospital admissions is proportional to the incidence of the disease, the number H of hospitalized individuals can be represented as H=a I + b Y, with a>>b due to the partial immunity of vaccinated or recovered individuals (which belong to class Y upon (secondary) contagion). Therefore, minimizing the burden on the healthcare system amounts to minimizing the number of individuals in the I class. Beyond non-pharmaceutical measures, I is minimized when individuals are transferred as fast as possible to the Y class, that is, maximizing vaccine supply and acceptance. In terms of our model parameters, this entails maximizing v and also θ (the maximum fraction of individuals eventually vaccinated), for instace through devoted awareness campaigns. These ideas have been included in the Discussion section.

Reviewer #1 (Significance (Required)):

The final message and some theoretical passages are not completely clear, at least to me.

Please discuss on the real message you want to convey to policy makers in the actual scenario of the pandemic.

As discussed above, we have modified the manuscript following the advice given by the Reviewer. We think that both the presentation and the theory are clearer now.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this paper, a compartmental model of the propagation of an infection with vaccination and reinfection is studied. The impact that changes in the rates of these two processes have on disease progression and on the number of deaths is analyzed. In order to highlight the overall effect of the demographic structure of populations and the propagation of a given disease among different groups, the population is divided into two subpopulations and the model is extended to the two-dimensional case. In addition to the study of equilibria and their relative stability, the model is then applied in the case of COVID-19. Different vaccination strategies are studied using real demographic data and with a population split between under 80 and over 80 individuals. It is observed that for low vaccination rates, the advisable strategy is to vaccinate the most vulnerable group first, in contrast to the case of sufficiently high rates, where it is appropriate to vaccinate the most connected group first. The simulations show also that with a low fatality ratio, the strategy that yields the greatest reduction in deaths is vaccination of the group with the most contacts, while the situation is reversed for higher fatality ratio.

The model and simulations presented are interesting and valuable. The comparison of the behavior of the model in the 4 different countries is very interesting, as well as the webpage created by the authors.

We thank the referee for the very positive evaluation and are very glad that the study is found interesting and valuable.

As minor comment, I think that the introduction of the model needs a more extensive literature review. For example, there is no mention of the classic SIR model of Kermack and McKendrick (1927) and other works on the introduction to epidemic models, which form the basis of the model presented by the authors.

The referee is right. There is a long history of extensions and applications since Kermack & McKendrick introduced the SIR model that we obviated. This has been amended by adding an introductory paragraph with several new references at the beginning of the Models section, page 3 in the main text.

Reviewer #2 (Significance (Required)):

The model presented by the authors is quite original and simple enough to be suitable to different contexts and scenarios.

Compared to previous work, this paper makes a twofold contribution, as explained by the authors. First, the introduction of reinfections shows the existence of long transients (or quasi-endemic states) that may precede the transition to a truly endemic state predicted for COVID-19. Second, the simplicity of model allows the characterization of systematic effects due to, at least, group size, demographic composition, and IFRs.

I am involved in the study and analysis of epidemic models accompanied by network effects. I think this paper is a good contribution, although preliminary, in the analysis of the vaccination process and in the search for the optimal strategy.

We thank the Reviewer and are glad that our goal, offering a model as simple as possible to obtain meaningful conclusions, is appreciated.

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Referee #2

Evidence, reproducibility and clarity

In this paper, a compartmental model of the propagation of an infection with vaccination and reinfection is studied. The impact that changes in the rates of these two processes have on disease progression and on the number of deaths is analyzed. In order to highlight the overall effect of the demographic structure of populations and the propagation of a given disease among different groups, the population is divided into two subpopulations and the model is extended to the two-dimensional case. In addition to the study of equilibria and their relative stability, the model is then applied in the case of COVID-19. Different vaccination strategies are studied using real demographic data and with a population split between under 80 and over 80 individuals. It is observed that for low vaccination rates, the advisable strategy is to vaccinate the most vulnerable group first, in contrast to the case of sufficiently high rates, where it is appropriate to vaccinate the most connected group first. The simulations show also that with a low fatality ratio, the strategy that yields the greatest reduction in deaths is vaccination of the group with the most contacts, while the situation is reversed for higher fatality ratio.

The model and simulations presented are interesting and valuable. The comparison of the behavior of the model in the 4 different countries is very interesting, as well as the webpage created by the authors.

As minor comment, I think that the introduction of the model needs a more extensive literature review. For example, there is no mention of the classic SIR model of Kermack and McKendrick (1927) and other works on the introduction to epidemic models, which form the basis of the model presented by the authors.

Significance

The model presented by the authors is quite original and simple enough to be suitable to different contexts and scenarios.

Compared to previous work, this paper makes a twofold contribution, as explained by the authors. First, the introduction of reinfections shows the existence of long transients (or quasi-endemic states) that may precede the transition to a truly endemic state predicted for COVID-19. Second, the simplicity of model allows the characterization of systematic effects due to, at least, group size, demographic composition, and IFRs.

I am involved in the study and analysis of epidemic models accompanied by network effects. I think this paper is a good contribution, although preliminary, in the analysis of the vaccination process and in the search for the optimal strategy.

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Referee #1

Evidence, reproducibility and clarity

The paper tackles an important problem regarding the effect of demographic dependent vaccination protocols on the reduction in the number of deaths with respect to the situation of no vaccination (say J). A compartmental SIRD model with reinfection Y is proposed, stratified in two (age dependent) groups, based on a binary reduction of a given contact map, and given infection fatality risk (IFR). Several countries are then analized.

As far as I understand we have a control variable v, parameters of the stratified model (i=1,2) tuned to match IFRi, and a control objective, i.e. minimization of J over one year.

The paper is well written. The final message and some theoretical passages are not completely clear, at least to me. I have the following observations that the authors may want to consider.

1)The study of stability of infection free and endemic equlibria should be better developed. The 5 equations can be reduced to 4 (neglecting D) and the characteristic of the reduced Jacobian used to characterize the local asymptotic stability of equlibria, instability, biforcation points etc... Alternatively, one can use a co-positive Lyapunov function (LF). For instance, if we take the LF V=S+I+Y+R, we get \dot V=-\mu_I I-\mu_Y Y \le 0. If \mu_I and \mu_y are strictly positive all equilibria are characterized by (S,0 0,R) and D=1-S-R. So, I don't understand the phrase after (7,8), notice that Y cannot be zero in finite time. For \mu_y=0 then Y can be nonzero. I guess that closed-form computation of S and R* is possible as function of the parameters at least in the case v=0. The stability result should be cast in function of the current reproduction number (not explicitated) wrt to S and R. The authors are invited to have a look at

1.1)Pagliara et al, "Bistability and Resurgent Epidemics in Reinfection Models", IEEE CSLetters, 2018,

for a theoretical analysis of stability on a similar (just a little bit simpler) model. What said before should be also extended to the stratified model, where a "network" Rt could be defined, see for instance

1.2)L. Stella et al, "The Role of Asymptomatic Infections in the COVID-19 Epidemic via Complex Networks and Stability Analysis", SIAM J Cont. Opt., 2021, (arxiv.org/pdf/2009.03649.pdf)

2)It is not clear whether the free contagion parameters of the model have been fitted on real data (identification from infection and reinfection data). Notice that the interplay between vaccination strategies and NPI is important, see e.g. 2.1) Giordano et al, Modeling vaccination rollouts, SARS-CoV-2 variants and the requirement for non-pharmaceutical interventions in Italy", Nature Medicine 2021, where progressive vaccination in reverse age order is considered together with different enforced NPI countermeasures.

3)In the model the immunity waning is not explicitly considered (flux from R to S or better from a vaccinated compartment to S). It is clear that this complicates the model. Please discuss why the indirect way the waning is considered here is justified.

3.1)Batistela et al, "SIRSi compartmental model for COVID-19 pandemic with immunity loss", Chaos Soliton and fractals, 2021.

3.2)McMahon et al, "Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected,Recovered) Modeling Using Empirical Infection Data", JMIR Public health and surveillance, 2020.

4)Reduction of deaths wrt no vaccination is of course important, but also reduction of stress in hospitals. This is particularly important now with the advent in Europe of the omicron variant. Please discuss on the real message you want to convey to policy makers in the actual scenario of the pandemic.

Significance

The final message and some theoretical passages are not completely clear, at least to me. Please discuss on the real message you want to convey to policy makers in the actual scenario of the pandemic.

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Reply to the reviewers

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Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This article focuses on one possible outcome of protein sequence evolution after duplication, in which the residue distribution at specific positions of a multiple sequence alignment becomes uncoupled from the distribution expected from the phylogeny of the protein family. The authors call these events "residue inversions" and interpret them as the result of functional pressures on family members with diverging cellular roles. Based on a theoretical model of residue evolution after duplication of the coding gene, the authors describe the criteria for categorizing a particular position in a protein as a "residue inversion" and develop an algorithm to identify such events in a multiple alignment. They then apply their approach to the family of Epidermal Growth Factor Receptors in Teleost fishes and identify 19 EGFR positions in a dataset of 88 fish genomes, which satisfy the criteria of "residues inversions". They provide support to the scoring scheme used in their approach through a simulated evolution run and conclude from a comparison of their positions to the ones predicted by SPEER to represent Specificity Determining Sites that the two are largely orthogonal and may therefore complement each other in sequence-based function prediction.

Major comments: 1. Throughout the paper, the functional involvement of positions subject to "residue inversions" is indirect, inferred from the literature, and in parts sparse and tenuous. It therefore remains unclear to what extent the interpretation that "residue inversions" represent functional adaptations is correct. The authors acknowledge this uncertainty in several places, including the Conclusions.

We agree with the reviewer that without experimental validation an uncertainty about the data interpretation remains, however testing protein function on a large scale and in non-model organisms is extremely challenging. Since we were aware of this obstacle, we validate our conclusions in different ways: 1. the theoretical model and the simulated MSA both show a lower chance of observing residue inversions than what we detected in the teleost fish EGFR example. 2. previous literature highlighted an identified inverted residue as the possible cause of sub-functionalization of teleost fish EGFR. 3 We generated the alpha fold models of teleost fish EGFR and performed molecular dynamic simulation of the two copies, in complex with the ligand. In our simulations, we see the same trend that we observe with the inter-paralog inversions at the functional level. The new results have been integrated in line 692-706.

"Residue inversion" is a very unintuitive term, which took me several readings to penetrate and made reading the article difficult. The authors may wish to reconsider this term. Naively, a residue inversion would be the swapping of residues between two positions, such that a residue expected in position A is found in position B, while the residue expected in B is found in A. That is what I suspect most readers will think.

We acknowledged that the terminology might be confusing. We therefore decided to define it as inter-paralog inversion of amino acids throughout all the text.

Is the phenomenon described here just a curiosity, or an important aspect of divergent evolution after duplication? The authors seem to be of two minds about it, calling the phenomenon "rare" in the Abstract, but an "important and understudied outcome of gene duplication" in the Introduction, then hedging again that it "might be rare" in the Conclusions. The benefits of recognizing such positions are also formulated with great caution, for example in lines 309-311: "In summary, the identification of residue inversion event has the potential to improve functional residue predictions".

We agree with the reviewer that we did not yet test the recurrence of this event on a large scale, however this does not exclude that this event is frequent. This work is focused on the observation, characterization, and implications of this event. Considering this comment and the one below we decided to perform a further analysis (see below for more details).

Additionally, the analysis of the frequency of this event at the whole-organism scale on multiple organisms, while interesting, would be out of the scope of this paper, if not just because it requires a totally different (large-scale) approach compared to the one used in here. This type of analysis is also limited by the absence of a database collecting intermediate knowledge that would speed up the initial part of ortholog classification at a broad range.

Finally, by rarity we mean the statistical chance of the event, not considering the effective chance of observing it from the real data. In fact, we rectified in the text using the reviewer’s observation.

OLD VERSION (ppXX):

Our work uncovers a rare event of protein divergence that has direct implications in protein functional annotation and sequence evolution as a whole.

NEW VERSION:

Our analysis shows a new way to investigate an important and understudied outcome of gene duplication.

It would probably strengthen the article substantially if the authors would (I) use their program to scan a large number of multiple alignments in order to establish more reliably how frequent this phenomenon actually is, and whether it is universal or a specifc aspect of eukaryotic, maybe even only vertebrate evolution; and then (II) mapped the positions identified on structural models for the proteins, obtained by homology modeling or AlfaFold prediction, in order to substantiate their potential origin as functional adaptations.

We thank the reviewer for the thoughtful suggestions. (I) we tested the inter-paralog inversion score at the proteome level using a reduced dataset (70) of reference teleost fish proteomes from Uniprot. We obtained 54 proteins that duplicated in the teleost specific whole genome duplication, then we run our pipeline on it. We found that the overall distribution of scores is more similar to the simulated evolution experiment rather than to the EGFR test case. We integrated the new results and discussion in a new paragraph and new figure in line 708-716.

(II) We considered also the analysis requested in the second point. Unfortunately, we could not extract any meaningful data from the AlphaFold models.

Reviewer #1 (Significance (Required)):

A method to improve the functional annotation of proteins in a paralogous family would be very useful, given the abundance of sequence data.

We thank the reviewer for acknowledging the importance of the question that we have addressed.

I am knowledgeable in varios aspects of molecular evolution and functional annotation. I am neither a mathematician, nor a developer of phylogenetic methods, so I cannot judge these aspects of the paper.

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Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Review of Pascarelli and Laurino titled “Identification of residue inversions in large phylogenies of duplicated proteins”

I find the topic of the paper very exciting and long overdue. Indeed, I was under the impression that the question of parallel evolution in paralogous copies must have been addressed long ago: to my surprise, having looked in depth at the literature, that is only partially so. The manuscript, therefore, addresses a relatively novel and fundamental question of broad interest.

We thank the reviewer for his positive comment.

Having said this, I also found the manuscript to suffer from an identity problem, which in many places encroaches on the underlying quality of the science. I will structure my review into three concerns: the identity issues, the novelty issue and the emergent quality issues from the two.

Identity issues:

The manuscript is primarily dealing with an evolutionary issue – or I am biased to see it this way as an evolutionary researcher myself. Nevertheless, much of the language and terminology of the paper either misuses evolutionary terms or invents new ones in its place with a bias towards a protein chemistry perspective. Specifically, what the authors call “residue inversions” is called “parallel evolution” or “convergent evolution” in the literature. Also, "residues" are typically used for physical amino acids in a structure. If we are talking about sequence level “amon acid” would be a better term. The issue is further confounded by the meaning of “inversion” in genetics as a single mutation that inverts the position of nucleotides (i.e. an “AT” becomes “TA”).

I strongly recommend for the authors to become familiarized with the common usage of existing and widely used terms in evolutionary biology that describe the phylogenetic patterns they see: parallel evolution, convergent evolution, homoplasy, etc, and to use them consistently throughout the manuscript.

The same goes for "mutation", which the authors confuse on two levels: evolutionary and biochemical. Sometimes the authors refer to “mutation” of amino acids (which can be entertained at some level, but from a genetic perspective only nucleotides mutate – in the protein biochemistry field this term is frequently applied to amino acid residues, which is the basis of the identity issue). However, since the authors also use “mutation” to refer to a “substitution” (which is what we call a mutation that has become fixed in evolution) this creates another level of confusion. I urge the authors to change this aspect of the language of the manuscript to better reflect evolutionary concepts.

As part of the language issues I am not sure how meta-functionalization in the author’s view differs either from neofunctionalization or specialization of duplicated genes.

We thank the reviewer to point out the terminology issue, this will also help reaching a broader audience. We clarify the confusion surrounding the terms “mutation” and “residue inversion” by changing the former to “substitution”, while the latter to “inter-paralog inversions” (see also other reviewer comments).

We understand the importance of the usage of the correct term to talk about this event of protein sequences evolution. Therefore, we used convergent and parallel evolution accordingly when we discussed the nuances between Metafunctionalization and parallel evolution in the text, in lines 188 and 399.

Novelty issues:

As I mentioned, the issue of parallel evolution of gene duplications is an extremely interesting topic. I was sure that the people who studied parallel evolution, or those interested in gene duplications, must have published extensively on this. However, my search of the literature revealed only a modest pre-existing effort. Nevertheless, previous efforts are not entirely non-existent and should be cited and discussed in this paper too. The most pertinent example is

https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-020-01660-1

which has an identical setup from what I can tell (compare Figure 1 in each paper).

This paper was not hard to find using "parallel evolution", thus my focus on the language issues in the previous section.

We thank the reviewer for his suggestion, we included the relevant papers in the text in lines 520-523. Interestingly, the cited paper shows that a comprehensive analysis of the fate of duplicated genes at the sequence level was done. However, in this paper, the ‘fate’ of a paralog is determined by counting the number of sites that support one or the other fate, independently of the orthologous relationship. In our study, we start from the orthologous relationship to pre-determine the fate of the paralogous protein, then we identify the sites that break this assumption. Our type of analysis is deemed to work only where the orthologous relationship is unequivocal. That is the reason why we chose an example with relatively short branch lengths after duplication (the teleost specific duplication). Our rationale is that with a higher genome coverage across organisms, resolving the orthologous relationship will get easier in time. However, our study focuses on a distinct case (asymmetric divergence) where the diverging paralogs converge to the same phenotype. In such a case, neutral substitutions related to the ancestral relationship of a protein can be filtered out to better search for functional adaptations.

Content issues:

The lack of attention to evolutionary concepts, in my opinion, provided some missed opportunities for the authors to attack the problem in a more convincing fashion. Specifically, in the setup to distinguish between parallel evolution of paralogues versus orthologues ("inversion" versus "species-specific adaptation" in the author's text) one must be able to distinguish between the two copies and assign true evolutionary relationship. In practice, that is not always possible based on tree lengths or topologies alone because of confounding factors such as independent duplications or gene conversion events.

I would feel better about the results of this study if the following two things were integrated.

The use of synteny to better determine homologous relationships (declare copies to be true paralogues if they occupy the same syntenic region). To compare the frequency or parallel evolution of paralogues versus orthologues as a null model of the expected number of parallel events in paralogous copies.

We agree that a synteny analysis has to be included. We tested it for the EGFR proteins in fish and the results support the orthologous relationship of EGFRa and EGFRb in the two groups compared (Cypriniformes versus other teleosts). The results were included in the text and in the Supplementary figure in lines 303-305.

The second point targets the way the model derives the expectations: at the author's own admission the model makes a number of unrealistic assumptions, ") equal branch length between the two paralogs; 2) only zero to one mutation can occur in each of the six branches; 3) after a mutation, each residue is equiprobable; 4) no selective pressure; 5) the probability of a mutation on a branch solely depends on the branch length (mutation rate). The authors do not really test the resulting tree on deviation from these assumptions (I am sure that it does not conform) but essentially comparing the occurrence of parallel events in paralogues versus orthologues may solve the problem with a less restrictive set of assumptions (that one expects an equal number of parallel events in paralogues and orthologues unless there is some paralogue-specific selection pressure, which is what the authors are looking for.

We compared the occurrence of the two outcomes in both the simulation and in the real data. In all cases, the two score distributions have a very similar shape, with a 99th percentile score of respectively 0.062 and 0.113. Most sites in an alignment (>99%) are not expected to be inverted and will have scores very close to 0, making the identification of inversions a quest for outliers. Furthermore, in case of the real data, each distribution can be independently affected by different selective pressures that might bias the background distribution. While the inversion in paralogs is expectedly involving few, functional, residues, the inversion in orthologs is expected to have a broad effect. For example, a temperature adaptation might shift the number of polar residues on the protein surface (see for example: https://academic.oup.com/peds/article/13/3/179/1466666). Also, a different protein chosen for analysis might generate a different background distribution of the two events. In the larger dataset, the similarity of the two distributions is even more (99th percentile of 0.07 and 0.08). Because of the shown similarity of the two event distributions, and the possible issues with different selective pressures, we leave the analysis suggested by the reviewer as a post-processing possibly performed by the user. We report a summary of this result born from the reviewer’s observation in line 478.

In summary, I believe that the topic is very interesting, the authors potentially found a new aspect of evolution of a specific gene family. However, in my opinion a major revision is needed to unite this text with the terms in the field, the previous publication and to integrate the two additional analyses I suggested.

Minor Comments:

I started adding these specific comments before generalizing the broader deviation from the common evolutionary language. There are more further along in the manuscript, but in the interest of time I will not articulate them here hoping that the authors will first try a major revision targeting these issues.

Line 64: While neutral mutations help to determine the phylogenetic position of a protein, mutations of functional residues are a signal of functional shifts that might occur independently of the phylogeny. - this is quite misleading. All substitutions (neutral or beneficial) have a phylogenetic signal. In any case, this is discussed here in phylogenetic terms: https://pubmed.ncbi.nlm.nih.gov/10742039/

We corrected the sentence to refer to divergence time instead of phylogenetic signal.

OLD VERSION:

While neutral mutations help to determine the phylogenetic position of a protein, mutations of functional residues are a signal of functional shifts that might occur independently of the phylogeny.

NEW VERSION:

While neutral substitutions are directly proportional to the time of divergence, a change in functional residues could be a signal of a functional shift that might occur independently of the divergence time.

Line 107: "under high evolutionary pressure" - I do not know what evolutionary pressure is nor why it can be high or low.

We corrected the term to “selective pressure”.

OLD VERSION:

Lorin et al. showed that both copies of EGFR might have been retained because they are involved in the complex process of skin pigmentation (40), which is under high evolutionary pressure in most fish.

NEW VERSION:

Lorin et al. showed that both copies of EGFR might have been retained because they are involved in the complex process of skin pigmentation (40), a trait that is under selective pressure in most fish

Line 112 "linearly inherited across orthologs" - linear is a poor choice of a word here. The first thing that comes to my mind is quadratic inheritance as an alternative. Perhaps the authors are looking for "vertical" versus "horizontal" - these are established terms in phylogenetics (think "horizontal gene transfer").

We corrected the term to “vertically inherited”.

OLD VERSION

Therefore, the power to predict functional residues is limited by our ability to track protein function on the phylogenetic tree when it is not linearly inherited by orthologs.

NEW VERSION

Therefore, the power to predict functional residues is limited by our ability to track protein function on the phylogenetic tree when it is not vertically inherited by orthologs.

It is my invariant practice to reveal my identity to the authors,

Fyodor Kondrashov

Reviewer #2 (Significance (Required)):

Addressed in the above

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Referee #2

Evidence, reproducibility and clarity

Review of Pascarelli and Laurino titled "Identification of residue inversions in large phylogenies of duplicated proteins"

I find the topic of the paper very exciting and long overdue. Indeed, I was under the impression that the question of parallel evolution in paralogous copies must have been addressed long ago: to my surprise, having looked in depth at the literature, that is only partially so. The manuscript, therefore, addresses a relatively novel and fundamental question of broad interest.

Having said this, I also found the manuscript to suffer from an identity problem, which in many places encroaches on the underlying quality of the science. I will structure my review into three concerns: the identity issues, the novelty issue and the emergent quality issues from the two.

Identity issues:

The manuscript is primarily dealing with an evolutionary issue - or I am biased to see it this way as an evolutionary researcher myself. Nevertheless, much of the language and terminology of the paper either misuses evolutionary terms or invents new ones in its place with a bias towards a protein chemistry perspective. Specifically, what the authors call "residue inversions" is called "parallel evolution" or "convergent evolution" in the literature. Also, "residues" are typically used for physical amino acids in a structure. If we are talking about sequence level "amon acid" would be a better term. The issue is further confounded by the meaning of "inversion" in genetics as a single mutation that inverts the position of nucleotides (i.e. an "AT" becomes "TA").

I strongly recommend for the authors to become familiarized with the common usage of existing and widely used terms in evolutionary biology that describe the phylogenetic patterns they see: parallel evolution, convergent evolution, homoplasy, etc, and to use them consistently throughout the manuscript.

The same goes for "mutation", which the authors confuse on two levels: evolutionary and biochemical. Sometimes the authors refer to "mutation" of amino acids (which can be entertained at some level, but from a genetic perspective only nucleotides mutate - in the protein biochemistry field this term is frequently applied to amino acid residues, which is the basis of the identity issue). However, since the authors also use "mutation" to refer to a "substitution" (which is what we call a mutation that has become fixed in evolution) this creates another level of confusion. I urge the authors to change this aspect of the language of the manuscript to better reflect evolutionary concepts.

As part of the language issues I am not sure how meta-functionalization in the author's view differs either from neofunctionalization or specialization of duplicated genes.

Novelty issues:

As I mentioned, the issue of parallel evolution of gene duplications is an extremely interesting topic. I was sure that the people who studied parallel evolution, or those interested in gene duplications, must have published extensively on this. However, my search of the literature revealed only a modest pre-existing effort. Nevertheless, previous efforts are not entirely non-existent and should be cited and discussed in this paper too. The most pertinent example is

https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-020-01660-1

which has an identical setup from what I can tell (compare Figure 1 in each paper).

This paper was not hard to find using "parallel evolution", thus my focus on the language issues in the previous section.

Content issues:

The lack of attention to evolutionary concepts, in my opinion, provided some missed opportunities for the authors to attack the problem in a more convincing fashion. Specifically, in the setup to distinguish between parallel evolution of paralogues versus orthologues ("inversion" versus "species-specific adaptation" in the author's text) one must be able to distinguish between the two copies and assign true evolutionary relationship. In practice, that is not always possible based on tree lengths or topologies alone because of confounding factors such as independent duplications or gene conversion events.

I would feel better about the results of this study if the following two things were integrated.

The use of synteny to better determine homologous relationships (declare copies to be true paralogues if they occupy the same syntenic region). To compare the frequency or parallel evolution of paralogues versus orthologues as a null model of the expected number of parallel events in paralogous copies.

The second point targets the way the model derives the expectations: at the author's own admission the model makes a number of unrealistic assumptions, ") equal branch length between the two paralogs; 2) only zero to one mutation can occur in each of the six branches; 3) after a mutation, each residue is equiprobable; 4) no selective pressure; 5) the probability of a mutation on a branch solely depends on the branch length (mutation rate). The authors do not really test the resulting tree on deviation from these assumptions (I am sure that it does not conform) but essentially comparing the occurrence of parallel events in paralogues versus orthologues may solve the problem with a less restrictive set of assumptions (that one expects an equal number of parallel events in paralogues and orthologues unless there is some paralogue-specific selection pressure, which is what the authors are looking for.

In summary, I believe that the topic is very interesting, the authors potentially found a new aspect of evolution of a specific gene family. However, in my opinion a major revision is needed to unite this text with the terms in the field, the previous publication and to integrate the two additional analyses I suggested.

Minor Comments:

I started adding these specific comments before generalizing the broader deviation from the common evolutionary language. There are more further along in the manuscript, but in the interest of time I will not articulate them here hoping that the authors will first try a major revision targeting these issues.

Line 64: While neutral mutations help to determine the phylogenetic position of a protein, mutations of functional residues are a signal of functional shifts that might occur independently of the phylogeny. - this is quite misleading. All substitutions (neutral or beneficial) have a phylogenetic signal. In any case, this is discussed here in phylogenetic terms: https://pubmed.ncbi.nlm.nih.gov/10742039/

Line 107: "under high evolutionary pressure" - I do not know what evolutionary pressure is nor why it can be high or low.

Line 112 "linearly inherited across orthologs" - linear is a poor choice of a word here. The first thing that comes to my mind is quadratic inheritance as an alternative. Perhaps the authors are looking for "vertical" versus "horizontal" - these are established terms in phylogenetics (think "horizontal gene transfer").

It is my invariant practice to reveal my identity to the authors,

Fyodor Kondrashov

Significance

Addressed in the above

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Referee #1

Evidence, reproducibility and clarity

This article focuses on one possible outcome of protein sequence evolution after duplication, in which the residue distribution at specific positions of a multiple sequence alignment becomes uncoupled from the distribution expected from the phylogeny of the protein family. The authors call these events "residue inversions" and interpret them as the result of functional pressures on family members with diverging cellular roles. Based on a theoretical model of residue evolution after duplication of the coding gene, the authors describe the criteria for categorizing a particular position in a protein as a "residue inversion" and develop an algorithm to identify such events in a multiple alignment. They then apply their approach to the family of Epidermal Growth Factor Receptors in Teleost fishes and identify 19 EGFR positions in a dataset of 88 fish genomes, which satisfy the criteria of "residues inversions". They provide support to the scoring scheme used in their approach through a simulated evolution run and conclude from a comparison of their positions to the ones predicted by SPEER to represent Specificity Determining Sites that the two are largely orthogonal and may therefore complement each other in sequence-based function prediction.

Major comments:

1. Throughout the paper, the functional involvement of positions subject to "residue inversions" is indirect, inferred from the literature, and in parts sparse and tenuous. It therefore remains unclear to what extent the interpretation that "residue inversions" represent functional adaptations is correct. The authors acknowledge this uncertainty in several places, including the Conclusions.
2. "Residue inversion" is a very unintuitive term, which took me several readings to penetrate and made reading the article difficult. The authors may wish to reconsider this term. Naively, a residue inversion would be the swapping of residues between two positions, such that a residue expected in position A is found in position B, while the residue expected in B is found in A. That is what I suspect most readers will think.
3. Is the phenomenon described here just a curiosity, or an important aspect of divergent evolution after duplication? The authors seem to be of two minds about it, calling the phenomenon "rare" in the Abstract, but an "important and understudied outcome of gene duplication" in the Introduction, then hedging again that it "might be rare" in the Conclusions. The benefits of recognizing such positions are also formulated with great caution, for example in lines 309-311: "In summary, the identification of residue inversion event has the potential to improve functional residue predictions".

It would probably strengthen the article substantially if the authors would (I) use their program to scan a large number of multiple alignments in order to establish more reliably how frequent this phenomenon actually is, and whether it is universal or a specifc aspect of eukaryotic, maybe even only vertebrate evolution; and then (II) mapped the positions identified on structural models for the proteins, obtained by homology modeling or AlfaFold prediction, in order to substantiate their potential origin as functional adaptations.

Significance

A method to improve the functional annotation of proteins in a paralogous family would be very useful, given the abundance of sequence data.

I am knowledgeable in varios aspects of molecular evolution and functional annotation. I am neither a mathematician, nor a developer of phylogenetic methods, so I cannot judge these aspects of the paper.

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Reply to the reviewers

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Reviewer #1 (Evidence, reproducibility and clarity (Required)):

**Summary:**

The authors characterized a new lncRNA locus named FLAIL that controls flowering time in Arabidopsis thaliana. The functional validation of this locus is strongly supported by the use of several different tools (CRISPR-Cas9 deletions, T-DNA insertion, amiRNA gene silencing, and transgene complementation of KO lines). It is also suggested that FLAIL lncRNA works in trans but not in cis. There are strong observations supporting that FLAIL works in trans.

Moreover, it is suggested that FLAIL regulates gene expression by interacting with distant chromatin loci. This was assessed using RNA-Seq and ChIRP-Seq. Yet, the overlap between DEGs in the flail mutant and FLAIL binding sites at the chromatin is very small, with only 12 genes. From those, only 2 flowering genes' expression was rescued by FLAIL transgene complementation. The final conclusion that FLAIL lncRNA represses flowering by direct inhibition of the 2 flowering genes expression is correlative, and lacks genetic validation.

#1.1 We plan to support the conclusions in the manuscript genetically as the reviewer suggests. We started these experiments yet they will require the timeframe of the full revision.

In addition inspection of the supplementary file shows that the ChIRP analysis was done without filtering for the FDR so that some of the positive hits have an FDR of 0,232.

#1.2 We strengthened the manuscript by implementing and FDR filter of ChIRP-seq results. The distribution of FLAIL binding sites in Fig. S7B and Table S4, and overlapping numbers between DEGs and FLAIL-ChIRP in Fig. S8A were correspondingly updated.

In addition, many of the peaks land in intergenic regions with is not mentioned in the text a graph with the position of the peaks in respect to nearby genes would help.

#1.3 Thank you for the suggestion, we strengthened the manuscript with the requested analysis. We implemented the FDR filter, then we used "tssRegion" in ChIPseeker to set distance to the nearest TSS as (-1000, 1000), then most peaks were located in promoter regions (67.24%) and in intergenic regions with 16.38%. Since many papers present the position of the peaks by ChIPseeker (PMID: 32338596, PMID: 28221134, PMID: 31081251, PMID: 32012197, PMID: 31649032, PMID: 32633672) we also applied a similar method to display a distribution of FLAIL binding loci relative to distance from the nearest TSS in Fig. S7C.

In one sentence, the authors used the right model system and methodology, including advanced techniques, to characterize a new trans-acting lncRNA important for controlling the flowering time in Arabidopsis but lack evidence supporting a mechanism of action that goes beyond the interaction with several chromatin loci.

**minor points:**

line#63-64 the authors say the COLDAIR and ASL work on FLC in cis in my view the original papers suggested/showed they work in trans.

#1.4 We increased precision by changing this sentence to ‘Vernalization-induced flowering associates with several lncRNAs such as ____COOLAIR____, COLDAIR____, ANTISENSE LONG (ASL), and COLDWRAP____ that in cis or in trans locally repress gene expression of FLOWERING LOCUS C (FLC), a key flowering repressor at different stages of vernalization’____.

Fig 1B please add some more protein-coding RNAs for the bio-info analysis for comparison

#1.5 ____done.

Order of Supplementary Fig citation is mixed with S2 coming before S1B

#1.6 Thank you, we ordered all figures by appearance in the text. __

__

It would help the reader to have a schematic of the crisper deletions, T-DNA insertion, and position of primers used for the RT-qPCR.

#1.7 We enhanced our presentation of Fig. 1A. It shows a schematic of them as well as positions of primers.

In the supplementary PDF file, some of the text is missing on page 3 beginning and end of lines.

#1.8 we ensured all text in new submission.

Reviewer #1 (Significance (Required)):

The use of several different tools to validate the biological function of FLAIL locus is a major strength of this work.

The authors propose that flowering time and its gene regulation are controlled by sense FLAIL lncRNAs. However, the sense transcription of FLAIL locus is not detected in wild-type plants by TSS-Seq, TIF-Seq, or plaNET-Seq.

#1.9.1 There appears to be some confusions. Transcription of sense FLAIL can be observed in chr-DRS, TSS-seq, TIF-seq in wild type and even in plaNET-seq in NRPB2-FLAG nrpb2-1 plant. We enhanced presentation of Fig. 1 and provided a more clear description in Line 81-99.

If the authors would have explored further the expression of FLAIL transcripts in different stages of development (vegetative and non-vegetative) and in response to different conditions, it would make their claims on the function of FLAIL lncRNAs more convincing. Additionally, flail mutants could have been obtained in the hen-2 background, since it's there where we can observe FLAIL transcription.

#1.9.2 Thank you for the suggestion. We included additional analyses in ____Fig. S2 for FLAIL transcription level in different tissues and different abiotic stress conditions base on 20,000 publicly available RNA-seq libraries (PMID: 32768600). Although many libraries are non-stranded, this analysis determined that sense FLAIL or total FLAIL (including sense and antisense) is broadly expressed over many tissues and induced in response to many abiotic stresses (Fig. S2A-B), therefore suggesting that FLAIL may be needed broadly in Arabidopsis.

FLAIL locus lays on the proximal promoter region of PORCUPINE (PCP), an important regulator of plant development. As flail mutants, pcp mutants display an early flowering phenotype. The authors show no link between FLAIL and PCP from the overlap between re-analysis of published RNA-Seq data for pcp and RNA-Seq and ChIRP-Seq from the authors. This analysis is not enough to exclude the involvement of PCP from the FLAIL function. PCP expression using RT-qPCR should be performed in flail mutants to further support that FLAIL works independently from PCP.

#1.10 We strengthened this conclusion by adding the requested experiment. PCP transcription level in flail3 mutant was provided by RT-qPCR and RNA-seq in Fig. S11A-B.

This work does not hypothesize any molecular mechanism besides the interaction of FLAIL lncRNAs with several chromatin loci. It was recently proposed in Arabidopsis that a trans-acting lncRNA interacts with distant loci via the formation of R-loops. The authors do not comment on that. This work would benefit in correlating FLAIL binding sites with R-loop-forming regions mapped in Arabidopsis, regardless of the results from this analysis. Additionally, the authors could attempt to look for a motif responsible for FLAIL binding.

Check R-loop forming data R-loops (Santos-Pereira and Aguilera, 2015) in Arabidopsis, determined by DRIP-seq (Xu et al., 2017).

#1.11 Thanks very much for this excellent suggestions.

First, we searched for a consensus DNA motif on FLAIL binding regions by Homer. We determined four commonly enriched DNA sequence motifs among FLAIL target genes (Fig. 4G). Notably, the target genes CIR1 and LAC8 contained consensus sequences that matched to all FLAIL binding motifs (Fig. 4G). These data are consistent with a model where FLAIL binds DNA targets through a sequence complementary mechanism. Functionally important sequences are frequently conserved among evolutionarily distant species, we observed three motifs that appeared to cross-species conserved (Fig. S9), suggesting a potential evolutionarily constrained role.

Second, we indeed identified R-loops peaks on several of FLAIL binding sites by DRIP-seq (Xu et al., 2017). For example, we observed R-loop formation over three FLAIL binding motifs at CIR1 locus and one at LAC8 (Fig. R1), indicating that R-loop formation may also be a factor determining FLAIL binding. Even though R-loop peaks are present at several FLAIL targets, full elucidation if R-loop formation determines FLAIL targeting requires further experimental evidence is beyond the scope of the current manuscript.

Fig. R1 Representative tracks at LAC8 and CIR1 showing R-loop formation by DRIP-seq on Watson strand (w-R loops), Crick strand (c-R loops). Undetectable R-loops after RNAse-H treatment was shown as negative control. Four conserved sequence regions of FLAIL binding motifs were indicated by red arrows at LAC8 and CIR1 loci. Gene annotation was shown at the bottom.

Most of the key conclusions are convincing, except for the flowering time control directly through CIR1 and LAC8, which should be mentioned as speculative

____#1.12____ Thank you for finding most key conclusions convincing. We plan strengthen the manuscript with additional genetic evidence to as part of the full revision.

The words locus and loci are latin and they should be written in italic. The word Brassicaceae, referring to the family should be in italic, and should not be "Brassicaceaes". The word analysis has the wrong spelling.

#1.13 We follow conventions given in Scientific Style and Format: The CBE Manual for Authors, Editors and Publishers (1994) Cambridge University Press, Cambridge, UK, 6th edn. The words locus and loci are common Latin terms and should not be italicized. However, should the format of the final prefer these words in italics we will change it later. We improved consistency of using italics. “Brassicaceaes” was changed to “Brassicaceae”.

"How much time do you estimate the authors will need to complete the suggested revisions: this is difficult to answer as it depends to which level the author would like to take their work. In my view, if all new experiments would have to be started from scratch it is too far away to be estimated.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this ms, the authors identified the FLAIL lncRNA that represses flowering in Arabidopsis from a locus producing sense and antisense transcripts. They use an allelic series involving T-DNA insertions, CRISPR/Cas9 and artificial miRNAs to study the role of FLAIL in flowering. A complementation series of constructs of the flail3 allele allowed them to show that the sense FLAIL lncRNA can act in trans. RNAseq revealed a small group of genes linked to the regulation of flowering whose expression is affected in the mutant and restored in the complementation line. To gain further insight into FLAIL function, the authors used a ChIRPeq approach to test whether the lncRNA can recognize potential target genes along the genome and they could show that FLAIL binds specific genomic regions. Clearly, this paper shows very nice evidence that the FLAIL lncRNA can act in trans to regulate gene expression. Nevertheless, there are certain points that need to be clarified to further support the action of the sense FLAIL transcript.

1.According to Fig. 1 A, the antisense FLAIL is "internal" to the DNA genomic area spanning the sense FLAIL. Hence, with direct RT-qPCR is very difficult to distinguish between these molecules as a minor "RT" activity of the Taq polymerase may lead to detection of low levels of antisense, idem if RDRs may generate low antisense levels. Although I think that the plaNET seq brings strong evidence about the start and ends of these molecules, to measure them by RT-qPCR is not trivial and requires the use of strand-specific RT-PCR using a 5' extension of the oligo and amplification with one oligo of the FLAIL sequence (sense or antisense) and the added oligo.

#2.1.1 Thanks for this good suggestion. We tested both sense and antisense FLAIL transcription using oligo linked gene specific reverse primers for RT and a pair of the linked oligo and gene specific forward primer for qPCR. Primer locations were shown in Fig. 1A and new data were in Fig. 1C-D, Fig. 2B-C, and Fig. S4B-C.

It is not clear how they could distinguish precisely sense and antisense particularly when both RNAs correlate as it is the case here in all alleles (Fig. 1 C and 1D). This should be more explicitly mentioned in the materials and methods section.

#2.1.2 We gave a description of strand specific RT-qPCR method in detail in Line 397-402.

2.In Fig. 2, what are the levels of antisense in the complementing lines with the sense transcript? And reciprocally sense levels in antisense constructs?

#2.2 We added this data in Fig. 2B-C and described in Line 136-143. We indeed observed that sense FLAIL transcripts in the transformed asFLAIL construct or asFLAIL transcripts in the transformed sense FLAIL construct was similar to the control 35S:GUS (Fig. 2B-C), validating that NOS terminator inhibits antisense transcripts. We also noted that the transformed 35S:GUS and sense FLAIL construct expressed higher asFLAIL compared to the flail3 mutant (Fig. 2C). This may be caused by a T-DNA insertion of the resulting transgenic plants.

This will definitively demonstrate the assumption that the T-NOS termination will not allow any expression on the other strand. At present, only one of the lncRNAs is measured in each experiment?

#2.3 We appreciate the next-level reflection of this reviewer, with so many regions initiating cryptic antisense transcription it is an interesting challenge to identify a 3´- terminator that initiates no or poor antisense transcription.

First, previous published data argue that the NOS terminator is largely abolishing initiation of antisense transcription (PMID: 33985972, PMID: 30385760, PMID: 27856735). All these studies address roles of antisense transcription by generating mutations abolishing antisense lncRNA transcription using the NOS terminator sequences.

Second, to satisfy the curiosity of this reviewer, we provide data below that from another manuscript of the lab in preparation. It’s a screenshot of plaNET-seq in fas2-4 NRPB2-FLAG nrpb2-1 mutant carrying a pROK2 construct. The pROK2 T-DNA coincidentally carries a NOS terminator. We mapped plaNET-seq reads to the pROK2 scaffold to display the reads. In pROK2, a NOS promoter activates NPTII expression (red) with NOS terminator as a terminator sequence. No antisense transcription (blue) is detectable by this sensitive method to detect nascent transcripts. Taken together, the selection of the NOS terminator as a region suppressing initiation of antisense transcription represents a valid choice.

Fig. R2 Genome browser screenshot of plaNET-seq at NPTII locus of pROK2 T-DNA vector in fas2-4 NRBP2-FLAG nrpb2-1 mutant. This mutant carries a pROK2 construct, in which a NOS promoter activates NPTII expression with NOS terminator a terminator sequence. Sense strand was shown in red and antisense strand in blue. pROK2 annotation was shown at the bottom.

3.In Fig. 3, it will be important to also show the FLAIL locus in the flail3 mutants (in comparison to the wt) as well as the transgene locus. Here the reads will be strand specific and furthermore this will allow to show that the transgene is not generating antisense transcripts (through RDRs for gene silencing?) and confirm that the sense FLAIL is required for the complementation.

#2.4 Thank you very much for this suggestion. NGS reads for endogenous FLAIL and transgenic FLAIL both map to the FLAIL locus, so we show the FLAIL locus in Fig 3B. This representation shows that sense FLAIL transcripts were significantly reduced in flail3 and rescued in complementation line comparing to wild type. These data argue against the idea of gene silencing and linked antisense production from the transgene. However, RNA-seq suggests that an isoform of asFLAIL appears to accumulate in flail3. Since we fail to identify this accumulation by strand specific RT-qPCR result in flail3 and in CRISPR-deletion lines, this may be an asFLAIL isoform resulting from the T-DNA insertion.

4.In Fig. S5, the expression of FLAIL is shown in the artificial miRNA lines. Is the antisense FLAIL affected "indirectly" by the cleavage of the amiRNA or remains constant? This is likely the case but should be shown.

#2.5 We added this result in Fig. S4C and expression level of asFLAIL remains constant compared to the transformed empty vector control.

5.The ChIRPseq data adds major novelty to the ms and brings new ideas about the way of action of FLAIL. However, are there any common epigenetic states between ChIRP targets (e.g. histone modifications, antisense RNA production, homologies "detected" in the conserved regions between Camelina and Arabidopsis and the target loci? Or others) that may highlight potential mechanisms leading to repression mediated by FLAIL of these loci? There are many databases that could be explored (even during flowering) to search for potential relationships. Although precise description of the mechanism is out of the scope of this ms, this can be discussed in more detail to further expand on the nice data obtained.

#2.6 We searched for a consensus DNA motif on FLAIL binding regions by Homer. We determined four commonly enriched DNA sequence motifs in target genes. Notably, the target genes CIR1 and LAC8 contained consensus sequences that matched to all FLAIL binding motifs (Fig. 4G). These data are consistent with a model where FLAIL binds DNA targets through a sequence complementarity mechanism. Functionally important sequences are frequently conserved among evolutionarily distant species, we observed three motifs that appeared to cross-species conserved (Fig. S9), suggesting a potential evolutionarily constrained role.

**Minor comments:**

6.In Fig. S3, a global alignment between FLAIL and two loci in Arabidopsis and Camelina is sown. What is the extent of homology? How conserved is this sequence at nucleotide level (small or very long?) to support the conservation of this lncRNA. Are there potential structures conserved among these lncRNAs?

#2.7 T____wo consensus regions of ____FLAIL____ sequences among eleven disparate Brassicaceae genomes were shown in Fig. S9. ____Camelina sativa_ shared 98-nucleotide_ conserved sequences with Arabidopsis thaliana. In the future, it will be interesting to explore evolutional conserved structures among Brassicaceae genomes. However, these analyses are beyond the scope of the current manuscript.

7.In Fig. S4B, arrows may help to understand which seeds were selected.

__#2.8 Thanks. Arrows were included.____

__

Reviewer #2 (Significance (Required)):

This paper is a very nice piece of work and demonstrate the action of a long non-coding RNA (lncRNA) in trans on specific targets involved in the regulation of a developmental process, flowering. There is growing evidences that the non-coding genome hides large number of lncRNAs and there is little detailed genetic support for the action of lncRNAs globally. In contrast to many descriptive papers in the field, this ms demonstrates genetically, through an allelic series and complementation experiments, that this lncRNA locus is involved in flowering regulation and that its sense lncRNA recognizes target loci genome-wide, bringing interesting perspectives on potential new mechanisms of transcriptional regulation mediated by non-coding RNAs.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In the manuscript by Jin et al authors characterize the FLAIL DNA locus in Arabidopsis (using a wide array of publicly available datasets), which produces a set of sense and anti-sense lncRNAs.

While our work on the FLAIL manuscript was ongoing we published the manuscripts where we presented these novel genomics methods and related data to capture nascent transcription and cryptic isoforms. We shared most data with TAIR, so we are happy to hear that these data are considered publically available.

Authors determined that the sense FLAIL lncRNA (or a set of sense lncRNAs, which isn't fully clear from the way the data are presented) is involved in flowering time in Arabidopsis based on the fact that the several flail mutants lead to the early flowering phenotype and this flowering defect is complemented by transgenic FLAIL DNA, meaning that FLAIL lncRNA acts in trans.

A series of experiments lead us to conclude that the sense isoform of FLAIL is responsible for the effect. We improved the data representation and writing of the manuscript to enhance accessibility.

The T DNA flail3 - mutant results in expression changes (up or down) of 1221 genes, including twenty genes linked to flowering in various ways. Expression of a group of these flowering-related genes could be either fully (for eight genes) or partially (for five genes) rescued by transgenic FLAIL. Authors also conducted the ChIRP-seq to determine which genes are physically bound by FLAIL lncRNA genome-wide. It was found that 210 genes in the genome are bound by FLAIL lncRNA. Comparison of the dataset of differentially expressed genes in the T-DNA flail3 mutant with the ChIRP-seq dataset of genes that are bound by FLAIL lncRNA revealed the 12 overlapping genes.

Among these twelve overlapping genes, four were found to be functionally connected to flowering with expression of these four genes being down in flail3 T-DNA mutant. Two out of these four genes were ruled out from being involved in the regulation of flowering by FLAIL. Authors conclude that the two other genes (Cir1 and Lac8) are responsible for the late flowering phenotype of flail mutants based on the three lines of evidence: (i) these genes expression is reduced in the flail mutant, (ii) FLAIL lncRNA directly interacts with these genes chromatin, (iii) the mutants of these genes were previously reported by others to display early flowering phenotypes too. While I find many of the findings reported in the manuscript very interesting, building a good foundation on which to expand the study and providing a very good leads for follow up experiments, I also have serious concerns about the manuscript in its current form.

Most importantly, this reviewer doesn't think that the mechanism of FLAIL lncRNA action was convincingly demonstrated. The main question would be how FLAIL lncRNA works and this question wasn't fully answered. It is great that FLAIL lncRNA binds directly to the two flowering-related genes, but what does it mean? Does it change any chromatin context of these genes quantitatively or qualitatively to affect the transcription? Or does it bind any components of transcriptional machinery and thus controls the transcriptional output?

#3.1 This manuscript addresses an important question in the field question: what is the evidence for functional elements in non-coding regions of genomes? Despite many efforts, convincing genetic support for these functions often remained limited. In addition to our strong genetic data, we provided new evidence that FLAIL recognizes targets with evolutionally conserved sequence motifs as part of the revision in Fig 4F and Fig. S9. Additionally, we plan to do ChIP-qPCR to identify histone modifications on FLAIL targets.

Additionally, flail3 T-DNA mutant affects the expression of 1221 genes and FLAIL lncRNA physically interact with 210 genes, so how can authors be fully sure that FLAIL lncRNA has only direct effect on these two genes and doesn't also contribute to the regulation of the upstream to Cir1 and Lac8 genes or even components of the transcriptional machinery that regulate these genes?

#3.2 We agree with this opinion. It is the reason why we felt stating this exact conclusion in our previous manuscript was justified. We improved accessibility of our manuscript in the revision, these clarify our model, that the trans-acting lncRNA sense FLAIL can interact with the chromatin regions of its target genes to directly or indirectly regulate gene expression changes involving flowering (Line 274).

Theoretically, doing RNA-seq in the amiR-FLAIL sense lncRNA mutant might have a chance of reducing the number of affected DEGs, making it easier to analyze the FLAIL targets, even if the allele can't be used for complementation experiments.

#3.3 Thanks for this suggestion. We plan to confirm key gene expression changes using amiRNA-FLAIL in full revision.

Also, auhors totally neglect putting the Cir1 and Lac8 genes into the context of flowering regulation, but it is something that needs to be done.

#3.4 ____We discussed roles of CIR1 and LAC8 in flowering regulation in Line 260-272. Flowering is fine-tuned to maximize reproductive success and seed production and by endogenous genetic cues and external environmental stimuli such as photoperiod. Nevertheless, many details of the flowering pathways and their integration remain to be investigated____. CIR1 is a circadian clock gene, induced by light and involved in a regulatory feedback loop that controls a subset of the circadian outputs and thus determines flowering time. Our GO analysis supports that a subset of DEGs are connected to the response to red or far red light that contains among other key flowering genes such as ____phytochrome interacting factor____ 4____ (PIF4) and CONSTANS (CO)____. FLAIL also binds the chromatin region of LAC8. LAC8 is a laccase family member that mainly modulates phenylpropanoid pathway for lignin biosynthesis____. Similar to flail, lac8 mutants flower early. While intermediates in this pathway or dysregulation of lignin-related genes could promote flowering in plants, the molecular connections of reduced LAC8 expression to effects on flowering time will require further investigation.

Lastly, the paper needs to be totally rewritten to be even properly evaluated. In its current state it reads like a very short draft.

#3.5 We reorganized the structure of manuscript, improved clarity and provided new mechanistic evidence in Fig. 4G and Fig. S9 to present a more complete manuscript.

The Abstract is weak, the Introduction is written in a such telegraphic style that it is barely readable, in many places there is no connections between sentences leading to the information appear to be presented as random, even if it isn't.

#3.6- We strengthened the Abstract by providing new evidence and improved for the Introduction.

The Results section is written rather rudimentary with information not being sufficiently provided to describe the results but rather scattered between the Results and Figure legends.

#3.7 Thanks for your suggestions, we described each FLAIL length and all constructs in detail in Results, put a schematic of T-DNA and CRISPR mutants in Fig. 1A, moved comparative genomics data to the end of Results and ensured all figures in order.

The Discussion is the best written part of the manuscript.

Thanks for your appreciation of the Discussion.

The Conclusion section carries no specific information and reads more like a little summary suitable for a review article rather than experimental paper.

#3.8 We agree this opinion, this paragraph fits Discussion better and Conclusion was removed.

Therefore, this reviewer thinks that regardless of how authors will choose to proceed with the current experimental version of the manuscript, it'd be in the authors' best interests to at least fully revise the paper before resubmitting anywhere. I'd also advise authors to seek professional editorial help specifically using an editor with the background in the plant sciences.

Authors might also want to consider moving Fig.3 into the Suppl. as it doesn't carry much weight or significance and perhaps make existing figures more meaningful and comprehensive and by including a better diagram of the locus (e.g., Fig. S1), etc.

#3.9 We thank this helpful suggestion. Fig.3 represents the RNA-seq data. In combination with supporting data in the supplementary material, it gives an easy visual readout of the reproducibility of the findings in replicates of stranded RNA-seq. In a new submission, we moved it to Fig. S5B and highlighted 13 differentially expressed flowering genes as well as sense FLAIL in flail3 that were rescued in complementation line in Fig. 3A. Moreover, we gave screenshots of FLAIL itself and four flowering related FLAIL targets in RNA-seq with a clear schematic representation of each locus. We believe these revisions improve Fig. 3.

It's not practical to list all issues with the writing as the paper requires total re-writing, so I can just make a few suggestions without any specific order to help authors improve the paper:

We are happy to improve our manuscript with the help of the reviewers. We addressed all comments including from reviewer #3 with a constructive spirit. However, since colleagues and reviewers #1 and #2 found the manuscript comprehensible to the point where they could make expert-level comments that illustrate understanding of the manuscript, a total re-writing did not feel like the most constructive suggestion to improve the manuscript.

--There is no statement anywhere that states the goal of the study.

#3.10____ We stated the goal of the study in line 50-69 and we think this is a misunderstanding. We summarized three issues currently exist in characterization of functional lncRNA in the last sentence of the first three paragraphs in Background: 1 in Line 50, the broad range of candidate hypotheses by which lncRNA loci may play functional roles call for multiple approaches to distinguish alternative molecular mechanisms. 2 in Line 59, functional characterization of trans-acting lncRNAs remains a key knowledge gap to understand the regulatory contributions of the non-coding genome. 3 in Line 69, the contribution of trans-acting lncRNAs to the regulation of distant flowering genes is currently unclear. So in the last paragraph of the background, we claimed that our goals are to address these questions through characterization of functional FLAIL lncRNA in flowering repression using multiple genetic approaches and various genomic data.

--No rational is provided on why authors decided to examine this specific genomic locus.

#3.11 For several years, our lab studies the rules and roles of non-coding transcription. We characterized and are characterizing several loci with evidence of non-coding transcription in a range of species. Early experiments suggested that FLAIL functioned in flowering, this manuscript clarifies that the function is executed as trans-acting lncRNA of the sense FLAIL isoform.

--Typically, the significance is in studying the function of lncRNA or a group of lncRNAs produced from a genomic locus, I don't think I ever encountered the instances when it was exciting to study just a specific genomic locus. If the locus does indeed have any significance for initiating the study, it needs to be explained.

#3.12 This study is remarkable in many aspects. We fully discuss key strengths in the discussion. First, we ____exhibit a trans-acting lncRNA FLAIL that represses flowering by promoting the expression of floral repressor genes as discussed in Line 247-281_; Second, in Line 284-306, we informed that this study provide a compelling model about how to apply _series of convincing genetic data____ to functionally characterize lncRNA loci. Third, in Line 307-312, evolutionary conserved FLAIL sequences across species is key to characterize the functional _microhomology in other _Brassicaceae.

--The locus can produce lncRNAs, but it can't harbor them.

#3.13 We clarified this confusion by enhancing ____presentation of Fig. 1 and providing a more clear description of each sequencing method and results in Line 81-99. Although we provided evidence that transcription of both sense and antisense FLAIL are more stable in hen2-2, they were clearly observed in chr-DRS in wild type and plaNET-seq in NRBP2-FLAG nrpb2-1 and sense FLAIL was even detected in TSS-seq and TIF-seq in wild type.

--No length of FLAIL lncRNAs or their range is provided in the first section of Results.

#3.14 We gave the length of sense FLAIL in Line 82 and antisense FLAIL in Line 86.

--On many occasions authors don't state rational for doing experiments, which leads to information often flowing as random.

#3.15 we enhanced clarity of the rational for each experiment and made some connections between sentences to make more fluent. For example, in sentences in Line 99, Line 113, Line 126, Line 159, Line 183, Line 214, and Line 219.

--What do authors mean by the subtitle "FLAIL characterizes a trans-acting lncRNA repressing flowering"? How can lncRNA FLAIL or FLAIL locus characterize lncRNA?

#3.16 We changed it to “FLAIL represses flowering as trans-acting lncRNA” in Line 112.

--Check all figures. E.g., Fig. 3B-E mentions only accession numbers for the genes.

#3.17 The systematic gene IDs are a valid way to represent data, in particular for genomics data since it facilitates cross-comparisons. To make it more accessible we also show systematic names of each gene in Fig. 3A-F, Fig. S6 and Table S3.

--It is not clear where exactly the T-DNA insertion is located relative to sense FLAIL in flail3 mutant (Fig. S4).

#3.18 We moved the schematic to clarify this to revised Fig. 1A and the exact T-DNA insertion site is mentioned in the legend.

--- What is the length of the complementing sense FLAIL lncRNA?

#3.19 We now include the length of the complementing sense and antisense FLAILs in Line 351-352.

--Check the description of each and every construct used and provide explanation for each in the Results. E.g., the pFLAIL:gFLAIL18/88 and pasFLAIL:gasFLAIL18/39 constructs aren't explained in Results, and can only be found in Fig. 2 legends.

#3.20 We described each construct including pFLAIL:gFLAIL18/88 and pasFLAIL:gasFLAIL18/39 constructs in Line 133, amiR-FLAIL-11 and amiR-FLAIL-11 in Line 149.

Reviewer #3 (Significance (Required)):

Tens of thousands of lncRNAs have been identified in various eukaryotes, but their biological roles have been shown only for a small fraction of them, and the mechanisms of their action are delineated for only a very few of them. Most of the advances on the field of lncRNAs are reported in metazoan, while the field of lncRNAs in plants is lagging far behind in terms of knowledge about lncRNAs with assigned biological functions or lncRNAs with delineated mechanisms of action. From this point of view, this reviewer is always excited to see any new functional plant lncRNAs for which either biological or mechanistic functions have been determined, and deems the information on this subject significant. The manuscript's findings are potentially very interesting and present a decent body of work that lays a very solid groundwork for future experiments. My main concern about the manuscript's significance in its current form is the fact that no real solid mechanism of action for the described lncRNA or a set of lncRNAs (?) has been demonstrated. The best mechanistically studied lncRNAs in Arabidopsis are involved in the regulation of flowering time, particularly those that function in the vernalization flowering pathway and to lesser extent in autonomous pathway. The new FLAIL lncRNA or lncRNAs (?) described in this manuscript also appear to regulate the flowering time in Arabidopsis, however more experiments would be needed to provide a definite conclusion about how direct FLAIL's effect is and how exactly it functions. That unfortunately obviously diminishes the significance of the manuscript and makes it potentially interesting only to researches studying flowering in Arabidopsis and even then the manuscript results would be incomplete to make solid conclusion.

Lots of functional phenotype have

Additionally, the manuscript requires complete re-writing.

We thank this reviewer for the appreciation of __a decent body of work and a very solid groundwork for future experiments. We are confident that our revisions make the manuscript more comprehensible to highlight the qualities of our manuscript more accessibly.____

__

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Referee #3

Evidence, reproducibility and clarity

In the manuscript by Jin et al authors characterize the FLAIL DNA locus in Arabidopsis (using a wide array of publicly available datasets), which produces a set of sense and anti-sense lncRNAs. Authors determined that the sense FLAIL lncRNA (or a set of sense lncRNAs, which isn't fully clear from the way the data are presented) is involved in flowering time in Arabidopsis based on the fact that the several flail mutants lead to the early flowering phenotype and this flowering defect is complemented by transgenic FLAIL DNA, meaning that FLAIL lncRNA acts in trans. The T-DNA flail3 mutant results in expression changes (up or down) of 1221 genes, including twenty genes linked to flowering in various ways. Expression of a group of these flowering-related genes could be either fully (for eight genes) or partially (for five genes) rescued by transgenic FLAIL. Authors also conducted the ChIRP-seq to determine which genes are physically bound by FLAIL lncRNA genome-wide. It was found that 210 genes in the genome are bound by FLAIL lncRNA. Comparison of the dataset of differentially expressed genes in the T-DNA flail3 mutant with the ChIRP-seq dataset of genes that are bound by FLAIL lncRNA revealed the 12 overlapping genes.

Among these twelve overlapping genes, four were found to be functionally connected to flowering with expression of these four genes being down in flail3 T-DNA mutant. Two out of these four genes were ruled out from being involved in the regulation of flowering by FLAIL. Authors conclude that the two other genes (Cir1 and Lac8) are responsible for the late flowering phenotype of flail mutants based on the three lines of evidence: (i) these genes expression is reduced in the flail mutant, (ii) FLAIL lncRNA directly interacts with these genes chromatin, (iii) the mutants of these genes were previously reported by others to display early flowering phenotypes too. <br /> While I find many of the findings reported in the manuscript very interesting, building a good foundation on which to expand the study and providing a very good leads for follow up experiments, I also have serious concerns about the manuscript in its current form. Most importantly, this reviewer doesn't think that the mechanism of FLAIL lncRNA action was convincingly demonstrated. The main question would be how FLAIL lncRNA works and this question wasn't fully answered. It is great that FLAIL lncRNA binds directly to the two flowering-related genes, but what does it mean? Does it change any chromatin context of these genes quantitatively or qualitatively to affect the transcription? Or does it bind any components of transcriptional machinery and thus controls the transcriptional output? Additionally, flail3 T-DNA mutant affects the expression of 1221 genes and FLAIL lncRNA physically interact with 210 genes, so how can authors be fully sure that FLAIL lncRNA has only direct effect on these two genes and doesn't also contribute to the regulation of the upstream to Cir1 and Lac8 genes or even components of the transcriptional machinery that regulate these genes? Theoretically, doing RNA-seq in the amiR-FLAIL sense lncRNA mutant might have a chance of reducing the number of affected DEGs, making it easier to analyze the FLAIL targets, even if the allele can't be used for complementation experiments. Also, authors totally neglect putting the Cir1 and Lac8 genes into the context of flowering regulation, but it is something that needs to be done. Lastly, the paper needs to be totally rewritten to be even properly evaluated. In its current state it reads like a very short draft. The Abstract is weak, the Introduction is written in a such telegraphic style that it is barely readable, in many places there is no connections between sentences leading to the information appear to be presented as random, even if it isn't. The Results section is written rather rudimentary with information not being sufficiently provided to describe the results but rather scattered between the Results and Figure legends. The Discussion is the best written part of the manuscript. The Conclusion section carries no specific information and reads more like a little summary suitable for a review article rather than experimental paper.

Therefore, this reviewer thinks that regardless of how authors will choose to proceed with the current experimental version of the manuscript, it'd be in the authors' best interests to at least fully revise the paper before resubmitting anywhere. I'd also advise authors to seek professional editorial help specifically using an editor with the background in the plant sciences. Authors might also want to consider moving Fig.3 into the Suppl. as it doesn't carry much weight or significance and perhaps make existing figures more meaningful and comprehensive and by including a better diagram of the locus (e.g., Fig. S1), etc.

It's not practical to list all issues with the writing as the paper requires total re-writing, so I can just make a few suggestions without any specific order to help authors improve the paper:

--There is no statement anywhere that states the goal of the study.

--No rational is provided on why authors decided to examine this specific genomic locus.

--Typically, the significance is in studying the function of lncRNA or a group of lncRNAs produced from a genomic locus, I don't think I ever encountered the instances when it was exciting to study just a specific genomic locus. If the locus does indeed have any significance for initiating the study, it needs to be explained.

--The locus can produce lncRNAs, but it can't harbor them.

--No length of FLAIL lncRNAs or their range is provided in the first section of Results.

--On many occasions authors don't state rational for doing experiments, which leads to information often flowing as random.

--What do authors mean by the subtitle "FLAIL characterizes a trans-acting lncRNA repressing flowering"? How can lncRNA FLAIL or FLAIL locus characterize lncRNA?

--Check all figures. E.g., Fig. 3B-E mentions only accession numbers for the genes.

--It is not clear where exactly the T-DNA insertion is located relative to sense FLAIL in flail3 mutant (Fig. S4). --- What is the length of the complementing sense FLAIL lncRNA?

--Check the description of each and every construct used and provide explanation for each in the Results. E.g., the pFLAIL:gFLAIL18/88 and pasFLAIL:gasFLAIL18/39 constructs aren't explained in Results, and can only be found in Fig. 2 legends.

Significance

Tens of thousands of lncRNAs have been identified in various eukaryotes, but their biological roles have been shown only for a small fraction of them, and the mechanisms of their action are delineated for only a very few of them. Most of the advances on the field of lncRNAs are reported in metazoan, while the field of lncRNAs in plants is lagging far behind in terms of knowledge about lncRNAs with assigned biological functions or lncRNAs with delineated mechanisms of action. From this point of view, this reviewer is always excited to see any new functional plant lncRNAs for which either biological or mechanistic functions have been determined, and deems the information on this subject significant. The manuscript's findings are potentially very interesting and present a decent body of work that lays a very solid groundwork for future experiments. My main concern about the manuscript's significance in its current form is the fact that no real solid mechanism of action for the described lncRNA or a set of lncRNAs (?) has been demonstrated. The best mechanistically studied lncRNAs in Arabidopsis are involved in the regulation of flowering time, particularly those that function in the vernalization flowering pathway and to lesser extent in autonomous pathway. The new FLAIL lncRNA or lncRNAs (?) described in this manuscript also appear to regulate the flowering time in Arabidopsis, however more experiments would be needed to provide a definite conclusion about how direct FLAIL's effect is and how exactly it functions. That unfortunately obviously diminishes the significance of the manuscript and makes it potentially interesting only to researches studying flowering in Arabidopsis and even then the manuscript results would be incomplete to make solid conclusion. Additionally, the manuscript requires complete re-writing.

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Referee #2

Evidence, reproducibility and clarity

In this ms, the authors identified the FLAIL lncRNA that represses flowering in Arabidopsis from a locus producing sense and antisense transcripts. They use an allelic series involving T-DNA insertions, CRISPR/Cas9 and artificial miRNAs to study the role of FLAIL in flowering. A complementation series of constructs of the flail3 allele allowed them to show that the sense FLAIL lncRNA can act in trans. RNAseq revealed a small group of genes linked to the regulation of flowering whose expression is affected in the mutant and restored in the complementation line. To gain further insight into FLAIL function, the authors used a ChIRPeq approach to test whether the lncRNA can recognize potential target genes along the genome and they could show that FLAIL binds specific genomic regions. Clearly, this paper shows very nice evidence that the FLAIL lncRNA can act in trans to regulate gene expression. Nevertheless, there are certain points that need to be clarified to further support the action of the sense FLAIL transcript.

1.According to Fig. 1 A, the antisense FLAIL is "internal" to the DNA genomic area spanning the sense FLAIL. Hence, with direct RT-qPCR is very difficult to distinguish between these molecules as a minor "RT" activity of the Taq polymerase may lead to detection of low levels of antisense, idem if RDRs may generate low antisense levels. Although I think that the plaNET seq brings strong evidence about the start and ends of these molecules, to measure them by RT-qPCR is not trivial and requires the use of strand-specific RT-PCR using a 5' extension of the oligo and amplification with one oligo of the FLAIL sequence (sense or antisense) and the added oligo. It is not clear how they could distinguish precisely sense and antisense particularly when both RNAs correlate as it is the case here in all alleles (Fig. 1 C and 1D). This should be more explicitly mentioned in the materials and methods section.

2.In Fig. 2, what are the levels of antisense in the complementing lines with the sense transcript? And reciprocally sense levels in antisense constructs? This will definitively demonstrate the assumption that the T-NOS termination will not allow any expression on the other strand. At present, only one of the lncRNAs is measured in each experiment?

3.In Fig. 3, it will be important to also show the FLAIL locus in the flail3 mutants (in comparison to the wt) as well as the transgene locus. Here the reads will be strand specific and furthermore this will allow to show that the transgene is not generating antisense transcripts (through RDRs for gene silencing?) and confirm that the sense FLAIL is required for the complementation.

4.In Fig. S5, the expression of FLAIL is shown in the artificial miRNA lines. Is the antisense FLAIL affected "indirectly" by the cleavage of the amiRNA or remains constant? This is likely the case but should be shown.

5.The ChIRPseq data adds major novelty to the ms and brings new ideas about the way of action of FLAIL. However, are there any common epigenetic states between ChIRP targets (e.g. histone modifications, antisense RNA production, homologies "detected" in the conserved regions between Camelina and Arabidopsis and the target loci? Or others) that may highlight potential mechanisms leading to repression mediated by FLAIL of these loci? There are many databases that could be explored (even during flowering) to search for potential relationships. Although precise description of the mechanism is out of the scope of this ms, this can be discussed in more detail to further expand on the nice data obtained.

Minor comments:

6.In Fig. S3, a global alignment between FLAIL and two loci in Arabidopsis and Camelina is sown. What is the extent of homology? How conserved is this sequence at nucleotide level (small or very long?) to support the conservation of this lncRNA. Are there potential structures conserved among these lncRNAs?

7.In Fig. S4B, arrows may help to understand which seeds were selected.

Significance

This paper is a very nice piece of work and demonstrate the action of a long non-coding RNA (lncRNA) in trans on specific targets involved in the regulation of a developmental process, flowering. There is growing evidences that the non-coding genome hides large number of lncRNAs and there is little detailed genetic support for the action of lncRNAs globally. In contrast to many descriptive papers in the field, this ms demonstrates genetically, through an allelic series and complementation experiments, that this lncRNA locus is involved in flowering regulation and that its sense lncRNA recognizes target loci genome-wide, bringing interesting perspectives on potential new mechanisms of transcriptional regulation mediated by non-coding RNAs.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The authors characterized a new lncRNA locus named FLAIL that controls flowering time in Arabidopsis thaliana. The functional validation of this locus is strongly supported by the use of several different tools (CRISPR-Cas9 deletions, T-DNA insertion, amiRNA gene silencing, and transgene complementation of KO lines). It is also suggested that FLAIL lncRNA works in trans but not in cis. There are strong observations supporting that FLAIL works in trans.

Moreover, it is suggested that FLAIL regulates gene expression by interacting with distant chromatin loci. This was assessed using RNA-Seq and ChIRP-Seq. Yet, the overlap between DEGs in the flail mutant and FLAIL binding sites at the chromatin is very small, with only 12 genes. From those, only 2 flowering genes' expression was rescued by FLAIL transgene complementation. The final conclusion that FLAIL lncRNA represses flowering by direct inhibition of the 2 flowering genes expression is correlative, and lacks genetic validation. In addition inspection of the supplementary file shows that the ChIRP analysis was done without filtering for the FDR so that some of the positive hits have an FDR of 0,232. In addition, many of the peaks land in intergenic regions with is not mentioned in the text a graph with the position of the peaks in respect to nearby genes would help.

In one sentence, the authors used the right model system and methodology, including advanced techniques, to characterize a new trans-acting lncRNA important for controlling the flowering time in Arabidopsis but lack evidence supporting a mechanism of action that goes beyond the interaction with several chromatin loci.

minor points:

line#63-64 the authors say the COLDAIR and ASL work on FLC in cis in my view the original papers suggested/showed they work in trans.

Fig 1B please add some more protein-coding RNAs for the bio-info analysis for comparison

Order of Supplementary Fig citation is mixed with S2 coming before S1B

It would help the reader to have a schematic of the crisper deletions, T-DNA insertion, and position of primers used for the RT-qPCR.

In the supplementary PDF file, some of the text is missing on page 3 beginning and end of lines.

Significance

The use of several different tools to validate the biological function of FLAIL locus is a major strength of this work.

The authors propose that flowering time and its gene regulation are controlled by sense FLAIL lncRNAs. However, the sense transcription of FLAIL locus is not detected in wild-type plants by TSS-Seq, TIF-Seq, or plaNET-Seq. If the authors would have explored further the expression of FLAIL transcripts in different stages of development (vegetative and non-vegetative) and in response to different conditions, it would make their claims on the function of FLAIL lncRNAs more convincing. Additionally, flail mutants could have been obtained in the hen-2 background, since it's there where we can observe FLAIL transcription.

FLAIL locus lays on the proximal promoter region of PORCUPINE (PCP), an important regulator of plant development. As flail mutants, pcp mutants display an early flowering phenotype. The authors show no link between FLAIL and PCP from the overlap between re-analysis of published RNA-Seq data for pcp and RNA-Seq and ChIRP-Seq from the authors. This analysis is not enough to exclude the involvement of PCP from the FLAIL function. PCP expression using RT-qPCR should be performed in flail mutants to further support that FLAIL works independently from PCP.

This work does not hypothesize any molecular mechanism besides the interaction of FLAIL lncRNAs with several chromatin loci. It was recently proposed in Arabidopsis that a trans-acting lncRNA interacts with distant loci via the formation of R-loops. The authors do not comment on that. This work would benefit in correlating FLAIL binding sites with R-loop-forming regions mapped in Arabidopsis, regardless of the results from this analysis. Additionally, the authors could attempt to look for a motif responsible for FLAIL binding.

Most of the key conclusions are convincing, except for the flowering time control directly through CIR1 and LAC8, which should be mentioned as speculative

The words locus and loci are latin and they should be written in italic. The word Brassicaceae, referring to the family should be in italic, and should not be "Brassicaceaes". The word analysis has the wrong spelling.

I was asked "How much time do you estimate the authors will need to complete the suggested revisions: this is difficult to answer as it depends to which level the author would like to take their work. In my view, if all new experiments would have to be started from scratch it is too far away to be estimated.

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Reply to the reviewers

Manuscript number: RC-2021-01118

Corresponding author(s): Jun, Nakayama and Kentaro, Semba

1. General Statements

We are grateful to all of the reviewers for their critical comments and insightful suggestions that have helped us considerably improve our paper. As indicated in the responses that follow, we have taken all of these comments and suggestions into account in the revised version of our paper, including the supplementary information.

In the revised manuscript, we focus on the existence of two cancer stem cell-like populations in TNBC xenograft model and patients. The response to each reviewer is described below.

Sincerely,

Jun Nakayama

Kentaro Semba

Department of Life Science and Medical Bioscience

School of Advanced Science and Engineering

Waseda University

E-mail: junakaya@ncc.go.jp or jnakayama.re@gmail.com to JN

ksemba@waseda.jp to KS

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)): * **Summary:** Nakayama and colleagues use their previously developed automated tissue microdissection punching platform to perform spatial transcriptomics on a breast cancer xenograft model. Using transcriptomics on multiple clumps of 10-30 cells from different regions in a tumor and a lymph node metastasis they identified different cell-type clusters. Two of these clusters expressed different cancer stem cell markers. This led the authors to suggest that two distinct cancer stem cell(-like) populations may exist within one (breast) tumor, which could potentially make tumors more drug-resilient.

**Major comments:** While the quality of the presented sequencing data is good and the manuscript is mostly written in a clear and accessible style, there are some concerns that limit the impact of this story. Most importantly, the manuscript in its present form does not convince me that the MDA-MB-231 xenografts indeed contain two distinct populations of cancer stem(-like) cells.

1.The data obtained are not single cell data, which makes it difficult -if not impossible- to draw conclusions about presence of cancer stem cells. Each data point is the average of 10-30 cells, and the interpretation of the data is severely limited by this. How can the quantification of expression of CD44/MYC/HMGA1 in clumps of 10-30 cells teach us something about the stemness of tumor cells? *

Answer: We would thank the comment. The reviewer’s suggestion is an important point; however, this is technical limitation of spatial transcriptomics technology. Most advanced spatial transcriptomics technologies, e.g. Visium (10x Genomics), also have the same problem. It means that our technology and the advanced technologies are technics to analyze gene expression and characteristics of tissues from 10-30 cells in each spot. Although high resolution spatial transcriptomics has been developed in 2021 [1], it is not generally used yet as described in the comment (Significance) from reviewer1.

From our spatial analysis, we identified that CD44, MYC, and HMGA1 were expressed from human cancer cell. Their expression profiles were distinct among specific parts of the tumor section. To validate the existence of two types of cancer stem-like cells in TNBC tumors, we performed the additional analysis with the public scRNA-seq datasets of high-metastatic MDA-MB-23-LM2 xenograft model (GSE163210) [2]. This study performed scRNA-seq analysis of primary tumor and circulating tumor cells in MDA-MB-231-LM2 xenograft model. We analyzed it with Seurat/R (Figure A-1). As a result of reanalysis, HMGA1 and CD44 expression were confirmed at single-cell resolution (Figure A-2,3). These results verified the existence of two cancer stem cell-like populations (HMGA1-high, CD44-high) in MDA-MB-231 xenograft. Hence, the study of MDA-MB-231 xenograft supported our findings from spatial transcriptomics.

Additionally, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As a result, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B). We believe that our findings are solid results because the findings were also validated by other methods.

In the revised manuscript, Figure A are incorporated as Figure 3B-E. Figure B is incorporated as Figure 3A. Hope our new results will be now accepted by the learned Reviewer and Editor.

Figure A-1. Reanalysis of scRNA-seq of metastatic MDA-MB-231 xenograft

Flowchart of the public single-cell RNA-seq (scRNA-seq) reanalysis using GSE163210 datasets.

Figure A-2. UMAP plots of xenograft and CD44/HMGA1 expression

UMAP plot of MDA-MB-231-LM2 xenograft tumors and circulating tumor cells (Left). Expression of CD44 and HMGA1 in the UMAP plot (Right).

Figure A-3. Pie chart of CD44/HMGA1 positive cancer cells in MDA-MB-231 xenograft

Pie chart of cancer stem cell-like population ratio in MDA-MB-231-LM2 xenografts.

Figure B. Fluorescent immuno-staining of MDA-MB-231 primary tumor

Representative images immunostained with CD44 and HMGA1 in primary tumor sections of the MDA-MB-231 xenograft model. Red: HMGA1, Green: CD44, and Blue: Nucleus. Scale bars, 20 μm (left), 10 μm (right). White arrows represent cancer cells that independently expressed or co-expressed.

* 2.Furthermore, the authors should better explain their data analysis strategy with identification of gene expression profiles. It is unclear how they found CD44, MYC, and HMGA1 other than by cherry-picking from the list of cluster markers. *Answer: In this research, to identify the characteristics of clusters, we analyzed differentially expressed genes (DEGs) by ‘FindAllMarkers’ function of Seurat. As a result, ‘Cluster 0’ significantly expressed HMGA1 gene, and ‘cluster 1’ significantly expressed CD44. HMGA1 and CD44 are popular cancer stem cell markers in triple-negative breast cancer [3, 4]. In this study, we focus on metastasis-related genes and cancer stem cell markers (described in introduction section). Therefore, we focus on cancer-stem cell markers in the presented study. Cancer stemness is an important concept in cancer metastasis [5-7]. These results suggested that the existence of two cancer stem cell-like populations could potentially make tumors more drug-resilient in xenograft models and clinical patients.

To improve the manuscript, we revised the description in the revised manuscript (Pages 5-6, Lines 97-105).

* 3.Following up on the above point: I looked in the supplementary tables, but couldn't find MYC. How did the authors conclude that MYC is involved in cluster 1? In fact, when I ran a quick analysis in EnrichR, I saw that putative MYC target genes were strongly enriched among the markers in the HMGA1 cluster, but not the CD44/MYC. That's opposite to what I would expect. *__Answer: __We apologize for our confusing data and description. First, we found the expression of CD44 and HMGA1 in each cluster. Therefore, we performed the up-stream enrichment analysis using gene signatures of FindAllMakers by Metascape. From the result of enrichment analysis, we found the MYC activation in CD44 high-cluster; therefore, we named the cluster “CD44/MYC-high” cluster.

To improve the manuscript, we revised the Figure2, Supplementary Table S3, and manuscript (Pages 5-6, Lines 103-106).

* 4.All data were produced from 1 primary tumor and 1 metastasis. Thus, reproducibility and robustness of the methodology cannot be evaluated. The interpretation of the data could be strengthened when xenografts from at least 3 different mice are shown. *__Answer: __We would thank the suggestion. As the reviewer’s comment, we performed 1 primary tumor and 1 metastasis lesion from a transplanted mouse. Since this experiment take a long time, we tried to validate the findings by other methods (Figure A: scRNA-seq analysis of MDA-MB-231 xenografts, Figure B: Immuno-staining of MDA-MB-231 primary tumor, Figure C: scRNA-seq analysis of TNBC patients).

First, we reanalyzed the public dataset which performed single-cell RNA-seq analysis of MDA-MB-231 xenografted tumor and circulating tumor cells in immunodeficient mice as shown in the answer to comment 1 (Figure A). Next, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As results, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B). Next, we performed the reanalysis of 19 scRNA-seq samples from integrated 3 TNBC cohorts (Figure C-1). In a UMAP plot, differences between CD44-positive cancer cell and HMGA1-positive cancer cell were observed; however, these cells did not visually form the specific clusters (Figure C-2). CD44 and HMGA1 expressed globally in the UMAP plot, but CD44 makes some specific clusters (cluster at right side). Additionally, following the comment, we performed the population analysis in each patient (Figure C-3 and C-4). Detection of double-positive population in TNBC patients suggested that the population may be more undifferentiated cancer stem cells diving into both CD44-positive cells and HMGA1-positive cells.

In addition, we reanalyzed primary tumors and metastasis lesions from other mice as a test trial sample (Figure D-1). The microspots including test trial samples showed 3 human clusters which were classified into CD44/MYC, HMGA1, and Marker-low clusters. We believe that our findings are solid results because the findings were also validated by other methods.

In the revised manuscript, Figure A are incorporated as Figure 3B-E. Figure B is incorporated as Figure 3A. Figure C is incorporated as Figure 5. We only showed Figure D in the response to the reviewer’s comment. Hope our new results will be now accepted by the learned Reviewer and Editor.

Figure C-1. Reanalysis of integrated TNBC patients scRNA-seq

A flowchart of the reanalysis of a public scRNA-seq dataset. We downloaded GSE161529, GSE176078, and GSE180286 (scRNA-seq data of 19 TNBC patients). Integrated datasets were analyzed with Seurat. Log normalization, scaling, PCA and UMAP visualization were performed following the basic protocol in Seurat. To extract the cancer cells, cells expressing EPCAM/KRT8 (epithelial marker) were filtered. A UMAP plot of cancer cell from 19 TNBC patients (right).

Figure C-2. CD44/HMGA1 expression in TNBC patients

Expression analysis of CD44 (Expression level > 2) and HMGA1 (Expression level > 2) with UMAP plots.

Figure C-3. CD44/HMGA1-positive cancer cell with UMAP plot

UMAP plots of CD44-high, HMGA1-high, HMGA1/CD44-high, and Negative cancer cells.

Figure C-4. Ratio of CD44/HMGA1-positive cancer cell in each patient

The bar plot showed the ratio of cancer cells that expressed CD44 and HMGA1.

Figure D-1. Analysis of microspots of MDA-MB-231 xenografts including test trial samples

UMAP plots of CD44-high, HMGA1-high, and Marker-low clusters with test trial samples (2 primary tumors and 1 lung metastasis). ‘Primary tumor 1’ has 20 microspots, ‘Primary tumor 2’ has 24 microspots, and ‘lung metastasis’ has 7 microspots. Most microspots of lung metastasis failed extraction of RNA; therefore, these spots classified into Marker-low cluster.

Figure D-2. Expression analysis of CD44, HMGA1, and MYC

Feature plot of CD44-high, HMGA1-high, and Marker-low clusters with test trial samples.

* 5.The only methodology is single cell RNA-sequencing. Immuno-staining on relevant markers such as CD44, MYC, HMGA1 plus human epithelium and cell cycle markers would provide strong additional support for the claims made by the authors, because it's a complementary technique and it allows quantification at single cell resolution. *__Answer: __We would thank the comment. As described in the responses to the reviewer’s comment 1 and 4, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As a result, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B).

In the revised manuscript, Figure B is incorporated as Figure 3A.

* 6.Line 173-175. The marker-low cluster look to me simply like spots containing a relatively high amount of dead/dying (tumor) cells. The identity/state of cells in the marker-low cluster should be characterized and discussed more extensively. *__Answer: __We would thank the comment. This suggestion is important. In fact, total count of RNA in the Marker-low cluster decreased as compared to HMGA1-high and CD44/MYC-high (Supplementary Figure S1B). Additionally, Ttr-high mouse cluster also has low total count of RNA (Supplementary Figure S1C).

Following the comment, we described that the Marker-low cluster and Ttr-high cluster have the possibility to include dead/dying cells (Page 13, Lines 268-279).

* 7.Figure 5 and accompanying text in line 182-194; the authors try to infer cell-to-cell interactions using a previously published tool. However, any biological interpretation is lacking. What can be concluded from this analysis? *__Answer: __Initially, algorithms of cell-to-cell interaction were reported with previously published tool [8, 9]; however, in this manuscript, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data) as previously described [10, 11]. We aimed to estimate the cell-to-cell interaction in each spot (including 10-30 cells). We think that this analysis will be helpful for discovering the cancer stem cell niche and metastatic niche [6].

However, in the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Therefore, CCI analysis in previous Figure 5 moved to Supplementary Figure S7. Previous Figure 6 is removed from revised manuscript.

* 8.Figure 6. Can the authors please explain more clearly what they mean by "PT" and "Mix" groups? I had a very hard time to understand what the data in figure mean. Again, an overall interpretation at the end (line 211) is lacking. *__Answer: __We apologize for the confusing result. We examined the combinations of human cancer cell cluster and mouse stromal cell cluster. To summarize, there are 10 combinations in the MDA-MB-231 xenograft. The combination groups in only primary tumor were named “PT”; on the other hand, the combination groups in both primary tumor and lymph-node metastasis were named “Mix”. These CCI analysis focused on cluster types of cancer cell and stromal cell. However, according to this revision, our presented study mainly focuses on the existence of two types of cancer stem cell-like population in TNBC xenograft and patients. Therefore, CCI analysis with cluster types was deleted from revised manuscript.

In the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Previous Figure 6 was removed from the revised manuscript.

* 9.Figure 7. I like the effort to align the results with public scRNA-seq data. But although the expression of the cluster-signatures is heterogeneous, there is no evidence for distinct (CSC-like) cell populations. Why don't these HMGA1 vs CD44 signature cells cluster away from each other in the UMAPs? Perhaps the patient-to-patient heterogeneity overwhelms differences within tumors, but in that case the authors could re-run their analysis for each patient separately, to make 6 patient-specific UMAPs. In its present form, this analysis does not convince me that two distinct CSC(-like) populations within one TNBC exist. *Answer: We would thank the comment. To improve the quality of reanalysis of clinical cohorts, we performed the reanalysis of 19 scRNA-seq samples from integrated 3 TNBC cohorts (Figure C-1). In a UMAP plot, there are differences between CD44-positive cancer cells and HMGA1-positive cancer cells; however, these cells did not visually form the specific clusters (Figure C-2). CD44 and HMGA1 were expressed globally in the UMAP plot, but CD44 made some specific clusters (cluster at right side). Additionally, following the comment, we performed the population analysis in each patient (Figure C-3 and C-4). There is double-positive population in TNBC patients suggesting that this population may be more undifferentiated cancer stem cells, dividing into both CD44-positive cells and HMGA1-positive cells.

In the revised manuscript, Figure C is incorporated as Figure 5.

* **Minor comments:** 10.In the Supplemental table 2 noticed that many of the marker genes have adjusted P values well above 0.05 (and even above 0.1). That makes the statistical analysis rather weak. This could especially be problematic since the authors entirely base their main claims on this marker analysis, and I recommend that the authors use more stringent P-value cut-offs in the cluster analysis. *Answer: We would thank the comment. We reshaped the list of differentially expressed genes (DEGs). Significantly expressed genes (adjusted p-value In mouse clusters, the enrichment analysis using significantly DEGs showed that only Tcell-like clusters had a lot of enriched terms. Citric acid (TCA) cycle, chemical stress response, and fatty acid oxidation were enriched in Tcell-like populations (Page 7, Lines 141-144).

In the revised manuscript, enrichment analyses are showed as Supplementary Figure S2 and S3B. We revised the sentence of enrichment analyses (Page 6, Lines 114-121), (Page 7, Lines 141-144). The network visualization of enrichment analysis was removed from the revised manuscript because this result did not support conclusions of the presented study.

* 11.Line 129/130. If I look at figure 3A, I don't see this tendency that the authors describe. Can the authors provide statistical support or visual aid to make their claim more apparent to the reader? *__Answer: __We would thank the suggestion. Following the comment, we performed the statistical analysis of spot position. The spots were categorized outer side (tumor edge) and Inner site (Center of tumor) in the primary tumor section (Figure E-1 upside). We counted the spot numbers of the clusters (Figure E-1 table) and performed statistical test by chi-test. As a result, CD44/MYC clusters significantly resided at outer side of primary tumor (Figure E-1 barplot). On the other hand, the spots in lymph-node metastasis are not readily defined the outer or inner. In addition, cell cycle analysis in the primary tumor and lymph node metastasis was performed with statistical test. As a result, HMGA1-high cluster and CD44/MYC-high cluster significantly proliferated in the lymph node metastasis section (Figure E-2).

Therefore, in the revised manuscript, we revised the sentence of spot position in lymph-node metastasis (Pages 8-9, Lines 159-172). Figure E-1 is incorporated as Figure 4D. Figure E-2 is incorporated as Figure 4F. Hope our new results will be now accepted by the Reviewer and Editor.

Figure E-1. Statistical analysis of spot position

Chi-test was performed by R. *p Figure E-2. Statistical analysis of cell cycle index

Fisher’s exact test was performed by R. *p * 12.Line 217; shouldn't this be 6 patients? I see six clusters and in the original paper six patients are mentioned. *Answer: We would thank the comment. ‘6 patients’ is correct, we revised it. However, in the revised manuscript, we added integrated analysis of TNBC as shown in the answer to comment 9.

Previous reanalysis of clinical scRNA-seq (previous Figure 7) was removed from the revised manuscript. The reanalysis using 3 integrated TNBC cohorts (Figure C) is incorporated as Figure 5.

Reviewer #1 (Significance (Required)): * Conceptual/biological impact: Showing the existence of distinct populations of CSCs within one (breast-)tumor potentially has a high impact on the field of fundamental and translational cancer research. As the authors state, it could be one key reason underlying drug resistance. However, the technology used by the authors does in my view not allow to make such a claim. First and foremost because the technology does not allow analysis at single cell resolution.

Technical impact: The platform used by the authors can be of interest for some applications, but they already published this in Scientic Reports a few years ago. I'm afraid that with the rapid recent developments in the field of spatial single cell transcriptomics (See for example Srivatsan et al Science 2021; 373: 111-117), the technical impact on the field is relatively low.

Audience: Researchers in the field of cancer biology with an interest to perform low-cost molecular analysis at low-resolution spatial-resolved tissue specimens (transcriptomics, but perhaps expanded with bisulfite sequencing, or ATAC sequencing) could be interested in the technology presented in this manuscript.

My expertise: single cell transcriptomics, (cancer) cell cycle, cancer drug resistance, cell plasticity, mouse models. *

**Referee Cross-commenting** I have read the comments and align mostly with reviewer #2. The authors need to improve this manuscript a lot before it's suitable for publication in any of the Review Commons journals. Answer: We are grateful to the reviewers. As indicated in the responses that follow, we have taken all of these comments and suggestions into account in the revised version of our paper, including the supplementary information.

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Reviewer #2 (Evidence, reproducibility and clarity (Required)): * This manuscript uses spatial transcriptomics to perform single cell-like expression analysis between a breast cancer cell line and tumor microenvironment in mice xenografted with these cells. Unfortunately, from the title, abstract, and introduction, it is difficult to understand exactly what the authors are focusing and discussing. It is also unclear the advantage of their technique for evaluating the populations observed within this manuscript. Furthermore, there is very little explanation of the results, and it does not appear to be a scientific logical structure. Hence, this manuscript is not suitable for acceptance in the journal. In order to improve the scientific quality of this study, the following concerns are presented.

**Major concerns:** 1.Is cell-cell interaction (CCI) analysis novel method? If so, please specify detail in the manuscript. If the basic concept and the principle of CCI analysis have not been published, please mention in the discussion section as a limitation that a manuscript on CCI analysis is under submission to the preprint. In addition, please revise the abstract and related text. *__Answer: __Initially, algorithms of cell-to-cell interaction were reported with previously published tool [8, 9]; however, in this manuscript, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data) as previously described [10, 11]. We aimed to estimate the cell-to-cell interaction in each spot (including 10-30 cells). We think that this analysis will be helpful for discovering the cancer stem cell niche and metastatic niche [6].

However, in the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Therefore, CCI analysis in previous Figure 5 is moved to Supplementary Figure S7. Previous Figure 6 are removed from the revised manuscript. We revised the description in the manuscript (Page 18, Lines 385-387).

* 2.The reviewer thinks that spatial transcriptomics plays an important role in your manuscript. Please describe the technique in the introduction. *__Answer: __We would thank the comments. Following the comments, we described the spatial technics in Introduction section. We revised the manuscript (Page 4, Lines 63-65) (Page 12, Lines 250-253).

* 3.The classification by expression profile (HMGA1, CD44/MYC and marker-low) lacks an explanation. Authors should mention in detail how these populations were extracted from breast cancer cell lines. *Answer: In this research, to identify the characteristics of clusters, we analyzed differentially expressed genes (DEGs) by FindAllmarkers function of Seurat. As a result, ‘Cluster 0’ significantly expressed HMGA1 gene, and ‘cluster 1’ significantly expressed CD44. Next, we performed the up-stream enrichment analysis using gene signatures of FindAllMakers by Metascape. From result of enrichment analysis, we found the MYC activation in CD44 high-cluster; therefore, we named the cluster “CD44/MYC-high” cluster.

HMGA1 and CD44 are popular cancer stem cell markers in triple-negative breast cancer [3, 4]; therefore, we focus on cancer-stem cell marker in presented study. Cancer stemness is an important concept in cancer metastasis [5-7].These results suggested that the existence of two cancer stem cell-like populations could potentially make tumors more drug-resilient in xenograft model and clinical patient.

To improve the manuscript, we revised the Figure2, Supplementary Table S2 and S4, and manuscript (Pages 5-6, Lines 97-106).

* 4.The description of the results is back and forth and confusing. Please reconsider the flow of the analysis. *__Answer: __We would thank the comment. We reconsidered the description and structure of manuscript. In revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients.

To improve the manuscript, we revised the Figure2 for examination of cluster characteristics by clustering and gene expression profiling. Figure 3 was revised for the validation of two cancer stem cell-like populations in TNBC xenograft model. Figure 4 was revised for the elucidation of spatial characteristics of each cluster. Figure 5 was revised for the validation of two cancer stem cell-like populations in TNBC patients.

* 5.How did you evaluate the outsides of the samples with very different spot positions in Figure 3A? Please mention your evaluation method in a scientific manner. In particular, authors should clearly indicate the outer evaluation for the metastatic case. *

Answer: We would thank the suggestion. Following the comment, we performed the statistical analysis of spot position. The spots were categorized outer side (tumor edge) and Inner site (Center of tumor) in primary tumor section (Figure E-1 upside). We counted the spot numbers of the clusters (Figure E-1 table) and performed statistical test by chi-test. As a result, CD44/MYC clusters significantly resided at outer side of primary tumor (Figure E-1 bar plot). On the other hand, the spots in lymph-node metastasis are not readily defined the outer or inner. In addition, cell cycle analysis in the primary tumor and lymph node metastasis was performed with statistical test. As a result, HMGA1-high cluster and CD44/MYC-high cluster significantly proliferated in the lymph node metastasis section (Figure E-2).

Therefore, in the revised manuscript, we revised the sentence of spot position in lymph-node metastasis (Pages 8-9, Lines 153-172). Figure E-1 are incorporated as Figure 4D. Figure E-2 are incorporated as Figure 4F. Hope our new results will be now accepted by the Reviewer and Editor.

Figure E-1. Statistical analysis of spot position

Chi-test was performed by R. *p Figure E-2. Statistical analysis of cell cycle index

Fisher’s exact test was performed by R. *p * 6.The spots in primary tumor have few counts derived from mouse stromal/immune cells, as shown in Figure S1A. Nevertheless, Figure 3C shows that mouse stromal/immune cells are evaluated in the same way in primary and metastatic sites. The reviewer thinks that the regions identified as Tcell-like in the metastatic site, where there are many mouse-derived counts, and in the primary, where there are few mouse-derived counts, do not have the same characteristics. If many mouse-derived counts were detected in a spot using the spatial transcriptomics, then there must be many mouse-derived cells in the spot. Please discuss how this expression is evaluated on this technique, which is not a single cell analysis. *__Answer: __We would thank the comment. The reviewer’s suggestion is an important point; however, this suggestion is technical limitation of spatial transcriptomics technology. Most advanced spatial transcriptomics technologies, e.g. Visium (10x Genomics), also have the same problem. It means that our technology and the advanced technologies are technics to analyze gene expression and characteristics of tissues from 10-30 cells in each spot.

In this spatial transcriptome analysis of mouse genes, we first performed the log normalization and scaling. Since Seurat used variable features among the samples for single-cell or spot clustering, we extracted the variable features for detection of clusters using the ‘FindVariableFeatures’ function. PCA and clustering using only mouse genes was performed for detecting the neighboring samples. After the clustering of mouse spots, we identified the character of clusters by finding the gene signatures. As the indication by the reviewer, the detected RNA counts and features are different, so it is difficult to define the exact character and cell type of stromal cells. Theoretically, spatial transcriptomics could only detect some kinds of stromal cells expressing the T-cell marker gene in the spot. Therefore, we named the cluster as “Tcell-like”. Not all of the Tcell-like cluster have the same characteristics or cell types, but they certainly express T-cell marker genes. This is also a technical limitation of spatial transcriptomics. Spatial transcriptomics with higher resolution probably is able to detect the stromal cells as a single-cell resolution, such as the one developed in previous research [1].

In the revised manuscript, we focused on the two types of cancer stem cell-like populations that were validated by other methods (scRNA-seq and Immuno-staining). As the method is not able to define the exact cluster characters, we moved CCI analyses to supplementary figures or removed partly.

We also revised the discussion in the revised manuscript (Pages 13-14, Lines 279-283).

* 7.Please explain how the gene symbols listed in Figure 4A were selected. Also, please indicate the characteristics of the gene groups that are not listed. *__Answer: __We selected the gene signature list from results of ‘FindAllMarker’ function in Seurat. ‘FindAllMarker’ function enables to extract the significantly expressed genes in each cluster. Heatmap in previous Figure 4A was drawn using these marker genes (Adjusted p-value 0.1). Highlighted genes in the heatmap have been reported as cancer-related genes or cell cycle-related genes.

The genes used for drawing heatmap are shown in Supplementary Table S2 and S4.

* 8.Please describe the details of the division and cycle index in lines 141-142. *__Answer: __Cell cycle index is a basic function of Seurat [12] (https://satijalab.org/seurat/archive/v3.1/cell_cycle_vignette.html). A list of cell cycle markers is loaded with Seurat. We can segregate this list into markers of G2/M phase and markers of S phase. We subjected this function into our spatial transcriptomics to estimate the cell cycle in each spot.

We revised the description manuscript (Page 16, Lines 331-332).

* 9.In Line 148-151, the expression and prognosis of TMSB10, CTSD, and LGALS1 is mentioned based on the previous reports. Aren't these findings the result of bulk? Is the HMGA1 cluster that the authors found involved in the prognosis of mice? Please clarify, as it is unclear what you want to discuss. *

Answer: We apologize for our confusing data and description. These highlighted genes (TMSB10, CTSD, LGALS1, CENPK, and CENPN) were extracted as DEGs of human cancer clusters (Supplementary Table S2). Previously, these genes have been reported as cancer-related genes or cell cycle-related genes, described in the manuscript (Page 6, Lines 107-110). To show the other expressed genes in each human cluster, we focused on these genes in the manuscript.

We extracted the gene signatures from DEGs and showed the gene signatures from HMGA1-high cluster correlated to poor prognosis in TNBC patients. Our data suggested that the HMGA1 signatures from the microspot resolution has the potential to be a novel biomarker for diagnosis, and HMGA1-high cancer stem cells may contribute to poor prognosis.

In this revision, since we reperformed DEGs analysis with significant threshold; therefore, survival analysis was reperformed with novel gene signatures with METABRIC TNBC cohorts (Figure F).

To improve the manuscript, we revised the description of DEGs extraction and heatmap (Page 6, Lines 106-112). Hope our Reviewer will approve this revised sentence.

Figure F. Survival analysis with gene signatures of HMGA1-high and CD44/MYC-high

Survival analysis of TNBC patients (claudin-low subtype and basal-like subtype) in METABRIC cohorts by the Kaplan-Meier method. (Left) Survival analysis with the expression of the HMGA1 signatures (High = 151, Low = 247). Shading along the curve indicates 95% confidential interval. Log-rank test, p = 0.012. (Right) Survival analysis with the expression of the CD44/MYC signatures (High = 333, Low = 65). Log-rank test, p = 0.079.

* 10.Please provide details of all statistical tests used in this manuscript and describe significance levels used in the p-values and FDR. *__Answer: __We performed the extraction of differentially expressed genes (DEGs) by ‘FindAllMarkers’ function with MAST method. MAST method identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data [13]. Adjusted p-value is calculated based on Bonferroni correction using all features in the dataset. In spatial spot analysis, statistical analyses were performed by Chi-test and Fisher’s exact test.

We revised materials and methods section in the manuscript (Page 19, Lines 391-394).

* 11.Please mention CCI score (line 198). *Answer: As described in answer to comment 1, the algorithms of CCI score calculation were performed using previously published tool [8, 9]; however, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data). We extracted the genes whose expression value was greater than 2. We selected the combinations representing ligand__-__receptor interactions, in which both ligand genes and receptor genes were expressed in the same spot.

We revised materials and methods section in the manuscript and Supplementary Legends (Page 18, Lines 385-387).

* 12.Lines 204-206 and Figure 6G show specific interaction of ITGB1 and CST3, but it is unclear why only these molecules were extracted. What about the other molecules? At least ITGB1 is not scored in mix5. *Answer: We selected genes that have been reported as cancer-related ones in breast cancer to discuss the interactions in primary tumor and lymph-node metastasis. However, according to this revision, our presented study mainly focused on the existence of two types of cancer stem cell-like population in TNBC xenografts and patients. Therefore, CCI analysis with cluster types moved to supplementary Figure or some were not shown now.

In the revised manuscript, previous Figure 6 is removed.

* 13.HMGA1 signature appears in Line 214, please explain in detail. *__Answer: __As described in answer to comment 7, we selected the gene signature list from results of ‘FindAllMarker’ function. ‘FindAllMarker’ function enables to extract the significantly expressed genes in each cluster. HMGA1 signature genes were selected from significantly differentially expressed genes of HMGA1-high clusters.

We revised the description in the revised manuscript (Pages 9-10, Lines 190-193).

* 14.Authors should discuss how the previously reported bulk expression data used in Figure 7E can be linked to the single-cell-like analysis in this study. *__Answer: __Previous research reported that gene signatures extracted from specific clusters in scRNA-seq study have the potential to be a prognosis marker [14]. We showed the gene signatures from HMGA1-high cluster correlated to poor prognosis in TNBC patients. Our results suggested that the gene signatures from the resolution of microspot (10-30 cells) could have the potential to be prognosis markers. This punching microdissection system enables to extract only the parts of a section that are necessary for diagnosis of cancer and to analyze at low-cost. It could be applied to diagnostics instead of the laser-capture microdissection methods.

We performed additional survival analysis with METABRIC cohorts. As described in this revision, since we reperformed DEGs analysis with significant threshold, survival analysis was reperformed with novel gene signatures with METABRIC TNBC cohorts (Figure F).

In revised manuscript, Figure F were incorporated as Figure 6. The usefulness of gene signatures from microspot resolution was additionally discussed (Page 12, Lines 242-245, 250-253).

* **Minor concerns:** 15.Please describe how the normalized centrality was calculated in UMAP algorithm and explain what this means in the results. __Answer: __The data showed that the expressional diversity in each cluster based on the network centrality of a correlational network with graph theory. The differences in the centrality among the clusters suggested expressional diversity in each (Supplementary Figure 4). Higher centrality represented lower expressional diversity and vice versa*. The detailed method for the calculation of centrality was previously shown to reveal the difference between smokers and never-smokers [10, 11].

We added the description in the Legend (Pages 7-8, Lines 145-150).

* 16.Please mention an explanation for the red X in Figure 1B to the legend. *__Answer: __The red X means failure spot for RNA extraction. We added the description in Figure 1B.

* 17.Please spell out the abbreviations in all figure legends. *__Answer: __We added the abbreviations in the legends of all figures.

* 18.Please explain what is meant by the color of the lines and the size of the circles in Figure 4D. *__Answer: __The network analysis was performed by Metascape (https://metascape.org/gp/index.html#/main/step1) [15]. The node size is proportional to the number of genes belonging to the term, and the node color represents the identity of the cluster. However, as described in the answer to reviewer’s comment 9, we reperformed enrichment analysis with significant DEGs. As a result, only CD44/MYC cluster had a lot of enrichment terms.

Therefore, network visualizations were removed from the revised manuscript.

* 19.Please mention an explanation for the color of the spots in Figure 5D and 5F to the legend. *__Answer: __The color showed the spots categorized into the selected group.

In the revised manuscript, previous Figure 5 was incorporated as Supplementary Figure S7. We added the description in Supplementary Figure S7 and S8 with the legends.

* 20.Is "S51" in Line 148 a typo for "S5A"? *Answer: Thank you. We revised “S5A”.

* 21.Please mention an explanation for the bars in Figure 6D and 6F to the legend. *__Answer: __The bars showed relative CCI scores. As described below, we removed the results of CCI analysis with cluster group (previous Figure 6) in the revised manuscript.

* 22.Please mention an explanation for the colors in Figure 7E to the legend. *__Answer: __The color showed patients’ group based on expression levels of gene signatures. We added the description in the Legend of Figure 6.

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Reviewer #2 (Significance (Required)): * The approach in Figure 5 is interesting, but the rest of the results do not take full advantage of the technology developed by the authors. The structure of the manuscript should be re-examined and new perspectives added. I look forward to the future of the authors' research.

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Reviewer #3 (Evidence, reproducibility and clarity (Required)): Microtissue transcriptome analysis of triple-negative breast cancer cell line MDA-MB-231 xenograft model using automated tissue microdissection punching techonology revealed that the existence of three cell-type clusters in the primary tumor and axillary lymph node metastasis. The CD44/MYC-high cluster showed aggressive proliferation with MYC expression, the HMGA1-high cluster exhibited HIF1A activation and upregulation of ribosomal processes. The cell-cell-interaction analysis revealed the interaction dynamics generated by the combination of cancer cells and stromal cells in primary tumors and metastases. The gene signature of the HMGA1-high cancer stem cell-like cluster has the potential to serve as a novel biomarker for diagnosis. The key conclusions are convincing. The data and methods are presented in a reproducible way. The experiments are adequately replicated and statistical analysis is adequate. Prior studies are appropriately referenced. The text and figures are clear and accurate. __Answer: __We would thank the valuable comments. As the reviewer mentioned, our findings showed that the existence of two cancer stem cell-like populations has the potential to make tumors more drug-resilient. Our results suggested that the gene signatures from the resolution of microspot (10-30 cells) could have the potential to be prognosis markers. This punching microdissection system enables to extract only the parts of a section that are necessary for diagnosis of cancer and to analyze at low-cost. It could be applied to diagnostics instead of the laser-capture microdissection methods.

In this revision, we focused on the existence of two cancer stem cell-like populations in TNBC xenografts and patients. Following the other reviewer’s comments, we performed the extraction of DEGs with significant threshold; therefore, we revised the results of enrichment analysis but it did not influence our main findings.

To validate the existence of two types of cancer stem-like cells in TNBC tumors, we performed the additional analyses (reanalysis of public scRNA-seq datasets and immuno-staining of MDA-MB-231 primary tumor). These results verified two cancer stem cell-like populations (HMGA1-high, CD44-high) in MDA-MB-231 xenograft and TNBC patients. We believe that our findings are solid results because the findings were also validated by other methods.

Again, we would thank kind reviewing our manuscript.

Reviewer #3 (Significance (Required)): * In the past several studies showed the heterogeneity of cell-cell interactions between cancer cells and stromal cells in situ (Andersson et al, 2021; Wu et al, 2021) and tumor microheterogeneity (Jiang et al, 2016; Liu et al, 2016; Zhang et al, 2020). Spatial transcriptomics methods are important to reveal microheterogeneity of cancer. As a physician working in gynecology and obstetrics in my opinion the results of the study and spatial transcriptomic methods could be relevant to detect new biomarkers for diagnosis and prognosis of breast cancer in future and to find novel therapeutic targets to overcome drug resistance and facilitate curative treatment of breast cancer.

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References in response letter

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2. Moravec JC, Lanfear R, Spector DL, Diermeier SD, Gavryushkin A. Cancer phylogenetics using single-cell RNA-seq data. bioRxiv. 2021:2021.01.07.425804. doi: 10.1101/2021.01.07.425804.
3. Liu H, Patel MR, Prescher JA, Patsialou A, Qian D, Lin J, et al. Cancer stem cells from human breast tumors are involved in spontaneous metastases in orthotopic mouse models. Proc Natl Acad Sci U S A. 2010;107(42):18115-20. Epub 2010/10/06. doi: 10.1073/pnas.1006732107. PubMed PMID: 20921380; PubMed Central PMCID: PMC2964232.
4. Pegoraro S, Ros G, Piazza S, Sommaggio R, Ciani Y, Rosato A, et al. HMGA1 promotes metastatic processes in basal-like breast cancer regulating EMT and stemness. Oncotarget. 2013;4(8):1293-308. Epub 2013/08/16. doi: 10.18632/oncotarget.1136. PubMed PMID: 23945276; PubMed Central PMCID: PMC3787158.
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7. Turdo A, Veschi V, Gaggianesi M, Chinnici A, Bianca P, Todaro M, et al. Meeting the Challenge of Targeting Cancer Stem Cells. Front Cell Dev Biol. 2019;7:16. Epub 2019/03/06. doi: 10.3389/fcell.2019.00016. PubMed PMID: 30834247; PubMed Central PMCID: PMC6387961.
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12. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, 3rd, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888-902.e21. Epub 2019/06/11. doi: 10.1016/j.cell.2019.05.031. PubMed PMID: 31178118; PubMed Central PMCID: PMC6687398.
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Referee #3

Evidence, reproducibility and clarity

Microtissue transcriptome analysis of triple-negative breast cancer cell line MDA-MB-231 xenograft model using automated tissue microdissection punching techonology revealed that the existence of three cell-type clusters in the primary tumor and axillary lymph node metastasis. The CD44/MYC-high cluster showed aggressive proliferation with MYC expression, the HMGA1-high cluster exhibited HIF1A activation and upregulation of ribosomal processes. The cell-cell-interaction analysis revealed the interaction dynamics generated by the combination of cancer cells and stromal cells in primary tumors and metastases. The gene signature of the HMGA1-high cancer stem cell-like cluster has the potential to serve as a novel biomarker for diagnosis.

The key conclusions are convincing. The data and methods are presented in a reproducible way. The experiments are adequately replicated and statistical analysis is adequate.

Prior studies are appropriately referenced. The text and figures are clear and accurate.

Significance

In the past several studies showed the heterogeneity of cell-cell interactions between cancer cells and stromal cells in situ (Andersson et al, 2021; Wu et al, 2021) and tumor microheterogeneity (Jiang et al, 2016; Liu et al, 2016; Zhang et al, 2020). Spatial transcriptomics methods are important to reveal microheterogeneity of cancer. As a physician working in gynecology and obstetrics in my opinion the results of the study and spatial transcriptomic methods could be relevant to detect new biomarkers for diagnosis and prognosis of breast cancer in future and to find novel therapeutic targets to overcome drug resistance and facilitate curative treatment of breast cancer.

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Referee #2

Evidence, reproducibility and clarity

This manuscript uses spatial transcriptomics to perform single cell-like expression analysis between a breast cancer cell line and tumor microenvironment in mice xenografted with these cells. Unfortunately, from the title, abstract, and introduction, it is difficult to understand exactly what the authors are focusing and discussing. It is also unclear the advantage of their technique for evaluating the populations observed within this manuscript. Furthermore, there is very little explanation of the results, and it does not appear to be a scientific logical structure. Hence, this manuscript is not suitable for acceptance in the journal. In order to improve the scientific quality of this study, the following concerns are presented.

Major concerns:

1.Is cell-cell interaction (CCI) analysis novel method? If so, please specify detail in the manuscript. If the basic concept and the principle of CCI analysis have not been published, please mention in the discussion section as a limitation that a manuscript on CCI analysis is under submission to the preprint. In addition, please revise the abstract and related text.

2.The reviewer thinks that spatial transcriptomics plays an important role in your manuscript. Please describe the technique in the introduction.

3.The classification by expression profile (HMGA1, CD44/MYC and marker-low) lacks an explanation. Authors should mention in detail how these populations were extracted from breast cancer cell lines.

4.The description of the results is back and forth and confusing. Please reconsider the flow of the analysis.

5.How did you evaluate the outsides of the samples with very different spot positions in Figure 3A? Please mention your evaluation method in a scientific manner. In particular, authors should clearly indicate the outer evaluation for the metastatic case.

6.The spots in primary tumor have few counts derived from mouse stromal/immune cells, as shown in Figure S1A. Nevertheless, Figure 3C shows that mouse stromal/immune cells are evaluated in the same way in primary and metastatic sites. The reviewer thinks that the regions identified as Tcell-like in the metastatic site, where there are many mouse-derived counts, and in the primary, where there are few mouse-derived counts, do not have the same characteristics. If many mouse-derived counts were detected in a spot using the spatial transcriptomics, then there must be many mouse-derived cells in the spot. Please discuss how this expression is evaluated on this technique, which is not a single cell analysis.

7.Please explain how the gene symbols listed in Figure 4A were selected. Also, please indicate the characteristics of the gene groups that are not listed.

8.Please describe the details of the division and cycle index in lines 141-142.

9.In Line 148-151, the expression and prognosis of TMSB10, CTSD, and LGALS1 is mentioned based on the previous reports. Aren't these findings the result of bulk? Is the HMGA1 cluster that the authors found involved in the prognosis of mice? Please clarify, as it is unclear what you want to discuss.

10.Please provide details of all statistical tests used in this manuscript and describe significance levels used in the p-values and FDR.

11.Please mention CCI score (line 198).

12.Lines 204-206 and Figure 6G show specific interaction of ITGB1 and CST3, but it is unclear why only these molecules were extracted. What about the other molecules? At least ITGB1 is not scored in mix5.

13.HMGA1 signature appears in Line 214, please explain in detail.

14.Authors should discuss how the previously reported bulk expression data used in Figure 7E can be linked to the single-cell-like analysis in this study.

Minor concerns:

15.Please describe how the normalized centrality was calculated in UMAP algorithm and explain what this means in the results.

16.Please mention an explanation for the red X in Figure 1B to the legend.

17.Please spell out the abbreviations in all figure legends.

18.Please explain what is meant by the color of the lines and the size of the circles in Figure 4D.

19.Please mention an explanation for the color of the spots in Figure 5D and 5F to the legend.

20.Is "S51" in Line 148 a typo for "S5A"?

21.Please mention an explanation for the bars in Figure 6D and 6F to the legend.

22.Please mention an explanation for the colors in Figure 7E to the legend.

Significance

The approach in Figure 5 is interesting, but the rest of the results do not take full advantage of the technology developed by the authors. The structure of the manuscript should be re-examined and new perspectives added. I look forward to the future of the authors' research.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Nakayama and colleagues use their previously developed automated tissue microdissection punching platform to perform spatial transcriptomics on a breast cancer xenograft model. Using transcriptomics on multiple clumps of 10-30 cells from different regions in a tumor and a lymph node metastasis they identified different cell-type clusters. Two of these clusters expressed different cancer stem cell markers. This led the authors to suggest that two distinct cancer stem cell(-like) populations may exist within one (breast) tumor, which could potentially make tumors more drug-resilient.

Major comments:

While the quality of the presented sequencing data is good and the manuscript is mostly written in a clear and accessible style, there are some concerns that limit the impact of this story. Most importantly, the manuscript in its present form does not convince me that the MDA-MB-231 xenografts indeed contain two distinct populations of cancer stem(-like) cells.

1.The data obtained are not single cell data, which makes it difficult -if not impossible- to draw conclusions about presence of cancer stem cells. Each data point is the average of 10-30 cells, and the interpretation of the data is severely limited by this. How can the quantification of expression of CD44/MYC/HMGA1 in clumps of 10-30 cells teach us something about the stemness of tumor cells?

2.Furthermore, the authors should better explain their data analysis strategy with identification of gene expression profiles. It is unclear how they found CD44, MYC, and HMGA1 other than by cherry-picking from the list of cluster markers.

3.Following up on the above point: I looked in the supplementary tables, but couldn't find MYC. How did the authors conclude that MYC is involved in cluster 1? In fact, when I ran a quick analysis in EnrichR, I saw that putative MYC target genes were strongly enriched among the markers in the HMGA1 cluster, but not the CD44/MYC. That's opposite to what I would expect.

4.All data were produced from 1 primary tumor and 1 metastasis. Thus, reproducibility and robustness of the methodology cannot be evaluated. The interpretation of the data could be strengthened when xenografts from at least 3 different mice are shown.

5.The only methodology is single cell RNA-sequencing. Immuno-staining on relevant markers such as CD44, MYC, HMGA1 plus human epithelium and cell cycle markers would provide strong additional support for the claims made by the authors, because it's a complementary technique and it allows quantification at single cell resolution.

6.Line 173-175. The marker-low cluster look to me simply like spots containing a relatively high amount of dead/dying (tumor) cells. The identity/state of cells in the marker-low cluster should be characterized and discussed more extensively.

7.Figure 5 and accompanying text in line 182-194; the authors try to infer cell-to-cell interactions using a previously published tool. However, any biological interpretation is lacking. What can be concluded from this analysis?

8.Figure 6. Can the authors please explain more clearly what they mean by "PT" and "Mix" groups? I had a very hard time to understand what the data in figure mean. Again, an overall interpretation at the end (line 211) is lacking.

9.Figure 7. I like the effort to align the results with public scRNA-seq data. But although the expression of the cluster-signatures is heterogeneous, there is no evidence for distinct (CSC-like) cell populations. Why don't these HMGA1 vs CD44 signature cells cluster away from each other in the UMAPs? Perhaps the patient-to-patient heterogeneity overwhelms differences within tumors, but in that case the authors could re-run their analysis for each patient separately, to make 6 patient-specific UMAPs. In its present form, this analysis does not convince me that two distinct CSC(-like) populations within one TNBC exist.

Minor comments:

10.In the Supplemental table 2 noticed that many of the marker genes have adjusted P values well above 0.05 (and even above 0.1). That makes the statistical analysis rather weak. This could especially be problematic since the authors entirely base their main claims on this marker analysis, and I recommend that the authors use more stringent P-value cut-offs in the cluster analysis.

11.Line 129/130. If I look at figure 3A, I don't see this tendency that the authors describe. Can the authors provide statistical support or visual aid to make their claim more apparent to the reader?

12.Line 217; shouldn't this be 6 patients? I see six clusters and in the original paper six patients are mentioned.

Significance

Conceptual/biological impact: Showing the existence of distinct populations of CSCs within one (breast-)tumor potentially has a high impact on the field of fundamental and translational cancer research. As the authors state, it could be one key reason underlying drug resistance. However, the technology used by the authors does in my view not allow to make such a claim. First and foremost because the technology does not allow analysis at single cell resolution.

Technical impact: The platform used by the authors can be of interest for some applications, but they already published this in Scientic Reports a few years ago. I'm afraid that with the rapid recent developments in the field of spatial single cell transcriptomics (See for example Srivatsan et al Science 2021; 373: 111-117), the technical impact on the field is relatively low.

Audience: Researchers in the field of cancer biology with an interest to perform low-cost molecular analysis at low-resolution spatial-resolved tissue specimens (transcriptomics, but perhaps expanded with bisulfite sequencing, or ATAC sequencing) could be interested in the technology presented in this manuscript.

My expertise: single cell transcriptomics, (cancer) cell cycle, cancer drug resistance, cell plasticity, mouse models.

Referee Cross-commenting

I have read the comments and align mostly with reviewer #2. The authors need to improve this manuscript a lot before it's suitable for publication in any of the Review Commons journals.

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I thank the Referees for their...

Referee #1

1. The authors should provide more information when...

Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

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Referee #3

Evidence, reproducibility and clarity

all OK

Significance

This is a valuable paper that make use of the rapid mitotic cycles of the Drosophila syncytial embryo to study the recruitment of proteins in mitotit centrosome maturation. The synchrony of these cycles make this an excellent experimental system in which to follow the relative timing of recruitment of individual molecules to the centrosome and, while the system may have idiosyncrasies that facilitate rapid cycling, it provides valuable information. This is a significant data set that shows the pulsatile recruitment of Spd2 and Polo kinase peaking in mid S-phase in contrast to the continuous recruitment of Cnn.

The authors carry out some interesting modelling to account for the pulsatile activity of Polo through recruitment to the centriole. As they have previously shown Polo recruitment to be dependent upon S-S/t motifs in An1 and Spd, the authors examine the effects of multiple mutations at these potential recruitment sites. Interestingly they show that mutation of 34 such sites in Ana1 has little effect on recruitment of Polo to old-mother centrioles but perturbs recruitment onto ne mothers. Expression of the multiply mutated Spd-2, on the other hand, perturbs the Polo pulse on both old- and new- mothers. Together this would be in line with their previous suggested role for Ana1 in initially recruiting Polo to centrioles and Spd2 having a role in expanding the PCM.

The modelling carried out by the authors is simple but effective. As with almost any cell cycle model, the models have their short-comings and the authors are largely aware of these. I thought it would be worthwhile to have some more discussion of what activates Polo kinase. It could be partially activated by the Polo-box binding to its receptor site but do other kinases carry out its T-loop phosphorylation? There are plenty of mitotic kinases around and so this could be discussed in greater detail. Moreover, although the pulsatile association of Polo with the centrosome does not have to correspond to pulsatile activity, this is likely. In which case, further discussion of the roles of opposing phosphatases would be in order.

All in all, however, this is a useful paper that comes up with a thorough description of the timing of events of centrosome maturation in Drosophila embryos.

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Referee #2

Evidence, reproducibility and clarity

Review of 'Mother centrioles generate a local pulse of Polo/PLK1 activity to initiate mitotic centrosome assembly' from Wong et al.

In this paper, Wong et al address the mechanisms of centrosome assembly in flies. They start with the interesting observation that Polo localized at centrosomes oscillates before cells enter mitosis, while Cnn (and with it centrosome maturation) either increases or reaches a plateau. The phenomenon is local, since Polo levels at in the cell are high during mitosis. They propose that the oscillation is driven by a negative feedback loop whereby Polo inhibits its own binding to the centrosome, Ana1 being the most likely relevant receptor. Finally, they discuss the possible meaning of this oscillatory behavior, in the light of the rapidity of the early embryonic cell cycles.

Major comments

1- One can imagine different reasons for the fact that the model displays different dynamics for Cnn and Spd-2/Polo. For example, a major difference may be due to the different dissociation rates of the clusters Cstar and Shat. These are governed by different laws and different parameters (kdis vs kidsCstar1/n). If I understand, both parameters and dependency on Cstar^2 are assumptions. Hence, it would be important to pinpoint which component of the model is more directly responsible for the observed behavior. The analysis should not be limited to the dissociation, but should be extended to the whole model. To this aim, one could test the robustness of the model's parameters. The results of this analysis will also be a prediction of the model.

2- The presence of a positive-feedback loop involving Cnn could offer an alternative and more robust explanation for the slower dynamics of Cnn. Such a loop between Cnn and Spd-2 was proposed by the authors (Conduit, eLife, 2014). I think some comment on this point would be interesting (eg, could the Cnn/Spd-2 loop proposed earlier work in this context? If not, why? If yes, should not this option be explored?).

3- The prediction presented in Figure 6 is very relevant. I wonder how robust this behavior is to changes in parameters values.

4- Additional testing of the model would be important to confirm that the negative feedback loop is actually in place, although I understand experiments may be difficult to be performed. Possible examples: constantly high levels of Polo are expected to decrease its centrosomal localization, is that correct and, if so, testable? Is it possible to delay one cycle, and then observe the decay in Cnn values? This latter experiment, for example, could help to distinguish positive feedback vs slow decay rates. If the experiments are not possible, it may be worth anyway to present some predictions worth testing.

5- The difference between Models 2 and 3 is not clear to me. In mathematical terms, they seem to be basically the same thing: reaction (50)=(33), (51)~(34) given (40) and (52)~(35) again given (40). This is precisely since the model comes with the assumption of a well-stirred system, and thus adding P in solution is not so different from assuming P=Rphat (40). I would have imagined that also Model 2 accounts for the fact that in Spd-2-S16T and Ana1-S347T Polo is recruited slower and for a longer period. Is it not true? If so, is model 3 really needed? More in general, assuming a role for an increase of local concentration of P* is quite a jump, especially given the small distances involved, and the fast diffusion occurring within cells.

Minor points

1-Could the authors use the FRAP data to estimate the different kdis? If so, a comparison with the 20-fold difference used in the model would be useful.

2- p. 6, The authors should state clearly for the worm-uneducated like me whether the fusions were done with the endogenous proteins or not.

3- p.7 Figure 1B, in the text it is referred to display 'levels of peaks' and in the figure and legend we find 'growth period'. Not clear how the two refer to the same quantity.

4- Spd2-mCherry is present in both Figure 1C and D, but with very different amplitudes. Why is that the case?

5- The fact that Polo peaks in mitosis is a key observation. Unfortunately, this is often reported as a personal communication. The authors never tried to produce this piece of data?

6- p.11 It is explained that NM and OM differ for their initial values because the OM starts with some PCM from the previous cycle. However in Figure 3A, for example, the values of Polo at the end of the cycle are identical in the two. Is not this in contrast with the explenation?

Still p11, there is reference to Figure 3C,D, but Figure 3D does not exist, I guess it should be 3A,C.

7- In the formulation of the model (page numbers in Suppl Mat are unfortunately missing..), one citation for the total amount of Polo being large is needed.

8- I do not understand this point: scaled c output is 1, and the initial condition for c=1 also?

9- It has been shown in different systems (from yeast -- haase winey reed, NCB, 2001-- to worms -- McCLeland O-Farrell CB 2008) that centrosome duplication can occur independently from the cell cycle oscillator. I was wondering whether the proposed negative feedback loop may play a role in this phenomenon. This is only a curiosity, which does not need to be addressed.

Significance

The new observation and hypotheses presented in the paper provide a sizeable advance. The presence of an oscillation in Polo, uncoupled from cellular levels, is new, and the model proposes a testable hypothesis to explain it. Some additional experiments to verify the model would strengthen the manuscript.

The work is probably more appropriate for experts in the centrosome field. My primary expertise for this review was in mathematical models.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Embryogenesis is characterized by rapid cell divisions without gap phases. How these cells achieve successive rounds of chromosome segregation in dozens of minutes without failure is of great interest to cell and developmental biologists. A key aspect of rapid divisions is the oscillatory nature of centrosome assembly, which aids in building the mitotic spindle during mitosis, and centrosome disassembly during mitotic exit. Polo kinase activation and localization to the centriole is essential for centrosome dynamics, but its molecular targets, timescales of activation and deactivation, and overall mechanism of action is still not fully determined.

This paper aims to build a mathematical model to tease out the mechanism of Polo recruitment to centrioles and transformation of centrosome scaffold proteins (Spd2/Cep192 and Cnn/CDK5RAP2) from inactive forms to functional, multimeric platforms. The authors posit that several features are critical to describe the dynamics of Polo, Spd-2, and Cnn: 1) a negative feedback loop that releases Polo from a centriole receptor, 2) a kinetic relay that allows Spd-2 to assemble, followed by Cnn, and 3) a disassembly mechanism driven by de-phosphorylation. They validate their model in several ways, most notably by introducing an Ana1 mutant that inhibits Polo binding to centrioles: their model predicts a delay in Polo accumulation which bears out in vivo.

The cell biology experiments of this paper are of high quality and well quantified, and I have no concerns there. However, the mathematical model elevates this study to the next level, and thus deserves greater scrutiny. I'm not concerned that the model doesn't get everything right, or that all of the parameters are correct. This is new territory. I think the value of models is their power to predict, rather than their power to explain existing data. The authors are giving the field a great hypothesis generator which we can use to plan experiments for the next 5 years. Then the model will be updated to be more accurate. Thus, this work represents a significant achievement.

Still, some key validations regarding phosphorylation rates are missing that could be easily tested. Furthermore, the study would be strengthened by greater understanding of the PCM disassembly. mechanism. Addressing these two points will improve my confidence in the mathematical model.

Major Comments.

1. This study builds a model that relies heavily on rates of phosphorylation and de-phosphorylation. Further, de-phosphorylation is assumed to be the key disassembly mechanism, but this has not been rigorously studied in fly embryos. Thus, two critical aspects of the model remain unverified.

Surely, the authors could test how changing phosphorylation rate (kcat S and kcat C) and de-phosphorylation rate (kdis) affects the recruitment and departure of Spd-2, Polo, and Cnn in vivo. This could be achieved by 1) titrating an inhibitor of Polo (e.g., BI-2536) or introducing a mutation in the T-loop of Polo (the equivalent of T210D or T210V in flies; T210D should raise kcat, while T210V should lower kcat; https://doi.org/10.1021/bi602474j), and 2) inhibiting a phosphatase such as PP2A, which is the presumed antagonist of Polo according to several C. elegans studies.

If their model adequately predicts the outcome of these two experiments (changing phosphorylation and dephosphorylation rates), I will be more convinced.

1. The models focus on Polo and Spd-2 pulses during mitosis, but ignore the disassembly phase of Cnn. Do Cnn levels drop during mitotic exit? Can this drop in Cnn be described by any of the authors' proposed models?
2. These models are described as the "simplest possible" yet have many unknown parameters. For example, Model 1 has 12 parameters, none of which have been determined experimentally. How did the authors land on these values? Is it possible that one could alter any combination of these parameters and achieve a similar outcome? Or, if the Kcat of Polo is changed two-fold, does the whole model fall apart (see above)?

Experimentally determining these parameters would greatly strengthen this paper, but I think that would require gargantuan effort that is beyond the scope of the current work. Instead, it is therefore critical to test how robust the model is by probing the parameter space. For example, could the authors show us what the model predicts (e.g., as in Figure 2C) when each parameter is changed by 2-fold? Presumably the authors have already done this, but I would like to see the outcomes.

Minor Comments.

-Figure 1. The authors should include representative images of centrosomes for the plots in panels A,C and D. The x-axes could have more informative labels (e.g., time relative to NEB). - Figure 1A. Much of Figure 1 has already been performed in C. Elegans, yet this fact is not mentioned until the discussion. For example, the pulsed nature of Polo and SPD-2 appearance and disappearance has been reported in C. elegans in Mittasch et al. 2020 and Magescas et al., 2019. These findings, and their implications for evolutionary conservation, should be mentioned in the main text (e.g. page 6 or 7). - Figure 2. It's hard to envision how a scaffold can both flux outward and be structurally strong. The mere fact that there is outward movement of scaffold chunks implies breaking of bonds, which indicates overall structural weakness. Are the authors talking about strength of the entire PCM, or just strength of the chunks? It would be great if the authors could clarify this. -Figure 2. One would think that scaffold flux and strength are anti-correlated. Perhaps this is the case? As far as I'm aware, previous studies of Cnn flux were performed primarily in S-phase, when there is presumably less need for PCM strength. What about during mitosis during chromosome segregation? Does the PCM become stronger during mitosis? Does Cnn flux decrease during mitosis? - Figure 2B. I would prefer a legend in the actual figure indicating what the different symbols mean. I found it difficult glancing back and forth between the text and the figure. - "We also allow the rate of 𝐶∗ disassembly to increase as the size of the 𝐶∗ scaffold increases, which appears to be the case in these embryos (Conduit et al, 2010)."

I can't find any analysis of PCM disassembly in this study. What are the authors referring to as "disassembly"? Do they mean departure of Cnn from the PCM in S-phase? Or, disassembly of the whole PCM during mitotic exit?

-"If the centriole and PCM receptors (Ana1 and Spd-2, respectively) recruit less Polo, the centriole receptor (Ana1) will be inactivated more slowly."

Is Ana1 a known substrate of Polo? This seems highly speculative. The authors should note that deactivation of Ana1 could be through various other mechanisms. Furthermore, Polo could be locally degraded as shown in human cells doi: 10.1083/jcb.200309035.

-"We note that our mathematical models are purposefully minimal to reduce the number of parameters and test possible mechanisms rather than to mimic experimental data." I appreciate this statement.

Significance

This paper aims to build a mathematical model to understand the cyclic nature of centrosome assembly and disassembly in fruit flies. Due to the conserved nature of the components (proteins in the system, such as Polo Kinase, Spd-2, and Cnn/CDK5RAP2), this model could likely be extended to a broad swath of eukaryotes. This approach is quite unique in the centrosome field, as only one other study (Zwicker et al., PNAS 2014) has tried seriously to model the growth kinetics of PCM, the outermost part of a centrosome. The field has been dominated by genetics and cell biology approaches, so implementing a mathematical model will advance the field and generate hypotheses, even if the model is not yet fully fleshed out. This paper represents a significant advance.

This study will be of broad interest to the centrosome field.

Expertise: centrosome biogenesis, mitosis, biophysics

Note: I am not sufficiently qualified to evaluate the mathematics underlying the model.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary: The Non-Photochemical Quenching (NPQ) protects photosystems from energy overloading by excess light exposure. The NPQ consists of multiple factors which function in different time scales and energy levels. One of the factors, qH, has been proposed based on chlorophyll fluorescence lifetime observation and the plastid lipocalin has been identified as the important player to regulate qH. It remains to link the qH phenotype and molecular mechanisms. The authors purify photosynthetic protein complexes from the qH mutants and tried to build a biophysical model to link qH phenomena and protein science based on chlorophyll fluorescence lifetime observation.

Response: Thank you for your constructive comments which we have addressed and complete the manuscript nicely.

Major comments: There are two major issues. One issue is, even many kinetics are presented, but the relationship between these values and qH phenotypes is not clearly stated or connected. One idea is to build the mathematical model(s) to explain these kinetics. The other issue is, lipid composition is not considered. Indeed, this phenomenon is observed or emphasized in low temperatures. Generally thinking, lipid composition bound to photosynthetic complexes would be disturbed or modified its conformation.

Response: ____We have not attempted to build a descriptive model of how the different molecular players in qH operate in the membrane to yield the observed fluorescence kinetics as this is beyond the scope of our study. However, we agree that lipid composition should be considered and we have now added two additional authors, text in the method and result sections and new Fig. 6 and Fig. S9 examining lipid composition of thylakoid extract, LHCII trimer and LHCII/Lhcb monomer fractions. No significant differences can be observed between the qH ON and OFF states in the main chloroplastic lipids.

Minor comments: Some datasets are less biological replicates or not clearly stated about the biological replicate number (Figure 2, Figure 4, Figure 5, Figure S2, Figure S5, and Figure S8). Normally, at least three independent biological replicates are required. Technical replicates are not acceptable.

Response: ____If by biological replicates, you mean three independent plant individuals, we agree that this would be the bare minimum required, and we apologize for the confusion. The definition of biological replicate (also referred to as biological experiment) in our study is each one represents a separate batch of several plant individuals pooled (n = 2 to 8) grown at independent times. Then within each biological experiment, we perform technical replicates (i.e. independent measurements of different aliquots from the same sample) which we believe are acceptable and necessary but we agree not sufficient. For most data, we have at least 2 biological experiments, and up to 3, for assessing the quenched nature of LHCII trimer and not LHCII/Lhcb monomer (Fig. 3). We have rephrased the text so this aspect is clearer and also provide more detail below about the aforementioned figures.

Fig. 2: TCSPC on thylakoids, n=3 technical replicates from 2 independent biological experiments; Two separate thylakoid preparations were made from independently grown plants (leaves from n > or = 5 plants were pooled each time). Fig. 4: CN-PAGE, n=3 technical replicates from 2 independent biological experiments (leaves from n > or = 3 plants were pooled each time). Fig. 5: TCSPC on isolated complexes, n=3 technical replicates from 2 independent biological experiments; Two separate thylakoid preparations were made from independently grown plants (leaves from n = 8 plants were pooled each time). Fig. S2: step solubilization, n=2 technical replicates; here 1 biological experiment was used from n = 8 plants. Fig. S5 contains the biological replicates 1 (n=2 plants) and 2 (n=8 plants) of the representative experiment shown in Fig3, biological replicate 3 (n=8 plants). Fig. S8: HPLC on isolated complexes from 2 independent biological experiments (leaves from n = 8 plants were pooled each time).

In Figure 3 and Figure S3, extend the length of the major tick for each axis. It is hard to distinguish between major tick and minor tick.

Response: Ok, done.

In Figure 3, mark the measured peak wavelength value on the top for readers.

Response: ____Ok, done, added in the legend “with maxima at 679 nm for all samples”.

In Figure 4, Why do not you present chlorophyll kinetics? I suspect it is possible to acquire if you used SpeedZen.

Response: In Figure 4, we present a measurement of fluorescence emission from separated pigment-protein complexes by CN-PAGE, there are no light-induced changes to be measured here hence we do not present chlorophyll fluorescence kinetics.

In Figure 6, decrease the thickness of the border for the bar graph or marker. Markers on the top of the bar graph are not visible.

Response: Ok, done.

Figures S3 and S6, provide the elution volume of protein standard in the chromatograph.

Response: We don’t make any statement regarding the molecular weights of the protein complexes from the chromatograms, so elution volumes of protein standards are not required. Composition of the different peaks were validated by Iwai et al. 2015 (Nat Plants 1: 14008) and further verified here (Fig. S6, S10).

Figures S11 and S12, describe the number of biological replicates.

Response: ____Ok, done (now Fig. S12 and S13).

Reviewer #1 (Significance (Required)): The topic is important for plant physiology especially photosynthesis regulation and biophysical characterization is straightforward to interpret molecular machinery. Other studies are only for chlorophyll observation for the whole plant body, but most importantly, this study is the challenging work on qH characterization with a biochemical approach.

Response: Thank you for your appreciation of our work!

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Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Authors observed qH in isolated LHCII trimers with Chl fluorescence changes (shorter), and concluded that no single major Lhcb isomers is necessary for qH.

Response: Thank you for your constructive comments which we have now addressed and make the manuscript clearer.

Major concern is: LHCII trimers are divided into S, M, L trimers with different compositions. Authors are requested to interpret their results in terms of L-, M-, S-trimers.

Response: Our solubilization conditions and isolation method don’t allow to distinguish between loosely (L), moderately (M) or strongly (S)-bound trimers, the LHCII trimer fraction is a pool of these trimers. We have now specified this aspect in the discussion and cannot interpret our results further than narrowing down the LHCII trimer as a quenching site. In future work, we will attempt their separation although getting entirely pure fractions of each is technically challenging.

Minor comments are: Authors describes qI as reversible NPQ, but qI with D1 damage is not reversible.

Response: ____D1 can be repaired thereby relaxing qI, see recent article from Nawrocki et al. Sci Adv 2021. We have clarified this point in the introduction.

In page 3 - 2nd paragraph, Authors define components of NPQ one by one, but the definition or recovery kinetics for qH is skipped, And authors suddenly start explaining molecular players of qH without changing paragraph.

Response: We have now clarified that the relaxation kinetics for sustained NPQ including qH are slow (hours to days) and changed paragraph to introduce the molecular players known to be regulating qH.

In Fig. S6, authors tried to confirm the trimer and monomer fractions they used by using Lhcb2 and Lhcb4 antibodies, respectively. But, the distribution of Lhcb2 only in Trimer fraction in WT, which is different from the distribution in other mutants. Contamination of Lhcb4 in Trimer fraction is also of concern. Authors may use BN-PAGE or Ultracentrifugal separation, rather than gel filtration.

Response: Regarding the different distribution of Lhcb2 between WT and mutants, we have now better labeled Fig. S6B so it is clear that WT is non-treated (non-stress condition) and the mutants underwent a cold and high light-treatment (stress condition). This difference may thus be explained by the trimers stability/propensity to be solubilized by the detergent varying between non-stress and stress conditions. It is not a concern as we’re not comparing mutants to WT. Contamination by Lhcb4 in the trimer fraction is neither a concern as its amount is similarly low between the compared samples: soq1 mutant cold HL (qH ON) and soq1 lcnp mutant cold HL (qH OFF). So presence of Lhcb4 cannot account for the observed difference in fluorescence quenching as its quantity does not differ between the ON and OFF states. Importantly, the monomeric fraction, enriched in Lhcb4, does not show fluorescence quenching. We have used CN-PAGE as a complementary approach that showed that LHCII trimers are quenched after a cold and high light-treatment in both WT and soq1 mutants (Fig. 4). These aspects are described page 7 in the results section “qH is observed in isolated major LHCII”. Here we chose not to use BN/CN-PAGE or sucrose gradient ultracentrifugation for the isolation of the trimeric and monomeric fractions for two reasons: they would not be as suitable for TCSPC experiments due to their acrylamide or sucrose content and they would take more time; gel filtration was preferred to limit buffer exchange and time required from plant protein extraction to measurement.

Reviewer #2 (Significance (Required)):

Localization of qH in LHCII trimers is interesting, but not surprizing.

So, authors are recommended to rewrite the significance of their findings.

Response:____ In the last paragraph of the introduction, we have now clarified that this study identifies qH quenching in the LHCII trimers but not in the minor monomeric Lhcbs. Prior to this work, the peripheral antenna as a whole was known to be required for qH, now this study identified the major trimeric LHCII as a quenching site. The novelty and significance of this work is further substantified by the isolation of quenched antenna directly from plants in physiological conditions, as opposed to artificial induction in vitro. Regarding the “surprising” nature of findings in general, please see answer below to reviewer #3.

My expertise: I am working on the movement of L and M trimers in plants under photoinhibitory illumination.

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Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The studies reported in this manuscript were designed to test the hypothesis that LCNP binds (or modifies) a molecule in the vicinity of (or within) the antenna proteins, under stress conditions. This in turn triggers a conformational change that converts antenna proteins from a light-harvesting to a dissipative state. Experiments were performed to locate the qH quenching site within the peripheral antenna of PSII and determine its sensitivity to Lhcb subunit composition. The authors were able to isolate antenna complexes with active qH that remained quenched after purification. Analysis of these complexes revealed that qH can occur in the major trimeric LHCII complexes. The elegant studies reported in this manuscript have made good use of appropriate molecular techniques and genetic resources. Genome editing and genetic crosses were used to demonstrate that qH is not restricted to inherent regulation of a specific major Lhcb subunit. The data are clearly presented and the data are convincing.

Response: Thank you very much for your appreciation of our work!

Reviewer #3 (Significance (Required)):

The studies reported in this manuscript build on a firm foundation of previous work by these authors and others. The conclusions are based on the analysis of Chl fluorescence lifetimes in intact leaves, thylakoids, and isolated antenna complexes in which qH was "ON" or "OFF". The findings are interesting and incremental in terms of increasing current understanding. However, the data extend our knowledge of the location of qH within the peripheral antenna of PSII. Rather unsurprisingly, the authors highlight the need to preserve thylakoid membrane macroorganisation for a full qH response.

Response: The philosophical concept of findings not being surprising could be discussed at length. To quote a commenter from this blog: ____https://blogs.uw.edu/ajko/2009/09/17/whats-surprising/____, just because one could have guessed the outcome of an experiment is not the same as empirically validating it. We hope you agree. Plus, as Fabrice Rappaport used to say, “we’re never sheltered from a discovery” and it could have been that isolated LHCII with qH ON showed short Chl fluorescence lifetimes similar to observed in leaves. We couldn’t know until we tried!

Data are presented showing that while qH occurs in the trimeric LHCII complexes, it does not require a specific Lhcb subunit and is insensitive to Lhcb composition. However, the discussion is rather speculative because data interpretation is limited by an absence of knowledge regarding what happens to the LHC trimers and qH during isolation of thylakoids and photosynthetic complexes. This point is considered appropriately in the discussion. The authors also acknowledge the existence of additional quenching sites beyond the LHCII trimers that are required for qH.

Response: Indeed, thank you we have addressed these points in the discussion, and have now added new data on the lack of changes in lipid composition in the LHCII trimer with qH ON or OFF. We view this study as an important milestone to obtain knowledge on the molecular origin of qH.

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Referee #3

Evidence, reproducibility and clarity

The studies reported in this manuscript were designed to test the hypothesis that LCNP binds (or modifies) a molecule in the vicinity of (or within) the antenna proteins, under stress conditions. This in turn triggers a conformational change that converts antenna proteins from a light-harvesting to a dissipative state. Experiments were performed to locate the qH quenching site within the peripheral antenna of PSII and determine its sensitivity to Lhcb subunit composition. The authors were able to isolate antenna complexes with active qH that remained quenched after purification. Analysis of these complexes revealed that qH can occur in the major trimeric LHCII complexes. The elegant studies reported in this manuscript have made good use of appropriate molecular techniques and genetic resources. Genome editing and genetic crosses were used to demonstrate that qH is not restricted to inherent regulation of a specific major Lhcb subunit. The data are clearly presented and the data are convincing.

Significance

The studies reported in this manuscript build on a firm foundation of previous work by these authors and others. The conclusions are based on the analysis of Chl fluorescence lifetimes in intact leaves, thylakoids, and isolated antenna complexes in which qH was "ON" or "OFF". The findings are interesting and incremental in terms of increasing current understanding. However, the data extend our knowledge of the location of qH within the peripheral antenna of PSII. Rather unsurprisingly, the authors highlight the need to preserve thylakoid membrane macroorganisation for a full qH response.

Data are presented showing that while qH occurs in the trimeric LHCII complexes, it does not require a specific Lhcb subunit and is insensitive to Lhcb composition. However, the discussion is rather speculative because data interpretation is limited by an absence of knowledge regarding what happens to the LHC trimers and qH during isolation of thylakoids and photosynthetic complexes. This point is considered appropriately in the discussion. The authors also acknowledge the existence of additional quenching sites beyond the LHCII trimers that are required for qH.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

Authors observed qH in isolated LHCII trimers with Chl fluorescence changes (shorter), and concluded that no single major Lhcb isomers is necessary for qH.

Major concern is: LHCII trimers are divided into S, M, L trimers with different compositions. Authors are requested to interpret their results in terms of L-, M-, S-trimers.

Minor comments are: Authors describes qI as reversible NPQ, but qI with D1 damage is not reversible.

In page 3 - 2nd paragraph, Authors define components of NPQ one by one, but the definition or revoery kinetics for qH is skipped, And authors suddenly start explaining molecular players of qH without changing paragraph.

In Fig. S6, authors tried to confirm the trimer and monomer fractions they used by using Lhcb2 and Lhcb4 antibodies, respectively. But, the distribution of Lhcb2 only in Trimer fraction in WT, which is diferetnf from the distribution in other mutants. Contamination of Lhcb4 in Trimer fraction is also of concern. Authors may use Bn-PAGE or Ultracentrigugal separation, rather than gel filtration.

provide evidences for

Significance

Localization of qH in LHCII trimers is interesting, but not surprizing.

So, authors are recommended to rewrite the significance of their findings.

My expertise: I am working on the movement of L and M trimers in plants under photoinhibitory illumination.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The Non-Photochemical Quenching (NPQ) protects photosystems from energy overloading by excess light exposure. The NPQ consists of multiple factors which function in different time scales and energy levels. One of the factors, qH, has been proposed based on chlorophyll fluorescence lifetime observation and the plastid lipocalin has been identified as the important player to regulate qH. It remains to link the qH phenotype and molecular mechanisms. The authors purify photosynthetic protein complexes from the qH mutants and tried to build a biophysical model to link qH phenomena and protein science based on chlorophyll fluorescence lifetime observation.

Major comments:

There are two major issues. One issue is, even many kinetics are presented, but the relationship between these values and qH phenotypes is not clearly stated or connected. One idea is to build the mathematical model(s) to explain these kinetics. The other issue is, lipid composition is not considered. Indeed, this phenomenon is observed or emphasized in low temperatures. Generally thinking, lipid composition bound to photosynthetic complexes would be disturbed or modified its conformation.

Minor comments:

Some datasets are less biological replicates or not clearly stated about the biological replicate number (Figure 2, Figure 4, Figure 5, Figure S2, Figure S5, and Figure S8). Normally, at least three independent biological replicates are required. Technical replicates are not acceptable.

In Figure 3 and Figure S3, extend the length of the major tick for each axis. It is hard to distinguish between major tick and minor tick.

In Figure 3, mark the measured peak wavelength value on the top for readers.

In Figure 4, Why do not you present chlorophyll kinetics? I suspect it is possible to acquire if you used SpeedZen. In Figure 6, decrease the thickness of the border for the bar graph or marker. Markers on the top of the bar graph are not visible.

Figures S3 and S6, provide the elution volume of protein standard in the chromatograph.

Figures S11 and S12, describe the number of biological replicates.

Significance

The topic is important for plant physiology especially photosynthesis regulation and biophysical characterization is straightforward to interpret molecular machinery. Other studies are only for chlorophyll observation for the whole plant body, but most importantly, this study is the challenging work on qH characterization with a biochemical approach.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

The manuscript reports the identification of a novel protein complex involved in denervation-induced desmin degradation. The first protein to be identified was the ATPAse Atad1. A clever isolation strategy was based on the fact that the ATPAse p97/VCP is involved in the extraction of ubiquitinated myofibrillar proteins but is not required for the removal of ubiquitinated desmin filaments. The authors reasoned that a related ATPAse might be specifically required for desmin filaments. Atad1 was identified by treating desmin filaments with a nonhydolyzable ATP analog and looking for ATPases that are associated with desmin filaments by proteomics. Knockdown of Atad1 causes a loss of desmin degradation and led to a loss of denervation-induced muscle atrophy. It seems that Atad1 binds desmin in a phsphorlation-dependent manner, although the binding maybe mediated by a protein that hasn't yet been identified. The authors went on and identified two additional proteins which together with Atad1 form a protein complex involved in recruiting calpain for desmin degradation.<br> Overall, this study is very convincing providing novel important insight. I have only some minor comments

Minor comments

1. I wondered whether Aatd1 is expressed at higher-than-normal levels in muscle and heart. I looked that expression pattern up and it seems that they are especially abundant in muscle and heart and expressed at lesser levels in smooth muscle and overall have a restricted expression.

We now analyzed ATAD1 levels in various tissues by Western Blotting and the new data is presented as Fig. S2. ATAD1 is present in many tissues and thus may have many cellular roles.

Maybe you have some data on their expression in muscle tissue. Did you perform some staining of muscle tissue at baseline and after denervation with regard to the protein localization by immunostaining?

The new associations between ATAD1 and its protein partners reported herein were further validated by an immunofluorescence staining of longitudinal sections from 7 d denervated muscles and super-resolution Structured illumination microscopy (SIM). The new data presented as Fig. 3E demonstrate colocalization of ATAD1 with calpain-1, PLAA and UBXN4. To confirm that these proteins in fact colocalize, we measured the average colocalization of ATAD1 with calpain-1, PLAA and UBXN4 using the spots detection and colocalization analysis of the Imaris software (Fig. 3E). Only spots that were within a distance threshold of less than 100 nm were considered colocalized (Fig. 3E, graph).

1. The string data presented in Figure 3C needs some further explanation with regard to the colors used for the different proteins. While the authors explained the meaning of the proteins labeled in red, there is no explanation for the other colors.

These were arbitrary colors assigned to protein nodes by the STRING database. The current color code we use is only meant to group the UPS enzymes based on function (e.g. E2s, E3s, DUBs etc). This information has now been added to figure legend.

1. Molecular weights in Fig. 2E, 3D needs to be 'repaired' and additional MW information is required in case of the ubiquitin blot shown in 3D.

All molecular weight values and protein ladders have been added.

1. Fiber size distributions shown in Fig. 1D and 4F. Have the differences been statistically tested?

We thank the reviewer for raising this important point because we just established an approach to quantitate these effects statistically using Vargha-Delaney A-statistics test and Brunner-Manzel test. Our new paper on this topic entitled “A semi-automated measurement of muscle fiber size using the Imaris software” by Gilda et al. was recently published in the AJP Cell Physiol. As requested by the reviewer, we now also apply A-statistics test and Brunner-Manzel test on the fiber size measurements presented in our current manuscript (Figs. 1C, 4F and Table I), which show a significant difference in size distributions of fibers expressing shAtad1 vs. adjacent non-transfected fibers. As indicated in our paper (Gilda et al, 2021), the A-statistics is a direct measure of the fiber size effect, and it shows significant beneficial effects on cell size by shAtad1 (Table I). Such effects can be simply missed by traditional measurements of median, average, and Student’s t-test.

1. For my taste the referral to the individual data (Fig. numbers) in the discussion section is too detailed and becomes a second results section. This should be substituted by a summary paragraph before the implications are discussed.

We agree and revised the discussion section accordingly.

1. The summary slide is very good. However, could you please add information, which protein of the three in the Atad1 complex is depicted by each symbol?

The model slide has been revised to include all enzymes studied in this paper, and a legend to improve clarity.

Reviewer #1 (Significance)

Novel insight into the proteins involved in desmin filament degradation. Since this is an important subject both in muscle and heart and plays an important role in muscle and heart disease, it is of significant clinical importance. Currently it has only been implicated in denervation-induced skeletal muscle atrophy, but it is likely that desmin filament metabolisms is also similarly regulated in the heart.

I am a researcher mainly focusing on the cardiac biology with some expertise also on muscle, however no specific knowledge about desmin filament biology. <br> Referee Cross-commenting Overall, I think all three reviewers agree that this is a significant and important paper. I think that the comments made by the reviewers are fair and probably add to the quality of the manuscript.

We are pleased that the reviewers found our paper novel and important.

Thus, both myself and reviewer 2 agree that it would be useful to visualize Atad1 and partners localization in muscle fibers by immunofluorescence. These data would provide independent support to the model the authors are proposing, which currently is only based on biochemical analysis.

These data have been added as new Fig. 3E.

I also support the proposed use of proximity ligation to provide further evidence of the presence of the Atad1, Ubxn4 and PLAA in a complex. However, this experiment depends on the quality of the available antibodies and I would consider this not absolutely required.

Because our antibodies are not suitable for proximity ligation assay (PLA), we used a super-resolution SIM microscope, immunofluorescence, and the spots detection and colocalization analysis of the Imaris software to confirm colocalization of ATAD1 and its partners (new Fig. 3E). Similar to PLA (where signal is generated only if two antibodies used for staining are 100nm apart), only spots that were within a distance threshold of less than 100 nm were considered colocalized (Fig. 3E, graph). In addition, we present immunoprecipitation (Fig. 3D) and use three independent mass spectrometry-based proteomic approaches to validate these new associations.

I also agree that some further information on the proteomics data (as suggested by reviewer 3) is required with regard to the method of filtering for UPS components was performed.

We agree and thank the reviewer for this comment. More information on the proteomics data have been added to the text and legend to Table II.

The proposed request for further information on the electroporation approach is a valid comment and if the authors have this information, it would be good to provide. However, I do not recommend further experiments as overall the data are very consistent and the findings are very significant and represent a major advance in our understanding of desmin degradation.

With regard to the electroporation approach, i) representative images have been added to Figs. 1C and 4F, ii) a statement was added to Methods under “in vivo electroporation” about the percent of transfection routinely used in our experiments (60-70%), iii) we determine transfection efficiency by dividing the number of transfected fibers (also express GFP) by the total number of fibers in the same muscle cross section (using the Imaris software). This approach was fully validated in our recent papers by Goldbraikh et al EMBO Rep, 2020 (see supplementary material) and Gilda et al AJP-Cell Physiol, 2021.

Reviewer #2 (Evidence, reproducibility and clarity)

In their manuscript the authors show the involvement of the AAA ATPase Atad1 in Desmin degradation. They identify PLAA and Ubxn4 as partners of Atad1 that participate to its function in desmin degradation.<br> A general comment is that some conclusions are overstated. The authors mention several times that Atad1 depolymerises desmin filaments. The data show that Atad1 participates to the degradation of Desmin and to its solubilization. "Depolymerisation" should be kept for the model presented in figure 8 but not used in the result section.

We respectfully disagree with the reviewer that our conclusions are overstated. Early studies from Fred Goldberg’s group showed that filaments are not accessible to the catalytic core of the proteasome (Solomon and Goldberg, JBC, 1996), and therefore must depolymerize before degradation. Accordingly, more recent studies by us and others identified distinct enzymes and cellular steps promoting disassembly and subsequent degradation of ubiquitinated desmin filaments (Cohen, JCB, 2012; Aweida, JCB, 2018) and myofibrils (Cohen, JCB, 2009; Volodin, PNAS, 2017). In the current manuscript, we employed a similar approach as we used before to analyze disassembly of filamentous myofibrils by p97/VCP (Volodin, PNAS, 2017), and demonstrate a critical role for ATAD1-PLAA-UBXN4 complex in promoting desmin IF disassembly and loss (figures 2C, 3D, 3G, 4C, 4G, 4H). We show that ATAD1 binds intact insoluble desmin filaments in an early phase during atrophy (3 d after denervation)(figures 2B, 2F) and later accumulates in the cytosol bound to soluble ubiquitinated desmin (figure 3D). Moreover, downregulation of ATAD1, PLAA or UBXN4 in mouse muscles prevents the solubilization of desmin IF (figures 2C, 3G, 4C) because in these muscles desmin accumulates as ubiquitinated insoluble filaments. Based on these data we conclude that Atad1 complex promotes desmin IF disassembly and subsequent loss.

Major comments:<br> 1) It would be useful to visualize Atad1 and partners localization in muscle fibers in immunofluorescence. Do they colocalize with desmin filaments, with calpain?

As requested, the new associations between ATAD1 and its protein partners reported herein were further validated by an immunofluorescence staining of longitudinal sections from 7 d denervated muscles and super-resolution Structured illumination microscopy (SIM). The new data presented as Fig. 3E demonstrate colocalization of ATAD1 with calpain-1, PLAA and UBXN4. To confirm that these proteins in fact colocalize, we measured the average colocalization of ATAD1 with calpain-1, PLAA and UBXN4 using the spots detection and colocalization analysis of the Imaris software (Fig. 3E). Only spots that were within a distance threshold of less than 100 nm were considered colocalized (Fig. 3E, graph). Given the antibodies in hand and new ones that we purchased, as well as the species of the antibodies, we were able to perform and optimize the staining only for the presented combinations of antibodies.

2) In the same line, interactors were obtained from large crosslinked complexes. It would make the model more convincing if direct interactions with Atad1 were shown, for example using Proximity Ligation Assays.

Because our antibodies are not suitable for proximity ligation assay (PLA), we used a super-resolution SIM microscope, immunofluorescence, and the spots detection and colocalization analysis of the Imaris software to confirm colocalization of ATAD1 and its partners (new Fig. 3E). Similar to PLA (where signal is generated only if two antibodies used for staining are 100nm apart), only spots that were within a distance threshold of less than 100 nm were considered colocalized (Fig. 3E, graph). In addition, we present immunoprecipitation (Fig. 3D) and use three independent mass spectrometry-based proteomic approaches to validate these new associations._

3) Evaluation of atrophy is made on cross-sections of muscles electroporated with shRNAs. Histology pictures should be shown.

As requested, representative images of transfected muscles were added to figures 1C and 4F.

4) What is the percentage of electroporated fibers? To evaluate the effect of shRNAs it is important to have this information. For example, if the efficiency is 50% it means that the reduction in expression of the target in electroporated fibers is twice the value reported for the whole muscle. Alternatively, immunofluorescence could be provided to see the decrease in targeted proteins in electroporated fibers.

We determine transfection efficiency by dividing the number of transfected fibers (also express GFP) by the total number of fibers in the same muscle cross section (using the Imaris software). This approach is fully validated in our recent papers by Goldbraikh et al EMBO Rep, 2020 (see supplementary material) and Gilda et al AJP-Cell Physiol, 2021. For our biochemical studies we always analyze muscles that are at least 60-70% transfected (added to methods).

As shown in figures 1B, 3F, and 4A-B, our shRNAs reduced gene expression by at least 40-50%, which in a whole muscle was sufficient to promote the beneficial effects on muscle (as mentioned in the text, shCAPN1 was validated in Aweida, JCB, 2018). Similar reduction in gene expression is commonly seen by the in vivo electroporation of a fully developed mouse muscles because transfection efficiency is never 100%. This means that the beneficial effects on muscle by the electroporated shRNA must underestimate the actual protective effects by gene downregulation. To prove that these beneficial effects on muscle result from specific gene downregulation, we compare and analyze in parallel in each experiment muscles transfected with shLacz scrambled control.

5) The same is true for all the experiments quantifying the effect of shRNAs in western blot. Since quantifications are probably made on whole muscles (ie a mix between electroporated and non electroporated fibers) and since the percentage of electroporated fibers is not given it is not possible to estimate the efficiency of the shRNAs in electroporated fibers.

As mentioned above and now also in the text, for our biochemical studies we always analyze muscles that are ~60-70% transfected. This methodology is very well established in our lab, and a reduction of 40-50% in gene expression by our shRNAs is sufficient to promote the beneficial effects on mouse muscle (see our papers in JCB, PNAS, Nat Comm, EMBO rep).

6) Figure 2C: by decreasing solubilization of desmin, one would expect a decrease in the levels of soluble desmin. Conversely the authors observe an increase in both insoluble and soluble desmin. Of course, this can be explained by reduced desmin degradation once solubilized but this should be demonstrated at least by showing that UPS inhibitors induces an increase in soluble ubiquitinated Desmin.

The reviewer raises an important point that we now discuss in the text. Soluble pool of desmin, its homolog vimentin as well as other Type III IF proteins is small as these proteins mostly exist in the cell assembled within filaments (see papers by RA Quinlan and WW Franke). This soluble pool of desmin may function either as precursors to the mature filament or as components released during filament turnover. Because we block desmin IF disassembly by downregulating Atad1, the soluble desmin that accumulates in the cytosol likely represents new precursors whose degradation also requires ATAD1. Therefore, we conclude that ATAD1 promotes degradation of desmin filaments and of soluble proteins (see also figures 2E and 4D).

As requested by the reviewer, we inhibited proteasome activity by injecting mice with Bortezommib and measured the effects on desmin content in denervated muscle (new figure 2D). Our new data clearly demonstrate accumulation of ubiquitinated desmin in atrophying muscles where proteasome activity was inhibited, indicating that in denervated muscles desmin is degraded by the proteasome.

7) Figure 2E: the levels of Atad1 in the insoluble fraction seem to be the same in the shLacZ and GSK3DN conditions, whereas the phosphor Ser is different. In other words, there should be more Atad1 in the insoluble fraction with shLacZ than with GSAK3DN since the phosphorylation level with shLacZ is significantly higher.

To quantitate the changes in ATAD1 association with desmin and avoid confusion by the reader, we performed densitometric measurements of ATAD1 and desmin, and depict in a graph the ratio of ATAD1 to desmin in the insoluble fraction. The new data was added to figure 2F and clearly demonstrate that ATAD1 association with desmin is significantly reduced in muscles expressing GSK3b-DN. These findings further support our conclusions that Atad1 association with desmin IF requires desmin phosphorylation.

8) Figure 4E: the authors state that phosphorylation decreases because of increased degradation (lanes 6-8). However, Calpain also increases degradation and phosphorylation is increased (lanes 2-4), so increasing degradation does not systematically cause a decrease in phosphorylation. Similarly, lane 5 Atad1 induces less degradation than Calpain, however, it causes a decrease in phosphorylation. Explain.

Here we use a cleavage assay, which was established and validated in our recent JCB paper (Aweida 2018). Desmin filaments were isolated from mouse muscle and the obtained preparation was divided between 9 tubes (hence there is no situation for “increase in phosphorylation” as indicated by the reviewer). Recombinant calpain-1 was then added to the tubes and cleavage of phosphorylated desmin was analyzed over time. Because the substrate for calpain-1 is phosphorylated desmin, we measured the content of both desmin and its phosphorylated form in the tube throughout the duration of the experiment. Only when cleavage of phosphorylated desmin by calpain-1 was accelerated (i.e., in the presence of Atad1), a rapid reduction in the amount of phosphorylated desmin could be detected (compare lanes 6-8 with 5) concomitantly with accumulation of small desmin fragments in short incubation times (compare lanes 6-7 with 2-3).

With respect to the reviewer’s comment that “Atad1 induces less degradation than Calpain” in lane 5, please note that Atad1 is not a protease and cleavage of desmin occurs in this experiment only in the presence of calpain-1. However, if there is a slight reduction in phosphorylated desmin, it should account for the ability of ATAD1 appears to slowly disassemble desmin IF (as our in vivo data by shATAD1 show).

9) The AAA ATPase VCP shares partners with Atad1 and is involved in muscle atrophy. It would greatly add to the manuscript if the authors inhibited VCP to compare its effect to Atad1

As stated in the text, we previously demonstrated that p97/VCP is not required for desmin filament loss: “the AAA-ATPase, p97/VCP disassembles ubiquitinated filamentous myofibrils and promotes their loss in muscles atrophying due to denervation or fasting (Piccirillo and Goldberg, 2012; Volodin et al., 2017). However, desmin IF are lost by a mechanism not requiring p97/VCP (Volodin et al., 2017). We show here that their degradation requires a distinct AAA-ATPase, ATAD1”. Therefore, our current studies were undertaken to specifically identify the AAA-ATPase that is involved in desmin filament disassembly and loss. Accordingly, p97/VCP was not detected by our mass spectrometry-based proteomic analyses presented here (stated in the discussion).

We did identify PLAA and UBXN4 as ATAD1 partners and show they are required for desmin loss, and therefore state in the text that “PLAA and UBXN4 are also known cofactors for p97/VCP (Liang et al., 2006; Papadopoulos et al., 2017), a AAA-ATPase that was not in our datasets, indicating that p97/VCP adaptors can bind and function with other AAA-ATPases”.

Minor comments:

1) The soluble fraction contains a large number of ubiquitinated proteins. Please explain how it can be stated that an increase in total soluble polyubiquitinated proteins corresponds to an increase in ubiquitinated desmin.

We do not state in the text that “an increase in total soluble polyubiquitinated proteins corresponds to an increase in ubiquitinated desmin”. We state that “stabilization of desmin filaments attenuates overall proteolysis. The reduced structural integrity of desmin filaments on denervation is likely the key step in the destabilization of insoluble proteins (e.g. myofibrils) during atrophy, leading to the enhanced solubilization and degradation in the cytosol”. We invite the reviewer to read our papers about this topic by Cohen 2012, Volodin 2017, and Aweida 2018. Using a dominant negative of desmin polymerization we show that disassembly of desmin filaments is sufficient to trigger myofibril destruction and consequently overall proteolysis (because myofibrils comprise ~70% of muscle proteins).

2) Page 11: the authors conclude that denervation enhance the interactions with Atad1. Figure 3D indeed show an increase for Ubxn4, but it is not clear for the other proteins.

Figure 3D shows that in 7 d denervated muscles there is an increase in associations between ATAD1 and ubiquitinated desmin, UBXN4, PLAA and calpain-1.

3) Figure 4 F: show muscle sections

A representative image was added as requested.

4) Page 21 in vivo transfection: it is stated "see details under immunofluorescence" but there is no immunofluorescence section in materials and methods.

Thank you. An immunofluorescence section has been added to Methods.

5) The authors show that Atad1 inhibition in innervated muscle is sufficient to induce muscle hypertrophy (Figure 4E). They conclude that the hypertrophic effect of Atad1 is due to the inhibition of Desmin degradation. However, this hypertrophic effect could be independent of the action of Atad1 on Desmin.

We believe the reviewer refers to figure 4F-H, where we show that downregulation of ATAD1 prevents the basal turnover of desmin and of soluble proteins and causes muscle fiber growth. Based on this data we speculate in the text that “ATAD1 attenuated normal muscle growth most likely by promoting the loss of desmin filaments and of soluble proteins … Thus, ATAD1 seems to function in normal postnatal muscle to limit fiber growth, and suppression of its activity alone can induce muscle hypertrophy”. We agree with the reviewer that in addition to these beneficial effects on desmin and soluble proteins, ATAD1 downregulation may contribute to muscle growth by additional mechanisms.

Reviewer #2 (Significance)

This is new information in the field since calpain cannot hydrolyze desmin insoluble filaments and that the mechanisms that give calpain access to desmin are not known.

The authors already made important contribution in the study of muscle atrophy and especially in desmin degradation. This work constitutes a new advance in their attempts to understand the molecular mechanisms leading to desmin degradation and muscle atrophy.

Audience: desmin is the main intermediate filament in skeletal muscle. This work will therefore interest scientists working on skeletal muscle.

Expertise of the reviewer: molecular and cellular biology of skeletal muscles, muscle atrophy.

Referee Cross-commenting

I fully agree with reviewer 1.

Reviewer #3 (Evidence, reproducibility and clarity)

Summary:

The manuscript by Aweida & Cohen introduces a novel complex formed by the AAA-ATPase ATAD1 and its interacting partners PLAA and UBXN4 as initiator of calpain-1-mediated disassembly of ubiquitylated desmin intermediate filaments (IF) during muscle atrophy. The authors use a denervation model of murine tibialis anterior muscles as their main resource for experimentation. They apply a kinase trap-assay and co-immunoprecipitation method followed by mass spectrometry as starting point for identifying novel interactors of desmin IF (Aweida et al. 2018 in JCB). They continue to analyze their candidates using immunoblotting, co-immunoprecipitation, shRNA-mediated intramuscular knock-down, gel filtration, mass spectrometry, and enzyme assays. In their experiments, thee authors show an accumulation of ATAD1 in the insoluble desmin filament fraction of denervated muscle fibers together with an increase in ubiquitylation of desmin filaments. Both proteomics experiments of size-exclusion chromatography of denervated muscles and ATAD1 immunoprecipitation identify several components of the ubiquitin-proteasome system as novel interactors of ATAD1, that are also bound to insoluble desmin filaments after muscle denervation. Following additional co-immunoprecipitation and knock-down experiments, the authors confirm PLAA and UBXN4 as novel cofactors of Atad1 that help in extracting previously GSK3-β-phosphorylated and TRIM32-ubiquitylated (Aweida et al. 2018 in JCB, Volodin et al. 2017 in PNAS) desmin from desmin IF. The authors further show that ATAD1 encourages calpain-1-dependent proteolysis of soluble desmin after extraction from the desmin IF in an in vitro enzymatic proteolysis assay.

Major comments:

The authors present clear and convincing arguments from in vivo and in vitro experiments for their proposed model of ATAD1/PLAA/UBXN4-aided calpain-1-mediated proteolysis of desmin IF.

In my opinion, no additional experimental evidence is essential to underlining their statement.

Data and methods are presented clearly and understandably to allow for the reproduction and the reapplication of the utilized methods for verifying the presented data and analyzing complementary aspects in a similar fashion.

A concern is with the presentation of mass spectrometry results, particularly regarding Table I: I am wondering whether the presented UPS components were the only proteins found in the proteomics screens or whether any filtering has taken place to only show UPS components in this manuscript. If so, please note the total number of proteins identified in the respective proteomics analyses and explain how filtering for UPS components was performed. This comment goes in line with the first minor comment on Figure 1A, see below.

We thank the reviewer for this valuable comment, as it helps clarify a point that was not completely lucid in the previous version of this manuscript. Because our paper focuses on protein degradation, we extracted from our datasets only UPS components that were identified with ³ 2 unique peptides using DAVID annotation tool-derived categories (Table II). Column 1 includes UPS components that were co-purified with ATAD1 by size exclusion chromatography (SEC)(20 out of 427 total proteins), and column 2 includes UPS components that were co-purified with ATAD1 by immunoprecipitation from muscle homogenates (17 out of 592 total proteins). These two proteomics experiments were oriented specifically towards identifying ATAD1-binding partners. To further validate our observations, we compared these lists of ATAD1-interacting components to our previous kinase-trap assay dataset (Aweida 2018, 1552 total proteins were identified) and included in column 3 only the proteins that overlapped with the other two proteomics approaches. The kinase trap assay was used to identify proteins that utilize ATP for their function and act on desmin, and as mentioned in the text, ATAD1 was one of the most abundant proteins in the sample. Of note is UBXN4, which was identified only by our kinase trap assay, and accumulated on desmin after denervation. These interactions between active enzymes in vivo must be transient and very dynamic, hence using three approaches did not identify the exact same subset of putative adaptors (see “discussion”). These points are now further elaborated in the text and the legend for Table II.

The relatively small number of individuals analyzed per experiment is owing to the limiting nature of mouse research and therefore acceptable. The observed alignment of the individual results is commendable, underlines the experimentator's ability, and strengthens the reached conclusion of the study.

We thank the reviewer for this comment.

Minor comments:

Figure 1A seems redundant, since the experimental approaches are described in the text and the Venn diagram does not integrate the identification of ATAD1 into the setting of the conducted screens, e.g. by showing how many additional proteins were identified in these two screens before the authors tended to their candidate ATAD1.

We agree and therefore removed Fig. 1A.

Word order mistake on page 6 in the sentence: "To test whether Atad1 is important for atrophy, we suppressed...".

Corrected.

Figure 1D: statistical analysis of the significance of the fiber area difference missing

Statistics for these effects is now included in new Table I. We quantitated the effects statistically using Vargha-Delaney A-statistics test and Brunner-Manzel test, based on our recent methodology paper in AJP Cell Physiol: “A semi-automated measurement of muscle fiber size using the Imaris software” (Gilda et al. 2021). The new statistical analyses show a significant difference in size distributions of fibers expressing shAtad1 vs. adjacent non-transfected fibers (Table I). As indicated in our paper (Gilda et al, 2021), the A-statistics is a direct measure of the fiber size effect.

Figure 2A: desmin ubiquitylation is not shown in these samples by immunoblotting against (poly-)ubiquitin, but only by the identification of high molecular weight bands of the desmin blot. I wonder about the specificity of the desmin antibody in this case and about the manner of sample extraction/isolation for this particular blot, as a detailed description is missing. There seems not to have been any muscle tissue fractionation beforehand, if I am correct?

This blot presents an analysis of desmin filaments isolated from mouse muscle, which are purified with associated proteins. In order to specifically detect ubiquitinated desmin filaments we must use a specific desmin antibody (antibody and methodology are validated in Cohen 2012 JCB, Volodin 2017 PNAS, and Aweida 2018 JCB). An antibody against ubiquitin conjugates will detect all proteins that are ubiquitinated in this insoluble preparation (e.g. proteins that bind desmin).

Orthography mistake "demin" instead of "desmin" on page 7 in sentence "It is noteworthy that the amount of ubiquitinated demin..."

Corrected.

Figure 3C: image quality is insufficient; some protein names are rather difficult to decipher

The figure has been revised to improve clarity.

Word missing on page 13 in sentence "In addition, by 10 minutes of incubation, phosphorylated ... due to their processive cleaveage by calpain-1 ..."

We thank the reviewer for reading the paper thoroughly and carefully. The missing word was added to the text.

Figure 4F: statistical analysis of the significance of the fiber area difference missing

Statistics is now included in new Table I. Asmentioned above, we quantitated the effects statistically using Vargha-Delaney A-statistics test and Brunner-Manzel test, based on our recent methodology paper in AJP Cell Physiol: “A semi-automated measurement of muscle fiber size using the Imaris software” (Gilda et al. 2021).

"ug" on page 21 in "Briefly, 20ug of plasmid DNA..." is probably supposed to be "µg". In general, please be aware of correct unit declaration and space character usage before units.

Corrected.

Please be aware of the usage of correct nucleic acid and protein nomenclature and style: When referring to gene or transcript levels mark the candidate characters in italic, e.g. Atad1 mRNA levels, shUbxn4, versus ATAD1 protein etc. In addition, please be aware to use the correct gene and protein name styles: e.g. shCapn1 instead of shCAPN1 for shRNA targeting the murine Capn1 transcript in Figure 4 in comparison to CAPN1 the protein. Helpful link: https://www.biosciencewriters.com/Guidelines-for-Formatting-Gene-and-Protein-Names.aspx

We thank the reviewer for this comment. The nomenclature for all genes and proteins have been revised accordingly.

Reviewer #3 (Significance)

Aweida & Cohen present evidence for the involvement of the AAA-ATPase ATAD1 not only in regulation of synaptic plasticity and the extraction of mislocalized proteins from the mitochondrial membrane, but also in a collaboration with the ubiquitin-binding proteins PLAA and UBXN4 in the disassembly of desmin intermediate filaments in muscle atrophy. The authors compare this newly discovered function of the AAA-ATPase ATAD1 to the numerous functions of the AAA+ ATPase p97/VCP and raise compelling arguments for their statement. Previously, E3 ligases that ubiquitylate sarcomere components in muscle atrophy have been identified, such as MuRF1 (Bodine et al. 2001 in Science) and TRIM32 (reviewed in Bawa et al. 2021 in Biomolecules), but the complete extraction mechanism of monomers from the diverse macromolecular fibrillary structures in muscle has been lacking.

Both, researchers of general proteostasis mechanisms, in particular their impact on muscle function and metabolism, as well as medical researcher investigating therapeutic roads may appreciate the authors' work. This study opens up various roads to follow with complementing investigations on the many functions of the UPS in the regulation of muscle fiber architecture and functionality.

I am working on proteostasis and particularly the UPS. I have a long-standing track record on muscle assmebly mechanisms, the regulation of E3 ligases and p97/VCP functions.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript by Aweida & Cohen introduces a novel complex formed by the AAA-ATPase ATAD1 and its interacting partners PLAA and UBXN4 as initiator of calpain-1-mediated disassembly of ubiquitylated desmin intermediate filaments (IF) during muscle atrophy. The authors use a denervation model of murine tibialis anterior muscles as their main resource for experimentation. They apply a kinase trap-assay and co-immunoprecipitation method followed by mass spectrometry as starting point for identifying novel interactors of desmin IF (Aweida et al. 2018 in JCB). They continue to analyze their candidates using immunoblotting, co-immunoprecipitation, shRNA-mediated intramuscular knock-down, gel filtration, mass spectrometry, and enzyme assays. In their experiments, thee authors show an accumulation of ATAD1 in the insoluble desmin filament fraction of denervated muscle fibers together with an increase in ubiquitylation of desmin filaments. Both proteomics experiments of size-exclusion chromatography of denervated muscles and ATAD1 immunoprecipitation identify several components of the ubiquitin-proteasome system as novel interactors of ATAD1, that are also bound to insoluble desmin filaments after muscle denervation. Following additional co-immunoprecipitation and knock-down experiments, the authors confirm PLAA and UBXN4 as novel cofactors of Atad1 that help in extracting previously GSK3-β-phosphorylated and TRIM32-ubiquitylated (Aweida et al. 2018 in JCB, Volodin et al. 2017 in PNAS) desmin from desmin IF. The authors further show that ATAD1 encourages calpain-1-dependent proteolysis of soluble desmin after extraction from the desmin IF in an in vitro enzymatic proteolysis assay.

Major comments:

The authors present clear and convincing arguments from in vivo and in vitro experiments for their proposed model of ATAD1/PLAA/UBXN4-aided calpain-1-mediated proteolysis of desmin IF. In my opinion, no additional experimental evidence is essential to underlining their statement. Data and methods are presented clearly and understandably to allow for the reproduction and the reapplication of the utilized methods for verifying the presented data and analyzing complementary aspects in a similar fashion.

A concern is with the presentation of mass spectrometry results, particularly regarding Table I: I am wondering whether the presented UPS components were the only proteins found in the proteomics screens or whether any filtering has taken place to only show UPS components in this manuscript. If so, please note the total number of proteins identified in the respective proteomics analyses and explain how filtering for UPS components was performed. This comment goes in line with the first minor comment on Figure 1A, see below. The relatively small number of individuals analyzed per experiment is owing to the limiting nature of mouse research and therefore acceptable. The observed alignment of the individual results is commendable, underlines the experimentator's ability, and strengthens the reached conclusion of the study.

Minor comments:

Figure 1A seems redundant, since the experimental approaches are described in the text and the Venn diagram does not integrate the identification of ATAD1 into the setting of the conducted screens, e.g. by showing how many additional proteins were identified in these two screens before the authors tended to their candidate ATAD1.

Word order mistake on page 6 in the sentence: "To test whether Atad1 is important for atrophy, we suppressed...".

Figure 1D: statistical analysis of the significance of the fiber area difference missing

Figure 2A: desmin ubiquitylation is not shown in these samples by immunoblotting against (poly-)ubiquitin, but only by the identification of high molecular weight bands of the desmin blot. I wonder about the specificity of the desmin antibody in this case and about the manner of sample extraction/isolation for this particular blot, as a detailed description is missing. There seems not to have been any muscle tissue fractionation beforehand, if I am correct?

Orthography mistake "demin" instead of "desmin" on page 7 in sentence "It is noteworthy that the amount of ubiquitinated demin..."

Figure 3C: image quality is insufficient; some protein names are rather difficult to decipher

Word missing on page 13 in sentence "In addition, by 10 minutes of incubation, phosphorylated ... due to their processive cleaveage by calpain-1 ..."

Figure 4F: statistical analysis of the significance of the fiber area difference missing

"ug" on page 21 in "Briefly, 20ug of plasmid DNA..." is probably supposed to be "µg". In general, please be aware of correct unit declaration and space character usage before units.

Please be aware of the usage of correct nucleic acid and protein nomenclature and style: When referring to gene or transcript levels mark the candidate characters in italic, e.g. Atad1 mRNA levels, shUbxn4, versus ATAD1 protein etc. In addition, please be aware to use the correct gene and protein name styles: e.g. shCapn1 instead of shCAPN1 for shRNA targeting the murine Capn1 transcript in Figure 4 in comparison to CAPN1 the protein. Helpful link: https://www.biosciencewriters.com/Guidelines-for-Formatting-Gene-and-Protein-Names.aspx

Significance

Aweida & Cohen present evidence for the involvement of the AAA-ATPase ATAD1 not only in regulation of synaptic plasticity and the extraction of mislocalized proteins from the mitochondrial membrane, but also in a collaboration with the ubiquitin-binding proteins PLAA and UBXN4 in the disassembly of desmin intermediate filaments in muscle atrophy. The authors compare this newly discovered function of the AAA-ATPase ATAD1 to the numerous functions of the AAA+ ATPase p97/VCP and raise compelling arguments for their statement. Previously, E3 ligases that ubiquitylate sarcomere components in muscle atrophy have been identified, such as MuRF1 (Bodine et al. 2001 in Science) and TRIM32 (reviewed in Bawa et al. 2021 in Biomolecules), but the complete extraction mechanism of monomers from the diverse macromolecular fibrillary structures in muscle has been lacking.

Both, researchers of general proteostasis mechanisms, in particular their impact on muscle function and metabolism, as well as medical researcher investigating therapeutic roads may appreciate the authors' work. This study opens up various roads to follow with complementing investigations on the many functions of the UPS in the regulation of muscle fiber architecture and functionality.

I am working on proteostasis and particularly the UPS. I have a long-standing track record on muscle assmebly mechanisms, the regulation of E3 ligases and p97/VCP functions.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

In their manuscript the authors show the involvement of the AAA ATPase Atad1 in Desmin degradation. They identify PLAA and Ubxn4 as partners of Atad1 that participate to its function in desmin degradation.

A general comment is that some conclusions are overstated. The authors mention several times that Atad1 depolymerises desmin filaments. The data show that Atad1 participates to the degradation of Desmin and to its solubilization. "Depolymerisation" should be kept for the model presented in figure 8 but not used in the result section. Major comments:

1. It would be useful to visualize Atad1 and partners localization in muscle fibers in immunofluorescence. Do they colocalize with desmin filaments, with calpain?
2. In the same line, interactors were obtained from large crosslinked complexes. It would make the model more convincing if direct interactions with Atad1 were shown, for example using Proximity Ligation Assays.
3. Evaluation of atrophy is made on cross-sections of muscles electroporated with shRNAs. Histology pictures should be shown.
4. What is the percentage of electroporated fibers? To evaluate the effect of shRNAs it is important to have this information. For example, if the efficiency is 50% it means that the reduction in expression of the target in electroporated fibers is twice the value reported for the whole muscle. Alternatively, immunofluorescence could be provided to see the decrease in targeted proteins in electroporated fibers.
5. The same is true for all the experiments quantifying the effect of shRNAs in western blot. Since quantifications are probably made on whole muscles (ie a mix between electroporated and non electroporated fibers) and since the percentage of electroporated fibers is not given it is not possible to estimate the efficiency of the shRNAs in electroporated fibers.
6. Figure 2C: by decreasing solubilization of desmin, one would expect a decrease in the levels of soluble desmin. Conversely the authors observe an increase in both insoluble and soluble desmin. Of course, this can be explained by reduced desmin degradation once solubilized but this should be demonstrated at least by showing that UPS inhibitors induces an increase in soluble ubiquitinated Desmin.
7. Figure 2E: the levels of Atad1 in the insoluble fraction seem to be the same in the shLacZ and GSK3DN conditions, whereas the phosphor Ser is different. In other words, there should be more Atad1 in the insoluble fraction with shLacZ than with GSAK3DN since the phosphorylation level with shLacZ is significantly higher.
8. Figure 4E: the authors state that phosphorylation decreases because of increased degradation (lanes 6-8). However, Calpain also increases degradation and phosphorylation is increased (lanes 2-4), so increasing degradation does not systematically cause a decrease in phosphorylation. Similarly, lane 5 Atad1 induces less degradation than Calpain, however, it causes a decrease in phosphorylation. Explain.
9. The AAA ATPase VCP shares partners with Atad1 and is involved in muscle atrophy. It would greatly add to the manuscript if the authors inhibited VCP to compare its effect to Atad1

Minor comments:

1. The soluble fraction contains a large number of ubiquitinated proteins. Please explain how it can be stated that an increase in total soluble polyubiquitinated proteins corresponds to an increase in ubiquitinated desmin.
2. Page 11: the authors conclude that denervation enhance the interactions with Atad1. Figure 3D indeed show an increase for Ubxn4, but it is not clear for the other proteins.
3. Figure 4 F: show muscle sections
4. Page 21 in vivo transfection: it is stated "see details under immunofluorescence" but there is no immunofluorescence section in materials and methods.
5. The authors show that Atad1 inhibition in innervated muscle is sufficient to induce muscle hypertrophy (Figure 4E). They conclude that the hypertrophic effect of Atad1 is due to the inhibition of Desmin degradation. However, this hypertrophic effect could be independent of the action of Atad1 on Desmin.

Significance

This is new information in the field since calpain cannot hydrolyze desmin insoluble filaments and that the mechanisms that give calpain access to desmin are not known.

The authors already made important contribution in the study of muscle atrophy and especially in desmin degradation. This work constitutes a new advance in their attempts to understand the molecular mechanisms leading to desmin degradation and muscle atrophy.

Audience: desmin is the main intermediate filament in skeletal muscle. This work will therefore interest scientists working on skeletal muscle.

Expertise of the reviewer: molecular and cellular biology of skeletal muscles, muscle atrophy.

Referee Cross-commenting

I fully agree with reviewer 1.

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Referee #1

Evidence, reproducibility and clarity

The manuscript reports the identification of a novel protein complex involved in denervation-induced desmin degradation. The first protein to be identified was the ATPAse Atad1. A clever isolation strategy was based on the fact that the ATPAse p97/VCP is involved in the extraction of ubiquitinated myofibrillar proteins but is not required for the removal of ubiquitinated desmin filaments. The authors reasoned that a related ATPAse might be specifically required for desmin filaments. Atad1 was identified by treating desmin filaments with a nonhydolyzable ATP analog and looking for ATPases that are associated with desmin filaments by proteomics. Knockdown of Atad1 causes a loss of desmin degradation and led to a loss of denervation-induced muscle atrophy. It seems that Atad1 binds desmin in a phsphorlation-dependent manner, although the binding maybe mediated by a protein that hasn't yet been identified. The authors went on and identified two additional proteins which together with Atad1 form a protein complex involved in recruiting calpain for desmin degradation.

Overall, this study is very convincing providing novel important insight. I have only some minor comments

Minor comments

1. I wondered whether Aatd1 is expressed at higher-than-normal levels in muscle and heart. I looked that expression pattern up and it seems that they are especially abundant in muscle and heart and expressed at lesser levels in smooth muscle and overall have a restricted expression. Maybe you have some data on their expression in muscle tissue. Did you perform some staining of muscle tissue at baseline and after denervation with regard to the protein localization by immunostaining?
2. The string data presented in Figure 3C needs some further explanation with regard to the colors used for the different proteins. While the authors explained the meaning of the proteins labeled in red, there is no explanation for the other colors.
3. Molecular weights in Fig. 2E, 3D needs to be 'repaired' and additional MW information is required in case of the ubiquitin blot shown in 3D.
4. Fibre size distributions shown in Fig. 1D and 4F. Have the differences been statistically tested?
5. For my taste the referral to the individual data (Fig. numbers) in the discussion section is too detailed and becomes a second results section. This should be substituted by a summary paragraph before the implications are discussed.
6. The summary slide is very good. However, could you please add information, which protein of the three in the Atad1 complex is depicted by each symbol?

Significance

Novel insight into the proteins involved in desmin filament degradation. Since this is an important subject both in muscle and heart and plays an important role in muscle and heart disease, it is of significant clinical importance. Currently it has only been implicated in denervation-induced skeletal muscle atrophy, but it is likely that desmin filament metabolisms is also similarly regulated in the heart.

I am a researcher mainly focusing on the cardiac biology with some expertise also on muscle, however no specific knowledge about desmin filament biology.

Referee Cross-commenting

Overall, I think all three reviewers agree that this is a significant and important paper. I think that the comments made by the reviewers are fair and probably add to the quality of the manuscript.

Thus, both myself and reviewer 2 agree that it would be useful to visualize Atad1 and partners localization in muscle fibers by immunofluorescence. These data would provide independent support to the model the authors are proposing, which currently is only based on biochemical analysis.

I also support the proposed use of proximity ligation to provide further evidence of the presence of the Atad1, Ubxn4 and PLAA in a complex. However, this experiment depends on the quality of the available antibodies and I would consider this not absolutely required.

I also agree that some further information on the proteomics data (as suggested by reviewer 3) is required with regard to the method of filtering for UPS components was performed.

The proposed request for further information on the electroporation approach is a valid comment and if the authors have this information, it would be good to provide. However, I do not recommend further experiments as overall the data are very consistent and the findings are very significant and represent a major advance in our understanding of desmin degradation.

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Reply to the reviewers

RESPONSE TO REVIEWER #1:

We wish to express our appreciation to Reviewer #1 for his or her insightful comments, which will significantly improve this paper. We thank the reviewers for giving us the opportunity to improve the manuscript. We have responded to all the comments pointed out. The revised sections are highlighted in red characters and yellow backgrounds in the preliminary revised manuscript.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): This manuscript "Histidine-rich protein 2: a new pathogenic 1 factor of Plasmodium falciparum malaria" by Iwasaki, et al. reports effects of recombinant HRP2 protein on various mammalian cell lines. The MS clearly demonstrates that recombinant HRP2 enters into HT1080 cells, causes inhibition of autolysosome fusion, increases lysosomal Ca ion concentration and reduces general autophagic degradation. The authors also show that the presence of FBS or metal chelators like EDTA and EGTA mitigate toxicity of HRP2, as the former traps HRP2 and the latter compete with HRP2 for Ca binding. The experiments are appropriately carried out with suitable controls in most of the cases. There are some concerns as listed below:

**Major concerns:** 1.HRP2 has been shown to be associated with virulence and causes vascular leakage, particularly cerebral malaria (references 37 and 38 ). Plasmodium falciparum histidine-rich protein II has been demonstrated to exacerbate experimental cerebral malaria in mice, which has been proposed to be associated with vascular leakage, activation of inflammasome and cytokine production (references 37, 38 and PMID: 31858717). This study complements the previous findings of the effect of HRP2 on mammalian cells. However, this study reveals another mechanism by which HRP2 might cause toxicity, which is inhibition of general autophagy and increase in lysosomal Ca concentration. However, whether these in vitro effects would translate in vivo needs to be shown.

Response: We sincerely appreciate the reviewer's effort to evaluate our work. As the reviewer pointed out, this is an in vitro study, so further in vivo validation is essential in the future. However, it is also true that we discovered new findings that have been overlooked because we conducted an artificial and simple in vitro experiment. In the future, it is necessary to demonstrate the cytotoxicity, autophagy inhibition, and lysosomal calcium concentration variation of PfHRP2 by in vivo studies using model animals. Concretely, we need to confirm whether PfHRP2 behaves as a similar virulence factor in vivo by animal experiments using PfHRP2-administrated or PfHRP2-overexpressing/deficient P. falciparum-infected mouse models. These future tasks have been added to the Discussion (page 9, lines 294–297 and 309–310; page 10, lines 339–342). We have also added the study (PMID: 31858717) reporting PfHRP2 elicits pro-inflammatory effect and induces vascular permeability as reference 40.

Furthermore, the title of the original paper was vague and gave the impression that it included in vivo experiments. Therefore, to avoid misunderstanding, we modified the paper's title to be more concrete, "Plasmodium falciparum histidine-rich protein II exhibits cell penetration and cytotoxicity with autophagy dysfunction".

Reference

P. Dinarvand, L. Yang, I. Biswas, H. Giri, A. R. Rezaie, Plasmodium falciparum histidine rich protein HRPII inhibits the anti-inflammatory function of antithrombin. J. Thromb. Haemost. 18, 1473–1483 (2020).

2.All the experiments are done with recombinant HRP2 and BSA as a control. The authors should show if similar effects happen with infected parasites.

Response: As the reviewer pointed out, it is required to perform in vivo experiments, i.e., to clarify whether the same phenomenon observed in the present study occurs in PfHRP2-administrated or P. falciparum-infected mouse models. However, in vivo studies are not possible immediately because we do not have the research facilities to carry out in vivo experiments. Therefore, we have added statements (page 9, lines 294–297 and 309–310; page 10, lines 339–342) to emphasize that the present findings are limited to in vitro and that further in vivo studies described above will be required in the future.

3.HRP2 is released in circulation, making it accessible to endothelial cells and immune cells. How would it reach to the equivalents of these cells in the human body?

Response: Since PfHRP2 induces vascular permeability as described in References 37–40, we propose that PfHRP2 can reach and contact cells in the human body after causing vascular leakage. I have added this possibility of contact between PfHRP2 and cells in the human body to Discussion (page 9, lines 287–290).

**Minor concerns** 1.p62 is an appropriate marker to assess autophagy cargo degradation. If possible, it would be good to support this with LC3 processing as well.

Response: Following the reviewer's advice, we will use LC3 as an autophagy marker as well as p62 to evaluate the autophagy inhibition of PfHRP2. Concretely, we plan to treat HT1080 cells with PfHRP2 (1 μM) for 12–60 hours and quantify the amount of LC3 protein by Western blotting. The results of this experiment will be added to Fig. 5 in the main manuscript.

2.HRP2 might affect general lysosomal degradation process. The authors can also check whether HPR2 affects degradation of a lysosomal substrate.

Response: Following the reviewer's advice, we will determine the effect of PfHRP2 on lysosomal degradation activity using the plasmid-based lysosomal-METRIQ (MEasurement of protein Transporting integrity by RatIo Quantification) probe, reported in a previous study (https://doi.org/10.1038/s41598-019-48131-2), to quantify lysosomal activity. The results of this experiment will be added to Fig. 5 in the main manuscript.

Reviewer #1 (Significance (Required)): This study compelements previous findings (references 37, 38 and PMID: 31858717). It identifies a new mechanism by which HRP2 might cause toxicity. However, it is completely an in vitro study, and the previous studies (references 37 and 38) have used in vivo models as well.

Response: We wish to thank the reviewer for this comment. As the reviewer pointed out, this study is completely in vitro, and further in vivo studies are essential in the future. Therefore, we have added statements (page 9, lines 294–297 and 309–310; page 10, lines 339–342) to emphasize that the present findings are limited to in vitro and that further in vivo studies are required in the future. We have also added the study (PMID: 31858717) reporting PfHRP2 elicits pro-inflammatory effect and induces vascular permeability as reference 40.

Furthermore, the title of the original paper was vague and gave the impression that it included in vivo experiments. Therefore, to avoid misunderstanding, we modified the paper's title to be more concrete.

Reference

P. Dinarvand, L. Yang, I. Biswas, H. Giri, A. R. Rezaie, Plasmodium falciparum histidine rich protein HRPII inhibits the anti-inflammatory function of antithrombin. J. Thromb. Haemost. 18, 1473–1483 (2020).

We thank you again for giving us the opportunity to improve our paper, and we hope that the changes are satisfactory.

RESPONSE TO REVIEWER #2:

We wish to express our appreciation to Reviewer #2 for his or her insightful comments, which will significantly improve this paper. We thank the reviewers for giving us the opportunity to improve the manuscript. We have responded to all the comments pointed out. The revised sections are highlighted in red characters and yellow backgrounds in the preliminary revised manuscript.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): This paper showed that recombinant Plasmodium falciparum HRPII generated in E. coli is internalized by human tumor derived cells lines and at high concentrations, induces calcium-dependent cell death. The authors propose that HRPII inhibits autolysosome formation and autophagy. Of major concern is the use of E. coli generated HRP2 without addressing the inherent confounders of copurified bacterial components, namely endotoxin LPS. It is crucial for validation of their conclusions that the authors address steps taken to remove endotoxin which is known to bind poly-histidine and HRPII, the quantification of endotoxin bound to purified protein, and the LPS sensitivity of model cell lines. Even small quantities of LPS have been shown to potentially inhibit endosome maturation (https://doi.org/10.1074/jbc.M114.611442). Would recommend caution with conclusions regarding cytotoxicity and autophagy inhibition without addressing this issue.

Response: We sincerely appreciate the reviewer's effort to evaluate our work. The reviewer points out that the endotoxin LPS may also affect the cytotoxicity and autophagy inhibition of PfHRP2 in this study. The reviewer's point is crucial, and we agree with the reviewer. In our study, recombinant PfHRP2 was captured by anti-FLAG antibody-immobilized affinity gel (Medical & Biological Laboratories Co., Ltd., Nagoya, Japan) and washed with 20-bed volumes of washing buffer (20 mM Tris-HCl pH7.4, 500 mM NaCl, 0.1% Triton X-100) to remove contaminants including endotoxin LPS according to the manufacturer's protocol (https://ruo.mbl.co.jp/bio/dtl/dtlfiles/3328R-ver4.0.pdf). After washing, affinity gel was equilibrated with 10-bed volumes of washing buffer without Triton X-100, and recombinant PfHRP2 was eluted by 10-bed volumes of elution buffer (20 mM Tris-HCl pH7.4, 500 mM NaCl, 0.1 mg/mL FLAG peptide: DYKDDDDK). However, we did not determine the residual endotoxin LPS bound to purified PfHRP2. To address the reviewer's concern, we will follow the reviewer's suggestion and quantify the residual endotoxin LPS in the purified PfHRP2 using the LAL Endotoxin Assay Kit. We also plan to test whether the same amount of endotoxin LPS alone as the residual endotoxin LPS affects cytotoxicity and autophagy inhibition. The results of additional experiments on endotoxin LPS will be added to Supplementary Information as Fig. S2. Furthermore, we have added additional information on the purification and washing of PfHRP2 to the Materials and methods section (page 11, lines 356–362).

Additional concerns for specific experiments are as follows: Figure 2A. There is an increase in BSA penetration at lower pH as well which suggests nonspecific increased cell permeability.

Response: As pointed out by the reviewer, the cell membrane permeability of BSA was enhanced at low pH (pH less than 5.8), and this result implies an increase in nonspecific cell permeability. Since we have reported in another study (https://doi.org/10.1093/bbb/zbab221) that BSA shows cell penetration to human gastric cancer cell lines at pH 5.0, the cell membrane permeability of BSA at low pH in this study is satisfactory. However, comparing pH 7.4 and pH 5.6, the net charge of BSA increased by 21.9 from -14.0 (pH7.4) to +7.9 (pH5.6), and the cell penetration increased by 34%. On the other hand, the net charge of PfHRP2 increased by 79.4 from -19.2 (pH7.4) to +60.2 (pH5.6), and the cell penetration increased by 246%. This suggests that the increase in cell membrane permeability of PfHRP2 under low pH conditions is due to the increase in net charge, not to the non-specific increase in cell permeability as seen in BSA. The above explanation has been added to lines 97–103.

Figure 3A, 3B, and 4C. There is inconsistency between the cell viability data. For example, in panel A, 1 μM of HRPII for 24 h showed 84% cell viability whereas in panel B, the cell viability is 61% for 1 μM HRP2 by 24 hours. Figure 3A and 4C (full length) differ at cell viability for 5 μM HRP2.

Response: We thank the reviewer for the critical remarks. There was an error in the time condition described in the graph of Fig. 3A. Correctly, Fig. 3A is the result of cell viability treated with 1 μM PfHRP2 for 3 hours, so we have corrected the time condition described in Fig. 3A. Namely, Fig. 3A and 3B show that a 3-hour treatment with 1 μM PfHRP2 results in 84% cell viability, but a 24-hour treatment with 1 μM PfHRP2 decreases cell viability to 61%. These results are correctly described in lines 119–120, highlighted in yellow.

On the other hand, as the reviewer points out, in Fig. 3A and Fig. 4C (full-length PfHRP2), the cell viability treated with 5 μM PfHRP2 for 24 hours was 5% and 26%, respectively. We believe that the discrepancy in these values is an experimental error. However, both Fig. 3A and Fig. 4C (full-length PfHRP2) agree that 5 μM PfHRP2 is statistically and significantly cytotoxic, which should not affect the claims of this study.

Figure 5C. It would be more informative if the cell viability data at 1 μM of HRP at timepoints beyond 60 hours and for bafilomycin treatment is also presented.

Response: We thank the reviewer for their suggestions. However, the purpose of the experiment in Figure 5C is to prove that PfHRP2 induces autolysosomal dysfunction. Since we confirmed that treatment of cells with 1 µM PfHRP2 for 60 hours resulted in accumulation of p62 in the same amount as the positive control, Bafilomycin A1, we believe that no further additional experiments are necessary.

Figure 3D. (Minor) Consider additional experimental detail regarding maintenance of cell cultures for 5 day. Are there interval media changes or supplement additions?

Response: We apologize for the insufficient information in the description of the experimental procedure in Fig. 3D. In the experiment in Fig. 3D, cell culture was maintained for 5 days by changing a fresh medium containing each concentration of proteins every day. We have added this information to the legends of Figure 3 (page 23, lines 653–655) and Figure S2 (page 4, lines 28–29).

Reviewer #2 (Significance (Required)): The authors present the novel finding of HRP2 permeability into human cells. The significance of these findings is limited given the major confounder with endotoxin and also since the experiments were conducted in tumor-derived cells lines with supraphysiologic concentrations of HRPII. Although the authors showed cell viability effects with lower concentrations over 3 and 5 days, the bulk of the experiments were at more than 10-fold mean physiological concentrations. Also, since these are early findings in tumor-derived cell lines, it is difficult to extrapolate the physiological relevance of these findings and use of calcium chelators as therapeutics. Several studies have proposed a pathogenic role for HRP2 including those cited in the paper regarding blood-brain barrier disruption (references 37 and 38), coagulation disruption (DOI: 10.1182/blood-2010-12-326876), and pro-inflammatory signaling (DOI: 10.1111/jth.14713). The challenge with all these studies is establishing the clinical relevance of the multitude of HRPII effects. If the issue of endotoxin is addressed, this paper could establish an interesting mechanism for further study in more clinically representative systems. Our lab has studied the many functions of HRPII including catalysis of heme polymerization, inhibition of antithrombin, brain endothelial disruption using tissue culture and mouse models.

Response: As pointed out by the reviewer, this study must clear up the effect of endotoxin LPS. In this regard, as mentioned above, we plan to quantify the residual endotoxin LPS in the purified PfHRP2 using the LAL Endotoxin Assay Kit. We will also check the effect of the endotoxin LPS itself on cytotoxicity and autophagy inhibition.

Furthermore, as the reviewer pointed out, this is an in vitro study using high concentrations of PfHRP2 and a tumor-derived cell line, so further in vivo validation is essential in the future. However, it is also true that we discovered new findings that have been overlooked because we conducted an artificial and simple in vitro experiment. In the future, it is necessary to demonstrate the cytotoxicity and autophagy inhibition of PfHRP2 by in vivo studies using model animals. Concretely, we need to confirm whether PfHRP2 behaves as a similar virulence factor in vivo by animal experiments using PfHRP2-administrated or PfHRP2-overexpressing/deficient P. falciparum-infected mouse models. We also need to demonstrate that calcium chelators such as EDTA have an in vivo therapeutic effect. These future tasks have been added to the Discussion (page 9, lines 294–297 and 309–310). We have also added the studies (DOI: 10.1182/blood-2010-12-326876, DOI: 10.1111/jth.14713) reporting PfHRP2 elicits pro-inflammatory effect and induces vascular permeability as reference 37 and 40.

Furthermore, the title of the original paper was vague and gave the impression that it included in vivo experiments. Therefore, to avoid misunderstanding, we modified the paper's title to be more concrete, "Plasmodium falciparum histidine-rich protein II exhibits cell penetration and cytotoxicity with autophagy dysfunction".

References

M. Ndonwi, et al., Inhibition of antithrombin by Plasmodium falciparum histidine-rich protein II. Blood 117, 6347–6354 (2011). P. Dinarvand, L. Yang, I. Biswas, H. Giri, A. R. Rezaie, Plasmodium falciparum histidine rich protein HRPII inhibits the anti-inflammatory function of antithrombin. J. Thromb. Haemost. 18, 1473–1483 (2020).

We thank you again for giving us the opportunity to improve our paper, and we hope that the changes are satisfactory.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

This paper showed that recombinant Plasmodium falciparum HRPII generated in E. coli is internalized by human tumor derived cells lines and at high concentrations, induces calcium-dependent cell death. The authors propose that HRPII inhibits autolysosome formation and autophagy.

Of major concern is the use of E. coli generated HRP2 without addressing the inherent confounders of copurified bacterial components, namely endotoxin LPS. It is crucial for validation of their conclusions that the authors address steps taken to remove endotoxin which is known to bind poly-histidine and HRPII, the quantification of endotoxin bound to purified protein, and the LPS sensitivity of model cell lines. Even small quantities of LPS have been shown to potentially inhibit endosome maturation (https://doi.org/10.1074/jbc.M114.611442). Would recommend caution with conclusions regarding cytotoxicity and autophagy inhibition without addressing this issue.

Additional concerns for specific experiments are as follows:

Figure 2A. There is an increase in BSA penetration at lower pH as well which suggests nonspecific increased cell permeability.

Figure 3A, 3B, and 4C. There is inconsistency between the cell viability data. For example, iIn panel A, 1 uM of HRPII for 24 h showed 84% cell viability whereas in panel B, the cell viability is 61% for 1 uM HRP2 by 24 hours. Figure 3A and 4C (full length) differ at cell viability for 5 uM HRP2.

Figure 5C. It would be more informative if the cell viability data at 1 uM of HRP at timepoints beyond 60 hours and for bafilomycin treatment is also presented.

Figure 3D. (Minor) Consider additional experimental detail regarding maintenance of cell cultures for 5 day. Are there interval media changes or supplement additions?

Significance

The authors present the novel finding of HRP2 permeability into human cells. The significance of these findings is limited given the major confounder with endotoxin and also since the experiments were conducted in tumor-derived cells lines with supraphysiologic concentrations of HRPII. Although the authors showed cell viability effects with lower concentrations over 3 and 5 days, the bulk of the experiments were at more than 10-fold mean physiological concentrations. Also, since these are early findings in tumor-derived cell lines, it is difficult to extrapolate the physiological relevance of these findings and use of calcium chelators as therapeutics.

Several studies have proposed a pathogenic role for HRP2 including those cited in the paper regarding blood-brain barrier disruption (references 37 and 38), coagulation disruption (DOI: 10.1182/blood-2010-12-326876), and pro-inflammatory signaling (DOI: 10.1111/jth.14713). The challenge with all these studies is establishing the clinical relevance of the multitude of HRPII effects. If the issue of endotoxin is addressed, this paper could establish an interesting mechanism for further study in more clinically representative systems.

Our lab has studied the many functions of HRPII including catalysis of heme polymerization, inhibition of antithrombin, brain endothelial disruption using tissue culture and mouse models.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

This manuscript "Histidine-rich protein 2: a new pathogenic 1 factor of Plasmodium falciparum malaria" by Iwasaki, et al. reports effects of recombinant HRP2 protein on various mammalian cell lines. The MS clearly demonstrates that recombinant HRP2 enters into HT1080 cells, causes inhibition of autolysosome fusion, increases lysosomal Ca ion concentration and reduces general autophagic degradation. The authors also show that the presence of FBS or metal chelators like EDTA and EGTA mitigate toxicity of HRP2, as the former traps HRP2 and the latter compete with HRP2 for Ca binding. The experiments are appropriately carried out with suitable controls in most of the cases. There are some concerns as listed below:

Major concerns:

1.HRP2 has been shown to be associated with virulence and causes vascular leakage, particularly cerebral malaria (references 37 and 38 ). Plasmodium falciparum histidine-rich protein II has been demonstrated to exacerbate experimental cerebral malaria in mice, which has been proposed to be associated with vascular leakage, activation ofinflamosome and cytokine production (references 37, 38 and PMID: 31858717). This study complements the previous findings of the effect of HRP2 on mammalian cells. However, this study reveals another mechanism by which HRP2 might cause toxicity, which is inhibition of general autophagy and increase in lysosomal Ca concentration. However, whether these in vitro effects would translate in vivo needs to be shown.

2.All the experiments are done with recombinant HRP2 and BSA as a control. The authors should show if similar effects happen with infected parasites.

3.HRP2 is released in circulation, making it accessibele to endothelial cells and immune cells. How would it reach to the equivalents of these cells in the human body?

Minor concerns

1.p62 is an appropriate marker to assess autophagy cargo degradation. If possible, it would be good to support this with LC3 processing as well.

2.HRP2 might affect general lysosomal degradation process. The authors can also check whether HPR2 affects degradation of a lysosomal substrate.

Significance

This study compelements previous findings (references 37, 38 and PMID: 31858717). It identifies a new mechanism by which HRP2 might cause toxicity. However, it is completely an in vitro study, and the previous studies (references 37 and 38) have used in vivo models as well.

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In this paper, Harterink and colleagues investigate the establishment of minus-end-out microtubule polarity in the anterior dendrite of C. elegans PVD neurons. These neurons offer an excellent model system due to their simplicity and well-defined microtubule polarity. The authors investigate the role of two proteins in particular, the well-studied Patronin protein and a newly identified homologue of Ninein (Noca-2). They show that these proteins are redundantly required for correct minus-end-out polarity. Absence of one of these proteins results in a low penetrant phenotype, but absence of both results in a strongly penetrant phenotype. Interestingly, in all cases the neurons display either almost fully retrograde or almost fully anterograde microtubule polarity, and not a mix of retrograde and anterograde microtubules. This is probably linked to the fact that the authors show that endosomes at the distal tip of the dendrite (that are known to mediate retrograde microtubule nucleation events) are either present or absent in these mutants (to differing degrees that reflect the polarity phenotypes of each mutant type). The authors further show that Noca-2, but not Patronin, is required for proper localisation of γ-tubulin to the distal endosomes, suggesting that the proteins influence microtubule polarity in different ways. They provide some evidence that Patronin clusters, while not colocalized to the distal endosomes, are somehow connected. The paper and figures are clear and the work should be reproducible.

Most conclusions are supported by the data, except for when the authors say: "Taken together, these results show that PTRN-1 (CAMSAP) and NOCA-2 (NINEIN) act in parallel in the PVD neuron during early development to establish minus-end out microtubule organization, and that this organization is important for proper dendritic morphogenesis." But the authors show that removal of Patr results in some neurons having a complete anterograde phenotype in the anterior genotype, but that no Patr neurons have a severe morphology defect (Fig 2). This would suggest that the severe morphology defects in Patr/Noca-2 double mutants are not simply due to the reversal of polarity in the anterior dendrite. This should be discussed.

We agree with the comment, and we will discuss this more clearly in a revised manuscript.

The paper could be strengthened with some biochemistry showing that Noca-2 can associate with γ-TuRCs i.e. do purified fragments of Noca-2 pull out γ-TuRCs from a cell extract (not necessarily a neuron cell extract)? This should be possible within 1 month.

We thank the reviewer for this suggestion. We will perform some biochemistry experiment to probe the association of NOCA-2 with γ-TuRCs. However, instead of doing the IP by overexpression of NOCA-2 and γ-TuRCs in cells, we will use the CRISPR knockin animals for NOCA-2 and γ-TuRCs, to exclude potential overexpression artifacts.

Minor comments

1) "However, in polarized cells such as neurons, most microtubules are organized in a non-centrosomal manner (Nguyen et al., 2011)." Need more up to date reference here, such as a recent review from Jens Lüders.

We will update the references in the revision version of the manuscript.

2) "and also in Drosophila Patronin was found important for dendritic microtubule polarity (Feng et al., 2019)." Also Wang et al., 2019 in eLife.

We will add this reference.

3) "In the non-ciliated PHC neuron or the ciliated URX neuron we did not observe microtubule organization defects in the ptrn-1 mutant (Supplemental figure 1A-B), which suggests that these neurons do less or do not dependent on PTRN-1." End of sentence needs re-phasing

We will rephrase the text.

Reviewer #1 (Significance (Required)):

Overall, the paper adds some interesting information to the field but does not make a conceptual advance that would make it attractive to a wide audience. It will, however, be of interest to those studying mt regulation in neurons. It is a shame that the molecular mechanism that allow Noca-2, and particularly Patronin, to establish microtubule polarity remain to be determined. Figuring out these mechanisms would significantly strengthen the paper.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Harterink comments:

In this manuscript He et al investigate the role of two key microtubule minus end regulators, Patronin/CAMSAP and NOCA-2/ninein, in establishing dendrite microtubule organization. The authors use a well-characterized branched sensory neuron in C. elegans for their analysis and make significant contributions to our understanding of neuronal microtubule organization. First, they show that C. elegans has not one, but two, ninein-like proteins, NOCA-1 and NOCA-2. Previously only NOCA-1 had been identified, and neuronal functions of ninein have remained elusive, perhaps in part because NOCA-2 had been missed. It had previously been shown that in epithelial cells NOCA-1 acts with gamma-tubulin as one arm of a microtubule minus end organizing pathway, while Patronin acts in parallel on minus ends. The current manuscript very nicely extends this functional map to neurons. The authors show that NOCA-2 helps recruit the gamma-tubulin ring complex (g-TuRC) to Rab11 endosomes that are important for microtubule nucleation at developing dendrite tips. As in epithelial cells, Patronin seems to act in parallel to this pathway and rather than being involved in recruiting the g-TuRC to Rab11 endosomes, is instead important for allowing the Rab11 endosomes to be transported to developing dendrite tips. In total this analysis not only identifies a new player in dendritic microtubule organization (NOCA-2), but also helps synthesize the functions of other players (g-TuRC, Patronin) into a model that makes sense in the broader context of microtubule organization across species and cell types.

• Specific points *

1. • NOCA-2 is described as a previously unidentified member of the ninein family. In order to evaluate this claim critically, it would be helpful to have a figure showing how similar NOCA-1 and NOCA-2 are to mammalian ninein. It is also critical to include a phylogeny to get a better feel for how NOCA-2 fits into the evolutionary history of the family. * We agree with the suggestion. We will perform this analysis and add it to a revised version of the manuscript.

• The nucleation assay used throughout is not very clear as one reads through the manuscript. The color-coding of Fig 1G could be better defined in the legend, and it would be helpful to have more information in the legend or results about what is meant here by microtubule nucleation. Is it simply initiation of new microtubule growth events? If so, how are these distinguished from catastrophe rescue? It would be good to use the same color coding in 2E. *

We thank the reviewer for pointing this out. In Fig 1G and 2E we indeed quantified EBP-2::GFP growth events. Although we later show that the microtubule nucleator gamma-tubulin localized to the distal segment where we observe increased microtubule growth events, we agree that we cannot distinguish microtubule nucleation from regrowth after catastrophe. Therefore, we will describe this more accurately in the text, legend and in the figure.

• The colocalization of Rab-11 and NOCA-2 seems to be supported only by a single overlapping puncta in a neuron before the anterior dendrite extends (Fig 4). It would be good to flesh out this data set more as it is an important part of the argument that NOCA-2 is involved in recruiting g-TuRC to Rab11 endosomes. *

We thank the reviewer for pointing this out. We will flesh out this data either by adding several examples and/or a movie to show the localization of Rab-11 and NOCA-2 in the revised version of the manuscript.

• Summary diagrams of results either as conclusions are made in the individual figures or synthesized at the end would help readers to understand the evidence that NOCA-2 and PTRN-1 function at different steps in establishment of MT polarity *

We agree that a summary diagram could be helpful, and we will consider adding this to the revised version of the manuscript.

• NOCA-1 is introduced at the beginning of the manuscript and appears to act in parallel to both NOCA-2 and PTRN-1. One is left with many questions, for example, is it also required to recruit g-TuRCs to Rab11 vesicles, or does it have some other role? However, I appreciate that it is beyond the scope of a single manuscript to answer all questions and the authors state a clear rationale for focusing on NOCA-2. *

We agree that the function of NOCA-1 is interesting to be investigated in the future, since we found it acts redundantly to PTRN-1 and NOCA-2. As NOCA-1 is an essential gene this brings along some technical difficulties to properly address its function and would require generating novel tools. We appreciate the reviewers understanding that this is beyond the scope of the current manuscript.

• It would be helpful to clearly state at some point which aspects of localization that are described are seen only in developing dendrites and which are seen in both developing and mature dendrites. For example, is there any similarity in localization of PTRN-1 NOCA-2 and gip2 in mature dendrites to that shown in immature? Is there any sign of continued localization to RAB11 vesicles, or is this only transient? Perhaps a diagram to summarize these findings would also be helpful. *

We thank the reviewer for pointing this out. We will better explain the localization of NOCA-2, PTRN-1, GIP-2 and RAB-11 vesicles in developing neurons vs mature neurons in a revised version of the manuscript.

• The authors propose several different ideas about how Patronin might contribute to Rab11 vesicle localization. However, I am not sure that they really describe the simplest one: that Patronin helps minus ends grow out from the cell body as shown in Drosophila (and Fig S6 here), and that these minus-end-out microtubules could be the tracks used to transport Rab11 into dendrites. Have I missed some reason why this model is not presented as a good fit for the data? *

We thank the reviewer for pointing this out. Feng et al indeed showed that EB proteins can track microtubule plus- and minus-end growth in the sensory neurons of Drosophila. Since the slower event co-localize with Patronin they suggested that these help to populate the minus-end out microtubules in the drosophila dendrites (Feng et al., 2019).

Although we do not have strong data against this model for the PVD dendrites in C. elegans, there are several observations that to us suggest that it is unlikely that minus-end growth is the driving force for the forward movement of the MTOC vesicles. These include: the MT being mixed in the distal segment, therefore it is hard to imagine how specifically one pool is growing; we do not see EBP-2 localize to the Camsap puncta as was seem in Drosophila; the Camsap dynamics at the growth cone seem very different (less processive) to the dynamics in the shaft (which indeed could be minus-end growth). We will make this reasoning more clear in the revised manuscript.

• There are some grammatical errors throughout, as well as a few typos (like PTNR-1 for PTRN-1). *

We will correct the text grammar and typos in the revision version of manuscript.

Reviewer #2 (Significance (Required)):

This analysis will help synthesize a more complete and meaningful understanding of how non-centrosomal microtubules are organized. The authors not only identify a new player in non-centrosomal microtubule organization, but also help fit together several existing players into a framework that brings together observations from other model systems and cell types into a more coherent whole.

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Referee #2

Evidence, reproducibility and clarity

Harterink comments:

In this manuscript He et al investigate the role of two key microtubule minus end regulators, Patronin/CAMSAP and NOCA-2/ninein, in establishing dendrite microtubule organization. The authors use a well-characterized branched sensory neuron in C. elegans for their analysis and make significant contributions to our understanding of neuronal microtubule organization. First, they show that C. elegans has not one, but two, ninein-like proteins, NOCA-1 and NOCA-2. Previously only NOCA-1 had been identified, and neuronal functions of ninein have remained elusive, perhaps in part because NOCA-2 had been missed. It had previously been shown that in epithelial cells NOCA-1 acts with gamma-tubulin as one arm of a microtubule minus end organizing pathway, while Patronin acts in parallel on minus ends. The current manuscript very nicely extends this functional map to neurons. The authors show that NOCA-2 helps recruit the gamma-tubulin ring complex (g-TuRC) to Rab11 endosomes that are important for microtubule nucleation at developing dendrite tips. As in epithelial cells, Patronin seems to act in parallel to this pathway and rather than being involved in recruiting the g-TuRC to Rab11 endosomes, is instead important for allowing the Rab11 endosomes to be transported to developing dendrite tips. In total this analysis not only identifies a new player in dendritic microtubule organization (NOCA-2), but also helps synthesize the functions of other players (g-TuRC, Patronin) into a model that makes sense in the broader context of microtubule organization across species and cell types.

Specific points

1. NOCA-2 is described as a previously unidentified member of the ninein family. In order to evaluate this claim critically, it would be helpful to have a figure showing how similar NOCA-1 and NOCA-2 are to mammalian ninein. It is also critical to include a phylogeny to get a better feel for how NOCA-2 fits into the evolutionary history of the family.
2. The nucleation assay used throughout is not very clear as one reads through the manuscript. The color-coding of Fig 1G could be better defined in the legend, and it would be helpful to have more information in the legend or results about what is meant here by microtubule nucleation. Is it simply initiation of new microtubule growth events? If so, how are these distinguished from catastrophe rescue? It would be good to use the same color coding in 2E.
3. The colocalization of Rab-11 and NOCA-2 seems to be supported only by a single overlapping puncta in a neuron before the anterior dendrite extends (Fig 4). It would be good to flesh out this data set more as it is an important part of the argument that NOCA-2 is involved in recruiting g-TuRC to Rab11 endosomes.
4. Summary diagrams of results either as conclusions are made in the individual figures or synthesized at the end would help readers to understand the evidence that NOCA-2 and PTRN-1 function at different steps in establishment of MT polarity
5. NOCA-1 is introduced at the beginning of the manuscript and appears to act in parallel to both NOCA-2 and PTRN-1. One is left with many questions, for example, is it also required to recruit g-TuRCs to Rab11 vesicles, or does it have some other role? However, I appreciate that it is beyond the scope of a single manuscript to answer all questions and the authors state a clear rationale for focusing on NOCA-2.
6. It would be helpful to clearly state at some point which aspects of localization that are described are seen only in developing dendrites and which are seen in both developing and mature dendrites. For example, is there any similarity in localization of PTRN-1 NOCA-2 and gip2 in mature dendrites to that shown in immature? Is there any sign of continued localization to RAB11 vesicles, or is this only transient? Perhaps a diagram to summarize these findings would also be helpful.
7. The authors propose several different ideas about how Patronin might contribute to Rab11 vesicle localization. However, I am not sure that they really describe the simplest one: that Patronin helps minus ends grow out from the cell body as shown in Drosophila (and Fig S6 here), and that these minus-end-out microtubules could be the tracks used to transport Rab11 into dendrites. Have I missed some reason why this model is not presented as a good fit for the data?
8. There are some grammatical errors throughout, as well as a few typos (like PTNR-1 for PTRN-1).

Significance

This analysis will help synthesize a more complete and meaningful understanding of how non-centrosomal microtubules are organized. The authors not only identify a new player in non-centrosomal microtubule organization, but also help fit together several existing players into a framework that brings together observations from other model systems and cell types into a more coherent whole.

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Referee #1

Evidence, reproducibility and clarity

In this paper, Harterink and colleagues investigate the establishment of minus-end-out microtubule polarity in the anterior dendrite of C. elegans PVD neurons. These neurons offer an excellent model system due to their simplicity and well-defined microtubule polarity. The authors investigate the role of two proteins in particular, the well-studied Patronin protein and a newly identified homologue of Ninein (Noca-2). They show that these proteins are redundantly required for correct minus-end-out polarity. Absence of one of these proteins results in a low penetrant phenotype, but absence of both results in a strongly penetrant phenotype. Interestingly, in all cases the neurons display either almost fully retrograde or almost fully anterograde microtubule polarity, and not a mix of retrograde and anterograde microtubules. This is probably linked to the fact that the authors show that endosomes at the distal tip of the dendrite (that are known to mediate retrograde microtubule nucleation events) are either present or absent in these mutants (to differing degrees that reflect the polarity phenotypes of each mutant type). The authors further show that Noca-2, but not Patronin, is required for proper localisation of γ-tubulin to the distal endosomes, suggesting that the proteins influence microtubule polarity in different ways. They provide some evidence that Patronin clusters, while not colocalized to the distal endosomes, are somehow connected. The paper and figures are clear and the work should be reproducible.

Most conclusions are supported by the data, except for when the authors say: "Taken together, these results show that PTRN-1 (CAMSAP) and NOCA-2 (NINEIN) act in parallel in the PVD neuron during early development to establish minus-end out microtubule organization, and that this organization is important for proper dendritic morphogenesis." But the authors show that removal of Patr results in some neurons having a complete anterograde phenotype in the anterior genotype, but that no Patr neurons have a severe morphology defect (Fig 2). This would suggest that the severe morphology defects in Patr/Noca-2 double mutants are not simply due to the reversal of polarity in the anterior dendrite. This should be discussed.

The paper could be strengthened with some biochemistry showing that Noca-2 can associate with γ-TuRCs i.e. do purified fragments of Noca-2 pull out γ-TuRCs from a cell extract (not necessarily a neuron cell extract)? This should be possible within 1 month.

Minor comments

1. "However, in polarized cells such as neurons, most microtubules are organized in a non-centrosomal manner (Nguyen et al., 2011)." Need more up to date reference here, such as a recent review from Jens Lüders.
2. "and also in Drosophila Patronin was found important for dendritic microtubule polarity (Feng et al., 2019)." Also Wang et al., 2019 in eLife.
3. "In the non-ciliated PHC neuron or the ciliated URX neuron we did not observe microtubule organization defects in the ptrn-1 mutant (Supplemental figure 1A-B), which suggests that these neurons do less or do not dependent on PTRN-1." End of sentence needs re-phasing

Significance

Overall, the paper adds some interesting information to the field but does not make a conceptual advance that would make it attractive to a wide audience. It will, however, be of interest to those studying mt regulation in neurons. It is a shame that the molecular mechanism that allow Noca-2, and particularly Patronin, to establish microtubule polarity remain to be determined. Figuring out these mechanisms would significantly strengthen the paper.

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Reply to the reviewers

Manuscript number: RC-2021-01129

Corresponding author(s): Koji Kikuchi

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Reviewer #1

Evidence, reproducibility and clarity (Required):

In this manuscript, Kikuchi et al describe the characterization of MAP7D2 and MAP7D1, two MAP7 family members in mouse with specific expression patterns. Focusing mostly on MAP7D2, they assess its expression pattern across the body and find that it is mostly expressed in certain neuronal subsets. They then characterize the MT-related properties of MAP7D2 based on previous knowledge of other MAP7 family members. They show that MAP7D2 binds MTs (via the N-terminus), determine the binding affinity, and show that it can stimulate MT polymerization (or stabilization) both in vitro and in vivo. Using a specific antibody, they localize MAP7D2 to centrosomes, midbody and neurites in N1-E115 cells. Functionally, they show that loss of MAP7D1/2 mildly affects microtubule stability as judged by acetyl-tubulin staining, and properties of these cells that rely on cytoskeletal elements such as cell migration and neurite growth. Interestingly, there might be a feedback loop regulating MAP7D1/2 expression, as knockdown of MAP7D1 upregulates MAP7D2.

Overall, the experiments and conclusions are very solid and convincing, such that I would not ask for further experiments. This is in part because the experiments are largely based on previous characterizations of other MAP7 family members, which are largely confirmed. The presentation of the data is also very clear.

Significance (Required):

I see the value of the study in the fact that it provides solid and specific research tools for MAP7D1/2 which could be very useful for the microtubule/neuronal cytoskeleton community.

Response: We thank the reviewer very much for appreciating the content of our manuscript.

\*Referees cross-commenting***

Reviewers 2 and 3 criticize that the evidence for an effect of MAP7D1/2 on MT dynamics is weak. I would agree in that ac-tub stainings and in vitro experiments are rather indirect. The experiments suggested by reviewer 2 should clarify this (esp. nocodazole should be easy). I also agree that an experiment addressing the potential involvement of kinesin-1 would help, the involvement of which seems to have been omitted by the authors. A kinesin-binding deficient mutant would add another MAP7D1/2 tool and increase the value for the community.

Response: As for the reviewer’s suggestions listed above, please refer to our responses to the comments of Reviewer #2.

Reviewer #2

Evidence, reproducibility and clarity (Required):

In this study, the authors investigate 2 members from the MAP7 family Map7D2 and Map7D1. They first address the tissue distribution of Map7D2, by northern blotting using a variety of rat tissues. To complement their analysis, they also raised an antibody to look at the protein distribution. From their studies, they concluded that Map7D2 is abundantly expressed in the brain and testis. The authors went on to perform a series of functional assays. First, they biochemically demonstrated that rat Map7D2 directly binds to MTs by MT co-sedimentation assay. The MT binding domain was mapped to the N-terminal half. They performed MT turbidity assay to demonstrate enhanced MT polymerisation in the presence of Map7D2, suggesting that this Map stabilises MTs. The authors went on to characterise in detail the subcellular localisation of Map7D2 which was predominantly present in the centrosome and partially localised to MTs including within neurites from N1-E115 cells. Kikuchi et al. further revealed the overlap in expression between Map7D2 and another family member, Map7D1. The authors continued these studies by a series of functional studies in N1-E115 cells where they performed single or combined knock-downs of Map7D2 and Map7D1 and studied the levels of acetylated and detyrosinated tubulins and the effect of the knock-downs on migration and neurite extension. The main conclusion from this work was that Map7D2 and Map7D1 facilitate MT stabilization through distinct mechanisms which are important in controlling cell motility and neurite outgrowth. Map7D2 is proposed to stabilise MTs by direct binding whereas Map7D1 does it indirectly by affecting acetylation.

Major comments:

The main conclusion from this work that Map7D2 and Map7D1 facilitate MT stabilization and that this is necessary for correct migration and neurite extension has not been convincingly demonstrated. In my opinion, a more detailed study of MT properties to demonstrate a role in MT stabilisation would greatly benefit the work, eg. experiments using MT destabilising agents such as nocodazole. In addition, a series of experiments aiming to study MT dynamics would help to understand the function of these MT regulators. The authors proposed an elevation in microtubule dynamics to explain the increase in migration and neurite extension but no experimental proof was provided.

Response: According to the reviewer’s suggestion, we plan to assess the role of MT stabilization in greater detail by analyzing the sensitivity to the MT-destabilizing agent, nocodazole.

To study MT dynamics, methods such as analyzing the velocity and direction of an EB1-GFP comet are commonly used. We have previously analyzed the roles of Map7 and Map7D1 in MT dynamics using HeLa cells stably expressing EB1-GFP (Kikuchi et al., EMBO Rep., 2018). However, no such tools have been developed for analyzing MT dynamics in N1-E115 cells, which were used in this study. In addition, it is difficult to analyze MT dynamics by transient expression of EB1-GFP because of the low plasmid transfection efficiency. Therefore, we instead plan to assess the effect on MT dynamics by measuring the EB1 comet length by immunofluorescence, referring to Fig. 7D in EMBO J. 32:1293–1306, 2013.

Moreover, considering the possibility that the Map7D2 dynamics are altered when MT stability is changed, e.g., before and after differentiation induction, we analyzed the Map7D2 dynamics at the centrosome by fluorescence recovery after photobleaching (FRAP) using N1-E115 cells stably expressing EGFP-rMap7D2. We found that the dynamics were altered between the proliferative and differentiated states (see the figure below). Compared to the proliferative state, the recovery rate of EGFP-Map7D2 was reduced (lower left panel), and the immobile fraction of Map7D2 was increased in the differentiated state (lower right panel). As these data suggest that the increase in immobile Map7D2 may enhance MT stabilization, we will present them in a new figure in our manuscript along with the results of the above two experiments.

It has been previously demonstrated that loss of MAP7D2 leads to a decrease in axonal cargo entry to axons resulting in defects in axon development and neuronal migration. The C-terminus is necessary for this function as it mediates interaction with Kinesin-1 (Pan et al., 2019). Such mechanisms could also explain the defects in migration and neurite growth that the authors observed. This possibility has not been considered but instead, the subtle changes in total α-tubulin led to suggest MT stabilisation as a key function without proof of causation. Could the authors provide some further experimental evidence to demonstrate that stability is the main contributor to the phenotypes observed? Eg. by rescuing migration and neurite phenotypes with a variant of MAP7D2 which cannot bind kinesin1.

Response: The reviewer states “Such mechanisms could also explain the defects in migration and neurite growth that the authors observed;” however, our results showed that loss of Map7D2 elevated the rates of both cell motility and neurite outgrowth (original Fig. 5). In contrast, it has been reported in several papers that when Kinesin-1 function is impaired, both cell motility and neurite outgrowth are reduced (Curr. Biol., 23: 1018–1023, 2013; Mol. Cell. Biol., 39: e00109–19, 2019; etc.). Therefore, it is likely that the phenotypes we observed are independent of the functions associated with Kinesin-1 in N1-E115 cells. It is indeed possible that the experiment suggested by the reviewer may reveal relationships between Map7D2 and kinesin-1 in terms of cell motility and neurite outgrowth, however, it is difficult to conduct such an experiment because transient expression of Map7D2 induces MT bundling, as shown in original Fig. 2F. Based on the above, we plan to add a discussion of the relationship between Map7D2 and Kinesin-1.

A key conclusion proposed by the authors is that Map7D2 and Map7D1 facilitate MT stabilization through distinct mechanisms. Such different roles in MT stabilisation are important in controlling cell motility and neurite outgrowth. In my opinion, their data does not fully support this statement and the findings using MT readouts do not match the defects in migration and neurite growth. Loss of Map7D2 leads to a very subtle phenotype on α-tubulin, while Map7D1 decreases both α-tubulin and acetylated tubulin, but Map7D1 seems to have a milder or similar effect on migration and neurite growth than Map7D2. Furthermore, it would be expected that the combined loss of function would lead to a stronger phenotype in cell migration when compared to the single loss of functions due to their distinct roles on MT stability, however, this seems not to be the case.

Response: The fact that no stronger phenotype was observed may be because, besides Map7D2 and Map7D1, other molecules are involved in MT stabilization. Another possible explanation is that the increases in both cell motility and neurite outgrowth caused by decreased MT stabilization are offset by Kinesin-1 dysfunction. We plan to add a discussion of the above two possibilities.

Minor comments:

1) In the first result section, the author refers to Fig. S3 to suggest the expression of MAP7D2 in the cerebral cortex, however, there are no transcripts in the cerebral cortex according to the figure. Similarly, the immunofluorescence analysis done by the authors shows marginal expression of MAP7D2 in the cerebral cortex.

Response: According to the reviewer’s comment, we have changed the order of the data shown in Fig. 1C, top panels. The data from the olfactory bulb, cerebellum, and hippocampus, in which Map7D2 expression was detected in the database, were arranged in the top three rows, and the data from the cerebral cortex, in which Map7D2 expression was not detected in the database, were moved to the bottom row as a negative control. In addition, we have revised the relevant part of the Results section as follows: “Based on RNA-seq CAGE, RNA-Seq, and SILAC database analysis (Expression Atlas, https://www.ebi.ac.uk/gxa/home/), Map7D2 expression was detected in the cerebellum, hippocampus, and olfactory bulb, and not in the cerebral cortex (Fig. S3). We further confirmed Map7D2 expression in the above four brain tissue regions of postnatal day 0 mice by immunofluorescence. Among these regions, Map7D2 was the most highly expressed in the Map2-negative area of the olfactory bulb, i.e., the glomerular layer (Fig. 1C). Weak signals were detected in the cerebellum, and marginal signals were observed in the hippocampus and cerebral cortex (Fig. 1C).” (page 5, lines 4–11)

2) The authors use γ-Tubulin as a housekeeping gene in Fig. 3D, since Map7D2 is enriched in centrosomes this may not be the most appropriate choice.

Response: γ-Tubulin is abundant in both the cytosol and the nuclear compartments of cells (Sig. Transduct. Target Ther. 3: 24, 2018). As it has been used for similar purposes in several other studies (Cancer Res., 61: 7713–7718, 2001; J. Biol. Chem., 291: 23112–23125, 2016; etc.), we considered it acceptable for use as a loading control for immunoblotting.

3) According to the authors, knockdown of Map7D2 leads to a decrease in the intensity of α-tubulin and Map7D1 (Fig. 4C and D). This data doesn't agree with the previous statement made by the authors where they show that Map7D2 knockdown or knockout did not affect Map7D1 expression by Western Blot Analysis (Fig. S2C and S5B)

Response: The immunoblotting results indicate that the total amount of Map7D1 in the cells is not affected by loss of Map7D2. In contrast, the immunofluorescence results indicate that the amount (distribution) of Map7D1 localized around the centrosome is decreased by loss of Map7D2, presumably due to a reduction in the number of MT structures that can serve as scaffolds for Map7D1. We plan to add this interpretation in the Results section.

4) Line 6 page 7 "Endogenous Map7D2 expression is suppressed in N1-E115 cells stably expressing EGFP-rMap7D2 and was restored by specific knock-down of EGFP-rMap7D2 using gfp siRNA (Fig. 3D)". No quantifications and stats are shown. Also, endogenous Map7D2 after knock-down of EGFP-rMap7D2 is not comparable to the control.

Response: According to the reviewer’s suggestion, we have quantified the amount of endogenous Map7D2 or EGFP-rMap7D2, normalized it to the amount of γ-tubulin, and calculated relative values to endogenous Map7D2 in the parental control. The amount of endogenous Map7D2 was decreased to 53% in N1-E115 cells stably expressing EGFP-rMap7D2, suggesting that EGFP-rMap7D2 expression suppressed endogenous Map7D2 expression. In this cell line, the total amount of Map7D2 (EGFP-rMap7D2 + endogenous Map7D2) was increased, however, when EGFP-rMap7D2 was depleted using sigfp in this cell line, endogenous Map7D2 was expressed to the same level as EGFP-rMap7D2 before knock-down. Together with the finding that Map7d1 knock-down increased the amount of Map7D2, these findings indicate that the amount of Map7D2 in the cells is regulated in response to the amount of Map7D1 and exogenous Map7D2. We have added this interpretation in the Results section. (page 7, lines 8–15)

In addition, we have changed the legend of the original Fig. 3D to clarify the quantification method, as follows: “(D) Generation of N1-E115 cells stably expressing EGFP-rMap7D2. To check the expression level of EGFP-rMap7D2, lysates derived from the indicated cells were probed with anti-GFP (top panel) and anti-Map7D2 (middle panel) antibodies. The blot was reprobed for γ-tubulin as a loading control (bottom panel). The amount of endogenous Map7D2 or EGFP-rMap7D2 was normalized to the amount of γ-tubulin, and the value relative to endogenous Map7D2 in the parental control was calculated.” (page 22, lines 18–20)

5) Line 8 page 7 "These results suggest that the expression of Map7D2 was influenced by changes in that of Map7D1" This statement seems in the wrong place, after the Map7D2 and EGFP-rMap7D2 experiment. Instead for clarity, it would be better placed after line 5 where the authors explain the effect of Map7D1 knock-down on the levels of Map7D2.

Response: According to the reviewer’s suggestion, we have rephrased the relevant sentence as “Interestingly, Map7d1 knock-down upregulated Map7D2 expression, as confirmed with three different siRNAs (Fig. S2C), suggesting that Map7D2 expression is affected by changes in Map7D1 expression, not by off-target effects of a particular siRNA.” (page 7, lines 7, 8)

6) Line 8 page 8 "Although the physiological role of the C-terminal region of Map7D2 is currently unknown..." This statement seems not adequate as there are several studies reporting the role of the C-terminal region of Map7D2 in Kinesin1- mediated transport. The authors mention such studies in the discussion.

Response: According to the reviewer’s suggestion, we plan to add a discussion of the relationship between Map7D2 and kinesin-1.

7) Line 6 page 9 " Further, the knock-down of either resulted in a comparable reduction of MT intensity (Fig. 4C and D) ..." This is not visible and/or justified by the images provided and would benefit from some sort of quantification at other regions such as neurites.

Response: Considering the cell motility, quantification of α-tubulin/Ace-tubulin/Map7D1/Map7D2 intensities in neurites is not appropriate. Instead, we have added arrowheads indicating α-tubulin/Ace-tubulin/Map7D1/Map7D2 in Fig. 4C, for better understanding.

8) In Fig. 2B, a band corresponding to his6-rMAP7D2 of molecular weight >97 kDa co-sedimented with the microtubules. However, the cloned rMAP7D2 had a molecular weight of 84.82 kDa and the addition of 6XHis-Tag would add another 2-3 kDa, therefore, the final protein band observed should be less than 90 kDa. It would be beneficial if the authors could specify the molecular weight of the purified protein after the addition of the V5-his tag and/or if there was addition of amino acids due to cloning strategy.

Response: In Fig. 2B, we used full-length GST-tagged rMap7D2, like in Fig. 2E and D; therefore, we have corrected His6-rMap7D2 as GST-rMap7D2. We apologize for the mistake.

9) In Fig. 2C, there is misalignment of the western blot with the panel or text underneath.

Response: We thank the reviewer for pointing this out; we have corrected the misalignment of the CBB staining in Fig. 2C.

10) In Fig. 3C the inset from the first panel seems to correspond to a different focal plane than the main image.

Response: We have revised the relevant part of the figure legend as follows: “In C, images of differentiated cells were captured by z-sectioning, because the focal planes of the centrosome and neurites are different. Each inset shows an enlarged image of the region indicated with a white box at each focal plane. Arrowheads indicate the centrosomal localization of Map7D2.”

11) In Fig. 4A, the cell type is not specified and is referred as "indicated cells", also the material and methods section seems to omit the specific cells used.

Response: We have added “in N1-E115 cells treated with each siRNA” in the legend of Fig. 4A.

12) Fig. S6 is not mentioned in the results.

Response: We apologize for having referred to Fig. S6 only in the Discussion section in the original manuscript. We plan to describe the findings shown in the original Fig. S6 to the Results section and renumber the figures accordingly.

Significance (Required):

MTs play essential roles in practically every cellular process. Their precise regulation is therefore crucial for cellular function and viability. MAPs are specialised proteins that interact with MTs and regulate their behaviour in different manners. Understanding their precise function in different cellular contexts is of utmost importance for many biological and biomedical fields.

MAPs are well known for their ability to promote MT polymerization, bundling and stabilisation in vitro (Bodakuntla et al., 2019). Several members of the Map7 family have been shown to regulate microtubule stability. For instance, MAP7 can prevent nocodazole-induced MT depolymerization and maintain stable microtubules at branch points in DRG neurons (Tymanskyj & Ma, 2019). Ensconsin, the Drosophila Map, is required for MT growth in mitotic neuroblasts by regulating the mean rate of MT polymerization (Gallaud et al., 2014). However, this family of Maps seems to have diverse functions encompassing a variety of mechanisms, as exemplified by a series of studies demonstrating the involvement of MAP7 family proteins in the recruitment and activation of kinesin1 (Hooikaas et al., 2019; Pan et al., 2019) and in microtubule remodelling and Wnt5a signalling (Kikuchi et al., 2018). Further understanding of this family of Maps and how its members differ in their function is important and will help to advance the field.

Response: We appreciate the reviewer’s comments. We believe that our revision plan will greatly improve the quality of our manuscript.

Reviewer #3

Evidence, reproducibility and clarity (Required):

Summary:

Microtubule Associated Proteins (MAPs) are important regulators of microtubule dynamics, microtubule organization and vesicular transport by modulating motor protein recruitment and processivity. In the current manuscript the authors have characterized 2 members of the MAP7 protein family, MAP7D1 and MAP7D2. The authors characterized MAP7D2 expression pattern in the brain and its microtubule binding properties in vitro and in cells. In cells both proteins localize to the centrosome and to microtubules and upon depletion centrosome localized microtubules seem reduced, and cell migration and neurite outgrowth are increased. Surprisingly, they find that microtube acetylation (a common marker for stable microtubules) is reduced upon MAP7D1 depletion but not MAP7D2 depletion. Based on this finding the authors conclude that these proteins have a distinct mechanism in stabilizing MTs to affect cell migration and neurite outgrowth; MAP7D2 stabilizes by binding to MTs, whereas MAP7D1 stabilizes MTs by acetylation.

Main comments:

- Both MAP7 proteins show strong localization to the centrosome and to a lesser degree to MTs. Knockdown of either protein leads to reduced MTs around the centrosome, which lead the authors to conclude the MAP7s are stabilizing the MTs. However, the effect could just as well be an indirect effect due to a function of these MAPs at the centrosome. To address this authors could e.g. quantify microtubule properties in postmitotic cells. In addition, antibody specificity should be tested using knockdown of knockout cells, as this centrosome localization was not observed in Hela cells (Hooikaas, 2019; Kikuchi, 2018). Maybe this localization is specific to rat MAP7s or to the cell line used.

Response: We think that this comment partly overlaps with the comments raised by Reviewer #2. We plan to assess the role of MT stabilization in greater detail by analyzing the sensitivity to the MT-destabilizing agent, nocodazole, and the effect on MT dynamics by measuring the EB1 comet length by immunofluorescence.

Regarding the reviewer’s concern about antibody specificity, we had carefully confirmed the antibody specificity, as shown in Fig. S2 of the original manuscript. Subsequently, Map7D2 localization was confirmed in N1-E115 cells stably expressing EGFP-rMap7D2, as shown in Fig. 3D, E of the original manuscript. In addition, we are currently conducting analyses using Map7d1-egfp knock-in mice, which confirmed that Map7D1 localizes around the centrosome in cortical neurons, as shown below (we would like to disclose these unpublished data to the reviewers only). Therefore, it is thought that the localization pattern of Map7D2 and Map7D1 differs depending on the cell type and cell line. We plan to add this interpretation to the Results section.

- Centrosome nucleated microtubules are typically highly dynamic and little modified. Therefore is the Ac-tub staining at the centrosome really MTs? I cannot identify MTs in the fluorescent images in 4C. Maybe authors could consider ac-tub/alpha-tub ratio in non centrosomal region (e.g. neurites). Moreover, as both Acetylation and detyrosination are associated with long-lived/stable MTs, it is surprising that only acetylated tubulin goes down on WB. Does this suggest that long-lived MTs are still present to normal level? If so, can one still argue that the loss of acetylation is the cause of the lower MT levels? This should at least be discussed.

Response: As for the reviewer’s statement “Centrosome nucleated microtubules are typically highly dynamic and little modified. Therefore is the Ac-tub staining at the centrosome really MTs?”, it has been previously reported that tubulin acetylation is observed around the centrosome in some cell lines (J. Neurosci., 30: 7215–7226, 2010; PLoS One, 13: e0190717, 2018; etc.). N1-E115 is one of the cell lines in which tubulin acetylation is observed around the centrosome.

It is not surprising that “only acetylated tubulin goes down on WB,” as it has been previously reported that acetylated and detyrosinated tubulins are sometimes not synchronous (J. Neurosci., 23: 10662–10671, 2003; J. Neurosci., 30: 7215–7226, 2010; J. Cell Sci., 132: jcs225805, 2019., etc.). For instance, Montagnac et al. (Nature, 502: 567–570, 2013) showed that defects in the α-tubulin acetyltransferase αTAT1-clathrin-dependent endocytosis axis reduce only tubulin acetylation, resulting in a shift from directional to random cell migration. Although the details of the molecular function of Map7D1 are beyond the main purpose of this study, we plan to add a discussion of the reduced tubulin acetylation by Map7d1 knock-down based on the above.

- MAP7D1 and MAP7D2 depletion leads to subtle defect in cell migration and neurite outgrowth, which the author suggest is caused by reduced MT stability. However, MAP7 proteins have well characterized functions in kinesin-1 transport, and thus the phenotypes may well be caused by defects in kinesin-1 transport. Ideally the authors would do rescue experiments with FL or just the MT binding N-termini to separate these functions. Moreover this is needed to substantiate the claim of the authors that MAP7D1 effect on MT stability is not mediated by direct binding.

Response: As this comment largely overlaps with the comments raised by Reviewer #2, please refer to our responses to the comments of Reviewer #2.

- The authors do not refer well to published work. Several papers have published very similar work (especially to Fig1+2) and it would help the reader much if this would be discussed/compared along the results section and not briefly mention these in the results section. In addition, authors overstate the novelty of their results e.g. page 3: these proteins are not "functionally uncharacterized" nor are their expression patter and biochemical properties analyzed for the first time in this manuscript; page 8 "Although the physiological role of the C-terminal region of Map7D2 is currently unknow, ..." There is a clear function for the C-terminus for the recruitment/activation of kinesin-1.

Response: According to the reviewer’s suggestion, we plan to add a comparison with data on the Map7 family members presented in previous papers in the Results section and rephrase the relevant part regarding the physiological role of the C-terminal region of Map7D2.

Minor comments

- P6 Map7D3 also binds with its N-terminus to MTs, like other MAP7s (Yadav et al)

Response: According to the reviewer’s comment, we have revised this as “Map7D3 binds through a conserved region on not only the N-terminal side, but also the C-terminal side (Sun, 2011; Yadav et al., 2014).” (page 6, lines 4, 5)

- P7 "As Map7D2 has the potential to functionally compensate for Map7D1 loss" where is this based on?

Response: For clarity, we have rephrased this as “As Ma7D2 expression was upregulated upon suppression of Map7D1 expression, Map7D2 has the potential to functionally compensate for Map7D1 loss.” (page 7, line 17, 18)

- Fig2F quality of black-white images is low potentially due to conversion issues

Response: We thank the reviewer for pointing out these conversion issues, and we have made the necessary corrections.

Significance (Required):

At this stage the conceptual advance is limited. Part of the findings are not novel. The finding that MAP7s depletion have a different effect on MTs acetylation may be interesting to cytoskeleton researchers, although the potential mechanism has not been addressed experimentally or textually.

However, their conclusion that this leads to reduced MTs and then to cellar migration and neurite formation defects is not sufficiently supported by experimental evidence.

Response: We appreciate the reviewer’s comments. We believe that our revision plan will greatly improve the quality of our manuscript.

\*Referees cross-commenting***

I completely agree with reviewer #2: At this stage the paper's conclusions are not sufficiently supported by the data. Important will be to further characterize the effect om the MTs (do they really have a different effect) and to look at the possible involvement of the motor recruitment. Maybe that a 3 to 6 months revision time would have been more accurate.

Response: Please refer to our responses to the comments of Reviewer #2.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Microtubule Associated Proteins (MAPs) are important regulators of microtubule dynamics, microtubule organization and vesicular transport by modulating motor protein recruitment and processivity. In the current manuscript the authors have characterized 2 members of the MAP7 protein family, MAP7D1 and MAP7D2. The authors characterized MAP7D2 expression pattern in the brain and its microtubule binding properties in vitro and in cells. In cells both proteins localize to the centrosome and to microtubules and upon depletion centrosome localized microtubules seem reduced, and cell migration and neurite outgrowth are increased. Surprisingly, they find that microtube acetylation (a common marker for stable microtubules) is reduced upon MAP7D1 depletion but not MAP7D2 depletion. Based on this finding the authors conclude that these proteins have a distinct mechanism in stabilizing MTs to affect cell migration and neurite outgrowth; MAP7D2 stabilizes by binding to MTs, whereas MAP7D1 stabilizes MTs by acetylation.

Main comments:

• Both MAP7 proteins show strong localization to the centrosome and to a lesser degree to MTs. Knockdown of either protein leads to reduced MTs around the centrosome, which lead the authors to conclude the MAP7s are stabilizing the MTs. However, the effect could just as well be an indirect effect due to a function of these MAPs at the centrosome. To address this authors could e.g. quantify microtubule properties in postmitotic cells. In addition, antibody specificity should be tested using knockdown of knockout cells, as this centrosome localization was not observed in Hela cells (Hooikaas, 2019; Kikuchi, 2018). Maybe this localization is specific to rat MAP7s or to the cell line used.
• Centrosome nucleated microtubules are typically highly dynamic and little modified. Therefore is the Ac-tub staining at the centrosome really MTs? I cannot identify MTs in the fluorescent images in 4C. Maybe authors could consider ac-tub/alpha-tub ratio in non centrosomal region (e.g. neurites). Moreover, as both Acetylation and detyrosination are associated with long-lived/stable MTs, it is surprising that only acetylated tubulin goes down on WB. Does this suggest that long-lived MTs are still present to normal level? If so, can one still argue that the loss of acetylation is the cause of the lower MT levels? This should at least be discussed.
• MAP7D1 and MAP7D2 depletion leads to subtle defect in cell migration and neurite outgrowth, which the author suggest is caused by reduced MT stability. However, MAP7 proteins have well characterized functions in kinesin-1 transport, and thus the phenotypes may well be caused by defects in kinesin-1 transport. Ideally the authors would do rescue experiments with FL or just the MT binding N-termini to separate these functions. Moreover this is needed to substantiate the claim of the authors that MAP7D1 effect on MT stability is not mediated by direct binding.
• The authors do not refer well to published work. Several papers have published very similar work (especially to Fig1+2) and it would help the reader much if this would be discussed/compared along the results section and not briefly mention these in the results section. In addition, authors overstate the novelty of their results e.g. page 3: these proteins are not "functionally uncharacterized" nor are their expression patter and biochemical properties analyzed for the first time in this manuscript; page 8 "Although the physiological role of the C-terminal region of Map7D2 is currently unknow, ..." There is a clear function for the C-terminus for the recruitment/activation of kinesin-1.

Minor comments:

• P6 Map7D3 also binds with its N-terminus to MTs, like other MAP7s (Yadav et al)
• P7 "As Map7D2 has the potential to functionally compensate for Map7D1 loss" where is this based on?
• Fig2F quality of black-white images is low potentially due to conversion issues

Significance

At this stage the conceptual advance is limited. Part of the findings are not novel. The finding that MAP7s depletion have a different effect on MTs acetylation may be interesting to cytoskeleton researchers, although the potential mechanism has not been addressed experimentally or textually.

However, their conclusion that this leads to reduced MTs and then to cellar migration and neurite formation defects is not sufficiently supported by experimental evidence.

Referees cross-commenting

I completely agree with reviewer #2: At this stage the paper's conclusions are not sufficiently supported by the data. Important will be to further characterize the effect om the MTs (do they really have a different effect) and to look at the possible involvement of the motor recruitment. Maybe that a 3 to 6 months revision time would have been more accurate.

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Referee #2

Evidence, reproducibility and clarity

In this study, the authors investigate 2 members from the MAP7 family Map7D2 and Map7D1. They first address the tissue distribution of Map7D2, by northern blotting using a variety of rat tissues. To complement their analysis, they also raised an antibody to look at the protein distribution. From their studies, they concluded that Map7D2 is abundantly expressed in the brain and testis. The authors went on to perform a series of functional assays . First, they biochemically demonstrated that rat Map7D2 directly binds to MTs by MT co-sedimentation assay. The MT binding domain was mapped to the N-terminal half. They performed MT turbidity assay to demonstrate enhanced MT polymerisation in the presence of Map7D2, suggesting that this Map stabilises MTs. The authors went on to characterise in detail the subcellular localisation of Map7D2 which was predominantly present in the centrosome and partially localised to MTs including within neurites from N1-E115 cells. Kikuchi et al. further revealed the overlap in expression between Map7D2 and another family member, Map7D1. The authors continued these studies by a series of functional studies in N1-E115 cells where they performed single or combined knock-downs of Map7D2 and Map7D1 and studied the levels of acetylated and detyrosinated tubulins and the effect of the knock-downs on migration and neurite extension. The main conclusion from this work was that Map7D2 and Map7D1 facilitate MT stabilization through distinct mechanisms which are important in controlling cell motility and neurite outgrowth. Map7D2 is proposed to stabilise MTs by direct binding whereas Map7D1 does it indirectly by affecting acetylation.

Major comments:

The main conclusion from this work that Map7D2 and Map7D1 facilitate MT stabilization and that this is necessary for correct migration and neurite extension has not been convincingly demonstrated. In my opinion, a more detailed study of MT properties to demonstrate a role in MT stabilisation would greatly benefit the work, eg. experiments using MT destabilising agents such as nocodazole. In addition, a series of experiments aiming to study MT dynamics would help to understand the function of these MT regulators. The authors proposed an elevation in microtubule dynamics to explain the increase in migration and neurite extension but no experimental proof was provided.

It has been previously demonstrated that loss of MAP7D2 leads to a decrease in axonal cargo entry to axons resulting in defects in axon development and neuronal migration. The C-terminus is necessary for this function as it mediates interaction with Kinesin-1 (Pan et al., 2019). Such mechanisms could also explain the defects in migration and neurite growth that the authors observed. This possibility has not been considered but instead, the subtle changes in total -tubulin led to suggest MT stabilisation as a key function without proof of causation. Could the authors provide some further experimental evidence to demonstrate that stability is the main contributor to the phenotypes observed? Eg. by rescuing migration and neurite phenotypes with a variant of MAP7D2 which cannot bind kinesin1.

A key conclusion proposed by the authors is that Map7D2 and Map7D1 facilitate MT stabilization through distinct mechanisms. Such different roles in MT stabilisation are important in controlling cell motility and neurite outgrowth. In my opinion, their data does not fully support this statement and the findings using MT readouts do not match the defects in migration and neurite growth. Loss of Map7D2 leads to a very subtle phenotype on -tubulin, while Map7D1 decreases both -tubulin and acetylated tubulin, but Map7D1 seems to have a milder or similar effect on migration and neurite growth than Map7D2. Furthermore, it would be expected that the combined loss of function would lead to a stronger phenotype in cell migration when compared to the single loss of functions due to their distinct roles on MT stability, however, this seems not to be the case.

Minor comments:

1. In the first result section, the author refers to Fig. S3 to suggest the expression of MAP7D2 in the cerebral cortex, however, there are no transcripts in the cerebral cortex according to the figure. Similarly, the immunofluorescence analysis done by the authors shows marginal expression of MAP7D2 in the cerebral cortex.
2. The authors use -Tubulin as a housekeeping gene in Fig. 3D, since Map7D2 is enriched in centrosomes this may not be the most appropriate choice.
3. According to the authors, knockdown of Map7D2 leads to a decrease in the intensity of -tubulin and Map7D1 (Fig. 4C and D). This data doesn't agree with the previous statement made by the authors where they show that Map7D2 knockdown or knockout did not affect Map7D1 expression by Western Blot Analysis (Fig. S2C and S5B)
4. Line 6 page 7 "Endogenous Map7D2 expression is suppressed in N1-E115 cells stably expressing EGFP-rMap7D2 and was restored by specific knock-down of EGFP-rMap7D2 using gfp siRNA (Fig. 3D)". No quantifications and stats are shown. Also, endogenous Map7D2 after knock-down of EGFP-rMap7D2 is not comparable to the control.
5. Line 8 page 7 "These results suggest that the expression of Map7D2 was influenced by changes in that of Map7D1" This statement seems in the wrong place, after the Map7D2 and EGFP-rMap7D2 experiment. Instead for clarity, it would be better placed after line 5 where the authors explain the effect of Map7D1 knock-down on the levels of Map7D2.
6. Line 8 page 8 "Although the physiological role of the C-terminal region of Map7D2 is currently unknown..." This statement seems not adequate as there are several studies reporting the role of the C-terminal region of Map7D2 in Kinesin1- mediated transport. The authors mention such studies in the discussion.
7. Line 6 page 9 " Further, the knock-down of either resulted in a comparable reduction of MT intensity (Fig. 4C and D) ..." This is not visible and/or justified by the images provided and would benefit from some sort of quantification at other regions such as neurites.
8. In Fig. 2B, a band corresponding to his6-rMAP7D2 of molecular weight >97 kDa co-sedimented with the microtubules. However, the cloned rMAP7D2 had a molecular weight of 84.82 kDa and the addition of 6XHis-Tag would add another 2-3 kDa, therefore, the final protein band observed should be less than 90 kDa. It would be beneficial if the authors could specify the molecular weight of the purified protein after the addition of the V5-his tag and/or if there was addition of amino acids due to cloning strategy.
9. In Fig. 2C, there is misalignment of the western blot with the panel or text underneath.
10. In Fig. 3C the inset from the first panel seems to correspond to a different focal plane than the main image.
11. In Fig. 4A, the cell type is not specified and is referred as "indicated cells", also the material and methods section seems to omit the specific cells used.
12. Fig. S6 is not mentioned in the results.

Significance

MTs play essential roles in practically every cellular process. Their precise regulation is therefore crucial for cellular function and viability. MAPs are specialised proteins that interact with MTs and regulate their behaviour in different manners. Understanding their precise function in different cellular contexts is of utmost importance for many biological and biomedical fields.

MAPs are well known for their ability to promote MT polymerization, bundling and stabilisation in vitro (Bodakuntla et al., 2019). Several members of the Map7 family have been shown to regulate microtubule stability. For instance, MAP7 can prevent nocodazole-induced MT depolymerization and maintain stable microtubules at branch points in DRG neurons (Tymanskyj & Ma, 2019). Ensconsin, the Drosophila Map, is required for MT growth in mitotic neuroblasts by regulating the mean rate of MT polymerization (Gallaud et al., 2014). However, this family of Maps seems to have diverse functions encompassing a variety of mechanisms, as exemplified by a series of studies demonstrating the involvement of MAP7 family proteins in the recruitment and activation of kinesin1 (Hooikaas et al., 2019; Pan et al., 2019) and in microtubule remodelling and Wnt5a signalling (Kikuchi et al., 2018). Further understanding of this family of Maps and how its members differ in their function is important and will help to advance the field.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Kikuchi et al describe the characterization of MAP7D2 and MAP7D1, two MAP7 family members in mouse with specific expression patterns. Focusing mostly on MAP7D2, they assess its expression pattern across the body and find that it is mostly expressd in certain neuronal subsets. They then characterize the MT-related properties of MAP7D2 based on previous knowledge of other MAP7 family members. They show that MAP7D2 binds MTs (via the N-terminus), determine the binding affinity, and show that it can stimulate MT polymerization (or stabilization) both in vitro and in vivo. Using a specific antibody, they localize MAP7D2 to centrosomes, midbody and neurites in N1-E115 cells. Functionally, they show that loss of MAP7D1/2 mildly affects microtubule stability as judged by acetyl-tubulin staining, and properties of these cells that rely on cytoskeletal elements such as cell migration and neurite growth. Interestingly, there might be a feedback loop regulating MAP7D1/2 expression , as knockdown of MAP7D1 upregulates MAP7D2.

Overall, the experiments and conclusions are very solid and convincing, such that I would not ask for further experiments. This is in part because the experiments are largely based on previous characterizations of other MAP7 family members, which are largely confirmed. The presentation of the data is also very clear.

Significance

I see the value of the study in the fact that,it provides solid and specific research tools for MAP7D1/2 which could be very useful for the microtubule/neuronal cytoskeleton community.

Referees cross-commenting

Reviewers 2 and 3 criticize that the evidence for an effect of MAP7D1/2 on MT dynamics is weak. I would agree in that ac-tub stainings and in vitro experiments are rather indirect. The experiments suggested by reviewer 2 should clarify this (esp. nocodazole should be easy). I also agree that an experiment addressing the potential involvement of kinesin-1 would help, the involvement of which seems to have been omitted by the authors. A kinesin-binding deficient mutant would add another MAP7D1/2 tool and increase the value for the community.

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Reply to the reviewers

Reviewer #1:

This is the first such piece of data to come from human infective parasites in the field. Technically this is a feat - because the small number of parasites that are present per mL of human blood at any given time during infection with T gambiense. Nevertheless they manage to identify up to 14 unique VSGs per patient sample. And this raises the first theoretical question: can they extrapolate to the average diversity load per human?

This is an intriguing question that we would like to eventually answer, but we do not believe we can make this estimate from the data we currently have. We know our sampling is insufficient based on the correlation between parasitemia and diversity, and we do not have sufficiently precise estimates of parasitemia that could be used to extrapolate total diversity in the blood. Moreover, our analysis was only performed on RNA extracted from whole blood samples. Recent studies indicate that significant populations of parasites reside in extravascular tissue spaces, and our analysis did not address antigenic diversity in these spaces. We believe it is unlikely that the blood alone reflects the full diversity of VSG expression in an infection, and an estimate based only on blood-resident parasites (if possible) could be misleading.

this is important because the timing of sample collection (ie that it occurred within a period of weeks) suggesting that an initial group of infected tsetse infected these patients (rather than a small number of interactions between a bloodmeal and a new infection - generally in itself on the order of 1 month or so). If parasitemia is low and diversity limited, this would explain both why CATT works as well as it does (because really it shouldn't at all!) and perhaps even the chronicity of infection (in the sense that the organism is unlikely to "run out" even of complete VSGs, never mind mosaics). The paper would benefit from a direct discussion on this.

Indeed, the timing of sample collection could inform our interpretation of the data. However, sample collection occurred over a period of six months. More importantly, patients were in both early and late stage disease at the time of sample collection, so we cannot estimate how long any individual patient had been infected. We have added text (line 180) to highlight this fact. Because some patients were infected at least 6 months apart (if not much more than that), it is unlikely that patients were infected around the same time by a small group of infected tsetse flies. Reviewer #1 introduces an interesting point about the efficacy of the CATT diagnostic test as it relates to antigenic diversity. We discuss CATT sensitivity in the introduction (lines 115-120) as well as the discussion, where regional sensitivity differences are mentioned (lines 715-718). Given uncertainty about total diversity and time since initial infection, we have refrained from speculating about how diversity/timing could affect CATT sensitivity.

An interesting feature of this new study is the apparent bias to type B N-terminal domain VSGs as well as the discovery that two patients share a specific VSG isolate (though it is not mentioned whether they are related by distance etc). This raises the possibility of substrains with different VSG archives that vary by geography.

We found two VSGs which were expressed in more than one patient. One was expressed in two patients from the same village (village C) while the other VSG was common between two cases originating from villages C and D, some 40 km apart. We agree that our data generally support the possibility that the VSG archive might vary geographically. We have performed additional analyses suggested by reviewer #2 that support this idea: we have now shown that Tbg patient VSGs classified in this study, which originated from the DRC, are distinct from the VSGs encoded by the reference strain Tbg DAL 972 which was isolated in Cote d’Ivoire. We mention this possibility on lines 721-724.

Alternatively it suggests that perhaps type B VSGs are picked up differentially by serology (and there the one feature of type B VSGs that could be shared, with regards to detection, is the O-hexose decoration on a number of type B VSG surfaces. Could CATT be detecting elements common to sugar decorated VSGs? Experimentally this is something that can be tested even with mouse infection materials.

This is indeed an intriguing possibility. We mention this in the discussion (lines 772-778): “In T. brucei, several VSGs have evolved specific functions besides antigenic variation [74]. Recently, the first type B VSG structure was solved [75], revealing a unique O-linked carbohydrate in the VSG’s N-terminal domain. This modification was found to interfere with the generation of protective immunity in a mouse model of infection; perhaps structural differences between each VSG type, including patterns of glycosylation, could influence infection outcomes.” While this is an experimentally tractable explanation for the type B VSG bias we observe, we believe such experiments are beyond the scope of the current paper.

Side comment: are the common VSGs mutated between patient samples?

We classified VSGs as common between patient samples if they had >98% nucleotide sequence identity as well as meeting the other quality cutoffs such as 1% expression level and consistency across technical replicates. This identity cutoff still allows for several mismatches between sequences, which we do occasionally observe. However, we cannot confidently rule out that the “mutations” we observe are sequencing or PCR errors. Thus, we cannot say for sure if there are mutations between common VSGs.

Reviewer #2

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1.Throughout the manuscript you observe 'diversity' in expressed VSG and its existence becomes a principal conclusion. I feel that the meaning of diversity and its significance is not sufficiently explained for the reader. In the abstract (l48) you say that there is 'marked diversity' in parasite populations. Presumably you mean parasite infrapopulations, i.e. within patients, not across the DRC? In any case, what is 'marked' about it, and relative to what? Why does it matter that there are multiple expressed VSG in a single patient at one time? Is this not a reasonable expectation for a population of (presumably) clones capable of switching the expressed VSG? How is this different to the view typical of the literature since 1970 that one VSG dominates while others wait in the background at low frequencies. If 'diversity' is the conclusion, then you need to define it and explain its significance more.

When we refer to diversity, we do mean infrapopulations of parasites within patients, or individual animals in this case, rather than across the DRC. We have edited the text to make this clear (see below). However, the study which benchmarked the application of VSG-seq to quantify VSG expression in vivo during mouse did not support the previously-held view that one VSG dominates while others wait in the background at low frequencies. Frequently we observe a handful of VSGs present at 10-20% of the population at any timepoint, and many VSGs (~50% of all detected variants) present at “In a proof-of-principle study, we used VSG-seq to gain insight into the number and diversity of VSGs expressed during experimental mouse infections [30]. This proof-of-principle study revealed significant VSG diversity within parasite populations in each animal, with many more variants expressed at a time than the few thought to be sufficient for immune evasion. This diversity suggested that the parasite’s genomic VSG repertoire might be insufficient to sustain a chronic infection, highlighting the potential importance of recombination mechanisms that form new VSGs.

2.Following on from 1., why does the analysis deal in counts of distinct VSG or N-terminal domains, and not then progress to their relative expression? The expression data are in Supp Table 3 and they show that, in most cases where many VSG are observed in the same patient, 1-3 of these are 'dominant', i.e. they account for >50% of the population.

The VSG-seq analysis pipeline does estimate the relative expression level of each identified variant in the population, and this information is available in the supplemental data (Supplemental Figure 1, Supplemental Table 3). However, we chose not to rely on these measurements too heavily because there was some variation between Tbg technical replicates, which is shown in the supplemental heatmap (Supplemental Figure 1). Replicate three tends to not agree with the first two replicates. We suspect that this was due to the order of sample processing and the fact that the parasite-enriched cDNA sample was repeatedly freeze-thawed between library preparations for technical replicates. Additionally, because our sampling did not reach saturation, some VSGs are not detected in all replicate libraries, making it difficult to estimate their abundance.

We have added a discussion of these issues to the text on lines 431-433: “Because our sampling did not reach saturation, resulting in some variability between technical replicates, we chose to focus only on the presence/absence of individual VSGs rather than expression levels within parasite populations.”

Figure 1 deals in VSG counts, but I would then expect another figure to illustrate the reality that only a minority of these observed VSG are likely to be clinically relevant (i.e. the subject of the immune response). This impacts the 'diversity' conclusion, as given in the discussion (ll 657-9), because you cannot afford to treat all these VSG equally when their abundances are quite different.

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We agree that relative expression level is a useful metric, but absent longitudinal sampling it is impossible to determine which VSGs are clinically relevant as defined by the reviewer: low abundance VSGs at one time point may be the predominantly expressed variant at another. Moreover, the threshold for triggering an anti-VSG antibody response remains unknown. Thus, we have chosen to treat all detected variants equally.

3.How related are these VSG? Were you able to ensure unique read mapping to the VSG assembly? Can you show that reads mapped to a single VSG only and therefore, that the RPKM values are reliable?

Our analysis accounts for the fact that VSGs can be very similar. We only considered uniquely mapping reads in our VSG-seq analysis. We also account for mappability in our quantification, so VSG sequences that are less unique (and thus have fewer uniquely mapping reads) are not artificially underrepresented in estimates of relative expression. We have specified the parameters used for alignment (line 274) in the methods.

4.The authors observed no orthology between expressed VSG and DAL972 genes. This is really interesting and deserves closer attention. Presumably there is microhomology? For T. brucei VSG, with constant recombination, we would predict that a comparison of the VSG in West and Central Africa would reveal a pattern of mosaicism, such that individual sequences in DRC would break down into motifs present in multiple genes in the West African reference. Question is, how many genes? What does that distribution look like? What is the smallest homology tract? There is an opportunity here to comment on how VSG repertoires diverge under recombination. How much of the expressed VSG sequence is truly unrepresented in the West African reference (or other T.b.gambiense genome sequences available in ENA). I can believe that none of the N-terminal domains in these data are present intact in DAL972, but I cannot believe that their components are not present without evidence.

We appreciate the reviewer’s suggestion to look at this more closely. We have performed additional analyses to address sequence similarity, or lack thereof, between the assembled DRC patient VSG and the West African reference TbgDAL972. We ran a nucleotide BLAST of expressed VSGs against the TbgDAL972 genome reference sequence pulled from TriTrypDB.org (release 54). We have added a supplemental figure depicting the results of this analysis (Supplemental Figures 6 and 7). Briefly, our analysis shows that most of the N-termini we identified have no significant similarity to DAL972 VSGs, even with very permissive search parameters. There are frequent hits in the VSG C-termini, however, which might be expected. Most BLAST hits are short spans 98% identity are short 20-25 bp regions. Given the large divergence from the reference, we were unable to infer any patterns of recombination in the VSGs. However, we believe this analysis supports our claim that the N-termini of VSGs assembled from DRC patients are novel, with their component parts largely unrepresented in the West African reference genome.

Figure 4 compares NTD type composition in the DRC data with previously published mouse experiments. The latter take place over very short timescales in maladapted hosts, while the timescales of the latter in natural hosts are unknown but plausibly very much longer. So are these data really comparable and are we learning anything from their comparison, given that the most likely explanation for the NTD bias in expressed VSG is the underlying genomic composition?

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Indeed, this is our intended conclusion from figure 4. The figure is meant to illustrate our claim that the expressed VSGs in each experimental set reflect the underlying genomic composition of their corresponding reference strains, despite fluctuations over time. The language and legend for Figure 4 has been clarified to emphasize this point. We have emphasized in the text that it is unknown whether these fluctuations occur over time in much longer natural infections.

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6.Please comment on the technical reproducibility of the data, there are multiple instances in Supp Table 3 where technical replicates expressed different VSG.

Three RNA-seq library technical replicates were prepared for each individual gHAT patient RNA sample. Replicates were prepared in batches together so all 1’s were done on the same day, for example. The original parasite-enriched cDNA sample was frozen and thawed between each batch. We suspect that the cDNA degraded after repeated freeze-thaw cycles, which is why replicate three tends to not agree with the other two as can be seen on the heatmap in supp fig. 1 and the expression data in supp table 3. We also suspect the fact that our sampling did not reach saturation resulted in the detection of different VSGs in individual replicate preps. We have edited the methods and mentioned this variability in the results section to communicate this issue more transparently.

• Lines 395-397 “Using RNA extracted from 2.5 mL of whole blood from each patient, we prepared libraries for VSG-seq in three separate batches for each technical replicate.”
• Lines 431-433: “Because our sampling did not reach saturation, resulting in some variability between technical replicates, we chose to only focus on the presence/absence of individual VSGs rather than relative expression levels within the population”

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Reviewer #3

1. In line 499, the authors conclude the due to the expressed VSGs being different in the blood and CSF being difference it may indicate that different organs harbor different VSG sets. Given that this is n=1 for patient samples I think this is too speculative a statement. There is also no indication as to whether the samples were taken at the same time or not.

This is absolutely correct. The precise timing of CSF sample collection is unknown for these samples. It likely occurred within hours to days after blood collection, but even on this short time scale, the unique CSF repertoire could represent the antibody-mediated clearance of one VSG population and replacement with another. We have scaled back our language and only point out that there are unique VSGs in this space (Lines 522 – 524).

I think that the authors need to be very careful as to the conclusions drawn about VSG expression over time in terms of hierarchy and N-terminal fluctuations. For any conclusions to be drawn on the hierarchy of VSG expression more data points are needed taken over time (this is obviously challenging when looking at patient samples). I find it too speculative to draw any conclusions when single time points are assessed and the assumption on the progression of the infection depends on whether it is a Tb or Tbr.

Reviewer #2 also pointed this out. We agree and have attempted to limit definitive conclusions in the text and instead discuss multiple possible explanations behind our observations.

I found some of the figure legends a bit terse. For example, in Figure 1 C, what do the black circles and lines represent? Perhaps a little more detail would help the reader.

Clarified legends for UpSet plots in figures 1C and 3C as follows: “The intersection of expressed VSG sets in each patient. Bars on the left represent the size of the total set of VSGs expressed in each patient. Dots represent an intersection of sets with bars above the dots representing the size of the intersection.”

In figure 2, I found it difficult to distinguish between the orange and dark red in (A) and the two lighter blue colors.

We have changed N-terminal type color palette for all plots to make red and blue hues more distinctive.

In line 389 – estimate

Corrected

In line 498 - should be reference been to figure 2C?

This should be a reference to Figure 3B. We have corrected the reference.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this work, So and Sudlow et al have used an established methodology - VSG-seq to assess the expressed VSG diversity in 12 patients infected with T. brucei gambiense. As with what is seen in mouse models, there is a diversity in VSG expression seen in patients. The application of this technology has not previously been used on patient samples and is now validated as a valuable tool to study antigenic variation in human populations. The authors have found that in addition to the VSG diversity seen there was a significant bais towards B type N-terminal domains and a restricted C-terminal types. This work, although on a small sample group, is an important step forward to applying this technology to understanding trypanosome immune evasion in the field.

Major comments:

I think that overall, the key conclusions on the expressed VSG diversity and that there are geographical variations are convincing and would agree with the conclusions that it is now feasible to study antigenic variation in the field. But given the sample size the I feel that two of the findings are overstated and should at least be qualified as speculative.

1.In line 499, the authors conclude the due to the expressed VSGs being different in the blood and CSF being difference it may indicate that different organs harbor different VSG sets. Given that this is n=1 for patient samples I think this is too speculative a statement. There is also no indication as to whether the samples were taken at the same time or not.

2.I think that the authors need to be very careful as to the conclusions drawn about VSG expression over time in terms of hierarchy and N-terminal fluctuations. For any conclusions to be drawn on the hierarchy of VSG expression more data points are needed taken over time (this is obviously challenging when looking at patient samples). I find it too speculative to draw any conclusions when single time points are assessed and the assumption on the progression of the infection depends on whether it is a Tb or Tbr. I don't believe that any other experiments are needed and the statistical analysis is adequate.

Minor comments:

I found some of the figure legends a bit terse. For example, in Figure 1 C, what do the black circles and lines represent? Perhaps a little more detail would help the reader.

In figure 2, I found it difficult to distinguish between the orange and dark red in (A) and the two lighter blue colors.

In line 389 - estimate

In line 498 - should be reference been to figure 2C?

Significance

Overall, this is an interesting study and shows the practical application of VSG-seq on the study of human infections. There is clearly interesting biology to be learned about both Tbg and Tbr infections and immune evasion by these parasites - which can now be done with the development and application of these technologies. I am a molecular cell biologist who specialises in trypanosome biology.

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Referee #2

Evidence, reproducibility and clarity

So et al. have analyzed the expression profiles of T.b.gambiense VSG genes in 12 natural human infections in DRC during a six month period of 2013, and compared these results to existing data for T.b.rhodesiense VSG and previously published data from mice. They use the VSGseq approach developed by the Mugnier lab over the last few years to good effect and provide a description of the expression profiles using phylogenetic and network approaches. The main conclusions are that parasite infrapopulations in each patient expression largely mutually exclusive VSG cohorts, with a couple of exceptions where patients 'shared' identical VSG transcripts. The authors note that these congolese VSG are not comparable with the West African T.b.gambiense reference sequence, and there is a pronounced bias in the systematic composition of expressed VSG (towards 'B-type VSG') that is not observed in other T. brucei subspecies. These latter observations lead to the suggestion that there may be substantial variation in expressed VSG repertoire among T. brucei populations that could have important consequences for pathology, although the spatial or temporal scale upon which this variation could be expected to occur cannot be inferred from these data. Overall, a competent study and a welcome addition to, if not extension of, recent work describing the dynamics of VSG expression in multiple African trypanosomes.

Major points:

1.Throughout the manuscript you observe 'diversity' in expressed VSG and its existence becomes a principal conclusion. I feel that the meaning of diversity and its significance is not sufficiently explained for the reader. In the abstract (l48) you say that there is 'marked diversity' in parasite populations. Presumably you mean parasite infrapopulations, i.e. within patients, not across the DRC? In any case, what is 'marked' about it, and relative to what? Why does it matter that there are multiple expressed VSG in a single patient at one time? Is this not a reasonable expectation for a population of (presumably) clones capable of switching the expressed VSG? How is this different to the view typical of the literature since 1970 that one VSG dominates while others wait in the background at low frequencies. If 'diversity' is the conclusion, then you need to define it and explain its significance more.

2.Following on from 1., why does the analysis deal in counts of distinct VSG or N-terminal domains, and not then progress to their relative expression? The expression data are in Supp Table 3 and they show that, in most cases where many VSG are observed in the same patient, 1-3 of these are 'dominant', i.e. they account for >50% of the population. Figure 1 deals in VSG counts, but I would then expect another figure to illustrate the reality that only a minority of these observed VSG are likely to be clinically relevant (i.e. the subject of the immune response). This impacts the 'diversity' conclusion, as given in the discussion (ll 657-9), because you cannot afford to treat all these VSG equally when their abundances are quite different.

3.How related are these VSG? Were you able to ensure unique read mapping to the VSG assembly? Can you show that reads mapped to a single VSG only and therefore, that the RPKM values are reliable?

4.The authors observed no orthology between expressed VSG and DAL972 genes. This is really interesting and deserves closer attention. Presumably there is microhomology? For T. brucei VSG, with constant recombination, we would predict that a comparison of the VSG in West and Central Africa would reveal a pattern of mosaicism, such that individual sequences in DRC would break down into motifs present in multiple genes in the West African reference. Question is, how many genes? What does that distribution look like? What is the smallest homology tract? There is an opportunity here to comment on how VSG repertoires diverge under recombination. How much of the expressed VSG sequence is truly unrepresented in the West African reference (or other T.b.gambiense genome sequences available in ENA). I can believe that none of the N-terminal domains in these data are present intact in DAL972, but I cannot believe that their components are not present without evidence.

5.Figure 4 compares NTD type composition in the DRC data with previously published mouse experiments. The latter take place over very short timescales in maladapted hosts, while the timescales of the latter in natural hosts are unknown but plausibly very much longer. So are these data really comparable and are we learning anything from their comparison, given that the most likely explanation for the NTD bias in expressed VSG is the underlying genomic composition?

6.Please comment on the technical reproducibility of the data, there are multiple instances in Supp Table 3 where technical replicates expressed different VSG.

Minor points:

1. Type 'estimates' line 389

Significance

The significance of this work relates to the application of VSG expression profiling to natural human infections, something not previously done largely because human infections are rare and materials difficult to obtain. The approach and the conclusions are not novel and do not represent substantial advances on previous efforts, but have an important aspect in confirming for natural infections what has been observed in quite artificial experimental settings. Sample size is small and this means that the conclusions remain speculative and cannot readily be extended to all HAT settings. This is not a criticism, since the analysis of any human samples is progress, but it does mean that the study raises interesting questions (e.g. variation across the population in N-terminal domain usage) rather than providing definitive conclusions. It is likely to interest trypanosome biologists with a specific interest in antigenic variation.

My own field concerns trypanosome genomics and the evolutionary dynamics of variant antigen genes.

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Referee #1

Evidence, reproducibility and clarity

In this paper by Mugnier and colleagues describe the repertoire of VSGs present within a cohort of human HAT cases that occurred at relatively close geographical distance.

VSG repertoires were first described by the senior author a few years ago already, from mouse infection data. This is the first such piece of data to come from human infective parasites in the field. Technically this is a feat - because the small number of parasites that are present per mL of human blood at any given time during infection with T gambiense. Nevertheless they manage to identify up to 14 unique VSGs per patient sample. And this raises the first theoretical question: can they extrapolate to the average diversity load per human? this is important because the timing of sample collection (ie that it occurred within a period of weeks) suggesting that an initial group of infected tsetse infected these patients (rather than a small number of interactions between a bloodmeal and a new infection - generally in itself on the order of 1 month or so). If parasitemia is low and diversity limited, this would explain both why CATT works as well as it does (because really it shouldn't at all!) and perhaps even the chronicity of infection (in the sense that the organism is unlikely to "run out" even of complete VSGs, never mind mosaics). The paper would benefit from a direct discussion on this.

An interesting feature of this new study is the apparent bias to type B N-terminal domain VSGs as well as the discovery that two patients share a specific VSG isolate (though it is not mentioned whether they are related by distance etc). This raises the possibility of substrains with different VSG archives that vary by geography. Alternatively it suggests that perhaps type B VSGs are picked up differentially by serology (and there the one feature of type B VSGs that could be shared, with regards to detection, is the O-hexose decoration on a number of type B VSG surfaces. Could CATT be detecting elements common to sugar decorated VSGs? Experimentally this is something that can be tested even with mouse infection materials.

Side comment: are the common VSGs mutated between patient samples?

Significance

Significance: high in the sense that this is the first in human field study of a disease that has been studied quite a lot in mouse models. Clearly from this work, there is still a lot to be learned from studying a disease in context.

Audience: parasitologists

My own expertise: parasitology and immunology

Referees cross-commenting

Nothing substantial to add. From the comments (all of which are worthwhile) I would suspect this would require minor revision.

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