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Referee #3

Evidence, reproducibility and clarity

Summary

Ichise et al., present a solid work describing the modality and time frame of action of NK control over seeding metastatic cells within the lung vasculature. Th authors use a variety of technique able to dissect how NK patrol lung vasculature, that they interact with cancer cells as they interact with the endothelial cells and they activate a ERK dependent activation leading to calcium influx in cancer cells leading to their death. The data support the notion that this NK control occur over an early time frame, 4h after cancer cells arrival and is mediated by Necl expression on cancer cells. After this time point cancer cells show a thrombin dependent loss of Necl expression on their surface and therefore become resistant to NK control.

Comments:

The data presented are supporting the conclusions. This work utilizes a variety of elegant strategy combining reporter strategy with in vivo imaging to assess the phenomenon of interaction, ERK activation, Calcium Inflax and Apoptosis activation directly in the lung. In term of experiments, I found the work thorough and complete. The data a presented well overall and the statistics seems adequate. I only have few suggestions:

Supplementary Figure S3, show the use of antiLy6G to deplete neutrophils in the lungs of C57BL/6 mice injected with melanoma B16F10 cells. It was recently shown that this antibody is not efficient in depleting neutrophils in this background, but only lead neutrophils to internalise the Ly6G so they cannot be detected by FACS. As shown in Boivin et al 2020 http://doi.org/10.1038/s41467-020-16596-9) neutrophils depletion in C57BL/6 mice can be achieved by using antiGr1 antibody. Therefore, if the authors aim to show this additional control, which I also agree is really good to have, I suggest performing the experiment accordingly to the best-known practice.

Figure 1E: in the text the experiment is described as 4T1 Akaluc cells were inoculated into the foot pad of BALB/c mice with either control antibody or αAGM1, but the legend states that mice subcutaneously injected with B16 Akaluc cells into footpad. As B16 melanoma cells are not in BALB/c background, I assume the legend needs to be corrected as the cells should be 4T1, however I wonder if injecting 4T1 breast cancer cells in the footpad could have let to the substantial growth required for lung metastasis without impairing the animal mobility. Could it be that cells where actually injected in the fat pad of the mice and this is just a misspelling in the text? In this case, the different in the tissue residence NK cells could also potentially explain why 4T1 are not cleared in the fat pad like the B6 cells are in the footpad.

The authors should comment on the difference in the in clearance of the cells at the injection site in Figure 1C VS Figure 1E.

Significance

The present work is highly relevant to the field of cancer metastasis. While it is known that NK are responsible for the first line of defence against metastatic seeding, most of the studies focuses on how they are suppressed or influenced by other immune cells. The present study provides a very accurate description of their mechanism of action, how they depend in the interaction with the endothelial cells and highlight the novel aspect of thrombin in inducing cancer cells NK resistance. What cause thrombin activation is the next relevant question, by in my opinion this study is complete and important.

My field of expertise is cancer metastasis and their interaction with the immune system and I personally enjoy very much reading this work.

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Referee #2

Evidence, reproducibility and clarity

The authors use in vivo imaging techniques to investigate the killing of lung metastasis by NK cells. They demonstrate that the cleavage of CD155 may result in resistance of killing by NK cells and suggest that this could be an immune evasion mechanism of metastatic tumor cells. Overall, the subject is highly relevant, and the in vivo imaging is an interesting and highly relevant technique. However, the message, that tumor cells escape the killing by NK cells by cleavage of CD155 is interesting, but not yet fully supported by the data.

Major comments:

1. Figure 6: To support their main claim the authors would need to transfect the tumor cells with a CD155 mutant, which cannot be cleaved by Thrombin and show that these tumor cells can no longer escape NK cell-mediated killing. This experiment is straight forward and feasible. Another important experiment along this line would be the use the CD155/CD112 deficient tumor cells (Which the authors use in figure 4) in the experiments shown in figure 1. One would expect that tumor control by NK cells within the first 24h is absent when using these tumor cells.
2. Figure 5: The demonstration that ERK is activated in this in vivo setting is novel. However, ERK activation is not DNAM-1 specific and the ERK inhibitor is significantly less effective that the depletion of NK cells. Therefore, the relevance of these data to the main message of the manuscript is unclear and the figure could be omitted.
3. In general, the issue of NK cell exhaustion should be addressed in more detail. The experiments do not address serial killing activity of NK cells and more data is needed to show that it is not an exhaustion of NK cells but the cleavage of CD155 from the tumor cells that prevents further killing.

Minor comments:

1. Figure 1C: The relevance of this experiment needs to be better explained.
2. Figure 3A: What does SHG stand for?
3. Figure 3: Please add a statistical analysis for these experiments.
4. Figure 4: The use of the caspase-3 and the calcium sensors may detect different cytotoxic mechanisms used by the NK cells. While caspase-3 can be activated by death receptor and perforin/granzyme B mediated killing, the calcium sensor may report mostly on perforin mediated membrane damage. These killing mechanisms have different kinetics and are differentially used during serial killing by NK cells. This should be addressed (at least in the discussion).

Significance

Investigating the in vivo cytotoxicity of NK cells against tumor cells by using live imaging technologies is highly relevant for the understanding of the dynamic relationship between tumor and killer cells. Therefore, the subject of this manuscript and the technologies used are very relevant, as in vivo killing activities do not always translate to the in vivo setting.

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Referee #1

Evidence, reproducibility and clarity

The authors aimed to understand the control and the elimination of disseminated tumor cells by NK cells within the lung, their main question being how pulmonary NK cells are able to prevent tumor cells from colonization in the lung.

To dissect this question, Hiroshi Ichise and colleagues took advantage of the ultra-sensitive bioluminescence whole body imaging system combined with intravital two-photon microscopy technology involving genetically-encoded biosensors tumor or NK cells to explore the behavior and functional competences of NK cells in an experimental lung metastasis model. First, the authors have monitored the fate of intravenously injected B16-Akaluc cells from 5 min to 10 days and observe that tumor cells decrease rapidly within the first 12-24 hours. In parallel, they performed asialoGM1+ and NK1.1+ cells depletion by injection of depleting anti-aGM1 and anti-NK1.1 antibodies in order to see the involvement of these populations on the elimination of the disseminated tumor cells. They conclude that a rapid decrease of the tumor cells is mediated by NK cells. Consisting with this first data, the authors observe also the same early NK cells mediated impact on two other syngenic mouse tumor cell lines : the BRAFV600E melanoma and the colon adenocarcinoma MC-38.

In a second part, the authors dissected NK cell dynamic behaviors in the pulmonary capillaries by taking advantage of the NKp46iCRExrosa26dtTomato mice where NKp46+ cells are fluorescents and performed 2P intravital imaging to follow the in situ the NKp46+ cells behavior. They could nicely observe that NK cells arrive from the capillaries and patrol on the lung epithelial cells in a stall-crawl-jump manner. Moreover, they also show that the attachment to the pulmonary capillaries is mediated by LFA-1. In the presence of B16F10 tumor model, they observe that NK cells stay longer in the capillaries and increase their duration time of crawling indicating that NK cells stay in contact longer with tumor cells.

The authors then explored the NK-mediated tumor killing in the lung by measuring tumor cell apoptosis using B16F10-SCAT3 cells (which leads to visualize caspase 3 activation) and Ca2+ influx in tumor cells expressing two Ca2+ sensors, GCaMP6s and R-GECO. They could observe casp3 activation but also Ca+ influx on tumor cells within few minutes after encountering NK cells. They also observe that evasion of NK cell surveillance is mediated by Nectin-5 and Nectin-2 expressed on tumor cells.

Then, they focus on NK cell activation by looking at ERK activation. To do so, they have isolated NK cells from Tg mice expressing a FRET-based ERK biosensor and performed in vitro killing assay against B16-R-GECO tumor cells but also in vivo experiments. For the in vivo experiments, they have developed reporter mice whose NK cells express the FRET biosensor for ERK. They observe that ERK-dependent NK cell activation contributes to the elimination of disseminated tumor cells within the first few hours but not after 24hours. Indeed, theu observe that B16F10-Akaluc tumor cells are equally eliminated when injected 24h after a first injection of B16F10 or PBS in mice. The authors concluded that tumor cell acquire the capacity to evade NK cell surveillance after 24h rather than a hypothesis toward NK cells loose tumoricidal activity over time. Finally, the authors have explored their last result on the potential tumor cell evasion of the NK cell surveillance. They show that this NK cell evasion is mediated by the shedding of cell surface Necl-5. They next show that clivage of extracellular domain of Necl-5 was mediated by thrombin in vitro and that anti-coagulation factors such as Warfarin, Edoxaban or Dabigatran Etexilate promote tumor elimination as observed by the bioluminescence experiments. This loss prevents the NK cell signaling needed for effective killing of tumor targets. However, most of the results remain correlations and have not been formally demonstrated or miss controls. B16F10 is a well known and characterized NK cell target in a in vivo model so the first part is not really knew except the in situ behavior of NK cells within the lung capillaries. The new mecanism of thrombin-mediated shedding of Necl-5 causing evasion from NK Cell surveillance is really concentrated on the last figure (Fig N{degree sign}6) and some supplemental experiments are mandatory and needed to really confirm this affirmation.

Significance

There are several points to address to improve the significance of these data.

Major points

1) A global point : 3 mice/group is to small to analyse and interprete data because of the heterogeneity of the mice. Mean +/- SEM have to represented instead of SD.

2) The authors used the well known polyclonal anti-asialoGM1 Ab to deplete NK cells. AsialoGM1 is also expressed by ILC1, T, NKT and gd+T cells but also basophils (Trambley J et al., Asialo GM1(+) CD8(+) T cells play a critical role in costimulation blockade-resistant allograft rejection. JCI, 1999). The authors checked the involvement only for the basophils. They have to check the depletion of each of these populations specifically in the lung to assume that the depletion impact only the NK cells or they must change their conclusion on the entire manuscrit and say that not only NK cells is responsible and involved in the control of the disseminated tumor cells but maybe also ILC1, NKT and or gd+T cells.

3) Lines 133 to 136 : The authors say that they « did not observe any significant difference in the relative increase of the bioluminescence signal between the control and αAGM1-treated mice, implying that NK cells eliminate disseminated melanoma cells primarily in the acute phase (< 24hrs) of lung metastasis » Please comment because the depletion of asGM1+ cells impact also the growth of the tumor until 8 days (fig 1B-E-G)

4) Fig S3A-B : The authors say that basophils express aGM1 so they performed basophils involvement on the elimination of B16F10 tumor cells with depleting aCD200R3 mab. They also checked the involvement of neutrophils and monocytes. They observed that basophils, neutrophils and monocytes are not involved on the B16F10 elimination. But what is the hypohesis to assess the role of neutrophils and monocytes ? Moreover, they did not explore Basophil roles in the other models including MC-38, BRAFV600E and 4T1 tumor cells.

5a) Fig 1D : Missing control : the author must add the WT Balbc + a-AGM1 as control.

5b) Lines 154 to 156 : the authors say that « T cell immunity does not contribute to tumor cell reduction » because tumor cells are eliminated in the nu/nu mice as efficiently as in the WT Balbc mice. This is not correct because they are looking in a window that correspond to innate immunity activation (up to 24h) so they cannot talk about T cell immunity, the adpative response will come more later around 8 days after.

6) Line 159 : (refer to point #2) To affirm that NK cells is critical and involved in the elimination of the disseminated tumor, authors have to perform experiment in a model of NK cell deficiency. The most relevant nowaday is the NKp46ICRExrosa26DTA mice that are deficients in NK cells but also ILC1 cells. Indeed, the authors have used the NKp46iCre mice model for other questions.

7a) Fig 2F : IC missing

7b) Lines 181-182 : Authors conclude that the effect of anti-LFA-1 on NK cells adhesion to the pulmonary endothelial cells is mediated primarily by LFA-1. It is not totally true because it is partially mediated as observed in the fig 2F. So authors should change their conclusion and precise that the involvement is partially mediated by LFA-1.

8) Fig S5B-C-D and S7: The authors talk about tumor cell death. But they are analyzing Ca2+ influx in vitro so it is a little bit different from the cell death. I'm wondering how the cell death is mesured espacially in the fig S5D and S7?

9) Fig 4H and lines 232-233 : the authors conclude that « damage to tumor cells is dependent on the engagement of DNAM-1 on NK cells ». There is any experiment performed to affirm this point so the authors cannot maintain this conclusion. First, the authors only analyzed Ca2+ influx at a specific time point. So this result only show that Nectin-5 and/or Nectin-2 expressed by B16F10 is involved in the Ca2+ influx following NK cell contact but there is any data on DNAM-1 contribution. So, the role on the NK cells and specifically DNAM-1+ NK cells have not been adressed here. To answer to that question, the author have to perform in vivo model of engrafted WT vs Necl-5/2 ko B16F10 in a WT vs DNAM1 deficient NK cells mouse model to ascertain the contribution of Necl-5/2-DNAM-1 on NK cells. Moreover, survival curve and bioluminescent experiments would be very appreciated.

10) Lines 253-254 : the authors talk about tumor apoptosis but they are looking at Ca2+ influx. So, they should change their conclusion or show killing experiment.

11) Fig 6 : the authors conclude that the trombin dependent shedding of Necl-5 causes evasion of NK cells surveillance. Moreover, all experiments are correlations and do not implicate in the same experiment Necl-5, DNAM-1+ NK cells and trombin or anti-coagulation factors. So, as in the comment #9, to adress this point, the authors should inject WT vs Necl-5 deficient B16F10-Akaluc into WT vs NK cell depleted mice and monitor the bioluminescence of the tumor cells within 24h following injection of anti coagulation factors as in the fig 6H. Moreover, the monitoring of the survival curve and the number of the lung metastasis would be also very important and informative to really answer to this point.

Minor points

1) Fig 2E: The authors assess the involvement of LFA-1 and MAC-1 on the NK cells attachement to the the pulmonary endothelial cells. But there is other adhesion molecules that are known to be expressed by NK cells as for example CR4 (CD11c/CD18). So, the attachement of NK cells could be also due to this molecule.

2) Lines 190 to 197 : Authors should put this methodology part in the « material and method » in order to be more clear on the message they want to deliver.

3) line 228 : There is any hypothesis or explanation regarding the use of Necl5/Necl2 deficient B16F10. Why authors decided to go and explore this pathway ? Authors could add some transition sentence and explanation to help readers.

4) The author could performed the same experiment as in Fig S7D and assessed ERK activation of DNAM+ vs - NK cells against WT vs Necl-5/Necl-2KO R-GEKO B16F10 cells.

5) Line 283 : Thanks to reformulate the sentence. Check the firgures associated with the text.

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Reply to the reviewers

We thank the reviewers for their insightful comments and suggestions. Addressing them will improve our work. Please find below our point-by-points answers to the issues raised. We also provide a partially revised version of the manuscript with changes indicated in blue.

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Reviewer #1 (Evidence, reproducibility and clarity (Required

**Summary**

The authors propose a mechanism through which voltage dependent water pore formation is key to the internalization of Cell permeable peptides (CPPs). The claim is based on an in-silico study and on several experimental approaches. The authors compare 5 peptides (R9, TAT-48-57, Penetratin, MAP and Transportan and use 3 distinct cell lines (Raji, SKW6.4 and HeLa cells), plus neurons in primary cultures. The also present in vivo experiment (mouse skin and zebrafish embryo). All in all, it is an interesting study, but it raises several issues that need to be addressed. Moreover, the length and structure of the manuscript make it very difficult to read (see below under "Reviewer statement")

**Reviewer statement**

The instructions are to use the "Major comments" section to answer 6 precise questions. Unfortunately, this is not possible due to the structure of the document to review. The main manuscript (22 pages) comes with 4 primary figures and 19 supplemental ones. Most of these figures have an enormous number of panels and their legends occupy 17 pages. To this, are added 6 supplemental tables and 7 supplemental movies (with 2 pages of legends), 28 pages of Material and Methods, and 146 References (109 for the main manuscript and 37 for Supplemental information). To be frank, I was often tempted to send the manuscript back, asking for the authors to submit a document facilitating the task of the reviewers.

Because of this complexity, my "Major comments" will come after a page by page, paragraph ({section sign}s) by paragraph and figure by figure "Detailed analysis" of the manuscript.

**Detailed analysis**

Q1. Page 4 {section sign} 3

The test is based on the ability of TAT-RasGAP to kill the cells. Although controls exist, this is worrying since necrotic death might participate in the rupture of the membrane and artificially amplify internalization after a first physiological entry of the peptide. It is also a bit dangerous to add a FITC group to a short peptide without controlling that it has no effect on the interaction with the membrane (FITC-induced local hydrophobicity can provoke peptide tilting and membrane shearing). In the same vein, the very high peptide concentrations often used in the study (40µM for Raji and SKW6.4 cells and 80µM on HeLa cells) can be highly toxic.

A1. We took advantage of the fact that TAT-RasGAP317-326 can kill cells to design a CRISPR/Cas9 screen based on cell survival for the identification of genes encoding proteins involved in CPP uptake. For this purpose, it was important therefore that the peptide was able to kill wild-type cells. Even if we consider the possibility that “necrotic death might participate in the rupture of the membrane and artificially amplify internalization after a first physiological entry of the peptide”, it remains that the cells that survived the screen did so because they were carrying mutations in genes that encoded potassium channels required for CPP uptake. And since the cells that survived the screen, by definition, were not dying, the issue raised by the reviewer is void in this case. The reviewer mentions that we included controls to validate the observations made with FITC-TAT-RasGAP317-326. Indeed, these controls were performed to address the potential problem raised by the reviewer. These controls, listed below, demonstrate that the genes identified through the CRISPR/Cas9 screen were also involved in the uptake of CPPs devoid of killing properties as well as CPPs that were not labelled with fluorophores.

i) Three different cell lines, lacking specific potassium channels identified through the CRISPR/Cas9 screen, were unable to allow a non-labelled, non-toxic CPP (TAT-PNA) to enter cells (Supplementary Fig. 8a).

ii) The Cre recombinase hooked to TAT, a construct that is not labelled with a fluorochrome and that is not toxic, did not enter Raji cells lacking the KCNQ5 potassium channels, also identified through a CRISPR/Cas9 screen (Supplementary Fig. 8b).

iii) The internalization of a TAT-conjugated FITC-labelled cell-protective therapeutic compound was inhibited, sometimes fully, in three different cell lines, lacking specific potassium channels identified through the CRISPR/Cas9 screen (Supplementary Fig. 8c).

Additionally, we are now reporting that the entry of FITC-labelled TAT, R9, and penetratin, all non-toxic CPPs, is impaired in Raji cells lacking the KCNQ5 potassium channel identified in the CRISPR/Cas9 screen. These new results will be incorporated in the revised version of our manuscript.

As supportive evidence that a potential toxicity effect of TAT-RasGAP317-326 is not a confounding factor in experiments recording the initial uptake of the peptide is that internalization is measured after one hour of incubation with the cells (Figure 1), time at which the peptide only minimally impacts the survival of cells (PNAS December 15, 2020 117 31871-31881).

Finally, please note that depolarizing cells, which is what happens in cells lacking the potassium channels identified through the CRISPR/Cas9 screen, not only blocked the uptake of TAT-RasGAP317-326, but also the uptake of a series of non-toxic CPPs (using short-time incubation protocols; Figure 2).

Page 5 {section sign} 1

Q2. Supp. Fig.1a shows no differences between the 3 cell types, even though they differ in their modes of peptide internalization, some favoring vesicular staining and others cytoplasmic diffusion.

A2. The images shown in panel A of this figure depicts, for each cell line, examples of cells that do not take up the CPP, those that only display vesicular staining, and those that additionally take up the peptides in their cytosol. These images were picked to depict these uptake phenotypes and this is why they are similar in the three cells lines. Panel A does not provide any quantitative information on the prevalence of these different uptake modes in the three cell lines. This is shown in panel B of Supplementary Fig. 1. There is, therefore, no discrepancies between the two panels.

Q3. Multiplying cell and peptide types contributes to the complexity of the manuscript without increasing its interest. If there is a conceptual breakthrough, as might be the case, it is obscured by the accumulation of useless images and data. A step into simplifying the manuscript would be (i), to concentrate on Raji cells (leaving out SKW6.4 and HeLa cells) and (ii) to only discuss the R9, TAT (including TAT-RasGAP) and Penetratin peptides.

A3. We are sorry that the inclusion of several cell lines and several CPPs was seen as confusing by the reviewer. Our current vision is that our observations are strengthened if we show that the observed effects are seen in several cell lines and with a variety of CPPs. We would like therefore to not exclude supportive evidence presented in our work because if we do remove some of the data shown in the manuscript, we will definitely weaken some of our claims. We nevertheless remain open with this point that can be further discussed with the editors.

Q4. TAT and R9 are poly-R peptides, which is not the case for Penetratin that has only 3 Rs. These 3 Rs are important (cannot be replaced by 3 Ks), but the two Ws absent in R9 and TAT are equally important as they cannot be replaced by Fs. This must be considered by the authors when they tend to generalize their model.

A4. The point raised by the reviewer concerning the importance of W and R residues in CPPs is well taken. We have now developed this in the discussion with the addition of the paragraphs shown below.

“An additional potential explanation to the internalization differences observed between arginine- and lysine-rich peptides is that even though both arginine and lysine are basic amino acids, they differ in their ability to form hydrogen bonds, the guanidinium group of arginine being able to form two hydrogen bonds1** while the lysyl group of lysine can only form one. Compared to lysine, arginine would therefore form more stable electrostatic interactions with the plasma membrane.

Cationic residues are not the only determinant in CPP direct translocation. The presence of tryptophan residues also plays important roles in the ability of CPPs to cross cellular membranes. This can be inferred from the observation that Penetratin, despite only bearing 3 arginine residues penetrates cells with similar or even greater propensities compared to R9 or TAT that contain 9 and 8 arginine residues, respectively (Supplementary Fig. 9g). The aromatic characteristics of tryptophan is not sufficient to explain how it favors direct translocation as replacing tryptophan residue with the aromatic amino acid phenylalanine decreases the translocation potency of the RW9 (RRWWRRWRR) CPP2. Rather, differences in the direct translocation promoting activities of tryptophan and phenylalanine residues may come from the higher lipid bilayer insertion capability of tryptophan compared to phenylalanine3-5. There is a certain degree of interchangeability between arginine and tryptophan residues as demonstrated by the fact that replacing up to 4 arginine residues with tryptophan amino acids in the R9 CPP preserves its ability to enter cells6. It appears that loss of positive charges that contribute to water pore formation can be compensated by acquisition of strengthened lipid interactions when arginine residues are replaced with tryptophan residues. This can explain why a limited number of arginine/tryptophan substitutions does not compromise CPP translocation through membranes**.”

Q5. Supp. Fig1c-d is not necessary (very little information in it) and Supp. Fig 1e is misleading as it takes a lot of imagination to see a difference between homogenous (top) and focal (bottom) diffusion.

A5. Since we perform cytosolic quantitation to infer direct translocation, it appears important to us, for allowing others to potentially replicate our results, that we precisely report how methodologically we perform our experiments. For Supplementary Fig. 1e, we agree that the examples shown are not easily interpretable. We have now removed this panel, as well as the accompanying panel f, from the Supplementary Fig. 1.

Q6. Supp. Fig.1g: How many cells are we looking at? Given the high variance, the result cannot be interpreted easily. A distribution according to fluorescence bits would be a better way to present the data.

A6. Over 230 cells have been quantitated per condition, which includes all cells where CPP entry has occurred regardless of the intensity or the type of entry. We did not only focus on cells with strong cytosolic staining to avoid any bias with regards to detection limitations. High variance can also be explained by the fact that CPP cellular entry is not synchronized. We tested the way of showing the data as suggested by the reviewer but this did not improve the visualization of the results in our opinion. We will therefore keep the initial presentation. Note that regardless of the way the data are presented, the conclusion remains the same, namely that illumination in our hands is not the cause of CPP membrane translocation.

Q7. Supp. Fig2i. This panel confirms that Raji cells differ from the two other cell types by showing clear temperature dependency. The explanation will come later with the energy barrier for low Vm-induced pore formation. This contradicts earlier reports showing that Penetratin translocation is not temperature-dependent, possibly because it was done on neurons naturally hyperpolarized. Or else because mechanisms are, at least in part, different from the one proposed here for R9 and TAT. This requires some clarification and supports the suggestion that, instead of multiplying models and peptides, it would be more efficient to compare TAT, R9 and Penetratin internalization by Raji cells and primary neurons.

A7. Supplementary Fig. 1i (not Supplementary Fig. 2i as indicated by the reviewer) was reporting the overall CPP uptake, both through direct translocation and endocytosis as a function of temperature. As there is limited endocytosis in Raji cells, the data shown for this cell type mostly correspond to direct translocation. For Hela and SKW6.4, endocytosis is not marginal however and we will perform a new set of experiments to define the role of temperature (4, 20, 24, 28, 32°C) in CPP direct translocation (i.e. cytosolic acquisition) in HeLa cells and SKW6.4 (using the CPPs listed by the reviewer). We have partially performed this for HeLa cells already and this shows that direct translocation is indeed inhibited by low temperatures (more than 10-fold at 4°C compared to 37°C). Bear in mind that no endosomal escape occurs in our settings (see Supplementary Fig. 7c). This indicates that the decrease in cytoplasmic fluorescence induced by low temperature is not a consequence of diminished CPP endocytosis.

Q8. Supp. Fig. 2a-f. Last sentence of the legend "Concentrations above 40µM led to too extensive cell death preventing analysis of peptide internalization". This confirms the warning against the use of concentrations varying between 40 µM and 80 µM and partially jeopardizes the validity of some experiments.

A8. The reviewer has truncated this sentence that actually reads “Note: concentrations above 40 mM of TAT-RasGAP317-326 led to too extensive Raji and SKW6.4 cell death, preventing analysis of peptide internalization at these concentrations.” As different cell lines display various sensitivities to potential toxic effects induced by CPPs (Raji and SKW6.4 cells being more sensitive than HeLa cells for example), we have adapted the concentrations of CPPs used to monitor cellular uptake so that cell death was minimal or non-existent in order to prevent the potential confounding effects mentioned by the reviewer. Hence in contrast to what the reviewer is stating, we are taking care of the toxicity effect and perform our experiments in conditions were toxicity is minimal. The logic of the reviewer to state that we “jeopardize[d] the validity of some experiments” is therefore unclear to us as we did take care of not exposing our cells to toxic CPP concentrations.

Page 6 {section sign} 2

Q9. The authors advocate 2 modes of entry, opposing transport across the membrane and endocytosis. In contrast with R9, TAT and Penetratin, Transportan or MAP seem to be purely endocytosed but, if they reach the cytoplasm, they still have to cross a membrane (unless "a miracle happens"). For Penetratin and R9/TAT, the authors consider that water pore and inverted micelle formation are incompatible. This is a bit rapid as inverted micelles might induce water pores through W/lipids interactions requiring less R residues and, possibly, less energy. This provides the opportunity to signal that, in spite of their very high number, key references are missing or hidden in cited reviews, some of them written by colleagues who are not among the main contributors to the CPP field.

A9. Transportan in our hands indeed appear to enter cells via endocytosis mostly. As reported by the reviewer, how Transportan reaches the cytosol remains unresolved.

Our data support a model where CPPs enter cells via water pores that are not made by the CPPs themselves but that are created by the megapolarization state of the membrane. Our data therefore do not support toroidal or barrel-stave pore models because these pores would be built as a result of CPP assemblage.

Inverted micelles have been hypothesized to mediate CPP translocation across membranes7 but to our knowledge, there is no in silico or cellular experimental evidence for this in the literature. To us, the data on which the involvement of inverted micelle in CPP translocation is based are also fully consistent with the water pore model. CPP translocation through water pores has been seen by several authors during in silico experiments but, to the best of our knowledge, simulations have not reported the formation of inverted micelles during CPP translocation across membranes.

Finally, we would be grateful to this reviewer if the “key references” that are apparently missing from our manuscript are disclosed so that we could acknowledge them appropriately.

Page 7 {section sign} 1

Q10.Fig. 1b confirms that Raji cells provide a good model for loss and gain of function (lovely rescue experiment) and that the authors should drop the two other cell types that provide no decisive information.

A10. Raji and HeLa cells display a stronger direct CPP uptake impairment phenotype when lacking a given potassium channel (KCNQ5 and KCNN4, respectively). In these cell lines, it appears that one potassium channel predominantly controls the plasma membrane potential. In contrast, in SKW6.4 cells, several potassium channels (e.g. KCNN4 and KCNK5) appear to be equally or redundantly involved in the control of the membrane potential. This probably explains the intermediate impact on the Vm and on CPP direct translocation when knocking out a given potassium channel in this cell line. When pharmacologically inducing cellular depolarization, a clear impairment in CPP translocation is however observed in this cell line. Thus, even though the Vm in SKW6.4 cells, is controlled predominantly by several potassium channels, it remains that an appropriate membrane potential is crucially required for these cells to take up CPP across their membrane. We agree with the reviewer that the stronger phenotypic effect observed in Raji and HeLa cells allows easy interpretation. On the other hand, it seems important to us that we provide data reporting intermediate situations so that readers can appreciate the variability that can be observed in different cell lines. Nevertheless, we would like to propose along the reviewer’s suggestion to move the SKW6.4 data from figure 1 to the supplemental data. Feedback from the editors would also be appreciated in this particular instance.

Page 8 {section sign} 1

Q11. A) Supp. Fig. 6b (no serum conditions) allows for the use of "normal" CPP concentrations and suggests that a fraction of the peptides may bind to serum components. No arrows in Supp. Fig.6b (but in 6c), and the R/pyrene butyrate interaction is not in 6c but in 6a. Still for Supp. Fig. 6c, the death of cells at 20µM (or less) even in the absence of K+ channels, confirms that we are borderline in term of peptide toxicity.

B)There is a confusion between Supp. Fig. 6d and 6e and a legend problem (6e is not described). Cell death is assessed in % of PI-positive cells. Does this securely distinguish between death and holes allowing for PI entry without death?

C) The CPP is incubated in the presence of Pyrene butyrate, making the KO cells less resistant. How does that demonstrate that the potassium channels are not involved in the killing if the peptide is already in? Unless the KO is done after internalization (but the cells should be already dead or dying?). This lacks clarity.

A11. We apologize for the lack of clarity in the legend of Supplementary Fig. 6. This will be corrected in the revised version of the manuscript.

A) Supp. Fig. 6b (no serum conditions) allows for the use of "normal" CPP concentrations and suggests that a fraction of the peptides may bind to serum components.

A) The reviewer is correct that CPPs interact with serum components. This is indeed what is reported in this figure. The presence or absence of serum has therefore an important impact in experiments performed with CPPs and should be reported to allow proper interpretation of our data.

No arrows in Supp. Fig.6b (but in 6c), and the R/pyrene butyrate interaction is not in 6c but in 6a.

Thank you for noting this. This is now corrected.

Still for Supp. Fig. 6c, the death of cells at 20µM (or less) even in the absence of K+ channels, confirms that we are borderline in term of peptide toxicity.

It has to be understood that in Supplementary Fig. 6c, we use the TAT‑RasGAP317‑326 peptide that is inducing cell death when translocating into cells8. This cell death response is not provided by the CPP portion of TAT‑RasGAP317‑326 (i.e. TAT) but by its bioactive cargo (i.e. RasGAP317‑326). The read-out in this particular experiment is therefore cell death and this should not be confused with general CPP toxicity.

B) There is a confusion between Supp. Fig. 6d and 6e and a legend problem (6e is not described).

B) This has now been fixed.

Cell death is assessed in % of PI-positive cells. Does this securely distinguish between death and holes allowing for PI entry without death?

The answer to this question is yes. In this manuscript we used PI in two very different experimental set-ups.

i) the conventional cell death detection assay where cells are incubated with 8 mg/ml PI prior to flow cytometry. In this set-up, dead cells with compromised membrane integrity have their nucleus brightly stained with PI.

ii) the detection of small pores in the plasma membrane (water pore) where cells are incubated with ~30 mg/ml PI and the fluorescence of PI measured in the cytosol by confocal microscopy. In this set-up, PI enters into the cytosol through small plasma membrane pores but PI does not stain the DNA in the nucleus. This protocol has been previously described9 and we have further validated it in the present work (Figure 3 and Supplementary Fig. 12).

PI does not fluoresce well unless it binds to DNA. In solution without cells, PI cannot be detected below 128 mg/ml (Supplementary Fig. 12e). At low PI concentrations (8 mg/ml), living cells (even when treated with compounds such as CPPs that create transitory pores) do not display cytosolic PI fluorescence. At high PI concentrations (32 mg/ml), the cytosol of CPP-treated cells becomes PI fluorescent. PI is positively charged and is attracted by the negative membrane potential of the cells. Its movement across the cell membrane is therefore unidirectional. This enables the PI molecules to accumulate/concentrate within the cytosol to values (> 64 mg/ml) allowing its detection (Supplementary Fig. 12a-c). PI and CPPs do no interact (Supplementary Figure 12d); hence they move independently from one another. If PI enters through the water pores induced by CPPs, the entry kinetics of PI and CPPs should be identical. Indeed, this is what we show now in a new figure (refer to our answer #31).

C) The CPP is incubated in the presence of Pyrene butyrate, making the KO cells less resistant. How does that demonstrate that the potassium channels are not involved in the killing if the peptide is already in? Unless the KO is done after internalization (but the cells should be already dead or dying?). This lacks clarity.

C) For the pyrene butyrate experiments the rationale was the following. The CRISPR/Cas9-identified potassium channels could either be involved in CPP internalization or they could be required for the killing activity of TAT-RasGAP317-326 when the peptide is already in the cytosol. To experimentally introduce TAT-RasGAP317-326 in the cytosol and to bypass any potential entry depending on potassium channels, we used pyrene butyrate that efficiently creates an artificial entry route for CPPs into cells. Our data show that when TAT-RasGAP317-326 is introduced in the cytosol through the use of pyrene butyrate, cells died whether they lack specific potassium channels or not. This led to our interpretation that potassium channels are not modulating the cell death activity of TAT-RasGAP317-326 once in the cytosol but that they are required for the entry of the CPP in the cytosol.

Page 9 {section sign} 1

Q12.The conclusion that the diffuse staining does not come from endosomal escape is based on the certainty that LLOME disrupts both endosomes and lysosomes. First, it should be verified with specific markers (rab5, rab7) that the fluorescent vesicles are endosomes. Second, the literature strongly suggests that LLOME primarily disrupts lysosomes and not endosomes. Finally, even if some endosomes are disrupted, the endosomal population is heterogenous and some CPPs may be in a subpopulation insensitive to LLOME. In addition, the importance of this issue is not well explained. In practice, access to the cytoplasm and nucleus requires crossing the plasma and/or the endosomal membrane and the latter, at least in early endosomes (thus the need of identifying the CPP-enriched vesicles), might not be very different from the plasma membrane.

A12. The conclusion that diffuse staining does not come from endosomal escape is based on experiments where HeLa cells were incubated in the presence of CPP for 30 minutes to allow CPP entry into cells, then the cells were washed to prevent further uptake (Supplementary Fig. 7c). We only monitored the cells that initially took up the CPP by endocytosis and not through direct translocation (for the HeLa cell line, there is always a substantial fraction of such cells; see Supplementary Fig. 1b). We measured the cytosolic CPP fluorescence intensity in these cells by time-lapse confocal microscopy for 4 ½ hours. The procedure to do this is now explained in new Supplementary Fig. 7c. We then assessed the CPP fluorescence intensity within the cytosol. No increase in cytosolic fluorescence was detected in this condition, speaking against the possibility that cytosolic acquisition of CPPs by the cells resulted from vesicular escape (the identity of the vesicles being unimportant in this context). Our set-up has the potential to detect CPPs in the cytosol if these CPPs leak out from vesicles because we could measure increased CPP fluorescence in the cytosol in cells treated with LLOME. It did not matter in this positive control experiment what types of CPP-containing vesicles are disrupted by LLOME. What was important to show in this control condition was that the disruption of at least some CPP-containing vesicles permitted us to detect a cytosolic signal.

Page 9 {section sign} 2

Q13. Is Supp. Fig. 7e really necessary? First, as mentioned several times, if 20 µM is a borderline concentration in term of toxicity, raising the concentration up to 100 µM is problematic. Secondly, what matters is not "binding" in general, but binding to the proper membrane components. As mentioned by the authors themselves (Supp. Fig. 1e and movie), there are privileged sites of entry that may correspond to the recognition of specific molecular entities/structures.

A13. The goal of the experiments presented in Supplementary Figure 7e was to determine whether the CRISPR/Cas9-identified potassium channels modulate CPP/membrane interaction. If those channels were to be required for the initial binding of the CPPs to the plasma membrane, this would have not hampered cells to take up the CPPs. Our data showed (Figure 7e) that Raji cells lacking the KCNQ5 potassium channel had a slightly decreased ability to bind TAT-RasGAP317-326 but importantly, these cells, at similar or even higher initial surface binding compared to wild-type cells (this was achieved by adequately varying the CPP concentrations), were still drastically impaired in taking up the peptide. Note that after one hour of incubation with TAT-RasGAP317-326 in the presence of serum there is only marginal amount of cell death (317-326, we have now performed an additional experiment with TAT that is not toxic to cells that confirms our data obtained with TAT-RasGAP317-326.

Page 9 {section sign} 3 and Page 10 {section sign} 1

Q14.The authors should have used a construct that does not kill the cells much earlier, just after the screening experiments based on resistance to necrosis induced by TAT-rasGAP. For Supp. Fig 8a and b: I am fully convinced by Raji cells and HeLa cells but not by the SKW6.4 cells.

A14. As mentioned in our answer to point 10, we agree that SKW6.4 cells present intermediate phenotypes probably because, unlike Raji and HeLa cells, a combination of ion channels seems to regulate the plasma membrane potential. As indicated above, we can move the SKW6.4 data to the supplementary information to clarify the message presented in the main text. Again, feedback from the editors is welcome here.

Page 10 {section sign} 2

Q15. A) Supp. Fig 9 is quite convincing but adds the information that 2 µM are sufficient in neurons. This again makes the 20 to 80 µM concentrations used on transformed cells unsatisfactory.

B) If one needs a cell line (more user friendly than primary cultures), there are several neural ones that can be differentiated (SHY, LHUMES, etc.) that may have an appropriate membrane potential (below -90mV). Indeed, it would then be important to verify if pore formation is still induced by TAT, R9 and Penetratin (separately) on "naturally" hyperpolarized cells.

C) Figure 2a confirms that changes in Vm are not solid for HeLa and SKW6.4 cells. This casts a doubt on the validity of the results obtained with the latter 2 cell lines.

A15. A) The experiments performed in Supplementary Fig. 9d with cortical rat neurons and HeLa cells were performed in the absence of serum accounting for the low concentrations used. We apologize for not emphasizing enough when experiments were performed in the presence or absence of serum, explaining the use of high CPP concentrations (40-80 mM) and low CPP concentrations (2-10 mM), respectively. We would like to emphasize however that we have adjusted the concentrations of CPPs in our study so as to get similar levels of CPP activity or CPP uptake between the different cell lines used. The concentrations used should not be compared as mere numbers, it is the CPP activity or uptake that should be considered.

B) We thank the reviewer for his/her suggestion. To address this point, we will perform a new experiment to determine if in neurons TAT, R9, and Penetratin induce pores (using the PI uptake approach).

C) Please see our answer to point 10.

Page 11 {section sign} 2

Q16. Why valinomycin was only tried on Raji cells?

A16. In this study, valinomycin was used on Raji and HeLa cells (Figure 2 and 3). We did not use valinomycin on SKW6.4 cells, as the drug-induced hyperpolarization levels were insufficient in this cell line. As we got a nice hyperpolarization in HeLa wild-type and KCNN4 KO cells through ectopic expression of the KCNJ2 potassium channels (which restored the ability of the KO cells to take up the CPPs), we did not perform the CPP uptake experiment with valinomycin in HeLa cells (although we had tested that valinomycin is able to hyperpolarize HeLa cells).

Page 12 {section sign} 2

Q17.A)Looking at Fig. 2c, it seems that low Vm increases the uptake of all CPPs, except Transportan. Is there any reason why this Figure does not provide the number of vesicles per cell in the hyperpolarized conditions?

B) In fact, if one goes to Supp. Fig. 9c, it appears that, among all peptides, only Penetratin is almost entirely cytoplasmic after 90' of incubation, whereas MAP and Transportan remain essentially vesicular. TAT and R9 are at mid-distance between these two extremes. This leads to send again the warning that all CPPs cannot be placed in a single category. The table that describes the sequences strongly suggests that, TAT and R9 uptake is due to the numerous Rs that cannot be replaced by Ks. In the case of Penetratin, that only has 3 Rs, the situation is thus different with the presence of 2 Ws previously shown to be mandatory for internalization, although absent in TAT ad R9.

C) In Supp. Fig9, panel g is useless.

D) A difference between peptides is also visible in Figure 2d where depolarization with KCl does not show the same efficiency on all peptides. The issue is whether these differences are significant and, if so, why? This discussion could be restricted to TAT, R9 and Penetratin.

E) Supp. Fig. 10a also suggests that all peptides do not respond similarly to depolarization and that the effects differ between cell types and concentrations used. However, given the high concentrations used and the high variance between replicates, this figure might not be a priority in the reorganization of the manuscript.

A17. A) As mentioned in the figure legend “Quantitation of vesicles was not performed in hyperpolarizing conditions due to masking from strong cytosolic signal.” This would create a bias towards underestimation of vesicles numbers in cells displaying strong cytosolic signal.

B) We agree with the reviewer that Transportan enters cells primarily through endocytosis. This is mentioned in the text as well as other differences that were observed with regards to the prevalence of endocytosis or direct translocation. These mentions are reported below.

Page 12: “With the notable exception of Transportan, depolarization led to decreased cytosolic fluorescence of all CPPs, while hyperpolarization favored CPP translocation in the cytosol (Fig. 2c, Supplementary Fig. 9h and 10a). Transportan, unlike the other tested CPPs, enters cells predominantly through endocytosis (Supplementary Fig. 9e), which could explain the difference in response to Vm modulation.”

Page 14: “Even though this extrapolation is likely to lack accuracy because of the well-known limitation of the MARTINI forcefield in describing the absolute kinetics of the molecular events, the values obtained are consistent with the kinetics of CPP direct translocation observed in living cells (Figure 1c and Supplementary Fig. 1b and 9e). With the exception of Transportan, the estimated CPP translocation occurred within minutes. This is consistent with our observation that Transportan enters cells predominantly through endocytosis and its internalization is therefore not affected by changes in Vm (Fig 2c-d and Supplemental Fig. 9e)”.

Page 20: “On the other hand, when endocytosis is the predominant type of entry, CPP cytosolic uptake will be less affected by both hyperpolarization and depolarization, which is what is observed for Transportan internalization in HeLa cells (Fig. 2c and Supplementary Fig. 10a).”

Concerning the roles of arginine and tryptophan residues, please refer to our answer #4.

C) We do not think this panel (now panel h) is useless as it shows representative examples of the quantitation shown in Figure 2c. We can however remove it if requested by the editors.

D) The reviewer is correct with the observation that KCl-induced depolarization does not lead to similar inhibition in uptake of the tested CPPs. As mentioned in the text, these differences can be explained by the prevalence of direct translocation in the cells. For example, transportan enters cells primarily through endocytosis, which as we show is not regulated/affected by the membrane potential (Figure 2c, lower graphs). Consequently, it is expected that KCl treatment will not impact on transportan cellular uptake.

E) The reviewer is correct in mentioning that there is quantitative heterogeneity between the different CPP tested. We mentioned these differences in the manuscript. These mentions are those that are reported under B, plus those listed below.

Page 19: “It is known for example that peptides made of 9 lysines (K9) poorly reaches the cytosol (Fig. 3f and Supplementary Fig. 9e) and that replacing arginine by lysine in Penetratin significantly diminishes its internalization10,11. According to our model, K9 should induce megapolarization and formation of water pores that should then allow their translocation into cells. However, it has been determined that, once embedded into membranes, lysine residues tend to lose protons12,13. This will thus dissipate the strong membrane potential required for the formation of water pores and leave the lysine-containing CPPs stuck within the phospholipids of the membrane. In contrast, arginine residues are not deprotonated in membranes and water pores can therefore be maintained allowing the arginine-rich CPPs to be taken up by cells.”

Page 21: “Therefore, the uptake kinetics of lysine-rich peptide, such as MAP, appears artefactually similar as the uptake kinetics of arginine-rich peptides such as R9 (Supplementary Fig. 11b).”

Page 21: “The differences between CPPs in terms of how efficiently direct translocation is modulated by the Vm (Fig. 2c-d and Supplementary Fig. 10a) could be explained by their relative dependence on direct translocation or endocytosis to penetrate cells. The more positively charged a CPP is, the more it will enter cells through direct translocation and consequently the more sensitive it will be to cell depolarization (Fig. 2c). On the other hand, when endocytosis is the predominant type of entry, CPP cytosolic uptake will be less affected by both hyperpolarization and depolarization, which is what is observed for Transportan internalization in HeLa cells (Fig. 2c and Supplementary Fig. 10a).”

However, what remains is that depolarization always affects CPP uptake, at most concentrations tested. The heterogeneity reported in Supplementary Fig. 10a for a given experimental condition in a given cell type is in itself of interest as it suggests that there are varying factors within a cell population (e.g. cell cycle, metabolism, etc.) that may impact on the ability of cells to take up CPPs. As per reviewer’s suggestion we may remove this panel from the figure if instructed to do so by the editors.

Page 12 {section sign} 3 and Page 13 {section sign} 1

Q18. The pH story is either too long or too short.

A18. One mechanism put forward to explain direct translocation relies on pH variation between the extracellular milieu and the cytosol14. It was therefore of interest in the context of the model we putting forward to see if pH is affecting the uptake of CPPs in our experimental model. Our data show that pH variations do not affect CPP direct translocation. This information should in our opinion be disclosed.

Page 14 {section sign} 2

Q19. At low Vm values, there is a decrease in free energy barrier. Does this modify temperature-dependency for internalization? Do cells really require energy when the Vm is very low, like is often the case for neurons?

A19. We thank the reviewer for this interesting comment. We will now address this by visualizing under a confocal microscope CPP direct translocation in rat cortical neurons incubated at various temperature (4°C, 24°C, 37°C).

Page 15 {section sign} 2

Q20. Figure 2e is not explained, not even in the legend while the statement that CPPs induce a local hyperpolarization is central to the study.

A20. As there is no Figure 2e, we believe that the reviewer is talking about Figure 3e, the legend of which was present in the initial version of the manuscript.

Page 16 {section sign} 1

Q21. It is confusing that the same agent, here PI, is used to measure internalization (2 nm pore formation in response to hyperpolarization,) and cell death. I have seen the explanation below, but I do not find it fully satisfactory.

A21. We have tried to explain this better under our answer to point 11B.

Page 16 {section sign} 2

Q22. Entry is not necessarily a size issue. Structure is an important parameter, including possible structure changes, for example in response to Vm modifications. Therefore, the statement that molecule with larger diameters are mostly prevented from internalization is not only vague ("mostly") but incorrect.

A22. We agree with the reviewer’s comment in the sense that the secondary structure of a molecule will also play an important role in its internalization. For that reason, we have used a series of molecules of identical structure (dextrans) but that have different molecular weights. In these experiments we saw that dextran of higher molecular weight enter less efficiently than that of lower molecular weight (Figure 3). We will rephrase some of our sentences so to precise that the size and the shape (structure) of molecules will determine their ability to enter cells through water pores that are characterized by a certain diameter.

Page 2: “Using dyes of varying sizes and shapes, we assessed the diameter of the water pores**.”

Page 4: “translocation and we characterize the diameter of the water pores used by CPPs**.”

Page 15: “cells were co-incubated with molecules of different sizes and structure and FITC-labelled CPPs at a peptide/lipid ratio of 0.012-0.018 (Supplementary Fig. 11c-d).”

Page 16: “3 kDa, 10 kDa, and 40 kDa dextrans, 2.3 ±0.38 nm, 4.5 nm and 8.6 nm (diameter estimation provided by Thermofisher), respectively, were used to estimate the diameter of the water pores formed in the presence of CPP.”

Page 16: “These results are in line with the in silico prediction of the water pore diameter obtained by analyzing the structure of the pore at the transition state.”

Page 16: “The marginal cytosolic co-internalization of dextrans was inversely correlated with their diameter.”

Page 35: “200 µg/ml dextran of different molecular weight in the presence or in the absence of the indicated CPPs in normal […]”.

Page 17 {section sign} 4 and Page 18 {section sign} 1

Q23. In Supp. Fig. 13b and c, since the GAP domain is mutated, death is not due to RasGAP activity. So what causes zebrafish death (hyperpolarization?) The results seem contradictory with those of Supp. Fig 13f where survival is 100% at 48 h.

A23. Indeed, it appears that valinomycin in water leads to zebrafish embryo death, as can be seen in Supplementary Fig. 13c. However, the main difference between Supplementary Fig. 13c and S13f is that in Supplementary Fig. 13f zebrafish were not incubated in valinomycin-containing water, but were locally injected with a CPP in the presence or in the absence of valinomycin. This has now been clarified in the text. We saw that local injections with the hyperpolarizing agent are much less toxic and are well tolerated by the zebrafish embryos.

Page 18 {section sign} 2

Q24. The formation of inverted micelles is not incompatible with that of pores. CPP-induced hyperpolarization (Vm) is not measured directly, but deduced from experiments involving artificial membranes and in silico modeling. It would be useful to distinguish between what takes place on live cells (in vitro and in vivo) and what is speculated (based on modeling and artificial systems).

A24.

The formation of inverted micelles is not incompatible with that of pores.

As mentioned above (point 9), we do also think that what has been presented as inverted micelles could have been in fact water pores.

CPP-induced hyperpolarization (Vm) is not measured directly, but deduced from experiments involving artificial membranes and in silico modeling. It would be useful to distinguish between what takes place on live cells (in vitro and in vivo) and what is speculated (based on modeling and artificial systems).

If we understand this point correctly, the reviewer is talking about the -150 mV hyperpolarization. This value is not a speculation but has been estimated from in silico experiments and also from experiments using live cells (not artificial membranes). In living cells, the hyperpolarization (megapolarization) has been estimated based on accumulation of intracellular PI over time in the presence or in the absence of CPP.

Page 19 {section sign} 3

Q25A. The model posits that the number of Rs influences the ability of the CPPs to hyperpolarize the membrane and, consequently, to induce pore formation. Since pore formation is key to the addressing to the cytoplasm, how can one explain that Penetratin which has only 3 Rs is transported to the cytoplasm more readily that TAT or R9? The authors should take this contradiction in consideration and should not leave aside, in the literature, what does not fit with their model.

A25A. We fully agree that this should be discussed and not left aside. Please refer to point 4 for detailed discussion about the role of arginine and tryptophan in the ability of CPPs to translocate across membranes.

Q25B. The fact that that Rs cannot be replaced by Ks, both in R9 and Penetratin is explained by differences in deprotonization. This is interesting but speculative. It might be that the interaction between Rs versus Ks with lipids and sugars are different and not only based on charge. After all their atomic structures, beyond charges, are different.

A25B. We do not claim that protonation differences between R and K is the definitive answer for their ability to promote CPP translocation. It is one possible explanation that we find sound. As suggested by the reviewer, the ability of K and R to bind lipids and sugars can also play a role. We can mention in this context that the guanidinium group of arginine residues can form two hydrogen bonds1, which allow for more stable electrostatic interactions while the lysyl group of lysine residues can only form one hydrogen bond. We have included these additional possibilities in the revised version of our manuscript as indicated under point 4.

Page 20 {section sign} 1 Q26. We still need to understand endosomal escape.

A26. We agree with the reviewer that endosomal escape is still poorly understood. This is an interesting research topic that deserves its own separate study.

**Major comments**

• The key conclusions are convincing for a subset of CPPs and cell types
• Yes, some claims should be qualified as speculative, but not preliminary
• Many experiments should be removed. Neuronal primary cultures should be introduced to verify the main conclusions, at least for the 3 mains CPPs (TAT, R9, Penetratin). Answers must be given to the concentration issue. Vesicles should be characterized as well as the localization of the peptides in or around the vesicles. See above for less decisive but still important experiments that would benefit to the study.
• Yes, the requested experiments correspond to a reasonable costs and amount of time (10 to 20,000 € and 3 to 5 months of work)
• Yes, the methods are presented with great details. -Yes, the experiments are adequately replicated and statistical analysis is adequate

**Minor comments (not so minor for some of them)**

• See "Detailed analysis"
• No, prior studies are not referenced appropriately (see above)
• No, the text and figures are not clear and not accurate (see above)
• (i) use Raji cells and primary neuronal cultures, plus in vivo model and forget the other cell types; (ii) forget MAP and Transportan and compare TAT/R9 and Penetratin; (iii) drastically reduce the number of figures, tables and movies (6 primary figures, 6 supplemental figures and 4 tables are reasonable numbers; movies are not absolutely necessary); (iv) limit to 6 (max) the number of panels per figure; (v) limit the number of references to less than 50 and cite the primary reports rather than reviews); (vi) reduce the size of the Material and Methods and the length of figure legends.

Reviewer #1 (Significance (Required)):

• The mode of CPP internalization is an unanswered question and the report, if revised, will represent a conceptual and technical advance.
• Bits and pieces of the conclusions can be found in previous reports. But the Vm-dependent pore formation as well as the CPP-induced "megapolarization" (even if only shown for a subset of CPPs) would be an important contribution. The authors must resist the tentation to generalize to all CPPs what might only be true for a few of them.
• I do not have the expertise for the in-silico work, but my field of expertise allows me to understand all other aspects of the manuscript.

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

In this manuscript, the authors investigated the effect of membrane potential on the internalization of CPPs into the cytosol of some cancer cell lines. Using a CRISPR/Cas9-based screening, they found that some potassium channels play an important role in the internalization of CPPs. The depolarization decreases the rate of internalization of CPPs and the hyperpolarization using valinomycin increases the rate. Using the coarse-grained MD simulations, the authors investigated the interaction of CPPs with a lipid bilayer in the presence of membrane potential. In the interaction of CPPs with the cells, propidium iodide (PI) enters the cytosol significantly. Based on this result, the authors concluded that pores with 2 nm diameter are formed in the plasma membrane.

This reviewer raises one main issue concerning CPP endocytosis. The reviewer challenges our method to investigate CPP direct translocation and specifically how do we make sure that what we consider direct translocation is not a combination of CPP endocytosis (followed or not by endosomal escape) and CPP plasma membrane translocation. As explained below in details our methodology is able to accurately distinguish CPP uptake by direct translocation from CPP endocytosis and we further demonstrate that endosomal escape does not occur in our experimental settings.

Q27. One of the defects in this manuscript is the method to determine the fraction of internalization of CPPs via direct translocation across plasma membrane. The authors estimated the fraction of the direct translocation of CPPs by the fluorescence intensity of the cytosolic region (devoid of endosomes) and the fraction of the internalization via endocytosis by the fluorescence intensity of vesicles. However, the CPPs can enter the cytoplasm via endocytosis, and thus the increase in the fluorescence intensity of the cytoplasm is due to two processes (via endocytosis and direct translocation). The authors should use inhibitors of clathrin-mediated endocytosis and macropinocytosis to determine the fraction of internalization of CPPs via direct translocation accurately. Low temperature (4 C) has been also used as the inhibitor of endocytosis (e.g., J. Biophysics, 414729, 2011; J. Biol. Chem., 284, 33957, 2009). Supplementary Figure 1i (the temperature dependence of internalization of TAT-RasGAP317-326) clearly shows that at 4 C the fraction of the internalization was very low, indicating that this peptide enters the cytosol mainly via endocytosis. The determination of the fraction of the internalization via endocytosis by the fluorescence intensity of vesicles in this manuscript is not accurate because it is difficult to examine all endosomes in cells and it is not easy to discriminate the fluorescence intensity due to the endosomes from that due to the cytosol.

It is important to follow a time course of the fluorescence intensity of single cells from the beginning of the interaction of CPPs with the cells (at least from 5 min) in the presence and absence of inhibitors of the endocytosis (J. Biol. Chem., 278, 585, 2003) to elucidate the process of the internalization of CPPs in the cytosol.

A27. The reviewer raises the possibility that the signal of fluorescent CPPs in endosomes somehow perturbs the acquisition of the signal in cytosol. This could occur in two ways: CPP endosomal escape and diffusion of the signal located in endosomes into adjacent cytosolic regions (halo effect). The second possibility can be readily dismissed because in situations where cells only take up fluorescent CPPs by endocytosis, the cytosol emits background fluorescence (autofluorescence). This can be seen in Supplementary Fig. 1a (“vesicular” condition) or in Supplementary Fig. 9h in the depolarized cells that cannot take up CPP by direct translocation. Also note that when we record the cytosolic signal we take great care of using regions of interest (ROI) that are distant from endosomes. In contrast to what the reviewer is saying (“it is not easy to discriminate the fluorescence intensity due to the endosomes from that due to the cytosol”), it is actually not difficult discriminating the cytosolic fluorescence from the endosome fluorescence. To illustrate this, we now provide examples of high magnification images of cells incubated with fluorescent CPPs (new Supplementary Fig. 1c, right[1]) to better explain/illustrate our methodology and to show that it is quite straightforward to find cytosolic areas devoid of endosomes. Such high magnification images are those that are used for our blinded quantitation. The other possibility is endosomal escape. We demonstrate in Supplementary Fig. 7c that in our experimental conditions, no endosomal escape is detected[2]. We may not have explained our methodology well enough in the earlier version. We will try and improve the description of our quantitation procedures better in the revised version. To this end, we have now added a scheme illustrating the experimental setup (now part of Supplementary Fig. 7c) that is used to assess endosomal escape.

The reviewer also questions the way we quantitate the CPP signals in endosomes. In the present paper, our goal is to characterize the direct translocation process of CPPs in to cells. We do not wish here to investigate in details the endocytic pathway taken by CPPs. This has been done in a separate study that we are currently submitting for publication. In a nutshell, this work shows that the endocytic pathway taken by CPPs is different from the classical Rab5- and Rab7-dependent pathway and that the CPP endocytic pathway is not inhibited by compounds that affect the classical pathway. Thus, even if we had wanted to use the inhibitors mentioned by the reviewer, they would not have blocked CPP endocytosis.

To sum up the issues raised under this point, we believe we have presented the reasons why there are no grounds to support the concerns raised by the reviewer.

[1] Supplementary Fig. 1c (right) is mentioned in the “Cell death and CPP internalization measurements” section of the methods.

[2] In this experiment, cells were incubated with CPPs for 30 minutes to allow CPP entry into cells. Then the cells were either washed (to prevent further uptake including uptake through direct translocation) or incubated in the continued presence of CPPs. In both conditions, cells where only endocytosis took place were followed by time-lapse confocal microscopy for 4 hours (i.e. these cells do not display any cytosolic CPP signal at the beginning of the recording). We then assessed the CPP fluorescence intensity within the cytosol (i.e. away from endosomes). From these experiments we saw that cytosolic fluorescence increased only in conditions where CPP was present in the media throughout the experiment. No increase of cytosolic fluorescence was detected in the condition where CPPs were washed out. In conclusion these results demonstrate that the cytosolic signal that we observed in our experiments is due to direct translocation and not endosomal escape. In these experiments we have used the LLOME lysosomotropic agent as a control to make sure that if endosomal escape had occurred (even if only from a subset of endosomes/lysosomes), we would have been able to detect it. Indeed, upon addition of LLOME we were able to record CPP release from endosomes to the cytosol. There is therefore no endosomal escape occurring in our experimental conditions. In conclusion, the observed cytosolic signal in our confocal experiments do not originate, even partly, from endosomal escape.

Supplementary Figure 1i (the temperature dependence of internalization of TAT-RasGAP317-326) clearly shows that at 4 C the fraction of the internalization was very low, indicating that this peptide enters the cytosol mainly via endocytosis.

The experiment shown in Supplementary Fig. 1i was analyzed by flow cytometry that cannot discriminate the cytosolic signal from the endosomal signal. We will therefore perform this experiment again but this time using confocal imaging to record the impact of temperature on CPP cytosolic acquisition. We have performed this for HeLa cells already and this shows that direct translocation is indeed inhibited by low temperatures (full blockage at 4°C). Bear in mind that no endosomal escape occurs in our settings (see Supplementary Fig. 7c). This indicates that the decrease in cytoplasmic fluorescence induced by low temperature is not a consequence of diminished CPP endocytosis.

Q28. Recently, it has been well recognized that membrane potential greatly affects the structure, dynamics and function of plasma membranes (e.g., Science, 349, 873, 2015; PNAS, 107, 12281, 2010). The results of the effect of membrane potential on the internalization of CPPs (depolarization decreases the rate of internalization and hyperpolarization increases the rate), which is main results of this manuscript, can be interpreted by various ways. For example, the rate of endocytosis may be greatly controlled by membrane potential, which can explain the authors' results.

A28. This reviewer may have missed the experiment presented in Figure 2c that clearly shows that CPP endocytosis is unaffected by depolarization or hyperpolarization of cells. We have also determined that transferrin uptake through endocytosis is not affected by potassium channel knockout (which also leads to depolarization). The possibility raised by the reviewer is therefore refuted by our experimental evidence.

Q29. A) The authors used the similar concentrations of various CPPs for their experiments (10 to 40 microM), and did not examine the peptide concentration dependence of the internalization. It has been recognized that the CPP concentration affects the mode of internalization of CPPs (e.g., J. Biol. Chem., 284, 33957, 2009). The authors should examine the peptide concentration dependence of the mode of internalization (less than 10 micorM, e. g., 1 microM).

B) In the case of depolarization, can higher concentrations of CPPs (e.g., 100 micorM) induce their internalization?

A29. A) We agree that CPPs/cell ratio might prompt one mode of entry over the other. It has been reported by imaging that at lower CPP concentrations endocytosis is favored since only vesicles were observed15-19. Our data confirm this (new Supplementary Fig. 9f).

B) In Supp. Fig. 7e we have incubated KCNQ5 KO Raji cells that are slightly more depolarized than WT cells in the presence of increasing CPP concentrations up to 100 m From the obtained results, we can see that at 100 mM, the uptake in depolarized cells is increased but does not reach the level of uptake seen in wild-type cells. Therefore, lack of hyperpolarization can be compensated to a mild extent by increased CPP availability.

Q30. A) The effects of membrane potential on plasma membranes and lipid bilayers have been extensively investigated experimentally and thus are well understood, although currently the coarse-grained MD simulations cannot provide quantitative results which can be compared with experimental results. In this manuscript, using the coarse-grained MD simulations, the authors applied 2.2 V to a lipid bilayer to examine the translocation of CPPs. However, it is well known the experimental results that application of such large voltage to a lipid bilayer induces pore formation in the membrane or its rupture (Bioelectrochem. Bioenerg., 41, 135, 1996; Sci. Rep., 7, 12509, 2017), but at low membrane potential (B) What is the probability of the existence of R9 in the surface of the membrane? R9 cannot bind to the electrically neutral lipid bilayers (such as PC) under a physiological ion concentration (Biochemistry, 55, 4154, 2016). Even if in the case of R9 the membrane potential reaches at -150 mV, the other CPPs have lower surface charge density than that of R9, and hence, the decrement of membrane potential is lower. The authors should provide the data of other CPPs.

C) It has been reported that the negative membrane potential increases the rate of entry of two kinds of CPPs into the lumen of giant unilamellar vesicles (GUVs) without leakage of water-soluble fluorescent probe (Stokes-Einstein radius; ~0.9 nm diameter), i.e., no pore formation in the GUV membrane (Biophys., 118, 57, 2020, J. Bacteriology, 2021, DOI: 10.1128/JB.00021-21). The authors should discuss the similarity and the difference between the results in these papers and the above results in this manuscript.

A30. A) As correctly stated by this Reviewer, we reported simulations with high transmembrane potential values, which is a common procedure in in silico simulations used to accelerate the kinetics of the studied process. In this manuscript we have additionally developed and carefully validated a novel protocol to estimate the free energy landscape of water pore formation and CPP translocation under physiological transmembrane potential (further details about the methodological procedure, the convergence and the validation of the free energy estimation are reported in Supplementary Fig. 15-19 of the manuscript). This protocol allowed us to demonstrate the impact of megapolarization (‑150 mV) on the free energy barrier corresponding to the CPP translocation process. The results exemplify how the megapolarization process modifies the uptake probability of the R9 peptide, reducing locally the free energy barrier of the membrane translocation (Fig. 3c-d). Moreover, we have also demonstrated how a single CPP produces a local transmembrane potential of about -150 mV, in agreement with our hypothesis (Fig. 3e).

Finally, the quantitative accuracy of the molecular simulations was found to be satisfactory because the water pore formation free energy in a symmetric DOPC membrane that we calculated is in excellent agreement with previous atomistic estimation (Table S5).

B) It has been demonstrated that CPP/membrane interactions are mostly electrostatic between positively charged amino acids carried by the CPPs and various negatively charged cell membrane components, such as glycosaminoglycans20-31 and phosphate groups32. It is in line with our model that the more positively charged CPPs are the better they should translocate into cells. Therefore, we agree with the reviewer that the level of megapolarization may vary according to the charges carried by the CPPs. However, our data clearly indicate that a certain membrane potential hyperpolarization threshold must be achieved to induce water pore formation. As suggested by the reviewer we will now conduct additional modeling experiments with other CPPs.

C) We have carefully read these papers and do not necessarily reach the same conclusions as the authors. In both papers, the translocation of CPPs in polarized GUVs is monitored through CPP acquisition on vesicles found within the GUVs (intraluminal vesicles; either smaller GUVs or LUVs). There is actually no evidence of the presence of luminal CPPs outside of the intraluminal vesicle membranes. We would therefore argue that these studies elegantly demonstrate that membrane potential increases CPP binding and insertion into the membrane of the mother GUVs but that the CPPs then move, by diffusion, from the lipidic boundary of the mother GUVs to the lipidic membranes of its intraluminal vesicles. This CPP diffusion would presumable occur when the intraluminal vesicles touch the outer membrane bilayer of the mother GUV. There is a marked lag between binding of the CPPs to the membrane of the mother GUV and appearance of CPPs on the intraluminal vesicles (Figure 3c of the Biophysical Journal paper). This lag is, according to us, more compatible with the explanation we are giving than with a translocation mechanism. If there were direct translocation of the CPP through the membrane of the mother GUV, such a large lag would not be expected to be seen (see next point). If there is no translocation of the CPPs across the GUV membrane, it could explain why the water soluble dye within the mother GUVs does not leak out.

Q31. The authors consider that the translocation of CPPs induces depolarization, and as a result, the pore closes immediately. This kind of transient pore cannot explain the authors' result of the significant entry of PI into the cytosol during the interaction of CPPs with the cells. The authors should explain this point.

A31. Our interpretation is that PI takes advantage of the water pore triggered by hyperpolarization to penetrate cells. PI is positively charged and is attracted by the negative membrane potential of the cells. Its movement across the cell membrane is therefore unidirectional. This enables the PI molecules to accumulate/concentrate within the cytosol (Supplementary Fig. 12). When PI is in the presence of a CPP, both molecules enter with similar kinetics (Supplementary Fig. 12a and the new quantitation provided in the partially revised version of the manuscript; Supplementary Fig. 12b). PI and CPPs do no interact (Supplementary Figure 12d); hence they move independently from one another.

Q32. In this manuscript, the authors used only cancer cell lines (Raji cell, SKW6.4 cell, and HeLa cell). The lipid compositions and the stability of the plasma membranes of these cells may be different from normal cells (e.g., 33; Cancer Res., 51, 3062, 1991). Is there a possibility that negatively charged lipids such as PS and PIP2 locate in the outer leaflet locally in these cells? At least, some discussions on this point is essential.

A32. We agree with the reviewer that plasma membrane composition may vary between cancerous and not cancerous cells and that this may impact on the ability of CPPs to cross cellular membranes. We now mention this in the discussion: “While the nature of the CPPs likely dictate their uptake efficiency as discussed in the precedent paragraph, the composition of the plasma membrane could also modulate how CPPs translocate into cells. In the present work, we have recorded CPP direct translocation in transformed or cancerous cell lines as well as in primary cells. These cells display various abilities to take up CPPs by direct translocation and the present work indicates that this is modulated by their Vm. But as cancer cells display abnormal plasma membrane composition33, it will be of interest in the future to determine how important this is on their capacity to take up CPPs”.

Q33. The authors found that PI enters the cytosol significantly when CPPs interact with these cells. Based on this result, the authors concluded that pores with 2 nm diameter are formed in the plasma membrane. However, they did not show the time courses of entry of PI and that of CPPs, and thus we cannot judge whether the pore formation in the plasma membrane is the cause of the entry of CPPs or the result of the entry of CPPs. We can reasonably consider that CPPs enters the cytosol via endocytosis and bind to the inner leaflet of the plasma membrane, inducing pore formation in the plasma membrane.

A33. The kinetics we are now showing in point A31 indicate co-entry of CPPs and PI, an observation that is in line with our model. Also note that we have demonstrated that CPPs do not escape endosomes (please see our answers to questions 12 and 28). These data are therefore not compatible with the reviewer’s interpretation.

Q34. It has been reported that the negative membrane potential increases the rate constant of antimicrobial peptide (AMP)-induced pore formation or local damage in the GUV membrane (J. Biol. Chem., 294, 10449, 2019; BBA-Biomembranes, 1862, 183381, 2020). These results are related to those in the present manuscript, because here the authors consider that CPPs induce pores in the plasma membrane in the presence of negative membrane potential.

A34. We thank the reviewer for mentioning these interesting articles. As we understand them, they demonstrate that antimicrobial peptides (AMPs) bind membranes better as a function of increasing negative membrane potential and that this favors their ability to form pores in the membrane, compromising membrane integrity and inducing the release of cytosolic or luminal content. These AMPs do not behave exactly like CPPs because the latter do not compromise the integrity of the membranes.

In conclusion, the results of the membrane potential dependence of the rate of the internalization of CPPs may be solid results, which is an important contribution. However, the other analyses and the interpretations are not conclusive at the current stage.

We thank the reviewer for the positive assessment of our results concerning the membrane potential dependence on CPP uptake. Hopefully we have clarified the remaining points with our answers developed above and with the new data we are presenting.

Reviewer #2 (Significance (Required)):

(1) Using a CRISPR/Cas9-based screening, the authors found that some potassium channels play an important role in the internalization of CPP TAT-RasGAP317-326. This result advances the field of CPPs.

(2) Several researches have suggested that the depolarization decreases the rate of internalization of CPPs into cell cytosol and the hyperpolarization increases the rate. It has been also reported that negative membrane potential increases the rate of entry of two kinds of CPPs into the lumen of GUVs of lipid bilayers. The authors provide a new genetic evidence that membrane potential plays an important role in the internalization of CPPs in the cytosol. However, modulation of membrane potential affects the structure, dynamics and function of plasma membranes greatly. At the current stage, it is difficult to judge which process of the internalization of CPPs is affected by the membrane potential.

(3) The researchers of CPPs and AMPs are interested in their results after they improve the contents of the manuscript.

(4) My field of expertise is membrane biophysics, especially the interaction of AMPs and CPPs with GUVs and cells.

References

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9 Bowman, A. M., Nesin, O. M., Pakhomova, O. N. & Pakhomov, A. G. Analysis of plasma membrane integrity by fluorescent detection of Tl(+) uptake. J Membr Biol 236, 15-26, doi:10.1007/s00232-010-9269-y (2010).

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11 Amand, H. L., Rydberg, H. A., Fornander, L. H., Lincoln, P., Norden, B. & Esbjorner, E. K. Cell surface binding and uptake of arginine- and lysine-rich penetratin peptides in absence and presence of proteoglycans. Biochim Biophys Acta 1818, 2669-2678, doi:10.1016/j.bbamem.2012.06.006 (2012).

12 Armstrong, C. T., Mason, P. E., Anderson, J. L. & Dempsey, C. E. Arginine side chain interactions and the role of arginine as a gating charge carrier in voltage sensitive ion channels. Sci Rep 6, 21759, doi:10.1038/srep21759 (2016).

13 Li, L., Vorobyov, I. & Allen, T. W. The different interactions of lysine and arginine side chains with lipid membranes. J Phys Chem B 117, 11906-11920, doi:10.1021/jp405418y (2013).

14 Herce, H. D., Garcia, A. E. & Cardoso, M. C. Fundamental molecular mechanism for the cellular uptake of guanidinium-rich molecules. J Am Chem Soc 136, 17459-17467, doi:10.1021/ja507790z (2014).

15 Kosuge, M., Takeuchi, T., Nakase, I., Jones, A. T. & Futaki, S. Cellular Internalization and Distribution of Arginine-Rich Peptides as a Function of Extracellular Peptide Concentration, Serum, and Plasma Membrane Associated Proteoglycans. Bioconjugate Chemistry 19, 656-664, doi:10.1021/bc700289w (2008).

16 Fretz, M. M., Penning, N. A., Al-Taei, S., Futaki, S., Takeuchi, T., Nakase, I., Storm, G. & Jones, A. T. Temperature-, concentration- and cholesterol-dependent translocation of L- and D-octa-arginine across the plasma and nuclear membrane of CD34+ leukaemia cells. The Biochemical journal 403, 335-342, doi:10.1042/BJ20061808 (2007).

17 Drin, G., Cottin, S., Blanc, E., Rees, A. R. & Temsamani, J. Studies on the internalization mechanism of cationic cell-penetrating peptides. J Biol Chem 278, 31192-31201, doi:10.1074/jbc.M303938200 (2003).

18 Duchardt, F., Fotin‐Mleczek, M., Schwarz, H., Fischer, R. & Brock, R. A Comprehensive Model for the Cellular Uptake of Cationic Cell‐penetrating Peptides. Traffic 8, 848-866, doi:10.1111/j.1600-0854.2007.00572.x (2007).

19 Ziegler, A., Nervi, P., Dürrenberger, M. & Seelig, J. The Cationic Cell-Penetrating Peptide CPPTAT Derived from the HIV-1 Protein TAT Is Rapidly Transported into Living Fibroblasts:  Optical, Biophysical, and Metabolic Evidence. Biochemistry 44, 138-148, doi:10.1021/bi0491604 (2005).

20 Ziegler, A. Thermodynamic studies and binding mechanisms of cell-penetrating peptides with lipids and glycosaminoglycans. Advanced Drug Delivery Reviews 60, 580-597, doi:https://doi.org/10.1016/j.addr.2007.10.005 (2008).

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23 Gonçalves, E., Kitas, E. & Seelig, J. Binding of Oligoarginine to Membrane Lipids and Heparan Sulfate:  Structural and Thermodynamic Characterization of a Cell-Penetrating Peptide. Biochemistry 44, 2692-2702, doi:10.1021/bi048046i (2005).

24 Rusnati, M., Tulipano, G., Spillmann, D., Tanghetti, E., Oreste, P., Zoppetti, G., Giacca, M. & Presta, M. Multiple Interactions of HIV-I Tat Protein with Size-defined Heparin Oligosaccharides. Journal of Biological Chemistry 274, 28198-28205, doi:10.1074/jbc.274.40.28198 (1999).

25 Butterfield, K. C., Caplan, M. & Panitch, A. Identification and Sequence Composition Characterization of Chondroitin Sulfate-Binding Peptides through Peptide Array Screening. Biochemistry 49, 1549-1555, doi:10.1021/bi9021044 (2010).

26 Åmand, H. L., Rydberg, H. A., Fornander, L. H., Lincoln, P., Nordén, B. & Esbjörner, E. K. Cell surface binding and uptake of arginine- and lysine-rich penetratin peptides in absence and presence of proteoglycans. Biochimica et Biophysica Acta (BBA) - Biomembranes 1818, 2669-2678, doi:https://doi.org/10.1016/j.bbamem.2012.06.006 (2012).

27 Ghibaudi, E., Boscolo, B., Inserra, G., Laurenti, E., Traversa, S., Barbero, L. & Ferrari, R. P. The interaction of the cell-penetrating peptide penetratin with heparin, heparansulfates and phospholipid vesicles investigated by ESR spectroscopy. Journal of Peptide Science 11, 401-409, doi:10.1002/psc.633 (2005).

28 Fuchs, S. M. & Raines, R. T. Pathway for polyarginine entry into mammalian cells. Biochemistry 43, 2438-2444, doi:10.1021/bi035933x (2004).

29 Ziegler, A. & Seelig, J. Contributions of Glycosaminoglycan Binding and Clustering to the Biological Uptake of the Nonamphipathic Cell-Penetrating Peptide WR9. Biochemistry 50, 4650-4664, doi:10.1021/bi1019429 (2011).

30 Ziegler, A. & Seelig, J. Interaction of the Protein Transduction Domain of HIV-1 TAT with Heparan Sulfate: Binding Mechanism and Thermodynamic Parameters. Biophysical Journal 86, 254-263, doi:https://doi.org/10.1016/S0006-3495(04)74101-6 (2004).

31 Hakansson, S. & Caffrey, M. Structural and Dynamic Properties of the HIV-1 Tat Transduction Domain in the Free and Heparin-Bound States. Biochemistry 42, 8999-9006, doi:10.1021/bi020715+ (2003).

32 Kawamoto, S., Takasu, M., Miyakawa, T., Morikawa, R., Oda, T., Futaki, S. & Nagao, H. Inverted micelle formation of cell-penetrating peptide studied by coarse-grained simulation: importance of attractive force between cell-penetrating peptides and lipid head group. J Chem Phys 134, 095103, doi:10.1063/1.3555531 (2011).

33 Szlasa, W., Zendran, I., Zalesinska, A., Tarek, M. & Kulbacka, J. Lipid composition of the cancer cell membrane. J Bioenerg Biomembr 52, 321-342, doi:10.1007/s10863-020-09846-4 (2020).

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors investigated the effect of membrane potential on the internalization of CPPs into the cytosol of some cancer cell lines. Using a CRISPR/Cas9-based screening, they found that some potassium channels play an important role in the internalization of CPPs. The depolarization decreases the rate of internalization of CPPs and the hyperpolarization using valinomycin increases the rate. Using the coarse-grained MD simulations, the authors investigated the interaction of CPPs with a lipid bilayer in the presence of membrane potential. In the interaction of CPPs with the cells, propidium iodide (PI) enters the cytosol significantly. Based on this result, the authors concluded that pores with 2 nm diameter are formed in the plasma membrane.

One of the defects in this manuscript is the method to determine the fraction of internalization of CPPs via direct translocation across plasma membrane. The authors estimated the fraction of the direct translocation of CPPs by the fluorescence intensity of the cytosolic region (devoid of endosomes) and the fraction of the internalization via endocytosis by the fluorescence intensity of vesicles. However, the CPPs can enter the cytoplasm via endocytosis, and thus the increase in the fluorescence intensity of the cytoplasm is due to two processes (via endocytosis and direct translocation). The authors should use inhibitors of clathrin-mediated endocytosis and macropinocytosis to determine the fraction of internalization of CPPs via direct translocation accurately. Low temperature (4 C) has been also used as the inhibitor of endocytosis (e.g., J. Biophysics, 414729, 2011; J. Biol. Chem., 284, 33957, 2009). Supplementary Figure 1i (the temperature dependence of internalization of TAT-RasGAP317-326) clearly shows that at 4 C the fraction of the internalization was very low, indicating that this peptide enters the cytosol mainly via endocytosis. The determination of the fraction of the internalization via endocytosis by the fluorescence intensity of vesicles in this manuscript is not accurate because it is difficult to examine all endosomes in cells and it is not easy to discriminate the fluorescence intensity due to the endosomes from that due to the cytosol.

It is important to follow a time course of the fluorescence intensity of single cells from the beginning of the interaction of CPPs with the cells (at least from 5 min) in the presence and absence of inhibitors of the endocytosis (J. Biol. Chem., 278, 585, 2003) to elucidate the process of the internalization of CPPs in the cytosol

Recently, it has been well recognized that membrane potential greatly affects the structure, dynamics and function of plasma membranes (e.g., Science, 349, 873, 2015; PNAS, 107, 12281, 2010). The results of the effect of membrane potential on the internalization of CPPs (depolarization decreases the rate of internalization and hyperpolarization increases the rate), which is main results of this manuscript, can be interpreted by various ways. For example, the rate of endocytosis may be greatly controlled by membrane potential, which can explain the authors' results.

The authors used the similar concentrations of various CPPs for their experiments (10 to 40 microM), and did not examine the peptide concentration dependence of the internalization. It has been recognized that the CPP concentration affects the mode of internalization of CPPs (e.g., J. Biol. Chem., 284, 33957, 2009). The authors should examine the peptide concentration dependence of the mode of internalization (less than 10 micorM, e. g., 1 microM). In the case of depolarization, can higher concentrations of CPPs (e.g., 100 micorM) induce their internalization? The effects of membrane potential on plasma membranes and lipid bilayers have been extensively investigated experimentally and thus are well understood, although currently the coarse-grained MD simulations cannot provide quantitative results which can be compared with experimental results. In this manuscript, using the coarse-grained MD simulations, the authors applied 2.2 V to a lipid bilayer to examine the translocation of CPPs. However, it is well known the experimental results that application of such large voltage to a lipid bilayer induces pore formation in the membrane or its rupture (Bioelectrochem. Bioenerg., 41, 135, 1996; Sci. Rep., 7, 12509, 2017), but at low membrane potential (< ~200 mV) a lipid bilayer is stable although they have transient pre-pores (Biophys. J., 85, 2342, 2003; Biophys. J., 80, 1829, 2001). In the main results obtained in the experiments of cells in this manuscript, the values of the membrane potential are less than -100 mV. Therefore, this description of their results of MD simulations is misleading. According to their theory, only at much lower Vm values (-150 mV) induces a large decrease in free energy barrier of the translocation of CPPs across the lipid bilayer. Is this due to the pore formation in the membrane? The description of this point is poor, and thus it is diffiult to understand it. The authors consider that normal membrane potential is much higher than -150 mV, and thus the binding of positively charged CPPs in the membrane surface must increase the negative membrane potential to decrease the free energy barrier. Then, the authors suggest that the presence of R9 in contact with lipid membrane decreases the transmembrane potential to -150 mV according to the MD calculation. What is the probability of the existence of R9 in the surface of the membrane? R9 cannot bind to the electrically neutral lipid bilayers (such as PC) under a physiological ion concentration (Biochemistry, 55, 4154, 2016). Even if in the case of R9 the membrane potential reaches at -150 mV, the other CPPs have lower surface charge density than that of R9, and hence, the decrement of membrane potential is lower. The authors should provide the data of other CPPs. It has been reported that the negative membrane potential increases the rate of entry of two kinds of CPPs into the lumen of giant unilamellar vesicles (GUVs) without leakage of water-soluble fluorescent probe (Stokes-Einstein radius; ~0.9 nm diameter), i.e., no pore formation in the GUV membrane (Biophys., 118, 57, 2020, J. Bacteriology, 2021, DOI: 10.1128/JB.00021-21). The authors should discuss the similarity and the difference between the results in these papers and the above results in this manuscript.

The authors consider that the translocation of CPPs induces depolarization, and as a result, the pore closes immediately. This kind of transient pore cannot explain the authors' result of the significant entry of PI into the cytosol during the interaction of CPPs with the cells. The authors should explain this point.

In this manuscript, the authors used only cancer cell lines (Raji cell, SKW6.4 cell, and HeLa cell). The lipid compositions and the stability of the plasma membranes of these cells may be different from normal cells (e.g., J. Bioenergetics Biomem., 52, 321, 2020; Cancer Res., 51, 3062, 1991). Is there a possibility that negatively charged lipids such as PS and PIP2 locate in the outer leaflet locally in these cells? At least, some discussions on this point is essential.

The authors found that PI enters the cytosol significantly when CPPs interact with these cells. Based on this result, the authors concluded that pores with 2 nm diameter are formed in the plasma membrane. However, they did not show the time courses of entry of PI and that of CPPs, and thus we cannot judge whether the pore formation in the plasma membrane is the cause of the entry of CPPs or the result of the entry of CPPs. We can reasonably consider that CPPs enters the cytosol via endocytosis and bind to the inner leaflet of the plasma membrane, inducing pore formation in the plasma membrane.

It has been reported that the negative membrane potential increases the rate constant of antimicrobial peptide (AMP)-induced pore formation or local damage in the GUV membrane (J. Biol. Chem., 294, 10449, 2019; BBA-Biomembranes, 1862, 183381, 2020). These results are related to those in the present manuscript, because here the authors consider that CPPs induce pores in the plasma membrane in the presence of negative membrane potential.

In conclusion, the results of the membrane potential dependence of the rate of the internalization of CPPs may be solid results, which is an important contribution. However, the other analyses and the interpretations are not conclusive at the current stage.

Significance

(1) Using a CRISPR/Cas9-based screening, the authors found that some potassium channels play an important role in the internalization of CPP TAT-RasGAP317-326. This result advances the field of CPPs.

(2) Several researches have suggessted that the depolarization decreases the rate of internalization of CPPs into cell cytosol and the hyperpolarization increases the rate. It has been also reported that negative membrane potential increases the rate of entry of two kinds of CPPs into the lumen of GUVs of lipid bilayers. The authors provide a new genetic evidence that membrane potential plays an important role in the internalization of CPPs in the cytosol. However, modulation of membrane potential affects the structure, dynamics and function of plasma membranes greatly. At the current stage, it is difficult to judge which process of the internalization of CPPs is affected by the membrane potential.

(3) The researchers of CPPs and AMPs are interested in their results after they improve the contents of the manuscript.

(4) My field of expertise is membrane biophysics, especially the interaction of AMPs and CPPs with GUVs and cells.

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

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Referee #1

Evidence, reproducibility and clarity

Summary

The authors propose a mechanism through which voltage dependent water pore formation is key to the internalization of Cell permeable peptides (CPPs). The claim is based on an in-silico study and on several experimental approaches. The authors compare 5 peptides (R9, TAT-48-57, Penetratin, MAP and Transportan and use 3 distinct cell lines (Raji, SKW6.4 and HeLa cells), plus neurons in primary cultures. The also present in vivo experiment (mouse skin and zebrafish embryo). All in all, it is an interesting study, but it raises several issues that need to be addressed. Moreover, the length and structure of the manuscript make it very difficult to read (see below under "Reviewer statement")

Reviewer statement

The instructions are to use the "Major comments" section to answer 6 precise questions. Unfortunately, this is not possible due to the structure of the document to review. The main manuscript (22 pages) comes with 4 primary figures and 19 supplemental ones. Most of these figures have an enormous number of panels and their legends occupy 17 pages. To this, are added 6 supplemental tables and 7 supplemental movies (with 2 pages of legends), 28 pages of Material and Methods, and 146 References (109 for the main manuscript and 37 for Supplemental information). To be frank, I was often tempted to send the manuscript back, asking for the authors to submit a document facilitating the task of the reviewers.

Because of this complexity, my "Major comments" will come after a page by page, paragraph ({section sign}) by paragraph and figure by figure "Detailed analysis" of the manuscript.

Detailed analysis

Page 4 {section sign} 3 The test is based on the ability of TAT-RasGAP to kill the cells. Although controls exist, this is worrying since necrotic death might participate in the rupture of the membrane and artificially amplify internalization after a first physiological entry of the peptide. It is also a bit dangerous to add a FITC group to a short peptide without controlling that it has no effect on the interaction with the membrane (FITC-induced local hydrophobicity can provoke peptide tilting and membrane shearing). In the same vein, the very high peptide concentrations often used in the study (40µM for Raji and SKW6.4 cells and 80µM on HeLa cells) can be highly toxic.

Page 5 {section sign} 1 Supp. Fig.1a shows no differences between the 3 cell types, even though they differ in their modes of peptide internalization, some favoring vesicular staining and others cytoplasmic diffusion. Multiplying cell and peptide types contributes to the complexity of the manuscript without increasing its interest. If there is a conceptual breakthrough, as might be the case, it is obscured by the accumulation of useless images and data. A step into simplifying the manuscript would be (i), to concentrate on Raji cells (leaving out SKW6.4 and HeLa cells) and (ii) to only discuss the R9, TAT (including TAT-RasGAP) and Penetratin peptides. TAT and R9 are poly-R peptides, which is not the case for Penetratin that has only 3 Rs. These 3 Rs are important (cannot be replaced by 3 Ks), but the two Ws absent in R9 and TAT are equally important as they cannot be replaced by Fs. This must be considered by the authors when they tend to generalize their model. Supp. Fig1c-d is not necessary (very little information in it) and Supp. Fig 1e is misleading as it takes a lot of imagination to see a difference between homogenous (top) and focal (bottom) diffusion. Supp. Fig.1g: How many cells are we looking at? Given the high variance, the result cannot be interpreted easily. A distribution according to fluorescence bits would be a better way to present the data.

Supp. Fig2i. This panel confirms that Raji cells differ from the two other cell types by showing clear temperature dependency. The explanation will come later with the energy barrier for low Vm-induced pore formation. This contradicts earlier reports showing that Penetratin translocation is not temperature-dependent, possibly because it was done on neurons naturally hyperpolarized. Or else because mechanisms are, at least in part, different from the one proposed here for R9 and TAT. This requires some clarification and supports the suggestion that, instead of multiplying models and peptides, it would be more efficient to compare TAT, R9 and Penetratin internalization by Raji cells and primary neurons. Supp. Fig. 2a-f. Last sentence of the legend "Concentrations above 40µM led to too extensive cell death preventing analysis of peptide internalization". This confirms the warning against the use of concentrations varying between 40µM and 80µ and partially jeopardizes the validity of some experiments.

Page 6 {section sign} 2 The authors advocate 2 modes of entry, opposing transport across the membrane and endocytosis. In contrast with R9, TAT and Penetratin, Transportan or MAP seem to be purely endocytosed but, if they reach the cytoplasm, they still have to cross a membrane (unless "a miracle happens"). For Penetratin and R9/TAT, the authors consider that water pore and inverted micelle formation are incompatible. This is a bit rapid as inverted micelles might induce water pores through W/lipids interactions requiring less R residues and, possibly, less energy. This provides the opportunity to signal that, in spite of their very high number, key references are missing or hidden in cited reviews, some of them written by colleagues who are not among the main contributors to the CPP field.

Page 7 {section sign} 1 Fig. 1b confirms that Raji cells provide a good model for loss and gain of function (lovely rescue experiment) and that the authors should drop the two other cell types that provide no decisive information.

Page 8 {section sign} 1 Supp. Fig. 6b (no serum conditions) allows for the use of "normal" CPP concentrations and suggests that a fraction of the peptides may bind to serum components. No arrows in Supp. Fig.6b (but in 6c), and the R/pyrene butyrate interaction is not in 6c but in 6a. Still for Supp. Fig. 6c, the death of cells at 20µM (or less) even in the absence of K+ channels, confirms that we are borderline in term of peptide toxicity. There is a confusion between Supp. Fig. 6d and 6e and a legend problem (6e is not described). Cell death is assessed in % of PI-positive cells. Does this securely distinguish between death and holes allowing for PI entry without death? The CPP is incubated in the presence of Pyrene butyrate, making the KO cells less resistant. How does that demonstrate that the potassium channels are not involved in the killing if the peptide is already in? Unless the KO is done after internalization (but the cells should be already dead or dying?). This lacks clarity.

Page 9 {section sign} 1 The conclusion that the diffuse staining does not come from endosomal escape is based on the certainty that LLOME disrupts both endosomes and lysosomes. First, it should be verified with specific markers (rab5, rab7) that the fluorescent vesicles are endosomes. Second, the literature strongly suggests that LLOME primarily disrupts lysosomes and not endosomes. Finally, even if some endosomes are disrupted, the endosomal population is heterogenous and some CPPs may be in a subpopulation insensitive to LLOME. In addition, the importance of this issue is not well explained. In practice, access to the cytoplasm and nucleus requires crossing the plasma and/or the endosomal membrane and the latter, at least in early endosomes (thus the need of identifying the CPP-enriched vesicles), might not be very different from the plasma membrane. Page 9 {section sign} 2 Is Supp. Fig. 7e really necessary? First, as mentioned several times, if 20µM is a borderline concentration in term of toxicity, raising the concentration up to 100µM is problematic. Secondly, what matters is not "binding" in general, but binding to the proper membrane components. As mentioned by the authors themselves (Supp. Fig. 1e and movie), there are privileged sites of entry that may correspond to the recognition of specific molecular entities/structures.

Page 9 {section sign} 3 and Page 10 {section sign} 1 The authors should have used a construct that does not kill the cells much earlier, just after the screening experiments based on resistance to necrosis induced by TAT-rasGAP. For Supp. Fig 8a and b: I am fully convinced by Raji cells and HeLa cells but not by the SKW6.4 cells..

Page 10 {section sign} 2 Supp. Fig 9 is quite convincing but adds the information that 2µM are sufficient in neurons. This again makes the 20 to 80µM concentrations used on transformed cells unsatisfactory. If one needs a cell line (more user friendly than primary cultures), there are several neural ones that can be differentiated (SHY, LHUMES, etc.) that may have an appropriate membrane potential (below -90mV). Indeed, it would then be important to verify if pore formation is still induced by TAT, R9 and Penetratin (separately) on "naturally" hyperpolarized cells. Figure 2a confirms that changes in Vm are not solid for HeLa and SKW6.4 cells. This casts a doubt on the validity of the results obtained with the latter 2 cell lines.

Page 11 {section sign} 2 Why valinomycin was only tried on Raji cells?

Page 12 {section sign} 2 Looking at Fig. 2c, it seems that low Vm increases the uptake of all CPPs, except Transportan. Is there any reason why this Figure does not provide the number of vesicles per cell in the hyperpolarized conditions? In fact, if one goes to Supp. Fig. 9c, it appears that, among all peptides, only Penetratin is almost entirely cytoplasmic after 90' of incubation, whereas MAP and Transportan remain essentially vesicular. TAT and R9 are at mid-distance between these two extremes. This leads to send again the warning that all CPPs cannot be placed in a single category. The table that describes the sequences strongly suggests that, TAT and R9 uptake is due to the numerous Rs that cannot be replaced by Ks. In the case of Penetratin, that only has 3 Rs, the situation is thus different with the presence of 2 Ws previously shown to be mandatory for internalization, although absent in TAT ad R9. In Supp. Fig9, panel g is useless. A difference between peptides is also visible in Figure 2d where depolarization with KCl does not show the same efficiency on all peptides. The issue is whether these differences are significant and, if so, why? This discussion could be restricted to TAT, R9 and Penetratin. Supp. Fig. 10a also suggests that all peptides do not respond similarly to depolarization and that the effects differ between cell types and concentrations used. However, given the high concentrations used and the high variance between replicates, this figure might not be a priority in the reorganization of the manuscript.

Page 12 {section sign} 3 and Page 13 {section sign} 1 The pH story is either too long or too short.

Page 14 {section sign} 2 At low Vm values, there is a decrease in free energy barrier. Does this modify temperature-dependency for internalization? Do cells really require energy when the Vm is very low, like is often the case for neurons?

Page 15 {section sign} 2 Figure 2e is not explained, not even in the legend while the statement that CPPs induce a local hyperpolarization is central to the study.

Page 16 {section sign} 1 It is confusing that the same agent, here PI, is used to measure internalization (2 nm pore formation in response to hyperpolarization,) and cell death. I have seen the explanation below, but I do not find it fully satisfactory.

Page 16 {section sign} 2 Entry is not necessarily a size issue. Structure is an important parameter, including possible structure changes, for example in response to Vm modifications. Therefore, the statement that molecule with larger diameters are mostly prevented from internalization is not only vague ("mostly") but incorrect.

Page 17 {section sign} 4 and Page 18 {section sign} 1 In Supp. Fig. 13b and c, since the GAP domain is mutated, death is not due to RasGAP activity. So what causes zebrafish death (hyperpolarization?) The results seem contradictory with those of Supp. Fig 13f where survival is 100% at 48 h.

Page 18 {section sign} 2 The formation of inverted micelles is not incompatible with that of pores. CPP-induced hyperpolarization (Vm) is not measured directly, but deduced from experiments involving artificial membranes and in silico modeling. It would be useful to distinguish between what takes place on live cells (in vitro and in vivo) and what is speculated (based on modeling and artificial systems).

Page 19 {section sign} 3 The model posits that the number of Rs influences the ability of the CPPs to hyperpolarize the membrane and, consequently, to induce pore formation. Since pore formation is key to the addressing to the cytoplasm, how can one explain that Penetratin which has only 3 Rs is transported to the cytoplasm more readily that TAT or R9? The authors should take this contradiction in consideration and should not leave aside, in the literature, what does not fit with their model. The fact that that Rs cannot be replaced by Ks, both in R9 and Penetratin is explained by differences in deprotonization. This is interesting but speculative. It might be that the interaction between Rs versus Ks with lipids and sugars are different and not only based on charge. After all their atomic structures, beyond charges, are different.

Page 20 {section sign} 1 We still need to understand endosomal escape.

Major comments

• The key conclusions are convincing for a subset of CPPs and cell types
• Yes, some claims should be qualified as speculative, but not preliminary
• Many experiments should be removed. Neuronal primary cultures should be introduced to verify the main conclusions, at least for the 3 mains CPPs (TAT, R9, Penetratin). Answers must be given to the concentration issue. Vesicles should be characterized as well as the localization of the peptides in or around the vesicles. See above for less decisive but still important experiments that would benefit to the study.
• Yes, the requested experiments correspond to a reasonable costs and amount of time (10 to 20,000 € and 3 to 5 months of work)
• Yes, the methods are presented with great details. -Yes, the experiments are adequately replicated and statistical analysis is adequate

Minor comments (not so minor for some of them)

• See "Detailed analysis"
• No, prior studies are not referenced appropriately (see above)
• No, the text and figures are not clear and not accurate (see above)
• (i) use Raji cells and primary neuronal cultures, plus in vivo model and forget the other cell types; (ii) forget MAP and Transportan and compare TAT/R9 and Penetratin; (iii) drastically reduce the number of figures, tables and movies (6 primary figures, 6 supplemental figures and 4 tables are reasonable numbers; movies are not absolutely necessary); (iv) limit to 6 (max) the number of panels per figure; (v) limit the number of references to less than 50 and cite the primary reports rather than reviews); (vi) reduce the size of the Material and Methods and the length of figure legends.

Significance

• The mode of CPP internalization is an unanswered question and the report, if revised, will represent a conceptual and technical advance.
  • Bits and pieces of the conclusions can be found in previous reports. But the Vm-dependent pore formation as well as the CPP-induced "megapolarization" (even if only shown for a subset of CPPs) would be an important contribution. The authors must resist the tentation to generalize to all CPPs what might only be true for a few of them.
  • I do not have the expertise for the in-silico work, but my field of expertise allows me to understand all other aspects of the manuscript.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** Previously, the authors showed the importance of contractile force in cell positioning and cell fate specification in preimplantation mouse development. In this study, the authors generated maternal-zygotic mutants of the non-muscle myosin-II heavy chain (NMHC) genes Myh9 and Myh10, and quantitatively analyzed their development using time-lapse microscopy and immunostaining. The authors first examined the expression of NMHCs. Myh9 and Mhy10 are present in preimplantation embryos, and Myh9 is maternally inherited. Single maternal-zygotic mutants of Myh9 or Myh10 revealed that maternal Myh9 plays a major role in actomyosin contractility. In maternal Myh9 mutants, compaction and contractility at the 8-cell stage were reduced. Maternal Myh9 mutants demonstrated a longer 8-cell stage, and mutant blastocysts had reduced cell numbers. Cell positioning was not affected; however, cell differentiation was slightly affected by reduced expression of TE and ICM markers. Maternal Myh9 mutants formed blastocoels, but lumen opening was observed earlier than that in wild-type embryos. In double maternal-zygotic mutants of Myh9 and Myh10, cytokinesis was severely affected. Nevertheless, TE fate was specified and embryos formed blastocoels. Interestingly, single-celled mutants swelled upon the formation of fluid-filled vacuoles in their cytoplasm. Similar TE fate specifications and cytoplasmic vacuoles were also observed with single-celled embryos produced by blastomere fusion. Based on these results, the authors concluded that maternal Myh9 is the major NMHC. However, Myh10 can significantly compensate for the loss of Myh9, and that cell fate specification and morphogenesis are independent of the success of cell division. **Minor comments:** Overall, the conclusions of this study are supported by high-quality data. However, I have a few minor concerns:

We thank the referee for her/his careful analysis of our manuscript.

1. Line 200~205. The authors showed the correlation between the cell number at the blastocyst stage and the 8-cell stage, and concluded that "the lengthened 8-cell stage of mzMyh9 is an important determinant of their reduced cell number at the blastocyst stage". This conclusion is not well supported because of several reasons. First, the timing of cell count is not clear. Cell number was compared at the blastocyst stage, but Figure 1c shows that mzMyh9 embryos initiate blastocoel formation earlier than wild-type embryos. Therefore, if cell count timing was determined based on the blastocyst morphology of the embryos, the timing of cell count (i.e., time after 3rd cleavage) for mzMyh9 mutants is earlier than that observed for wild-type embryos. This shorter culture time likely contributes to the reduced cell number of mzMyh9. Second, the authors only showed a correlation, and no experimental data supporting this conclusion were shown. If the cell number was counted at the same time after the 3rd cleavage, and if the authors' hypothesis is correct, then culturing mzMyh9 mutants for an additional three hour, which is the difference in the duration of the 8-cell stage, should make the cell numbers of mutants comparable to those of wild-type blastocysts.

Although, this correlation provides the best explanation we had based on the data, we agree that the statement above is weakly supported by our study. We do not want to make a strong point about it since we do not think it brings much to the narrative of the study. We have removed the sentence.

Discussion. In the paragraph starting from line 405, the authors discussed the inconsistencies in the observation of the phenotypes of mzMyh9 and mzMyh10 mutants with the conclusions of previous studies by others about cell polarization. It will be informative to also discuss about inconsistency with their previous observations on cell fate. In their previous report (reference 8), the authors concluded that without contractile forces, blastomeres adopt an inner-cell-like fate regardless of their position. This is clearly opposite of the phenotype of mzMyh9;mzMyh10 mutants, in which all the cells are specified to TE. Please add a discussion addressing this discrepancy.

The data provided here are consistent with the ones from ref 8 (Maître et al, 2016): reduced contractility (Myh9 KO, double Myh9;Myh10 KO or Blebbistatin treatment) leads to reduced CDX2 levels. In ref 8, CDX2 and YAP are checked at the 16-cell stage, before the definitive differentiation into TE and ICM, whereas here we present data at the mid-blastocyst stage (~64 cells). We had not checked SOX2 in ref 8 since it is not expressed at such early stage, so we cannot conclude about this marker.

We want to clarify that, as stated in the manuscript, in mzMyh9;mzMyh10 KO we detect CDX2 in 5/7 embryos only and therefore not all cells are correctly specified into TE. However, SOX2 could be detected in the inner cell of the one embryo that produced an inner cell. We had not discussed this issue further since it is difficult to conclude much from such rare events and we would prefer to keep it as such.

To strengthen our argument about reduced differentiation in NMHC mutant embryos, we now provide YAP immunostaining (Fig S4). YAP is correctly patterned in Myh10 mutants and shows slightly less defined nuclear localization in Myh9 mutants, in agreement with our previous observations on CDX2 in the present study and previous observations on YAP at the 16-cell stage (Maître et al 2016).

Together, we can conclude that, at the 16-cell stage, when ICM fate is not engaged yet (no detectable SOX2 expression), “inhibition of contractility causes (…) blastomeres to become inner-cell-like with respect to (…) Yap localization and Cdx2 levels, despite their external position” (Maître et al, 2016). At the blastocyst stage embryos with chronically impaired contractility can succeed in some but not all cases to produce TE (this study). Between these two developmental stages, blastomeres are exposed to prolonged signals from the apical domain and can be strongly deformed by the growing lumen. Based on the literature (Hirate et al 2013, Dupont et al 2011), both of these stimuli could potentially favor YAP nuclear localisation despite low contractility.

Throughout the paper, the description of gene and protein symbols should follow the rules of MGI's guidelines for nomenclature of genes (http://www.informatics.jax.org/mgihome/nomen/gene.shtml#gene_sym). Gene and allele symbols are italicized. Protein symbols use all uppercase letters and are not italicized.

We have corrected this.

Line 163. The term "contact angles" are used without any explanation or definition. The term should be introduced with a brief explanation in the text, preferably with a figure. It should help facilitate the understanding of the scientists working in different fields.

We have labelled a contact angle on Fig 1A and specified this in the text and in the figure legend.

Reviewer #1 (Significance (Required)): The importance of actomyosin contractility in compaction, cell polarization, cell positioning, and cell fate specification in preimplantation embryos has been reported by several groups, mostly using chemical inhibitors, except for the study cited in reference 8, in which chimeras of wild-type and mMyh9 mutant embryos were used. This is the first genetic analysis of the roles of actomyosin contractility in the development of preimplantation embryos. Thus, the major advancement of this study is the genetic dissection of the roles of actomyosin contractility in preimplantation mouse development, and clarifying the contribution of maternal/zygotic Myh9 and Myh10 genes. While the phenotypes of reduced compaction and blastomere contractility are consistent with those observed in previous studies, polarization and TE fate specification of the mutant cells appear inconsistent with the conclusions of previous inhibitor experiments, which show defects in polarization processes and fate specification to ICM. These are potentially important issues, but detailed analyses were not performed. The requirement of actomyosin contractility for the cytokinesis of preimplantation embryos is also a novel finding, although it is expected from studies conducted in other systems. Vacuole formation in single-celled mzMyh9;mzMyh10 mutants in a timely manner suggested that fluid accumulation is a cell autonomous process and that cell differentiation occurs independently of cell division. These are also novel findings, although the latter is somewhat expected from previous studies performed using cell number manipulated embryos. In summary, the conceptual advance offered by this study is small. However, this is a high-quality study and makes critical observations in the field of preimplantation mouse development. Scientists in the field of developmental biology, especially those working on preimplantation development, should be interested in this paper. My field of expertise is preimplantation development.

We thank the reviewer for her/his appreciation of our work. We want to argue that we did perform a very detailed analysis of the development of the NMHC mutant embryos, with multiple quantitative image and data analyses to thoroughly and objectively characterise the phenotypes of these mutants. If by “detailed analysis”, the reviewer meant a molecular dissection of the phenotype, we argue that 1/ checking the end result (i.e. presence of TE and ICM markers, presence of polarised fluid transport) was sufficient to assess the functionality of biological processes without checking every steps of a signalling cascade; 2/ we now provide additional molecular information on the state of YAP and apico-basal polarisation (Fig S3-4).

Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this manuscript, Schliffka et al. report that maternally deposited Myh9 is the major NMHC in preimplantation embryonic morphogenesis and complete removal of both Myh9 and Myh10 caused severe cytokinesis failure similar to tissue culture cells. Interestingly, although the mutant embryos completely failed cytokinesis thus forming single-celled embryos, they initiated trophoblast gene expression and vacuolization (likely similar to blastocoel formation), suggesting that the timing of preimplantation developmental events is independent from cell number and morphogenetic events.

We thank the reviewer for her/his appreciation of our work.

Major comments Vacuolization in single-celled embryos is interesting. In the images, there looks to be two types of vacuoles, F-actin positive and negative. The authors speculate the similarity to blastocoel formation. To support this, it is important to stain them with some basolateral markers like Na+ ATPase, E-cadherin and B-catenin. It is also important to confirm if the apical domain is properly formed by staining the apical domain markers like aPKC and Pard6.

We thank the reviewer for this suggestion. We now provide immunostaining of single Myh9 or Myh10 and double Myh9;Myh10 mutants for aPKC (PRKCz), Na/K ATPase (ATP1A1), Aquaporin-3 (AQP3), the best basolateral marker in our hands, which is also very relevant to fluid pumping, CDH1 and F-actin (Fig S3). We observe that these markers localise similarly in multiple-celled and single-celled embryos, suggesting that vacuoles de facto substitute for the basolateral compartment normally consisting of cell-cell contacts and the lumen. This suggests that the same machinery is at the origin of the fluid inside the lumen and inside vacuoles.

Minor comments All gene names should be Italicized.

We have corrected this.

L157. Myh10 and Myh9 should be mMyh10 and mMyh9.

We have corrected this.

L294 1/8 embryos. What does this mean?

This means this was observed in 1 embryo out of 8 in total.

L333 6/25 embryos. Does this mean 6 out of 25 embryos combined all maternal double mutants?

Precisely.

L438-442. I do not find these embryos are similar to tetraploid embryos. I suggest to remove the sentences.

We have removed the sentences.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): This study investigates the roles of non-muscle myosin in development, reporting a requirement for maternal and zygotic Mhy9 and 10. Strengths of the study include robust genetic techniques, innovative nested imaging to visualize events over different timescales within the same embryos, and analysis of morphological as well as transcriptional/cell fate phenotypes. However, the somewhat superficial phenotype analysis limits the authors' ability to draw strong mechanistic conclusions about what is going on in these mutants. Is cell polarization normal? Is cell signaling (HIPPO signalling) normal?

We thank the reviewer for carefully assessing our study. We argue that we have thoroughly characterized the phenotypes of the NMHC mutants, which allowed us to draw many important mechanistic conclusions (such as the ability of NMHC mutants to polarise, or to pump fluid in a cell autonomous manner). Each mutant embryo has been imaged at multiple time scales, stained and genotyped. The time-lapses and immunostaining have been extensively quantified using manual as well as automated methods such as particle image velocimetry. We also provided fusion experiments, which phenocopy some aspects of the mutants to provide evidence of the mechanisms causing the observed phenotype.

Nevertheless, we agree that one can always do more and that we had focused on the biological processes (lineage specification, morphogenesis and cleavages) rather than molecular characterisation. Although polarised fluid pumping ascertains a functioning epithelial polarity, we now provide immunostaining of polarity markers in mutant embryos. Although CDX2 and SOX2 staining inform on the output of the signalling cascade leading to effective TE and ICM differentiation, we now provide YAP immunostaining of mutant embryos. We hope this satisfies the request from the reviewer.

What determines whether an embryo can form an inside cell or not?

This is an outstanding question. Cells can internalise by oriented cell division or contractility mediated cell sorting. Contractility-mediated internalisation functions with only 2 cells (as when doublets of 16-cell stage blastomeres form a cell-in-a-cell structure) but requires to grow above a tension asymmetry threshold (of 1.5 in WT and most likely above 3 in these mutants due to their poor compaction, see Maître et al 2016). Oriented cell division only works if there is a cell-cell contact to push dividing cells in between. Therefore, at least 3 cells are required for an inner cell to be internalised by this mechanism.

In double mutants, the average cell number is 2.9. No embryo consisting of only 2 cells contained an inner cell, about half of embryos with 3-5 cells contained a single inner cell and all embryos with 6 cells or more contained inner cells (Fig 4D). Based on the low contractility of double mutants, we can speculate that they do not succeed in overcoming the tension asymmetry threshold. This would explain why no inner cell is observed in embryos with only 2 cells. We can speculate that with 3-5 cells, oriented divisions could occur thanks to the presence of functional polarity (Korotkevitch et al 2017).

We have added a discussion about this important matter.

Similarly, the manuscript would benefit from rewriting to reframe the authors' discoveries within the context of what is known regarding lineage specification (e.g., why does CDX2/SOX2 expression indicate normal lineage specification). Additional minor comments are listed below.

We elaborate on these points.

**Minor comments:** • Introduction focuses overly on the work of the PI and his mentor, giving the presentation an unnecessarily biased quality.

We have corrected this to the best of our ability. Please note that, to our knowledge, there are 8 studies (Anani et al., 2014; Maître et al., 2015; Samarage et al., 2015; Maître et al., 2016; Zhu et al., 2017; Zenker et al., 2018; Chan et al., 2019; Dumortier et al., 2019) looking in more or less details into the contractility of the preimplantation embryo. We mention and cite all of these studies.

• The text asserts that Myh9 levels are highest during zygote stage, on the basis of qPCR (Fig. S1A), and that this is also observed by RNA-seq (Fig. S1B). However, this conclusion is not supported by the data shown.

We have corrected this.

• Would be nice to repeat the qPCR on the mz null.

We agree with the referee that this would help in assessing the level of compensation between NMHC paralogs in individual mutants. Our qPCR protocol requires a few tens of embryos to be able to amplify the different paralogs. Unfortunately, pooling embryos from our current mating strategy would result in pooling homozygote and heterozygote mutants as we cannot know a priori which embryo is of which genotype.

We believe that, as nice as this information would be, the current study does not require this information, which would be technically challenging.

• Were the measurements shown in Fig. S1F taken from the images shown in Fig. S1E? If so, the authors should clarify how the measurements were normalized, since the images in Fig. S1E were clearly taken with different camera settings (as judged by background fluorescence level surrounding the embryos).

The camera settings were identical but the LUT are set differently (to the maximal signal of a given genotype) so that some signal is visible. The signal intensities are so different between genotypes that if set to a common LUT, we either get the maternal GFP as a saturated white circle or the other genotypes as black images. We explain our LUT settings both in the methods and figure legends.

As an alternative to the current data presentation, we would be fine to have the same LUT for all images and show almost black images for WT and paternal GFP.

• Can't really conclude that Myh9 is essential for compaction since compaction occurs (albeit abnormally) in the absence of Myh9 (line 177-178).

Our statement is “we conclude that maternal Myh9 is essential for embryos to compact fully”. WT and mzMyh10 mutants increase their contact angles by 60° whereas mzMyh9 only grow by 30°. Double mutants compact less than single Myh9 mutants. Therefore, the compaction movement is halved in mzMyh9 and the residual weak compaction could be explained by compensation from Myh10. We stand by our statement.

• Line 211: "observe" rather than "measure".

We have corrected this.

• If the embryos achieve proper ICM/TE ratio, in spite of having half the number of cells in the mutants, is that to be expected? Would/do halved embryos also possess the same ICM/TE ratio? Or is this outcome peculiar to the mutants?

This is an interesting question on which we had not sufficiently elaborated. Our experiments with cell fusion at the 4-cell-stage (Fig. S5) produced embryos with reduced cell number. These resemble Myh9 mutant embryos in the aspect that they show a reduced cell number while maintaining the total embryonic cell mass. In both cases, the ICM/total cell ratio is similar to control embryos. This indicates a robust mechanism of ICM/TE ratio setting that is robust to the cell number change observed in the single mutant. We have added a discussion about this.

• Line 222: what is the evidence that Cdx2 and Sox2 are TE and ICM markers?

We have added references to the studies from Strumpf et al., 2005 and Avilion et al., 2003 to support these claims.

• Is the reported reduction in CDX2 and SOX2 levels due to a stage-delay? What would the comparison look like in wt embryos with half as many cells? Timing of lumen formation may or may not indicate developmental timing...

We address this point by fusing embryos to half the cell number and find that the fate marker levels are specifically affected as a result of mutation of Myh9 (Fig S5).

We agree that the timing of lumen formation is unlikely to be a good reference for staging and we did not use this event. We do synchronise embryos based on lumen opening only when comparing lumen growth rate.

• Line 240 - what was the correction on the multiple pairwise comparisons (multiple t tests)?

To compare lumen growth rate, individual growth rates of mutants are compared to those of WT using Student’s t test. Growth rates are considered as normally distributed and independent (not pairwise).

• Lumen forms on time in mutants, despite having fewer cells. Alternatively, lumen forms early, prior to acquisition of proper cell number. Is there a reason the authors did not consider this alternative?

The referee is correct. Lumens form with fewer cells in mutant embryos and therefore prior to the acquisition of proper cell number.

• Lines 306 and 339: why does lack of SOX2 expression suggest that the lineage specification program is intact? Why does expression of CDX2 suggest TE initiation has occurred normally? The regulation of these two markers was not introduced.

We have better introduced and justified this aspect.

• Line 349: why is blastocoel formation a cell-autonomous property when it clearly occurs extracellularly? Does this also happen in wild type embryos?

Blastocoel formation is clearly a multi-cellular process. We argue that fluid accumulation is not. The implications for WT embryos are that fluid can be accumulated in the blastocoel entirely trans-cellularly (no need for fluid to flow through cell-cell junction).

• Speculate in Discussion on why the ML-7/Blebbistatin experiments results could differ from the genetic results produced here.

Blebbistatin experiments are in agreement with the mutant data. ML-7 experiments are partially in agreement with the mutant data. The discrepancy lies in the effect on cell polarity. ML-7 affects kinases other than the MLCK, such as PKC, which is a known regulator of cell polarity during preimplantation development. Although this is speculative, we specify this in the revised manuscript.

• Can these mutant embryos implant?

We grow colonies of heterozygous mutants, therefore mMyh9, mMyh10 and mMyh9;mMyh10 embryos are viable and must be able to implant. As for homozygous mutants, they are not viable and we do not know whether they can implant.

Reviewer #3 (Significance (Required)): The study provides the first strong evidence of a requirement for non-muscle myosin in epithelialization. This is significant to embryology and to epithelial biology.

We thank the reviewer for appreciating the significance of our study. We want to clarify that our study provides evidence for NMHC as NOT being required for de novo epithelialization.

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Referee #3

Evidence, reproducibility and clarity

This study investigates the roles of non-muscle myosin in development, reporting a requirement for maternal and zygotic Mhy9 and 10. Strengths of the study include robust genetic techniques, innovative nested imaging to visualize events over different timescales within the same embryos, and analysis of morphological as well as transcriptional/cell fate phenotypes. However, the somewhat superficial phenotype analysis limits the authors' ability to draw strong mechanistic conclusions about what is going on in these mutants. Is cell polarization normal? Is cell signaling (HIPPO signalling) normal? What determines whether an embryo can form an inside cell or not? Similarly, the manuscript would benefit from rewriting to reframe the authors' discoveries within the context of what is known regarding lineage specification (e.g., why does CDX2/SOX2 expression indicate normal lineage specification). Additional minor comments are listed below.

Minor comments:

• Introduction focuses overly on the work of the PI and his mentor, giving the presentation an unnecessarily biased quality.

• The text asserts that Myh9 levels are highest during zygote stage, on the basis of qPCR (Fig. S1A), and that this is also observed by RNA-seq (Fig. S1B). However, this conclusion is not supported by the data shown.

• Would be nice to repeat the qPCR on the mz null.

• Were the measurements shown in Fig. S1F taken from the images shown in Fig. S1E? If so, the authors should clarify how the measurements were normalized, since the images in Fig. S1E were clearly taken with different camera settings (as judged by background fluorescence level surrounding the embryos).

• Can't really conclude that Myh9 is essential for compaction since compaction occurs (albeit abnormally) in the absence of Myh9 (line 177-178).

• Line 211: "observe" rather than "measure".

• If the embryos achieve proper ICM/TE ratio, in spite of having half the number of cells in the mutants, is that to be expected? Would/do halved embryos also possess the same ICM/TE ratio? Or is this outcome peculiar to the mutants?

• Line 222: what is the evidence that Cdx2 and Sox2 are TE and ICM markers?

• Is the reported reduction in CDX2 and SOX2 levels due to a stage-delay? What would the comparison look like in wt embryos with half as many cells? Timing of lumen formation may or may not indicate developmental timing...

• Line 240 - what was the correction on the multiple pairwise comparisons (multiple t tests)?

• Lumen forms on time in mutants, despite having fewer cells. Alternatively, lumen forms early, prior to acquisition of proper cell number. Is there a reason the authors did not consider this alternative?

• Lines 306 and 339: why does lack of SOX2 expression suggest that the lineage specification program is intact? Why does expression of CDX2 suggest TE initiation has occurred normally? The regulation of these two markers was not introduced.

• Line 349: why is blastocoel formation a cell-autonomous property when it clearly occurs extracellularly? Does this also happen in wild type embryos?

• Speculate in Discussion on why the ML-7/Blebbistatin experiments results could differ from the genetic results produced here.

• Can these mutant embryos implant?

Significance

The study provides the first strong evidence of a requirement for non-muscle myosin in epithelialization. This is significant to embryology and to epithelial biology.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Schliffka et al. report that maternally deposited Myh9 is the major NMHC in preimplantation embryonic morphogenesis and complete removal of both Myh9 and Myh10 caused severe cytokinesis failure similar to tissue culture cells. Interestingly, although the mutant embryos completely failed cytokinesis thus forming single-celled embryos, they initiated trophoblast gene expression and vacuolization (likely similar to blastocoel formation), suggesting that the timing of preimplantation developmental events is independent from cell number and morphogenetic events.

Major comments

Vacuolization in single-celled embryos is interesting. In the images, there looks to be two types of vacuoles, F-actin positive and negative. The authors speculate the similarity to blastocoel formation. To support this, it is important to stain them with some basolateral markers like Na+ ATPase, E-cadherin and B-catenin. It is also important to confirm if the apical domain is properly formed by staining the apical domain markers like aPKC and Pard6.

Minor comments

All gene names should be Italicized.

L157. Myh10 and Myh9 should be mMyh10 and mMyh9.<br> L294 1/8 embryos. What does this mean?

L333 6/25 embryos. Does this mean 6 out of 25 embryos combined all maternal double mutants?

L438-442. I do not find these embryos are similar to tetraploid embryos. I suggest to remove the sentences.

Significance

In this manuscript, Schliffka et al. report that maternally deposited Myh9 is the major NMHC in preimplantation embryonic morphogenesis and complete removal of both Myh9 and Myh10 caused severe cytokinesis failure similar to tissue culture cells. Interestingly, although the mutant embryos completely failed cytokinesis thus forming single-celled embryos, they initiated trophoblast gene expression and vacuolization (likely similar to blastocoel formation), suggesting that the timing of preimplantation developmental events is independent from cell number and morphogenetic events.

Major comments

Vacuolization in single-celled embryos is interesting. In the images, there looks to be two types of vacuoles, F-actin positive and negative. The authors speculate the similarity to blastocoel formation. To support this, it is important to stain them with some basolateral markers like Na+ ATPase, E-cadherin and B-catenin. It is also important to confirm if the apical domain is properly formed by staining the apical domain markers like aPKC and Pard6.

Minor comments

All gene names should be Italicized.

L157. Myh10 and Myh9 should be mMyh10 and mMyh9.<br> L294 1/8 embryos. What does this mean?

L333 6/25 embryos. Does this mean 6 out of 25 embryos combined all maternal double mutants?

L438-442. I do not find these embryos are similar to tetraploid embryos. I suggest to remove the sentences.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Previously, the authors showed the importance of contractile force in cell positioning and cell fate specification in preimplantation mouse development. In this study, the authors generated maternal-zygotic mutants of the non-muscle myosin-II heavy chain (NMHC) genes Myh9 and Myh10, and quantitatively analyzed their development using time-lapse microscopy and immunostaining. The authors first examined the expression of NMHCs. Myh9 and Mhy10 are present in preimplantation embryos, and Myh9 is maternally inherited. Single maternal-zygotic mutants of Myh9 or Myh10 revealed that maternal Myh9 plays a major role in actomyosin contractility. In maternal Myh9 mutants, compaction and contractility at the 8-cell stage were reduced. Maternal Myh9 mutants demonstrated a longer 8-cell stage, and mutant blastocysts had reduced cell numbers. Cell positioning was not affected; however, cell differentiation was slightly affected by reduced expression of TE and ICM markers. Maternal Myh9 mutants formed blastocoels, but lumen opening was observed earlier than that in wild-type embryos. In double maternal-zygotic mutants of Myh9 and Myh10, cytokinesis was severely affected. Nevertheless, TE fate was specified and embryos formed blastocoels. Interestingly, single-celled mutants swelled upon the formation of fluid-filled vacuoles in their cytoplasm. Similar TE fate specifications and cytoplasmic vacuoles were also observed with single-celled embryos produced by blastomere fusion. Based on these results, the authors concluded that maternal Myh9 is the major NMHC. However, Myh10 can significantly compensate for the loss of Myh9, and that cell fate specification and morphogenesis are independent of the success of cell division.

Minor comments:

Overall, the conclusions of this study are supported by high-quality data. However, I have a few minor concerns:

1. Line 200~205. The authors showed the correlation between the cell number at the blastocyst stage and the 8-cell stage, and concluded that "the lengthened 8-cell stage of mzMyh9 is an important determinant of their reduced cell number at the blastocyst stage". This conclusion is not well supported because of several reasons. First, the timing of cell count is not clear. Cell number was compared at the blastocyst stage, but Figure 1c shows that mzMyh9 embryos initiate blastocoel formation earlier than wild-type embryos. Therefore, if cell count timing was determined based on the blastocyst morphology of the embryos, the timing of cell count (i.e., time after 3rd cleavage) for mzMyh9 mutants is earlier than that observed for wild-type embryos. This shorter culture time likely contributes to the reduced cell number of mzMyh9. Second, the authors only showed a correlation, and no experimental data supporting this conclusion were shown. If the cell number was counted at the same time after the 3rd cleavage, and if the authors' hypothesis is correct, then culturing mzMyh9 mutants for an additional three hour, which is the difference in the duration of the 8-cell stage, should make the cell numbers of mutants comparable to those of wild-type blastocysts.
2. Discussion. In the paragraph starting from line 405, the authors discussed the inconsistencies in the observation of the phenotypes of mzMyh9 and mzMyh10 mutants with the conclusions of previous studies by others about cell polarization. It will be informative to also discuss about inconsistency with their previous observations on cell fate. In their previous report (reference 8), the authors concluded that without contractile forces, blastomeres adopt an inner-cell-like fate regardless of their position. This is clearly opposite of the phenotype of mzMyh9;mzMyh10 mutants, in which all the cells are specified to TE. Please add a discussion addressing this discrepancy.
3. Throughout the paper, the description of gene and protein symbols should follow the rules of MGI's guidelines for nomenclature of genes (http://www.informatics.jax.org/mgihome/nomen/gene.shtml#gene_sym). Gene and allele symbols are italicized. Protein symbols use all uppercase letters and are not italicized.
4. Line 163. The term "contact angles" are used without any explanation or definition. The term should be introduced with a brief explanation in the text, preferably with a figure. It should help facilitate the understanding of the scientists working in different fields.

Significance

The importance of actomyosin contractility in compaction, cell polarization, cell positioning, and cell fate specification in preimplantation embryos has been reported by several groups, mostly using chemical inhibitors, except for the study cited in reference 8, in which chimeras of wild-type and mMyh9 mutant embryos were used. This is the first genetic analysis of the roles of actomyosin contractility in the development of preimplantation embryos. Thus, the major advancement of this study is the genetic dissection of the roles of actomyosin contractility in preimplantation mouse development, and clarifying the contribution of maternal/zygotic Myh9 and Myh10 genes. While the phenotypes of reduced compaction and blastomere contractility are consistent with those observed in previous studies, polarization and TE fate specification of the mutant cells appear inconsistent with the conclusions of previous inhibitor experiments, which show defects in polarization processes and fate specification to ICM. These are potentially important issues, but detailed analyses were not performed. The requirement of actomyosin contractility for the cytokinesis of preimplantation embryos is also a novel finding, although it is expected from studies conducted in other systems. Vacuole formation in single-celled mzMyh9;mzMyh10 mutants in a timely manner suggested that fluid accumulation is a cell autonomous process and that cell differentiation occurs independently of cell division. These are also novel findings, although the latter is somewhat expected from previous studies performed using cell number manipulated embryos. In summary, the conceptual advance offered by this study is small. However, this is a high-quality study and makes critical observations in the field of preimplantation mouse development. Scientists in the field of developmental biology, especially those working on preimplantation development, should be interested in this paper. My field of expertise is preimplantation development.

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Reply to the reviewers

We thank Review Commons and its three reviewers for your supportive and insightful responses to our manuscript. Below, we provide detailed responses to the reviewers’ individual comments and how we plan to address them during the revision.

Reviewer #1: **Major comments:**

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?

For all experiments, the cells were grown in medium containing 5% FBS. Throughout the manuscript, “hormone-free” refers to medium containing 5% FBS with no dexamethasone added. Technically, this medium is not hormone-free as FBS contains low levels of cortisol. However, the levels of cortisol from the FBS in our medium seems insufficient to elicit a transcriptional response or DNA binding by GR based on experiments comparing charcoal stripped and medium containing regular 5% FBS. However, we acknowledge that it should be made clear to the reader that growth conditions technically were not hormone-free. We will make sure to include this information in both the methods and results section of a revised manuscript. In addition, we will state explicitly that our naiive cells are those that have not been exposed to a high dose of hormone.

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.

The ChIP-seq peaks for GR are the intersect of two biological replicates. This is described in the Methods section (page 7). For the ATAC data, we used two biological replicates for the vehicle treated cells and treated two different hormones (dexamethasone and cortisol) as replicates. In a revised manuscript, we will add a supplemental figure to show the concordance between the replicates.

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.

This is a good idea. As suggested by this reviewer (and also in response to a comment by one of the other reviewers), we will revise this figure panel to make the overlap between GR binding and opening and closing sites more obvious. Here are the numbers:

A549 cells:

opening sites: 49%

closing: 10%

nonchanging: 18%

U2OS cells:

opening: 54%

closing: 0.2%

nonchanging: 7%

Regarding the use of browser shots, obviously these are cherry picked examples, however in our opinion they serve a purpose beyond illustrating examples of individual loci that open or close as they also give the reader an idea of the quality of the ATAC-seq data.

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 proposed group of control peaks is essentially what we included as “non-changing” peaks. For the revision, we will refine this group and compare the H3K27ac signal between GR-occupied and non-occupied groups. Regarding reduced H3K27ac signal upon Dex treatment at non-changing sites: Notably, this comparison is based on a single ChIP-seq replicate. In our experience, ChIP-seq experiments show quite some variability between biological replicates, which limits our ability to compare signal levels quantitatively. Thus, the difference could simply reflect a difference in ChIP efficiency between the treated and untreated cells. Alternatively, it could be that there is a general redistribution of H3K27ac signal towards GR-occupied opening sites. To pin down which of these explanations is valid, we would need to perform additional experiments, e.g. using spike-ins. However, this is beyond what we can do at the moment and therefore, we will instead revise the text to make sure that the interpretation of these results is somewhat speculative.

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.

We will revise figure 1 as requested. In our initial submission, the data was always sorted by signal intensity of the feature shown. We will revise this and sort by ATAC-signal and keep a consistent sorting order for other features shown (and stratify each group into GR-occupied or not).

- 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?

We will revise the figures as suggested to show the same time point for ChIP and ATAC-seq data.

- Fig. 1f. The authors state "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.

This is a fair point and was also raised by the other reviewers. During the revision, we will make textual changes to acknowledge that GR binding is enriched near repressed genes, albeit less so than for activated genes. In addition, we will include genome browser shots of genes with nearby peaks that are repressed by GR.

- 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.

See our response to the first major comment above.

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.

We agree that that would be interesting but this is something that we cannot include for now.

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.

A trivial explanation for the overlaying H3K27ac and H3K27me3 marks at the ZBTB16 locus is that the ChIP results represent a population average. From our single-cell FISH experiments, we found that only a subset of cells activates ZBTB16 expression upon hormone treatment. Thus, a potential explanation is that the cells of the population that respond are responsible for the H3K27ac signal whereas the non-responders are decorated with H3K27me3. We will include this information in a revised discussion.

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

When compared to FKBP5, ZBTB16 indeed has very low levels of pre-hormone expression. However, this is unlikely to explain the observed “memory” for ZBTB16 given that there are other genes with similarly low pre-hormone levels that do not show more robust responses upon repeated hormone exposure (see Fig. 3B,D). For the FISH experiments, we decided to include a non-primed gene (FKBP5 as control). We agree that adding additional control genes with comparable basemean levels would be informative. For example, this would tell us if a response of only a subset of cells in the population to hormone is specific to ZBTB16. Based on single cell studies by others (PMID: 32170217), most GR target genes show a response in only a subset of cells indicating that this is unlikely a unique feature of ZBTB16 explaining the priming observed. Rather than performing additional experiments, we will revise the discussion to acknowledge the difference in basemean and the potential role of cell-to-cell variability in explaining the observed “memory” for the ZBTB16 gene.

**Minor comments:**

- 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).

We are aware of these studies and agree that they should be included when listing the possible mechanisms by which GR can repress genes. We will revise the text accordingly.

- 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.

We will pick a more appropriate reference indicative of tethering as a mechanism by which GR might repress genes.

-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 careful phrasing here was to include the possibility that GR might bind DNA even when hormone is completely washed out. However, a more likely explanation is that the washout is incomplete. The residual GR binding we find in our ChIP assays shows us that a subset of GR is indeed still chromatin-bound which implies that some GR is still 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.

When revising the manuscript, we will see if implementiung this suggestion is feasible.

- Figures have CAPS panel letters (A,B,C, etc) while the text calls for lower case letter (a,b,c...)

We will fix this as part of the revision. Reviewer #2: **Major Comments**

We agree that long-term and repeated GC treatment would be very interesting to study and would yield insights that are more likely to be relevant to, for example, emerging GC-resistance during therapeutic use. We are aware of the limitations of our study and will make sure that these are acknowledged in the revised manuscript and we will point out the speculative nature of translating our findings to an in-vivo setting.

2a.) 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).

A similar suggestion was made by reviewer 1. We agree that using the same sort order for the datasets makes it easier to link the different types of data we generated. We will present the data with a consistent sorting order and stratified by GR-occupied or not when we revise the manuscript.

2b.) 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.

The first issue raised will be addressed by using the same sort order across different types of data. It might also shed light on features associated with GR binding sites that do not change accessibility or H3K27ac. Once we implement the revised sorting order, we will evaluate if the statements mentioned are indeed supported by the data.

2c.) 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).

We will include a motif analysis comparing opening, closing and non-changing sites (stratified into GR-occupied or not) in a revised version of the manuscript. In addition, we will further investigate the redistribution of p300 upon Dex-treatment e.g. to test the correlation between p300 loss at closing sites lacking GR occupancy and transcriptional repression. We agree that the “squelching model” is just one of several explanations for repression and will provide a more comprehensive list of possible explanations beyond squelching as part of the revision.

We will discuss the difference in receptor levels between the cell lines, the different number of genomic GR binding sites and its possible implication in the observed residual binding after wash-out in U2OS-GR cells as suggested.

We agree that the coverage plots do not take the fraction of binding sites with signal into account. However, by also showing the heat maps, this information is also available to the reader. In our opinion, the coverage plots provide a straight-forward way to compare the signal for the different categories of peaks. The violin plots are an interesting alternative way to present the data, which also captures the diversity in the signal within each group. We will add violin plots to the supplementary data as requested.

We see your point. However, based on the ATAC-signal (Fig. 5D) the changes in nucleosomal occupancy upon GC treatment are the same for naiive and primed cells and revert to their base-line level after hormone withdrawal. This indicates that these loci have comparable nucleosome occupancy after wash-out. Yet, the levels for these histone modifications do not differ between primed and naiive cells indicating that these histone marks do not “mark” the promoter of primed genes after wash-out.

We are reluctant to put p-values on every chart, especially for experiments with few replicates. Importantly, we always plot the values for each individual data point, so the reader can gage if they differ between conditions. We will add p-values for figure 4 to test (support) our claim that ZBTB16 is primed whereas other GR target genes are not.

A similar suggestion was brought up by reviewer #1, here is the response we gave to this comment: When compared to FKBP5, ZBTB16 indeed has very low levels of pre-hormone expression. However, this is unlikely to explain the observed “memory” for ZBTB16 given that there are other genes with similarly low pre-hormone levels that do not show more robust responses upon repeated hormone exposure (see Fig. 3B,D). For the FISH experiments, we decided to include a non-primed gene (FKBP5 as control). We agree that adding additional control genes with comparable basemean levels would be informative. For example, this would tell us if a response of only a subset of cells in the population to hormone is specific to ZBTB16. Based on single cell studies by others (PMID: 32170217), most GR target genes show a response in only a subset of cells indicating that this is unlikely a unique feature of ZBTB16 explaining the priming observed. Rather than performing additional experiments, we will revise the discussion to acknowledge the difference in basemean and the potential role of cell-to-cell variability in explaining the observed “memory” for the ZBTB16 gene.

The fact that we do not observe elevated expression of other genes upon repeated expression could be due to the relatively short length of the hormone treatment, 4 hours, which was chosen to enrich for direct target genes of GR. These four hours might be insufficient for transcription, translation and ultimately gene regulation by the ZBTB16 protein. We have not looked at ZBTB16 protein levels.

**Minor Comments**

We will include this information in a revised version of the manuscript.

We will add the requested peak-centric view. Based on a previous study (PMID: 29385519), we expect that binding is a poor predictor of gene regulation of nearby genes, especially for repressed genes.

In our analysis, we looked at opening and closing peaks independently. If a peak is in the vicinity of multiple genes, it will only be assigned to the closest one. Thus, genes that have both and opening and a closing peak in the 50kb window will be included in both the analysis of closing sites and opening sites. We have not looked at clusters of binding sites, but agree that this would be interesting to see if the combinatorial action of multiple peaks makes regulation of the gene more likely. We will look into this during the revision process.

1. 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.

Even though several studies indicate that tethered binding can be captured using formaldehyde-only fixed cells (e.g. PMID: 32619221, PMID: 15879558), we agree that the ChIP-assay might have blind spots, for instance for tethered binding, and will revise our statements as suggested.

This might be related to comment #4 given that P300 is brought to the DNA by other transcription factors whereas H3K27ac is directly DNA-bound which likely influences the cross-linking efficiency. By resorting the heat-maps, we will be able to determine the overlap between p300 recruitment and changes in H3K27ac levels (the other main enzyme that deposits this mark is CREBBP (a.k.a. CBP)).

We will include this information in a revised version of the manuscript.

We have not looked into this but a previous study by the Reddy lab (PMID: 22801371) has investigated binding sites in A549 cells that are occupied at very low Dex concentrations. They found that this is not driven by a specific GR motif but rather by the presence of binding sites for other transcription factors and chromatin accessibility.

This data for the GILZ gene is shown in Figure S2C. When we revise the manuscript we will add this information to main figures 1 and 2 as suggested.

This is shown in figure S3C and shows that expression levels of certain genes (ZBTB16 and FKBP5 but not GILZ) stay high after Dex washout (but not cortisol wash-out) consistent with persistent GR binding at a subset of GR-occupied loci for the experiments using Dex.

For both S2C and S3C, cells were treated for 4h with Dex before the wash-out. For the ZBTB16 and FKBP5 genes, the persistent GR binding after wash-out is accompanied by a preserved Dex response after wash-out. For GILZ, GR binding at one of the peaks near the GILZ gene is also preserved, yet the expression of this gene reverses to its pre-treatment levels after wash-out. A possible explanation is that the residual binding at the GILZ gene is observed for only one of several nearby GR peaks. Previous studies, where we deleted GR binding sites near the GILZ gene, have shown that the combined action of multiple GR-occupied regions is needed for robust induction of this gene (PMID: 29385519).

A trivial explanation for the overlaying H3K27ac and H3K27me3 marks at the ZBTB16 locus is that the ChIP data represents a population average. From our single-cell FISH experiments, we found that only a subset of cells activates ZBTB16expression upon hormone treatment so a potential explanation is that the cells of the population that respond are responsible for the H3K27ac signal whereas the non-responders are decorated with H3K27me3. We will include this information in a revised discussion. On a single histone, H3K27me3 and H3K27ac are mutually exclusive. However, given that a nucleosome has 2 copies of histone H3, both modifications can in principle co-exist.

We’re guessing here, but we assume the reviewer refers to the potentially slightly higher H3K27me3 levels upon Dex treatment for ChIP-seq whereas the qPCR indicates that the levels do not change? The change seen in the ChIP-seq experiment is marginal and based on a single experiment. In contrast, the qPCR data shows the results from three biological replicates and therefore is probably a more reliable source of information.

We will include this information in a revised version of the manuscript.

Cancer cell lines often have variable karyotypes and our FISH data suggests that the ZBTB16 locus is present in more than 2 copies in some of the A549 cells. Here’s the info from the ATCC website describing the karyotype of A549 cells: …” This is a hypotriploid human cell line with the modal chromosome number of 66, occurring in 24% of cells. Cells with 64 (22%), 65, and 67 chromosome counts also occurred at relatively high frequencies; the rate with higher ploidies was low at 0.4%.....”.

Upon quick inspection, we find that GR target genes are typically not marked by H3K27me3, however ZBTB16 does not appear to be the only one. When we revise the manuscript, we will look more systematically at the link between gene regulation by GR and genes marked by H3K27me3 to determine how “special” the presence of this mark is, which will also inform us about the likelihood that it is linked to the transcriptional memory observed for the ZBTB16 gene.

We are not sure if ZBTB16 regulation by GR is tissue independent. However, in contrast to most GR target genes that are regulated in a cell-type-specific manner, ZBTB16 is regulated in both cell lines we examined and has also been reported to be a GR target gene in other cell types e.g. in macrophages (PMID: 30809020).

Reviewer #3 **Major Comments:**

For sure the washout time matters and we do not doubt that the persistent changes observed upon shorter wash-out by the Hager lab are real. One of the reasons we chose the 24h period was to see if the changes observed by Lightman and Hager might persist for extended periods of time as suggested by Zaret and Yamamoto. Our findings suggest that this is not the case and that the majority of GR-induced changes are short-lived. Perhaps future studies can shed light on how long changes persist. However, given the slow dissociation between GR and Dex, we expect that it might be hard to dissect if persistent changes are indeed persisting in the absence of GR binding or reflect an incomplete hormone wash-out.

The objective of this study was to find out if persistent changes as observed in Ref33 are the exception or the rule not to test if the original observation is correct (importantly, another cell line was used in Ref33 which makes a 1:1 comparison impossible to begin with). We believe that we have convincingly shown that, for the cell lines we assayed, persistent changes are rare if occurring at al. Given that no convincing persistent changes were observed after a 24h washout, we think that it is very unlikely that such changes would be observable after even longer wash-out periods. We do not intend to include experiments using longer wash-out but will revise the discussion to emphasize that the lack of persistent changes we found might be specific to the cell lines we chose for our studies.

We agree that adding this percentage is a good idea as this would allow for a more quantitative comparison between the different groups. Here are the numbers:

A549 cells:

opening sites: 49%

closing: 10%

nonchanging: 18%

U2OS cells:

opening: 54%

closing: 0.2%

nonchanging: 7%

We will include this information in a revised version of the manuscript.

For the ATAC-seq experiments, we treated the dex-treated and cort-treated experiments as replicates to find candidate regions with persistent chromatin changes. For the ATAC-seq data, a site is 'persistent' if called (by MACS2, e.g. DEX vs EtOH) upon treatment and then again 24h after washout (DEX washout vs EtOH washout). For the ATAC-qPCR experiments, we performed 4 biological replicates and will perform a t-test to determine if the small difference we observe at some sites between the EtOH and washout is statistically significant. Given the overlapping error bars and the very small difference, don’t expect the difference to be significant even for these most promising candidates from our genome-wide analysis.

Indeed we did not find a mechanistic explanation for the ZBTB16-specific memory. Possible explanations are discussion in the following section of the results (page 14-15): “… Mirroring what we say in terms of chromatin accessibility, transcriptional responses also seem universally reversable with no indication of priming-related changes in the transcriptional response to a repeated exposure to GC for any gene with the exception of ZBTB16. Although several changes in the chromatin state occurred at the ZBTB16 locus, none of these changes persisted after hormone washout arguing against a role in transcriptional memory at this locus (Fig. 5). Similarly, the increased long-range contact frequency between the ZBTB16 promoter region and a GR-occupied enhancer does not persist after washout (Fig. 5e). Notably, our RNA FISH data showed that ZBTB16 is only transcribed in a subset of cells, hence, it is possible that persistent epigenetic changes occurring at the ZBTB16 locus also only occur in a small subset of cells and could thus be masked by bulk methods such as ChIP-seq or ATAC-seq. Another mechanism underlying the priming of the ZBTB16 gene could be a persistent global decompaction of the chromatin as was shown for the FKBP5 locus upon GR activation [35]. Likewise, sustained chromosomal rearrangements, which we may not capture by 4C-seq, could occur at the ZBTB16 locus and affect the transcriptional response to a subsequent GC exposure. Furthermore, prolonged exposure to GCs (several days) can induce stable DNA demethylation as was shown for the tyrosine aminotransferase (Tat) gene [71]. The demethylation persisted for weeks after washout and after the priming, activation of the Tat gene was both faster and more robust when cells were exposed to GCs again [71]. Interestingly, long-term (2 weeks) exposure to GCs in trabecular meshwork cells induces demethylation of the ZBTB16 locus raising the possibility that it may be involved in priming of the ZBTB16 gene [72]. However, it should be noted that our treatment time (4 hours) is much shorter. Finally, enhanced ZBTB16 activation upon a second hormone exposure might be the result of a changed protein composition in the cytoplasm following the first hormone treatment. In this scenario, increased levels of a cofactor produced in response to the first GC treatment would still be present at higher levels and facilitate a more robust activation of ZBTB16 upon a subsequent hormone exposure. Although several studies have reported gene-specific cofactor requirements [73], the 14 fact that we only observe priming for the ZBTB16 gene would make this an extreme case where only a single gene is affected by changes in cofactor levels……”.

**Minor Comments**

We will include a motif analysis for opening and closing sites in a revised version of the manuscript.

We will revise the label in a revised version of the manuscript as suggested.

We actually prefer the MA plots as they also provide information regarding the basemean counts for regulated genes. This allows one, for example, to see that other GR-regulated genes with similar basemean counts do not show a “memory” suggesting that the low expression level for ZBTB16 likely does not explain the observed priming.

We will include this information in a revised version of the figure.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this study, Bothe et al investigated the functional role of glucocorticoids on gene regulation and chromatin accessibility using ATAC-seq, RNA-seq data in A549 and U2OS-GR cells under different conditions. They focused on transcriptional memory of GR activation, meaning a more robust transcriptional response upon repeated hormone stimulation (transcriptional memory). A previous study reported persistent changes after more than 9 days, but here the authors focused on a 24-hour washout period which they reasoned would be more likely to reveal persistent changes. However, they found that only identified a single gene, ZBTB16, with this characteristic. The studies are well performed, but the reader is left confused as to whether the difference between the present and previous result is a timing issue. That needs to be addressed. In parallel, the authors found that chromatin accessibility was also reversible after hormone withdrawal. This was also true for the ZBTB16 gene and thus could not explain the transcriptional memory for this gene. The authors suggest that priming increases ZBTB16 output by increasing the fraction of cells responding to hormone treatment as well as by augmenting activation by individual cells. This is interesting, but the reason why the ZBTB16 gene is special is not explained. Moreover, since ZBTB16 was the only gene where hormone-induced changes were not reversible, the conclusion that "hormone can induce gene-specific changes in the response to subsequent exposures which may play a role in habituation to stressors and changes in glucocorticoid sensitivity" seems an overstatement especially since the title states that changes are universally reversible. Also the discussion ends by arguing that it is still likely that individual cells remember previous hormone exposure, even though the present paper argues strongly against that except for a single gene where the mechanism of the memory is completely unclear. This discrepancy between what "might be the case" and what the authors actually observe needs to be corrected.

Major Comments:

1. Although the authors reported that only ZBTB16 displayed transcriptional memory, would more genes emerge with less stringent cutoffs, for example Fold Change> 1.5 & adjusted p value < 0.05?
2. One question the authors should consider is whether the washout time matters. What if it were reduced to a shorter time, for example 8 or 12 hrs? This might especially alter the conclusions about dexamethasone, which Lightman and Hager have suggested to have a long half life of binding to the GR in cells.
3. The authors point out that Ref. 33 focused on persistent changes after more than 9 days, but the authors state that they focused on a 24-hour washout period which they reasoned would be more likely to reveal persistent changes. However, that was not the case, and the present findings seem to be at odds with the conclusion drawn in Ref. 33. This begs the question of whether the original report was correct and authors would have seen persistent changes (by whatever mechanism) after 9 days, or whether there almost no persistent changes at all as the present study would suggest. To address this and advance the field on this point, it is imperative that the authors do the "positive control" of repeating the protocol used in the original report, to determine if the difference is quantitative (timing) or qualitative (true discrepancy between two groups).
4. The authors state that "opening sites are often GR-occupied whereas closing sites are not occupied by GR" in Figs 1B and C. What is the fraction of opening sites with GR binding?
5. In Fig. 2C the authors show SLC9A8 as an example of a gene which maintained a reduced level of open chromatin when assessed by ATAC-seq. To "validate" this they performed ATAC-PCR, and in the results shown in Fig. 2D any differences were not found to be statistically significant. However, these are two different assays, and both have potential flaws and experimental error. Were biological replicates of ATAC-PCR performed, and if so were the differences in ATAC-seq signal between EtOH and washout statistically significant? And is this true of other genes with similar patterns, such as FKBP5, PTK2B, and others?
6. The authors suggest that priming increases ZBTB16 output by increasing the fraction cells responding to hormone treatment, but no explanation was found to explain why this happens to ZBTB16 but not all of the other GC-induced genes. This needs to be discussed.

Minor Comments

1. What motifs are enriched at the ATAC sites that open and close?
2. Fig. 1F would be improved by rephrasing the labels using terms "without site/peak" and "with site/peak". Otherwise, readers may think they are all GR peaks.
3. For Figs. 3B-3D, volcano plots are a better way to present the differentially expressed genes.
4. p values should be shown in Figs. 6C, 6D, 6F and 6G.

Significance

This is important since glucocorticoids are important hormones and drugs. A limitation of this study is that it's all in cell lines. One concern is that the conclusions differ from those in reference 33 and that will minimize the significance unless this is addressed by the authors, for example by using the 9 day protocol that was used in ref 33 to determine if those results are reproducible.

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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.

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

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.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

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.

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

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.

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

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Reply to the reviewers

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

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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.

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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

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

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Referee #1

Evidence, reproducibility and clarity

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.

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

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Referee #1

Evidence, reproducibility and clarity

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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