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Referee #2

Evidence, reproducibility and clarity

Summary

Bothe and colleagues studied the effect of repeated glucocorticoid exposure on DNA accessibility and gene expression in A549 and U2OS-GR cells and show that most of the glucocorticoid receptor (GR) induced changes are reversible in both cell lines and after long-term (20 hrs) and short-time (4 hrs) dexamethasone (Dex) or cortisone treatment. They identified a single gene that seem to have persisting memory of previous Dex exposure, namely ZBTB16.

Major Comments

1. The authors used the cancer cell lines A549 and U2OS-GR as model systems the latter additionally overexpresses GR. In order to make the work more translatable an in-vivo model comparing the effect of long-term, short-term and repeated glucocorticoid (GC) treatment on DNA accessibility and gene expression is necessary. The authors should clearly emphasizes this limitation of their study in the discussion or add in-vivo data (e.g. qPCRs) to strengthen the translatability.
2. The authors draw conclusions of the association of DNA accessibility, H3K27ac, P300 and GR occupancy from independent heatmaps. This cannot be easily done from the current way the data is presented. A direct link between accessibility, H3K27ac and mRNA expression of the associated gene for example is missing. a.) The authors show several heatmaps to indicate changes in accessibility, H3K27ac and P300 upon Dex treatment as well as GR binding patterns in Fig. 1 and S1. Those are sorted by decreasing signal strength (I assume). To make those results more comparable, I suggest to sort them all in the same way (e.g. by descending ATAC-Seq signal or fold-change). b.) In line with a.), it is unclear to the reader if those sides opening /closing are the same sides showing increased/decreased H3K27ac or P300 occupancy and if those sides bind GR. Integrating this data together with mRNA e.g as correlation plots would strengthen the author's argument that accessibility, H3K27ac and mRNA changes are indeed correlated. What about the GR binding sites that do not change accessibility or H3K27ac? What makes those different? Therefore, the statement "Furthermore, closing peaks, which show GC-induced loss of H3K27ac levels and lack GR occupancy (Fig. S1c-f), were enriched near repressed genes" on page 10 as well as the statement "suggesting that transcriptional repression by GR does not require nearby GR binding." in the abstract and discussion cannot be made from how the data is presented. c.) Several recent studies have shown that GR's effect on gene expression and chromatin modification at enhancers might be locus-/context-specific ("tethering", competition, composite DNA binding) and/or recruitment of different co-regulators (see Sacta et al. 2018 (doi: 10.7554/eLife.34864), Gupte et al. 2013 (doi.org/10.1073/pnas.1309898110) and many more). Defining the GR-bound or opening/closing sides in terms of changing H3K27ac (or having H3K27ac or not) more closely would help to link those to gene expression changes e.g. in violin plots. Furthermore, the authors could include a motif analysis to see if the different enhancer behaviours can be explained by differences in the GR motif sequence or co-occurring motifs. Thereby more closely defining the mechanism of chromatin closure a sites that lack GR binding e.g. by displacement of other transcription factors as described for p65 in macrophages (Oh et al. 2017 (doi.org/10.1016/j.immuni.2017.07.012)). In general a more detailed analysis of the data is required before the authors could state "Instead, our data support a 'squelching model' whereby repression is driven by a redistribution of cofactors away from enhancers near repressed genes that become less accessible upon GC treatment yet lack GR occupancy." on page 10. The results might also be explained by competitive transcription factor binding, tethering or selective co-regulator recruitment (e.g. HDACs).
3. The authors use U2OS-GRa cells as a second cell line. Those cells overexpress rat GRa (see DOI: 10.1128/mcb.17.6.3181) in a cell line that usually does not express GR. I am wondering to what extend the overexpression reflects residence times and GR binding kinetics of cells endogenously expressing GR (mostly to at a lower protein level). At least the number of GR binding sites as well as the number of opening chromatin sites is much higher in U2OS-GR cells the A549 cells. The authors should discuss this point with respect to the observed preservation of some GR-binding sites U2OS-GR cells after Dex treatment and washout.
4. In figure 1 and S1, the authors show coverage plots on top of the heatmaps to show the mean signal in ATAC-Seq, GR, H3K27ac or GR signal between the different subset. These plots are statistically inappropriate as a significant portion of the enhancers does not have a signal and a few enhancers show a very strong signal (at least for H3K27ac, P300 and GR) which skews the mean. Plotting the signal distribution or the distribution of the Dex-dependent change in signal (fold-change, e.g. as violin plots) more accurately reflects the diversity in the signal response.
5. ChIP qPCRs against histone marks in figures 5B and S2C are not normalized for histone H3, but the author's clearly see changes in nucleosomal occupancy at those sides by ATAC-Seq. Additional normalization by total H3 is highly recommended.
6. Figures 1C, 2D, 4A/B, 5B/C/E, 6C/F, S2C/E and S3A-D lack statistics.
7. In figure 6, the authors compare the ZBTB16 locus with FKBP5, a locus that as by the data presented is very different from the ZBTB16 locus in terms of expression level (Fig 6C/F) and H3K27me3 occupancy (Fig. 5B). The authors should compare ZBTB16 to a locus with similar expression level and H3K27me3 deposition. Especially the co-occurrence of H3K27me3 and H3K4me3 (Fig. 5B) at the ZBTB16 promoter indicates its poised chromatin state whereas the FKBP5 promoter is marked by an active chromatin state.
8. ZBTB16 itself is a transcriptional regulator, but its elevated expression upon repeated Dex treatment does not affect other genes. How do the authors explain this observation? Is ZBTB16 elevated on the protein level as well?

Minor Comments

1. The authors nicely explained the data analysis of their ATAC-Seq data, I recommend to include some more information on if and how the ChIP-Seq data was normalized (library size, scaling factors or spike-ins) even if most of the data sets are published.
2. In figures 1F and S1F, the authors show the association of opening/closing an non-changing sites and GR peaks with genes that are up/down-regulated or unchanged upon Dex treatment. This gene-centric analysis is skewed by the different sizes of up-/down regulated gene sets and opening/closing chromatin (especially for the U2OS-GR cells that have 15.6x more opening sites then closing sites). Could the authors also include a peak-centric view showing how many closing/opening and non-changing sites are associated with down/up-regulated or unchanged genes? How good is the association (correlation)?
3. In the figures 1F and S1F it is unclear how the authors handled genes with associated peaks (within +/-50kb) that show different characteristics e.g. a gene with a peak that gains and another peak that loses accessibility. How do the authors account for >1 opening or closing peaks per gene? In relation to this. Do opening/closing sites cluster around up/down-regulated genes? What is the stoichiometry as 1.6x more closing sites (then opening sites) relate to 1/3 of repressed when compared to activated genes?
4. The authors claim on p10 that "We could validate several examples of opening and closing sites and noticed that opening sites are often GR-occupied whereas closing sites are not occupied by GR". As most of the ChIP-Seq experiments were performed on formaldehyde-only fixed cells, the authors might miss "tethered" sides, which are mostly linked to gene repression. You might rephrase this part to most closing sites lack direct DNA binding.
5. The P300 ChIP-Seq in Fig S1B shows less sides with P300 occupancy then sides with H3K27ac. Is this a ChIP quality issue or do other factors mediated changes in H3K27ac? Similar to mayor comment 1a, are the P300 sites on the top the same sites as the top H3K27ac sites?
6. Please indicate the primer position of qPCR primers if the genome browser tracks are displayed. That makes the comparison of sequencing and qPCR results easier.
7. The authors nicely show that GR binding sites with persisting accessibility after Dex treatment and washout in U2OS-GR cells show residual GR binding and are bound by GR at Dex concentrations of 0.1nM. Could the authors specify if differences in the GR motif exist between those and the non-persisting sites?
8. The authors focus on ZBTB16, FKBP5 and GILZ to show the priming effect of glucocorticoid treatment on ZBTB16 (Fig. 4), but GILZ was not included in the initial ATAC-Seq (Fig. 1) and ATAC-Seq washout (Fig. 2) experiments. For better comparison, I recommend adding qPCR results on GILZ in figures 1 and 2.
9. The authors indicate that the washout of Dex does restore gene expression in A549 cells to pre-Dex levels (Fig. 4). These cells did not show any persisting GR binding, so. How does the gene expression in U2OS cells behave? E.g. for the genes displayed in Fig. S2C.
10. In Fig. S3C, the authors observe that Gilz expression in U2OS-GR cells is similarly induced upon 1st and 2nd stimulation with Dex using 4hrs treatment. How does this relate to the preserved Dex response after 20hrs treatment and washout (Fig. S2C)? Was the expression of GILZ altered after 20hrs (see comment 9)? Are H3K27ac and GR signal after 4hrs Dex stimulation and washout comparable as well? Please comment on the differences observed between the 20hrs and 4hrs experiments.
11. The GR enhancer of ZBTB16 seems to be simultaneously marked H3K27ac and H3K27me3 (Fig. 5A). Please comment. Is this an artefact of bulk ChIP-Seq? Is this due to the different timings (H3K27me3 after 1h and H3K27ac after 3hrs)? Can both marks co-exists or do they reflect allelic differences?
12. Please comment on the observed differences in H3K27me3 response to Dex between ChIP-Seq (Fig. 5A) and the ChIP qPCR (Fig. 5B). Is this a timing issue?
13. Please indicate the number of replicates for the ChIP-Seq experiments in the figure legends.
14. The statement "Upon hormone treatment, both the number of transcripts per cell and the number of transcriptional foci increases." on page 13 is confusing. Most cells only have two alleles (max. two transcription foci). Is ZBTB16 duplicated in A549 cells?
15. ZBTB16 is marked by H3K27me3 (Fig. 5A/B). How many GR binding sites do overlap H3K27me3 in A549 cells? How many genes associated with GR/H3K27me3 sites are expressed in A549 cells? Is ZBTB16 the only one?
16. Is ZBTB16 a GR target gene that is regulated by GR tissue-independently (like GILZ and FKBP5)?

Significance

The work is of significant interest as glucocorticoids (GCs) are physiologically secreted with circadian and ultradian rhythms, but widely prescribed with repeated dosing during the day (in order to maintain high GC levels) in patients during chemotherapy (doi: 10.1016/j.critrevonc.2018.04.002, doi: 10.1186/1471-2407-8-84 ) or anti-inflammatory therapy in rheumatoid arthritis (doi: 10.1186/ar4686, doi:10.1093/rheumatology/kes086) for example. Therefore the assessment of long-term versus short-term as well as the effect of repeated GC exposure on various cell types is of high interest to understand adverse effects of GC therapy. However, the choice of cell lines as model system dampens the overall translatability of the findings, as does the choice of those cell lines. Alveolar epithelial cells (A549) are not classically known as a cell type affected by GC side effects in therapy. However, GR is widely known to regulate tissue-specific gene programs (doi: 10.1038/emboj.2013.106, doi: 10.1016/j.steroids.2016.05.003, doi: 10.1016/j.molcel.2011.06.016). Hepatic, skeletal muscle cells or fat cells would reflect those tissues more accurately. Obtaining in-vivo data is hampered by the cofounding effect of endogenous glucocorticoids and their circadian expression (doi: 10.1016/j.molcel.2019.10.007 ), but primary cells would overcome those limitations and still be a closer model system the cancer cell lines.

That the glucocorticoid receptor mostly binds accessible genomic regions and changes the DNA accessibility of a subset of binding sites after short-term treatment with Dex was previously described (doi: 10.7554/eLife.35073, doi: 10.1093/nar/gkx1044, doi: 10.1038/ng.759 ), but the reversibility of these effects were not studied before. Therefore, this study adds an interesting conceptual finding.

The observation that ZBTB16 expression can be boosted by repeated Dex treatment is interesting and seems to be tissue independent. Again, in-vivo or patient data confirming this observation would strengthen the conclusions from this paper and exclude an artefact from immortalized (cancer) cell lines. The impact of this observation depends on ZBTB16 function and if ZBTB16 is elevated on the protein level as well.

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Referee #1

Evidence, reproducibility and clarity

Bothe et al investigated whether GR induced chromatin changes could be somehow preserved after inactivation of the receptor. They performed ATAC-seq to examine the status of chromatin accessibility under several treatment conditions in two different human cell lines. Their main finding is that GR changes to chromatin are universally reversable, with the exception of a tissue-specific single locus (ZBTB16). Additionally, the authors claim their data support a squelching mechanism for transcriptional repression by GR.

Major comments:

Are the key conclusions convincing? Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Are the data and the methods presented in such a way that they can be reproduced? Are the experiments adequately replicated and statistical analysis adequate?

The manuscript is very well written. The data is clearly presented. The methods are explained in sufficient detail with a few exceptions mentioned below, and statistical analysis are adequate. There are some concerns and suggestions about the experimental design and data presentation.

• Drug treatments. It is not clear whether the cells were previously grown on charcoal-stripped serum before hormone treatments. From methods, it seems they were grown in 5% FBS and directly treated with the hormones. Also, what "hormone-free medium" mean? Is it charcoal stripped Serum or not Serum at all? Replicates for these data sets? The ATAC and Chip-Seq should have at least 2. The concordance of the ATAC-seq and Chip-seq replicates should be described and shown in supplemental figures. Fig1A - The ATAC-seq HM should be clustered to show which peaks in opening/closing and unchanged peaks also have called GR chip peaks. Showing browser shots as in Fig1B is cherry picking data and can be put in a supplementary figure as an example. This is a main point of emphasis of the manuscript so show the data. The atac peaks that do overlap with GR chip peaks should be sorted by GR peak intensity. The QPCR is then only needed to confirm the quantitative changes.

To show both the ATAC sites and H3K27ac sites are specific to hormone treatment, a random set of 15K peaks not in this peak set also should be shown in HMs and should not change with the treatments. Why does the H3K27ac go down in the 6768 non changing sites with dex?

The D & E parts of Fig1 can then be eliminated to become parts of Fig1A. Its not clear in the text that the HMs in Fig1 are all sorted in the same way.

• Fig. 1b (and d). The ChIP data is from 3h-hormone treatment while the ATAC-seq data is from a 20h hormone treatment. It seems a bit misleading to directly compare GR occupancy with the state of the chromatin at different time windows. Shouldn't the authors show their ATAC-seq 4h treatment data (shown in Fig S1) here instead?
• Fig. 1f. The authors sate "downregulated genes only show a modest enrichment of GR peaks". However, there is a significant enrichment of GR-peaks in repressive genes compared to non-regulated genes. It would be interesting to see how some of these peaks look in a browser shot. While the general conclusion "transcriptional repression, in general, does not require nearby GR binding", seems valid, the observation that many GR peaks appear directly bound to nearby repressed genes ought to be more emphatically recognized in the text.
• Concept of naïve cells (Fig. 3A). If cells are normally grown in serum-containing media, which is known to have some level of steroids, can the cells described here as "Basal expression" be truly free of a primed state? In the first part of the experimental design (+/- 4h hormone), which type of media is present here? Is it 5% FBS? A concern is that the authors may require the assumption that the (4h + 24h) period a is sufficient to erase all memory of the cells, which is exactly what they are trying to test.

The transcriptional memory is a second major emphasis of the paper.

The RNA primers (Table 1) span within an exon or across 2 exons to best measure mRNA levels. The QPCR primers should span exon intron boundaries to better reflect transcriptional activity (prior to mRNA splicing) at the collection time point.

It would be interesting to do a time course of the hormone-free period of the washout to determine the memory of the chromatin environment that results in the enhanced transcriptional response instead of just 24 and 48 hrs in A549 cells.

Fig 5A appears to show H3K27ac overlaying H3K27me marks near the promoter of ZBTB16 and at the GR sites within the gene locus with no reduction in H3K27me levels. This seems counterintuitive and should be explained or addressed especially since the authors use quantitative comparisons of H3K27ac levels with and without treatment in other figures.

Showing the changes of ZBTB16 upon 2nd stimulation via FISH is not terribly surprising and is even the most expected reason for higher RNA levels. Why does it only occur at that gene is a better question and is touched on in the discussion. It is more likely that this gene has a very low level of pre-hormone transcription compared to FKBP5 (see Fig 3e and the FISH images). ZBTB16 is in the lower 3rd of basemean RNA levels of GR responsive genes according to the RNAseq data. Selection of 1 or 2 other genes with similar basemean levels of RNA (from the RNA-Seq data) would make the data more

Minor comments:

Specific experimental issues that are easily addressable. Are prior studies referenced appropriately? Are the text and Figures clear and accurate? Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

• In the Intro (paragraph two), the authors explain the different mechanisms by which GR might repress genes. One alternative the authors appear to have missed is the possibility of direct binding to GREs while, for example, recruiting a selective corepressor such as GRIP1 (Syed et al., 2020). There are many recent critics to the notion that transrepression via tethering is responsible for GR repressive actions at all (Escoter-Torres et al., 2020; Hudson et al., 2018; Weikum et al., 2017).
• When the authors introduce the concept of tethering to AP-1, they go way back to the first description of tethering. However, one of the references (Ref 20) actually goes against the tethering model as they did not detect protein-protein interactions between AP-1 and GR, and also, they conclude that repression requires the DNA-binding domain. -Figure 2. The authors state "This suggests that the few sites with persistent opening are likely a simple consequence of an incomplete hormone washout and associated residual GR binding". The authors should check the subcellular distribution of GR after their washout protocol. If the washout is not completed, GR should still be in the nuclear compartment.
• The first part of the manuscript (Repression through "squelching") seems a bit disconnected from the rest of the results (reversibility in accessibility). The abstract is structured in a way that this disconnection seems much less obvious. Perhaps the authors could try to present their squelching part in the middle of the manuscript, following the flow of the abstract? This is just a suggestion.
• Figures have CAPS panel letters (A,B,C, etc) while the text calls for lower case letter (a,b,c...)

Escoter-Torres, L., Greulich, F., Quagliarini, F., Wierer, M., and Uhlenhaut, N.H. (2020). Anti-inflammatory functions of the glucocorticoid receptor require DNA binding. Nucleic Acids Res 48, 8393-8407. Hudson, W.H., Vera, I.M.S., Nwachukwu, J.C., Weikum, E.R., Herbst, A.G., Yang, Q., Bain, D.L., Nettles, K.W., Kojetin, D.J., and Ortlund, E.A. (2018). Cryptic glucocorticoid receptor-binding sites pervade genomic NF-kappaB response elements. Nat Commun 9, 1337. Syed, A.P., Greulich, F., Ansari, S.A., and Uhlenhaut, N.H. (2020). Anti-inflammatory glucocorticoid action: genomic insights and emerging concepts. Curr Opin Pharmacol 53, 35-44. Weikum, E.R., de Vera, I.M.S., Nwachukwu, J.C., Hudson, W.H., Nettles, K.W., Kojetin, D.J., and Ortlund, E.A. (2017). Tethering not required: the glucocorticoid receptor binds directly to activator protein-1 recognition motifs to repress inflammatory genes. Nucleic Acids Res 45, 8596-8608.

Significance

The study tackles two important questions. One is regarding the effects of inducible transcription factors on chromatin structure after inactivation. The second is on the mechanisms behind transcriptional repression.

The effect of GR inactivation on chromatin accessibility has already been addressed in previous work for a single locus (Refs 38) or genome wide (Ref 33). However, the 24h temporal windows have not been addressed before. In this sense, the manuscript sheds some new light into the matter. Even though the authors conclude that accessibility is globally reversable, they only studied in detail the mechanism behind a single-locus exception.

Regarding the mechanisms behind transcriptional repression, the authors present data supporting the squelching mechanism, which is still highly controversial.

The manuscript will be of interest to the molecular and cell biology communities, especially those working on chromatin structure, transcription factors, gene regulation, and nuclear receptors. Overall, this is an interesting paper with somewhat limited novel findings that is suitable for publication after addressing the above comments. The rigor of the findings needs to be better described via replicates and if they have not been done, it should be a major requirement of revision.

The reviewers specialize in transcription factor dynamics.

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Reply to the reviewers

We thank both reviewers for their comments on our manuscript. Our responses to their specific comments and plan to modify the manuscript are described bellow.

Response to reviewer #1

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> Figure 1C is difficult to interpret. Am I supposed to see anything in particular in the two insets? Please provide a descriptive interpretation of the inset and let the reader know if anything in particular is to be noted.

We agree that it is a bit difficult to interpret although the goal of these images is to show that spermatogenesis appears globally not disturbed until the histone-to-protamine transition in distorter males. We will change the legend of the figure to clarify this particular point.

> Figure 1D. Elsewhere, in supplemental S1, they have an image for SD5/CyO. This should be provided here as a control. As presented, the genetics don't prove that the interaction between SD5 and Gla is the cause of the phenotype. As presented in the figure, the effect could be caused by SD5 alone, independent of Gla. In S1, this is not the case - SD5 with CyO doesn't produce the phenotype. Likewise, I think they should provide the SD5/CyO image in S1A in Figure 1C.

We can add the images of the SD5/CyO genotype (currently in FigS1) in Fig1C (whole testis) and in Fig1D (single cyst). We also suggest to present in this figure the other distorter genotype cn bw/CyO (which is currently in Fig S1). However, because the modified figure is going to be too big, we also suggest to split Figure 1 in two Figures with Figure 2 presenting FISH results including all controls. In this case, we will remove the supplemental figure 1.

> Figure 1E. These images are the formal proof (especially for Gla/SD5 genotype) that the large Rsp array is on the chromosomes that seem destined for removal from the cyst. However, there is no control. The authors should provide FISH results for the genotypes Gla/CyO and, ideally, also cn1 bw1/Cyo.

We agree with reviewer #1 and will provide images of the Gla/CyO control and cn1 bw1/Cyo in a new Figure 2 as explained above.

> Figure 2. Keeping consistent with other figures, can the Gla/SD5 panel be in the middle?

Yes. We swapped the Gla/SD5 and cn bw/SD-Mad panels.

Also, shouldn't there be SD5/CyO in Figures 2, 3 and 4, to demonstrate that the phenotypes are the result of the interaction rather than just SD5? I am OK with providing just the cn 1 bw1/SD-Mad here alone, since it is simply contrasted with Gla/SD5.

We agree that it would be better to show also the SD5/CyO controls. However, we chose to show only one control (Gla/CyO) to make the figures easier to read. We thus suggest to provide all images of the SD5/CyO genotype in supplemental figures.

> Figure 3A. In the scheme, can you provide greater detail as to where F-Actin is expected?

The scheme was modified to clarify this point.

> Figure 3B. It is stated that there is a size difference in the nuclei for IC stage and greater variation in ProtB-GFP staining within bundles. Can there be an effort to quantify these observations?

It would be difficult to quantify ProtB-GFP signal intensity and nuclear size in IC stage cyst because nuclei are very close to each other. The best way would be to squash testes to spread spermatid nuclei but there might be a bias on nuclear size/shape due to the squashing procedure. In addition, on squashed preparations, it is difficult to be sure that the nuclei analyzed and compared belong to the same cyst. We agree that quantification would help to describe the phenotype better but we think that the best read-out of the different SD phenotypes is the quantification of number of abnormally compacted nuclei in seminal vesicles which is provided later in the manuscript.

> Figure S4. There doesn't appear to be the same phenotype for Gla/SD-Mad (DAPI, ProtB-GFP) in post-IC stage bundles compared to what is seen in 3C for Gla/SD-5. In particular, in figure 3, the defective nuclei seem to be trailing, but in S4, while the bundle appears disorganized, there doesn't appear to be the trailing nuclei. Is this difference real or is it just the result of a single picture contrast? Some clarification could be helpful.

Actually, the images that were shown on Figure 3C for Gla/SD5 post-IC probably show the SD5 nuclei of one cyst (the normal one) and the Rsp nuclei being eliminated from another cyst (these are trailing behind nuclei which are too far to be included in the same image). We thus changed the images for Gla/SD5 for an image which looks like the one shown for Gla/SD-5 genotype for clarity.

We did not mention this observation in the manuscript but we actually see cysts in which abnormally-shaped nuclei are trailing behind the normal nuclei and sometimes IC cones around the abnormally shaped nuclei seem to be stuck close to the normal nuclei which are already individualized. It might be possible that IC progression around abnormal nuclei is slowed down compared to normal nuclei. The difference could not reflect different phenotypes but more likely different states of a dynamic process.

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Response to reviewer #2

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Reviewer #2 had no specific comments.

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Referee #2

Evidence, reproducibility and clarity

SD is a multi-component system, where two major factors Sd (a truncation allele of RanGAP that mislocalizes) and Rsp, a satellite DNA (whose copy number determines sensitivity to RanGAP distorting allele).

This study by Herbette et al. provide cytological characterization of Drosophila SD (segregation distortor), a male meiotic drive system, focusing on the process of histone-to-protamine transition. By thoroughly studying multiple alleles of SD, they find that the mechanisms by which SD accomplishes segregation distortion are not uniform. In some cases, spermatogenesis is perturbed at the level of protamine incorporation and in other cases, mature sperm can be generated yet they exhibit distorted segregation.

In one combination Gla/SD5, histone elimination is delayed (never complete), whereas cn bw/SD-Mad exhibit normal timing in histone elimination/protamine incorporation, although these two combinations result in similar, severe degree of distortion. They further show that DNA compaction is incomplete in these SD alleles (again more severe in Gla/SD5 condition) by using dsDNA antibody. Interestingly, defective spermatids in Gla/SD5 combination never progress to sperm maturation and enter seminal vesicle, defective spermatids in cn bw/SD-Mad combination are capable of entering seminal vesicle, but likely fail to fertilize or develop after fertilization, resulting in distortion.

This is a well-done study and provides important insights into the mechanisms of segregation distortion in the Drosophila melanogaster SD system. The quality of data is high, and I don't have any major concerns on this manuscript. Of course, the exact mechanisms of how SDs drive (i.e. why Rsp(S) alleles fail to condense properly, and how it is related to the Rsp copy number) remains unclear, this study provides a significant step forward to tackle this fascinating phenomenon of segregation distortion.

Significance

This study provides important insights into the underlying mechanism of segregation distortion during D. melanogaster spermatogenesis. Segregation distortion is a fascinating phenomenon of significant interest in evolutionary biology. Thorough cytological characterization of spermatogenesis phenotype that leads to segregation distortion provides much needed information, and this study is a significant step forward to understand how meiotic drivers might exploit the system to distort segregation for their advantage.

Referees cross-commenting

I think I and reviewer #1 seems to be in good agreement. I don't have anything in particular to add. This is a nice paper.

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Referee #1

Evidence, reproducibility and clarity

This is a very nice paper that combine cytology and genetics to provide insight into the mechanism of segregation distortion in the Drosophila SD system. The conclusions are well supported with multiple different experiments from different in angles. By using different genetic backgrounds - their conclusion that Rsp abundance dictates distinct outcomes is well supported. My primary suggestion is that they include a few more controls and provide some additional quantitative analysis. In some cases, quantitative conclusions are made without sufficient support.

Specific Comments.

Figure 1C is difficult to interpret. Am I supposed to see anything in particular in the two insets? Please provide a descriptive interpretation of the inset and let the reader know if anything in particular is to be noted.

Figure 1D. Elsewhere, in supplemental S1, they have an image for SD5/CyO. This should be provided here as a control. As presented, the genetics don't prove that the interaction between SD5 and Gla is the cause of the phenotype. As presented in the figure, the effect could be caused by SD5 alone, independent of Gla. In S1, this is not the case - SD5 with Cyo doesn't produce the phenotype. Likewise, I think they should provide the SD5/CyO image in S1A in Figure 1C.

Figure 1E. These images are the formal proof (especially for Gla/SD5 genotype) that the large Rsp array is on the chromosomes that seem destined for removal from the cyst. However, there is no control. The authors should provide FISH results for the genotypes Gla/CyO and, ideally, also cn1 bw1/Cyo.

Figure 2. Keeping consistent with other figures, can the Gla/SD5 panel be in the middle? Also, shouldn't there be SD5/CyO in Figures 2, 3 and 4, to demonstrate that the phenotypes are the result of the interaction rather than just SD5? I am OK with providing just the cn 1 bw1/SD-Mad here alone, since it is simply contrasted with Gla/SD5.

Figure 3A. In the scheme, can you provide greater detail as to where F-Actin is expected?

Figure 3B. It is stated that there is a size difference in the nuclei for IC stage and greater variation in ProtoB-GFP staining within bundles. Can there be an effort to quantify these observations?

Figure S4. There doesn't appear to be the same phenotype for Gla/SD-Mad (DAPI, protoB-GFP) in post-IC stage bundles compared to what is seen in 3C for Gla/SD-5. In particular, in figure 3, the defective nuclei seem to be trailing, but in S4, while the bundle appears disorganized, there doesn't appear to be the trailing nuclei. Is this difference real or is it just the result of a single picture contrast? Some clarification could be helpful.

I think this is a nice paper and I enjoyed reading it very much. The combination of the genetics (different RSP alleles from nature and the different X chromosomes) with the cytology provide a very reasonable explanation for why different genotypes seem to yield different effects. It provides some reconciliation among previous studies.

Significance

I think it is very significant.

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Reply to the reviewers

We are grateful for the careful read and constructive comments provided by the 3 reviewers assigned to our manuscript. Each reviewer provided thoughtful and clearly structured comments that helped us to better clarify points or summarize results in the manuscript that they indicated were not presented clearly or completely. We have revised the manuscript to address the points raised by the reviewers, incorporating edits and additional text throughout the manuscript, figure legends, and supplemental materials. We feel the revised version of the manuscript is much improved as a result of the revisions in response to the reviewers.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The authors demonstrate a powerful method utilizing mNGS of individual mosquitoes utilizing reference-free analysis. This allows researchers to combine the resulting datasets of mosquito identification, blood-meal source, microbiome, viral sequencing, etc. Such knowledge could be a useful tool in detecting and responding to transmission of mosquito-borne diseases that affect human or animal populations, even though the technology is currently likely too expensive for widespread use (as acknowledged by the authors).

Major Comments:

No major revisions requested.

The authors provide their detailed methodology, including code, allowing for replication by other groups.

Minor Comments:

The authors' discussion of using this technique in order to detect pathogens should be qualified regarding detection vs possible transmission. Detecting a virus in an engorged mosquito does not necessarily mean that said mosquito can transmit the virus, but may have simply acquired it from a recent blood meal. The same can be said of detecting a plant pathogen following a recent sugar meal.

From the methods, it seems that mosquitoes were not washed prior to processing. This may make it difficult to discriminate between internal and external microbiota as well as lead to cross-contamination of surface microbiota between mosquitoes collected in the same trap.

Significance

This work currently would be of interest to other research groups examining the co-occurence of pathogens, other microbiota, and blood meals for field collected mosquitoes. While of great potential application to public health surveillance, the current cost is likely prohibitive.

My field of expertise is virology and vector biology with minimal background in NGS.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, the authors utilized unbiased meta-transcriptomic in sequencing 148 diverse wild-caught mosquitoes (Aedes, Culex, and Culiseta ​mosquito species) collected in California, with main aim of detecting sequences of eukaryotic, prokaryotic and viral origin. Their results show that majority of their sequenced data assembled into contigs corresponding to viral genomes. In their data, 7.4 million viral reads clustered as +ssRNA viruses including ​Solemoviridae, Luteoviridae, Tombusviridae, Narnaviridae, Flaviviridae, Virgaviridae, and Filovirida​ whereas 2.25 million viral reads identified as -ssRNA viruses comprising of ​Peribunayviridae, Phasmaviridae, Phenuiviridae, Orthomyxoviridae, Chuviridae, Rhabdoviridae, and Ximnoviridae​. With 0.94 million viral reads, dsRNA viruses formed the third most abundant virus category with viruses under families ​Chrysoviridae, Totiviridae, Partitiviridae, and Reoviridae. Under the prokaryotic taxa, Wolbachia​ species was the dominant group, followed by other lower abundance bacterial taxa that includes Alphaproteobacteria, Gammaproteobacteria, Terrabacteria group, and Spirochaetes. Trypanosomatidae was the most dominant eukaryotic taxa, followed up by reads from ​Bilateria​ and Ecdysozoa taxa. Ultimately, this study demonstrates that single mosquito meta-transcriptomic analysis has potential in identifying vectors of human health significance, potent emerging pathogens being transmitted by them and their reservoirs all in one assay.

Major comments:

1.Are the key conclusions convincing? The conclusions are accurate.

2.Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? None. The study's results, discussion and conclusion are appropriate.

3.Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

As much as the authors describe the use of mNGS as a tool in validating mosquito species and providing an unbiased look at the vector-associated pathogens, it is still prudent for them to use qPCR to validate the obtained RNASeq data (e.g. validation of the viral sequences).

4.Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. The outlined methodology is realistic.

5.Are the data and the methods presented in such a way that they can be reproduced? The methodology is reproducible.

6.Are the experiments adequately replicated and statistical analysis adequate? Yes

Minor comments:

1.Specific experimental issues that are easily addressable. qPCR validation the obtained RNASeq data should be conducted.

2.Are prior studies referenced appropriately? The recently publications about mosquito microbiome/virome should be added. (eg.  doi: 10.1128/mSystems.00640-20.)

3.Are the text and figures clear and accurate? The resolution for Fig 4, Fig 6, SFig 2, SFig 4, and SFig 5 is poor. The author should update them.

4.Do you have suggestions that would help the authors improve the presentation of their data and conclusions? (1)in the method section, the mosquito has been washed to avoid the contamination from the environment before RNA extraction? (2)most part of non-host reads are matched to the viruses (10.5M), however only few of them were belong to the prokaryotes, does it means mosquito carries more viruses than prokaryotes. (3)none of the mosquito-borne virus known to occur in California (eg. WNV, SLEV, WEEV, ) has been found in Table 1 for the virus detected with complete genome in this study. In contigs level, did the author detected any mosquito-borne virus known to occur in California. Since the mNGS is very sensitive and this study include large sample numbers, why no known mosquito-borne virus was detected in their study should be discussed.

Significance

1.Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. With the existential threat of emerging novel pathogens of global health concern, efficient and rapid public health surveillance strategies are crucial in monitoring and possibly averting such eventual calamities. Specifically, mosquitoes are widely diverse and are known to harbor and transmit various pathogenic agents to humans and animals. Thus, this rapid identification of relevant vector species, pathogens and their reservoirs in one assay is a promising and convenient aspect of surveillance in the public health sector.

2.Place the work in the context of the existing literature (provide references, where appropriate). Shi et al reported the first single mosquito viral metagenomics study, in which her and the team demonstrated the feasibility of using single mosquito for viral metagenomics, a methodology that has potential to provide much more precise virome profiles of mosquito populations. In the present study, the authors have gone a step higher by aiming to combine three objective points in single mosquito meta-transcriptomic, as described in brief in their abstract and the comprehensive methodology outline.

Reference: Shi, C., Beller, L., Deboutte, W. et al. Stable distinct core eukaryotic viromes in different mosquito species from Guadeloupe, using single mosquito viral metagenomics. Microbiome 7, 121 (2019). https://doi.org/10.1186/s40168-019-0734-2

3.State what audience might be interested in and influenced by the reported findings. The methodology and findings described in this manuscript are important in advancing the public health field of vector surveillance. The identification of relevant vector species, pathogens and their reservoirs in one assay is a promising and convenient aspect of surveillance.

4.Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I am an Associate Professor at a research institute. My lab research work focuses on Arbovirology studies, more specifically vector surveillance of known and novel viruses associated with mosquitoes and ticks, mosquito-transcriptomic studies, mosquito viruses tropism studies and other related mosquito-virus interaction studies.

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Referee #1

Evidence, reproducibility and clarity

This is a very interesting and well designed study on mNGS of mosquitoes. The authors demonstrate that they can distill valuable information on the vector species, the source of the blood meals and the microbiome/virome using a simple experimental approach and using single mosquitoes. A highlight of the work is that the paper is very comprehensive with an overwhelming dataset and thoughtful analysis. It is a showcase how sequencing data from a relative compact number of mosquitoes specimens can be used to conduct sophisticated computational analysis leading to meaningful conclusions. The authors make a strong case for the power of mNGS of mosquitoes that may be applicable to other (invertebrate) species. Especially the phylogenetic analysis based on SNP distance without have reference genomes and the grouping of contigs by means of co-occurence in datasets is original. We feel that the work deserves to be published.

Significance

We have a number of comments that the authors may consider in further improving the quality of their manuscript:

What is the impact of this paper?

I think it is possible that the paper will have a decent impact on the mosquito arbovirus field, because it adequately shows the possibilities that individual mosquito sequencing can bring (e.g. co-occurrence analysis). It may shift the balance to doing more individual mosquito sequencing instead of pools. The paper is also very extensive in the analyses that it does on this very rich data set. Below, some suggestions are given for additional analysis, which should be interpreted as a compliment to the interesting data set acquired. It should however be noted that the ideas and approaches taken are not entirely new. Sequencing individual mosquitoes, co-occurrence analysis and metagenomic sequencing have been done before, although not to this extent and not in this field. Several novel possibilities:

1. An unbiased way to check if you have the correct mosquito species and the ability to detect subspecies. Using the genetic distance of the transcriptomes they have likely corrected the missed identification in some samples, where these calls had a logical mistake made. The fact that subspecies overlapped with the sites of capture is very interesting and confirms the relevance of looking at the genetic distance also within species.
2. Blood-meal analysis from sequence data. Here they can get to species level for 10 out of 40 blood-engorged mosquitoes. The idea is interesting, as you would be able to get a lot more information if you can determine blood-meal origin from RNA-seq data (as shown in this paper). However, I feel that in the current paper (and this may be intentional) they do not properly show that RNA-seq is an adequate alternative to DNA sequencing of the blood. To convince me, I would have liked to have these results compared to DNA sequencing and see how much overlap there is. I understand however that the choice was made not to do this, but I do have a small note for the information given now. It was mentioned that 1 contig with an LCA of vertebrates is enough for a 'blood-meal origin' call. I am however left to wonder how reliable is 1 read? Are there really no contigs with an LCA in vertebrates in the non blood-fed mosquitoes? Also, what do we think happened in the mosquitoes that were visibly bloodfed but nothing was found; any speculation?
3. The study of co-occurrence, although not novel, is a nice addition to the mosquito virome/microbiome determination field. Identifying novel segments and missed segments of viruses is very nice. I do however wonder: did it ever occur that co-occurrence finds a 'linked' fragment that was clearly wrong? Were some post-analyses done to check if the results make sense? It seems, especially because the paper elaborates on examples, that you need some follow-up. This is not problematic, but a nice addition to the paper would be (as is also described below) to mention which segments were added to viral genomes by co-occurrence and if some checks were done to verify these hits.
4. Being able to say something about differences in viruses within the same mosquito species is super interesting. Pools do not give the possibility to say something about profiles and prevalence and the large size (148 mosquitoes) allows to find interesting correlations.

What parts do you think are problematic?

1. We question the validity 'blood-meal calls' as outlined above.
2. In this study they use % of non-host reads as a measure for the abundance of a pathogen (see e.g. Figure 3). I don't understand this at all... If you have more pathogens, then the amount of non-host reads would have to go up right? It seems to assume that the amount of non-host reads you have is similar in all samples? It becomes even more problematic when the trend is mentioned that having a higher % of non-host reads for Wolbachia is related to a lower % of non-host reads for viruses. This seems to be trivial as the amount of non-host reads goes up with increased Wolbachia infection, and therefore the % of non-host reads for viruses goes down due to the larger denominator. A different number than 'non-host reads' should be taken to normalise the data and say something about abundance. E.g. host reads or spiked RNA?

What are the most relevant questions you are left with?

1. I am curious about the limited overlap with Sadeghi et al., 2018, who sequenced so many Culex mosquitoes in California. I would suggest to say a little but more about these discrepancies and their potential causes in the discussion.
2. What do the authors think are in those 'dark reads'? Is the amount of dark reads the same across the different samples? Similarly, are the 'tetrapoda' reads reduced/absent in mosquitoes with a reference genome available?
3. In the first part of the results, mention is made to being able to characterize to kingdom level 77% of the 13 million non-host reads (also see comment on non-host reads below). I am however puzzled with the description in the text and supplemental figure 3: which 3 million contigs were not able to be characterized? Where in supplemental figure 3 are they? This is especially puzzling as the main text mentions that 11 million non-host reads are from complete viral genomes, 0.9 million to eukaryotic taxa and 0.7 million to prokaryotic taxa?
4. There seem to be 131 bars, corresponding to individual mosquitoes, in figure 3? Where are the remaining 17?

What are your tips (in addition to responses to above questions)?

1. I think the definition of 'non-host reads' needs to be clearly made and used consistently across the document. At the end of the paragraph 'Comprehensive and quantitative analysis of non-host sequences detected in single mosquitoes' the concept of "...13 million non-host reads..." is introduced. At first glance of supplemental figure 3 it seems that "non-host reads" could also be defined as the 16.7 aligned reads that are left after putative host sequences are removed. Although it is true that the derivation of 13 million is explained in the figure text of supplemental figure 3, it may be easier for the reader (as it cost me some time) to explain this in the main text. In addition, is the definition of 'non-host reads' (corresponding to 13-million reads) corresponding to "classified non-host reads" in the following excerpt: "For every sample, "classified non-host reads" refer to those reads mapping to contigs that pass the above filtering, Hexapoda exclusion, and decontamination steps. "Non-host reads" refers to the classified non-host reads plus the reads passing host filtering which failed to assemble into contigs or assembled into a contig with only two reads."? This caused some confusion.
2. I believe it would be a valuable addition to add a table for the viruses which includes: 1) How it was determined that the complete genome is there, 2) The percentage overlap for those segments that were identified with blast and 3) Which viruses were already known.
3. Have the numbers of the caught mosquitoes somewhere written out in the materials and methods.
4. Pg2 L1-3: "Metagenomic sequencing..... a single assay." Perhaps a bit early for this statement. Would suggest to place it two paragraphs later before:"Here, we analyzed...."
5. Figure S4 is too pixelated to read. Perhaps due to pdf conversion, but please do check before submission.

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Reply to the reviewers

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

Many flatworm species reproduce asexually, by fission, and the process relies on the activity of stem cells (neoblast), which drive regeneration. The question that this work tries to address is what is the dynamics of stem cells in this process, including how many stem cells contribute to regeneration, what are the mutation rates and selection mechanisms, if any. Towards this, the authors tracked one specimen of planarian Girardia tigrina for more than ten rounds of fission, and re-sequenced its genome at multiple time points and applied methods of population genetics to analyze and model the data. The main conclusion of the work is that there is high somatic mutation rate, rapid loss of heterozygosity, and a small size of the stem cell population that contributes to regeneration after fission.

Reviewer #1 (Significance (Required)):

The work has value, since it provides a framework to address the evolutionary aspects of stem cell dynamics in flatworms. However, as the authors point multiple times, there are many unknown biological parameters, such as, for example, the ratio of cell to organism regeneration (g), and simplifications, which can significantly influence the results. For this reason, the authors provide a range of estimates for somatic mutation rates and the effective stem cell population size, rather than some final conclusions. As the authors point out, further work will be needed to refine the model but generating new data for that is beyond the scope of this manuscript. As such, I find this manuscript is an important initial contribution to the field of stem cell population dynamics in flatworms, and its methods, results and conclusions convincing. I don't have further suggestions for improving this manuscript.

Thank you very much for your positive assessment of our work.

**Referees cross-commenting**

I agree with the suggestion of Reviewer #2 that repeating the analysis on additional contings, instead of focusing only on one longest contig in the assembly, will be useful.

We will process and analyze a few additional contigs to evaluate genomic variation in transmission of somatic variants in this system.

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

**Summary:**

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

The authors aimed to use population genetics to determine the number of stem cells active in each cycle of regeneration or the equality of their relative contributions in planarians. They approached this by establishing a population with serial fission from one wild isolate of Girardia cf. tigrina collected in Italy. They used next generation sequencing to sample variants of regenerated worms at different generations of fissioning. They estimated the effective population size of stem cells to be a few hundreds, besides calculation of nucleotide diversity and somatic mutation rate. They propose small effective number of propagating stem cells might contribute to reducing reproductive conflicts in clonal organisms.

**Major comments:**

• Are the key conclusions convincing?

The mutation rate is reasonable. The effective stem cell population size and the genetic diversity may vary between different species. A small effective stem cell population size is not counter intuitive.

Generally, the work is interesting and deserves to be published.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

The current analysis is based on many assumptions, one single set of experiments and a genome that is not well assembled. The authors have been careful with their language and documented the limitations in discussion.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

I will feel more comfortable if the authors can repeat the analysis with two more random long contigs to have a better idea if the localization of markers impacts the conclusion. The concern is if different parts of the genome behave differently and if the Girardia genome is highly repetitive. As the pipeline of analysis is established, I expect this can be completed in a month with no experimental cost.

We will process and analyze a few additional contigs to evaluate genomic variation in transmission of somatic variants in this system.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

Yes

• Are the data and the methods presented in such a way that they can be reproduced?

Yes

• Are the experiments adequately replicated and statistical analysis adequate?

Yes

**Minor comments:**

• Specific experimental issues that are easily addressable.

Yes.

• Are prior studies referenced appropriately?

Yes.

• Are the text and figures clear and accurate?

Yes.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Yes. Please also see the significance section.

Specifically, my concerns are about writing and the context of current study.

"Small effective number of propagating stem cells might contribute to reducing reproductive conflicts in clonal organisms." is confusing. Not a good abstract ending sentence for the work presented. Reproductive conflicts need clarification. How the work relates to that concept needs support. I recommend keeping the interpretations simple and focused on the data.

Please see response under “Significance”.

Reviewer #2 (Significance (Required)):

Both questions the authors attempt to address, the genetic diversity of clonal animals and the number of stem cells contributing to regeneration, are interesting and important. The combination of these two is a bit odd in the manuscript. In other words, the population genetics approach did not address the cell biology question:how many or what proportion of stem cells are active in each cycle of regeneration. I would recommend the authors to focus the writing on one question only: the genetic diversity and evolution of a clonal species, which is driven by stem cell genome evolution and the process of regeneration. The cell biology question, phrased by the author in the abstract and introduction, need to be resolved by cell biologists. I understand the appeal to put the current study in the context of regeneration research. A balance should be achieved. Currently, the second sentence of the abstract and the first paragraph of introduction are odd and misleading. The first paragraph of the introduction can be a second paragraph to introduce the planarian system for the study.

We will restructure the manuscript to clearly separate the findings that arose directly from experimental (sequencing) data i.e. magnitude and inheritance pattern of somatic variation, and the findings that were inferred from our approximate population genetic model and depend on the unknown parameter g i.e. the effective number of stem cells and the somatic mutation rate. We will emphasize the distinction. The statements that are tangentially relevant and are not directly supported by our analyses will be modified or removed.

In the context of genetic diversity of clonal species, many studies shall be referenced. It is interesting as well to draw comparisons with other species. Asexual planarians are unique and interesting in that space.

Thus said, the attempt to examine stem cell population genetics is especially interesting and important as the fissiparous planarians do not undergo bottleneck selection by zygotes. In the context of recent progress studying planarian genetic diversity (Nishimura, O. et al. 2015, Guo, L. et al. 2016), Asgharian H. et al.'s work is timely and an important contribution to planarian researchers and evolutionary biologists. The question has general interest to cancer biologists as well. The manuscript does not have the data and is not written in a way to reach such broader audiences yet. A community is growing to address these questions.

We agree with the reviewer’s point about the pioneering works of Nishimura et al. 2015 and Guo et al. 2016. Both papers were indeed cited in our manuscript. We will cite more studies pertaining to the question of somatic genetic diversity in planarians.

The study of planarian genetic diversity has just started with two publications (Nishimura, O. et al. 2015, Guo, L. et al. 2016). It is reasonable to have lots of limitations and assumptions in the manuscript. The work is an interesting piece to be published, assuming the major points listed in the review is addressed. The reported findings will be part of the early literature and inspiration for planarian researchers and evolutionary biologists. I expect many more future manuscripts will be published, either to reexamine the reported findings or to push our understanding of the question deeper.

Thank you very much for this assessment. We fully agree.

My expertise is with planarian biology, genome, genetics, and diversity. I do not have sufficient expertise to evaluate the equations used in the study.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

The authors aimed to use population genetics to determine the number of stem cells active in each cycle of regeneration or the equality of their relative contributions in planarians. They approached this by establishing a population with serial fission from one wild isolate of Girardia cf. tigrina collected in Italy. They used next generation sequencing to sample variants of regenerated worms at different generations of fissioning. They estimated the effective population size of stem cells to be a few hundreds, besides calculation of nucleotide diversity and somatic mutation rate. They propose small effective number of propagating stem cells might contribute to reducing reproductive conflicts in clonal organisms.

Major comments:

• Are the key conclusions convincing?

The mutation rate is reasonable. The effective stem cell population size and the genetic diversity may vary between different species. A small effective stem cell population size is not counter intuitive.

Generally, the work is interesting and deserves to be published.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

The current analysis is based on many assumptions, one single set of experiments and a genome that is not well assembled. The authors have been careful with their language and documented the limitations in discussion.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

I will feel more comfortable if the authors can repeat the analysis with two more random long contigs to have a better idea if the localization of markers impacts the conclusion. The concern is if different parts of the genome behave differently and if the Girardia genome is highly repetitive. As the pipeline of analysis is established, I expect this can be completed in a month with no experimental cost.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

Yes

• Are the data and the methods presented in such a way that they can be reproduced?

Yes

• Are the experiments adequately replicated and statistical analysis adequate?

Yes

Minor comments:

• Specific experimental issues that are easily addressable.

Yes.

• Are prior studies referenced appropriately?

Yes.

• Are the text and figures clear and accurate?

Yes.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Yes. Please also see the significance section. Specifically, my concerns are about writing and the context of current study.

"Small effective number of propagating stem cells might contribute to reducing reproductive conflicts in clonal organisms." is confusing. Not a good abstract ending sentence for the work presented. Reproductive conflicts need clarification. How the work relates to that concept needs support. I recommend keeping the interpretations simple and focused on the data.

Significance

Both questions the authors attempt to address, the genetic diversity of clonal animals and the number of stem cells contributing to regeneration, are interesting and important. The combination of these two is a bit odd in the manuscript. In other words, the population genetics approach did not address the cell biology question:how many or what proportion of stem cells are active in each cycle of regeneration. I would recommend the authors to focus the writing on one question only: the genetic diversity and evolution of a clonal species, which is driven by stem cell genome evolution and the process of regeneration. The cell biology question, phrased by the author in the abstract and introduction, need to be resolved by cell biologists. I understand the appeal to put the current study in the context of regeneration research. A balance should be achieved. Currently, the second sentence of the abstract and the first paragraph of introduction are odd and misleading. The first paragraph of the introduction can be a second paragraph to introduce the planarian system for the study.

In the context of genetic diversity of clonal species, many studies shall be referenced. It is interesting as well to draw comparisons with other species. Asexual planarians are unique and interesting in that space.

Thus said, the attempt to examine stem cell population genetics is especially interesting and important as the fissiparous planarians do not undergo bottleneck selection by zygotes. In the context of recent progress studying planarian genetic diversity (Nishimura, O. et al. 2015, Guo, L. et al. 2016), Asgharian H. et al.'s work is timely and an important contribution to planarian researchers and evolutionary biologists. The question has general interest to cancer biologists as well. The manuscript does not have the data and is not written in a way to reach such broader audiences yet. A community is growing to address these questions.

The study of planarian genetic diversity has just started with two publications (Nishimura, O. et al. 2015, Guo, L. et al. 2016). It is reasonable to have lots of limitations and assumptions in the manuscript. The work is an interesting piece to be published, assuming the major points listed in the review is addressed. The reported findings will be part of the early literature and inspiration for planarian researchers and evolutionary biologists. I expect many more future manuscripts will be published, either to reexamine the reported findings or to push our understanding of the question deeper.

My expertise is with planarian biology, genome, genetics, and diversity. I do not have sufficient expertise to evaluate the equations used in the study.

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---

Referee #1

Evidence, reproducibility and clarity

Many flatworm species reproduce asexually, by fission, and the process relies on the activity of stem cells (neoblast), which drive regeneration. The question that this work tries to address is what is the dynamics of stem cells in this process, including how many stem cells contribute to regeneration, what are the mutation rates and selection mechanisms, if any. Towards this, the authors tracked one specimen of planarian Girardia tigrina for more than ten rounds of fission, and re-sequenced its genome at multiple time points and applied methods of population genetics to analyze and model the data. The main conclusion of the work is that there is high somatic mutation rate, rapid loss of heterozygosity, and a small size of the stem cell population that contributes to regeneration after fission.

Significance

The work has value, since it provides a framework to address the evolutionary aspects of stem cell dynamics in flatworms. However, as the authors point multiple times, there are many unknown biological parameters, such as, for example, the ratio of cell to organism regeneration (g), and simplifications, which can significantly influence the results. For this reason, the authors provide a range of estimates for somatic mutation rates and the effective stem cell population size, rather than some final conclusions. As the authors point out, further work will be needed to refine the model but generating new data for that is beyond the scope of this manuscript. As such, I find this manuscript is an important initial contribution to the field of stem cell population dynamics in flatworms, and its methods, results and conclusions convincing. I don't have further suggestions for improving this manuscript.

Referees cross-commenting

I agree with the suggestion of Reviewer #2 that repeating the analysis on additional contings, instead of focusing only on one longest contig in the assembly, will be useful.

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Reply to the reviewers

We want to thank the reviewers for their careful evaluation of our work and their helpful suggestions. We provide at the end of this letter a point by point response of how we aim to address their concerns, which can be summarised in the following main points:

1-We will provide further evidence for the efficiency and dynamics of beta-catenin deletion in adult neural stem cells in vivo (point raised by both reviewers).

We fully agree that although we tested for the disappearance of beta-catenin transcripts in sorted NSCs after deletion, providing further proof of the absence of beta-catenin protein in these cells will help strengthen our conclusions. For this, we are performing additional stainings for beta-catenin and Wnt/beta-catenin targets, together with neural stem cell markers, to quantify the loss of beta-catenin and Wnt/beta-catenin signalling in NSCs at P90 (30 days after deletion), as well as new P150 samples (90 days after deletion).

2-We will investigate in further detail the effects (or lack of effect) of beta-catenin deletion on adult neurogenesis.

The focus of our work is the effect of Wnt/beta-catenin signalling on NSCs. Nevertheless, we agree with reviewer 2 that extending our analysis to later stages in the neurogenic process will be of importance to better contrast our results with previous reports identifying a role for Wnt in neuronal production in the adult hippocampus. We are currently processing new material from mice in which beta-catenin was deleted at P60 and brains collected after 3 months to evaluate the long-term effects of beta-catenin deletion on the neurogenic output of NSCs. We will also perform stainings of Wnt-responsive neuronal genes, such as NeuroD1 and Prox1, at P90 and P150 in both control and beta-catenin cKO mice.

3- We are aiming to confirm that the in vitro effects of CHIR99021 on NSCs are mediated by beta-catenin. We already provide evidence that stimulation with Wnt3a has the same effect as inhibition of GSK3beta by CHIR99021. To further prove the link of the observed effects to Wnt-beta-catenin signalling, we will repeat some of our key experiments using beta-catenin floxed cells (both induction of neuronal differentiation and re-activation from quiescence) as reviewer 1 suggests.

Reviewer #1

Overall, the results are reliable and important for the field. However, several points need to be addressed and clarified to support their conclusion. I am hopeful that the authors find my comments helpful and constructive.

Many thanks for your insightful comments, we believe they will indeed help us improve our manuscript.

1. • Validation of cKO in vivo.*

• Although the authors validated cKO of beta-catenin in vivo using FACS/qPCR at the transcript level, it would be important to check when and to what extent beta-catenin proteins are downregulated in qNSC/activeNSCs in vivo. This will be easily assessed by immunohistochemistry. In the same line, although the authors confirmed the reduction of beta-catenin signaling using beta-gal signaling in cKO mice, it would be important to check if this can be cross-checked by staining the nuclear localization of beta-catenin. This confirmation would strength the authors statement and clear that some remained beta-catenin at the plasma membrane may not be compensating their function.*

• Independent of the confirmation of beta-catenin cKO, it would be important to check if the downstream targets of Wnt/beta-catenin signals (ex. Expression of Axin2) were also attenuated. This point should be addressed both in vivo and in vitro. *

We are performing immunohistochemistry and quantification of beta-catenin in control and cKO brain samples, as suggested by the reviewer. Unfortunately, we have not yet found an antibody and labelling protocol that allows us to detect nuclear beta-catenin, even in control samples, so with our current antibody, we won’t be able to show a reduction in nuclear localization of beta-catenin in the cKO samples. We are testing alternative beta-catenin antibodies that could help us overcome this limitation. As the reviewer mentions, we do see a reduction in reporter expression in BATGAL mice upon deletion of beta-catenin. In order to further demonstrate effective Wnt signalling attenuation in our mutant mice we are testing antibodies for Wnt targets such as Axin2, CcnD1 and NeuroD1.

• Wnt/beta-catenin signals in qNSC and active NSC in vitro.*

The authors indicated that the depletion of beta-catenin had no effect on qNSCs and active NSCs in vitro. However, it is not clear whether Wnt/beta-catenin signaling is activated in their culture conditions. If there are no inputs of Wnt signaling in cultured cells, the depletion of beta-catenin will not lead any impacts. Therefore, it would be critical to check if the Wnt-signaling is activated in control cells in their culture condition, and if the downstream targets of Wnt-signaling are downregulated in cKO qNSCs/active NSCs.

We agree that this is an important conceptual point that needs to be clarified. From our data (see Figure S3C), we can see that deletion of beta-catenin in NSCs in vitro blocks their response to Wnt stimulation (with CHIR99021) but it did not lower the levels of Axin2. From this, we can deduce that Wnt signalling is indeed not significantly activated in proliferating NSCs in vitro, despite the expression of Wnt ligands by these cells (Figure 3). We will perform further analysis of Wnt target genes in control and cKO NSCs in vitro to confirm this observation. Of note, the lack of Wnt signalling activity in NSCs would further support our claim that Wnt is dispensable for their proliferation and maintenance. We will make this point clearer in the manuscript.

• ChIR treatment on cKO cells.*

The authors only use WT cells for ChIR treatment. To investigate whether the effect of ChIR come through the beta-catenin signaling pathway, why don't they use cKO NSCs for ChIR treatment (Fig5-7)?

This is a great suggestion and we are performing these experiments with control and cKO NSCs.

Different Wnt signaling levels between in vivo and in vitro.

The authors indicated that different levels of Wnt signaling could results in different outcomes based on in vitro observation. What are the levels of Wnt signaling in vivo compared to in vitro ChIR treatment? Activation of Wnt/beta-catenin in vivo is much weaker than in vitro CHIR treatment, therefore the contribution of Wnt signaling at endogenous levels is negligible? This may help to explain why Wnt/beta-catenin is dispensable in vivo, at least in young state. This can be addressed by probing the levels of downstream targets.

Levels of Wnt signalling are indeed central to our conclusions and we agree that a comparison of Wnt/beta-catenin signalling levels between our in vitro interventions and the in vivo situation would be important. However, we find that directly comparing the levels of downstream Wnt targets between the two systems might prove challenging due to differences in methodology (immunolabeling is not a reliably quantitative method, especially when performed on such different sample types, with different fixation conditions, etc). We will nevertheless attempt such quantifications using immunolabelings for CcnD1, Axin2 and NeuroD1 both in vivo and in vitro. We also want to point out that CHIR is not the only way in which we have stimulated Wnt signalling in NSCs in vitro. In Figure S5, we demonstrate that treatment with Wnt3a can reactivate quiescent neural stem cell in a dose-dependent manner, showing that the effect of Wnt signalling on NSCs can be achieved also with a more physiological intervention.

Reviewer #2

A major challenge is to separate cell adhesion functions of beta-catenin from its function in the canonical Wnt/beta-catenin signaling pathway. The authors tested two different conditional bcat alleles (bcatdel ex2-6 ; bcatdel ex3-6) to delete bcat from stem cells. It is a bit unfortunate that the authors chose to test two conditional alleles that would affect cell adhesion and transcriptional activity instead of the Ctnnb1dm allele (Draganova et al. 2015, Stem Cells), which would have been a cleaner way to specifically address the contribution of beta-catenin transcriptional activity in adult hippocampal neural stem cells. Was there a specific reason not to use the Ctnnb1dm conditional mice? Please comment / discuss.

We agree with the reviewer that the Ctnnb1dm allele would better differentiate between cell adhesion and transcriptional effects of beta-catenin deletion. However, as we see no effect of beta-catenin deletion, we did not find it necessary to further dissect the differential contribution of cell adhesion and the Wnt/beta-catenin pathway in this particular case. We will add a comment on this point to the discussion.

The authors control for downregulation of beta-catenin signaling activity in the bcatdel ex2-6 through the analysis of the BATGAL reporter. 30 days after recombination, they observe a drop in reporter activity (from 31% to 13%). While this drop shows that at the time of analysis beta-catenin signaling activity was reduced, the lack of complete downregulation of reporter activity raises the issue whether long-term stability of the b-catenin protein may be a confounding factor at this time-point. In particular effects of b-catenin on the DCX population, which to a significant extent is generated several days to weeks before the time-point of analysis, may not be revealed. Data on the time-course of downregulation of the BATGAL reporter could help for the interpretation of the data as would analysis of beta-catenin protein levels in recombined cells. In addition, analysis of bcatdel ex2-6 at a later time-point after recombination, at which beta-catenin signaling activity is further downregulated, would strengthen the surprising finding that loss of beta-catenin signaling activity does not hamper neuronal differentiation in the adult hippocampus.

We will monitor the disappearance of beta-catenin using immunohistochemistry for beta-catenin and downstream targets of Wnt in control and cKO brains, both at P90 and at a longer time after deletion (P150), as the reviewer suggests. Of note, when we deleted beta-catenin in vitro in NSCs, we could confirm the disappearance of the protein by 48 hours, and therefore beta-catenin stability cannot explain the lack of effect of the deletion (Figure S3B).

Was quantification performed only in recombined (i.e., reporter positive) cells or in recombined and non-recombined cells? I could not locate that information. Given the evidence for feed-back regulation from intermediate precursor cells / immature neurons to stem cells (e.g. Lavado et al. 2010, Plos Biology), it is important to separately evaluate the development of recombined and non-recombined cells to evaluate the behavior of beta-catenin signaling deficient stem cells.

The quantifications were always performed in YFP+ recombined cells. The efficiency of recombination was very high (from 83 to 97%), therefore allowing no room for confounding effects of unrecombined cells. We will convey this information in a clearer way in our revised manuscript.

Reports from (Kuwabara et al. 2009, Nat Neurosci), (Gao et al. 2009, Nat Neurosci) and (Karalay et al. 2011, PNAS) suggest that beta-catenin signaling activity drives dentate granule neuron identity through regulating the expression of Neurod1 and Prox1. Given that in these studies neither loss of Neurod1 nor of Prox1 affects neuronal fate commitment but long-term survival and that the studies by (Gao et al. 2007, J Neurosci) and (Heppt et al. 2020, EMBO J) suggest that loss-of-beta-catenin affects neuronal survival, it may be interesting to evaluate a) whether a dentate granule neuron identity, b) long-term survival of adult generated neurons are affected. At the minimum these studies should be more extensively discussed.

As mentioned in our response summary, our main aim is to test the effects of Wnt/beta-catenin signalling on NSCs. Nevertheless, these are excellent suggestions and we are currently performing immunohistochemistry for NeuroD1 and Prox1 to test whether they are downregulated in cKO brain samples. We have also performed a longer deletion of beta-catenin (deletion at P60 and analysis at P150) to test whether neurogenesis is affected in the cKO mice in the longer term.

It has been suggested that the neural stem cell population in the adult hippocampus may be heterogenous with one population being responsible for baseline neurogenesis and being resistant to age-associated depletion and a second population driving high levels of neurogenesis in young adults (see also Urban, Bloomfield and Guillemot 2019, Neuron). The observation that beta-catenin signaling is only active in a small fraction of stem cells and their progeny raises the question whether it fulfills only a function in a specific subpopulation. Such possibility should at least be discussed.

This is a very interesting point, which we will include in the discussion of our revised manuscript. We are also performing immunohistochemistry for Id4 together with beta-catenin or downstream targets of Wnt and NSC markers to determine whether the resting population (described in Urban et al. 2016 and Harris et al. 2021), which has low levels of Id4 is more responsive to Wnt than the dormant population.

The recently published studies by (Rosenbloom et al. 2020, PNAS) and (Heppt et al. 2020, EMBO J) strongly suggest that beta-catenin signaling dynamics are critical for the regulation / modulation of adult hippocampal neurogenesis. The aspect of beta-catenin signaling dynamics should be discussed.

We will include a discussion of beta-catenin signalling dynamics in the revised version of the manuscript.

**Significance:**

Adult neurogenesis is considered an important factor in hippocampal plasticity and its disturbance is thought to contribute to the pathogenesis in several psychiatric and degenerative diseases. Wnt/beta-catenin signaling is considered central to the regulation of adult hippocampal neurogenesis. In this regard, the manuscript describes the potentially very important and surprising finding that deletion of beta-catenin from neural stem cells does not generate major neurogenesis phenotypes. The concern with the present manuscript is, that the lack of phenotype requires additional analyses to exclude that phenotypes develop with a delay because of long-term stability of the beta-catenin protein.

We believe the revisions outlined above will address these concerns.

The significance of the manuscript and its interest to a wider audience would in addition be greatly enhanced, if the authors could provide some mechanistic data that would explain the discrepancies between published functions of Wnt/beta-catenin-signaling dependent regulation of neurogenesis and their own findings. The manuscript would also gain significance if the authors would provide solid data for their interesting hypothesis that beta-catenin-signaling contributes to the regulation of adult hippocampal neurogenesis in response to extrinsic stimuli. In this regard one potential approach would be to analyse whether extrinsic stimuli such as running would be able stimulate the activation of stem cells.

Both finding a mechanism to explain the observed discrepancies and demonstrating that Wnt has a role in the response of NSCs to extrinsic stimuli are excellent follow-up suggestions to our work and we thank the reviewer for these recommendations. However, addressing these points would take many months (if not years) and is not necessary to support the current conclusions of our work. We therefore believe they are out of the scope of this current manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Wnt/beta-catenin signaling is considered central to the regulation of adult hippocampal neurogenesis. In this manuscript Austin and colleagues interrogate the function of beta-catenin-dependent signaling using in vivo beta-catenin conditional knockout and gain-of-function approaches combined with in vitro pharmacological and genetic approaches. The authors confirm previous reports of Wnt/beta-catenin signaling in adult hippocampal neurogenesis and report the surprising findings that • Deletion of beta-catenin from stem cells does not affect stem cell numbers and their activation / proliferation in vivo and in vitro • Deletion of beta-catenin from stem cells does not affect neuronal differentiation in vivo and in vitro Moreover, the authors show that expression of a stabilized form of beta-catenin affects stem cell positioning in vivo and that the effects of treatment of cultured hippocampal stem/progenitor cells with a pharmacological stimulator of Wnt/beta-catenin signaling are dose and time-dependent. The authors discuss that their findings suggest that Wnt/beta-catenin signaling is dispensable for neural stem cell homeostasis and that Wnt/beta-catenin signaling may have a function in the response of stem cells to external stimuli.

Comments:

A major challenge is to separate cell adhesion functions of beta-catenin from its function in the canonical Wnt/beta-catenin signaling pathway. The authors tested two different conditional bcat alleles (bcatdel ex2-6 ; bcatdel ex3-6) to delete bcat from stem cells. It is a bit unfortunate that the authors chose to test two conditional alleles that would affect cell adhesion and transcriptional activity instead of the Ctnnb1dm allele (Draganova et al. 2015, Stem Cells), which would have been a cleaner way to specifically address the contribution of beta-catenin transcriptional activity in adult hippocampal neural stem cells. Was there a specific reason not to use the Ctnnb1dm conditional mice? Please comment / discuss.

The authors control for downregulation of beta-catenin signaling activity in the bcatdel ex2-6 through the analysis of the BATGAL reporter. 30 days after recombination, they observe a drop in reporter activity (from 31% to 13%). While this drop shows that at the time of analysis beta-catenin signaling activity was reduced, the lack of complete downregulation of reporter activity raises the issue whether long-term stability of the b-catenin protein may be a confounding factor at this time-point. In particular effects of b-catenin on the DCX population, which to a significant extent is generated several days to weeks before the time-point of analysis, may not be revealed. Data on the time-course of downregulation of the BATGAL reporter could help for the interpretation of the data as would analysis of beta-catenin protein levels in recombined cells. In addition, analysis of bcatdel ex2-6 at a later time-point after recombination, at which beta-catenin signaling activity is further downregulated, would strengthen the surprising finding that loss of beta-catenin signaling activity does not hamper neuronal differentiation in the adult hippocampus.

Was quantification performed only in recombined (i.e., reporter positive) cells or in recombined and non-recombined cells? I could not locate that information. Given the evidence for feed-back regulation from intermediate precursor cells / immature neurons to stem cells (e.g. Lavado et al. 2010, Plos Biology), it is important to separately evaluate the development of recombined and non-recombined cells to evaluate the behavior of beta-catenin signaling deficient stem cells.

Reports from (Kuwabara et al. 2009, Nat Neurosci), (Gao et al. 2009, Nat Neurosci) and (Karalay et al. 2011, PNAS) suggest that beta-catenin signaling activity drives dentate granule neuron identity through regulating the expression of Neurod1 and Prox1. Given that in these studies neither loss of Neurod1 nor of Prox1 affects neuronal fate commitment but long-term survival and that the studies by (Gao et al. 2007, J Neurosci) and (Heppt et al. 2020, EMBO J) suggest that loss-of-beta-catenin affects neuronal survival, it may be interesting to evaluate a) whether a dentate granule neuron identity, b) long-term survival of adult generated neurons are affected. At the minimum these studies should be more extensively discussed.

It has been suggested that the neural stem cell population in the adult hippocampus may be heterogenous with one population being responsible for baseline neurogenesis and being resistant to age-associated depletion and a second population driving high levels of neurogenesis in young adults (see also Urban, Bloomfield and Guillemot 2019, Neuron). The observation that beta-catenin signaling is only active in a small fraction of stem cells and their progeny raises the question whether it fulfills only a function in a specific subpopulation. Such possibility should at least be discussed.

The recently published studies by (Rosenbloom et al. 2020, PNAS) and (Heppt et al. 2020, EMBO J) strongly suggest that beta-catenin signaling dynamics are critical for the regulation / modulation of adult hippocampal neurogenesis. The aspect of beta-catenin signaling dynamics should be discussed.

Significance

Significance:

Adult neurogenesis is considered an important factor in hippocampal plasticity and its disturbance is thought to contribute to the pathogenesis in several psychiatric and degenerative diseases. Wnt/beta-catenin signaling is considered central to the regulation of adult hippocampal neurogenesis. In this regard, the manuscript describes the potentially very important and surprising finding that deletion of beta-catenin from neural stem cells does not generate major neurogenesis phenotypes. The concern with the present manuscript is, that the lack of phenotype requires additional analyses to exclude that phenotypes develop with a delay because of long-term stability of the beta-catenin protein.

The significance of the manuscript and its interest to a wider audience would in addition be greatly enhanced, if the authors could provide some mechanistic data that would explain the discrepancies between published functions of Wnt/beta-catenin-signaling dependent regulation of neurogenesis and their own findings. The manuscript would also gain significance if the authors would provide solid data for their interesting hypothesis that beta-catenin-signaling contributes to the regulation of adult hippocampal neurogenesis in response to extrinsic stimuli. In this regard one potential approach would be to analyse whether extrinsic stimuli such as running would be able stimulate the activation of stem cells.

Expertise:

Adult neurogenesis, stem cell biology, signaling

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Referee #1

Evidence, reproducibility and clarity

Summary

Wnt/beta-catenin signaling has been studies in the context of adult neurogenesis for decades. It has been shown that modulation of Wnt signaling regulates adult neurogenesis, but the consequences were not always consistent. In this study, the authors developed conditional knockout mouse lines to test whether beta-catenin is essential for the regulation of adult neurogenesis.

First, using a published single cell seq-data and a reporter TG mouse system, they validated the expression of Wnt-pathway molecules in qNSCs and active NSCs. Then, beta-catenin conditional cKO mice were analyzed. The authors did not find any changes in total number of NSCs, the activation of NSCs, and the number of IPCs as well as neuroblasts. Subsequently, using in vitro culture system, the authors addressed if the proliferation and differentiation are affected in vitro conditions. Both proliferation and activation from the quiescent state were not affected in cKO NSCs. Finally, they demonstrated that an artificial stimulation of Wnt signaling by CHIR can induce differentiation or proliferation depending on cellular states and doses, thus NSCs can respond to Wnt signaling. Based on these data, they concluded that beta-catenin is dispensable for the maintenance/activation of NSCs in vivo, although NSCs can respond to Wnt/beta-catenin signaling. Overall, the results are reliable and important for the field. However, several points need to be addressed and clarified to support their conclusion. I am hopeful that the authors find my comments helpful and constructive.

1. Validation of cKO in vivo. Although the authors validated cKO of beta-catenin in vivo using FACS/qPCR at the transcript level, it would be important to check when and to what extent beta-catenin proteins are downregulated in qNSC/activeNSCs in vivo. This will be easily assessed by immunohistochemistry. In the same line, although the authors confirmed the reduction of beta-catenin signaling using beta-gal signaling in cKO mice, it would be important to check if this can be cross-checked by staining the nuclear localization of beta-catenin. This confirmation would strength the authors statement and clear that some remained beta-catenin at the plasma membrane may not be compensating their function. Independent of the confirmation of beta-catenin cKO, it would be important to check if the downstream targets of Wnt/beta-catenin signals (ex. Expression of Axin2) were also attenuated. This point should be addressed both in vivo and in vitro.
2. Wnt/beta-catenin signals in qNSC and active NSC in vitro The authors indicated that the depletion of beta-catenin had no effect on qNSCs and active NSCs in vitro. However, it is not clear whether Wnt/beta-catenin signaling is activated in their culture conditions. If there are no inputs of Wnt signaling in cultured cells, the depletion of beta-catenin will not lead any impacts. Therefore, it would be critical to check if the Wnt-signaling is activated in control cells in their culture condition, and if the downstream targets of Wnt-signaling are downregulated in cKO qNSCs/active NSCs.
3. ChIR treatment on cKO cells The authors only use WT cells for ChIR treatment. To investigate whether the effect of ChIR come through the beta-catenin signaling pathway, why don't they use cKO NSCs for ChIR treatment (Fig5-7)?
4. Different Wnt signaling levels between in vivo and in vitro<br> The authors indicated that different levels of Wnt signaling could results in different outcomes based on in vitro observation. What are the levels of Wnt signaling in vivo compared to in vitro ChIR treatment? Activation of Wnt/beta-catenin in vivo is much weaker than in vitro CHIR treatment, therefore the contribution of Wnt signaling at endogenous levels is negligible? This may help to explain why Wnt/beta-catenin is dispensable in vivo, at least in young state. This can be addressed by probing the levels of downstream targets.

Significance

Significant.

A genetic approach to address the role of Wnt/Beta-catenin signaling is critical for the field. The audience would be interested if this study make it clear previously reported discrepancy.

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Reply to the reviewers

Response to reviewers

We first thank Review Commons for recruiting such knowledgeable reviewers to comment on our manuscript. We appreciate their diverse set of useful and constructive comments, which should help us improve the manuscript substantially. Please see our response to each reviewer’s comments below.

Reviewer #1:

**Summary:** The authors describe a useful modified fluctuation assay that couples conventional mutation rate analysis with mutational spectrum characterization of forward mutations at the S. cerevisiae CAN1 locus. They nicely showed that wild yeast isolates display a wide range of mutation rates with strains AAR and AEQ displaying rates ~10-fold higher than the control lab strain. These two strains also showed a bias for C>A mutations, and were the only strains analyzed that had a mutation spectrum statistically different from the lab control. Together, these data provide a compelling proof-of-principle of the applicability of the modified fluctuation analysis approach described in this manuscript. Overall, the manuscript is very well written, and the work reported in it does represent a valuable contribution to the field. However, two primary shortcomings were identified that can be addressed to strengthen the conclusions prior to publication. Both points described below pertain to the analysis of the possible C>A specific mutator phenotype in strains AAR and AEQ.

Response:

We thank the reviewer for this positive response. We have made a plan, detailed below, to address the shortcomings the reviewer has highlighted.

**Major comments:**

1. The work presented in the manuscript does suggest that these two haploids are likely to display the C>A mutator phenotype. Yet, the authors fell short of providing a full and unambiguous demonstration that would elevate the significance of their discovery. They could have directly tested the predicted C>A specific mutator phenotype by conducting additional experiments, one of which is relatively simple. Specifically, they could have performed a simple reversion-based mutation assay to validate the reported C>A mutator phenotype displayed by AAR and AEQ. For example, into AAR, AEQ, and a wild type control, the authors could introduce an engineered auxotrophic marker allele (e.g., ura3 mutation) caused by an A to C substitution, which upon mutation back to A restores prototrophic growth in minimal media (ie. reversion from ura3-C to URA3-A). Such specific reversible allele should be relatively easy to integrate into the AAR and AEQ genomes, as well as in the control strain. Based on the authors' prediction, AAR and AEQ should display a very large increase (far higher than 10 fold) in the reversion rate when compared to a control haploid. To demonstrate the specificity of the mutation spectrum, the authors could test the reversion rates of a different engineered allele requiring a reversion mutation in the opposite direction (ie. reversion from ura3-A to URA3-C). If the AAR and AEQ mutator is specific C>A, one would predict that all three strains should have similar mutation rates for a reversion in the A>C direction. This additional genetic work would thoroughly validate the central discovery and would reinforce the usefulness of the method described in the manuscript.

Alternatively, a conventional mutation accumulation and whole genome re-sequencing experiment with parallel lines of AAR, AEQ and a control strain would also very effectively validate the C>A mutator prediction, and it would also answer the authors' discussion point about specificity to the CAN1 locus. However, it would be more costly and much more time consuming.

Response:

We thank the reviewer for these detailed, clear suggestions regarding additional methodology for further validating our results. We appreciate that parallel independent validations always add credibility to unexpected results like the ones presented in our manuscript. We’ve been considering these suggestions seriously, but our concern is that it is much less straightforward to engineer the genomes of these wild yeast than one might expect based on experiments with standard laboratory strains. Unforeseen roadblocks related to the biology of AAR and AEQ could end up making the URA3 reversion assay take even longer than an MA study. As we understand it, the two main concerns that might necessitate this additional undertaking are that either our novel assay for ascertaining mutations in CAN1 doesn’t work properly, or that the mosaic beer strains mutate significantly differently outside CAN1. Below we describe revisions to the text that we think will clearly represent these caveats and the relatively modest uncertainty associated with them.

To further justify the soundness of our claim that AAR and AEQ have distinctive mutation rates and spectra, we plan to add additional discussion of the validation approaches that are presented in the manuscript to verify the accuracy of our pipeline. Although the ability of fluctuation assays to estimate mutation rates is well established, the identification of the spectra using our next-generation-sequencing-based pipeline is novel, so we used Sanger sequencing to validate the exact de novo mutations it ascertained in a select control strain. Our Sanger sequencing test found our assay to have an undetectably low false positive rate and a false negative rate that was much too low to account for the differences we measured between AAR, AEQ, and the standard lab strains. The fact that we also observed similar mutation spectra from control lab strains used in previous CAN1-based studies further demonstrates the reliability of our method, and it is notable that most natural isolates were measured to have very similar mutation spectra to lab strains (Figure 4 and Supplementary Figure S8-S9). We agree that further validation would be needed to read much into the more subtle differences in mutation rates and spectra that we saw hints of between other strains, and for that reason, we focused this paper on the differences that well exceed what we measured to be our measurement pipeline’s margin of error.

It is true that the genome-wide mutation rate might differ somewhat from the mutation rate at the CAN1-locus, but the mutation spectrum at the CAN1 locus measured in a previous study (Lang and Murray, 2008) was very similar to the genome-wide mutation spectra obtained from MA studies (Sharp et al., 2018), with just a small overall increase of mutations with C/G nucleotides (the second to last paragraph on page 17 and Supplementary Figure S13). Moreover, we have avoided making any claims of seeing distinct mutation rates or spectra based on “apples-to-oranges” comparisons between mutation spectra measured at CAN1 and spectra measured across the whole genome.

We also note that the enrichment of C>A mutations in AEQ and AAR is not only observed from our de novo mutation data in CAN1, but also seen in rare natural polymorphisms genome-wide (Figure 1B, 5A,B). Rare natural polymorphisms are recent mutations that occurred during the history of the strain, and the fact that they disproportionately enrich in C>A mutations in these strains indirectly shows that the C>A enrichment occurs not only at CAN1, as measured in our experiment, but has also been occurring during natural mutation accumulation genome-wide.

The second concern is in regard to the relatively extensive conclusions drawn about the possible evolutionary significance of the possible C>A mutator in AAR and AEQ. The authors should be more cautious and conservative in the proposed interpretation. As the authors note:

'Three of the four C>A-enriched mosaic beer strains, AAR, AEQ, and SACE_YAG, are all haploid derivatives of the [highly heterozygous] diploid Saccharomyces cerevisiae var diastaticus strain CBS1782, which was isolated in 1952 from super-attenuated beer.'

From this statement, and because the paper cited provided few details on the isolation of CBS1782, it is presumed that these haploid derivatives were most likely isolated as recombinant spores. Furthermore, it is unclear when this isolation occurred, and for how many generations strains AAR and AEQ have been propagated in a haploid state.

Herein lies a critical point: AAR and AEQ were recently derived from a diploid background with a "high level of heterozygosity". In a heterozygous diploid context, deleterious point mutations (and any resulting mutator phenotypes) would likely be masked by the presence of wild-type alleles. Now, as haploids, they express a novel genotype (i.e., combination of defective or incompatible parental alleles), which manifests as a mutator phenotype. In this respect, AAR and AEQ appear analogous to the spore derivatives of the incompatible cMLH1-kPMS1 isolate referred to in the manuscript as a notable exception. The analysis of strains harboring incompatible MLH1-PMS1 mutations by Raghavan et al. demonstrated that the heterozygous diploid parents were not themselves mutators, but that haploid spores which had inherited the pair of incompatible alleles displayed mutator phenotype. Collectively, while it can certainly be argued that the strains AAR and AEQ (like the MLH1/PMS1 incompatible strains) are mutators now, this fact alone does not support the conclusion that they have adapted to survive the expression of an extant mutator phenotype. This premise could be tested by analyzing the mutation rates/spectra of four new spores derived from a single tetrad of CBS 1782. Do the four sibling spores display similar or different mutational rates and spectra? If all four spores from a single tetrad exhibit the 10-fold increase in CAN1 mutation rate and the C>A transversion bias, then it can be inferred that the diploid parent is also a mutator in the same manner. Further direct analysis of mutation rates and spectrum in the parent diploid CBS 1782 would complete the work. This finding would be quite significant, and would provide strong evidence that wild strains can in fact tolerate the expression of a chronic mutator allele.

Response:

We thank the reviewer for suggesting additional study of the ancestral diploid strain CBS 1782, and we agree this could add a lot to the manuscript, especially given the high level of heterozygosity in the diploid and the link to the previous MLH1-PMS1 incompatibility story. We have obtained a sample of CBS 1782 and plan to knock out its HO locus using CRISPR, perform tetrad dissection of spores freshly derived from the diploid, and then measure mutation rates and spectra in all four segregants derived from a single tetrad (provided that all four spores end up growing). We plan to collect and sequence about 50 mutations to get qualitative results on the mutation rates and spectra of these segregants. We also plan to sequence the whole genome of the strain CBS 1782 and examine polymorphisms together with the 1011 strains to check for any signal of C>A enrichment. We recognize that our pipeline as currently implemented will not let us directly measure the mutation spectrum of the diploid, which is inaccessible to our pipeline given its two functional copies of CAN1 and the recessive nature of canavanine resistance. That being said, the elevation of the C>A fraction in natural polymorphisms found in AAR and AEQ provides evidence for prolonged activity of the mutator phenotype in the wild and/or in the domesticated environment from which CBS 1782 was derived. However, we acknowledge we have limited information about how these haploids were propagated before they were banked.

**Minor comments:** A final, relatively minor point. That the new haploids AAR and AEQ show distinct mutation rates and spectra opens the door to an interesting line of inquiry, which may help to identify the causative mutator allele in a manner more efficient than searching for missense mutations. It is stated, and it is understandable, that the identification of the possible causal mutations is beyond the scope of the present manuscript. In this spirit, it would be much more appropriate to restrict such considerations to the Discussion section. Specifically, while the authors make a plausible case for OGG1 being a candidate gene responsible for the C>A mutator phenotype, no experimental demonstration was attempted. As such, that text segment should be moved from the Results to the Discussion section.

Response:

We agree with the reviewer of lacking genetic evidence on OGG1 in the current manuscript and we will move that section from the results to the discussion. Future work is underway to test and identify the causal loci for the mutator phenotype.

Reviewer #1 (Significance (Required)): As stated in the summary section above, the manuscript by Jiang et al represents a substantial contribution to the fields of genome stability and genome evolution. The method described is likely to be useful beyond budding yeast. The work will be appreciated by a broad audience of geneticists. The additional work and text modifications proposed above would likely further elevate the impact of this work.

Response:

We are very grateful for this generous assessment and we likewise hope our planned revisions will further elevate the paper’s potential impact.

Reviewer #2:

Mutation is a fundamental force in organismal evolution, and therefore understanding the evolution of mutational mechanisms are important in evolutionary studies. In this manuscript, the authors used strains of S. cerevisiae as a model system to study the variations of rates and spectra in mutations with bioinformatic and experimental approaches. First, the authors analyzed the polymorphism data from 1011 strains by PCA analysis and show the variations in spectra. Second, the authors used fluctuation test combined with deep sequencing of the resistance gene to identify mutation rates and spectra in 18 strains, which show ~10-fold mutation rate variations and increased C-to-A mutations in two strains.

For the second part, the experimental procedures and statistical analysis are mostly solid. For the first part, as what authors said in the introduction, polymorphism is not equal to the mutation spectra. I think the authors did a good job by being cautious in the wording and having no over-inference after the analysis. It is thus inevitable that the conclusion of this part sounds mostly descriptive. The overall writing is very clear. I will recommend the publication in field-specific journals.

Response:

We thank the reviewer for these positive comments. We will address each minor point below.

**Minor comments:** P9 - It is very hard to not wonder how the 16 strains were picked in the fluctuation tests. Some comments on that will be appreciated. E.g., was that informed by the results of Fig 1?

Response:

We actually did not pick strains based on the results of Figure 1, one reason being that the CAN1 reporter method only works on haploid strains with a canavanine sensitivity phenotype. We also restricted our analysis to strains without known aneuploidies to maximize our ability to accurately measure the spectra of the strains’ polymorphisms. When possible, given these constraints, we included at least two randomly selected strains from each clade of the 1011 collection whenever possible. These constraints are currently explained on the second to last paragraph on page 9, and will be explained in more detail in revision.

P17- In the paragraph "natural selection might contribute ..." , is there any example of "certain mutation types are more often beneficial than others"?

Response:

One example of this is that transitions are more often synonymous than transversions are (Freeland and Hurst, 1998), and mutations that create or destroy CpG sites are more likely to alter gene regulation than other mutation types are (in species other than yeast where CpGs are methylated). We recognize that these effects are likely not large, which is one reason we don’t think natural selection is a great explanation for mutation spectrum difference among groups.We will mention these examples explicitly in the revised text.

P20 - Extra ')' in the sentence "Adjacent indels were merged if their frequencies differed by less than 10%)."

Response:

We will fix this in revision.

In the discussion, it might be good to add a paragraph to compare the rate and spectra reported here and the ones found by MA and then NGS approach(e.g., Zhu et al. 2014).

Response:

We’ll be sure to add a reference to the Zhu et al. (2014) spectrum in the discussion, extending our existing comparison of mutation spectra previously reported using CAN1 (Lang and Murray, 2008) and the MA approach (Sharp et al., 2018) (currently discussed on the second to last paragraph on page 17, Supplementary Figure S13). Our CAN1 method also obtains results that are consistent with the Lang et al 2008 study on the same control strain (the last paragraph on page 11).

Reviewer #2 (Significance (Required)): The significance of this manuscript will be relatively specific to evolutionary biologists and geneticists, especially those who use yeasts as a model system. For example, I expect the variation of mutation rates and spectra found in this manuscript will impact the following population-genetic analysis in this collection of 1011 strains and motivate more studies on the molecular machineries which affect mutation rates and spectra.

In addition, in terms of methodological novelty, adding a novel step of reporter-gene sequencing is a reasonable way to get some information on mutation spectra as it is less labor-intensive than NGS of MAs. Other statistical or experimental procedures in this manuscript mostly follow the approaches which have been developed in previous literature and thus show not much novelty.

Response:

We thank the reviewer for this positive assessment. Since evolutionary biology, population genetics, and model organism genetics are three of eLife’s major focus areas, we are hoping to communicate our results to this journal’s broad audience rather than restrict ourselves to a journal focusing too narrowly on just one of these focus areas.

Reviewer #3:

**Summary** The authors show that certain yeast strains have altered mutation rates/bias. The study is well motivated, genetic variation in mutation rates are not easily uncovered, and capitalizes on yeast and a high-throughput mutation rate/bias method that validates findings of C>A bias from yeast polymorphism data. The results are solid and clearly presented and I have no major concerns.

Response:

We are very grateful for this positive response. Please find our response to each minor comment below.

**Major comments** None

**Minor comments** Should have comma: "In addition, environmental ..."

Response:

We will fix this in revision.

Using S. paradoxus to classify derived vs ancestral alleles may not work as well as allele frequency. A 1/100 rare variant is 100x more likely derived than common variant. But with S. paradoxus divergence of say 5%, 5% polymorphic sites are misclassified or NA. Of course, since you used both, this is not a concern. But the number of variants included/excluded in each analysis should be reported. Also, I was a bit surprised that the rare variants are more noisy since most variants are rare.

Response:

We agree that the heuristic of classifying rare alleles as derived will do the right thing the majority of the time, but this could potentially create artifactual differences between the mutation spectra of different populations because the exact ratio of rare derived alleles to common derived alleles depends on the population’s demographic history and true site frequency spectrum. If two populations had the same mutation spectrum but very different proportions of variants that are polarized incorrectly, this could create the appearance of a mutation spectrum difference where none exists. In the revision, we will be sure to report the total number of variants filtered because of the variation present in S. paradoxus.

The reviewer is right to point out that rare variants are generally more abundant than common variants, but this pattern is much more pronounced in a species like humans that has undergone recent population expansion than it appears to be in S. cerevisiae, which appears to have a higher proportion of older, shared variation. We hope this clarifies why the rare variant mutation spectrum PCA appears noisier than the plot made from variation across more frequency categories.

In regards to variation in mutation rate based on canavanine resistanct. There is a caveat that some strains may be more canavanine resistant - due to differences in transporter abundanced or some other aspect of metabolism. Thus, the same mutation would survive and grow (barely) in one strain background, but not another. This caveat is very unlikely to have much of an impact but it would be worth discussing.

Response:

Thanks for pointing this out. We also considered the possibility that our mutation rate estimates could be confounded by slight differences in canavanine resistance between strains, and will address this point in the discussion.

The explanation for synonymous mutations is hitchhikers or errors. However, they could also disrupt translation, here's one possibility PMC4552401.

Response:

Thanks for pointing this out. We will expand our statement on the possible significance of synonymous mutations to include modification of transcription and translation efficiency.

Are there CAN allele differences between strains? If there are some, it might be worth mentioning why you do/don't think this influences the mutation rate. E.g. CGG is one step from stop but CGT is not.

Response:

The reviewer makes a good point that there are segregating differences among these strains in the sequence of CAN1. We plan to add an analysis where we calculate the number of opportunities for missense mutations and nonsense in each strain, as a function of its CAN1 sequence, to put a bound on the amount that these differences could affect our estimates of mutation rates in each strain.

For the allele counts in Figure 5B. 2 indicates a variant is present in one strain so there are only 9 mutations present in AAR and not found in ANY other strain or just not found in the four listed? Likewise AAR has 36 for count 4, meaning that there are 36 variants present in AAR and one other strain, where other strains are just the 4 shown in the table, or other strains being any of the 1011?

Response:

The allele count in Figure 5B represents the number of times the derived allele is present in the whole population. In this case, the whole population refers to the 1011 strains minus 336 strains that are so closely related to other strains in the panel that they are effectively duplicates. An allele of count 2 might be homozygous in AAR and absent from all other strains, or present as one heterozygous copy in AAR as well as one heterozygous copy in another strain. We will explain this more clearly in the revised manuscript.

"To our knowledge, this is one of the first" This is an odd way to put it and could be rephrased. As it stand you are either the first and not knowledgeable or knowledgeable and not the first.

Response:

Thanks. We will revise this to state that to our knowledge, we are the first to report such a discovery.

"humans, great apes, .." Could you put the citations in the discussion too. I was a little surprised there was no mention of C>A bias as it relates to studies in bacteria and cancer, where there has been a lot of work on mutational spectra. A comment on this literature or whether the C>A biases are not found elsewhere would be nice.

Response:

We will add citations and discussion of bacteria and cancer in the revised manuscript. The reviewer is right to point out that C>A mutations do come up in cancer signatures, for example in familial adenomatous polyposis disorders where excision repair of 8-oxoguanine is compromised.

Reviewer #3 (Significance (Required)):

I am an evolutionary geneticist with expertise in genomics and bioinformatics. In addition to reviewing papers I also regularly handle papers as an editor. The manuscript provides rare insight into population variation in mutation rates. While differences in mutational biases are well known between species and in some cases within a species, we typically don't know what causes this biases. Environmental factors are often thought to be involved; this work clearly shows that genetic (mutator strains) exist and impact polymorphism in yeast. The manuscript does a nice job in the introduction of explaining the background on mutation rate research and motivation for the work. It also clear explains the advantage of an experimental highthroughput mutation rate/spectra approach. Thus, I believe this new angle on a long-standing problem will be of interest to the community of evolutionary geneticists outside of yeast researchers.

Response:

We appreciate this very generous assessment, thank you!

Reference

Freeland, S. J. and Hurst, L. D. (1998) ‘The genetic code is one in a million’, Journal of molecular evolution, 47(3), pp. 238–248.

Lang, G. I. and Murray, A. W. (2008) ‘Estimating the Per-Base-Pair Mutation Rate in the Yeast Saccharomyces cerevisiae’, Genetics, 178(1), pp. 67–82.

Sharp, N. P. et al. (2018) ‘The genome-wide rate and spectrum of spontaneous mutations differ between haploid and diploid yeast’, Proceedings of the National Academy of Sciences of the United States of America, 115(22), pp. E5046–E5055.

Zhu, Y. O. et al. (2014) ‘Precise estimates of mutation rate and spectrum in yeast’, Proceedings of the National Academy of Sciences of the United States of America, 111(22), pp. E2310–8.

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Referee #3

Evidence, reproducibility and clarity

Summary

The authors show that certain yeast strains have altered mutation rates/bias. The study is well motivated, genetic variation in mutation rates are not easily uncovered, and capitalizes on yeast and a high-throughput mutation rate/bias method that validates findings of C>A bias from yeast polymorphism data. The results are solid and clearly presented and I have no major concerns.

Major comments

None

Minor comments

Should have comma: "In addition, environmental ..."

Using S. paradoxus to classify derived vs ancestral alleles may not work as well as allele frequency. A 1/100 rare variant is 100x more likely derived than common variant. But with S. paradoxus divergence of say 5%, 5% polymorphic sites are misclassified or NA. Of course, since you used both, this is not a concern. But the number of variants included/excluded in each analysis should be reported. Also, I was a bit surprised that the rare variants are more noisy since most variants are rare.

In regards to variation in mutation rate based on canavanine resistanct. There is a caveat that some strains may be more canavanine resistant - due to differences in transporter abundanced or some other aspect of metabolism. Thus, the same mutation would survive and grow (barely) in one strain background, but not another. This caveat is very unlikely to have much of an impact but it would be worth discussing.

The explanation for synonymous mutations is hitchhikers or errors. However, they could also disrupt translation, here's one possibility PMC4552401.

Are there CAN allele differences between strains? If there are some, it might be worth mentioning why you do/don't think this influences the mutation rate. E.g. CGG is one step from stop but CGT is not.

For the allele counts in Figure 5B. 2 indicates a variant is present in one strain so there are only 9 mutations present in AAR and not found in ANY other strain or just not found in the four listed? Likewise AAR has 36 for count 4, meaning that there are 36 variants present in AAR and one other strain, where other strains are just the 4 shown in the table, or other strains being any of the 1011?

"To our knowledge, this is one of the first" This is an odd way to put it and could be rephrased. As it stand you are either the first and not knowledgeable or knowledgeable and not the first.

"humans, great apes, .." Could you put the citations in the discussion too. I was a little surprised there was no mention of C>A bias as it relates to studies in bacteria and cancer, where there has been a lot of work on mutational spectra. A comment on this literature or whether the C>A biases are not found elsewhere would be nice.

Significance

I am an evolutionary geneticist with expertise in genomics and bioinformatics. In addition to reviewing papers I also regularly handle papers as an editor. The manuscript provides rare insight into population variation in mutation rates. While differences in mutational biases are well known between species and in some cases within a species, we typically don't know what causes this biases. Environmental factors are often thought to be involved; this work clearly shows that genetic (mutator strains) exist and impact polymorphism in yeast. The manuscript does a nice job in the introduction of explaining the background on mutation rate research and motivation for the work. It also clear explains the advantage of an experimental highthroughput mutation rate/spectra approach. Thus, I believe this new angle on a long-standing problem will be of interest to the community of evolutionary geneticists outside of yeast researchers.

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Referee #2

Evidence, reproducibility and clarity

Mutation is a fundamental force in organismal evolution, and therefore understanding the evolution of mutational mechanisms are important in evolutionary studies. In this manuscript, the authors used strains of S. cerevisiae as a model system to study the variations of rates and spectra in mutations with bioinformatic and experimental approaches. First, the authors analyzed the polymorphism data from 1011 strains by PCA analysis and show the variations in spectra. Second, the authors used fluctuation test combined with deep sequencing of the resistance gene to identify mutation rates and spectra in 18 strains, which show ~10-fold mutation rate variations and increased C-to-A mutations in two strains.

For the second part, the experimental procedures and statistical analysis are mostly solid. For the first part, as what authors said in the introduction, polymorphism is not equal to the mutation spectra. I think the authors did a good job by being cautious in the wording and having no over-inference after the analysis. It is thus inevitable that the conclusion of this part sounds mostly descriptive. The overall writing is very clear. I will recommend the publication in field-specific journals.

Minor comments:

P9 - It is very hard to not wonder how the 16 strains were picked in the fluctuation tests. Some comments on that will be appreciated. E.g., was that informed by the results of Fig 1?

P17- In the paragraph "natural selection might contribute ..." , is there any example of "certain mutation types are more often beneficial than others"?

P20 - Extra ')' in the sentence "Adjacent indels were merged if their frequencies differed by less than 10%)." In the discussion, it might be good to add a paragraph to compare the rate and spectra reported here and the ones found by MA and then NGS approach(e.g., Zhu et al. 2014).

Significance

The significance of this manuscript will be relatively specific to evolutionary biologists and geneticists, especially those who use yeasts as a model system. For example, I expect the variation of mutation rates and spectra found in this manuscript will impact the following population-genetic analysis in this collection of 1011 strains and motivate more studies on the molecular machineries which affect mutation rates and spectra.

In addition, in terms of methodological novelty, adding a novel step of reporter-gene sequencing is a reasonable way to get some information on mutation spectra as it is less labor-intensive than NGS of MAs. Other statistical or experimental procedures in this manuscript mostly follow the approaches which have been developed in previous literature and thus show not much novelty.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The authors describe a useful modified fluctuation assay that couples conventional mutation rate analysis with mutational spectrum characterization of forward mutations at the S. cerevisiae CAN1 locus. They nicely showed that wild yeast isolates display a wide range of mutation rates with strains AAR and AEQ displaying rates ~10-fold higher than the control lab strain. These two strains also showed a bias for C>A mutations, and were the only strains analyzed that had a mutation spectrum statistically different from the lab control. Together, these data provide a compelling proof-of-principle of the applicability of the modified fluctuation analysis approach described in this manuscript. Overall, the manuscript is very well written, and the work reported in it does represent a valuable contribution to the field. However, two primary shortcomings were identified that can be addressed to strengthen the conclusions prior to publication. Both points described below pertain to the analysis of the possible C>A specific mutator phenotype in strains AAR and AEQ.

Major comments:

1. The work presented in the manuscript does suggest that these two haploids are likely to display the C>A mutator phenotype. Yet, the authors fell short of providing a full and unambiguous demonstration that would elevate the significance of their discovery. They could have directly tested the predicted C>A specific mutator phenotype by conducting additional experiments, one of which is relatively simple. Specifically, they could have performed a simple reversion-based mutation assay to validate the reported C>A mutator phenotype displayed by AAR and AEQ. For example, into AAR, AEQ, and a wild type control, the authors could introduce an engineered auxotrophic marker allele (e.g., ura3 mutation) caused by an A to C substitution, which upon mutation back to A restores prototrophic growth in minimal media (ie. reversion from ura3-C to URA3-A). Such specific reversible allele should be relatively easy to integrate into the AAR and AEQ genomes, as well as in the control strain. Based on the authors' prediction, AAR and AEQ should display a very large increase (far higher than 10 fold) in the reversion rate when compared to a control haploid. To demonstrate the specificity of the mutation spectrum, the authors could test the reversion rates of a different engineered allele requiring a reversion mutation in the opposite direction (ie. reversion from ura3-A to URA3-C). If the AAR and AEQ mutator is specific C>A, one would predict that all three strains should have similar mutation rates for a reversion in the A>C direction. This additional genetic work would thoroughly validate the central discovery and would reinforce the usefulness of the method described in the manuscript.

Alternatively, a conventional mutation accumulation and whole genome re-sequencing experiment with parallel lines of AAR, AEQ and a control strain would also very effectively validate the C>A mutator prediction, and it would also answer the authors' discussion point about specificity to the CAN1 locus. However, it would be more costly and much more time consuming.

1. The second concern is in regard to the relatively extensive conclusions drawn about the possible evolutionary significance of the possible C>A mutator in AAR and AEQ. The authors should be more cautious and conservative in the proposed interpretation. As the authors note:

'Three of the four C>A-enriched mosaic beer strains, AAR, AEQ, and SACE_YAG, are all haploid derivatives of the [highly heterozygous] diploid Saccharomyces cerevisiae var diastaticus strain CBS1782, which was isolated in 1952 from super-attenuated beer.'

From this statement, and because the paper cited provided few details on the isolation of CBS1782, it is presumed that these haploid derivatives were most likely isolated as recombinant spores. Furthermore, it is unclear when this isolation occurred, and for how many generations strains AAR and AEQ have been propagated in a haploid state.

Herein lies a critical point: AAR and AEQ were recently derived from a diploid background with a "high level of heterozygosity". In a heterozygous diploid context, deleterious point mutations (and any resulting mutator phenotypes) would likely be masked by the presence of wild-type alleles. Now, as haploids, they express a novel genotype (i.e., combination of defective or incompatible parental alleles), which manifests as a mutator phenotype. In this respect, AAR and AEQ appear analogous to the spore derivatives of the incompatible cMLH1-kPMS1 isolate referred to in the manuscript as a notable exception. The analysis of strains harboring incompatible MLH1-PMS1 mutations by Raghavan et al. demonstrated that the heterozygous diploid parents were not themselves mutators, but that haploid spores which had inherited the pair of incompatible alleles displayed mutator phenotype. Collectively, while it can certainly be argued that the strains AAR and AEQ (like the MLH1/PMS1 incompatible strains) are mutators now, this fact alone does not support the conclusion that they have adapted to survive the expression of an extant mutator phenotype. This premise could be tested by analyzing the mutation rates/spectra of four new spores derived from a single tetrad of CBS 1782. Do the four sibling spores display similar or different mutational rates and spectra? If all four spores from a single tetrad exhibit the 10-fold increase in CAN1 mutation rate and the C>A transversion bias, then it can be inferred that the diploid parent is also a mutator in the same manner. Further direct analysis of mutation rates and spectrum in the parent diploid CBS 1782 would complete the work. This finding would be quite significant, and would provide strong evidence that wild strains can in fact tolerate the expression of a chronic mutator allele.

Minor comments:

A final, relatively minor point. That the new haploids AAR and AEQ show distinct mutation rates and spectra opens the door to an interesting line of inquiry, which may help to identify the causative mutator allele in a manner more efficient than searching for missense mutations. It is stated, and it is understandable, that the identification of the possible causal mutations is beyond the scope of the present manuscript. In this spirit, it would be much more appropriate to restrict such considerations to the Discussion section. Specifically, while the authors make a plausible case for OGG1 being a candidate gene responsible for the C>A mutator phenotype, no experimental demonstration was attempted. As such, that text segment should be moved from the Results to the Discussion section.

Significance

As stated in the summary section above, the manuscript by Jiang et al represents a substantial contribution to the fields of genome stability and genome evolution. The method described is likely to be useful beyond budding yeast. The work will be appreciated by a broad audience of geneticists. The additional work and text modifications proposed above would likely further elevate the impact of this work.

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Reply to the reviewers

Answers to the reviewers’ comments

We deeply appreciate the reviewers for their thoughtful, critical and constructive comments, which have undoubtedly provided us with valuable opportunities to improve our manuscript.

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

Extravasation of lymphocytes from HEV in the lymph nodes is mediated by the interaction between lymphocyte L-selectin and PNAd-carrying sulfated sugars expressed by HEVs. Multiple steps of lymphocyte migration interacting with ECs at the luminal side of HEVs have been studied intensively; however, post-luminal migration steps are unclear. In this study, using intravital confocal microscopy of peripheral lymph nodes (pLNs), the authors found that GlcNAc6ST1 deficiency, required for sulfation of PNAd, delays trans-fibroblastic reticular cell (FRC) migration of lymphocytes, and hot spots of trans-HEV EC migration and trans-FRC migration. Interestingly, hot spots of trans-FRC migration are often associated with dendritic cells (DCs). Thus, the authors concluded that FRCs delicately regulate the transmigration of T and B cells across the HEV wall, which could be mediated by perivascular DCs.

**Main comments**

1. This study focused on pLNs, which are quite different from mesenteric lymph nodes (mLNs) in many ways. The authors should include mLNs in their study to make the general statement with regard to the T/B cell entry into lymph nodes. In addition, it will be more significant if this study includes challenged pLNs.

We thank the reviewer for raising the important point. We agree that mesenteric lymph nodes are quite different from peripheral lymph node that this study focuses on. Therefore, we specified the popliteal or peripheral lymph node in the revised manuscript as follows.

In the Abstract (page 2), “… Herein, we performed intravital imaging to investigate post-luminal T and B cell migration in popliteal lymph node, consisting of trans-EC migration, crawling in the perivascular channel (a narrow space between ECs and FRCs) and trans-FRC migration. … These results suggest that HEV ECs and FRCs with perivascular DCs delicately regulate T and B cell entry into peripheral lymph nodes.”

In the Introduction (page 4), “Herein, we clearly visualized the multiple steps of post-luminal T and B cell migration in popliteal lymph node, including trans-EC migration, intra-PVC crawling and trans-FRC migration, using intravital confocal microscopy and fluorescent labelling of ECs and FRCs with different colours.

In the Discussion (page 21), “… These results imply that pericyte-like FRCs, the second cellular barrier of HEVs, regulate the entry of T and B cells to maintain peripheral lymph node homeostasis more precisely and restrictively than we previously thought.”

In addition, we discussed the difference in lymphocyte migration across HEVs between peripheral lymph node, mesenteric lymph node, and peyer’s patches in the Discussion of the revised manuscript. We also discussed inflamed lymph nodes in the Discussion as follows.

In the Discussion (page 20), “… Although this work focused on peripheral lymph node, the other lymphoid organs have different lymphocyte homing efficiency61 due to organ-specific gene expression on HEVs62. B cells home better to mesenteric lymph nodes and peyer’s patches than peripheral lymph nodes61 by CD22-binding glycans expressed preferentially on the HEVs of mesenteric lymph nodes and peyer’s patches62.

Inflamed peripheral lymph node become larger by recruiting more lymphocytes and even L-selectin-negative leukocytes that are excluded in the steady state63,64. Inflamed HEV ECs show different gene expression, such as downregulation of GLYCAM1 and GlcNAc6ST-160. In addition, inflamed HEV integrity may be loosen due to markedly increased leukocyte influx although the HEV FRCs can prevent bleeding by interacting with platelet CLEC-248. CD11c+ DCs are associated with inflamed HEV EC proliferation that is functionally associated with increased leukocyte entry65. The stepwise migration of lymphocyte across inflamed HEVs and their hot spots with perivascular CD11+ DCs will be interesting topic for future study.”

The finding that GlcNAc6ST1 deficiency delays lymphocyte trans-FRC migration but not trans-HEV EC migration is surprising. However, the reason this occurs is neither shown nor discussed. Is GlcNAc6ST1 also expressed in FRCs? Or does GlcNAc6ST1 expression on HEV license lymphocytes to transmigrate across FRCs?

This is valid point to be addressed. GlcNAc6ST-1 is predominantly involved in PNAd expression on the abluminal side rather than on the luminal side. Therefore, our results that GlcNAc6ST-1 deficiency increased the time required for trans-FRC migration but not that for trans-EC migration, could be attributable to deficiency of GlcNAc6ST-1-synthesizing L-selectin ligands in the abluminal side of HEV.

In addition to PNAd expression in the luminal and abluminal sides of endothelial cells in HEV, PNAd expression has been observed in reticular network close to HEV as following figures. We believe that PNAds are expressed in FRCs close to HEV and can affect lymphocyte migration such as trans-FRC migration and parenchymal migration. By looking at the data (Table S1, Rodda et al., Immunity 2008), GlcNAc6ST-1 (Chst2) is expressed in T-cell-zone reticular cells while GlcNAc6ST-2 (Chst4) is absent. Therefore, it is presumable that FRC-expressed GlcNAc6ST1 may regulate trans-FRC migration in some extent.

Figures. PNAD expression on HEVs (arrows) and reticular network (arrow heads) close to the HEVs

We included these points in the Discussion of the revised manuscript (page 15) as follows.

“… GlcNAc6ST-1 is predominantly involved in PNAd expression on the abluminal side rather than on the luminal side, although GlcNAc6ST-1 deficiency also modestly affects the luminal migration of lymphocytes by increasing the rolling velocity9. GlcNAc6ST-1 deficiency increased the time required for trans-FRC migration but not that for trans-EC migration. This could be attributable to deficiency of GlcNAc6ST-1-synthesizing L-selectin ligands in the abluminal side of HEV. In addition to the abluminal side of HEV endothelial cells, FRCs also express GlcNAc6ST-1, but not GlcNAc6ST-227, implying that FRC-expressed GlcNAc6ST-1 may regulate trans-FRC migration in some extent. … Thus, PNAds expressed at the endothelial junction and on the abluminal side of HEVs facilitate the efficient transmigration of lymphocytes across the HEV wall but do not slow transmigration in the perivascular region. GlcNAc6ST-1 deficiency and MECA79 antibody also decreased the parenchymal B and T cell velocities immediately after extravasation, respectively, probably because of blockade of parenchymal expression of PNAd in close proximity to HEV6,21,28.”

Because of the adoptive transfusion experiment, the actual number of transmigrating lymphocytes in Fig. 3F is underestimated.

We agree with the reviewer’s comment. We corrected the y-axis label in Fig. 3F from ‘average number of cells transmigrating at one site’ to ‘average number of labeled cells transmigrating at one site.’

Whether DCs covering FRCs have a role for lymphocyte trans-migration is not shown.

We leaved this work as future research and discussed about the potential mechanisms in the Discussion (page 17-18) that the DC may regulate lymphocyte entering by interacting FRC with LTβR or CLEC-2 signaling. We also included ‘Martinez et al Cell Rep 2019 (ref.51)’ in the discussion of the revised manuscript (page 18). In addition, we also discussed about better characterization of the CD11c+ DC in the Discussion of the revised manuscript (page 19) as follows.

In the Discussion (page 18), “The podoplanin of FRCs also controls FRC contractility49,50 and ECM production51 by interacting with the CLEC-2 of DCs in inflamed lymph nodes. In the steady state, resident DCs in lymph nodes express CLEC-252. Thus, it is conceivable that CLEC-2+ resident DCs may control the contractility of FRCs and remodel ECM surrounding HEVs to facilitate the trans-FRC migration of T and B cells. Thus, the CLEC-2/podoplanin signalling may represent a key molecular mechanism underlying our discovery that trans-FRC migration hot spots preferentially occur at FRCs covered by CD11c+ DCs.”

In the Discussion (page 19), “… In addition, better characterization of the CD11c+ DCs located in the hot spots of HEVs is required to differentiate them from the other CD11c+ DCs observed in the non-hot-spot regions of HEVs. Some T-cell-zone resident macrophages can also express CD11c54. Imaging of a triple-transgenic mouse with Zbtb46-cre;tdTomato and CD11b-GFP will be able to differentiate 3 types of DCs and macrophages potentially associated with the hot spots: Zbtb46+CD11b- cDC1, Zbtb46+CD11b+ cDC2, and Zbtb46-CD11b+ macrophage54,55.”

In Fig. 1, time required for trans HEV EC migration and trans-FRC migration of T cells is shorter than that of B cells; however, this finding is not observed in Fig. 2C and E.

Although the statistical comparison between T and B cells are not shown in Fig. 2C-F and S5., there are actually significant difference between T and B cells, which are similar results as Fig. 1 except for the dwell time in PVC. P values between T and B cells in wildtype mice are 0.0003, In the Result (page 6), “… The mean velocity of T cells (5.3 ± 1.7 μm/min) was significantly higher than that of B cells (4.1 ± 1.4 μm/min) during intra-PVC migration (Fig. 1E), while the dwell time and total path length in the PVC were not significantly different between T and B cells (Fig. 1, H and I). Similar results were obtained when both cells were imaged simultaneously, except that B cells had significant longer dwell time than T cells (Fig. 2C-F and Fig. S5). Interestingly, more than half of the T and B cells crawled from 50 μm to 350 μm inside the PVC (Fig. 1I), …”

In the legend of Fig. 2, “… P values between T and B cells in wild-type mice were 0.0003 (C), …”

In the legend of Fig. S5, “… P values between T and B cells in wild-type mice were 0.0240 (A), 0.3614 (B), 0.7518 (C) and 0.1337 (D). …”

**Minor comments**

1. Please provide evidence for GlcNAc6ST1 deficiency in HEV and surrounding tissues.

Previous studies (Uchimura et al., JBC 2004, Nat. Immunol. 2005; ref9 and 10, respectively, in the manuscript) confirmed systemic deficiency of GlcNAc6ST-1 in peripheral lymph nodes of the GlcNAc6ST-1 KO mice.

Images for delayed trans-FRC migration in GlcNAc6ST1 KO mice relative to WT are not convincing (Fig. 2G and H).

We think the reason why the images look unconvincing is probably because it is not easy to quickly determine the images corresponding to the trans-FRC migration in the image sequence. To make the transmigration images easier to recognize, we added arrow heads indicating the transmigration site in Fig. 2G and 2H, and Fig. S4 as follows.

Provide actual time periods required for Fig. 3F and G. Lack of isotype control IgG experiment in Fig. S3.

We added the time periods (3 hours) in the figure legend as follows.

“… (F) Average numbers of labeled T and B cells transmigrating at one site for 3 hours. (G) Ratio of hot spots to total transmigration sites for 3 hours. …”

The purpose of Fig. S3 was to confirm that the anti-ER-TR7 antibody injection for labeling FRC do not alter normal T cell motility, rather than to confirm the function of ER-TR7. Therefore, we used non-injected group as control rather than control antibody injection group.

Line 12 on page 11, "the ratio of hot spots to the total “observed” transmigration sites..." is not appropriate. The ratio must be calculated by hot spots to the total "potential" transmigration sites, although it is challenging to find total potential sites.

We corrected the expression from ‘the total observed transmigration sites’ to ‘the total potential transmigration sites’.

Please correct typos of angiomoduin to angiomodulin (page 16), ET-TR7 to ER-TR7 (page 17), Anti-CD3 to anti-CD3 (page 22), half the dose to half dose (page 22), the Multiple step to the multiple step (page 23).

We thank the reviewer for finding those errors. We corrected them and performed proofreading repeatedly to correct typos and grammatic errors.

Please provide an additional explanation of why actin-DsRed in HEVs is more strongly expressed than surrounding tissues such as FRCs in Fig. 1 although actin-DsRed should be expressed in all cell types in mice.

We were also surprised when we found that HEV ECs expressed red fluorescence more strongly compared to surrounding tissues. Although the other cells such as FRCs and endogenous lymphocytes also express DsRed under control of a promotor gene, beta-actin, we believe that HEV ECs express more strongly, which is sufficient to image only HEV-EC by adjusting an image contrast. We revised the explanation of this point in the Methods (page 21) as follows.

“HEV ECs of actin-DsRed mouse popliteal lymph node expressed red fluorescence much stronger than the surrounding stromal cells and endogenous lymphocytes, which was sufficient to image only HEV ECs by adjusting an image contrast (Fig. 1, A and B).”

Reviewer #1 (Significance (Required)):

The study focused on lymphocytes post extravasation of HEV, which is an understudied question, using intravital imaging. The in vivo imaging study was deliberately and beautifully performed, and the finding is insightful for understanding lymphocyte trafficking in lymph nodes. However, additional experimental should be performed to address some weaknesses listed in our comments.

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

The present study by K. Choe meticulously monitored the stepwise transmigration behavior of T cells and B cells, respectively, through the high endothelial venules of the mouse popliteal lymph node using the laser scanning confocal microscopy. In particular, the study focused on the post-luminal migration of T and B cells and reported the following. (1) Mice deficient in GlcNAc6ST-1 which is necessary for PNAd expression on the abluminal side of HEV showed significantly reduced abluminal migration of both T and B cells, (2) the footpad injection of the ER-TR7 antibody did not affect T cell transmigration across HEVs but marginally increased the parenchymal T cell velocity when compared with injection of control antibody, (3) T cells and B cells tended to share FRC migration hot spots but this was not the case with trans-EC migration hot spot, (4) the trans-FRC migration was observed at the FRCs closely associated with CD11c+ dendritic cells in HEV.

While the present study is obviously the product of very meticulous and time-consuming work, it basically describes only a phenomenology, just reporting the lymphocyte behavior within and outside lymph node HEVs, without sufficiently analyzing the mechanistic aspect of the individual event they observed. The only antibody blocking experiments they performed to obtain mechanistic insights was by the use of commercially available monoclonal antibodies, all of which unfortunately contained a preservative, sodium azide, which potently blocks lymphocyte migration in vivo (Freitas AA & Bognacki J, Immunol 36:247, 1979). Therefore, the results of these antibody blocking experiments cannot be taken at face value.

We thank the reviewer for raising the important point. Freitas et al used pre-treated lymphocytes with sodium azide in vitro for 1 hour while we injected the antibody into the footpad of recipient mouse 3 hours before lymphocyte injection via tail vein and imaging. Sodium azide might be highly diluted in vivo condition. In addition, Fig. S3 shows no significant difference in T cell migration in HEV between anti-ER-TR7 antibody-injected and non-injected groups although the anti-ER-TR7 antibody also contains sodium azide. We believe that the effect of sodium azide on our convincing results of the PNAd-blocking antibody compared to the control antibody (Fig. S8) may be insignificant. The potential side effect of sodium azide was mentioned in the Methods of the revised manuscript (page 22) as follows.

“All antibodies we used contains sodium azide that has potential side effects on lymphocyte migration in lymph node57. However, Fig. S3 shows no significant difference in T cell migration in HEV between anti-ER-TR7-injected and non-injected groups.”

Reviewer #2 (Significance (Required)):

Real time imaging experiments were performed very carefully. However, as mentioned above, authors used sodium azide-containing antibodies for blocking experiments, and hence, these experiments cannot be interpreted properly.

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

This study presents a detailed investigation of T and B cell entry into lymph nodes (LN) via HEV. Substantial high quality intravital imaging is used to examine trans-EC and trans-FRC migration and define the role of PNAds in this process. The authors find that T and B cells use 'hot spots' to cross EC and FRC barriers, which supports prior similar observations by others. They also show that where T and B cells cross EC and FRC layers can differ, with regions of shared trans-FRC migration but more distinct EC crossing sites. This may relate to differences in the structure of these cellular layers, but provides novel insight into the mechanisms of cell entry into LNs via HEV. Assessment of the dependence on PNAd using antibodies or GlcNAc6ST-1 KO mice revealed perivascular and parenchymal cell behavior is also influenced by these signals. Lastly, examination of DCs that sit on the perivascular FRCs suggested that cells may prefer to cross at sites co-localized by DCs, although the reasons for this are not explored.

This is a well performed study, with high quality imaging data and analysis. The results are convincing, with sufficient numbers of mice and adequate statistical analysis. There are a number of minor grammatical errors throughout the text, which should be easy to fix.

We thank the reviewer for the positive evaluation. We carefully performed proofreading repeatedly to correct typos and grammatical errors.

Reviewer #3 (Significance (Required)):

Although 'hot spots' have been proposed by others, this detailed analysis provides new knowledge of how lymphocytes can cross the HEV and FRC barriers to enter LNs. This is an important study to advance our understanding of cell recruitment to lymph nodes. The role of perivascular and parenchymal PNAd signals observed here should also be of interest to immunologists to help define the signals required for immune cell motility in tissues.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

The authors have used a combination of intravital confocal imaging and transgenic models to study the migration of T and B cells through the HEVs. They move on from Moscacci et al. and Park et al., studies on lymphocyte migration. This study focuses on visualization and molecular mechanism of post-trans-EC migration, including the intra-PVC and trans-FRC migration of T and B cells in HEVs. They have been able to show how lymphocytes migrate through the HEV into the parenchyma. Using the GlcNAc6sT-1 (catalyst for sulfation of PNAds) KO model (and MECA control for PNAds blocking) they identify the role of L-selectin/PNAd for lymphocyte transmigration. The identification of hot spots of T and B cell transmigration in HEVs is novel and extremely interesting for the field however the data shown is not entirely convincing in their current form. The hot spots were defined as areas where the lymphocytes migrate through the HEV epithelial cells and pericyte (FRC) regions. These are areas where migration was greatly shared T and B cells. Using the CD11c-YFP mouse model they identified CD11c+ cells in proximity to the FRCs located at the migration hotspots which can drive further speculation regarding the mechanism by which these areas of the HEVs are more permissive.

**Major comments**

1) Intravital imaging of T and B cell transmigration across HEVS composed of ECs and FRCs

• Figure 1: The authors mention that they performed similar experiments for B cells. Authors should show comparative data for T cells and B cells.

• Panel S1B should be provided for both T and B cells in figure 1.

We added the image sequence of B cell migration and the panels (Fig S1B of previous manuscript) showing intra-PVC segments of T or B cells in Fig. 1C of the revised manuscript as follows.

2) T and B cells preferentially share hotspots for trans-FRC migration not EC-migration

• Figure 4: This data is important to the storyline but as presented it is difficult to understand. Results are overstated in the text however it is difficult to see where these conclusions come from based on the figure. In Figure 4B the authors should show percentages on the Venn diagram or remove it entirely. In Figure 4C the authors should add labels to their y-axis and separate the data in order to assist with the storyline and convince of the presence of hot spots.

We agree with the reviewer’s opinion. We removed the Venn diagram, separated the Fig. 4C into 4B and 4C, and added y-axis labels in the figures. In addition, we revised the figure legends and the text in the Results to make it easier to understand as follows.

In the figure legend, “…(B-C) The round and diamond symbols represent predicted and observed values, respectively, for the percentage of T cell hot spots in B cell hot spots (B), for the percentage of B cell hot spots in T cell hot spots (C). …”

In the Results (page12), “Simultaneously imaging T and B cells showed that some T and B cells transmigrated across FRCs at the same site (Fig. 4A and Movie S8). To investigate whether T and B cells share their hot spots preferentially or accidentally, we compared the percentage of T cell hot spots in total B cell hot spots (diamond symbols in Fig. 4B) with its predicted value that is the possibility of accidently sharing T and B cell hot spots (round symbols in Fig. 4B). The predicted value can be calculated as the percentage of T cell hot spots in total transmigration sites. To note, the percentage of hot spots in total sites for trans-FRC migration was higher than that for trans-EC migration (Fig. 3G and round symbols in Fig. 4B) maybe because the number of trans-FRC migration sites was less than that of trans-EC migration sites. It implies that the possibility of accidently sharing T and B cell hot spots for trans-FRC migration is higher than that for trans-EC migration. However, surprisingly, the percentage of T cell hot spots in B cell hot spots was significantly higher than its predicted value of accidently sharing hot spots for trans-FRC migration (Fig. 4B). Similarly, the percentage of B cell hot spots in T cell hot spots was also significantly higher than its predicted value for trans-FRC migration (Fig. 4C). These results imply that T and B cells preferentially share trans-FRC migration hot spots beyond the prediction for accidently sharing. However, there were no significant differences between observed and predicted values for trans-EC migration (Fig. 4B and 4C), which implies T and B cells just accidently share their trans-EC migration hot spots.”

3) T and B cells prefer to transmigrate across FRCs covered by perivascular CD11c+ DCs

• DCs drive changes to FRC phenotype and contractility. The interaction between CLEC-2 (on DCs and platelets) is important for driving permeability of the HEVs. The authors use the CD11c-YFP mouse model in Figure 5 (and the supporting figures) to show the proximity of the CD11c+ cells and FRCs. Data from Baratin et al., (Immunity, 2017) suggest that CD11c+ cells in the parenchyma are also T cell zone macrophages (TZMs) that were previously characterized as DCs. Macrophages have previously been shown important for perivascular transmigration of neutrophils during bacterial skin infection (Abtin et al.2014- Nat Immun). CD11c-YFP alone does not show the cells proximal to FRCs are DCs so the authors should try to stain them with CLEC-2 or use the CLEC9a-cre mouse model to better characterise these cells.

We thank the reviewer for raising important point. We agree that the perivascular CD11c+ cells could be T-cell-zone macrophages (TZMs). Better characterization of the CD11c+ cells located in the hot spots of HEVs is required to determine if they are DCs or macrophages, and also to differentiate them from the other CD11c+ cells observed in the non-hot-spot regions of the HEVs. To differentiate DCs from TZMs, Zbtb46-GFP mouse can be used for imaging because Zbtb46-GFP are highly expressed in conventional DCs (cDCs) but not monocytes, macrophages, or other lymphoid or myeloid lineages (Satpathy et al, JEM 2012). However, endothelial cells also express Zbtb46-GFP. To visualize only DCs in HEVs, we need to make a chimeric mouse by adoptive transfer of Zbtb46-GFP bone-marrow cells into irradiated wild-type mouse. Furthermore, using a triple transgenic mouse with Zbtb46-cre;tdTomato and CD11b-GFP will be able to differentiate 3 types of DCs and TZMs potentially associated with the hot spots: Zbtb46+CD11b- cDC1 (red), Zbtb46+CD11b+ cDC2 (yellow), and Zbtb46-CD11b+ macrophage (green). However, since generation or obtaining of those transgenic mice models including CLEC9a-cre mouse will take long time, we will leave this work as future research and discussed this point in the Discussion of the revised manuscript as follows. In addition, we think that it will be difficult to differentiate the CLEC2 of perivascular DCs from that of platelets by in vivo labeling by injection of anti-CLEC2 antibody conjugated with a fluorescent dye because the CLEC2 of platelets maintains HEV integrity with interacting of FRC podoplanin (Herzog et al, Nature 2013).

In the Discussion (page 19), “… In addition, better characterization of the CD11c+ DCs located in the hot spots of HEVs is required to differentiate them from the other CD11c+ DCs observed in the non-hot-spot regions of HEVs. Some T-cell-zone resident macrophages can also express CD11c54. Imaging of a triple-transgenic mouse with Zbtb46-cre;tdTomato and CD11b-GFP will be able to differentiate 3 types of DCs and macrophages potentially associated with the hot spots: Zbtb46+CD11b- cDC1 (red), Zbtb46+CD11b+ cDC2 (yellow), and Zbtb46-CD11b+ macrophage (green)54,55.”

**Minor comments**

1) Intravital imaging of T and B cell transmigration across HEVS composed of ECs and FRCs

• The velocity differences observed could be due to location of HEV in the parenchyma. Furthermore, FRC plasticity can cause differences in secretion of chemokine gradients based on the location of cells and their niche (Rhoda et al., Immunity 2018). HEVs regulation of lymphocyte entry can be influenced by their niche (Veerman et al., Cell Reports 2019). The authors should comment on the HEV position relative to B cell areas.

We included this point with the references (Rhoda et al, immunity 2018, ref 27; Veerman et al., Cell Rep. 2019, ref 60) in the Discussion of the revised manuscript (page 19-20) as follows.

“Compared to T cell, B cells took a longer time to pass EC and FRC layers in HEV and had lower velocity in PVC and parenchyma just after extravasation. Furthermore, the adhesion rate of B cells to HEV EC in luminal side is lower than that of T cells5. These could be attributed to lower expression of L-selectin and CCR7 on B cells than T cells18,59. The difference in homing efficiency between T and B cells may vary depending on the HEV location due to the heterogeneous expression of chemokines and integrins on HEV EC and surrounding FRCs in peripheral lymph node27,60. The HEVs imaged in this work were located around 40-70 μm depth from the capsule where might be close to B cell follicles. B cell homing efficiency in the deeper paracortical T cell zone could be different from our data probably due to less CXCL13 that is chemoattractant for B cells highly expressed in follicles. …”

• Images shown in Fig1A is the same as Fig S1A/B. I presume this is an error.

Fig. 1A and Fig. S1A correspond to a 20-um-thick maximum intensity projection and single z-frame without projection, respectively. To avoid the confusion, we changed Fig.1A to the single z-frame (Fig S1A) and remove the 20-um thick maximum projection.

• Figure S3: Data for Ab treated appears to be identical to what is shown for T cells in Fig 1. I presume this is an error and the correct control will be shown.

We used the data of Fig. 1D-1I as the Ab-injected group in Fig. S3. We are sorry for the lack of clear explanation about this. We included the explanation in the figure legend as follows.

In the legend of Fig. S3, “(A-E) There is no significant difference between antibody-injected group (Ab) and non-injected group (Non) in T cell migration from trans-EC migration to trans-FRC migration. Non-injected means that no substance is injected into a footpad of mouse. We used the data of Fig. 1D-1I as the antibody-injected group. …”

2) Non-redundant role of L-selectin/PNAd interactions in post-luminal migration of T and B cells in HEV

• Could the authors clarify the number of mice used for this analysis (same applies to figure 1)

In the legends of Fig. 1-2, S6 and S8, there is the number of mice we used. In Fig. 1, “Four and 3 mice were used for the analysis of T and B cells, respectively.” In Fig. 2, “Four mice were analysed for each group.” In Fig. S6, “Three mice were analysed for each group.” In Fig. S8, “Five and 4 mice were analysed for the control Ab and MECA79 groups, respectively.”

In addition, we added the number of mice in the legend of Fig. S7. In Fig. S7, “The images are representative of 4 popliteal lymph nodes of 2 mice and 2 popliteal lymph nodes of a mouse for MECA79 and control IgM antibody, respectively.”

• Figure S6: further to percentages of T cell populations the authors should also provide the number of T cells (CD4, CD8, CM and naive) for both wildtype and KO.

We included the analyzed cell number by FACS in Fig. S6 and revised the figure legend as follows.

In the Fig. S6, “… (B) Analyzed cell numbers by FACS for 3 control and 3 KO mice. (C) Percentage of each type of T cells in DsRed+ T cells. No difference in the percentage of homing central memory, Naïve CD4 and CD8 T cells between wild-type and KO mice. …”

**Methods** for the flow cytometry analysis could the details of how samples were processed (or reference) be provided.

We added the details in the Methods (page 24) as follows.

“Popliteal and inguinal lymph nodes were harvested and single-cell suspensions were prepared by mechanical dissociation on a cell strainer (RPMI-1640 with 10% FBS). Cell suspensions were centrifuged at 300g for 5 min. Erythrocytes in lymph nodes were lysed with ACK lysis buffer for 5 min at RT. Cell suspensions were washed and filtered through 40um filters. Non-specific staining was reduced by using Fc receptor block (anti-CD16/CD32). Cells were incubated for 30 min with varying combinations of the following fluorophore-conjugated monoclonal antibodies: anti-CD3e (clone 145-2C11, BD pharmigen), anti-CD4 (clone GK1.5, BD Pharmingen), anti-CD8 (clone 53-6.7, eBioscience), anti-CD44 (clone IM7, Biolegend) and anti-CD62L (clone MEL-14, eBioscience) antibodies (diluted at a ratio of 1:200) in FACS buffer (5% bovine serum in PBS). After several washes, cells were analyzed by FACS Canto II (BD Biosciences) and the acquired data were further evaluated by using FlowJo software (Treestar).

**References:** The discussion covers key references in the field, but more recent studies should be included. Some examples have been suggested in the comments sections. Key references missing that can help discussion/interpretation of the data include: 1) Veerman et al 2019, Cell reports. The data in that paper shows the heterogeneity of the HEV and different regulation of genes that control lymphocyte entry. This can also be linked to the comments above regarding section 1 and 2. 2) Rhodda et al 2018, Immunity that focuses on niche-associated heterogeneity of lymph node stromal cells. The authors should also include Webster et al., 2006, JEM which describes the role of DCs in regulating vascular growth in the lymph node.

We thank the reviewer for suggesting good references to discuss. We included the references #1 and #2 in the revised manuscript as we responded to the minor comment #1. We also cited Webster et al., JEM 2006 (as ref 65) in the Discussion of the revised manuscript (page 20) as follows.

“Inflamed peripheral lymph node become larger by recruiting more lymphocytes and even L-selectin-negative leukocytes that are excluded in the steady state63,64. Inflamed HEV ECs show different gene expression, such as downregulation of GLYCAM1 and GlcNAc6ST-160. In addition, inflamed HEV integrity may be loosen due to markedly increased leukocyte influx although the HEV FRCs can prevent bleeding by interacting with platelet CLEC-248. CD11c+ DCs are associated with inflamed HEV EC proliferation that is functionally associated with increased leukocyte entry65. The stepwise migration of lymphocyte across inflamed HEVs and their hot spots with perivascular CD11+ DCs will be interesting topic for future study.”

Reviewer #4 (Significance (Required)):

This paper asks important questions and can make a significant contribution to the field if all revisions are addressed. The authors identified PNAd as an important factor for T cell migration. Further to previous studies in the field suggesting non-random transmigration sites. The authors used intra-vital confocal imaging to identify how lymphocytes cross the epithelial cells and FRCs of the HEVs to migrate to the parenchyma. The authors identify hotspots used by lymphocytes to transmigrate. Finally, the authors show that CD11c+ cells are proximal to FRCs hotspots and might have a role in driving lymphocyte transmigration.

Audience: Lymphocyte/immune cell biology, stomal immunology, FRC and lymph node inflammation. My expertise: Stomal immunology, immunology, innate immunity

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Referee #4

Evidence, reproducibility and clarity

The authors have used a combination of intravital confocal imaging and transgenic models to study the migration of T and B cells through the HEVs. They move on from Moscacci et al. and Park et al., studies on lymphocyte migration. This study focuses on visualization and molecular mechanism of post-trans-EC migration, including the intra-PVC and trans-FRC migration of T and B cells in HEVs. They have been able to show how lymphocytes migrate through the HEV into the parenchyma. Using the GlcNAc6sT-1 (catalyst for sulfation of PNAds) KO model (and MECA control for PNAds blocking) they identify the role of L-selectin/PNAd for lymphocyte transmigration. The identification of hot spots of T and B cell transmigration in HEVs is novel and extremely interesting for the field however the data shown is not entirely convincing in their current form. The hot spots were defined as areas where the lymphocytes migrate through the HEV epithelial cells and pericyte (FRC) regions. These are areas where migration was greatly shared T and B cells. Using the CD11c-YFP mouse model they identified CD11c+ cells in proximity to the FRCs located at the migration hotspots which can drive further speculation regarding the mechanism by which these areas of the HEVs are more permissive.

Major comments:

1) Intravital imaging of T and B cell transmigration across HEVS composed of ECs and FRCs

• Figure 1: The authors mention that they performed similar experiments for B cells. Authors should show comparative data for T cells and B cells.
• Panel S1B should be provided for both T and B cells in figure 1.

2) T and B cells preferentially share hotspots for trans-FRC migration not EC- migration

• Figure 4: This data is important to the storyline but as presented it is difficult to understand. Results are overstated in the text however it is difficult to see where these conclusions come from based on the figure. In Figure 4B the authors should show percentages on the Venn diagram or remove it entirely. In Figure 4C the authors should add labels to their y-axis and separate the data in order to assist with the storyline and convince of the presence of hot spots.

3) T and B cells prefer to transmigrate across FRCs covered by perivascular CD11c+ DCs

• DCs drive changes to FRC phenotype and contractility. The interaction between CLEC-2 (on DCs and platelets) is important for driving permeability of the HEVs. The authors use the CD11c-YFP mouse model in Figure 5 (and the supporting figures) to show the proximity of the CD11c+ cells and FRCs. Data from Beratin et al., (Immunity, 2017) suggest that CD11c+ cells in the parenchyma are also T cell zone macrophages (TZMs) that were previously characterised as DCs. Macrophages have previously been shown important for perivascular transmigration of neutrophils during bacterial skin infection (Abtin et al.2014- Nat Immun). CD11c-YFP alone does not show the cells proximal to FRCs are DCs so the authors should try to stain them with CLEC-2 or use the CLEC9a-cre mouse model to better characterise these cells.

Minor comments:

1) Intravital imaging of T and B cell transmigration across HEVS composed of ECs and FRCs

• The velocity differences observed could be due to location of HEV in the parenchyma. Furthermore FRC plasticity can cause differences in secretion of chemokine gradients based on the location of cells and their niche (Rhoda et al., Immunity 2018).HEVs regulation of lymphocyte entry can be influenced by their niche (Veerman et al., Cell Reports 2019).The authors should comment on the HEV position relative to B cell areas.
• Images shown in Fig1A is the same as Fig S1A/B. I presume this is an error.
• Figure S3: Data for Ab treated appears to be identical to what is shown for T cells in Fig 1. I presume this is an error and the correct control will be shown.

2) Non-redundant role of L-selectin/PNAd interactions in post-luminal migration of T and B cells in HEV

• Could the authors clarify the number of mice used for this analysis (same applies to figure 1)
• Figure S6: further to percentages of T cell populations the authors should also provide the number of T cells (CD4, CD8, CM and naive) for both wildtype and KO.

Methods:

for the flow cytometry analysis could the details of how samples were processed (or reference) be provided.

References:

The discussion covers key references in the field but more recent studies should be included. Some examples have been suggested in the comments sections.Key references missing that can help discussion/interpretation of the data include: 1) Veerman et al 2019, Cell reports. The data in that paper shows the heterogeneity of the HEV and different regulation of genes that control lymphocyte entry. This can also be linked to the comments above regarding section 1 and 2. 2) Rhodda et al 2018, Immunity that focuses on niche-associated heterogeneity of lymph node stromal cells. The authors should also include Webster et al., 2006, JEM which describes the role of DCs in regulating vascular growth in the lymph node.

Significance

This paper asks important questions and can make a significant contribution to the field if all revisions are addressed. The authors identified PNAd as an important factor for T cell migration. Further to previous studies in the field suggesting non-random transmigration sites. The authors used intra-vital confocal imaging to identify how lymphocytes cross the epithelial cells and FRCs of the HEVs to migrate to the parenchyma. The authors identify hotspots used by lymphocytes to transmigrate. Finally the authors show that CD11c+ cells are proximal to FRCs hotspots and might have a role in driving lymphocyte transmigration.

Audience: Lymphocyte/immune cell biology, stomal immunology, FRC and lymph node inflammation.

My expertise: Stomal immunology, immunology, innate immunity

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 study presents a detailed investigation of T and B cell entry into lymph nodes (LN) via HEV. Substantial high quality intravital imaging is used to examine trans-EC and trans-FRC migration and define the role of PNAds in this process. The authors find that T and B cells use 'hot spots' to cross EC and FRC barriers, which supports prior similar observations by others. They also show that where T and B cells cross EC and FRC layers can differ, with regions of shared trans-FRC migration but more distinct EC crossing sites. This may relate to differences in the structure of these cellular layers, but provides novel insight into the mechanisms of cell entry into LNs via HEV. Assessment of the dependence on PNAd using antibodies or GlcNAc6ST-1 KO mice revealed perivascular and parenchymal cell behaviour is also influenced by these signals. Lastly, examination of DCs that sit on the perivascular FRCs suggested that cells may prefer to cross at sites co-localised by DCs, although the reasons for this are not explored.

This is a well performed study, with high quality imaging data and analysis. The results are convincing, with sufficient numbers of mice and adequate statistical analysis. There are a number of minor grammatical errors throughout the text, which should be easy to fix.

Significance

Although 'hot spots' have been proposed by others, this detailed analysis provides new knowledge of how lymphocytes can cross the HEV and FRC barriers to enter LNs. This is an important study to advance our understanding of cell recruitment to lymph nodes. The role of perivascular and parenchymal PNAd signals observed here should also be of interest to immunologists to help define the signals required for immune cell motility in tissues.

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Referee #2

Evidence, reproducibility and clarity

The present study by K. Choe meticulously monitored the stepwise transmigration behavior of T cells and B cells, respectively, through the high endothelial venules of the mouse popliteal lymph node using the laser scanning confocal microscopy. In particular, the study focused on the post-luminal migration of T and B cells and reported the following. (1) Mice deficient in GlcNAc6ST-1 which is necessary for PNAd expression on the abluminal side of HEV showed significantly reduced abluminal migration of both T and B cells, (2) the footpad injection of the ER-TR7 antibody did not affect T cell transmigration across HEVs but marginally increased the parenchymal T cell velocity when compared with injection of control antibody, (3) T cells and B cells tended to share FRC migration hot spots but this was not the case with trans-EC migration hot spot, (4) the trans-FRC migration was observed at the FRCs closely associated with CD11c+ dendritic cells in HEV.

While the present study is obviously the product of very meticulous and time-consuming work, it basically describes only a phenomenology, just reporting the lymphocyte behavior within and outside lymph node HEVs, without sufficiently analyzing the mechanistic aspect of the individual event they observed. The only antibody blocking experiments they performed to obtain mechanistic insights was by the use of commercially available monoclonal antibodies, all of which unfortunately contained a preservative, sodium azide, which potently blocks lymphocyte migration in vivo (Freitas AA & Bognacki J, Immunol 36:247, 1979). Therefore, the results of these antibody blocking experiments cannot be taken at face value.

Significance

Real time imaging experiments were performed very carefully. However, as mentioned above, authors used sodium azide-containing antibodies for blocking experiments, and hence, these experiments cannot be interpreted properly.

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Referee #1

Evidence, reproducibility and clarity

Extravasation of lymphocytes from HEV in the lymph nodes is mediated by the interaction between lymphocyte L-selectin and PNAd-carrying sulfated sugars expressed by HEVs. Multiple steps of lymphocyte migration interacting with ECs at the luminal side of HEVs have been studied intensively; however, post-luminal migration steps are unclear. In this study, using intravital confocal microscopy of peripheral lymph nodes (pLNs), the authors found that GlcNAc6ST1 deficiency, required for sulfation of PNAd, delays trans-fibroblastic reticular cell (FRC) migration of lymphocytes, and hot spots of trans-HEV EC migration and trans-FRC migration. Interestingly, hot spots of trans-FRC migration are often associated with dendritic cells (DCs). Thus, the authors concluded that FRCs delicately regulate the transmigration of T and B cells across the HEV wall, which could be mediated by perivascular DCs.

Main comments:

1. This study focused on pLNs, which are quite different from mesenteric lymph nodes (mLNs) in many ways. The authors should include mLNs in their study to make the general statement with regard to the T/B cell entry into lymph nodes. In addition, it will be more significant if this study includes challenged pLNs.
2. The finding that GlcNAc6ST1 deficiency delays lymphocyte trans-FRC migration but not trans-HEV EC migration is surprising. However, the reason this occurs is neither shown nor discussed. Is GlcNAc6ST1 also expressed in FRCs? Or does GlcNAc6ST1 expression on HEV license lymphocytes to transmigrate across FRCs?
3. Because of the adoptive transfusion experiment, the actual number of transmigrating lymphocytes in Fig. 3F is underestimated.
4. Whether DCs covering FRCs have a role for lymphocyte trans-migration is not shown.
5. In Fig. 1, time required for trans HEV EC migration and trans-FRC migration of T cells is shorter than that of B cells; however, this finding is not observed in Fig. 2C and E.

Minor comments:

1. Please provide evidence for GlcNAc6ST1 deficiency in HEV and surrounding tissues.
2. Images for delayed trans-FRC migration in GlcNAc6ST1 KO mice relative to WT are not convincing (Fig. 2G and H).
3. Provide actual time periods required for Fig. 3F and G. Lack of isotype control IgG experiment in Fig. S3.
4. Line 12 on page 11, "the ratio of hot spots to the total;observed' transmigration sites..." is not appropriate. The ratio must be calculated by hot spots to the total "potential" transmigration sites, although it is challenging to find total potential sites.
5. Please correct typos of angiomoduin to angiomodulin (page 16), ET-TR7 to ER-TR7 (page 17), Anti-CD3 to anti-CD3 (page 22), half the dose to half dose (page 22), the Multiple step to the multiple step (page 23).
6. Please provide an additional explanation of why actin-DsRed in HEVs is more strongly expressed than surrounding tissues such as FRCs in Fig. 1 although actin-DsRed should be expressed in all cell types in mice.

Significance

The study focused on lymphocytes post extravasation of HEV, which is an understudied question, using intravital imaging. The in vivo imaging study was deliberately and beautifully performed, and the finding is insightful for understanding lymphocyte trafficking in lymph nodes. However, additional experimental should be performed to address some weaknesses listed in our comments.

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Reply to the reviewers

Our response to reviewers has been provided as a formatted typeset pdf file. This includes the original review comments (bolded) and our responses. In particular, our responses include several figures. Our intention is to include the full set of reviews and responses as supplementary information in our manuscript once published at a journal - we would also be happy to have this document uploaded to biorXiv for readers as well.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Kannan et al start with the good idea of using Shannon entropy as a way to temporally classify the development of cells, quantifying their maturation status by implementing it on single cell gene expression as measured by scRNAseq. The idea behind is that as cells develop, genes are silenced and hence the overall GeX entropy goes down. This approach would allow a robust method to compare heterogeneous datasets, an important problem that current scRNAseq analysis methods (such as Monocle) using dimensionality reduction are unable to robustly perform this task. Unfortunately the analysis and calculation of the entropy and also the results obtained do not generate convincing proof that Entropy is actually a good metric for comparing development in diverse datasets/cell types.

Major Comments:

-The calculation of the entropy is not clear enough (or not performed correctly).Shouldn't Pi be the GeX distribution of Gene i across all cells? The authors seem to have calculated Pi as the probability of expression in one cell then summed across. Unless I am wrong, this does not make sense and invalidates all the analysis.

-Entropy score correlated only moderately with pseudotimes for the three methods. This is a major problem that needs to be explained. One would expect entropy to give a higher correlation if it is a robust measure of development.

-One of the main purposes of the approach is to classify maturation of in vitro datasets, but basically no entropy changes are found. They are minimal in figures 5c. Following with this, the developmental times of the datasets as shown by color codes do not match the changes in entropy (see Figs 4b, 5a/b.

Minor Comments:

-Also Pi being a probability, how was the normalization performed so that the sum of the probability is 1. Given the variability in gene expression, scRNAseq platforms and number of cells it would be good to have a metric estimating the quality of the distribution. -why is the entropy not compared between the Kannan dataset and Wang and Yao? This would prove that indeed entropy is a good measure as opposed to UMAP+monocle.

Fig 3 should be in the supplement.

Significance

The idea behind this study is of potential significance as well stated by the authors, but the implementation of these ideas lacks scientific rigor. Entropy analysis needs to be repeated or clarified/better explained.

Referees cross-commenting

After reading the other reviewers comments showing the relevance of the approach developed by the authors, I do feel that with some clarification/discussion regarding the technical questions of the analysis solving the doubts I expressed, the manuscript could be of interest.

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Referee #2

Evidence, reproducibility and clarity

The manuscript does a fairly exhaustive job of comparing and bench-marking different single cell/nucleus RNA-seq on in vivo cardiomyocytes and in vitro cardiomyocyte differentiation protocols. The analyses is clearly described.

Minor comments, questions and clarifications sought:

It may be useful to emphasize that matching the entropy score of in vivo cardiomyocytes (or a given CM developmental state) is not a sufficient indication of matching the expression patterns of the in vivo counterpart. Compare entropy scores from cardiomyocytes from snRNA-seq on post mortem tissue (Litviňuková, et al. Nature volume 588, pages466-472(2020)) There are differences in cardiomyocytes obtained from different regions of the human heart (atrial vs. ventricular, left vs. right, etc.). It will be informative to compare the many in vitro differentiation datasets (and protocols) that may give result in atrial-like or ventricular-like CM to their in vivo counterparts. This question pertains to in vitro CM differentiation: Is entropy score sensitive to cell-types that differentiate into alternative lineages during in vitro differentiation (issue of purity)? Different cell lineages may have different maturation rates and if they are not excluded, the non-cardiomyocyte cells could contribute to noisy measurements. If the entropy score is calculated after a first round of clustering, on identified CM among the population (as opposed to cardiac progenitor cells, for example), I would be more confident of the entropy score.

This also pertains to in vitro CM differentiation: Even within the cardiomyocyte lineage, there may be different rates of development that ultimately lead to the same end point. Therefore there may be the need to coarse-grain the developmental time-points to account for the precocious ones and the 'late bloomers'. It may be useful to anchor the developmental trajectory based on entropy score to biological milestones (such as when the CM's start beating in plates). Can the authors comment on this, please?

CM's are interesting in that they are post-mitotic and as such, will attain a level or maturity at the end of the maturation process. I can imagine this not being the case for cells that continue to cycle and divide. It would be interesting to compare the change in entropy score for such cells. How about cells that differentiate when activated by an external stimulus (e.g., immune cells)? As long as a cell has high transcriptional variability or is transcriptionally active (e.g., as stress response) it may still show high entropy score. How would one interpret Entropy scores in such situations?

The authors note "higher mtGENE in differentiated cells and later time points."- Fig 2a. Could this be related to difficulty in dissociation, as part of stress response? The authors note "In particular, 10x Chromium and STRT-seq datasets appeared to have systematically higher percentages of ribosomal protein-coding genes than other protocols." Could this simply be due to higher transcript capture rate of these protocols? These protocols/techniques may not be statistically sampling a cell's transcripts at the same rate as the techniques with "lower" capture efficiency.

Can entropy score be used in the context of activation (under external stimulus) or deactivation (when the external stimulus is removed)?

What do the black dots represent in Fig 2c?

Significance

The manuscript, "Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level" by Kannan et al. introduces an interesting phenomenon, transcriptional entropy to track the rate of maturation in an important in PSC-derived cardiomyocytes. The need for cardiomyocyte in translational and clinical research along with the difficulty in getting live, mature cardiomyocytes from humans and make it imperative that in vitro systems are sought. Being able to characterize the rate of differentiation and maturation in these in vitro systems is also valuable and in that respect, the manuscript does a fairly exhaustive job of comparing and benchmarking different cardiomyocyte differentiation protocols that have been profiled by sc/snRNA-seq to date. Most importantly, comparing entropy scores between in vitro and in vivo counterparts is a simple and elegant way to anchor in vitro differentiation to pre- and post-natal development. Another interesting aspect of transcriptional entropy measure in a single cell is that it is independent of neighboring cells, and is therefore a conceptually different and novel way to characterize single cell data that, to date, have been analyzed by techniques that group cells by each cell's similarity to others. The study is well conceived and systematically explored. The manuscript is also well written. I recommend that the manuscript be accepted for publication.

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Referee #1

Evidence, reproducibility and clarity

Kannan et al. have developed an approach based on the quantification of gene distributions to assess pluripotent stem cell (PSC)-derived cell and tissue maturation. Methodologically, they combined single cell RNA-seq (scRNA-seq) with bioinformatic and statistical approaches to calculate transcriptomic entropy scores to benchmark cellular maturation. Their findings address unresolved issues regarding the developmental state of isolated cells and current problems associated with cell population heterogeneity. As model systems, the authors focused on cardiomyocytes (CMs) from mouse heart and on CMs generated through in vitro differentiated of PSCs from human. The authors examine a spectrum of CMs from mouse heart as a function of developmental time and provide evidence showing that scRNA-seq captures maturation related changes. Using a modification of the Shannon entropy of scRNA-seq and CMs isolated from embryonic, fetal, neonatal and early adult mouse hearts, they show that transcriptomic entropy scores decrease with developmental time. The authors then extend their results to human cells and perform a meta-analysis of publicly available scRNA-seq datasets. When cross-study comparisons were performed, meaningful comparisons could only be generated after gene and cell filtration. The output of the resulting workflow and computed entropy scores show good concordance among cells generated using different in vitro differentiation and different isolation techniques, and between stage-matched mouse and human tissues. The authors go on to show that in vitro derived CMs or reprogrammed CMs (from fibroblasts) undergo an apparent developmental block to maturation in vitro. The relevance of their approach to other cell systems was demonstrated using datasets from pancreatic beta cells and hepatocytes. In summary, the calculated entropy scores recapitulate known CM maturation gene expression profiles, making this approach invaluable for future comparisons between engineered and in vivo derived tissues.

Comments:

The key conclusions of the manuscript by Kannan et al. are supported by an examination of multiple datasets and the use of extensive and complementary bioinformatic and statistical analyses. The authors utilized a digestion and cell sorting approach that permits the isolation of viable CMs from mouse heart. The choice of scRNA-seq approaches eliminated cell type heterogeneity (either physically or bioinformatically) from otherwise complex cell populations. The authors then employed a variety of analytical approaches to identify limitations to cross-data comparisons and to define the maturation state of the cells. By minimizing protocol-related biases, resolving mismapping of mitochondrial reads to pseudogenes, taking into account variations in study sensitivity, and excluding datasets of relative poor quality, they were able to develop an informative workflow to generate meaningful entropy scores to benchmark maturation in cross-study and cross-species comparisons. These comparisons were validated using reprogrammed fibroblasts, hepatocytes and pancreatic beta cells. Overall, the experiments were well designed, the experimental and bioinformatic limitations addressed, and the conclusions supported by robust datasets, entropy scores, bioinformatics and statistics. This leads me to conclude that their validated approach will be of significant value to other researchers who need to benchmark cell maturation using a quantitative, transcriptome-based approach.

A few experimental additions or discussion points would have strengthened the overall impact of this study.

First, the process of cell dissociation coupled with cell sorting may be associated with a time lag in sample preparation that might be expected to affect RNA stability. If comparisons were performed between scRNA-seq and bulk RNA-seq, would the entropy scores have been equally informative or would differences have been observed from RNA instability that may have affected the entropy scores? While this test would be difficult with in vivo acquired cells, such a comparison could have been made using purified (but not sorted) hPSC-CMs. An answer to this question might be valuable to investigators who wish to use your approach to examine existing bulk RNA-seq datasets. Basically, is the workflow only applicable for scRNA-seq data where problems of cell heterogeneity can be eliminated, even though you provide evidence on how to exclude non-CMs from your datasets using transcriptome profiles?

Second, would mouse strain differences or sex differences cause a shift in the entropy scores or pseudotime analyses, even if only marginally? Not all mouse models develop at the same rate and sex is known to affect both murine fetal and infant growth.

Third, when performing the entropy scores and pseudotime analyses, were there specific transcripts or groups of transcripts that were more informative of specific stages of maturation? You mention that ~81.5% were identified as differentially expressed by all methods and some transcript profiles are shown in Figure 4e, but were any informative genes or gene sets (i.e., markers) more useful for assessing maturation that would not require scRNA-seq? This information (which could be added in the supplement) might make your approach more accessible to the broader research community (i.e., the identification of new and informative markers of CM development or differentiation). Alternatively, it may be that scRNA-seq is required. If so, then this should be discussed. Finally, could you comment further on the application of entropy scores to study maturation and how your approach may be of value to the research community? A number of situations beyond comparisons of engineered and in vivo tissues, and somatic cell reprogramming protocols might include an evaluation of PSC-CMs for pharmaceutical and toxicity testing, and the prediction of pathways that may be essential for maturation of cells either through a gene regulatory network or through individual signaling pathways. While these experiments and discussion points are not necessary to support your conclusions, an evaluation of these points and limitations in the Discussion may broaden the paper's impact and significance.

As minor critiques, there are a few typos (e.g., celltypes [cell types]), redundancies (e.g., ...transcript and protein level expression [...transcript and protein levels.]), and some improvements to the figures that could be made. For the latter, the font sizes are often too small (Figs 1, 3, 4, 5), as are some of the timepoints listed on the x axis (Fig 3a,d, 4b). Otherwise, the figures are visually informative, and the supplemental data are necessary to the assessment of the procedure.

Significance

The approach describe by Kannan et al. represents a significant advance over existing strategies to benchmark maturation states of PSC derivatives. Gene expression studies1 and transcriptome-based studies2-4 have been useful to estimate the developmental state of mouse and human PSC-CMs; however, most published studies have relied either on an assessment of a few markers or on data from a limited number of in vivo derived samples. These earlier studies were further limited by the confounding problem of heterogeneous cell populations. Omics based quantitative approaches have been proposed for improved maturation benchmarking and have proved valuable to study the differentiation of stem cells to progenitors and to committed lineages. 5-9 In this paper, Kannen et al. have improved upon these approaches and report the use of entropy scores to benchmark in vitro PSC-CM maturation against a gold standard of in vivo counterparts. The result is a reference resource that captures transcriptomic profiles from mouse CMs across a broad range of developmental states that will be particularly valuable to the cardiac field. By extending the assessments to include meta-analyses and cross-species comparisons (mouse versus human), they have established a workflow that results in a meaningful benchmark a cell's maturation state. Kannan et al., thus, have developed a quantitative and reproducible approach (entropy score) that simultaneously resolves issues of cell heterogeneity and estimates then in vivo maturation state of in vitro derived cells. This quantitative approach is likely to advance studies designed to assess drug and toxicity testing of more "adult-like" CMs, and adoption of this approach by the broader stem cell community will likely prove invaluable for the assessment of engineered tissues made from complex cell populations and for applications to regenerative medicine.

Keywords: Reviewer's field of expertise Cardiovascular Physiology, Stem Cell Biology, Omics

References:

1. AC Fijnvandraat, et al., Cardiomyocytes derived from embryonic stem cells resemble cardiomyocytes of the embryonic heart tube. Cardiovascular Research 58, 399-409 (2003).
2. E Poon, et al., Transcriptome-guided functional analyses reveal novel biological properties and regulatory hierarchy of human embryonic stem cell-derived ventricular cardiomyocytes crucial for maturation. PLoS ONE 8, e77784 (2013).
3. CW van den Berg, et al., Transcriptome of human foetal heart compared with cardiomyocytes from pluripotent stem cells. Development (Cambridge, England) 142, 3231-3238 (2015).
4. H Uosaki, et al., Transcriptional Landscape of Cardiomyocyte Maturation. Cell Reports 13, 1705-1716 (2015).
5. D Grun, et al., De Novo Prediction of Stem Cell Identity using Resource De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data. Cell Stem Cell 19, 266-277 (2016).
6. W Chen, AE Teschendorff, Estimating Differentiation Potency of Single Cells Using Single- Cell Entropy (SCENT). Comput. Methods for Single-Cell Data Analysis 1935, 125-139 (2019).
7. M Guo, EL Bao, M Wagner, JA Whitsett, Y Xu, SLICE : determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res. 45, 1-14 (2017).
8. AE Teschendorff, T Enver, Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome. Nat. Commun. 8, 1-15 (2017).
9. GS Gulati, et al., Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405-411 (2020).

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Reply to the reviewers

To whom it may concern:

We are thank the reviewers for their kind assessment of our work and its potential impact. Here we have outlined key points that we plan to address during revisions.

1. The erect wing story could be investigated a bit further. We agree the erect wing phenotype is intriguing, and will try to improve our understanding. We plan to use fat-body specific c564-Gal4 or BaraA-Gal4 to express UAS-BaraA and attempt to rescue the phenotype. In this way, we will also give insight into whether erect wing can be rescued by immune-tissue or BaraA-endogenous tissue effects. We will note that the cause of erect wing may be due to a lack of BaraA during development and/or during the immune response, which will require careful investigations in the future.
2. The in vitro antifungal data are modest. We agree. We will perform additional experiments to further corroborate these data to increase confidence in the trends observed.
3. The nature of the genetic backgro__unds is not clear.__ We will do our best to explain the genetic background complications in the main text. We use w; **∆BaraA flies as an independent means of confirming isogenic data (and vice versa). We had to backcross the ∆BaraA mutation with an arbitrary genetic background prior to experiments to remove an off-site mutation that we detected in the antifungal gene Daisho2 (formerly IM14). As such, there is no appropriate wild-type control for these flies as the background is mixed. We include OR-R as a generic wild-type representative. OR-R flies survive bacterial infection like w; **∆BaraA in multiple assays, and so we feel that different immune competences of the genetic backgrounds is unlikely to explain major susceptibilities to fungal infection. We have additional data for bassiana R444 infection (Fig. 4C-D) with a second wild-type that we can include if desired, which shows similar trends when compared to w; **∆BaraA. We will also perform additional experiments with newly-generated isogenic flies to increase confidence in the trends, and to better inform on interactions between BaraA and other immune effectors. For other minor points, we will be happy to make suggested changes to improve clarity of the figures or methodology.

Best regards,

Mark Hanson and Bruno Lemaitre

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Referee #3

Evidence, reproducibility and clarity

Summary:

Hanson et al. have set out to investigate the BaraA gene, and show that the gene encodes for several immune induced molecule (IM) peptides, namely IM10, IM12 (and its sub-peptide IM6), IM13 (and its sub-peptides IM5 and IM8), IM22, and IM24. Flies lacking BaraA are viable but susceptible to specific infections, notably by the entomopathogenic fungus Beauveria bassiana. Furthermore, they show that BaraA is antimicrobial and, when combined with the antifungal Pimaricin, it inhibits fungal growth. In principle, this is a nicely written paper with interesting findings. The authors show induction of BaraA with different micro-organisms and where BaraA is expressed, using a BaraA reporter. The exploration of the genomic area, showing the duplication of the BaraA locus is really nice work. Also, the survival experiments show quite clear phenotypes and therefore effects for BaraA.

Major comments:

Line 153, related results: Fold induction of BaraA is greater with E. coli (~50) than with C. albicans (~20) or M. luteus (~6) - any comments on this? Also, infection times with these microbes are different - some comments about BaraA kinetics? Based on Fig 1B, BaraA looks to be highly induced by E. coli, although in Fig 1C, after 60h, reporter induction by E. coli is much less than with M. luteus. Some clarification about the kinetics of BaraA in these different infection models is needed.

Erect wing phenotype in males: This is a bit surprising finding/interpretation. I have also seen erect wings in E. faecalis-infected flies, but I am not sure now in which flies I saw this; I have never tried to quantify this nor made any notes about females/males in this context. I normally use Myd88 RNAi (VDRC #25399) as a control in my experiments, and if they were the ones showing the erect wing phenotype in a prevalent manner, they would also lack BaraA (which is dependent on the Toll pathway function). At the time of doing my experiments, I just interpreted this in the way that the flies looked "sick", they were lifting their wings up and walking around rather than flying. When monitoring my survival experiments, I assumed that the ones with wings up were the ones dying next (the sickest). What is your interpretation; are the flies still ok or very sick, when this erect wing starts to appear?

Minor comments:

Wording: In the intro, line 78: "Many of the genes that encode these components of the immune peptidic secretome have remained largely unexplored." - I would say "had remained" until recently - especially the quite recent Bomanin work and work with Daisho1 & 2 have brought about a lot of new information about this "immune peptidic secretome".

Fig 1A: What is BaraA called in DeGregorio et al? Can't find it (easily) from their lists. Please write BaraA into the Fig 1A graph, to make it clearer. Also, write somewhere in the text or Figure legend what the gene is called in DeGregorio et al (CG33470? CG18278? something else?)

Line 238 Reference to Supplementary data file 1: In the supplementary data files I downloaded, I can't see the files numbered as data files 1 and so on. Instead, there are folders (Fly stocks, NF-kappaB sites, Primers used) and the files have names. Please clarify that the supplement names match the text.

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. State what audience might be interested in and influenced by the reported findings.

I think the significance of this work is great for Drosophila immunity researchers. The nature and mode of action of many of the Toll pathway -induced peptides is not known, so more information on them is much appreciated by the field. Also, studying molecules with potential antimicrobial activities is also potentially interesting for wider audience.

• Place the work in the context of the existing literature (provide references, where appropriate).

The main Drosophila immunity pathways are the Toll and the Imd pathway, and when activated, several immune effector genes are induced. In 2015, a group of Toll pathway target genes was identified by mass spectrometry, that the authors here call "the immune peptidic secretome". (Clemmons AW et al., PLoS Pathogens 2015). Many of these peptide genes have been uncharacterized, although emerging studies have shed light to these findings in the past three years (Lindsay SA et al J. Inn. Imm. 2018; Cohen LB et al. Front.Imm. 2020). This research brings about new information on yet uncharacterized peptides in this group.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Drosophila melanogaster, innate immunity, humoral immunity, cellular immunity Toll pathway, Imd pathway, immune-induced molecules

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Referee #2

Evidence, reproducibility and clarity

Summary:

Hanson et al. investigated an antifungal gene they named BaraA, which codes for a protein that is proteolytically processed into 8 smaller peptides. BaraA expression is induced by Toll pathway signaling with minor input from the Imd pathway. It is expressed in the fat body upon immune challenge and expressed in other tissues such as head and eyes. Overexpression of BaraA increased the survival of animals defective in both IMD and Toll pathways. In vitro, the combination of the three major BaraA peptides displayed modest inhibitory effect on fungal pathogens when combined with the antifungal drug Pimaricin, however BaraA peptides alone showed little or no antifungal activity. BaraA deficient mutants showed little to no significant difference in bacterial resistance but appeared to show susceptibility to fungal infection; this fungal susceptibility was independent of the Bomanins. Male BaraA mutants also displayed an erected-wings phenotype when subjected to infection.

There are 3 key findings:

• BaraA overexpression conferred protection against fungal infection.
• BaraA-derived peptides displayed antifungal activity in vitro in conjunction with Pimaricin, in vitro
• Loss of BaraA decreased fungal resistance.

Major Concerns:

The results from the overexpression experiments were clear. However, the second and third findings were less convincing.

• The cocktail of IM10-like BaraA peptides showed significant synergy with Pimaricin in killing C. albicans at only one dose out of the five tested, and this combination has modest (19-29%) inhibition on hyphae growth of B. bassiana. The in vitro antifungal experiments might be more compelling if other fungi were examined and/or combinations with other antifungals were investigated, where synergy might be more robust.
• The most problematic issue with this data is the control of genetic background in the study of the BaraA mutant strains. Much of the survival data compares mutant strains (BaraA and/or Bom∆) with Oregon-R as a wildtype. As best we can tell, the BaraA and Bom strains are not in the genetic background and neither is particularly similar to OR-R. If the authors can justify the use of OR-R as the wildtype control for these experiments, they should do so explicitly. Otherwise, these experiments are very difficult to interpret. This issue is highlighted by other data, where genetic background is carefully controlled, in the iso-w background, and the survival phenotypes are much more mild, and do reach significance is some infections, by log-rank analysis. All experiments should be performed in this controlled background to enable firm conclusions and interpretations.

Minor comments:

• Figure 1A mined data from a previous published study, which is acceptable, but this data presentation lacks proper description of the methodology, reproducibility, and statistics.
• The authors need to clarify the condition of the flies in Figures 1D to G (as well as S1C and D). Infected? Baseline? It is not clear.
• There is no visualization of the genomic location of the BaraA deletion, which should be added to figure 2C.
• The authors should include the full genotype information for the Bloomington stocks, since the BL numbers may change over time.
• In Figure 2C, the authors should include some information about which lines possess the single BaraA locus and which lines have the duplication event.
• The author should elaborate on what is known about Dso2 and how the aberrant Dso2 locus might affect their assays. The info here is incomplete and confusing.
• Does the Ecc15 strain used in the paper innately resist Ampicillin? If yes, then the result of Ecc15 resisting the combination of IM cocktail and Ampicillin does not reveal much.
• It is unclear what the concentration of pimaricin was used for Figure 3E.
• The authors should include a clear genetic explanation for their conclusion that BaraA and Bomanins function independently. The text describing this double mutant analysis could be more informative.
• BaraA overexpression significantly improved female survival against M. luteus (Figure S4C, p=0.006), this is interesting but not mentioned in the text.
• The author should be clear and consistent about the pathogen source (lab grown vs. commercial) and method of infection (natural infection vs. septic injury). The authors should explain the difference in virulence between different infection models and methods.
• The sex-specific erected wings phenotype is interesting, but does not contribute to the overall significance of the manuscript. The authors should consider moving Figure 6 to the supplement.

Significance

This work is a potential step in characterizing the immune effectors downstream of the Toll pathway that contribute to the Drosophila defense against fungal pathogens. These effectors so far have not been characterized and understood. We are familiar with the Toll pathway and its effectors, but in no way are experts.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The authors use the fruitfly Drosophila melanogaster as a model to study innate immunity. In this manuscript, they study the effects of a set of antimicrobial peptides (AMPs) that are produced by furin cleavage of a larger precursor (Baramicin A, BaraA). Bara A is immune-induced in a Toll-dependent manner and has antifungal activity. Somewhat in line with expression in non-immune tissues, BaraA mutants show ab erect-wing phenotype in males.

Major comments:

The experiments are well-presented in a reproducible and statistically sound way. In particular care is taken to control effects of the genetic background. The immune phenotype of BaraA mutants is somewhat subtle but convincing and in line with recent findings by the same authors that some of the recently created CRISPR/Cas mutants in antimicrobial peptides have broader effects while others target intruders in a more specific manner or in combination with other AMPs. These are very relevant studies, which provide a balanced view of innate immunity in particular AMP action. I have one comment about the (BarA dependent, male-specific) erect wing phenotype: this is an interesting observation, which could stimulate work by others, I guess this is one reason why it was included in the manuscript. On its own it stands out a bit in the manuscript since in contrast to other parts, where insight into the underlying mechanisms is provided, this is not the case for the erect wing phenotype. The authors speculate about the non-immune expression, which may be responsible. One might use tissue-specific knockdown or rescue to check up on this (wing muscle or nervous system). This would be cost effective but delay publication for a few months. It depends a bit on the respective journal policy and the plans for further investment of the groups involved whether the phenotype is considered part of BaraA pleiotropism (which I could buy) or is considered too descriptive and should be used later for a later publication. Along similar lines, while sex-specific immune phenotypes are highly interesting, they open up many discussions about the underlying causes, both proximal and ultimate.

Minor comments:

The experiments look sound and previous work is mentioned sufficiently. The experimental design and results are easy to follow. I have mentioned some concerns about the erect wing phenotype (see above). Is there any evidence for metabolic regulation of BaraA (TF binding sites for example) in particular in the promoter fragment used for the reporter line? Did any of the fat body drivers show the same effect as the ubiquitous actin driver (this would increase specificity).<br> Why was pimaricin used, it seems presently as a representative of membrane-active antifungal drugs, which BaraA peptides are likely not. Still, using combinations with other insect (Drosophila) antifungal AMPs would be more physiological, maybe this was tried and did not work, but should still be discussed. Or do the authors want to imply that physiologically the Daisho peptides or Bomanins have this effect? Perhaps elaborate on this.

In Fig. 1: part H is missing although mentioned in the legend.

In the abstract:

it should be more clearly mentioned that the erect wing phenotype was observed in the mutants. line 27 and 28, replace one "characterized" line 28: contribute line 33: entomopathogenic

other places:

line 68: AMPs

Significance

Significance:

The evolutionary relevance and therapeutic potential of AMP synergism is an emerging topic both within insect immunity, innate immunity in general and its use in patient treatment [1, 2]. The latter aspect may be interesting to justify the use of pimaricin. Thus, the work presented here in combination with previous work from the authors leads to a more balanced view of the action of insect AMPs (the authors call that the logic of the Drosophila effector response) with implications for human innate immunity and perhaps even therapy of diseases. Therefore, the data will be interesting for a broad audience. The use of models such as Drosophila, which can be manipulated in a targeted manner has provided insights that are beyond the study of single AMPs in vitro. Still, using overexpression as done in some cases here should be interpreted cautiously and should - if available - compared to data on in vivo concentration of AMPs (the authors try to derive estimates from the MS data), which may be difficult in case there are large local differences.

My own field:

primarily insect immunity with a background in mammalian immunity, although I am not able to keep up with all recent development in mammalian immunity.

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Reply to the reviewers

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

Trypanosoma brucei causes African sleeping sickness and related cattle disease, both diseases that urgently need new therapeutics. One reason for the lack of a drug or a vaccine is the parasite's way to escape the immune system: their cell surface is covered by the variant surface glycoprotein (VSG) of which many variants exist, but only one is expressed. The switching between the different VSG forms is called antigenic variation and involves a not fully understood epigenetic mechanism. It is essential for the parasite's survival that the VSG surface coat is very dense at any given time: antibodies of the host should not be able to recognise any invariant proteins on the cell surface that are 'hidden' in between the VSG molecules. Consequently, the VSG protein is the most abundant protein in the cell (10% of total). This high protein abundance is achieved by both transcriptional and posttranscriptional mechanisms. One major posttranscriptional mechanism is the stabilisation of the VSG mRNA. Two cis-elements in the VSG mRNA 3´UTR have been known for a long time to be essential for this stability (an 8-mer and a 16-mer). However, nothing was known about the underlying mechanism of VSG mRNA stabilisation. In this work, the authors have addressed this problem. They have purified the VSG mRNA from trypanosomes in two very different ways and, in both approaches, they found the cyclin F-box protein 2 (CFB2) to co-purify. They have defined the full complex that binds to the VSG mRNA. Most importantly, the authors could clearly show the very specific effect on VSG mRNA stability when CFB2 was RNAi depleted. Moreover, CFB2 RNAi mostly phenocopied the phenotype that was previously described for VSG RNAi. The CFB2 protein is present in a very low copy number and the authors provide data suggesting that it may be tightly autoregulated by interaction with SKP1. The authors further show that the regulation of VSG mRNA stability by CFB2 depends on the 16-mer cis-element, but not on the 8-mer. The data are, throughout, very convincing, experiments are done with all the essential controls and the data are well presented. The conclusions are supported by the data. The authors have, beyond any doubt, finally identified the major posttranscriptional regulator protein that is responsible for VSG mRNA stability, a milestone in the field, and provide a mechanism on how it could work and be autoregulated. I only have one major point (and a few very minor points)

My main criticism is on the introduction: major information is missing here or presented far too short. People from outside of the trypanosome field will find the paper almost impossible to understand. It is important to explain the life cycle and its stages (as these are mentioned later) as well as the parasites special transcription of mRNAs by PolI and PolII in more detail. Trypanosome translation initiation factors and PABPs should be introduced. Nomenclature of the VSG is also a confusing throughout. Why switching to VSG4 in Figure 8 for example. Also, it would be beneficial to phrase the question better and stress the importance of why this needs to be answered to understand the basic biology of the parasite.

R: We have extended the Introduction section as suggested. The reason for the switch is now explained.

**Minor stuff:** Line 76: ' supporting direct binding to mRNA in vivo' Is this true? I thought the poly(A) oligos can also purify protein complexes? (but I may be wrong)

1. Yes, but probably not very much when the complexes have been washed with lithium chloride and urea. In any case, the readers can find in the supplementary Table 1 the false discovery rate (FDR) values obtained for each identified protein for both purifications (oligodT and VSG/Tub antisense) taking into consideration the data from the control experiments.

   Line 104: 'Kinetoplastid specific'. Better 'Trypanosome specific' if its absent in Leishmania? The correlation between presence of antigenic variation and number of CFB could be worked out a little better, perhaps presented in a main Figure.

2. CFB genes are absent in Leishmania; thus, we have edited it as suggested. Since we do not actually know whether it has any biological meaning, we have also removed the association between the presence of multiple copies of CFB genes and antigenic variation.

   Line 161: Tb927.8.1945, ad: 'encoding a hypothetical protein of unknown function'.

3. Done. Line 202: MG132, better: 'the proteasome inhibitor MG132' R. Done. Line 310-311: no, best to delete this sentence.

We prefer to leave it in.

Reviewer #1 (Significance (Required)):

There is no doubt about this being a truly significant contribution to the trypanosome field. Method-wise, it is also a nice example of how mRNA binding proteins can be identified and validated and there are clear mechanistic insights here into the regulation of the VSG mRNA. This is not frequently found, in any organism. I believe that this work will be publishable in any parasitology journal, and, once the introduction has been changed (see above) also in any RNA journal.

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

The current study describes the isolation and characterisation of Variant Surface Glycoprotein (VSG) mRNA-bound proteins in the bloodstream form African trypanosome. CFB2 is identified as a VSG mRNA positive regulator which depends upon a conserved 16mer in the VSG mRNA 3'-UTR.

1. The authors state in their abstract that "CFB2 is essential for VSG mRNA stability". They also "describe cis-acting elements within the VSG 3'-untranslated region that regulate the interaction". Expression of a GFP reporter appears to be reduced by only ~3-fold in bloodstream-form cells when the relevant cis-acting element (the 16mer) is removed, however (Fig. 8B). This would suggest that the mRNA lacking the 16mer could still be relatively stable ("VSG mRNA is extremely stable, having a half-life of 1-2h compared with less than 20 min for most other mRNAs").

Was half-life measured for an mRNA lacking a 16-mer or for VSG mRNA in cells lacking CFB2?

1. Yes, this was previously published, references are in the Introduction. The presence of the 16-mer in VSG is essential for survival in the T. brucei bloodstream stage (PMID: 28906055). Could CFB2 impact mRNA maturation rather than stability?

2. The reporter experiments rule this out since in Kinetoplastids, the 3'-UTR sequence has no role in controlling polyadenylation, beyond a preference for sites with several A residues. This is now explicitly stated. Also, which data demonstrate an altered interaction between CFB2 and the mRNA lacking a 16mer? The authors could consider adjusting these statements and also the quantitative impact that CFB2 has on VSG mRNA stability, as well as evidence supporting differing interactions between CFB2 and mRNAs containing or lacking the 16mer.

We do now show new data demonstrating that binding of CFB2 to the reporters depends on the VSG 3'-UTR and is unaffected by the 8-mer mutation. Unfortunately, the GFP-VSGm16mer mRNA was too low in abundance to quantitate, even by qPCR. The 16-mer and 8-mer are the only sequences in the 3'-UTR that are conserved in different VSG mRNAs. Binding to the upstream UC-rich region remains a theoretical possibility but it seems very unlikely since this region is variable and such sequences are present in numerous other 3'-UTRs (for example, the alpha tubulin 3'-UTR, which is the first we looked at, includes the sequence CCUUCCUUCCCCUU). Our preliminary results suggest indeed that region is not involved (Suppl. Fig 13E). And in that case, why would mutating the 16-mer affect the response to CFB2 expression? We cannot rule out the possibility that CFB2 binds to m6A - it's a chicken-and-egg problem, because mutation of the 16-mer eliminates the methylation. However, this too seems unlikely since m6A is by no means restricted to VSG (https://doi.org/10.1101/2020.01.30.925776; PMID: 30573362). To find out it would be necessary to identify the m6A "writers", and reduce their expression; this is well beyond the scope of this manuscript and is being actively pursued in another lab. An alternative would be to express soluble CFB2 for in vitro binding studies, but so far this has not been possible despite several attempts.

In relation to point 1 above, Fig. 2A and Fig. 3D show CFB2 binding to the VSG 3'-UTR, to the 16mer in the latter case. This interaction could be presented as a 'model' whereas it seems too speculative to be included in the current data-Figures. Indeed, the authors "suggest that CFB2 recognizes the 16mer" in their Discussion and do also consider alternatives. A caveat has been added to the Figure 3D legend.

Given the emphasis on the experimental approach and "the potential to supply detailed biological insight into mRNA metabolism in any eukaryote" (end of abstract), can the authors explain how their method improves upon / differs from the approach of Theil et al., 2019 and other similar approaches?

Our approach is slightly different to the one described by Theil et al. (antisense oligo length, incubation temperature) and a detailed description of our protocol can be found in the Methods section. We have stressed the method because there is only one previous successful example attempting the purification of the protein bound to a native mRNA. Our intention is not to compare approaches but to encourage researchers willing to perform these experiments in a variety of other organisms.

**Other points:** i. Fig. 2B: Why does N-GFP- SBP migrate more slowly in the Tet+ eluate? Also why does the slower-migrating form of the protein appear to dominate in Fig. 2C?

1. N-GFP-SBP protein migrates as a single band. In Fig. 2C, the membrane was first probed with anti-RBP10 and then with anti-GFP antibodies. What is observed in the input and flow-through (I/FT) is RBP10 signal and not GFP. The concentration of N-GFP-SBP in the eluate is much higher than in the I/FT (it is the only protein visualized upon Ponceau staining in eluates). That causes the band to appear in the eluate as “ghost band” (ECL reagent is consumed in the middle region of the band) while in the I/FT, the concentration is still not enough to give a signal. The same occurs in Fig. 2B. The faint bands that are seen in the I/FT in Fig. 2B are likely products of cross-reactivity.

   ii. Fig. 3D: What's the evidence that SKP1 interacts with VSG-mRNA-bound CFB2? Is this protein enriched in the data shown in Fig. 1C and can the relevant data-point be labelled?

2. Our interactome capture results (PMID: 26784394) suggest that in bloodstream form, Skp1 (Tb927.11.6130) do not bind poly(A) RNA directly; thus, it is not enriched in the VSG mRNA-bound proteome. What we know is that Skp1 interacts, in a Y2H setting, with CFB2 and that mutations in the CFB2 F-box domain abolish this interaction. The data we have presented suggest the interaction with Skp1 regulates CFB2 levels. We actually do not know whether Skp1 binds to free or to VSG-mRNA-bound CFB2.

   iii. There are four other highly abundant mRNAs in Fig. 4C. Are these related to VSG expression?

They are tubulins, EF1, HSP83 and HSP70.

iv. Lines 85-88: Suggest citing the studies used to prioritise RBPs, expressed only in the bloodstream form, that increase mRNA stability or translation when "tethered" to an mRNA. R. References have been added.

Is CFB2 expressed only in the bloodstream form?

Yes, this is described in more detail later.

v. We spotted a number of other potential corrections, including: Lines 161 and 171; should '4E' be '4C'? Line 202; explain MG132. Define RPM, ns, BS, ++ etc in the Figures. Yeast-2-hybrid and CAT may be standard assays, but we suggest briefly describing them in the Methods section. Done.

Reviewer #2 (Significance (Required)):

Post-transcriptional control of gene expression by mRNA binding proteins (RBPs) is an area of major current research interest and activity. Much remains unknown regarding control of mRNA stability, nuclear export or translation and there are many uncharacterised or only partially characterised RBPs in eukaryotic cells. Trypanosomes present an important model in this context since global polycistronic transcription places a major emphasis on post-transcriptional controls. They are also important parasites. The variant surface glycoprotein is a key virulence factor and one of the few genes that is under transcriptional control in African trypanosomes, yet RBPs are thought to be important for generating/maintaining the highly abundant VSG mRNA in bloodstream form cells (and for low abundance in the insect stage), possibly via interaction with the highly conserved regulatory elements in the 3'-UTR.

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Referee #2

Evidence, reproducibility and clarity

The current study describes the isolation and characterisation of Variant Surface Glycoprotein (VSG) mRNA-bound proteins in the bloodstream form African trypanosome. CFB2 is identified as a VSG mRNA positive regulator which depends upon a conserved 16mer in the VSG mRNA 3'-UTR.

1. The authors state in their abstract that "CFB2 is essential for VSG mRNA stability". They also "describe cis-acting elements within the VSG 3'-untranslated region that regulate the interaction". Expression of a GFP reporter appears to be reduced by only ~3-fold in bloodstream-form cells when the relevant cis-acting element (the 16mer) is removed, however (Fig. 8B). This would suggest that the mRNA lacking the 16mer could still be relatively stable ("VSG mRNA is extremely stable, having a half-life of 1-2h compared with less than 20 min for most other mRNAs"). Was half-life measured for an mRNA lacking a 16-mer or for VSG mRNA in cells lacking CFB2? Could CFB2 impact mRNA maturation rather than stability? Also, which data demonstrate an altered interaction between CFB2 and the mRNA lacking a 16mer? The authors could consider adjusting these statements and also the quantitative impact that CFB2 has on VSG mRNA stability, as well as evidence supporting differing interactions between CFB2 and mRNAs containing or lacking the 16mer.
2. In relation to point 1 above, Fig. 2A and Fig. 3D show CFB2 binding to the VSG 3'-UTR, to the 16mer in the latter case. This interaction could be presented as a 'model' whereas it seems too speculative to be included in the current data-Figures. Indeed, the authors "suggest that CFB2 recognizes the 16mer" in their Discussion and do also consider alternatives.
3. Given the emphasis on the experimental approach and "the potential to supply detailed biological insight into mRNA metabolism in any eukaryote" (end of abstract), can the authors explain how their method improves upon / differs from the approach of Theil et al., 2019 and other similar approaches?

Other points:

i. Fig. 2B: Why does N-GFP- SBP migrate more slowly in the Tet+ eluate? Also why does the slower-migrating form of the protein appear to dominate in Fig. 2C?

ii. Fig. 3D: What's the evidence that SKP1 interacts with VSG-mRNA-bound CFB2? Is this protein enriched in the data shown in Fig. 1C and can the relevant data-point be labelled?

iii. There are four other highly abundant mRNAs in Fig. 4C. Are these related to VSG expression?

iv. Lines 85-88: Suggest citing the studies used to prioritise RBPs, expressed only in the bloodstream form, that increase mRNA stability or translation when "tethered" to an mRNA. Is CFB2 expressed only in the bloodstream form?

v. We spotted a number of other potential corrections, including: Lines 161 and 171; should '4E' be '4C'? Line 202; explain MG132. Define RPM, ns, BS, ++ etc in the Figures. Yeast-2-hybrid and CAT may be standard assays, but we suggest briefly describing them in the Methods section.

Significance

Post-transcriptional control of gene expression by mRNA binding proteins (RBPs) is an area of major current research interest and activity. Much remains unknown regarding control of mRNA stability, nuclear export or translation and there are many uncharacterised or only partially characterised RBPs in eukaryotic cells. Trypanosomes present an important model in this context since global polycistronic transcription places a major emphasis on post-transcriptional controls. They are also important parasites. The variant surface glycoprotein is a key virulence factor and one of the few genes that is under transcriptional control in African trypanosomes, yet RBPs are thought to be important for generating/maintaining the highly abundant VSG mRNA in bloodstream form cells (and for low abundance in the insect stage), possibly via interaction with the highly conserved regulatory elements in the 3'-UTR.

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

Trypanosoma brucei causes African sleeping sickness and related cattle disease, both diseases that urgently need new therapeutics. One reason for the lack of a drug or a vaccine is the parasite's way to escape the immune system: their cell surface is covered by the variant surface glycoprotein (VSG) of which many variants exist, but only one is expressed. The switching between the different VSG forms is called antigenic variation and involves a not fully understood epigenetic mechanism. It is essential for the parasite's survival that the VSG surface coat is very dense at any given time: antibodies of the host should not be able to recognise any invariant proteins on the cell surface that are 'hidden' in between the VSG molecules. Consequently, the VSG protein is the most abundant protein in the cell (10% of total). This high protein abundance is achieved by both transcriptional and posttranscriptional mechanisms. One major posttranscriptional mechanism is the stabilisation of the VSG mRNA. Two cis-elements in the VSG mRNA 3´UTR have been known for a long time to be essential for this stability (an 8-mer and a 16-mer). However, nothing was known about the underlying mechanism of VSG mRNA stabilisation. In this work, the authors have addressed this problem. They have purified the VSG mRNA from trypanosomes in two very different ways and, in both approaches, they found the cyclin F-box protein 2 (CFB2) to co-purify. They have defined the full complex that binds to the VSG mRNA. Most importantly, the authors could clearly show the very specific effect on VSG mRNA stability when CFB2 was RNAi depleted. Moreover, CFB2 RNAi mostly phenocopied the phenotype that was previously described for VSG RNAi. The CFB2 protein is present in a very low copy number and the authors provide data suggesting that it may be tightly autoregulated by interaction with SKP1. The authors further show that the regulation of VSG mRNA stability by CFB2 depends on the 16-mer cis-element, but not on the 8-mer.

The data are, throughout, very convincing, experiments are done with all the essential controls and the data are well presented. The conclusions are supported by the data. The authors have, beyond any doubt, finally identified the major posttranscriptional regulator protein that is responsible for VSG mRNA stability, a milestone in the field, and provide a mechanism on how it could work and be autoregulated. I only have one major point (and a few very minor points)

My main criticism is on the introduction: major information is missing here or presented far too short. People from outside of the trypanosome field will find the paper almost impossible to understand. It is important to explain the life cycle and its stages (as these are mentioned later) as well as the parasites special transcription of mRNAs by PolI and PolII in more detail. Trypanosome translation initiation factors and PABPs should be introduced. Nomenclature of the VSG is also a confusing throughout. Why switching to VSG4 in Figure 8 for example. Also, it would be beneficial to phrase the question better and stress the importance of why this needs to be answered to understand the basic biology of the parasite.

Minor stuff:

Line 76: ' supporting direct binding to mRNA in vivo' Is this true? I thought the poly(A) oligos can also purify protein complexes? (but I may be wrong)

Line 104: 'Kinetoplastid specific'. Better 'Trypanosome specific' if its absent in Leishmania? The correlation between presence of antigenic variation and number of CFB could be worked out a little better, perhaps presented in a main Figure.

Line 161: Tb927.8.1945, ad: 'encoding a hypothetical protein of unknown function'.

Line 188, 216, 246: typos/grammar, also: 8mer or 8-mer (decide for one)

Line 202: MG132, better: 'the proteasome inhibitor MG132'

Line 310-311: no, best to delete this sentence.

Significance

There is no doubt about this being a truly significant contribution to the trypanosome field. Method-wise, it is also a nice example of how mRNA binding proteins can be identified and validated and there are clear mechanistic insights here into the regulation of the VSG mRNA. This is not frequently found, in any organism. I believe that this work will be publishable in any parasitology journal, and, once the introduction has been changed (see above) also in any RNA journal.

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Reply to the reviewers

We wish to thank the reviewers for their detailed and constructive comments on our manuscript. This valuable feedback has resulted in substantial improvements to our paper. A detailed list addressing the reviewers’ comments and the changes to our manuscript since the first submission is outlined below:

Reviewer #1:

The manuscript by Martens et al investigates the mechanisms of Bnip3-mediated cell damage during hypoxia. The Authors show that modulation of prostaglandin (PG) E1 signaling with misoprostol prevents cardiac dysfunction, mitochondrial impairment and cell death induced by hypoxia. In addition, they show that the effect of misoprostol is dependent on PKA-mediated Thr181 phosphorylation. The Authors also suggest that there is a possible interaction between Bnip3 and 14-3-3beta that prevents ER Ca2+ release and mitochondrial Ca2+ overload. The Authors conclude that Bnip3 phosphorylation plays a key role in the regulation of cardiac and metabolic dysfunction and identify misoprostol treatment as a therapeutic intervention to prevent hypoxia-induced cardiac injury.

**Major Comments:**

1.Results presented in Figs. 1, 2 and 3 pertaining to hypoxia-induced changes in Bnip3 expression, changes in mitochondrial function, cell death, Bnip3-dependent Ca2+ transfer from ER to mitochondria and the effect of misoprostol have partially been demonstrated in a previous publication from the same group (PMID: 30275982).

Thank you for noting our previous work using predominantly the HCT-116 cell line and a rat model of neonatal hypoxia published in the journal Cell Death Discovery. The work in the current manuscript builds on our previous papers, and extends these findings utilizing a neonatal mouse model, primary neonatal ventricular myocytes, human iPSC-derived cardiomyocytes, and H9c2 cells. These models not only demonstrate the robust nature of the effects of misoprostol treatment on the hypoxic neonatal cardiomyocytes, but they also allowed us to utilize powerful genetic models, such as the Bnip3 knockout mouse, and knockout mouse embryonic fibroblasts (MEFs), which phenocopy many of the effects of misoprostol treatment. These findings strongly implicate Bnip3 as a primary target of misoprostol treatment in the hypoxic neonatal heart in rodents.

In addition, Figure 1 contains very important in vivo endpoints that we have not previously utilized, including echocardiography to assess neonatal cardiac function, transmission electron microscopy to assess mitochondrial ultrastructure, cardiac ATP and lactate levels, an array of gene expression, and HMGB1 immunofluorescence (Fig.1 I in the current version of the manuscript) implicating a necro-inflammatory phenotype that is modulated by misoprostol treatment. Moreover, in Figures 2 and 3 we confirm previous observations related to hypoxia- and Bnip3-mediated mitochondrial function, and calcium signaling, but also extend these observations to include the impact of hypoxia, Bnip3 and misoprostol treatment on mitochondrial morphology and necro-inflammatory markers, in additional to utilizing human cardiomyocytes and knockout MEFs. Finally, Figures 4-7 of our manuscript describes a novel mechanism by which misoprostol treatment can therapeutically target Bnip3 function both in vivo and in cell models (see below).

Although based on our previous observations, we feel the work in the present manuscript is highly novel and original, and adds substantially to our knowledge of both Bnip3 function, and neonatal hypoxic injury, which currently represents a world-wide health crisis that is underrepresented in the biomedical literature.

2.The very same publication from 2018 shows that misoprostol treatment of pups exposed to hypoxia for 7 days is able to prevent the increase in Bnip3 protein levels. Yet, in the present manuscript misoprostol treatment had no effect on Bnip3 protein levels in the same model (Fig. 1E). This raises some concerns regarding the solidity and soundness of the results presented. Thank you for noting this in our previous work. In our 2018 paper, we treated hypoxic neonatal rats with misoprostol and observed a complete repression of Bnip3 expression in the gut and hippocampus, but only a partial repression in the heart. This observation prompted us to explore other mechanisms by which misoprostol could inhibit Bnip3 function. We have increased our sample size for the data in Figure 1 J and K to be more statistically conclusive. The evidence is now stronger that misoprostol only the partial represses Bnip3 expression in the neonatal mouse heart. In addition, we provided a representative western blot (Fig. 1 J), which is consistent with the result in Figure 3C in MEF cells.

3.The validation of the custom antibody against p-Thr181 needs to be shown. Fig. 4E shows that p-Bnip3 band is quite strong in H9c2 cells, despite total endogenous Bnip3 levels are barely detectable. In addition, phosphorylation of the Bnip3 Thr181 residue in cells and/or in vivo should be confirmed by mass spectrometry.

Our plan in the next revision, we will be to provide additional validation of the p-Thr181 antibody, as we have done previously (PMID: 33044904). In addition, we will re-run the western blots noted above to improve their quality, as the difference between total Bnip3 and p-Bnip3 in H9c2 cells is likely due to different exposures of the two blots. Confirmation of Bnip3 phosphorylation using mass spectrometry in extracts from intact cells was previously published (PMID: 26102349), however, the nature of the signaling pathways leading to phosphorylation was not determine, nor was the mechanism of Bnip3 inhibition previously determined.

4.Fig. 4L shows that misoprostol treatment of H9c2 cells leads to an increase in Bnip3 phosphorylation, but this does not seem to be the case in normoxic conditions in vivo (Fig. 4N). Moreover, shouldn't this presumable increase in phosphorylation induced by misoprostol in normoxic conditions lead to Bnip3 accumulation in the cytosol thereby reducing its colocalization with mitochondria (Fig. 6B)? The results obtained with the colocalization method should be corroborated using different methods, such as cell fractionation.

In the previous version of the manuscript, we reported that misoprostol treatment increases Bnip3 phosphorylation in H9c2 cells following acute exposure (Supplement 5 B). In the updated version of the manuscript, we confirmed this in vitro observation in PVNC’s, demonstrating that in culture, acute misoprostol drug treatments during normoxia result in Bnip3 phosphorylation (Supplement 5 C). However, when we increased our N in the revised manuscript this difference remained not statistically sustained after 7 days of misoprostol treatment in vivo (Fig. 4 M, N). Importantly though, our observation that hypoxia exposure resulted in reduced Bnip3 phosphorylation, and that misoprostol drug treatment was sufficient to restore it is particularly novel, and ties together with our new colocalization data (Fig. 4 M, N). What is very intriguing about the data in Figure 6B is that myc-Bnip3 did not colocalize with the mitochondrial matrix-targeted mito-Emerald under normoxic conditions, and thus was not impacted by misoprostol treatment in normoxia. However, in hypoxic cells the colocalization coefficient between myc-Bnip3 and mito-Emerald increased and was abrogated by misoprostol treatment. This observation suggests that Bnip3 is actively translocated deeper into the mitochondria ultrastructure during hypoxic stress, and that misoprostol treatment can prevent this phenomenon. This observation is consistent with the pBnip3 data shown in Figure 4M. In the most recent version of the manuscript, we have performed additional confocal experiments to substantiate this novel observation. New data clearly demonstrates that hypoxia exposure in vivo increases the colocalization of Bnip3 with the inner mitochondrial membrane protein Opa1 (Fig. 6 C). However, when mice are treated with misoprostol, the colocalization with Opa1 is reduced and the colocalization of Bnip3 with 14-3-3b increases (Fig. 6 M). We have also shown in H9c2 cells that expression of Opa1 prevents Bnip3-induced mitochondrial fission (Fig. 3 J), and that when both Bnip3 and 14-3-3b are ectopically expressed, misoprostol treatment can increase their colocalization (Fig. 6 N, O), suggesting that this is regulated by post-translational modification and not alterations in Bnip3 expression due to hypoxia. Our plan is to include additional fractionation experiments in the next revision of the manuscript; however, this approach may not be as sensitive as confocal microscopy.

5.In relation to Fig. 4 M, N (page 19), the Authors concluded that the reduction in Bnip3 phosphorylation suggests an increase in Bnip3 activity in the hypoxic neonatal hearts. Nevertheless, this has not been demonstrated.

Thank you for pointing this out. At this time, we do not have data to suggest that a reduction in Bnip3 phosphorylation increases its activity in vivo. In the revised manuscript, we have new confocal-based colocalization experiments using fixed sections from hypoxic and misoprostol treated hearts that provide insightful information into the subcellular localization of Bnip3 (Please see above; Fig. 6 C, M). Importantly, based on our data in figure 5, particularly Fig.5F using Bnip3-null MEFs, the protective effect of misoprostol is completely prevented by reconstitution of the T181A mutant, but not wild-type Bnip3, suggesting phosphorylation at T181 is an important mechanism by which misoprostol inhibits Bnip3-induced mitochondrial depolarization. Finally, we have also been careful not to overstate our conclusions is the most recent version of the manuscript, have been more specific with our language, and have avoided vague terms like ‘activity’.

6.Along that line, the Authors concluded that misoprostol-induced cytoprotection is dependent on PKA Thr181 phosphorylation. Nevertheless, this dependence has not been convincingly demonstrated in hypoxic cells and in vivo.

The new data described outline above, in point #4 and #5, have provided assurances regarding the role of Bnip3 phosphorylation on its subcellular location in vivo and in cultured cells. To further address the dependency of PKA on T181 phosphorylation, we have performed experiments using the PKA inhibitor, H89, in cellular experiments and evaluate whether the protective effect of misoprostol is lost in the presence of this inhibitor. This new data has been added to the most recent version of the manuscript (Fig. 4 G).

7.Previous studies showed that Bnip3 induces mitochondrial fragmentation and mitophagy (PMID: 16645637, 20436456). What is the hypothesis for the inhibition of mitochondrial fragmentation induced by misoprostol in the present study? Does it prevent Bnip3 interaction with Opa1 or is this event downstream of ER Ca2+ release and mitochondrial Ca2+ overload? Does misoprostol affect mitophagy?

We have added new experimental data to address the hypothesis that Bnip3 colocalizes with Opa1 to induce mitochondrial fragmentation (as noted by the reviewer, they were previously shown to physically interact), and that this is inhibited by misoprostol treatment (Fig. 6 C). We have also added new data to the supplemental material demonstrating that misoprostol inhibits hypoxia- and Bnip3-induced mitophagy. Based on our data, we proposal that misoprostol inhibits both Bnip3-induced ER-calcium release and Opa1-dependent mitochondrial fusion. This is based on our data that misoprostol prevents Bnip3 accumulation at both the ER and mitochondria, respectively.

8.The link between Bnip3 interaction with 14-3-3 and Bnip3 Thr181 phosphorylation, if there is any, is not clear. The Authors mention that Thr181 lies within the 14-3-3 binding domain. Is Thr181 phosphorylation required for 14-3-3 binding or are these events unrelated? What is the significance of these events in hypoxia, does 14-3-3 binding to Bnip3 occur in vivo? Is Bnip3 localization affected by hypoxia, 14-3-3 binding and/or misoprostol treatment in vivo?

Previously, we described the role of phosphorylation of Bnip3L (Nix) at Ser-212 how this regulates the interaction with 14-3-3 (PMID: 33044904). This phosphorylation site is conserved in Bnip3 as T181. Interestingly, phosphorylation of Nix by PKA was not required for interested with 14-3-3b, but the interaction between Nix and 14-3-3b was enhanced by phosphorylation. Our plan is to perform similar experiments with Bnip3 and 14-3-3b to determine if this mechanism is conserved. However, as noted above we have new in vivo data showing that misoprostol increased the colocalization of Bnip3 and 14-3-3b in the hypoxic heart (Fig. 6 M).

9.Fig. 6P shows the presence of myc-tag after IP for HA-tag, even when HA-14-3-3 was not expressed (middle lane). How is this possible?

This appears to be a small amount of non-specific interaction between the HA antibody and myc-Bnip3. This is relatively small compared to the band in lane 3, which demonstrates specificity, and the importance of including this control condition. Our plan is to re-run this CO-IP to improve the western blot quality.

**Minor Comments:**

1.Please co-stain with the cardiomyocyte marker in Fig. 2A (such as alpha-actinin).

Yes, good suggestion.

2.The Methods are not sufficiently detailed. For instance, it is not clear what is the Ca2+ concentration used for Ca2+ pulses in the CRC experiment. The fact that cardiac mitochondria are able to uptake only two Ca2+ pulses raises some concerns regarding the quality of mitochondrial preparation. What is the reason for isolating mitoplasts instead of intact mitochondria?

We have provided more detail in the revised manuscript.

3.TMRM fluorescence should be measured before and after FCCP administration, to account for the difference in plasma membrane potential (the results should be expressed as F/FFCCP).

We can provide some additional control experiments in the revised manuscript, if necessary.

4.Measurement of extracellular acidification is mentioned in the methods, but the relative results are not shown.

Thank you, this has been removed.

5.RNAi experiments targeting Bnip3 are also mentioned in the methods, but the results are not described.

Thank you. This has been fixed.

Reviewer #1 (Significance (Required)):

Previous studies have demonstrated that Bnip3 is upregulated by hypoxia and plays a key role in inducing mitochondrial dysfunction and PTP opening that eventually results in cell death (PMID: 12169648, 10922063). Along that line, misoprostol has been shown to prevent damaging effects of hypoxia by repressing Bnip3 and promoting the expression of pro-survival alternative splicing isoforms (PMID: 30275982). Indeed, the same study showed that misoprostol treatment prevents loss of mitochondrial membrane potential, ROS formation and impairment in mitochondrial oxygen consumption caused by hypoxia in primary neonatal cardiomyocytes. The present manuscript recapitulates these previously published findings. The truly novel findings concern the identification of Bnip3 residue Thr181 as target for PKA phosphorylation and the possible interaction of Bnip3 with 14-3-3. However, the role and/or involvement of these events has not been thoroughly investigated in relation to hypoxia and misoprostol treatment in cells or in vivo.

Thank you for noting our previous work and identifying the novelty in our present work. As stated above, for Reviewer #1 comments 4, 6, and 8. We have provided additional mechanistic and in vivo data to more fully describe the role of T181 phosphorylation and the interaction with 14-3-3 chaperones in the revised manuscript.

Reviewer #2:

Systemic hypoxia, a major complication associated with reduced gestational time, affects more 60% of preterm infants and is a known driver of hypoxia-induced Bcl-2-like 19kDa-interacting protein 3 (Bnip3) expression in neonatal heart. At the level of the cardiomyocyte, Bnip3 activity plays a prominent role in the evolution of necrotic cell death, disrupting subcellular calcium homeostasis and initiating mitochondrial permeability transition (MPT). Emerging evidence suggests both a cardioprotective role for protein kinase A (PKA) through stimulatory prostaglandin (PG) E1 signalling during prolonged periods of hypoxia, and a cytoprotective role for Bnip3 phosphorylation, indicating that post-translational modifications of Bnip3 may be a point of convergence for these two protective pathways. Using a combination of in vivo and multiple cell models, including human iPSC-derived cardiomyocytes, the authors tested if the PGE1 analogue misoprostol is cardioprotective during neonatal hypoxic injury by altering the phosphorylation status of Bnip3. Here we report that hypoxia exposure significantly increases Bnip3 expression, mitochondrial-fragmentation, -ROS, -calcium accumulation and -permeability transition, while reducing mitochondrial membrane potential, all of which were restored to control levels with addition of misoprostol, despite elevated Bnip3 protein expression. Through both gain- and loss-of function genetic studies, the authors show that misoprostol-induced protection directly affects Bnip3, preventing mitochondrial perturbations. They demonstrate that this is a result of PG EP4 receptor signalling, PKA activation, and direct Bnip3 phosphorylation at threonine-181. Furthermore, when this PKA phosphorylation site within Bnip3 is neutralized, the protective misoprostol effect is lost. They also provide evidence that misoprostol traffics Bnip3 away from the ER through a physical interaction with 14-3-3β, thereby preventing aberrant ER calcium release and MPT. In vivo studies further demonstrate that misoprostol treatment increases Bnip3 phosphorylation at threonine-181 in the mouse heart, while both misoprostol treatment and genetic ablation of Bnip3 prevented hypoxia-induced reductions in contractile function. Taken together, these results demonstrate a foundational role for Bnip3 phosphorylation in the molecular regulation of cardiomyocyte contractile and metabolic dysfunction and identifies EP4 signaling as a potential pharmacological mechanism to prevent hypoxia-induced neonatal cardiac injury. While this work is interesting, a number of issues remain.

1.English expression needs some attention. For example, the first sentence of the abstract - "more than 60%...."; Page 20, line 9 "We observed that misoprostol's ability to to". Many sections should be broken into 2 or 3 sentences.

Thank you, we have made these changes and have fully proof-read our manuscript.

2.Evidence from In vivo studies such as those described in section 3.7 is minimal. Much more in vivo evidence is needed. It is unclear the authors established this in vivo model of hypoxia - supposedly gestational hypoxia should be considered. Consider citing these reviews on maternal over- and under-nutrition for postnatal heath (PMID 33181042; 22982026).

Thank you, we will cite these papers and have clarified our in vivo model, related to comparable human gestation time, in the Methods section. We have also revised the Introduction and Discussion to be more consistent with our in vivo model. In addition, and noted above, we have preformed addition in vivo experiments in both the hypoxia/misoprostol model, and in Bnip3 KO mice to more fully support our conclusions. Additional HMGB1 immunofluorescence has already been added to Figures 1 and 7, and we have include additional confocal-based colocalization experiments from fixed tissues (described in more detail above; Fig. 7 D).

3.Which one does misoprostol exactly execute its action? Phosphorylation through PKA or trafficking Bnip3 away from the ER through a physical interaction with 14-3-3β?

This is a very good question. Based on our previous work on Bnip3L (Nix; PMID: 33044904,) PKA-induced phosphorylation of the transmembrane domain increased the physical interaction with 14-3-3b, which acts as a chaperone to translocate Nix away from the mitochondria and ER/SR. We will preform similar experiments with Bnip3 and 14-3-3b for the next revision to provide additional support for this conclusion.

4.More in vivo proof of concept studies are needed to validate the signaling mechanism - this is an invitro-based study (hypoxia challenge occurs in vitro).

We have already included additional in vivo immunofluorescence to Figure 1 and 7, and have performed additional colocalization experiments to validate the signaling pathway in this revision. Many of these experiments are described above.

5.Quality of figures is somewhat poor.

Our images are the highest possible resolution within the confines of the figure size limit. Perhaps the reviewer received a web-optimized version of the figures for review.

Reviewer #2 (Significance (Required)):

Relatively high - although in vivo evidence is needed.

Thank you. This is provided in the revised manuscript.

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Referee #2

Evidence, reproducibility and clarity

Systemic hypoxia, a major complication associated with reduced gestational time, affects more 60% of preterm infants and is a known driver of hypoxia-induced Bcl-2-like 19kDa-interacting protein 3 (Bnip3) expression in neonatal heart. At the level of the cardiomyocyte, Bnip3 activity plays a prominent role in the evolution of necrotic cell death, disrupting subcellular calcium homeostasis and initiating mitochondrial permeability transition (MPT). Emerging evidence suggests both a cardioprotective role for protein kinase A (PKA) through stimulatory prostaglandin (PG) E1 signalling during prolonged periods of hypoxia, and a cytoprotective role for Bnip3 phosphorylation, indicating that post-translational modifications of Bnip3 may be a point of convergence for these two protective pathways. Using a combination of in vivo and multiple cell models, including human iPSC-derived cardiomyocytes, the authors tested if the PGE1 analogue misoprostol is cardioprotective during neonatal hypoxic injury by altering the phosphorylation status of Bnip3. Here we report that hypoxia exposure significantly increases Bnip3 expression, mitochondrial-fragmentation, -ROS, -calcium accumulation and -permeability transition, while reducing mitochondrial membrane potential, all of which were restored to control levels with addition of misoprostol, despite elevated Bnip3 protein expression. Through both gain- and loss-of function genetic studies, the authors show that misoprostol-induced protection directly affects Bnip3, preventing mitochondrial perturbations. They demonstrate that this is a result of PG EP4 receptor signalling, PKA activation, and direct Bnip3 phosphorylation at threonine-181. Furthermore, when this PKA phosphorylation site within Bnip3 is neutralized, the protective misoprostol effect is lost. They also provide evidence that misoprostol traffics Bnip3 away from the ER through a physical interaction with 14-3-3β, thereby preventing aberrant ER calcium release and MPT. In vivo studies further demonstrate that misoprostol treatment increases Bnip3 phosphorylation at threonine-181 in the mouse heart, while both misoprostol treatment and genetic ablation of Bnip3 prevented hypoxia-induced reductions in contractile function. Taken together, these results demonstrate a foundational role for Bnip3 phosphorylation in the molecular regulation of cardiomyocyte contractile and metabolic dysfunction and identifies EP4 signaling as a potential pharmacological mechanism to prevent hypoxia-induced neonatal cardiac injury. While this work is interesting, a number of issues remain.

1.English expression needs some attention. For example, the first sentence of the abstract - "more than 60%...."; Page 20, line 9 "We observed that misoprostol's ability to to". Many sections should be broken into 2 or 3 sentences.

2.Evidence from In vivo studies such as those described in section 3.7 is minimal. Much more in vivo evidence is needed. It is unclear the authors established this in vivo model of hypoxia - supposedly gestational hypoxia should be considered. Consider citing these reviews on maternal over- and under-nutrition for postnatal heath (PMID 33181042; 22982026) .

3.Which one does misoprostol exactly execute its action? Phosphorylation through PKA or trafficking Bnip3 away from the ER through a physical interaction with 14-3-3β?

4.More in vivo proof of concept studies are needed to validate the signaling mechanism - this is an invitro-based study (hypoxia challenge occurs in vitro).

5.Quality of figures is somewhat poor.

Significance

relatively high - although in vivo evidence is needed

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Martens et al investigates the mechanisms of Bnip3-mediated cell damage during hypoxia. The Authors show that modulation of prostaglandin (PG) E1 signaling with misoprostol prevents cardiac dysfunction, mitochondrial impairment and cell death induced by hypoxia. In addition, they show that the effect of misoprostol is dependent on PKA-mediated Thr181 phosphorylation. The Authors also suggest that there is a possible interaction between Bnip3 and 14-3-3beta that prevents ER Ca2+ release and mitochondrial Ca2+ overload. The Authors conclude that Bnip3 phosphorylation plays a key role in the regulation of cardiac and metabolic dysfunction and identify misoprostol treatment as a therapeutic intervention to prevent hypoxia-induced cardiac injury.

Major Comments:

1.Results presented in Figs. 1, 2 and 3 pertaining to hypoxia-induced changes in Bnip3 expression, changes in mitochondrial function, cell death, Bnip3-dependent Ca2+ transfer from ER to mitochondria and the effect of misoprostol have partially been demonstrated in a previous publication from the same group (PMID: 30275982).

2.The very same publication from 2018 shows that misoprostol treatment of pups exposed to hypoxia for 7 days is able to prevent the increase in Bnip3 protein levels. Yet, in the present manuscript misoprostol treatment had no effect on Bnip3 protein levels in the same model (Fig. 1E). This raises some concerns regarding the solidity and soundness of the results presented.

3.The validation of the custom antibody against p-Thr181 needs to be shown. Fig. 4E shows that p-Bnip3 band is quite strong in H9c2 cells, despite total endogenous Bnip3 levels are barely detectable. In addition, phosphorylation of the Bnip3 Thr181 residue in cells and/or in vivo should be confirmed by mass spectrometry.

4.Fig. 4L shows that misoprostol treatment of H9c2 cells leads to an increase in Bnip3 phosphorylation, but this does not seem to be the case in normoxic conditions in vivo (Fig. 4N). Moreover, shouldn't this presumable increase in phosphorylation induced by misoprostol in normoxic conditions lead to Bnip3 accumulation in the cytosol thereby reducing its colocalization with mitochondria (Fig. 6B)? The results obtained with the colocalization method should be corroborated using different methods, such as cell fractionation.

5.In relation to Fig. 4 M, N (page 19), the Authors concluded that the reduction in Bnip3 phosphorylation suggests an increase in Bnip3 activity in the hypoxic neonatal hearts. Nevertheless, this has not been demonstrated.

6.Along that line, the Authors concluded that misoprostol-induced cytoprotection is dependent on PKA Thr181 phosphorylation. Nevertheless, this dependence has not been convincingly demonstrated in hypoxic cells and in vivo.

7.Previous studies showed that Bnip3 induces mitochondrial fragmentation and mitophagy (PMID: 16645637, 20436456). What is the hypothesis for the inhibition of mitochondrial fragmentation induced by misoprostol in the present study? Does it prevent Bnip3 interaction with Opa1 or is this event downstream of ER Ca2+ release and mitochondrial Ca2+ overload? Does misoprostol affect mitophagy?

8.The link between Bnip3 interaction with 14-3-3 and Bnip3 Thr181 phosphorylation, if there is any, is not clear. The Authors mention that Thr181 lies within the 14-3-3 binding domain. Is Thr181 phosphorylation required for 14-3-3 binding or are these events unrelated? What is the significance of these events in hypoxia, does 14-3-3 binding to Bnip3 occur in vivo? Is Bnip3 localization affected by hypoxia, 14-3-3 binding and/or misoprostol treatment in vivo?

9.Fig. 6P shows the presence of myc-tag after IP for HA-tag, even when HA-14-3-3 was not expressed (middle lane). How is this possible?

Minor Comments:

1.Please co-stain with the cardiomyocyte marker in Fig. 2A (such as alpha-actinin).

2.The Methods are not sufficiently detailed. For instance, it is not clear what is the Ca2+ concentration used for Ca2+ pulses in the CRC experiment. The fact that cardiac mitochondria are able to uptake only two Ca2+ pulses raises some concerns regarding the quality of mitochondrial preparation. What is the reason for isolating mitoplasts instead of intact mitochondria?

3.TMRM fluorescence should be measured before and after FCCP administration, to account for the difference in plasma membrane potential (the results should be expressed as F/FFCCP).

4.Measurement of extracellular acidification is mentioned in the methods, but the relative results are not shown.

5.RNAi experiments targeting Bnip3 are also mentioned in the methods, but the results are not described.

Significance

Previous studies have demonstrated that Bnip3 is upregulated by hypoxia and plays a key role in inducing mitochondrial dysfunction and PTP opening that eventually results in cell death (PMID: 12169648, 10922063). Along that line, misoprostol has been shown to prevent damaging effects of hypoxia by repressing Bnip3 and promoting the expression of pro-survival alternative splicing isoforms (PMID: 30275982). Indeed, the same study showed that misoprostol treatment prevents loss of mitochondrial membrane potential, ROS formation and impairment in mitochondrial oxygen consumption caused by hypoxia in primary neonatal cardiomyocytes. The present manuscript recapitulates these previously published findings. The truly novel findings concern the identification of Bnip3 residue Thr181 as target for PKA phosphorylation and the possible interaction of Bnip3 with 14-3-3. However, the role and/or involvement of these events has not been thoroughly investigated in relation to hypoxia and misoprostol treatment in cells or in vivo.

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Reply to the reviewers

We thank both reviewers for their insightful comments and suggestions. We propose to address these as described below.

Reviewer 1

**Major points:**

Point 1

1. A logical question comes up and I do not think the authors addressed, in a human body what happens to the extracted drugs after loading on HDLs? This requires some mentioning in the discussion.

1. This is indeed a good question. We have now added in the discussion what may happen to the HDL-extracted drugs in a whole organism. It reads as follows: The likely fate of HDL-extracted drugs in humans is that they are carried to the liver by HDLs. Scavenger receptors such as SR-BI expressed by hepatocytes can then bind HDLs carrying the extracted drugs allowing the drugs to be taken up by the cells. In hepatocytes, the drugs may be inactivated and excreted in the bile (https://doi.org/10.1016/j.cld.2016.08.001, https://doi.org/10.1161/CIRCRESAHA.119.312617). Point 2

2. Is the effect specific to the fully mature HDL molecule or do apo-lipoproteins that compose HDLs have similar effects?

1. This is an interesting question. Apo-AI is the characteristic and most abundant apolipoprotein found in HDLs. It is however not trivial to compare the activities of ApoAI and HDLs because of the difficulty of producing large amounts of ApoAI. In the present paper, the lowest concentration of HDLs that induces drug efflux is 0.125 mM. As there are about 3 molecules of Apo-AI per HDL molecule, we should use 0.375 (3 x 0.125) mM Apo-AI to see if the Apo-AI content of these HDLs can mediate or mimic the drug efflux capacity of the lipoproteins. About 100 mg of recombinant Apo-AI would be required to make 10 ml of a ~0.3 mM Apo-AI cell culture solution. This is an enormous task requiring substantial time and money investment. We are therefore not in a position to perform this experiment that would be of interest but which is not central for supporting the main message of our manuscript. Point 3

2. What are non-SERCA-mediated effects of TG?

1. The SERCA-independent toxic effects of TG have been shown to be a consequence of mitochondrial dysfunction resulting from the ability of TG to induce mitochondrial permeability transition (DOI: 10.1046/j.1432-1327.1999.00724.x). This is now mentioned in the discussion. Point 4

2. Why don't HDLs protect cells from low dose TG despite its removal?

1. Our data indicate indeed that HDLs do not affect the ability of TG to inhibit SERCA and the low ER stress response that ensues. This can be explained by the fact that very low concentrations of TG inhibit SERCA in an irreversible manner (Ki values of 0.2, 1.3, and 12 nM for SERCA1b, SERCA2b, and SERCA3a, respectively) (DOI:https://doi.org/10.1074/jbc.M510978200). Hence, even though HDLs can remove a substantial amount of TG from cells, the concentration of TG that remains in cells is presumably still sufficient to fully inhibits the SERCA pumps. This explanation is now included in the discussion. Point 5

Line 144. No information on the siRNA was given (refer to the materials section to guide the reader).

The siPOOLs we have used correspond, for each targeted gene, to a pool of 30 optimally-designed proprietary siRNAs from Biotech. The company does not disclose the sequences of these siRNAs.

Minor comments:

Point 6

1. There needs to be an abbreviation section. Make sure that you only abbreviate the terms that are used more than once in the text.

1. An abbreviation list is now provided. Point 7

2. Lines 104, 277, 283 and anywhere else: use TG instead of thapsigargin.

1. Thank you for noting this. This has now been done. Point 8

2. Line 262: you don't have to redefine SERCA.

1. Done Point 9

2. I suggest adding structures of the used drugs.

1. The structures of the drugs used in this work are now presented in Figure S9. Point 10

2. I suggest using a table for the RT-PCR primers. Protein Direction Number Sequence Description NCBI entryh-SERCA2 Fwd #1612 5'ATG GGG CTC CAA CGA GTT AC nucleotides 648-667 of human SERCA2, variant a NM_001681.4

1. Thank you for this suggestion that we have now followed and that indeed facilitates the reading of the RT-PCR method section. Point 11

2. Line 93: DMEM (Gibco; ref 61965-059;) the lot number is missing.

1. The lot number is now indicated. Point 12

2. Line 102: 500'000 (and all other thousand numbers) the apostrophe's place is strange.

1. We have now removed the apostrophe in numbers. Point 13

2. Line 381: cholesterol carriers.

1. This typo has now been corrected Reviewer #2 (Evidence, reproducibility and clarity (Required)):

**Major concerns**

Point 14

1. 1. Figure 2, The authors should perform western blot to evaluate the protein expression levels (not only mRNA levels by Q-PCR)

1. We have performed these experiments in the past in MIN6 cells (Pétremand et al. Diabetes 2012 May; 61(5): 1100-1111; Figure 2). This earlier work showed that HDLs reduce the induction of TG-induced ER stress markers at the protein (CHOP and BiP) and functionality (IRE1 activity on XBP1 splicing). We will repeat these experiments in DLD1 cells as per the reviewer’s suggestion. Point 15.

1. Could the authors evaluate whether HDL treatment reduces the amount of SERCA (mRNA/protein) in their cells? The loss of SERCA could explain the reduced accumulation of the BODIPY-TG in the cell?

We would argue that it is unlikely that a reduction in SERCA expression from cells has any significant impact on TG cell loading as the cell-associated drug is certainly in vast excess compared to the number of SERCA molecules in cells. We will nevertheless perform the requested experiment using DLD-1 cells and assess whether HDLs modulate their SERCA2 expression.

Point 16.

1. To generalize their observation, It would have been interesting to test more lipophilic/hydrophilic drugs to quantitatively validate that HDLs are selective of lipophilic drugs.

We will test 2 new lipophilic (letermovir and lumefantrine) and 2 new hydrophilic drugs (levetiracetam and cefepime) for their ability to be extracted by HDLs (experiment set-up as in Figure 4).

Point 17.

1. The ABC transporter part in this manuscript has to be improved with the down-regulation of extinction of ABCA1 and ABCG1 to determine in a comprehensive manner the effect of these transporters in the pro-survival role of HDL.

We will invalidate the genes encoding ABCA1, ABCB1, ABCG1, and ABCG2 using the CRISPR/Cas9 technology and test the ability of the invalidated cells to promote efflux of thapsigargin to HDLs (experiment set-up as in Figure 6) and to protect them from the drug (experiment set-up as in Figure 6). The choice of the cell lines to be used for the invalidation depends on what ABC transporters they express. No single cell line expresses all four ABC transporters to high levels. The following cell lines will be used because, according to the literature or to the Human Protein Atlas (https://www.proteinatlas.org/), they display strong expression of the indicated transporters: for ABCA1: HCT116; for ABCB1: HEK293T; for ABCG1 and ABCG2: MCF7. For consistency with the experiments already performed in the manuscript, the invalidation will also be performed in the DLD1 cell line.

**Minor point:** Point 18.

1. 1. ABCB1 blot in figure 7B is not convincing and should be improved.

1. We will redo this WB to improve the quality of the blot.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Christian Widmann and colleagues describe how HDLs can protect cells by promoting the extraction of lipophilic drugs such as thapsigargin (TG). The authors observe that HDLs do not affect the ability of TG to inhibit SERCA but instead decrease lipophilic drug content inside cells and therefore protect cells against their lethal effects. Using some compounds (probably not enough to conclude), the authors claim that HDLs can promote the exclusion of lipophilic drugs while hydrophilic drugs or compounds like doxorubicin hydrochloride, an anticancer drug, or Rhodamine 123, were not extracted from cells. Finally using small interfering RNA, the authors reveal that ABCB1 mediates some of the drug effluxes to HDLs. This study is sound and well-written. Although of interest from a therapeutic standpoint, this manuscript should address some questions to strengthen these data.

Major concerns

1. Figure 2, The authors should perform western blot to evaluate the protein expression levels (not only mRNA levels by Q-PCR)
2. Could the authors evaluate whether HDL treatment reduces the amount of SERCA (mRNA/protein) in their cells? The loss of SERCA could explain the reduced accumulation of the BODIPY-TG in the cell?
3. To generalize their observation, It would have been interesting to test more lipophilic/hydrophilic drugs to quantitatively validate that HDLs are selective of lipophilic drugs.
4. The ABC transporter part in this manuscript has to be improved with the down-regulation of extinction of ABCA1 and ABCG1 to determine in a comprehensive manner the effect of these transporters in the pro-survival role of HDL.

Minor point:

1. ABCB1 blot in figure 7B is not convincing and should be improved.

Significance

This study can interest a large scientific audience. Some additional experiments have to be performed to render more convincing some part of this study.

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Referee #1

Evidence, reproducibility and clarity

It was my pleasure to evaluate the work submitted to Review Commons. I have reviewed the work and my comments are as follows: This manuscript entitled "HDLs extract lipophilic drugs from cells" by Zheng and colleagues describes a new mechanistic picture of how HDLs protect cells against death. The authors meticulously describe a novel ability of HDLs to extract hydrophobic xenobiotics from cells akin to their cholesterol-extracting function. I would like to thank the authors for a pleasurable read and their well-defined experimental design. This manuscript is of great value and significance to the fields of clinical chemistry and pharmacology. I therefore do think this manuscript merits publication after tending to these major and minor comments.

Major points:

• A logical question comes up and I do not think the authors addressed, in a human body what happens to the extracted drugs after loading on HDLs? This requires some mentioning in the discussion.
• Is the effect specific to the fully mature HDL molecule or do apo-lipoproteins that compose HDLs have similar effects?
• What are non-SERCA-mediated effects of TG?
• Why don't HDLs protect cells from low dose TG despite its removal?
• Line 144. No information on the siRNA was given (refer to the materials section to guide the reader). Minor comments:
• There needs to be an abbreviation section. Make sure that you only abbreviate the terms that are used more than once in the text.
• Lines 104, 277, 283 and anywhere else: use TG instead of thapsigargin.
• Line 262: you don't have to redefine SERCA.
• I suggest adding structures of the used drugs.
• I suggest using a table for the RT-PCR primers. Protein Direction Number Sequence Description NCBI entry h-SERCA2 Fwd #1612 5'ATG GGG CTC CAA CGA GTT AC nucleotides 648-667 of human SERCA2, variant a NM_001681.4
• Line 93: DMEM (Gibco; ref 61965-059;) the lot number is missing.
• Line 102: 500'000 (and all other thousand numbers) the apostrophe's place is strange.
• Line 381: cholesterol carriers.

Significance

This manuscript is of great value and significance to the fields of clinical chemistry and pharmacology.

Referees cross-commenting

I agree with the experiments suggested by reviewer #2

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• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>