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Reply to the reviewers

1. General Statements [optional]

We thank the reviewers for their critical review of our manuscript. We are excited to see that the reviewers agree that we have presented high-quality data that advances the centrosome field and is worthy of publication following revision. The authors also agree with the reviewers that the data presentation requires improvement, that some experiments require additional replicates with robust statistical analyses and that a model or summary would help clarify the differences between previously published results and ours. We will address all these concerns in the revised version of our manuscript. The reviewer comments in their entirety can be found below in italic followed by our response in bold.

Considering that the manuscript was very well received we believe it makes a strong candidate for publication in eLife. In terms of editors at eLife, we believe that Anna Akhmanova and Jeremy Reiter would be very well suited to handle this manuscript.

We hope that you will concur with us that the revision plan detailed below adequately addresses the reviewers’ comments.

2. Description of the planned revisions

Reviewer 1, Major points

1. • Previous data suggested that an important role of TRIM37 was to limit accumulation of CEP192 levels, yet here CEP192 levels appeared unchanged in TRIM37 knockout cells that stably express wild-type or RING domain mutant TRIM37. However, in agreement with previous work, transient expression of TRIM37 reduced CEP192 levels along with those of other PCM and centriole components in an E3-dependent manner. These data are rather confusing in light of the literature, and the current report does not really deal with these discrepancies but to me they suggest that high levels of TRIM37 can target multiple centrosome components for degradation, but this may be an experimental artefact.* We agree that acutely overexpressed TRIM37 results in decreased CEP192 levels and is consistent with published results. We also provide evidence that CEP192 levels are not correspondingly increased in the absence of TRIM37, nor are they decreased in a cell line that stably overexpresses FLAG-BirA TRIM37. This suggests that the decreased CEP192 (and PCNT and CEP120) after acute overexpression of TRIM37 might be short-lived or a consequence of overexpression. We will discuss this possibility more clearly in the revised mansucript. In addition, we will perform Western blots for TRIM37 in wild type cells, cells stably expressing FLAG-BirA TRIM37 and cells induced to express TRIM37-3xFLAG to more directly compare the amount of TRIM37 present in these cell lines.

• The choice of cells for particular experiments is not always stated or explained. For instance, in Figure 3A: Trim37 KO pool used while in Figure 3B TRIM37 single KO. These are then combined with both transient and stable expression of TRIM37 mutants.*

We apologize for this and will clarify the choice of cell lines in the results section. Importantly, because some of our results challenge previously published reports, we performed critical experiments using multiple cell lines. For example, we show that centrinone B-induced growth arrest is independent of TRIM37 E3 ligase activity using a single RPE-1 TRIM37-/- clone, an RPE-1 TRIM37-/- pool and an A375 TRIM37-/- pool. We feel this is a highlight of our work and this new data will be included in the revised version of the manuscript and will be emphasized.

• Two different concentrations (200 nM and 500 nM) of centrinone were used to compare responses of too many or no centrosomes in RPE1 and A375 . While these concentrations result in centrosome amplification (200 nM) and loss (500 nM) in RPE1 cells, the phenotypes seem much less clear-cut in A375 cells. At 200nM 70% of cells have 0 or 1 centrioles (~35% each category) and only about 15% have centrosome amplification, whereas centrosome amplification occurs in 30% of RPE1 with 0-1 centrioles seen in fewer than 10% (Figure 4 - figure supplement 1H). Hence the different outcomes of centrinone treatment makes conclusions about cell-type specific responses difficult. This difference may be due to differences in drug uptake/efflux, PLK4 activity or in expression of other components of these pathways. In fact, 167nM centrinone B in A375 cells would have been a much closer match to the 200nM treatment of RPE-1. These points should be discussed as they impact the conclusions.*

The reviewer rightly points out that the response to centrinone appears to differ between cell types, as shown previously (Meitinger et al., 2020 and Yeow et al., 2020), and that this difference may impact our conclusions. Although we don’t think that the major conclusions drawn will change, we will discuss these caveats within the results and discussion of the manuscript.

• I find the different outcomes of stable versus acute expression of TRIM37 ligase mutant confusing. Here, stable expression of TRIM37 ligase mutant increases mitotic length compared to that of TRIM37 wild-type, which contradicts a recent report by (Meitinger et al. 2021). What could be the potential reason for these differences? *

It is unclear why we obtain results that differ from Meitinger et al. We are using similar cell lines (RPE-1 hTert vs. RPE-1 hTert Cas9) with similar TRIM37 constructs (TRIM37-3xFLAG) that are induced in similar ways (both are doxycycline inducible but using different systems). For our experiment, we used a single TRIM37 KO clone. As an independent validation, we will repeat this experiment using our TRIM37 KO pools in both RPE-1 and A375 cells and discuss these results and implications.

What could be the mechanism for TRIM37 action in regulating spindle assembly/mitotic duration and cell proliferation upon centrosome loss? How do those acentrosomal MTOCs form that decrease mitotic duration and promote proliferation?

These are insightful questions that we feel lie at the heart of TRIM37 function. Current models posit that in the absence of TRIM37, PLK4 condensates form and are required to nucleate ectoptic accumulations of PCM components (ex. CEP192) that facilitate mitosis (Meitinger et al. 2020). A number of our findings are not consistent with this model. First, PLK4 is detected in the Cenpas/condensates only using a single antibody (Wong et al., 2015) (two other antibodies have been reported to be used (Sillibourne et al., 2010, Moyer et al., 2015) and we have used another (Millipore MABC544 clone 6H5) - none of these three detect PLK4 at the condensates). Additionally, the PLK4 signal observed is not sensitive to PLK4 siRNA (Balestraet al. 2021, Figure 4 – figure supplement 1I). In our manuscript we also provide evidence that overexpressed PLK4-3xFLAG cannot be detected (using PLK4 or FLAG antibodies) at these strucures. Moreover, our experiments using TRIM37 mutants show that Cenpas formation and ectopic PCM assembly are mechanistically distinct; Cenpas are not resolved after expression of TRIM37 C18R, yet ectopic PCM structures are suppressed (Figure 5E and G). Our data do, however, suggest that the ability to form ectopic PCM structures is inversely correlated to growth arrest activity (i.e. cells that form ectopic PCM fail to arrest). How these structures form and how they affect growth arrest are still critical, open questions. We will discuss these possibilities further in the revised manuscript.

Do the authors find a difference in the % of cells expressing TRIM37 mutants upon stable or acute expression? This part needs a better summary, and again a table would help. I also wonder about protein expression levels; wild-type FB-TRIM37 seems to be expressed at much lower levels than the mutants in Figure 5B.

The differences in overall abundance are not due to heterogenous expression within the population. The TRIM37 mutants are expressed in all cells after stable and acute expression. We will provide quantification of immunofluorescence images and statistics to show this. TRIM37 mediates its own degradation in an E3-dependent manner (Meitinger et al. 2021, Figure 3f). Our results are consistent with this as the TRIM37 C18R and TRIM37 __DRING mutants have a higher overall abundance compared to TRIM37 or TRIM37 D__505-709. These experiments are ongoing and we will discuss this further in the revised manuscript and provide a summary table.

• Other means of centrosome depletion (Cenpj, SAS6 etc) would have been useful to include in the manuscript in support of E3 ligase dependent and independent roles of TRIM37. It is not essential to perform these experiment but if data are available, including these would improve the paper. *

We will generate new data using a double TRIM37 KO, SASS6 KO line to address TRIM37 ligase-dependent and -independent functions.

• The authors show that TRIM37 regulates PLK4 phosphorylation and that this modification could only be observed in HEK293T and not in RPE1. Why would there be a difference between HEK293 and RPE1?*

We will address this by surveying a panel of cell lines to determine if there any cell type dependent differences in TRIM37 modification. Any potential differences will be addressed in the discussion.

• Statistical analysis for graphs should be included. Figure 5 is ok but graphs in Figures 3, 4, 6, 7 would benefit.*

This point is well taken. In the revised manuscript, we will ensure that all experiments are performed in biological triplicate and that proper statistical analyses are included to support our conclusions.

• The authors characterise TRIM37 localisation. They detect it at centrosomes (as shown by Yeow et al 2021) and more specifically at the PCM, but apparently the signal is not present in all cells. They should also provide a quantification of the % of cells with centrosomal TRIM37 signal and compare this to cells expressing Flag-tagged Trim37. The specificity of the antibody signal using TRIM37-/- should be confirmed. *

We will perform immunofluorescence experiments using wild type and TRIM37-/- cells to demonstrate the specificity of the antibody signal. We will also provide a more detailed analysis regarding TRIM37 localization noting 1) the number of cells with centrosomal TRIM37 2) cell cycle correlation with centrosomal TRIM37 and 3) a comparison with FLAG-BirA tagged TRIM37.

Reviewer 1, Minor points

1.Page 3: "A recent screen for mediators of supernumerary centrosome-induced arrest identified PIDDosome/p53 and placed the distal appendage protein ANKRD26 within this pathway [31]". It appears that the reference for Burigotto et al. is missing.

This reference will be inserted.

2.Page 6: The authors state that: TP53BP1, USP28 and CDKN1A are also suppressors in the Nutlin-3a screen and suggest that they act in a general p53 pathway. However Meitinger et al (2016) showed that depletion of TP53BP1 or USP28 did not affect the upregulation of p53 and p21 upon Mdm2 inhibition.

Our data is consistent with previous reports that TP53BP1 and USP28 are required for cell arrest after Nutlin-3a treatment (Cuella-Martin R et al. 2016). We will discuss possible explanations for the results observed by Meitinger et al.

3.Page 9: "First, we performed live cell imaging to measure mitotic length in cells grown in centrinone". For consistency the authors should say centrinone B here as wellI

We will change the text to indicate using centrinone B.

4.Page 9: "Cells lacking TRIM37 suppressed the growth arrest from 150 to 500 nM centrinone B in RPE-1 and 167 to 500 nM in A375 cells". The growth data for the A375 cells seem to be missing from the figures.

We refer to Figure 4D and Figure 4 – figure supplement 1G that contain the RPE-1 and A375 growth data, respectively. We will modify the text to more clearly refer to the data.

5.Page 10: "Our results confirmed that PLK4 and TRIM37 form a complex in RPE-1 cells (Figure 3G)" It appears the authors referred to the wrong figure, it should be Figure 4B.

Our apologies. The correct figure reference will be used.

6.Figure 1C: The nuclear p53 signal is not apparent with 500 nM centrinone B in the exemplary cells. Did the authors use thresholding to quantify p53/p21 positive cells?

The p53 staining in centrinone-treated cells is somewhat variable. To quantify the data, we used automated image analysis and set a cut off based on p53 intensity in DMSO-treated cells to indicate p53-positive cells. To improve the figure we will repeat the experiment and use a lower magnification image to show a more representative field of cells stained for p53. The quantification pipeline will be better explained in the methods section.

7.Figure 4D and Figure 4 - Figure supplement 1G: The graph is misleading and should not be presented as a continuous line.

We are sorry that the reviewer finds the graph misleading. We will change the way this data is presented to make it easier to understand and to facilitate indicating statistical differences. Instead of a scatter plot of all the data, we will present the data as individual boxplots at each centrinone B concentration with statistical differences indicated. We hope this will address any confusion regarding these data.

8.Figure 5A and C: A direct and statistical comparison mitotic timing upon expression different Trim37 mutants to wildtype and trim37-/- cells is missing

In Figure 5A we compare RPE-1 WT to TRIM37-/- at each centrinone B concentration and within each line we compare each centrinone B concentration to DMSO. Perhaps we do not understand the reviewer’s concern here, but we do not think any comparisons are missing from this panel. In Figure 5C, we compare the mitotic lengths between cell lines expressing TRIM37 WT or TRIM37 C18R since we focus on the requirement for the E3 ligase activity of TRIM37. For this experiment we did not include a wild-type control, but we will perform statistical analyses between control cells expressing FLAG-BirA and those expressing FB-TRIM37 WT or FB-TRIM37 C18R. We hope this addresses this concern.

9.Figure 6B: A loading control/Ponceau staining is missing as well as the quantification of protein levels

This experiment will be repeated for proper quantification and we will include a loading control for our representative results.

10.Figure 6D: It is unclear if the centrosomal signal intensity was quantified in interphase or mitotic cells

The centrosomal signal was quantified in mitotic cells only. This results and figure legend will be updated to more clearly indicate this.

11.Figure 7C: A loading control/Ponceau staining is missing

The experiment will be repeated and a sample will be taken prior to immunoprecipitation to indicate the input amounts for each sample.

12.Figure 2 - figure supplement 2F and G: It would help if the authors could highlight the cell line, e.g. RPE-1 (F) or A375 (G) in the venn diagrams.

In Figure 2 – figure supplement 2G we highlight the genes found in RPE-1 and A375 screens only in the overlap of the Venn diagram using font colour. We will colour code the hits from each cell line in panels (F) and (G). We thank the reviewer for this suggestion.

13.Figure 4 - figure supplement 1E: it appears that the BirA antibody gives only an unspecific signal. It would be useful to show if the different TRIM37 variants are able to localise to the centrosomes. Furthermore it appears that centrosomes are missing in the C18R and 505-709 variants. It would be useful if the authors quantify centrosome numbers upon expression of different Trim37 variants as shown in Figure 4 - figure supplement 1. To make the identification of the cell easier it would help to include a DNA signal or indicate the outline of the cell.

The anti-BirA antibody does give a slightly diffuse signal, although we disagree that it is unspecific considering that the BirA signal is only observed in cells expressing FLAG-BirA alone or BirA fusion proteins.

We agree with this reviewer that we did not make any statements about the centrosomal localization of the TRIM37 mutants. We will re-analyze our images to quantify relative centrosomal localization of these proteins. The images as displayed in this Figure panel appear to be somewhat confusing to the reviewer. In terms of scale, only a small portion of the cell surrounding the centrosome is shown, therefore a nuclear or cell outline cannot be displayed on these images. In each image a centrosome is present, even in the C18R and 505-709 samples. We will show images of entire cells with insets to highlight the region surrounding the centrosome.

14.The generation of stable and dox-inducible cell lines is missing in the material and methods

We apologize for this omission. This information will be added.

Reviewer 2, Major points

1. • The centrosomal localization of endogenous TRIM37 should be validated by comparing control and knockout/knockdown cells.* We will perform these experiments as outlined in response to Reviewer 1, Major point 8.

• Some of the quantifications are derived from only two experiments and in many cases no statistical testing was done. The authors should test the observed effects and add extra replicates to make the data more robust, where required. *

We will ensure experiments are performed in biological triplicate and that appropriate statistical analyses are performed (see comment to Reviewer 1, Major point 7)

• Fig. 5 supplements: panels showing effects on marker proteins in cells by IF lack quantification of the claimed effects. Without providing some type of quantifications for key findings, it is unclear how strong or penetrant the effects are.*

Quantification and statistical testing will be performed for these experiments.

Reviewer 2, Minor points

I would suggest a final, summarizing schematic that illustrates the main findings in a cartoon/flow chart manner.

We will improve the discussion of our main findings as well as provide a model/table of comparisons to improve the clarity of our manuscript.

1. • Please revise incorrect abstract sentence: "We identify TRIM37 as a key mediator of growth arrest when PLK4 activity is partially or fully inhibited but is not required for growth arrest triggered by supernumerary centrosomes." __In our screens, we find that TRIM37 is required for growth arrest after treating cells with 200 and 500 nM centrinone B. Treatment of cells with 200 nM centrinone B causes centriole overduplication and our initial hypothesis was that centriole overduplication alone is inducing growth arrest. To test this in a parallel manner, we also overexpressed PLK4 to induce centriole overduplication. Surprisingly, but consistent with recently published results (Evans et al*., 2020), TRIM37 was not required for growth arrest after PLK4 overexpression. Thus, TRIM37 is required for growth arrest after 200 nM centrinone treatment, but not PLK4 overexpression, yet both of these conditions induce centriole overduplication. This concept will be highlighted, discussed and clarified in the text. We will change the abstract sentence to ‘We identify TRIM37 as a key mediator of growth arrest when PLK4 activity is partially or fully inhibited, but it is not required for growth arrest after PLK4 overexpression’__.

Please also see similar comment to Reviewer 3, Major point 1.

• In various figures and supplements showing centrosome and condensates/Cenpas, these are very difficult to distinguish due to their small size. I suggest to magnify regions of interest and/or add arrowheads in different colors marking the specific structures.*

This comment is similar to Reviewer 1, Minor point 13. We will use coloured arrowheads to indicate different structures. Where possible, we will use magnified regions to improve clarity.

• Fig. 2A: What is the purpose of the schematics on the right of panel A? The labels in the graph are unreadable and the network diagram without any labels is also not very useful. This could be removed. *

The schematics on the right indicate a ‘generic analysis’ using the NGS sequencing data. We agree it is not essential and it will be removed.

• Fig. 2B: The network presentation is not very easy to read. What are the functional groups/pathways here? The clusters should be labeled accordingly. What is the meaning of the different sizes of the circles? Maybe key interactions (e.g. TRIM37) could be indicated in a different color shade to highlight these? *

In our figure we tried to highlight 1) the connectivity among screening conditions and 2) complexes that were identified by the screens. In our figure, each node (other than the six hub nodes that denote a screen condition) represents a hit from the screens. Thus, the nodes are connected by edges only to the screening conditions, not to each other. In this scenario, highlighting TRIM37 ‘interactions’ would only highlight the screening conditions for which TRIM37 was a hit (200 nM RPE-1, 500 nM RPE-1, 200 nM A375, 500 nM A375). We could try to overlay functional enrichment data on the graph, but this data is presented separately in Figure 2 – figure supplement A-D. The large circles represent hits found in previous screens and is indicated in the legend. Given the challenges of this figure we will modify it to improve its clarity.

Reviewer 3, Major points

1. • The presentation throughout the manuscript sometimes made it difficult to follow exactly what the authors meant when they referred to the various doses of Centrinone used in their experiments-often using the terms "low" or "high" without specifying exactly what they mean. In Figure 1A, for example, they present a growth inhibition curve using a log10 scale of Centrinone concentration, and they conclude that growth was inhibited "at concentrations above 150nM, with full inhibition observed at concentrations greater than 200nM". I presume this is just sloppy language, as it appears that growth is significantly inhibited at 150nM and full growth inhibition is achieved at 200nM. However, in Figure 4D, the authors show another growth inhibition curve (this time presented on a linear scale) where significant growth inhibition is seen well below 100nM and full inhibition appears to be achieved at ~125nM. The discrepancy between these experiments is not noted, nor any reason for it explained. We agree with the reviewers and apologize for using ‘low’ and ‘high’ as they are ambiguous. We will ensure that we refer specifically to each concentration of centrinone B used (ex. 50 nM, 150 nM etc.). The comparison between Figure 1A and Figure 4D is not straightforward. The experiments presented were performed approximately 6 years apart and in slightly different ways. As reviewer 3 indicates, Figure 1A is presented in a log scale; this makes it difficult for the reader to determine the exact concentrations of centrinone B used. For this panel, we used, 0 (DMSO), 10, 30, 75, 165, 200 and 500 nM centrinone B. For Figure 4D, we used 0, 50, 125, 150, 167, 200 and 500 nM. The only point that might be anomalous is 75 nM in Figure 1A. We do see approximately 25% inhibition using 50 nM centrinone B in Figure 4D, but no inhibition using 75 nM in Figure 1A. We can offer two explanations for this discrepancy. First, we noticed small deviations in the potency of centrinone B batches. Second, for Figure 1A, cells were assayed using a passaging assay where they are continuously plated, counted and re-seeded. Cells in Figure 4D were assayed using a clonogenic assay where cells are plated at low density and allowed to grow over the course of approximately two weeks. It is possible that a combination of these factors led to the highlighted discrepancy. We feel that the discrepancy is a minor one and we propose the following as a solution. We will present the growth data in Figure 1A as a scatter / box plot using only 200 and 500 nM centrinone B since these are the drug concentrations we use for the screen conditions and the key conclusions are derived only from these concentrations (i.e. both concentrations result in p53-dependent growth arrest where centrioles are overduplicated after 200 nM centrinone B, while centrioles are lost after treatment with 500 nM). We hope that this explanation and changes satisfy the reviewers.

While discrepancies such as this may seem trivial, they make it hard to interpret some of the authors conclusions. For example, in their initial screen, the "low" dose of Centrinone (200nM) leads to centriole amplification and genes that block centriole duplication or PIDDosome function (which normally signals the presence of extra centrioles) are required for the growth arrest triggered by this concentration of the drug (Figure 1B). To me, this suggests that centriole amplification is required for this growth arrest at 200nM. However, when the authors test a more graded series of concentrations they conclude "excess centrioles might not be the trigger for this arrest at low Centrinone B concentrations". I assume they are using "low" here to indicate concentrations at or below 150nM (even though they use low to mean 200nM in their initial screen)? In the Discussion, they state that TRIM37 is "required for the growth arrest in response to partially or fully inhibited PLK4, but this activity was independent of the presence of excess centrioles". Again, it is not clear to which experiments they are referring when they talk about "partially" or "fully" inhibited PLK4, but, if this is correct, then why are genes required for centriole duplication and PIDDosome function identified in their initial screen as being required for the growth arrest at 200nM but not 500nM? Do they consider 200nM to be fully inhibiting PLK4? *

We observed that cells arrested after treatment with either 200 or 500 nM centrinone B. Additionally, we observed centriole over-duplication after 200 nM but centriole loss at 500 nM. Our initial hypothesis was therefore that either centriole overduplication or loss resulted in growth arrest. Our subsequent results with TRIM37 caused us to question this simple interpretation. To determine if centriole overduplication caused by 200 nM centrinone B triggers growth arrest in this case, we induced centriole overduplication by overexpressing PLK4 and, surprisingly, TRIM37 was not required for growth arrest in these conditions, similar to that observed by __Evans et al., 2020. Thus, we have two conditions where centriole overduplication is observed where the growth arrest in only one condition is dependent on TRIM37. This is an important difference that we will better highlight in our revised manuscript. We will also present a better model and/or table outlining our most salient results. Briefly, it is thought that partially inhibited PLK4 blocks its own auto-phosphorylation and therefore blocks its degradation. The overall abundance of PLK4 therefore increases under these conditions and overduplication occurs. In our hands, we consider PLK4 to be partially inhibited in RPE-1 or A375 cells at any concentrations of centrinone B at 200 nM or lower.__

Please also see similar comment to Reviewer 2, Minor point 1.

Presumably it will only require textual changes to address this point, but it is hard to assess the broader significance of the paper until these points are clarified: is the main point of this paper that the cells response to Centrinone treatment is complicated and the role of TRIM37 equally so; or, is there a narrative that leads to a clear hypothesis that can explain these surprising findings?

We don’t currently have a model that explains all the results we observe with TRIM37. We have data that is consistent with some previously published results and data that challenges some of these recent reports. The current model suggests that TRIM37 E3-dependent remodeling of CEP192 underlies its growth arrest activity after centriole loss. Importantly, we find that TRIM37 supports growth arrest in an E3-ligase-independent manner. We will discuss this further in our revised manuscript, as well as providing additional hypotheses based on our other observations of TRIM37 function.

• It seems a striking omission that the authors show that p53 and p21 are induced by 200nM and 500nM Centrinone (Figure 1D), but they don't assay these proteins at any concentration lower than this. Perhaps they are saving this data for a subsequent manuscript, but the authors certainly seem to draw conclusions from several experiments they perform at concentrations below 200nM, so they should at least explain why they don't assay p53 and p21 status in these experiments. *

We apologize for not including this data in the original version of the manuscript. It will be included in the revised version.

Reviewer 3, Minor points

1. • In the abstract the authors claim that the way in which altered centrosome numbers cause a p53-dependent growth arrest is evolutionarily conserved. This is misleading, as it implies that the loss and gain of centrosomes trigger the same arrest (which is probably not correct), and most of the data to date suggests that flies and worms (two popular models for centrosome research) do not have such a growth-arrest pathway.* This is a good point. We will modify this statement to indicate that p53-dependent arrest is confined to mammalian cells: “Altered centrosome numbers cause a p53-dependent growth arrest in both mouse and human cells through mechanisms that are still poorly defined”.

Reviewer 3, comment in ‘significance’

I could not discern, however, whether one could draw any broader conclusions than this, in part due to the presentation problems described above. Moreover, in the abstract the authors propose that altering PLK4 activity alone is sufficient to signal growth arrest. This would be an important conclusion, and I presume this refers to the very low dosage Centrinone experiments that trigger growth arrest without altering centrosome numbers and which does not require TRIM37? If so, this arrest is poorly characterised here and will be the subject of a future investigation, so it seems to strange to have this as a major conclusion in the abstract.

We agree. As reviewer 3 points out, based on our findings we hypothesize that altered PLK4 activity could itself signal growth arrest. As this is not supported experimentally, we will remove it from the abstract and discuss this tantalizing possibility within the discussion.

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

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

Most of the experiments are currently ongoing and the preliminary results we have obtained discussed in the previous section. The revised manuscript will be modified to address each and every concern of the three reviewers as detailed above.

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

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

We will carry out all the experiments requested by the reviewers as detailed above.

References

Balestra FR et al., TRIM37 prevents formation of centriolar protein assemblies by regulating Centrobin. Elife. 2021 Jan 25

Cuella-Martin R et al., 53BP1 Integrates DNA Repair and p53-Dependent Cell Fate Decisions via Distinct Mechanisms. Mol Cell. 2016 Oct 6;64(1):51-64

Evans LT et al., ANKRD26 recruits PIDD1 to centriolar distal appendages to activate the PIDDosome following centrosome amplification. EMBO J. 2021 Feb 15;40(4)

Meitinger F et al., TRIM37 controls cancer-specific vulnerability to PLK4 inhibition. Nature. 2020 Sep;585(7825):440-446

Moyer TC et al., Binding of STIL to Plk4 activates kinase activity to promote centriole assembly. J Cell Biol. 2015 Jun 22;209(6):863-78

Sillibourne JE et al.,Autophosphorylation of polo-like kinase 4 and its role in centriole duplication. Mol Biol Cell. 2010 Feb 15;21(4):547-61

Wong YL et al., Cell biology. Reversible centriole depletion with an inhibitor of Polo-like kinase 4. Science. 2015 Jun 5;348(6239):1155-60

Yeow ZY et al., Targeting TRIM37-driven centrosome dysfunction in 17q23-amplified breast cancer. Nature. 2020 Sep;585(7825):447-452

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this manuscript, Tkach et al. analyse the molecular pathways that lead to the growth arrest of either RPE-1 or A375 cells in response to varying doses of the PLK4 inhibitor Centrinone B (hereafter Centrinone). They show that both 200nM and 500nM Centrinone cause a strong growth arrest, but the lower concentration actually leads to centrosome amplification, while the higher concentration leads to centrosome loss. They identify the Ubiquitin E3 ligase TRIM37 as a key mediator of the growth arrest at both drug concentrations, although they confirm previous findings that TRIM37 is not required for the growth arrest induced by the supernumary centrosomes that are formed when PLK4 is overexpressed. Perhaps most importantly, the authors test the ability of various mutated forms of TRIM37 to function in the growth arrest induced by Centrinone treatment, and they conclude that, surprisingly, the E3 ligase activity of TRIM37 is not required for this growth arrest.

The experiments presented here are generally of a high quality, although I found some aspects of the presentation a little confusing (as detailed below).

Major Comments:

1. The presentation throughout the manuscript sometimes made it difficult to follow exactly what the authors meant when they referred to the various doses of Centrinone used in their experiments-often using the terms "low" or "high" without specifying exactly what they mean. In Figure 1A, for example, they present a growth inhibition curve using a log10 scale of Centrinone concentration, and they conclude that growth was inhibited "at concentrations above 150nM, with full inhibition observed at concentrations greater than 200nM". I presume this is just sloppy language, as it appears that growth is significantly inhibited at 150nM and full growth inhibition is achieved at 200nM. However, in Figure 4D, the authors show another growth inhibition curve (this time presented on a linear scale) where significant growth inhibition is seen well below 100nM and full inhibition appears to be achieved at ~125nM. The discrepancy between these experiments is not noted, nor any reason for it explained.

While discrepancies such as this may seem trivial, they make it hard to interpret some of the authors conclusions. For example, in their initial screen, the "low" dose of Centrinone (200nM) leads to centriole amplification and genes that block centriole duplication or PIDDosome function (which normally signals the presence of extra centrioles) are required for the growth arrest triggered by this concentration of the drug (Figure 1B). To me, this suggests that centriole amplification is required for this growth arrest at 200nM. However, when the authors test a more graded series of concentrations they conclude "excess centrioles might not be the trigger for this arrest at low Centrinone B concentrations". I assume they are using "low" here to indicate concentrations at or below 150nM (even though they use low to mean 200nM in their initial screen)? In the Discussion, they state that TRIM37 is "required for the growth arrest in response to partially or fully inhibited PLK4, but this activity was independent of the presence of excess centrioles". Again, it is not clear to which experiments they are referring when they talk about "partially" or "fully" inhibited PLK4, but, if this is correct, then why are genes required for centriole duplication and PIDDosome function identified in their initial screen as being required for the growth arrest at 200nM but not 500nM? Do they consider 200nM to be fully inhibiting PLK4?

Presumably it will only require textual changes to address this point, but it is hard to assess the broader significance of the paper until these points are clarified: is the main point of this paper that the cells response to Centrinone treatment is complicated and the role of TRIM37 equally so; or, is there a narrative that leads to a clear hypothesis that can explain these surprising findings?

1. It seems a striking omission that the authors show that p53 and p21 are induced by 200nM and 500nM Centrinone (Figure 1D), but they don't assay these proteins at any concentration lower than this. Perhaps they are saving this data for a subsequent manuscript, but the authors certainly seem to draw conclusions from several experiments they perform at concentrations below 200nM, so they should at least explain why they don't assay p53 and p21 status in these experiments.

Minor comments:

In the abstract the authors claim that the way in which altered centrosome numbers cause a p53-dependent growth arrest is evolutionarily conserved. This is misleading, as it implies that the loss and gain of centrosomes trigger the same arrest (which is probably not correct), and most of the data to date suggests that flies and worms (two popular models for centrosome research) do not have such a growth-arrest pathway.

Significance

Significance and comparison to existing literature:

The question of how centrosome loss or amplification leads to senescence or apoptosis in many cell types is currently a hot topic, and TRIM37 has previously been identified as a potentially important player-most recently in two high-profile papers from the Oegema/Loncarek (Meitinger et al, Nature 2021) and Holland/Chapman (Yeow at al., Nature 2021) labs. In these papers, TRIM37 is shown to be overexpressed in certain cancer cells, where it appears to degrade PCM components (most notably Cep192) to prevent the formation of ectopic spindle poles that help to ensure mitotic fidelity in these abnormal cells. Moreover, mutations in TRIM37 cause Mulibrey nanism, which has recently been shown to be associated with the formation of ectopic Centrobin-dependent PCM condensates (Balestra et al., eLife 2021; Meitinger et al., JCB, 2021).

This manuscript makes an important contribution to this area, and it will be of considerable interest to researchers in several fields (most obviously the centrosome, but also ubiquitin ligase, cancer and Mulibrey fields). In its current form, this contribution is largely to illustrate that treating cells with Centrinone (which is widely used by many centrosome researchers) triggers a complex cellular response that varies with drug dosage, and that the role of TRIM37 in triggering this response also appears to be surprisingly complicated. These are significant points that are of sufficient importance to warrant publication.

I could not discern, however, whether one could draw any broader conclusions than this, in part due to the presentation problems described above. Moreover, in the abstract the authors propose that altering PLK4 activity alone is sufficient to signal growth arrest. This would be an important conclusion, and I presume this refers to the very low dosage Centrinone experiments that trigger growth arrest without altering centrosome numbers and which does not require TRIM37? If so, this arrest is poorly characterised here and will be the subject of a future investigation, so it seems to strange to have this as a major conclusion in the abstract.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The study by Tkach et al. investigates the molecular basis of the previously described, p53-dependent growth arrest that is triggered by manipulation of PLK4 kinase activity, a master regulator of centrosome biogenesis. To address this, they use CRISPR/Cas9 screening in human cell lines, gene-specific knockout and rescue experiments, and biochemical interaction assays. As in previously conducted similar screens they identify the E3 ligase TRIM37 as a key mediator of growth arrest after PLK4 inhibition, but not growth arrest induced by increased centrosome number. Importantly, contrary to suggestions in previous studies, they find that TRIM37 function in growth arrest is independent of E3 ligase function, but may involve regulation of PLK4.

Major comments:

Overall, I found the key conclusions convincing, assuming the claimed effects are significant. In this regard, some data requires quantification and some of the quantifications may require additional replicates.

1) The centrosomal localization of endogenous TRIM37 should be validated by comparing control and knockout/knockdown cells.

2) Some of the quantifications are derived from only two experiments and in many cases no statistical testing was done. The authors should test the observed effects and add extra replicates to make the data more robust, where required.

3) Fig. 5 supplements: panels showing effects on marker proteins in cells by IF lack quantification of the claimed effects. Without providing some type of quantifications for key findings, it is unclear how strong or penetrant the effects are.

Minor comments:

Overall, I felt that the presentation of the data can be improved. After reading the abstract, it was not clear at all to me, what message the authors want to convey, also in comparison to previous work. In particular the final part of the abstract should be improved. The results part is well written, but may still be improved, by providing more summarizing statements that extract the key conclusion from particular experiments and by explaining better why particular experiments were done. The specific rationale may be clear to expert readers but less so to non-experts. Only after reading the discussion, the findings and how they relate to previous work became clearer. I would suggest a final, summarizing schematic that illustrates the main findings in a cartoon/flow chart manner.

1) Please revise incorrect abstract sentence: "We identify TRIM37 as a key mediator of growth arrest when PLK4 activity is partially or fully inhibited but is not required for growth arrest triggered by supernumerary centrosomes."

2) In various figures and supplements showing centrosome and condensates/Cenpas, these are very difficult to distinguish due to their small size. I suggest to magnify regions of interest and/or add arrowheads in different colors marking the specific structures.

3) Fig. 2A: What is the purpose of the schematics on the right of panel A? The labels in the graph are unreadable and the network diagram without any labels is also not very useful. This could be removed.

4) Fig. 2B: The network presentation is not very easy to read. What are the functional groups/pathways here? The clusters should be labeled accordingly. What is the meaning of the different sizes of the circles? Maybe key interactions (e.g. TRIM37) could be indicated in a different color shade to highlight these?

Significance

While the authors start out by essentially reproducing results from previously conducted screens, which may seem to be of limited novelty, the current work reaches conclusions that differ in important aspects from those in previous studies. Moreover, the current work nicely compares in different cell backgrounds PLK4 partial inhibition (extra centrosomes), full inhibition (less/no centrosomes), and p53 pathway inhibition, to obtain an integrated view of the mechanisms involved in growth arrest and tease apart molecular requirements. The results challenge some of the conclusions from previous studies, including high-profile papers where this pathway has been identified as a potential target for cancer treatment. For these reasons I consider this very important work.

My expertise is in centrosome biology and microtubule organization including mitotic spindle assembly.

Referee Cross-commenting

Hi everyone,

Overall it seems that we all agree that this is an important study. However, as noted by several comments, the presentation definitely needs to be improved and the new findings need to be highlighted better and contrasted with previous studies. I had relatively few major concerns, but, after reading the other reviews, I found the additional comments also important and useful.

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Referee #1

Evidence, reproducibility and clarity

Centrosome loss and gain both elicit a p53-dependent cell cycle arrest but the molecular pathways involved are still not fully understood. To address this question the Pelletier lab performed several genome wide CRISPR screens using two different concentrations of centrinone that cause centrosome amplification (low) or loss (high) in RPE1 and A375 cells. In order to distinguish between pathways that act by regulating p53 levels in cells vs those that mediate p53 response to abnormal centrosome numbers, they also performed a screen in cells where p53 levels were artificially elevated by Nutlin treatment. The top hits from the low/high centrinone screen confirmed previous results from other groups, highlighting the importance of the 53BP1/USP28/p53 complex and PIDDisome/ANKR26 complex in the cell cycle response. TRIM37 was shared between both centrinone conditions, while being absent from the Nutlin screen, and thus the authors focused their analysis on the function of TRIM37. Overall the data quality and presentation are both good and the manuscript reads well. The Crispr-Cas9 screens have been performed to a high standard and it is reassuring that the same candidates emerge as from previous screens focusing on centrosome loss and gain.

TRIM37 has been the subject of several high profile papers over the past year. This current manuscript has the potential to clarify some of the outstanding questions but in its present form the manuscript brings more confusion than clarity to his area of research. Although the authors conduct a careful analysis of TRIM37 function, unless someone is a die-hard specialist, it is difficult to follow what is already known, what the authors find and how or why their data fits/contradicts previous work. The key observations are that i) TRIM37 may not actually control CEP192 levels (unless overexpressed transiently), ii) its E3 ligase activity and its binding to PLK4 are independent of its ability to promote growth arrest upon centrinone treatment, iii) its influence on mitotic duration is independent of its E3 activity or its role in growth arrest upon centrinone treatment. The result that a TRIM37-dependent growth arrest may also exist without increased mitotic duration is another interesting finding, as is the link between TRIM37 and condensates of centrosomal proteins. Including a table that summarises which roles of TRIM37 require PLK4 binding, E3 ligase activity etc would be useful not only to non-specialists. Some of the data contradicts current models for TRIM37 function in growth suppression, so the authors should consider showing a revised model, too.

Major Points:

1. Previous data suggested that an important role of TRIM37 was to limit accumulation of CEP192 levels, yet here CEP192 levels appeared unchanged in TRIM37 knockout cells that stably express wild-type or RING domain mutant TRIM37. However, in agreement with previous work, transient expression of TRIM37 reduced CEP192 levels along with those of other PCM and centriole components in an E3-dependent manner. These data are rather confusing in light of the literature, and the current report does not really deal with these discrepancies but to me they suggest that high levels of TRIM37 can target multiple centrosome components for degradation, but this may be an experimental artefact.
2. The choice of cells for particular experiments is not always stated or explained. For instance, in Figure 3A: Trim37 KO pool used while in Figure 3B TRIM37 single KO. These are then combined with both transient and stable expression of TRIM37 mutants.
3. Two different concentrations (200 nM and 500 nM) of centrinone were used to compare responses of too many or no centrosomes in RPE1 and A375 . While these concentrations result in centrosome amplification (200 nM) and loss (500 nM) in RPE1 cells, the phenotypes seem much less clear-cut in A375 cells. At 200nM 70% of cells have 0 or 1 centrioles (~35% each category) and only about 15% have centrosome amplification, whereas centrosome amplification occurs in 30% of RPE1 with 0-1 centrioles seen in fewer than 10% (Figure 4 - figure supplement 1H). Hence the different outcomes of centrinone treatment makes conclusions about cell-type specific responses difficult. This difference may be due to differences in drug uptake/efflux, PLK4 activity or in expression of other components of these pathways. In fact, 167nM centrinone B in A375 cells would have been a much closer match to the 200nM treatment of RPE-1. These points should be discussed as they impact the conclusions.
4. I find the different outcomes of stable versus acute expression of TRIM37 ligase mutant confusing. Here, stable expression of TRIM37 ligase mutant increases mitotic length compared to that of TRIM37 wild-type, which contradicts a recent report by (Meitinger et al. 2021). What could be the potential reason for these differences? What could be the mechanism for TRIM37 action in regulating spindle assembly/mitotic duration and cell proliferation upon centrosome loss? How do those acentrosomal MTOCs form that decrease mitotic duration and promote proliferation? Do the authors find a difference in the % of cells expressing TRIM37 mutants upon stable or acute expression? This part needs a better summary, and again a table would help. I also wonder about protein expression levels; wild-type FB-TRIM37 seems to be expressed at much lower levels than the mutants in Figure 5B.
5. Other means of centrosome depletion (Cenpj, SAS6 etc) would have been useful to include in the manuscript in support of E3 ligase dependent and independent roles of TRIM37. It is not essential to perform these experiment but if data are available, including these would improve the paper.
6. The authors show that TRIM37 regulates PLK4 phosphorylation and that this modification could only be observed in HEK293T and not in RPE1. Why would there be a difference between HEK293 and RPE1?
7. Statistical analysis for graphs should be included. Figure 5 is ok but graphs in Figures 3, 4, 6, 7 would benefit.
8. The authors characterise TRIM37 localisation. They detect it at centrosomes (as shown by Yeow et al 2021) and more specifically at the PCM, but apparently the signal is not present in all cells. They should also provide a quantification of the % of cells with centrosomal TRIM37 signal and compare this to cells expressing Flag-tagged Trim37. The specificity of the antibody signal using TRIM37-/- should be confirmed.

Minor Points

• Page 3: "A recent screen for mediators of supernumerary centrosome-induced arrest identified PIDDosome/p53 and placed the distal appendage protein ANKRD26 within this pathway [31]". It appears that the reference for Burigotto et al. is missing.

• Page 6: The authors state that: TP53BP1, USP28 and CDKN1A are also suppressors in the Nutlin-3a screen and suggest that they act in a general p53 pathway. However Meitinger et al (2016) showed that depletion of TP53BP1 or USP28 did not affect the upregulation of p53 and p21 upon Mdm2 inhibition.

• Page 9: "First, we performed live cell imaging to measure mitotic length in cells grown in centrinone". For consistency the authors should say centrinone B here as well

• Page 9: "Cells lacking TRIM37 suppressed the growth arrest from 150 to 500 nM centrinone B in RPE-1 and 167 to 500 nM in A375 cells". The growth data for the A375 cells seem to be missing from the figures.

• Page 10: "Our results confirmed that PLK4 and TRIM37 form a complex in RPE-1 cells (Figure 3G)" It appears the authors referred to the wrong figure, it should be Figure 4B.

• Figure 1C: The nuclear p53 signal is not apparent with 500 nM centrinone B in the exemplary cells. Did the authors use thresholding to quantify p53/p21 positive cells?

• Figure 4D and Figure 4 - Figure supplement 1G: The graph is misleading and should not be presented as a continuous line.

• Figure 5A and C: A direct and statistical comparison mitotic timing upon expression different Trim37 mutants to wildtype and trim37-/- cells is missing

• Figure 6B: A loading control/Ponceau staining is missing as well as the quantification of protein levels

• Figure 6D: It is unclear if the centrosomal signal intensity was quantified in interphase or mitotic cells

• Figure 7C: A loading control/Ponceau staining is missing

• Figure 2 - figure supplement 2F and G: It would help if the authors could highlight the cell line, e.g. RPE-1 (F) or A375 (G) in the venn diagrams.

• Figure 4 - figure supplement 1E: it appears that the BirA antibody gives only an unspecific signal. It would be useful to show if the different TRIM37 variants are able to localise to the centrosomes. Furthermore it appears that centrosomes are missing in the C18R and 505-709 variants. It would be useful if the authors quantify centrosome numbers upon expression of different Trim37 variants as shown in Figure 4 - figure supplement 1. To make the identification of the cell easier it would help to include a DNA signal or indicate the outline of the cell.

• The generation of stable and dox-inducible cell lines is missing in the material and methods

Significance

Centrosome loss in mammalian cells triggers a somewhat mysterious p53-dependent irreversible cell cycle arrest that bears similarities with senescence. A key modulator of this arrest is the E3 ubiquitin ligase TRIM37; TRIM37-overexpressing cells show increased sensitivity to centrosome loss whereas TRIM37 deletion restores normal growth to cells lacking centrosomes. The precise function of TRIM37 in this process is still not clear.

The authors here report a two-pronged approach to improve our understanding; first, they perform several genome-wide Crispr/Cas9 screens in two cell lines to identify new players that modulate growth arrest following inhibiton of centrosome duplication, and second, they analyse the function of TRIM37, their top candidate, in this process. Whereas the screens recapitulate previous reports by identifying a near identical set of genes, the functional work of TRIM37 provides interesting new data that go beyond (and at places contradict) published work. They describe a complex relationship between TRIM37 function, PLK4 inhibition and growth arrest, and suggest that TRIM37 acts via modulating PLK4 phosphorylation/stability and perhaps its role in autophagy also contributes to the overall phenotype. These possibilities will need to be tested in the future but the current manuscript contains enough interesting and potentially important data that it is worthy of publication following revision.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): In this paper, the authors use a previously published method SHAP for interpreting deep learning (DL) models (specifically LSTMs) that are trained for predicting physicochemical attributes of peptides (such as antigenicity and collisional cross section). The paper shows that it's capable of identifying some amino acid residues contributing to the prediction results of the DL models. Reviewer #1 (Significance (Required)):

1. One main ideas of the paper is to use SHAP for determine the significant amino acids at each position (or pairs of AA at each position) contributing to the prediction. Some of the interpretation results are consistent with findings reported previously. This is very nice; however, most of these findings are statistical results such "XX is often present at the second position for the peptides with the positive outcome", which are relatively straightforward and may be derived by using some statistical methods without using DL models. We expect more complex patterns can be discovered in addition to these statistical observations.

We thank the reviewers for these comments.

First, to the point about discovering complex patterns, we note that one use of PoSHAP we discuss later in the paper is that PoSHAP enables interposition dependence analysis, which depends on interactions between residues and would not be reflected by summary statistics.

Second, we agree it is important to show whether PoSHAP produces different residue importance maps than simple statistical summaries of amino acids in each group. The strongest binding peptides, or the highest mobility for the CCS model, were determined by taking only peptides that fall above a linear regression best fit of the ranked experimental values. Statistical summary heatmaps were created and then compared to those from PoSHAP revealing some similarities but also many differences. We added the following text and new figure to the results section to illustrate these points:

“We wondered whether the patterns revealed by PoSHAP simply reflect the summary statistics for the high-binding or high-CCS subset of peptides. As expected, due to known differences in amino acid abundance across the proteome, the prevalence of amino acids was different across the training data and were also heterogeneous across positions (Figure 5A). To determine the subset of high CCS peptides, peptides were ordered in the training set by their CCS rank and then linear regression was performed to get the average trend line (Figure 5B). Any peptide above that trendline was defined as “high CCS”, and the frequency of amino acids at each position in this set was summarized using a heatmap (Figure 5C). Compared to the statistical amino acid frequencies, PoSHAP suggests a greater importance to arginine at both termini, the importance of tryptophan to increase CCS becomes apparent, and interior glutamic acid contributes less to high CCS than the frequencies would suggest (Figure 5D). The same analysis was repeated for MHC data (Supplementary Figures 9 and 10). This demonstrates that PoSHAP found non-linear relationships between the inputs and the outputs that are not present by simple correlation. “

Figure 5: Amino acid summary statistics differ from PoSHAP values for the CCS data. (A) Amino acid counts as a function of position for training data. (B) Procedure for picking the ‘top peptides’ with the highest CCS. Linear regression was performed on the peptides ranked by their actual CCS value. Any peptide that fell above the trendline and overall mean were defined as ‘top peptides’. (C) Counts of amino acids for the top peptides were summarized in a heatmap. (D) Mean SHAP values across amino acids and positions from PoSHAP analysis.

We also added the corresponding supplemental figures showing the same examples for the MAMU A001 model and human MHC models:

Supplemental Figure 9: Amino acid summary statistics differ from PoSHAP values for the A001 MAMU MHC I data. (A) Amino acid counts as a function of position for training data. (B) Procedure for picking the ‘top peptides’ with the highest CCS. Linear regression was performed on the peptides ranked by their actual CCS value. Any peptide that fell above the trendline and overall mean were defined as ‘top peptides’. (C) Counts of amino acids for the top peptides were summarized in a heatmap. (D) Mean SHAP values across amino acids and positions from PoSHAP analysis. For the MAMU model, the amino acid frequencies of the input peptides show no obvious preference for amino acid position, but some amino acids are over-represented overall. The presence of the “end” token is more likely to be a high binder statistically (C), but the PoSHAP reveals that this end token is not the main determinant of binding (D).

Supplemental Figure 10: Amino acid summary statistics differ from PoSHAP values for the human A1101 MHC I data. (A) Amino acid counts as a function of position for training data. The distribution of amino acids in this data. (B) Procedure for picking the ‘top peptides’ with the highest CCS. Linear regression was performed on the peptides ranked by their actual CCS value. Any peptide that fell above the trendline and overall mean were defined as ‘top peptides’. (C) Counts of amino acids for the top peptides were summarized in a heatmap. (D) Mean SHAP values across amino acids and positions from PoSHAP analysis. There are clear differences between the summary statistics of top peptides (C) and PoSHAP heatmap (D). For example, the end token is prominent in the summary statistics absent from the PoSHAP interpretation. Also, the preference for S/T/V at position two is tempered according to PoSHAP, but would be determined to be very important by the summary statistics.

Although the interpreting results reported in the paper largely agree with previous reports, the paper did not explicitly model the frequency of different amino acid in the training data. For instance, if the amino acid 'A' happens to be over-represented in the positive samples of peptides in the training data, the DL model may consider it as to contribute to the positive prediction, which may not be not true. This issue might become more serious when pairs of amino acids are considered. The authors may want to analyze this potential issue in their results.

We agree and understand the concern for the overrepresentation of amino acids that might skew the training of our models. To determine if this is an issue, as part of the response to the previous question, we looked at the amino acid counts for all peptides (Figure 5A, Supplemental Figures 9A and 10A). In general, the PoSHAP heatmaps (panel Ds in the same figures) look very different from the frequencies of amino acids (panel Cs in the figures), suggesting that amino acid frequencies have not caused any problem.

Even on a balanced training dataset, the LSTM model to be interpreted may still contain arbitrary bias due to invertible overfitting, which the authors did not discuss. It will be more convincing by training multiple models using different hyper-parameters and optimization algorithms, and then see if similar interpretation results can be reached among most or all of these models.

We assume the reviewer meant ‘inevitable overfitting’ instead of “invertible overfitting”? If so, the original manuscript did assess overfitting in Figure S4 based on the training and validation loss over training epochs.

We think the reviewer makes a good point that different models might produce different interpretations, so we trained new models without optimization and with different hyperparameters and with a different optimizer (RMS prop). We see essentially the same PoSHAP interpretations. We added the following text to the results section along with these three new supplemental figures:

“Given the dependence of the model interpretation results on the model used, the same model architecture trained with different parameters might result in different model interpretation. Given this, models for each of the three tasks mentioned here were retrained with different hyperparameters including the “RMS prop” optimizer. Each model produces similar or better prediction performance compared to the earlier version, and the model interpretation by PoSHAP was almost identical to the previous results in all three cases (Supplementary figures 12, 13, 14). This suggests that the model architecture drives the differences in interpretation, not the model training process.”

Supplemental Figure 12. PoSHAP Analysis of Mamu A001 With Unoptimized Hyperparameters and RMSprop. A new model for the Mamu data was trained using the same architectures but with different hyperparameters and RMSprop as the optimization algorithm. Loss was plotted as mean squared error compared to the validation data. (A) Similar metrics for MSE, r, and p-values were obtained (B). Similar patterns are also observed for the PoSHAP heatmap of A001. (C) A dependence plot for A001 shows similar patterns to the Adam optimized model, including the positional dependence of proline at position two for high SHAP values of serine and threonine.

Supplemental Figure 13. PoSHAP Analysis of A:11*01 With Unoptimized Hyperparameters and RMSprop. A new model for the A:11*01 data was trained using the same architectures but with different hyperparameters and RMSprop as the optimization algorithm. Loss was plotted as mean squared error compared to the validation data. (A) Similar metrics for MSE, r, and p-values were obtained (B). Similar patterns are also observed for the PoSHAP heatmap of A:11*01. (C) The SHAP ranges by position plot for A:11*01 shows similar patterns to the Adam optimized model, including the largest range of SHAP values at position two, nine, and ten.

Supplemental Figure 14. PoSHAP Analysis of CCS With Unoptimized Hyperparameters and RMSprop. A new model for the CCS data was trained using the same architectures but with different hyperparameters and RMSprop as the optimization algorithm. Loss was plotted as mean squared error compared to the validation data. (A) Similar metrics for MSE, r, and p-values were obtained (B). Similar patterns are also observed for the PoSHAP heatmap of CCS. (C) Dependence analysis was performed on the dataset and the combined distance-interaction type bar plot shows similar relationships between the groupings, notably charge repulsion’s split.

For the dependence analysis, it is not completely clear why the distance is used as the variable, while the relative position of the amino acid residue in the peptide is ignored. For example, if there is a strong interaction between the first and the last residues in the peptide, their distance changes depending on the peptide length. In figure 6, the authors showed strong interactions between amino acid that are 8-9 residues apart may suggest the peptide length actually plays a role here.

We used distance because as the dependence analysis is a calculation of the difference in means between two distributions of SHAP values, dependent of the amino acid at another position. We believe that the distance between these interacting points is a natural choice and among the most informative metrics to explain these interactions. We agree with the reviewer that peptide length is important to the magnitude of the interactions between amino acids. We also recognize that there may be interactions between the peptide termini that could be obscured by the interactions of the longer peptides. To better explore this possibility, we performed the dependence analysis on each of the different peptide lengths separately (8, 9, or 10 here) to see if this is the case. Unfortunately, given the smaller size of these data subsets, we were unable to show significant differences in the interaction groupings. Though, interestingly enough, the significant interactions for the peptides of length eight only occurred between neighboring amino acids or the termini. This may suggest an interaction between termini that could be explored in the future.

We added the following text and supplemental figure 11 to the results:

“Finally, to try to ask if the absolute positions of amino acids in the peptide are relevant for the interaction, the data was split into 8, 9, or 10mers before analysis (Supplemental Figure 11). This revealed that there may be interactions between the termini, but this effect may be difficult to observe because there are significantly fewer 8mers and 9mers in the CCS dataset.”

Supplemental Figure 11. SHAP Values of Collisional Cross Section by Peptide Length. The impact of peptide length on SHAP values was explored for the CCS data. The dataset was split into peptides of length 8, 9, and 10. All SHAP values were plotted as violin plots. The mean SHAP values were plotted in heatmaps by position and amino acid and standardized. Significant interactions by dependence analysis were plotted in bar charts by distance between interactions.

To further support our decision to use distance as an interaction metric, we have also now included an additional box plot for Figure 7, demonstrating the interactions between each of the categories combined with distance. We have found that some of the bimodality of the interaction categories are explained by the distance at which they interact. Most strikingly is charge repulsion that decreases CCS when neighboring but increases CCS when the interaction is further.

We added the following text and updated Figure 7 to the results section:

“Additionally, there are interesting differences in the interactions of the amino acid among the significant set of interactions (Figure 76B). All significant interactions from the CCS data (Supplemental Table 3, adj. p-value Though it is evident that the mean of each interaction type corresponds to the expected impact those interactions would have on CCS, each of the interaction dependence plots are bimodal, with some interactions increasing CCS and some decreasing it. To dissect this observation further, we combined the two methods of splitting the data to see if the bimodality of interaction types would be resolved by distance (Figure 7C). Though definitive conclusions cannot be made for most categories, likely due to the ever decreasing sample size by splitting, of note is the difference between neighboring charge repulsion and non-neighboring charge repulsion. Neighboring charge repulsion seems to decrease CCS while distant charge repulsion increases CCS (see adjusted p-value from Tukey’s posthoc test in Figure 7D). When distant, charge repulsion makes intuitive sense as the amino acids are forced apart, linearizing the peptide and increasing the surface area. When neighboring, it is possible that the repulsion causes a kink in the linear peptide, decreasing the cross section. Overall, these analyses demonstrate that the models were able to learn fundamental chemical properties of the amino acids and through PoSHAP analysis we were able to uncover them.”*

Figure 7. Dependence analysis of CCS model. (A) Significant (Bonferroni corr. P-value = charge repulsion, * = other, and δ = polar. For the distance analysis, interactions were grouped into three categories, neighboring (distance = 1), near (distance = 2, 3, 4, 5,6), and far (distance = 7, 8, 9). * indicates significance (ANOVA with Tukey’s post hoc test p-value

Also, it would be better to show that how the result looks like when applying this method to peptides in the negative samples (e.g., the peptides that are not bound by MHC in the antigenicity prediction experiment). Will the interpreting results also be negative?

We agree this is an interesting idea. We updated the supplemental figure showing PoSHAP of top peptide subsets to also show PoSHAP of bottom peptide subsets (supplemental figure 8). The results suggest that certain amino acid positions are detrimental to binding, for example D/E at various positions. We updated this section to add:

“We also performed the same analysis with the eight peptides with the lowest binding predictions (Supplemental Figure 8). These PoSHAP heatmaps are primarily composed of negative SHAP values, suggesting that using this subset reveals amino acids at certain positions that are detrimental to MHC binding.”

Supplemental Figure 8. Pooled PoSHAP for bottom and top predicted subsets of the data. The mean SHAP values for each amino acid at each position were calculated for the peptides with the bottom (A) or top (B) 0.013% predicted intensity (top 8 peptides) for the “A” Mamu alleles. Due to the small sample size, most of the amino acid positions have a value of zero. The positions with extreme values, however, illustrate important amino acids for prediction. Notably for A001 and A002, aspartic acid and glutamic acid contribute to low prediction along the peptide, suggesting charge may inhibit binding. For the top predictions, phenylalanine or leucine are important at the first position for both A001 and A008. A serine or threonine at position two is important for A001, A002, and A008. All alleles demonstrate the importance of a proline near the middle of the peptide.

Finally, it will be interesting to see the interpreting results when the method is applied to the DL models on more challenging tasks such as the prediction of tandem mass spectra of peptides. The authors may want to discuss these applications.

We agree it would be very interesting to apply this method to interpret predictions of tandem mass spectra. In this paper we already demonstrated PoSHAP on three different datasets with three different models, so we feel that adding a fourth model is out of the scope of this work. We do agree that we would like to explore this option in the future. We added this idea to the discussion section:

“Altogether the advances described herein are likely to find widespread use for interpreting models trained from biological sequences, including models not covered here such as those to predict tandem mass spectra (reviewed in 33).”

I am primarily interested in algorithmic and statistical problems in genomics and proteomics. We have develop deep learning models for predicting the full tandem mass spectrum of peptides, and am interested model interpretation methods to explain the fragmentation mechanism resulting in non-conventional fragment ions in tandem mass spectra of peptides. I review the paper in collaboration with my Ph.D students, who are developing deep learning models for computational mass spectrometry.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Comments to the Authors** In this study, the authors developed a framework named PoSHAP for the interpretation of neural networks trained on biological sequences. The current manuscript can be stronger if the following issues can be clearly addressed.

1. As interpreting model with SHAP is a vital part of this manuscript, it would be better to provide descriptions of the underlying principles of SHAP to enable the readers to understand the paper easily.

We recognize that understanding the principles of SHAP is vital. To better explain SHAP, we have added the following text to the introduction:

“SHAP is a perturbation-based explanation method where the contribution of an input is calculated by hiding that input and determining the effect on the output. SHAP expands this using the game theoretic approach of Shapely values that ensures the contributions of the inputs plus a calculated baseline sum to the predicted output.”

It is emphasized in the manuscript that PoSHAP is introduced to interpret neural networks trained on biological sequences. However, it is not clear why the authors choose the Model Agnostic Kernel SHAP, which is based on Linear LIME. Although it can be used for any model, the performance of which may not be optimal. In this regards, perhaps Deep SHAP or Gradient SHAP is more appropriate, both of which are designed for deep learning networks [1]. It would be better to provide some additional experiments on Deep SHAP and this work will be more convincing if the same or similar contribution of each position on each peptide as that of Kernel SHAP. [1] Lundberg, S., and S. I. Lee. "A Unified Approach to Interpreting Model Predictions." Nips 2017.

Our goal in using KernelExplainer was to demonstrate that PoSHAP was not dependent on model specific interpretation methods. However, we have realized that this intention may not have been clearly stated or demonstrated. To expand on this, we have included a new Figure 8, which shows PoSHAP analysis comparisons to other classes of machine learning models, all using Kernel Explainer. This result was interesting because it revealed that even though the XGboost model technically performed better at prediction (Figure 8A, reduced MSE and higher spearman rho), and produced a similar PoSHAP motif heatmap, the interpositional dependences from the perspective of distance (Figure 8C) or chemical interactions (Figure 8D) were substantially muted. This is also apparent with the other standard machine learning model ExtraTrees. This result shows that the choice of model architecture is important, and this direct comparison would not be possible if we used the DeepExplainer.

We added the following text and figure to the manuscript:

“ PoSHAP uses the SHAP KernelExplainer method, which is based on Local interpretable model-agnostic explanations (LIME). Using the general KernelExpplainer method enables direct comparison of interpretations produced by different models trained from the same data. To ask whether PoSHAP interpretation changes based on the model used, the CCS data was used to train XGboost or ExtraTrees models. Surprisingly, the XGboost model performed better than the LSTM model with regard to MSE and spearman rho between true and predicted values in the test set (Figure 8A). ExtraTrees was slightly worse than the other two models. The model interpretation heatmaps from PoSHAP were similar between the LSTM and XGboost, but the interpretation from the ExtraTrees model was missing the high average SHAP due to n-terminal histidine or arginine (Figure 8B). Even though XGboost produced a similar PoSHAP heatmap, the interpositional dependence with regard to distance (Figure 8C) and chemical interactions (Figure 8D) was muted. This shows that the choice of model is important for revealing amino acid interactions.”

Figure 8. CCS PoSHAP of Various Machine Learning Models. PoSHAP analysis was performed on two additional machine learning models, Extra Trees and Extreme Gradient Boosting (XGB). Predictions were plotted against experimental values and the Mean Squared Error and r values are reported for each model (A). PoSHAP heatmaps were created for each model (B), illustrating an increase in model complexity as more sophisticated models are used. Dependence analysis was performed on each model and the significant interactions are plotted by distance (C) and by combined distance and interaction type (D).

As described in the manuscript, "Correlations between true and predicted values were assessed by MSE, Spearman's rank correlation coefficient, and the correlation p-value." As an important indicator for evaluation, the exact p-values should be provided in the seven subgraphs in Figure 2, not p=0.0.

We agree with the reviewer that reporting accurate p-values can assist in evaluation. We have updated the figures to reflect the p-values as far as we were able to determine them. Unfortunately, we are limited by the nature of the double data type in python and so reported that the p-value was less than the minimum value allowed by a double in six of the seven graphs. Additionally, the scales have been marked symmetrically as you mentioned in comment 4.

It should be noted that the coordinate scales of Figure 2B and Figure 2C need to be marked symmetrically. And from Figure 2B, we can see that, the IC50 with smaller (0.8) values cannot be well predicted. Can the authors provide a detailed explanation about these results?

We understand the reviewer’s concern with poor prediction of extreme values. Figure B represents the IC50 prediction for the A1101 human allele which was the smallest of the datasets we used for training. It only consists of 4,522 entries, around 1/10 of the data used for the Mamu alleles and CCS. Because of this, it is likely that there were not enough examples of datapoints at the extremes to reliably train the model to account for them. However, given the limited size of the dataset, we were surprised with the satisfactory predictions. More importantly, the purpose of our paper is model interpretation not model prediction accuracy, and this shows that even when predictions are not perfect, the model interpretation by PoSHAP can still be effective. We thank the reviewer for noticing this and added the following statement to the results:

“Remarkably, this was achieved for A\11:01 using a total dataset of only 4,522 examples, which shows that PoSHAP can be effective with even less than 10,000 training examples. “*

References are needed in some descriptions in the manuscript. For example, "one might train a network to take an input of peptide sequence and predict chromatographic retention time", "RNNs have found extensive application to natural language processing, and by extension as a similar type of data, predictions from biological sequences such as peptides or nucleic acids".

We apologize for missing these references. We have now cited these statements and have added many additional references as part of our revision.

The description of the adopted three models in the section "Model architecture" is a bit confusing. As described in this section, "The LSTM layer outputs a 50x128 dimensional matrix to a dropout layer where a proportion of values are randomly set to 0", "a second LSTM layer outputs a tensor with length 128 and a second dropout layer then randomly sets a proportion of values to 0". But as shown in the Supplemental Figure 3, the output size of the first LSTM was 10x128. Also, as shown in Table 1, the dropout rates were not 0. Therefore, the section should be adjusted for clear clarification.

We apologize for the confusing wording. We meant that dropout layers randomly set values=0, not that the dropout proportion was 0. We reworded this part to read:

“The LSTM layer outputs a 10x128 dimensional matrix to a dropout layer where a proportion of values are randomly “dropped”, or set to 0. For the MHC models, a second LSTM layer outputs a tensor with length 128 to a second dropout layer. Then in all models, a dense layer reduces the data dimensionality to 64. For the MHC models, the data is then passed through a leaky rectified linear unit (LeakyReLU) activation before a final dropout layer, present in all models.”

Reviewer #2 (Significance (Required)): Pls refer to my comments provided as above.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): **Summary:** The main goal of the work is to provide the interpretation of Deep Neural Networks (LSTM in the paper) trained on biological sequences. For this purpose authors used the framework introduced earlier - SHapley Additive exPlanations (SHAP), in particular - the slight adaptation of this method called positional SHAP (PoSHAP), because they are interested in the impact of each position of the input sequence to the model output. They demonstrate this on three regression tasks that predict peptide properties. **Major comments** The main contribution, highlighted in the paper: authors showed how PoSHAP discloses amino acid motifs that influence MHC I binding. Further they described how PoSHAP enables understanding of interpositional dependence of amino acids that result in high affinity predictions. Also they argued that this work also contributes to a method for accurate prediction of peptide-MHC I affinity using peptide array data enabled by novel application of a neural network that combines amino acid embedding and LSTM layers.

There are some comments about the statements above: 1.Why was the LSTM model chosen? Recent publications showed the success of the Transformer model for biological sequences; however this direction was not covered in the related work overview. The architecture choice then should be better justified. Also the choice of LSTM for the biological sequences is not new and authors should better claim their statement about "novel application of a neural network that combines amino acid embedding and LSTM layers ". Where exactly is the novelty? Could the community use the pretrained embeddings for their purpose?

The reviewer is correct that transformer models are highly effective for making predictions from biological sequences. In fact, many models do well, and there is no single correct choice of model for this task. Though there are many models to choose from, our models are sufficiently accurate. Importantly, the main contribution of our manuscript is not to train the most accurate models, but rather to demonstrate a strategy for positional model interpretation based on SHAP. Related to that point, please note our response to reviewer #2’s second comment that our approach uses the kernel explainer and can be applied to any model. However, we do agree that we neglected coverage of the transformer model in the introduction and have added a paragraph to the introduction covering some of the recent work in this area:

“Many effective deep learning model architectures are available for making predictions from inputs of biological sequences, and there is currently no single correct choice. CNN models such as MHCflurry 2.0 (40) and LSTM models are effective at predicting MHC binding of peptides (41). Even simpler models, such as random forests, have been used to predict MHC binding (42,43). Prediction of other peptide properties like tandem mass spectra are often done with CNN or LSTM models (33). More recently, given the extraordinary performance of transformer models like BERT (44) and GPT-3 (45) for NLP, there is interest in transformer models for biological sequences (46).”

We also want to be sure we do not overstate the novelty of our contributions. We have updated our discussion to better reflect the nature of our contributions. We reworded the statement quoted above to read:

“Overall, the three modeling examples laid out herein serve as a tutorial for PoSHAP interpretation of almost any model trained from almost any biological sequence.”

The attention mechanism itself provides the great opportunity to interpret the model predictions. In the introduction section authors made a statement that attention layers may limit the flexibility of model architecture when designing new models. Could they better explain this limit? Because recent state of the art models successfully work with long biological sequences and show better results then any other models (one example could be found here: https://openreview.net/pdf?id=YWtLZvLmud7). Authors should cover these limits more, that also related to the motivation of the LSTM choice.

We added a paragraph to our introduction to expand on attention and its limitations:

“Attention mechanisms have been successful in recapitulating experimentally defined binding motifs, but require that the model be constructed with attention layers. This may limit the flexibility of model architecture when designing new models. For example, attention mechanisms are specific to neural networks. Simpler models, such as random forests and XGboost, may also be more suitable for some applications, and these cannot utilize attention. Also, while attention mechanisms are currently very effective, there is always a possibility that new architectures will emerge that make interpretations using attention infeasible. Beyond this, attention is a metric of the model itself, while SHAP values are calculated on a per input basis. By looking at the model through the lens of the inputs, we can understand the model’s “reasoning” behind any peptide. Attention mechanisms also do not enable dissection of interpositional dependencies between amino acids. Thus, new methods for model agnostic interpretation are desirable.”

Another statement was made about the PoSHAP - adaptation of the SHAP method. It is hard to follow through the explanation of this adaptation - it is not clear what exactly is this adaptation. For example, Kernel SHAP from the original paper computes feature importance, in this paper authors compute the impact of each position, that is basically also the feature importances. Thus authors should better explain the statement about PoSHAP novelty. Will it be possible to use PoSHAP for any other model trained for the same purpose? If yes, for better reproducibility, authors should provide the place where exactly in the repo is the code for this. Also mathematical notations are missing in the Positional SHAP (PoSHAP) section - it is better to explain the adaptation with them to increase the understanding of the section.

We apologize for the ambiguous wording in the abstract stating that “PoSHAP adapts SHAP”. We have reworded this statement to “PoSHAP utilizes SHAP”. The novelty of this approach is taking the feature importance values calculated by SHAP and structuring them to include each position’s index to allow for the interpretation of biological sequences. As we demonstrate here, this allows for novel interpretations of previously published data and will enable model interpretation in future studies that learn from biological sequences. Although this is practically very simple, we are not yet aware of any examples in the literature that do this.

The following two SHAP force plots demonstrate the difference between using SHAP as-is versus PoSHAP. There is a demonstrated need for such a framework, considering the dearth of biological sequence model interpretation using SHAP and the ambiguity within biological sequence SHAP interpretation. For example, Meier et al., Nature Communications, 2021 performed an analysis like our Figure S7C, which just shows the range of SHAP values per residue. Although we can learn something about which AAs are important based on the range of their SHAP values, SHAP as-is doesn’t reveal a motif. While our position indexing is a simple change, it enables all the rich, sequence dependent analysis we performed in this paper. We added the following text to the results section with this new supplementary figure:

“PoSHAP utilizes the standard SHAP package but adapts the analysis by simply appending an index to each input and maintaining positional information after the kernelExplainer interpretation, which enables tracking of each input postion’s contribution to an output prediction (supplementary figure 5, showing force plot with and without index).”

Supplemental Figure 5. SHAP Forceplots Demonstrating PoSHAP Indexing. Two forceplots were created with the SHAP forceplot method of the third peptide in the CCS testing set. (A) shows the plot with encoded inputs mapped to their amino acid. (B) shows the plot with the encoded inputs mapped to their amino acid and position. The addition of positional indexing removes the ambiguity of contributions, for example, glutamine having both a positive and a negative SHAP contribution to the prediction of the third peptide.

We have updated the repository to include a tutorial that demonstrates PoSHAP on provided data and explains how to use PoSHAP with your own model and data.

In the experimental section, authors first compare the results with previously known. For example, for the human MHC allele A*11:01 model PoSHAP analysis shows the similar results as was shown with another approach. Based on the provided explanation, it is not clear why PoSHAP is better than the previously published method. The advantage of the PoSHAP should be better explained.

We agree with the reviewer that the benefits of our approach should be as clear as possible. The referenced section of the paper is to validate our approach compared to another model interpretation technique. We added a new third paragraph to the discussion section to clearly explain the benefits of PoSHAP:

“There are several benefits of PoSHAP over competing methods. First, PoSHAP determines important residues despite biases in the frequencies of amino acids (Figure 5, Supplementary Figures 9 and 10). PoSHAP is also applicable to any model trained from sequential data (Figure 8), and enables dissection of interpositional dependencies (Figures 6 and 7). Finally, we include a clearly explained jupyter notebook on Github that will take any model and dataset and perform PoSHAP analysis.”

In the experimental section, after the PoSHAP performance verification, hypothesis generation was introduced. However, it is not clear how many hypotheses were generated; how many of them were known before; what kind of other categories are inside these hypotheses (unknown, possible and potentially interesting, etc).

We are unsure as to how to quantify the number of hypotheses generated by our approach. In a sense, the SHAP value of each amino acid at each position within a heatmap represents a hypothesis of the contribution of that amino acid to the metric being predicted. Each significant interaction listed in the first three supplemental tables represents a hypothesis of the interactions between two given amino acids at two positions. To make these into testable hypotheses requires some analysis, as we have discussed. i.e. the two binding motifs (L-T-P, F-S-P) of A001, or the distance-type interactions within the CCS.

The README section in the GitHub repo is not easily understandable. An additional explanation for each step is required (e.g., links to the folders where the calculated SHAP values, the trained models, all splits and all-important benchmarks are).

We have updated the README and repository to explain how to use PoSHAP, and explanations of each item in the repository.

**Minor comments**

1. The prior studies should be covered better (see Major comments).

We apologize for not better covering prior studies. We have significantly expanded the introduction by adding two new paragraphs and at least 10 additional citations.

The work consists of some typos, for example: "However, because many reports forgo model interpretation" - "t" is missed.

We did intend to use the word “forgo” not “forget” in that sentence. We have checked again thoroughly for spelling and grammar mistakes.

The hyperparameters table, hyperparameter search section should be moved to the supplemental material, that's technical details.

We moved this table to the supplementary materials.

Reviewer #3 (Significance (Required)): Interpretation of the model results is an important topic for biology. New findings here could lead to new interactions opening, new drugs development etc. That is relevant for the applied ML Researches and computational biologists. This paper aims to provide a way to do it. Because my field of interest and expertise lies in Machine Learning for healthcare, language modelling of biological sequences and Natural Language Processing, this work is of great interest to me. So I mostly evaluated ML methodology presented in the paper.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The main goal of the work is to provide the interpretation of Deep Neural Networks (LSTM in the paper) trained on biological sequences. For this purpose authors used the framework introduced earlier - SHapley Additive exPlanations (SHAP), in particular - the slight adaptation of this method called positional SHAP (PoSHAP), because they are interested in the impact of each position of the input sequence to the model output. They demonstrate this on three regression tasks that predict peptide properties.

Major comments

The main contribution, highlighted in the paper: authors showed how PoSHAP discloses amino acid motifs that influence MHC I binding. Further they described how PoSHAP enables understanding of interpositional dependence of amino acids that result in high affinity predictions. Also they argued that this work also contributes to a method for accurate prediction of peptide-MHC I affinity using peptide array data enabled by novel application of a neural network that combines amino acid embedding and LSTM layers.

There are some comments about the statements above:

1.Why was the LSTM model chosen? Recent publications showed the success of the Transformer model for biological sequences, however this direction was not covered in the related work overview. The architecture choice then should be better justified. Also the choice of LSTM for the biological sequences is not new and authors should better claim their statement about "novel application of a neural network that combines amino acid embedding and LSTM layers ". Where exactly is the novelty? Could the community use the pretrained embeddings for their purpose?

1. The attention mechanism itself provides the great opportunity to interpret the model predictions. In the introduction section authors made a statement that attention layers may limit the flexibility of model architecture when designing new models. Could they better explain this limit? Because recent state of the art models successfully work with long biological sequences and show better results then any other models (one example could be found here: https://openreview.net/pdf?id=YWtLZvLmud7). Authors should cover these limits more, that also related to the motivation of the LSTM choice.
2. Another statement was made about the PoSHAP - adaptation of the SHAP method. It is hard to follow through the explanation of this adaptation - it is not clear what exactly is this adaptation. For example, Kernel SHAP from the original paper computes feature importance, in this paper authors compute the impact of each position, that is basically also the feature importances. Thus authors should better explain the statement about PoSHAP novelty. Will it be possible to use PoSHAP for any other model trained for the same purpose? If yes, for better reproducibility, authors should provide the place where exactly in the repo is the code for this. Also mathematical notations are missing in the Positional SHAP (PoSHAP) section - it is better to explain the adaptation with them to increase the understanding of the section.
3. In the experimental section, authors first compare the results with previously known. For example, for the human MHC allele A*11:01 model PoSHAP analysis shows the similar results as was shown with another approach. Based on the provided explanation, it is not clear why PoSHAP is better than the previously published method. The advantage of the PoSHAP should be better explained.
4. In the experimental section, after the PoSHAP performance verification, hypothesis generation was introduced. However, it is not clear how many hypotheses were generated; how many of them were known before; what kind of other categories are inside these hypotheses (unknown, possible and potentially interesting, etc).
5. The README section in the GitHub repo is not easily understandable. An additional explanation for each step is required (e.g. links to the folders where the calculated SHAP values, the trained models, all splits and all important benchmarks are).

Minor comments

1. The prior studies should be covered better (see Major comments).
2. The work consists of some typos, for example: "However, because many reports forgo model interpretation" - "t" is missed.
3. The hyperparameters table, hyperparameter search section should be moved to the supplemental material, that's technical details.

Significance

Interpretation of the model results is an important topic for biology. New findings here could lead to new interactions opening, new drugs development etc. That is relevant for the applied ML Researches and computational biologists. This paper aims to provide a way to do it. Because my field of interest and expertise lies in Machine Learning for healthcare, language modelling of biological sequences and Natural Language Processing, this work is of great interest to me. So I mostly evaluated ML methodology presented in the paper.

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

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Referee #2

Evidence, reproducibility and clarity

Comments to the Authors

In this study, the authors developed a framework named PoSHAP for the interpretation of neural networks trained on biological sequences. The current manuscript can be stronger if the following issues can be clearly addressed.

1. As interpreting model with SHAP is a vital part of this manuscript, it would be better to provide descriptions of the underlying principles of SHAP to enable the readers to understand the paper easily.
2. It is emphasized in the manuscript that PoSHAP is introduced to interpret neural networks trained on biological sequences. However, it is not clear why the authors choose the Model Agnostic Kernel SHAP, which is based on Linear LIME. Although it can be used for any model, the performance of which may not be optimal. In this regards, perhaps Deep SHAP or Gradient SHAP is more appropriate, both of which are designed for deep learning networks [1]. It would be better to provide some additional experiments on Deep SHAP and this work will be more convincing if the same or similar contribution of each position on each peptide as that of Kernel SHAP. [1] Lundberg, S., and S. I. Lee. "A Unified Approach to Interpreting Model Predictions." Nips 2017.
3. As described in the manuscript, "Correlations between true and predicted values were assessed by MSE, Spearman's rank correlation coefficient, and the correlation p-value." As an important indicator for evaluation, the exact p-values should be provided in the seven subgraphs in Figure 2, not p=0.0.
4. It should be noted that the coordinate scales of Figure 2B and Figure 2C need to be marked symmetrically. And from Figure 2B, we can see that, the IC50 with smaller (<0) and larger (>0.8) values cannot be well predicted. Can the authors provide a detailed explanation about these results?
5. References are needed in some descriptions in the manuscript. For example, "one might train a network to take an input of peptide sequence and predict chromatographic retention time", "RNNs have found extensive application to natural language processing, and by extension as a similar type of data, predictions from biological sequences such as peptides or nucleic acids".
6. The description of the adopted three models in the section "Model architecture" is a bit confusing. As described in this section, "The LSTM layer outputs a 50x128 dimensional matrix to a dropout layer where a proportion of values are randomly set to 0", "a second LSTM layer outputs a tensor with length 128 and a second dropout layer then randomly sets a proportion of values to 0". But as shown in the Supplemental Figure 3, the output size of the first LSTM was 10x128. Also, as shown in Table 1, the dropout rates were not 0. Therefore, the section should be adjusted for clear clarification.

Significance

Pls refer to my comments provided as above.

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Referee #1

Evidence, reproducibility and clarity

In this paper, the authors use a previously published method SHAP for interpreting deep learning (DL) models (specifically LSTMs) that are trained for predicting physicochemical attributes of peptides (such as antigenicity and collisional cross section). The paper shows that it's capable of identifying some amino acid residues contributing to the prediction results of the DL models.

Significance

1. One main ideas of the paper is to use SHAP for determine the significant amino acids at each position (or pairs of AA at each position) contributing to the prediction. Some of the interpretation results are consistent with findings reported previously. This is very nice; however, most of these findings are statistical results such "XX is often present at the second position for the peptides with the positive outcome", which are relatively straightforward and may be derived by using some statistical methods without using DL models. We expect more complex patterns can be discovered in addition to these statistical observations.
   1. Although the interpreting results reported in the paper largely agree with previous reports, the paper did not explicitly model the frequency of different amino acid in the training data. For instance, if the amino acid 'A' happens to be over-represented in the positive samples of peptides in the training data, the DL model may consider it as to contribute to the positive prediction, which may not be not true. This issue might become more serious when pairs of amino acids are considered. The authors may want to analyze this potential issue in their results.
   2. Even on a balanced training dataset, the LSTM model to be interpreted may still contain arbitrary bias due to invertible overfitting, which the authors did not discuss. It will be more convincing by training multiple models using different hyper-parameters and optimization algorithms, and then see if similar interpretation results can be reached among most or all of these models.
   3. For the dependence analysis, it is not completely clear why the distance is used as the variable, while the relative position of the amino acid residue in the peptide is ignored. For example, if there is a strong interaction between the first and the last residues in the peptide, their distance changes depending on the peptide length. In figure 6, the authors showed strong interactions between amino acid that are 8-9 residues apart may suggest the peptide length actually plays a role here.
   4. Also, it would be better to show that how the result looks like when applying this method to peptides in the negative samples (e.g., the peptides that are not bound by MHC in the antigenicity prediction experiment). Will the interpreting results also be negative?
   5. Finally, it will be interesting to see the interpreting results when the method is applied to the DL models on more challenging tasks such as the prediction of tandem mass spectra of peptides. The authors may want to discuss these applications.

I am primarily interested in algorithmic and statistical problems in genomics and proteomics. We have develop deep learning models for predicting the full tandem mass spectrum of peptides, and am interested model interpretation methods to explain the fragmentation mechanism resulting in non-conventional fragment ions in tandem mass spectra of peptides. I review the paper in collaboration with my Ph.D students, who are developing deep learning models for computational mass spectrometry.

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Reply to the reviewers

We thank reviewers for helping us clarify our manuscript. Some key information was only in the Supporting Information document, and was not obvious to find. We have now introduced some of this information into the main text, and otherwise clarified to which specific sub-paragraph of the Supporting Information document we refer every time we mention it. Another aspect which we have clarified is the relevance of controls previously published in our paper PLOS Comp Biol 16: 1-23. These controls address many of the remarks raised by the reviewers, regarding for instance rhythm detection methods, detection threshold, the effect of normalization of time-series data in rhythm detection, the consideration of biological replicates in time-series data, or the relationship between rhythms and highly expressed genes. We have now introduced some of these results within the main text to clarify these points, or have specified to which specific result of our previous paper we refer.

REVIEWER #1

Major comments:

They assumed the optimal constant level would be the maximum over the rhythm period when rhythmic regulation is absent. They also assumed the trade-off between the benefits of not producing proteins when they are not needed (costs saved) and the costs involved in making it rhythmic (costs of complexity), which they argued lead to the expectation that costlier genes be more frequently rhythmic. However, there was no explicit definition for the trade-off, so it is unclear how it leads to the expectation. [...]

Second, the "costs of complexity" were not defined

We have now clarified these points:

Thus, a first evolutionary advantage given by rhythmic biological processes would be an optimization of the overall cost (over a 24-hour period), compared to a constant expression at a high level of proteins, when this high level is necessary **for fitness at least at some point of time.

• Thus, a first evolutionary advantage given by rhythmic biological processes would be an optimization of the overall cost (over a 24-hour period), compared to the costs generated over the same period by optimizing a constant level of proteins. The reasonable assumption that the optimal constant level would be the maximum over the rhythm period strengthens the case for selection on expression cost.

• Our results suggest that rhythmicity of protein expression has been favored by selection for cost control of gene expression, while keeping optimal expression levels. In the case of rhythmic genes, what would that optimal constant level be? We can propose two hypotheses. The first is that it would be the mean expression over the period, since this maintains the same overall amount of protein. The second is that it would be the maximum over the rhythm period, since that is the level needed at least at some point. The second hypothesis explains better the existence of this maximum level during the cycle. Of note, it also strengthens the case for selection on expression cost. Thus, for the case of rhythmic genes, the optimal constant level should at least correspond to the mean expression level (Fig 1d). We provide results obtained using both the maximum and the mean of expression in Fig. 2a. We have modified Fig. 1d accordingly, and specified in Supp Fig. S2 that the delta value was calculated from mean expression levels.

We assume that the maximal expression level gives an estimation of the level that would be constantly maintained in the absence of rhythmic regulation

• We assume that, in the absence of rhythmic regulation, the constant optimal level is included between the mean and the maximum expression level observed in rhythmic expression. Here, we studied the evolutionary costs and benefits that shape the rhythmic nature of gene expression at the RNA and protein levels. For this, we analysed characteristics we presume to be part of the trade-off.

• Here, we studied the evolutionary costs and benefits that shape the rhythmic nature of gene expression at the RNA and protein levels. For this, we analysed characteristics we presume to be part of the trade-off determining the rhythmic nature of gene expression between its advantages (cost economy over 24h, non-ribosomal occupancy) and disadvantages (costs of complexity related to precise temporal regulation). The evolutionary** origin of maintaining large cyclic biological systems, in term of adaptability, can be seen as a trade-off between disadvantages such as cost or noise induced by the added complexity, and advantages such as economy over a daily time-scale, temporal organization, or adaptability.

• Most rhythmic genes are tissue-specific (Zhang et al. 2014, Boyle et al. 2017), which means that their rhythmic regulation is not a general property of the gene and is therefore expected to be advantageous only in those tissues in which they are found rhythmic. This argues that rhythmic regulation has costs, since it is not general. These costs are **probably related to the complexity of regulation** to maintain precise temporal organisation. Thus, cyclic biological systems are expected to have adaptive origins.

  It would be more convincing to define a fitness function or cost function to demonstrate their argument that costlier genes have fitness advantages if they are rhythmic.

Considering rhythmicity as an economy strategy is quite intuitive and our results confirm what is currently accepted (Wang et al. 2015). We show and discuss to which extent this is true by comparing expression costs at different expression levels. Defining more precisely a fitness function in our case would require an experiment where we could compare fitness between two populations (e.g. prokaryote growth rates): WT versus a strain whose promoter of the costliest genes would be controlled by non-cyclic transcriptional factors. We do not feel that this is a reasonable extension of this work, but a whole new research program.

First, when proteins are not needed, it can be either the case of not producing extra proteins (cost saved) or the case of degrading excessive proteins (cost incurred). […]

The cost function presented in this paper may be oversimplified. It only takes into account the costs to produce protein. The authors argued that a more complex cost calculation would not change the observation, but without proving it. However, protein degradation, including ubiquitination and proteolysis, requires energy; for a rhythmic gene, it is also necessary to consider the cost of maintaining the rhythmicity, including the temporally precise regulation of protein expression when the proteins are needed and of protein destruction when they are not.

We have now clarified this in Section 4.1 of the Supporting Information document:

Protein decay can be due to spontaneous decay of unstable molecules (no cost), cellular dilution (no cost), or active protein degradation, which has a cost which has been shown to be negligible. Costs of protein decay are negligible enough to not be opposed by selection. Indeed, Lynch and Marinov (2015) and Wagner (2005) have shown that “degradation in a lysosome may cost essentially nothing, and amino-acid export back to the cytoplasm consumes ∼1 ATP for every 3 to 4 amino acids”. Compared with the unique cost of producing one single nucleotide which consume 49~P, protein decay costs becomes negligible comparatively to transcriptional costs, which are themselves negligible comparatively to translational costs. All the more, given that amino acids from degradation are reused and do not need to be produced by the cell, which therefore economizes around 30 ~P per amino-acid (~P: high-energy phosphate bonds).

In Section 3 of the Supporting Information document, we also show why rhythmic and highly expressed proteins are costlier for the cell per time-unit than rhythmic and lower expressed proteins, even considering decay costs or proteins half-lives.

Thus, the order of costs between genes is not expected to be affected by a more complex calculation accounting for protein decay and protein half-lives.

We think these points should be in Supporting Information document since they are not novel. Lynch and Marinov as well as Wagner have studied and reported these points in detail in their work. We have replicated their results and have used them to understand rhythmicity, which is the focus of our manuscript.

The authors claimed that cycling genes are enriched in highly expressed genes, by showing rhythmic proteins are costlier than non-rhythmic proteins (based on the expression cost function) in several species. However, only the first 15% of proteins based on p-values ranking from their rhythm detection algorithms were classified rhythmic. One potential artifact of this classification is that the identified rhythmic genes are biasedly highly expressed genes because the lower-amplitude genes are harder to detect and excluded by the algorithm. If changing the threshold for rhythmicity to include more rhythmic genes with intermediate p-values (p-value Since the results of this paper would be sensitive to the accuracy of identifying rhythmicity at both mRNA and protein levels, it is crucial to validate the rhythm detection algorithm by cross-checking algorithm-generated results with those known rhythmic genes. Can the authors estimate the false positive and false negative rates in each group of the rhythmic and non-rhythmic proteins or mRNAs identified by their algorithm?

Our 2020 paper (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007666) addresses these issues, but we did not make this sufficiently clear here. We have now added some details of our previous results in the main text to clarify, as this a logical limitation remark. We mostly use GeneCycle based on the results of the benchmarking in that paper; it notably produces a uniform distribution under the null hypothesis and a skew towards low p-values for all empirical data.

Furthermore, cycling genes have been shown to **be over-represented among highly expressed genes (Laloum & Robinson-Rechavi 2020, Wang et al. 2015).

• Furthermore, we have shown in our previous work that rhythmic genes are largely enriched in highly expressed genes, and that the differences in rhythm detection obtained between highly and lowly expressed genes either reflect true biology or a lower signal to noise ratio in lowly expressed genes (Laloum & Robinson-Rechavi 2020).

  Higher gene expression usually leads to lower genetic noise. The authors thus applied a definition of the stochastic gene expression (SGE) that controls the biases associated with the correlation between the expression mean and variance to evaluate expression noise. They found lower noise with rhythmic transcripts. However, they did not explain, mechanistically, why rhythmic RNA has lower noise and what is the biological meaning behind this finding. It is also unclear whether they considered the phase difference between signal and noise that usually exists in an oscillatory system.

Please see answer to second reviewer.

Minor comments:

It would be helpful if the authors could interpret their observations including where the results may not be as significant. A few examples are listed below.

1) In tissue-specific studies, they used the transcriptomics datasets from 11 mouse tissues to compare the difference in expression levels (based on z-score) of each gene between tissue groups of rhythmic and non-rhythmic expression and found higher gene expression in rhythmic tissues. However, proteins showed a bimodal distribution, and it would be helpful to add interpretation or discussion regarding this bimodal distribution.

Note that for proteins, the delta was calculated based on only 3 or 4 tissues, which limits a lot our detection power. We now proposed the hypothesis:

• We also provide results obtained from other datasets in supplementary Table S3, although they must be taken with caution since only 2 to 4 tissues were available, and sometimes data were coming from different experiments. Of note, for proteomic data, the distributions of ẟ are bimodal (Fig. S3), separating rhythmic proteins into two groups, with low or high protein levels in the tissues in which they are rhythmic. **A hypothesis is that for some tissue-specific proteins the rhythmic regulation is not tissue-specific, making them rhythmic also in tissues where they are lowly expressed. But the very small sample size does not allow us to test it, and we caution against any over-interpretation of this pattern before it can be confirmed.

  2) They also calculate partial correlation for rhythmicity with expression level over tissues for all tissue-specific genes (tau>0.5) and found Spearman's correlation coefficient is skewed towards negative (suggesting a correlation), but Pearson's correlation showed a positive peak. It indicates that a subset of genes is less rhythmic in the tissues where they are most expressed. Is this positive peak significant or expected? What are these genes? Any evolutionary benefits? Can the authors discuss the functional difference between these genes and other genes that follow the predictions?

While Spearman’s correlation is clearly skewed towards negative correlations, i.e. lower p-values thus stronger signal of rhythmicity in the tissue where genes are more expressed, Pearson’s correlation also has a smaller peak of positive correlations (Fig. S4), suggesting a subset of genes which are less rhythmic in the tissues **where they are most expressed.

• While Spearman’s correlation is clearly skewed towards negative correlations, i.e. lower p-values thus stronger signal of rhythmicity in the tissue where genes are more expressed, Pearson’s correlation also has a smaller peak of positive correlations (Fig. S4a), suggesting a subset of genes which are less rhythmic in the tissues where they are most expressed. We show that tissue-specific genes which are mostly rhythmic in tissues where they are highly expressed are under stronger selective constraint than those which are rhythmic in tissues where they are lowly expressed (Fig. S4b). Thus, rhythmic expression of this second set of genes might be under weaker constraints.**

We added Fig. S4b in Supplementary figures.

3) In SGE analysis, the scRNA data of Arabidopsis was from roots, while the data for detecting the rhythmicity was from leaves. Without knowing whether the gene expression patterns in these two different parts are comparable, it is hard to judge the results. The authors may want to provide some discussion.

Indeed, this limits the interpretation for Arabidopsis, as noted in the results and in the discussion. We still prefer to report this pattern than to remove it. But, we have now moved the results obtained for Arabidopsis into Supplementary Table S5.

• In Arabidopsis, the single-cell data used are from the root, while transcriptomic time-series data used to detect rhythmicity are from the leaves, which limits the interpretation. Despite this limitation, we found no evidence of lower noise for genes that are rhythmic at the protein level (Table 1b and 1e, and Supplementary Table S5), **and trends towards lower noise in almost all cases for genes with rhythmic mRNAs (Table 1a, 1c, and 1d).
• Our results in mouse are consistent with all of these considerations (Table 1 and Supplementary Table S5), although it was not fully the case for Arabidopsis (Supplementary Table S5). However, this last point might be explained by the tissue-specificity of rhythmic gene expression. Indeed, for Arabidopsis, the time-series dataset come from leaves whereas single-cell RNA data come from roots.

  For Mouse tissues, while most show lower noise for rhythmic genes, they saw the opposite in Muscle. Is this significant? Any discussion?

For mouse muscle, we had not mentioned it since it was the only tissue showing such a trend. We now added comment regarding this in the main text:

• In mouse, tissue muscle gave opposite result, possibly because skeletal muscle is one of the most un-rhythmic tissues in the body.

  In various places of the text, the authors only pointed the readers to "Supporting information" without explicitly referring to a specific supplemental figure by its number. It would be helpful to cite a table or figure explicitly.

We agree, and have corrected this. See first General Statements.

Figure 2 does not have legends in the graphs.

This is now corrected, thank you for your attention.

REVIEWER #2

Major comments:

• Our major concern regards the identification of rhythmic genes.

Despite we are not experts in the specific method used (details are not provided in the manuscript), a method looking for a statisical significant periodicity in a noisy signal will provide a high p-value for a signal sufficiently above the noise level. Gene expression data are noisy because of stochastic gene expression and technical noise (e.g., the sampling noise due to RNA capture in RNAseq data). This noise scales with the average level of expression. Lowly expressed genes generally display larger relative fluctuations (e.g., sampling noise is essentially Poisson-like). As a result, the method will identify with a higher probability genes that are highly expressed as rhythmic genes since the signal to noise ratio is generally higher.

This could significantly bias the subsequent analysis, since most of the claims are related to a link between expression levels and rhythmicity.

[There is not even an obvious separtation of timescales that can be invoked between a possible 24-hour periodic signal and the fluctuations. For example, the timescale of protein fluctuations can be largely set by dilution and thus have a timescale comparable to the cell cycle.]

The authors should discuss this issue, which is overlooled in the current manuscript.

How much this potential bias affects the selection of rhythmic genes can probably be assessed using synthetic data.

• It would be useful to clarify in the main text what are the units of measurement of gene expression at the mRNA and at the protein level. If we understood correctly, the authors used FPKM and protein counts respectively. The dynamics in time could in principle be different if an absolute or a normalized level of expression is considered. For example, the cell cycle can be correlated with the circadian clock (as reported for example in cyanobacteria). Since the absolute amount of total proteins has to approximately double during a cell cycle (for cell size homeostasis), this can create a periodic signal in protein counts with a 24-hour period.

The same reasoning does not hold true if the measurement is normalized, as in the FPKM case.

The authors should discuss this issue or simply show that the results for proteins are robust if the protein count is normalized (for example with respect to the total protein amount).

We haven’t focused the present manuscript on these issues since we recently published another paper which addresses these points: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007666

We have now added some details of our previous results in the main text to make the work more relevant.

• The expression cost defined in the manuscript seems dominated by the expression level.

It would be useful to report the scatter plot and the correlation level of cost versus average expression. A high correlation between these two quantities can largely recapitulate the results in Figure 2 (even though the results presented are still interesting per se). In other words, the relation between cost and rhytmicity sounds like a simple rephrasing of the relation between average expression level and rhythmicity (previously reported as correctly referenced in the manuscript).

We now provide these results in Fig. S2 (Supplementary figures) and show a negative and significant correlation between the order of the rhythmicity signal and the total expression cost (calculated from the mean expression level). Since our previous benchmark show that the order of genes from most to less rhythmic genes is not very reliable for known methods, including the one used here, we prefer to present this result in the Supplementary figures document.

• The empirical observation of a relation between noise and rhythmicity in mRNA expression is interesting, but we cannot fully understand its link with the theoretical arguments proposed.

The Authors suggest that perodicity in mRNA expression could decrease protein noise at the peak of mRNA expression (Fig.S1). But this is not what they can measure in the single-cell data analyzed, where cell-to-cell variability is reported at a single timepoint for a cell population. If the oscillations are not syncronized in the cell population, an oscillating transcript would simply display a high cell-to-cell variability dominated by the amplitude of oscillations. Even if the oscillations are syncronized, there is no information in the dataset about the mRNA dynamics. Thus, mRNA cell-to-cell variability could have been measured at any point of its (putative) cyclic dynamics.

Thus, we propose to make more clear the connections between the theoretical arguments and the empirical observation about noise in gene expression.

Thank you for pointing out this issue. We have clarified the following in the main text:

These considerations lead to predictions which we test here: i) a decreased stochasticity strategy for genes with rhythmically accumulated mRNAs ...**.

• These considerations lead to predictions which we test here: i) a strategy to periodically decrease stochasticity for genes with rhythmically accumulated mRNAs .... Assuming that genes with low noise have noise-sensitive functions (and thus noise is tightly controlled), these results support the hypothesis that noise is globally reduced thanks **to rhythmic regulation at the transcriptional level.

• Our results show that noise is globally reduced for genes with rhythmic regulation at the transcriptional level. Since rhythmic genes are not all in the same phase (Fig. S9a in Supporting information), we expect this result obtained for a given time-point (noise estimation based on a single time-point scRNA dataset) to be general to all time-points (section 6.3 in Supporting information). Assuming that genes with low noise have noise-sensitive functions (and thus noise is tightly controlled), these results suggest that rhythmic genes have their noise periodically and drastically reduced through periodic high accumulation of their mRNAs.

• Thus, since we find lower noise among rhythmic transcripts, rhythmic expression of RNAs might be a way to periodically reduce expression noise of highly expressed genes (Figure 2 and Fig. S1-S2), which are under stronger selection. Indeed, we found that genes with rhythmic transcripts are under stronger selection, even controlling for expression level effect. As proposed by Horvath et al. (2019) and supported by results in mouse by Barroso et al. (2018) genes under strong selection could also be less tolerant to high noise of expression. Thus, periodic accumulation of mRNAs might be a way to periodically reduce expression noise of noise-sensitive genes (Fig 1c), i.e. genes under stronger selection. **However, our results are limited by the fact that noise estimation is based on a single time-point measurement since no scRNA time-series data are currently available for these species. Since the peak time of rhythmic transcripts is distributed across all times (Supporting Information Fig. S9a), the mean noise estimated at a given time-point includes the noise of the genes that are peaking at that time (lowest noise) and all the others that have a higher noise than those at their own peak time-point (Supporting Information Fig. S9b). Our results suggest that rhythmic genes peaking at the time-point of the scRNA measurement have sufficiently low noise for the mean noise of rhythmic genes to be much lower than that of non-rhythmic genes.

  • As a simple additional test of robustness of the rhythmic gene selection, biological replicates can be used, although this would not resolve the possible bias discussed above. As explained by the Authors, some of the datasets analyzed have biological replicates. It would be interesting to know the robustness of the detection method across replicates. How much is the set of genes identified as rhythmic conserved if estimated on different replicates? Spearman correlation or simply the overlap between the sets (maybe assessed with a hypergeometric test) can be used.

These points have been already addressed in our 2020 paper https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007666 (paragraph “The importance of having an informative dataset”) as well as in recent guidelines (Hughes et al. 2017). We specified in Methods that we considered replicates as new cycles as recommended.

Minor comments:

• The claim that "transcriptional noise is known to be the main driver of overall expression noise", which is present in the discussion is questionable.

For example, the quantitative large-scale dataset referenced by the Authors for E.coli (Taniguchi et al) shows instead that the dominant source of noise is extrinsic for many of the genes tested.

We have clarified in the main text that by “main driver of the overall noise” we refer to the relative contribution of transcriptional versus translational noise into the overall noise.

We have also added the section 6.1 into Supporting Information document:

• Relatively to translational noise, transcriptional noise is the main driver of the overall noise (Raj and van Oudenaarden 2008) and should give a good estimation of the output noise. Indeed, based on estimations of coefficient of variations (CV, cell-to-cell variations of protein level) for diverse transcription and translation rates in E. coli and S. cerevisiae, Hausser et al. (2019) have shown that for a fixed transcriptional rate, CV is almost constant for diverse translational rates. Thus, changes in protein level have little to no impact on gene expression noise. The availability of mRNA molecules seems to drive the final noise. I.e., comparatively to the noise caused by the translational activity, the availability of low number molecules such as transcriptional factors (subject to the stochasticity of diffusion and binding in the cell environment) is the main factor of the output cell-to-cell variation in protein abundances. And have modified the main text:

Indeed, transcriptional noise, which we measure here, is known to be the main driver of overall expression **noise (Raj & van Oudenaarden 2008).

• Relatively to translational noise, transcriptional noise is the main source of the overall noise (Raj & van Oudenaarden 2008) (section 6.1 in Supporting information) In addition, highly expressed proteins are all precisely expressed and they display little variation in noise (also shown by Hausser et al. (2019) who reused Taniguchi et al. (2010) data). The noise of these highly expressed proteins is also just above a limit which is the noise floor. This "noise floor" is dominated by extrinsic noise as suggested by Hausser et al. and Taniguchi et al.: “The extrinsic noise in the last three terms in Eq. 4 (of the noise floor) might originate from fluctuations in cellular components such as metabolites, ribosomes, and polymerases and dominates the noise of high copy proteins” (Taniguchi et al.). Thus, highly expressed proteins are precisely expressed and their residual noise is similar to the noise floor, which is due to the extrinsic noise (imperfect synchrony of cell states inherent or due to the environment).

• We suggest to avoid explicit statements about a causal link between expression level and rhythmicity, as in the caption title of Figure 2. A detected correlation is not a proof of a causal relation.

We have corrected the sentence as follows:

Rhythmic proteins are costly proteins due to their high level of expression.

• High level of expression is the main factor explaining the higher cost observed in rhythmic proteins.

  • Supplementary Figures attached at the end of the main text and Supplementary Figures in the Supporting Information file have the same numbering...so there are two different versions of Fig.S1 S2 etc.

This complicates the work of the reader.

We have modified the numbering of figures to make them easier to follow.

-The legend of Fig 2 is missing (the legend is instead reported in Fig.S1).

This is now corrected, thank you for your attention

Other modifications:

We also show how cost can explain the tissue-specificity of rhythmic gene expression. Indeed, the nycthemeral transcriptome has long been known to be tissue-specific (Zhang et al. 2014, Boyle et al. 2017, Korenˇciˇc et al. 2014), i.e. a given gene can be rhythmic in some tissues, and constantly or not expressed in others.

• Furthermore, the nycthemeral transcriptome has long been known to be tissue-specific (Zhang et al. 2014, Boyle et al. 2017, Korenˇciˇc et al. 2014), i.e. a given gene can be rhythmic in some tissues, and constantly or not expressed in others. Here, we provide a first explanation for the tissue-specificity of rhythms in gene expression by showing that genes are more likely to be rhythmic in tissues where they are specifically highly expressed.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The manuscript proposes an interesting hypothesis to explain the widespread presence of rhythmicity in gene expression. The Authors suggest that rhythmicity can be the combined result of cost optimization and control of gene expression noise. To support this hypothesis, they analyzed several proteomic and RNA sequencing datasets across different species. Specifically, putative rhythmic genes were identified using a published tool from time-series datasets. Their first claim concerns the typical expression cost (Cp) for rhythmic vs. non-rhythmic genes. The evaluated Cp is empirically (slightly but significantly) higher for rhythmic genes, mainly because these genes on average show higher expression levels than non-rhythmic genes. The analysis of tissue-specific expression data further supports this relation between expression levels and rhythmicity. Genes are more likely to be rhythmic in tissues where they are specifically highly expressed. To investigate the additional hypothesis of a relation with noise control, the Authors compared expression fluctuations of rhythmic and non-rhythmic genes, measuring noise only at the mRNA level (and using a specific noise measure). According to this measure, genes displaying rhythmicity, in particular at the transcript level, are indeed in most cases less noisy than non-rhythmic genes.<br> Finally, the analysis of protein evolutionary conservation between rhythmic and non-rhythmic genes suggests that genes with rhythmic transcription are under strong purifying selection.

The paper is concise and well written. The data used are described in sufficient detail to reproduce the results.

Major comments:

• Our major concern regards the identification of rhythmic genes.

Despite we are not experts in the specific method used (details are not provided in the manuscript), a method looking for a statisical significant periodicity in a noisy signal will provide a high p-value for a signal sufficiently above the noise level. Gene expression data are noisy because of stochastic gene expression and technical noise (e.g., the sampling noise due to RNA capture in RNAseq data). This noise scales with the average level of expression. Lowly expressed genes generally display larger relative fluctuations (e.g., sampling noise is essentially Poisson-like). As a result, the method will identify with a higher probability genes that are highly expressed as rhythmic genes since the signal to noise ratio is generally higher. This could significantly bias the subsequent analysis, since most of the claims are related to a link between expression levels and rhythmicity. [There is not even an obvious separtation of timescales that can be invoked between a possible 24-hour periodic signal and the fluctuations. For example, the timescale of protein fluctuations can be largely set by dilution and thus have a timescale comparable to the cell cycle.]

The authors should discuss this issue, which is overlooled in the current manuscript. How much this potential bias affects the selection of rhythmic genes can probably be assessed using synthetic data.

• It would be useful to clarify in the main text what are the units of measurement of gene expression at the mRNA and at the protein level. If we understood correctly, the authors used FPKM and protein counts respectively. The dynamics in time could in principle be different if an absolute or a normalized level of expression is considered. For example, the cell cycle can be correlated with the circadian clock (as reported for example in cyanobacteria). Since the absolute amount of total proteins has to approximately double during a cell cycle (for cell size homeostasis), this can create a periodic signal in protein counts with a 24-hour period.

The same reasoning does not hold true if the measurement is normalized, as in the FPKM case. The authors should discuss this issue or simply show that the results for proteins are robust if the protein count is normalized (for example with respect to the total protein amount).

• The expression cost defined in the manuscript seems dominated by the expression level. It would be useful to report the scatter plot and the correlation level of cost versus average expression. A high correlation between these two quantities can largely recapitulate the results in Figure 2 (even though the results presented are still interesting per se). In other words, the relation between cost and rhytmicity sounds like a simple rephrasing of the relation between average expression level and rhythmicity (previously reported as correctly referenced in the manuscript).
• The empirical observation of a relation between noise and rhythmicity in mRNA expression is interesting, but we cannot fully understand its link with the theoretical arguments proposed. The Authors suggest that perodicity in mRNA expression could decrease protein noise at the peak of mRNA expression (Fig.S1). But this is not what they can measure in the single-cell data analyzed, where cell-to-cell variability is reported at a single timepoint for a cell population. If the oscillations are not syncronized in the cell population, an oscillating transcript would simply display a high cell-to-cell variability dominated by the amplitude of oscillations. Even if the oscillations are syncronized, there is no information in the dataset about the mRNA dynamics. Thus, mRNA cell-to-cell variability could have been measured at any point of its (putative) cyclic dynamics. Thus, we propose to make more clear the connections between the theoretical arguments and the empirical observation about noise in gene expression.
• As a simple additional test of robustness of the rhythmic gene selection, biological replicates can be used, although this would not resolve the possible bias discussed above. As explained by the Authors, some of the datasets analyzed have biological replicates. It would be interesting to know the robustness of the detection method across replicates. How much is the set of genes identified as rhythmic conserved if estimated on different replicates? Spearman correlation or simply the overlap between the sets (maybe assessed with a hypergeometric test) can be used.

Minor comments:

• The claim that "transcriptional noise is known to be the main driver of overall expression noise", which is present in the discussion is questionable. For example, the quantitative large-scale dataset referenced by the Authors for E.coli (Taniguchi et al) shows instead that the dominant source of noise is extrinsic for many of the genes tested.
• We suggest to avoid explicit statements about a causal link between expression level and rhythmicity, as in the caption title of Figure 2. A detected correlation is not a proof of a causal relation.
• Supplementary Figures attached at the end of the main text and Supplementary Figures in the Supporting Information file have the same numbering...so there are two different versions of Fig.S1 S2 etc. This complicates the work of the reader. -The legend of Fig 2 is missing (the legend is instead reported in Fig.S1).

Significance

The hypothesis of a link between rhythmic expression, expression cost and noise control is intriguing and can be of interest for a large audience of scientists from computational and evolutionary biologists to interdisciplinary researchers interested in models of gene expression.

Our combined expertise (keywords):

Physical biology, mathematical modelling, stochastic gene expression, transcriptomic data, quantititative cell physiology, genomics.

Referee Cross-commenting

The other report looks fair to me too. We seem to agree on the relevance of the questions asked, but also on some major concerns about the methods used to support the conclusions. Thanks!

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Referee #1

Evidence, reproducibility and clarity

Summary:

This paper explored the evolutionary advantages of having nycthemeral rhythmicity in many genes, using genome-wide transcriptomics and proteomics datasets from bacteria, plants, animals, and specific mouse tissues. As the main findings of this paper, the authors first applied a cost function with the proteomics data in four species and showed that rhythmic proteins are costlier. They also evaluated the stochastic gene expression (SGE) using single-cell RNA (scRNA) data from several plant and animal species and found that genes with rhythmic mRNAs had lower noise than non-rhythmic mRNAs. They argued that rhythmic genes are evolutionarily selected because of the cost-saving advantage at the protein level and the noise control strategy at the mRNA level.

In addition to their main findings, the authors also compared the protein evolutionary conservation between rhythmic and non-rhythmic genes using dN/dS data (the ratio of non-synonymous to synonymous substitutions). They found that genes with rhythmic transcripts were more conserved even after controlling for the effect of gene expression and suggested that rhythmic transcripts are important for genes under strong purifying selection.

Major comments:

The finding that rhythmic genes are costlier does not convincingly lead to the conclusion that protein rhythmicity has a cost-saving advantage. To make sense of this conclusion, the authors made several assumptions that lack convincing support. They assumed the optimal constant level would be the maximum over the rhythm period when rhythmic regulation is absent. They also assumed the trade-off between the benefits of not producing proteins when they are not needed (costs saved) and the costs involved in making it rhythmic (costs of complexity), which they argued lead to the expectation that costlier genes be more frequently rhythmic. However, there was no explicit definition for the trade-off, so it is unclear how it leads to the expectation. First, when proteins are not needed, it can be either the case of not producing extra proteins (cost saved) or the case of degrading excessive proteins (cost incurred). Second, the "costs of complexity" were not defined. It would be more convincing to define a fitness function or cost function to demonstrate their argument that costlier genes have fitness advantages if they are rhythmic. The cost function presented in this paper may be oversimplified. It only takes into account the costs to produce protein. The authors argued that a more complex cost calculation would not change the observation, but without proving it. However, protein degradation, including ubiquitination and proteolysis, requires energy; for a rhythmic gene, it is also necessary to consider the cost of maintaining the rhythmicity, including the temporally precise regulation of protein expression when the proteins are needed and of protein destruction when they are not.

The authors claimed that cycling genes are enriched in highly expressed genes, by showing rhythmic proteins are costlier than non-rhythmic proteins (based on the expression cost function) in several species. However, only the first 15% of proteins based on p-values ranking from their rhythm detection algorithms were classified rhythmic. One potential artifact of this classification is that the identified rhythmic genes are biasedly highly expressed genes because the lower-amplitude genes are harder to detect and excluded by the algorithm. If changing the threshold for rhythmicity to include more rhythmic genes with intermediate p-values (p-value<=0.05), will this change the results? Since the results of this paper would be sensitive to the accuracy of identifying rhythmicity at both mRNA and protein levels, it is crucial to validate the rhythm detection algorithm by cross-checking algorithm-generated results with those known rhythmic genes. Can the authors estimate the false positive and false negative rates in each group of the rhythmic and non-rhythmic proteins or mRNAs identified by their algorithm? Higher gene expression usually leads to lower genetic noise. The authors thus applied a definition of the stochastic gene expression (SGE) that controls the biases associated with the correlation between the expression mean and variance to evaluate expression noise. They found lower noise with rhythmic transcripts. However, they did not explain, mechanistically, why rhythmic RNA has lower noise and what is the biological meaning behind this finding. It is also unclear whether they considered the phase difference between signal and noise that usually exists in an oscillatory system.

Minor comments:

It would be helpful if the authors could interpret their observations including where the results may not be as significant. A few examples are listed below.

1) In tissue-specific studies, they used the transcriptomics datasets from 11 mouse tissues to compare the difference in expression levels (based on z-score) of each gene between tissue groups of rhythmic and non-rhythmic expression and found higher gene expression in rhythmic tissues. However, proteins showed a bimodal distribution, and it would be helpful to add interpretation or discussion regarding this bimodal distribution.

2) They also calculate partial correlation for rhythmicity with expression level over tissues for all tissue-specific genes (tau>0.5) and found Spearman's correlation coefficient is skewed towards negative (suggesting a correlation), but Pearson's correlation showed a positive peak. It indicates that a subset of genes is less rhythmic in the tissues where they are most expressed. Is this positive peak significant or expected? What are these genes? Any evolutionary benefits? Can the authors discuss the functional difference between these genes and other genes that follow the predictions?

3) In SGE analysis, the scRNA data of Arabidopsis was from roots, while the data for detecting the rhythmicity was from leaves. Without knowing whether the gene expression patterns in these two different parts are comparable, it is hard to judge the results. The authors may want to provide some discussion. For Mouse tissues, while most show lower noise for rhythmic genes, they saw the opposite in Muscle. Is this significant? Any discussion?

In various places of the text, the authors only pointed the readers to "Supporting information" without explicitly referring to a specific supplemental figure by its number. It would be helpful to cite a table or figure explicitly. Figure 2 does not have legends in the graphs.

Significance

The paper attempts to understand the origins of why many genes display nycthemeral rhythmicities. The question, if addressed, would have a significant impact in the fields of computational systems biology and evolutionary biology. But the findings of this study do not provide a satisfying answer to the question, thus reducing the significance. The conclusions are too overarching without providing significant biological insights and interpretation. Our field of expertise is in systems biology, but we do not have sufficient expertise to evaluate computational tools used to classify genome-wide gene expression data.

Referee Cross-commenting

I have reviewed other reports, which look fair to me. I have no comments. Thanks!

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Reply to the reviewers

First of all, I sincerely appreciate the critical reading of our manuscript by the reviewers.

Point-by-point responses to the reviewer #1’s comments

Most of the key conclusions are valid but the main one should be either reinforced or tuned down.

Through our study, we want to indicate that MTCL2 preferentially associates with perinuclear MTs accumulated around the Golgi complex, and its target is not necessarily restricted to “Golgi-associated (nucleated) MTs.” In this sense, the sentences in the previous manuscript, such as “MTCL2 preferentially associates with Golgi-associated MTs” and “MTCL1 and 2 …. are specifically condensed on Golgi-associated MTs,” were overstatements and completely misleading.

According to reviewer#1’s comment, we carefully revised these sentences throughout the manuscript and eliminated ambiguity on this point as far as possible.

The corresponding revisions are as follows.

In particular, the authors tend to give central role to MTCL2 in regulating the formation and organization of Golgi-associated MT network, and conversely in organizing Golgi elements, without considering the other factors identified (the authors cite relevant papers though but do not discuss this). They should analyze the function of MTCL2 in relation to the role of CLASP2, AKAP450, Golgi-g-Tubulin, or even EB proteins (like EB3).

I agree with the above comment since it is important to analyze how MTCL2 preferentially associates with the perinuclear MTs accumulated around the Golgi complex.

In the revised manuscript, we included new data analyzing knockdown effects of CLASP1/2 and AKAP450 on the subcellular localization of MTCL2 (Fig. 7A). These data indicate that CLASPs but not AKAP450 are required for the preferential localization of MTCL2 to the perinuclear MTs around the Golgi. We also demonstrate that the minimum Golgi-localizing region of MTCL2 (the N-terminal coiled-coil region) physically associates with CLASP2 (Fig. 7B), further supporting the idea that CLASPs mediate the Golgi association of MTCL2. Additional involvement of another Golgi element, giantin, is also suggested through Fig. 7C and Appendix Fig. S6. We believe that these revisions significantly improved the weakness previously pointed out by the reviewer.

I also do not think that carrying out super resolution microscopy is enough to "reveal the possibility that MTCL2 mediates the association of the Golgi membrane with stabilized MTs". More generally, the authors cannot conclude that MTCL2 preferentially associated to Golgi-MT only from their immunofluorescence and KD experiments. The centrosome (the main MTOC) is indeed also localized in the perinuclear area. Easy to do additional experiments may help to confirm these conclusions (see below). Also, the authors could strengthen the way the study how MTCL1 and MTCL2 binds to microtubules and Golgi (see below). The localization or interaction of MTCL2 with Golgi-associated MT is not directly shown.

Previously, we demonstrated that the N-terminal region of MTCL2 shows clear Golgi-localization activity, whereas the C-terminal region directly binds to MTs. These data support our conclusion that MTCL2 mediates the association of the Golgi membrane with general MTs (although not with Golgi-associated or stabilized MTs).

In the revised manuscript, we reinforced these data by newly revealing that four-point mutations (4LA) in the first coiled-coil motif disrupt the Golgi localization of the N-terminal region of MTCL2 (Fig. 4D). Thereafter, we found that introduction of the same mutations in full-length MTCL2 abolished its preferential association to the perinuclear MTs accumulating around the Golgi without affecting its localization to MTs (Fig. 4E and F). In addition, we provide data on candidate molecules mediating the Golgi association of MTCL2, as stated above (Fig. 7). These results reinforce our immunofluorescence analysis results (Fig. 2) and indicate that the preferential association of MTCL2 to perinuclear MTs accumulating around the Golgi is facilitated by physical interactions between the N-terminal region of MTCL2 and the Golgi-resident proteins, such as CLASPs and giantin.

The title should be changed also. I am not sure I understand what an asymmetric microtubule network means in this context. I guess that the authors mean non-centrosomal microtubule network.

We acknowledge the confusion caused by our previous manuscript. By “an asymmetric MT network” we meant not equivalent to “non-centrosomal MT network.”

In many cases, microtubules do not elongate radially (symmetrically) from the centrosome but intensely accumulate around the Golgi area and show asymmetric organization (see Meiring et al. Curr. Opin. Cell Biol. 62: 86-95, 2020). “An asymmetric MT network” in the title corresponds to this asymmetric array of general MTs accumulating around the GA.

The present findings that MTCL2 depletion severely disrupted MT accumulation around the Golgi and induced random and rather symmetric arrays of MTs (Fig. 5A) are very impressive. We believe that the knockdown/rescue experiments in this study strongly support the title by demonstrating that MTCL2 facilitates MT accumulation around the Golgi through its dual binding activity to MTs and the Golgi membrane.

We changed the title in the revised manuscript but still used the term “asymmetric microtubule organization” based on these rationalities.

The authors also state that tubulin acetylation is induced by MTCL1 C-MTBD but it may simply be stabilized. They should also clarify if MTCL2 regulates Golgi-dependant nucleation microtubules.

Yes, we think that MTCL1 C-MTBD enhances tubulin acetylation by simply stabilizing the polymerization state of MTs (see Kader et al. PLos One 12: e0182641, 2017). As for the second point, please see our response below to the comment (7).

I was not convinced by the use of the quantification of "skewness", in particular in figure 5B. Whether a Wilcoxon test is adequate is unclear to me.

I understand that utilization of skewness, a measure of the asymmetry of distribution, might not be popular in previous studies. In fact, the skewness of tubulin signal distribution in pixels does not indicate in which way MTs distribute asymmetrically by themselves. However, quantification of this statistical parameter does not require any arbitrary factors and thus eliminates the chance of using discretion as far as possible. Therefore, we are confident that this is the best way to estimate the asymmetric organization of microtubules, which are severely affected by various conditions, without any preconception.

The two biological phenomena we attempted to elucidate here (microtubule arrays and Golgi ribbon expansion) are thought to be context-dependent in each cell (for example, cell cycle, cell densities, etc.). Therefore, we do not have any substantial reason to assume a normal distribution for variation of the two values (skewness of tubulin signal distribution and Golgi ribbon expansion angle) in our cell population. Therefore, we considered that the Wilcoxon test, being a non-parametric rank test, was the most appropriate and safest test to use.

To demonstrate that MTCL2 associated to Golgi-MT, microtubule regrowth experiments following nocodazole treatment have to be conducted (time course). Another efficient way to analyze such events, as shown by the Kaverina and the Akhmanova labs for example, is to use fluorescent EB proteins (e.g. EB3) to image microtubule plus ends and back-track them to identify nucleation points. Carrying out such an experiment (nocodazole way-out and EB tracking) in the presence or absence of MTCL2 would allow to confirm, or not, the functional hypothesis of the authors.

We did not want to demonstrate that MTCL2 preferentially associates with “Golgi-MTs.” From this point of view, we do not think the experiments suggested by reviewer#1 were necessarily required for our study.

However, there is no doubt that one of the main components of the “perinuclear MTs accumulating around the Golgi” is “Golgi-associated (nucleated) MTs.” In this sense, we still agree with reviewer#1’s comment that it is better to examine whether MTCL2 is involved in MT nucleation from the Golgi membrane. The results of these experiments will be informative for readers particularly because we previously reported that MTCL1 stabilizes Golgi-associated (nucleated) MTs.

In keeping with the above consideration, we have performed both experiments (nocodazole way-out and EB tracking) according to the previous studies (for example, Sanders et. al. M.B.C. vol. 28; 3181-3192, 2017). However, we ultimately decided against the inclusion of the data as we could not overcome large cell-to-cell deviations.

Nevertheless, we believe that our current dataset adequately answers and supports the specific questions we explored. Briefly, if these experiments succeed to demonstrate the functional importance of MTCL2 for the development of Golgi-nucleated microtubules, they will not necessarily indicate the physical interaction of MTCL2 with Golgi-associated microtubules. In this respect, as described above, we have significantly supplemented data on the molecular mechanisms by which MTCL2 mediates MT–Golgi interactions. This improvement must sufficiently compensate lack of data from the experiments suggested by reviewer#1.

Several circumferential data suggest that MTCL2 is not involved in the development of Golgi-associated (nucleated) MTs in contrast to MTCL1. We discussed this issue in the “Discussion” of the revised manuscript.

Additionally, carrying electron microscopy analysis would be important to qualify better the effects observed on Golgi complexes upon depletion. The authors mention the effects on the "morphology of Golgi ribbon" but it is rather unclear.

We did not perform electron microscopy analysis, because we are not implicating a change in the ultrastructure of the Golgi apparatus in MTCL2-knockdown cells. We specifically want to demonstrate that MTCL2 knockdown changes the assembly structures of the Golgi ribbons, and we believe that it is feasible to do so by light microscopy. We realize that using the term “Golgi morphology” may be misleading in this context. In the revised manuscript, we replaced this term with appropriate ones, such as “assembly structures of the Golgi stacks” or “compactness of the Golgi ribbon.”

Last, because the authors compare the way MTCL1 and MTCL2 bind microtubules, and suggest intriguing differences, domain swapping experiments between these two isoforms would be important to carry out.

We conducted the suggested experiments and obtained interesting results. However, we ultimately decided against their inclusion given that the functional difference between MTCL1 and 2 is not the main point of discussion in our study.

Some studies are referred but the published data not actually used (with the exception of the final scheme). The authors should comment on the fact that other Golgi-associated MT binding proteins have been shown to be involved in the mechanisms highlighted here. Why they would not take over in the absence of MTCL2 should be properly discussed.

In the revised manuscript, we included data regarding the involvement of CLASPs and AKAP450 in the Golgi association of MTCL2. Accordingly, we introduced their roles in the development of Golgi-associated MTs as far as possible in the “Introduction” (see lines 29-36 and 38-42), “Results” (see lines 306-309 and 344-347), and “Discussion” (see lines 398-402 and 442-444).

Similarly, in the discussion, the authors indicate that SOGA has been found as an interacting partner of CLASP2. As CLASP2 is a microtubule binding protein also localized at the Golgi complex and binding to acetylated microtubules, the authors should at least comment on the putative role of the interaction between MTCL2 and CLASP2 in the phenotypes they described. The role of the interaction between CLASP2 and MTCL2 should be discussed and ideally tested.

As described above, we provided the data indicating the role of the interaction between MTCL2 and CLASP2 in the revised manuscript.

In the introduction, page 3 line 74-77, the authors wrote « The resultant N-terminal fragment is released into the cytoplasm to suppress autophagy by interacting with the Atg12/Atg5 complex, whereas the C-terminal fragment is secreted after further cleavage (see Fig. 1A, boxed illustration). » while on the Fig1 the boxed area indicates that SOGA bears Atg16 and Rab5 binding domains. Please double check the interacting partners of SOGA1.

Thank you for pointing this out. The sentence in the “Introduction” was revised to “… interacting with the Atg12/Atg5/Atg16 complex” (Rev. Endocr. Metab. Disord. 15, 137–147, 2014).

Figure 1 B and C are not cited in the main text.

These figures were cited in the “Introduction” section (line 65 in the previous manuscript). In the revised manuscript, these figures were replaced with Fig. EV1 A and C and cited in the “Introduction” section (line 59) as well as in the legend to Fig. 1 (line 757).

Figure 1E: a loading control is needed to evaluate the expression level of SOGA/MTCL2 in the mouse tissues.

Sample loading in each lane shown in previous Fig. 1E (Fig. 1D in the revised manuscript) was normalized by total protein amount (25 mg), as indicated in the figure legends. However, we have decided to add the data for a-tubulin expression in each lane as a reference, although they are not equal for each lane.

In the liver, the size of the bands is different than in other tissues (smaller size). The authors might comment if these smaller bands correspond to the cleaved version of SOGA that was previously described in mouse hepatocyt

In Fig. 1D of the revised manuscript, we added arrowheads indicating the bands of smaller sizes observed in some tissues such as the liver. In addition, we commented on them in the corresponding part of the “Results” section by describing that “we cannot exclude a possibility that MTCL2 is subjected to the reported cleavage and works as SOGA in these tissues.”

Figure 2A: single color picture for the anti-tubulin immunolabeling would help to see the distribution of microtubules in the perinuclear area. The perinuclear region is a crowded area with many intracellular compartments accumulating there as well as cytoskeleton elements.

We completely revised Fig. 2 following the reviewers’ suggestion. To provide single-color pictures for the anti-MTCL2 and anti-tubulin immunolabeling, we added new pictures examining colocalization of MTCL2 with MTs at the peripheral regions where densities of both signals are rather low. In Fig. 2B, the colocalization was further examined via a line scan analysis across MTs. Finally, we have included new data demonstrating that exogenously expressed MTCL2 similarly colocalized with MTs even at the peripheral regions when its expression was suppressed to the endogenous level (Fig. 2C).

Figure 2C: same comment as above, a single-color picture for the anti-MTCL2 and anti-GM130 immunolabeling are required.

Owing to the space limitation, we could not include a single-color picture for the anti-GM130 immunolabeling in Fig. 2, although we enlarged their merged figure so that readers easily agree with our statement: “some overlapped with the Golgi marker signals” (lines 146-147).

Alternatively, we included a new Appendix Fig. S8, in which immunofluorescence signals of MTCL2 and CLASP1/2 (A) or giantin (B) are compared at a super-resolution microscopic level. In these figures, we included single-color pictures together with merged data.

page 7, line 132-134: the authors state: « Close inspection using super-resolution microscopy further revealed the possibility that MTCL2 mediates the association of the Golgi membrane with stabilized MTs (Fig. 2D, arrows). » To my opinion, the data are over-interpreted. The signals partially co-localize but this does not indicate a function of MTCL2 in mediating the interaction.

We deleted the previous Fig. 2D and the corresponding sentence. By doing so, we ceased to suggest that MTCL2 functions to mediate MT–Golgi interactions only based on immunofluorescence data.

Figure 3: Another way of merging the anti MTCL2 and GS28 pictures have to be provided. The pictures are difficult to interpret with the current display.

We deleted the previous Fig. 4 and ceased to discuss colocalization of MTCL2 with Golgi proteins only based on immunolabeling data as mentioned above.

Figure 4C: please indicate the meaning of « ppt »

We included the explanation of “ppt” in the legends to the corresponding figure (Fig. 3C in the revised manuscript) as follows (lines 801-802):

“ppt represents the MT precipitate obtained after centrifugation (200,000 × g) for 20 min at 25°C.”

Figure 5B and C: for easier reading of the figure, it would be useful to annotate with MTCL2 construct is overexpressed following doxycycline treatment (MTCL2 WT (A) and MTCL2 delta C-MTBD (C)).

We followed the suggestion. Please see new Fig. 5 and Fig. EV4 and 5.

Figure 6 A and C: the labels are wrong. Bottom pictures correspond to anti-GM130 immunostaining not anti-tubulin. If I am not mistaken, it is MTCL2 delta C which is studied in panel C.

Thank you for pointing this out. We corrected this error in Fig. EV5 (previous Fig. 6) in the revised manuscript.

Page 11, line 212: Supplementary Figure 2 (knockdown in RPE1 cells) is intended to be cited not Supplementary Figure 3.

Thank you for pointing this out. We corrected the error in the revised manuscript appropriately.

Figure 8A: single color pictures are needed to appreciate the distribution of the signals

One of the major comments of three reviewers have been provided on Fig. 8, which reports that MTCL1 and 2 differentially regulate microtubules. We agree that the previous data in Fig. 8 A–C are rather preliminary. Although we could improve these figures according to the reviewers’ comments, we decided to omit these data and cease the discussion that MTCL1 and 2 localize with microtubules in a mutually exclusive manner, as this was not the main focus of the study.

Point-by-point responses to the reviewer #2’s comments

In figure 1D, a loading control should be included for the Western Blot probing for V5-mMTCL2 in HEK293T cells.

We did include loading controls for the indicated lanes. However, because the HEK293T cell extract in lanes 1–3 was diluted, the signals were too weak to be visualized in this figure (Fig. 2C in the revised manuscript).

The authors use the anti-SOGA antibody to detect MTCL2. However, in Figure 1A they do not show the sequence similarity between this region in MTCL1 and MTCL2. The authors should include this, as well as show that the anti-SOGA antibody is specific for MTCL2 and does not detect MTCL1.

In new Fig. EV1, we included amino acid sequence alignment data for the region corresponding to the used anti-SOGA1 antibody epitope. The data indicate significant divergence of the sequence from MTCL1 (6% homology, 23% similarity).

We also included new western blot data (Fig. 1B in the revised manuscript) demonstrating that anti-SOGA1 antibody does not react with MTCL1 exogenously expressed in HEK293T cells.

Line 132-134. The authors conclude that MTCL2 possible mediates association between Golgi membrane and stabilized MTs based on localization microscopy only. This is an overstatement and should be corrected. Not only is the microscopy technique used able to produce resolution of 140nm, which is not enough to show direct association; the staining techniques used (double antibody staining) ensures the fluorophores are approximately 20-30nm away from the intended target (MTs, MTCL2, or Golgi). Thus, the conclusion drawn is overstated and should be refined at this point in the manuscript.

I agree with reviewer#2’s comment that the previous data in Fig. 2D are insufficient to draw the conclusion that MTCL2 mediates the association between the Golgi membrane and stabilized MTs. We deleted the figure and the corresponding sentence reviewer #2 indicated.

We want to demonstrate that “MTCL2 mediates the association between the Golgi membrane and MTs (not restricted to the stabilized MTs).” In this sense, we have already obtained supportive data in the previous manuscript that the N-terminal region of MTCL2 has clear Golgi-localization activity, whereas the C-terminal region directly binds to MTs.

In the revised manuscript, we reinforced these data by revealing that four-point mutations (4LA) in the first coiled-coil motif disrupt the Golgi localization of the N-terminal region of MTCL2 (Fig. 4D). Thereafter, we found that introduction of the same mutations in full-length MTCL2 abolished its preferential association to the perinuclear MTs accumulating around the GA without affecting its colocalization to MTs (Fig. 4E and F). We also provide data on candidate molecules mediating the Golgi association of MTCL2 (Fig. 7). These results reinforce our immunofluorescence analysis results (Fig. 2) and indicate that the preferential association of MTCL2 to perinuclear MTs accumulating around the Golgi is facilitated by physical interactions between the N-terminal region of MTCL2 and the Golgi-resident proteins, such as CLASPs and giantin.

The authors should include some quantification of MTCL2 signals along stabilized microtubules near the Golgi and in peripheral regions of the cell in Figure 2. This will show that MTCL2 preferentially localizes to MTs in the Golgi region but not the periphery, as the authors claim (lines 124-130). This quantification could be in the form of linescans along or across MT signals.

We included a line scan data across peripheral MTs to confirm MTCL2 colocalization with MTs (Fig. 2C). However, it is difficult to perform a line scan for the perinuclear regions where both signals of MTCL2 and MTs are too dense. Therefore, we demonstrate the preferential colocalization of MTCL2 to the perinuclear MTs by comparing peripheral signals of MTCL2 with that of MAP4 (Fig. 2D).

The authors show that ectopic expression of the C-terminus of MTCL2 can rescue MTCL2 siRNA phenotypes. Since the N-terminus localizes strongly to the Golgi membrane, the authors should do corresponding experiments with this fragment, to determine if membrane binding of MTCL2 can have a similar rescue effect or if MT binding is essential. This is especially important for the Golgi-ribbon organization (Figure 6).

We did not include data indicating rescue activity of the C-terminal fragment of MTCL2. In the previous Fig. 5 and 6, we demonstrated that MTCL2 lacking the C-terminal microtubule-binding region does not show rescue activities. Therefore, we did not follow reviewer#2’s suggestion directly.

However, we included new data indicating that an MTCL2 mutant (4LA) that associates with MTs but not with the Golgi membrane also lacks rescue activities for asymmetric MT organization and Golgi ribbon compactness (new Fig. 5 and Fig. EV4). I hope these revisions are satisfactory.

Line 261-2. The authors claim that MTCL1 and MTCL2 function in a mutually exclusive manner. As with point 3, this is an overstatement based solely on localization microscopy. The authors cannot draw this conclusion from the data associated with this statement (Figure 8A) and it should be refined to reflect that they only comment on the respective localization patterns of MTCL1 and MTCL2. Additionally, to show that MTCL1 and MTCL2 do not overlap on MTs, the authors should include linescans along MTs showing the anti-V5 and anti-MTCL1 intensities.

One of the major comments of three reviewers have been provided on Fig. 8, which reports that MTCL1 and 2 differentially regulate microtubules. We agree that the previous data in Fig. 8 A–C are rather preliminary. Although we could improve these figures according to the reviewers’ comments, we decided to omit these data and cease the discussion that MTCL1 and 2 localize with microtubules in a mutually exclusive manner, as this was not the main focus of the study.

In Figure 8C the authors show acetylated tubulin staining in cells depleted of MTCL2. Based on this localization pattern, it seems the MT network is not grossly altered, as was shown in Figure 5 where perinuclear accumulation of MTs was lost. The authors should comment on whether acetylated tubulin presence and localization is altered in MTCL2-depleted cells. This is also mentioned in the discussion where the authors conclude that the major function of MTCL2 is to crosslink and accumulate MTs in the Golgi region. However, based on acetylated tubulin staining patterns, stable MTs seem to still accumulate in the Golgi region. The authors need to show this accumulated population of stable MTs is no longer crosslinked in the absence of MTCL2 to support their claim.

Acetylated microtubules represent a minor fraction of the perinuclearly accumulated microtubules. From the point of this view, it could be possible that the accumulation of perinuclear microtubules is severely affected, whereas that of acetylated microtubules is not. MTCL1 might crosslink these acetylated microtubules.

In any case, we have decided to delete the previous Fig. 8 A–C, as stated above.

To investigate potential functional overlap between MTCL1 and MTCL2, the authors should include a double depletion experiment where MT organization and Golgi organization are investigated. The currently shown experiments do not test a functional relationship between the two paralogs. Additionally, the authors should show Western Blot analysis of MTCL1 levels in MTCL2-depleted cells, and vice versa. While there does not seem to be an overlap in localization patterns of the two proteins, that does not mean there is no functional relationship.

We did not follow reviewer#2’s comment because of the reason stated above.

Lines 120-30 and 297-9. The authors state that based on the localization pattern of MTCL2 it mostly localizes along MTs in the perinuclear region (shown in Figure (2). Then, in the discussion they state MTCL2 preferentially localizes to Golgi membranes. Please clarify which of the two sites MTCL2 localizes to preferentially.

We agree that we should be more careful while describing the subcellular localization of MTCL2. We revised the information in the manuscript to indicate that MTCL2 preferentially localizes to perinuclearly accumulated microtubules showing partial colocalization to the Golgi membrane.

Loss of Golgi organization as described in Figures 6 does not appear in polarized cells in Figure 7. The authors should comment on the loss of the phenotype in polarized cells.

Since RPE1 cells cultured at high density show abnormally elongated shapes, as described in the original text (line 238; in the revised text, line 326), Golgi ribbons in these cells do not appear to be as expanded. However, their loss of compactness in MTCL2-knockdown cells can be easily recognized in the previous Fig. 7C (corresponding to Fig. 6C in the revised manuscript).

The authors should consider using colorblind friendly palettes in figures. For example, magenta/green instead of red/green and magenta/cyan/yellow instead of red/blue/green. Additionally, for tri-color images the combination red/green/white (Figure 4B, 7C) should be avoided, as overlapping red/green signals will show up as yellow which is difficult to distinguish from the white signals. Finally, human eyes detect shades of red much poorer than for example green. Therefore, the main point of a figure should not be in red. For example, MTCL2 is frequently shown as red signal in a merged image and should be replaced with a different color.

We incorporated the reviewer’s suggestion.

The authors claim the mouse MTCL2 protein lacks 203 N-terminal amino acids. Authors should clarify in the text that this is relative to mouse MTCL1. The authors should also include the human comparisons, as they work on human cell lines in the majority of the manuscript.

I am afraid that this comment is based on a misunderstanding by reviewer #2, because we did not claim that mouse MTCL2 lacks 203 N-terminal amino acids. Instead, we described that SOGA, a mouse MTCL2 isoform, lacks 203 N-terminal amino acids compared to the full-length mouse MTCL2, the cDNA of which was used in this work.

In Figure 1D the authors show Western Blots where various amounts of HEK293T extracts were probed for exogenously expressed MTCL2. As a control, authors should include a non-transfected control. From Figure 1E, it would be expected that HEK293 (kidney cells) would not express endogenous MTCL2, but the control should be included anyway.

In the revised Fig. 2B, we included a lane in which a non-transfected HEK293T cell extract was loaded, according to reviewer #2’s comment (see lanes indicated as mock).

In Figure 3, the color scheme in the final column of images should be changed. Red/white contrast is very poor and no conclusions can be drawn from these images. Additionally, the authors should include a box to show where the inset is located in the overview images.

In the revised manuscript, we deleted the “final column of images using red/white contrast” from Fig. 2D (previous Fig. 3), to avoid drawing a conclusion on the interaction between MTCL2 and the Golgi membrane only from immunofluorescence data.

In addition, we included boxes in the overview images to show where the inset is located, wherever it is required in the revised manuscript.

Authors claim that MTCL2 is not detected near more dynamic MTs in the periphery of the cell and references Figures 2A and 3. They should include annotation in the figures to highlight this. This can be done with arrowheads or other markings, or with additional insets enlarging a peripheral region of the cell.

To respond to the comment, we separately provided enlarged views of perinuclear and peripheral regions in the revised Fig. 2.

The authors should clarify in the main text and figure legend which superresolution microscopy technique was used in Figure 2D.

As mentioned above, we deleted the previous Fig. 2D.

The authors use methanol fixation to examine localization of MTCL2, MTs, and Golgi. Methanol extracts lipids and thus affects intracellular membrane compartments, and can affect the localization pattern of GM130, a Golgi matrix protein. The authors should include samples fixed with a crosslinking fixative to ensure their conclusions drawn from methanol-fixed samples are not affected by the choice of fixative.

According to the reviewer’s suggestion, we included additional data obtained using PFA fixations (Fig. EV2). PFA fixation also revealed a similar localization pattern of MTCL2 to that obtained by methanol fixation.

In Supplementary Figure 1B a third, relatively high expressing cell can be seen in the top panel. The GM130 signal for this cell seems to be comparable to non-transfected cells in the same image. Can the authors address this? Alternatively, to show differences in expression levels between these three cells in that panel and others, authors could use a heatmap LUT of the V5 signal to differentiate expression levels more clearly in different cells.

I am unsure whether the reviewer is referring to the cell located at the bottom-left corner of the panel in the previous Supplementary Fig. 1B (Appendix Fig. S1B in the revised manuscript). The cell shows a rather normal distribution pattern of exogenous MTCL2 similar to the endogenous one. We think this is the reason why it maintains a rather normal assembly structure of the Golgi ribbon. We included the word “frequently” in the sentence (line 153 in the revised text) to indicate that high levels of exogenous MTCL2 do not disrupt the normal Golgi ribbon structure. We do not think it is necessary to differentiate the expression levels of exogenous MTCL2 more clearly by using a heatmap, since this issue is not critical for the conclusions of this paper.

Line 139. How was the ectopic expression 'suppressed to endogenous levels'? The panels in Suppl Fig. 1 of 'low expression' clearly show increased MTCL2 signal when compared to non-transfected cells in the same panel still. This would suggest ectopic expression is still above endogenous levels.

We did not suppress the expression actively. We identified the cells expressing exogenous MTCL2 at low levels comparable to those of endogenous MTCL2. The information provided in line 139 of the previous text is not accurate. Thank you for pointing out this issue; we revised the sentence as follows: “However, when the expression levels were similar to the endogenous levels, … (lines 154-155 in the revised text)”

Figure 5C. The label for MTCL2 construct should read mMTCL2 ΔC-MTBD to clarify the expression construct used.

Since the labeling in previous Fig. 5 and 6 was confusing, we revised them all by adding the name of the expressed MTCL2 mutant under the label “+dox” (see Fig. 5, Fig. EV4, and Fig. EV5 in the revised manuscript).

In Figures 6A and 6C the label shows a-tubulin, but the staining is of a Golgi marker.

Thank you for pointing this out. We corrected this error in the corresponding figure (Fig. EV5) in the revised manuscript.

In Figures 6B and 6D the different conditions should be separated more in the graph, the datapoints overlap.

In the revised manuscript, we significantly improved the presentation of the statistical data shown in the previous Figs. 5 and 6 (Fig. 5 and Figs. EV4 and 5 in the revised manuscript). In these improvements, we determined to only include data of biological replicates in a single typical experiment in the main figures. Automatically, data points in the previous Fig. 6B and D were decreased in number and do not overlap anymore (see Figs. EV4 and EV5D). Instead, we have included new figures (Appendix Fig. S4) in which the results of technical replicates (three independent experiments) are presented.

Lines 246-7. The authors claim the Golgi-associated and centrosomal MTs can be easily distinguished in MTCL2 knockdown cells. They should include annotation in the corresponding figures to highlight these different populations.

We followed the reviewer’s suggestion by adding arrows in Fig. 6C of the revised manuscript.

Figure 8A. A horizontal line is missing in the panel showing MTCL/a-tub merge.

Thank you for pointing this out. As mentioned above, we deleted the previous Fig. 8A from the manuscript.

Figures 8C and 8D. The acetylated tubulin staining in control cells (control RNAi and GFP) in these panels vary greatly. Can the authors comment on this?

Expression of MTCL1 C-MTBD induces tubulin acetylation intensely. Therefore, to obtain appropriate pictures under non-saturated conditions, we had to decrease the gain of photomultiplier of the confocal microscopy system for the previous Fig. 8D. This is why acetylated tubulin signals in control cells appear to be too weak in the previous Fig. 8D than those in Fig. 8C.

In any case, we deleted the previous Fig. 8C in the revised manuscript as stated above. The previous Fig. 8D is solely included in Fig. EV3.

Additionally, there appears to be an increase in acetylated tubulin on the Western Blot (8E) shown in cells expressing GFP-MTCL2 CMTB that is not reflected in the image in Figure 8D. Since a significant population of GFP-MTCL2 CMBT localizes to the nucleus, it is possible that the functional population of GFP-MTCL2 CMBT that can stabilize MTs is much lower than GFP-MTCL1 CMBT despite showing equal levels in the Western Blot. The author should compare signal intensity in the cytosol of GFP-expressing cells and base their analysis of acetylated tubulin levels on cells where cytosolic levels are comparable.

We agree with this reviewer’s comment and did not include WB data in Fig. EV3B corresponding to the previous Fig. 8D.

As for quantification of the fluorescence data in Fig. 8D, we provided a typical result on the acetylate-tubulin signals normalized by GFP and a-tubulin signals in the boxed regions where cytosolic GFP signals are comparable.

Point-by-point responses to the reviewer #__3’s comments__

While the standard fluorescence images are of good quality, the quality of the super-resolution microscopic images is quite low and insufficient. Fig. 8A looks like an enlarged standard laser scanning microscope image, but does not achieve the resolution of a super-resolution image by far, which should be well below the µm range. However, such a resolution would be required to support the claim that MTCL1 and 2 locate on MTs in a mutually exclusive manner. (Negative) data from immunoprecipitation experiments also provide only weak evidence for the absence of a heterocomplex. I also fear that the fixation process creates artifacts. Experiments to image living cells would definitely bolster the data and also provide information about the dynamics of the interactions.

One of the major comments of three reviewers have been provided on Fig. 8, which reports that MTCL1 and 2 differentially regulate microtubules. We agree that the previous data in Fig. 8 A–C are rather preliminary. In the revised manuscript, we deleted these data and ceased to discuss that MTCL1 and 2 localize with microtubules in a mutually exclusive manner, as this was not the main focus of the study.

We also deleted the previous Fig. 2D (showing another super-resolution image) and the corresponding sentence. By doing so, we ceased to suggest that MTCL2 functions in mediating MT–Golgi interactions only based on immunofluorescence data.

It would also be relevant to confirm that the results are not a cell line artifact in HeLa cells.

In the previous manuscript, we included data indicating that the knockdown effects observed in HeLa-K cells (reduced accumulation of MTs around the Golgi as well as lateral expansion of the Golgi ribbon) are also induced in RPE1 cells by MTCL2 knockdown (Supplementary Fig. 2 in the previous manuscript). We included the same figure in the revised manuscript as Appendix Fig. S4.

A standard method for detecting microtubule association in cultured cells would be to use an extraction protocol. This has to be done to show that MTCL2 actually behaves like a microtubule-associated protein (MAP).

In the revised manuscript, we included new immunofluorescence data obtained using PFA fixation with or without pre-extraction, which revealed a similar localization pattern of MTCL2 to that obtained by methanol fixation (Fig. EV2). Pre-extraction was performed using BRB80 buffer supplemented with 0.5% TX-100 and 4 mM EGTA for 30 s, according to a protocol provided by Dr. Mitchison Laboratory.

I don't see that the study proves that MTCL2 is essential for the organization of an asymmetric microtubule network as the title claims. The experiments shown in Fig. 5 demonstrate a change in the skewness of the pixel intensity distribution dependent on the presence of MTCL2, which may indicate a contribution of MTCL2 (provided that the fixation and staining do not produce an artifact). However, they do not prove that MTLC2 is essential.

We cannot understand how an artifact due to the fixation and staining may be responsible for the results shown in the previous Fig. 5 (Fig. 5 and Figs. EV4 and 5 in the revised manuscript).

In many cases, microtubules do not elongate radially (symmetrically) from the centrosome but intensely accumulate around the Golgi area and show asymmetric organization (see Meiring et al. Curr. Opin. Cell Biol. 62: 86-95, 2020). “An asymmetric MT network” in the title corresponds to this asymmetric array of general MTs accumulating around the Golgi complex.

In this respect, our findings that MTCL2 depletion severely disrupted MT accumulation around the Golgi and induced random and rather symmetric arrays of MTs (Fig. 5A) are very impressive. We believe that the knockdown/rescue experiments in this study strongly support the title by demonstrating that MTCL2 facilitates MT accumulation around the Golgi through its dual binding activity to MTs and the Golgi membrane.

We are unable to comprehend the reviewer’s standpoint in not allowing us to conclude the essential role of MTCL2 in the organization of an asymmetric microtubule. However, the title in the revised manuscript was changed as follows.

“MTCL2 promotes asymmetric microtubule organization by crosslinking microtubules on the Golgi membrane”

There is also a large oversampling of the data by plotting each individual cell from only two separate experiments. It would be better and more reliable to present the data as the mean of the experiments (then of course more than 2 would be required). The same applies to the experiments in which the "Golgi ribbon expanding angle" was determined (Fig. 6).

In my opinion, statistical theories based on an ideal assumption cannot simply be applied to the quantitative analysis of biological phenomena. In our case, the MT distributions, as well as the Golgi ribbon expansion angles significantly deviate in a context-dependent manner in each cell (for example, cell cycle, cell densities, etc.). The deviation of these values between each cell (in biological replicates) is much larger than the experimental deviation, which is mainly dependent on the stochastic element (in technological replicates). I understand that this is the reason why many journals in cell biology do not necessarily require “three” independent experiments for statistical analysis.

In the revised manuscript, however, we included data from three independent experiments for all rescue experiments (Fig. 5, Figs. EV4 and 5, and Appendix Fig. S4) to further demonstrate the reliability of our data.

In the main figures (Fig. 5, Figs. EV4 and 5), we included statistical data of a single typical experiment to demonstrate reproducibility in biological replicates in each condition. To compensate for these figures, we listed statistical data for each biological replicate of all experiments in Appendix Fig. S4 A. In Appendix Fig. S4 B and C, we further provided statistical data of technical replicates (three independent experiments) by comparing the average of each biological replicate. We concluded that this is the best way to statistically demonstrate the reliability of the biological analysis.

We believe that the data collectively presented by these figures strongly support the reliability of our conclusions.

It would be good to support the claim that MTCL2 affects the Golgi ribbon structure through ultrastructural analysis (EM).

We did not perform electron microscopy analysis, because we are not implicating a change in the ultrastructure of the Golgi apparatus in MTCL2-knockdown cells. We specifically want to demonstrate that MTCL2 knockdown changes the assembly structures of the Golgi ribbons, and we believe that it is feasible to do so by light microscopy. We realize that using the term “Golgi morphology” may be misleading in this context. In the revised manuscript, we replaced this term with appropriate ones, such as “assembly structures of the Golgi stacks” or “compactness of the Golgi ribbon.”

The critical mechanistic question is which molecule on the Golgi side interacts with MTCL2, since the experiments with the deletion constructs would suggest that it is not the microstructure of the microtubules. As shown, the study is mainly descriptive in relation to this aspect.

We significantly improved this weakness by including new data indicating the possible involvement of CLASPs and giantin in mediating the Golgi association of MTCL2 (see Fig. 7 and Appendix Figs. S5–7).

We also revealed that four-point mutations (4LA) in the first coiled-coil motif disrupt the Golgi localization of the N-terminal region of MTCL2 (Fig. 4D). Thereafter, we found that introduction of the same mutations in full-length MTCL2 abolished its preferential association to the perinuclear MTs accumulating around the GA without affecting its colocalization to MTs (Fig. 4E and F).

These results reinforce our immunofluorescence results (Fig. 2) and indicate that the preferential association of MTCL2 to perinuclear MTs accumulating around the Golgi is facilitated by physical interactions between the N-terminal region of MTCL2 and the Golgi-resident proteins, such as CLASPs and giantin.

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Referee #3

Evidence, reproducibility and clarity

Matsuoka et al. describe an MTCL1 paralogue (MTCL2) that is present in vertebrates and binds to the Golgi membrane and interacts with microtubules. In contrast to MTCL1, MTCL2 contains only one microtubule binding region and does not stabilize any microtubules. The authors provide evidence that MTCL2 may be involved in accumulating microtubules on the Golgi and promote directed migration. The study is based on experiments with cell lines, predominantly HeLa cells, and relies heavily on the immunofluorescence staining of methanol-fixed cells. While the concept of a functional Golgi-microtubule interaction is interesting and may be relevant for directed migration, I am not convinced of the experimental support and interpretation provided by the authors.

1. The study relies entirely on the examination of cell lines, mainly HeLa cells, and the immunofluorescence of fixed cells. While the standard fluorescence images are of good quality, the quality of the super-resolution microscopic images is quite low and insufficient. Fig. 8A looks like an enlarged standard laser scanning microscope image, but does not achieve the resolution of a super-resolution image by far, which should be well below the µm range. However, such a resolution would be required to support the claim that MTCL1 and 2 locate on MTs in a mutually exclusive manner. (Negative) data from immunoprecipitation experiments also provide only weak evidence for the absence of a heterocomplex. I also fear that the fixation process creates artifacts. Experiments to image living cells would definitely bolster the data and also provide information about the dynamics of the interactions.
2. It would also be relevant to confirm that the results are not a cell line artifact in HeLa cells.
3. A standard method for detecting microtubule association in cultured cells would be to use an extraction protocol. This has to be done to show that MTCL2 actually behaves like a microtubule-associated protein (MAP).
4. I don't see that the study proves that MTCL2 is essential for the organization of an asymmetric microtubule network as the title claims. The experiments shown in Fig. 5 demonstrate a change in the skewness of the pixel intensity distribution dependent on the presence of MTCL2, which may indicate a contribution of MTCL2 (provided that the fixation and staining do not produce an artifact). However, they do not prove that MTLC2 is essential. There is also a large oversampling of the data by plotting each individual cell from only two separate experiments. It would be better and more reliable to present the data as the mean of the experiments (then of course more than 2 would be required). The same applies to the experiments in which the "Golgi ribbon expanding angle" was determined (Fig. 6).
5. It would be good to support the claim that MTCL2 affects the Golgi ribbon structure through ultrastructural analysis (EM).
6. The critical mechanistic question is which molecule on the Golgi side interacts with MTCL2, since the experiments with the deletion constructs would suggest that it is not the microstructure of the microtubules. As shown, the study is mainly descriptive in relation to this aspect.

Significance

The study is based on experiments with cell lines, predominantly HeLa cells, and relies heavily on the immunofluorescence staining of methanol-fixed cells. While the concept of a functional Golgi-microtubule interaction is interesting and may be relevant for directed migration, I am not convinced of the experimental support and interpretation provided by the authors.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this work, Matsuoka et al. describe a novel microtubule (MT) crosslinking factor, MTCL2. They use Western Blot analysis to show the presence of MTCL2 in various tissues and use a previously developed antibody to show its localization in cultured cells. The authors show that MTCL2 localizes along MTs in the Golgi region and that upon depletion of MTCL2, these MTs do not accumulate in the Golgi and Golgi organization is affected, leading to defects in migration. Through deletion mutant analysis, they show that MTCL2 C-terminus binds to MTs and that the N-terminus binds to Golgi membranes, though this may be lost or reduced in the full length protein. Expression of the C-terminal fragment rescues the phenotypes observed in MTCL2-depleted cells. Finally, the authors show that MTCL1 and MTCL2 show non-overlapping localization patterns and conclude they may have different functions in crosslinking and stabilizing MTs and Golgi organization.

Major comments:

1. In figure 1D, a loading control should be included for the Western Blot probing for V5-mMTCL2 in HEK293T cells.
2. The authors use the anti-SOGA antibody to detect MTCL2. However, in Figure 1A they do not show the sequence similarity between this region in MTCL1 and MTCL2. The authors should include this, as well as show that the anti-SOGA antibody is specific for MTCL2 and does not detect MTCL1.
3. Line 132-134. The authors conclude that MTCL2 possible mediates association between Golgi membrane and stabilized MTs based on localization microscopy only. This is an overstatement and should be corrected. Not only is the microscopy technique used able to produce resolution of 140nm, which is not enough to show direct association; the staining techniques used (double antibody staining) ensures the fluorophores are approximately 20-30nm away from the intended target (MTs, MTCL2, or Golgi). Thus, the conclusion drawn is overstated and should be refined at this point in the manuscript.
4. The authors should include some quantification of MTCL2 signals along stabilized microtubules near the Golgi and in peripheral regions of the cell in Figure 2. This will show that MTCL2 preferentially localizes to MTs in the Golgi region but not the periphery, as the authors claim (lines 124-130). This quantification could be in the form of linescans along or across MT signals.
5. The authors show that ectopic expression of the C-terminus of MTCL2 can rescue MTCL2 siRNA phenotypes. Since the N-terminus localizes strongly to the Golgi membrane, the authors should do corresponding experiments with this fragment, to determine if membrane binding of MTCL2 can have a similar rescue effect or if MT binding is essential. This is especially important for the Golgi-ribbon organization (Figure 6).
6. Line 261-2. The authors claim that MTCL1 and MTCL2 function in a mutually exclusive manner. As with point 3, this is an overstatement based solely on localization microscopy. The authors cannot draw this conclusion from the data associated with this statement (Figure 8A) and it should be refined to reflect that they only comment on the respective localization patterns of MTCL1 and MTCL2. Additionally, to show that MTCL1 and MTCL2 do not overlap on MTs, the authors should include linescans along MTs showing the anti-V5 and anti-MTCL1 intensities.
7. In Figure 8C the authors show acetylated tubulin staining in cells depleted of MTCL2. Based on this localization pattern, it seems the MT network is not grossly altered, as was shown in Figure 5 where perinuclear accumulation of MTs was lost. The authors should comment on whether acetylated tubulin presence and localization is altered in MTCL2-depleted cells. This is also mentioned in the discussion where the authors conclude that the major function of MTCL2 is to crosslink and accumulate MTs in the Golgi region. However, based on acetylated tubulin staining patterns, stable MTs seem to still accumulate in the Golgi region. The authors need to show this accumulated population of stable MTs is no longer crosslinked in the absence of MTCL2 to support their claim.
8. To investigate potential functional overlap between MTCL1 and MTCL2, the authors should include a double depletion experiment where MT organization and Golgi organization are investigated. The currently shown experiments do not test a functional relationship between the two paralogs. Additionally, the authors should show Western Blot analysis of MTCL1 levels in MTCL2-depleted cells, and vice versa. While there does not seem to be an overlap in localization patterns of the two proteins, that does not mean there is no functional relationship.
9. Lines 120-30 and 297-9. The authors state that based on the localization pattern of MTCL2 it mostly localizes along MTs in the perinuclear region (shown in Figure 2). Then, in the discussion they state MTCL2 preferentially localizes to Golgi membranes. Please clarify which of the two sites MTCL2 localizes to preferentially.
10. Loss of Golgi organization as described in Figures 6 does not appear in polarized cells in Figure 7. The authors should comment on the loss of the phenotype in polarized cells.

Minor comments:

1. The authors should consider using colorblind friendly palettes in figures. For example, magenta/green instead of red/green and magenta/cyan/yellow instead of red/blue/green. Additionally, for tri-color images the combination red/green/white (Figure 4B, 7C) should be avoided, as overlapping red/green signals will show up as yellow which is difficult to distinguish from the white signals. Finally, human eyes detect shades of red much poorer than for example green. Therefore, the main point of a figure should not be in red. For example, MTCL2 is frequently shown as red signal in a merged image and should be replaced with a different color.
2. The authors claim the mouse MTCL2 protein lacks 203 N-terminal amino acids. Authors should clarify in the text that this is relative to mouse MTCL1. The authors should also include the human comparisons, as they work on human cell lines in the majority of the manuscript.
3. In Figure 1D the authors show Western Blots where various amounts of HEK293T extracts were probed for exogenously expressed MTCL2. As a control, authors should include a non-transfected control. From Figure 1E, it would be expected that HEK293 (kidney cells) would not express endogenous MTCL2, but the control should be included anyway.
4. In Figure 3, the color scheme in the final column of images should be changed. Red/white contrast is very poor and no conclusions can be drawn from these images. Additionally, the authors should include a box to show where the inset is located in the overview images.
5. Authors claim that MTCL2 is not detected near more dynamic MTs in the periphery of the cell and references Figures 2A and 3. They should include annotation in the figures to highlight this. This can be done with arrowheads or other markings, or with additional insets enlarging a peripheral region of the cell.
6. The authors should clarify in the main text and figure legend which superresolution microscopy technique was used in Figure 2D.
7. The authors use methanol fixation to examine localization of MTCL2, MTs, and Golgi. Methanol extracts lipids and thus affects intracellular membrane compartments, and can affect the localization pattern of GM130, a Golgi matrix protein. The authors should include samples fixed with a crosslinking fixative to ensure their conclusions drawn from methanol-fixed samples are not affected by the choice of fixative.
8. In Supplementary Figure 1B a third, relatively high expressing cell can be seen in the top panel. The GM130 signal for this cell seems to be comparable to non-transfected cells in the same image. Can the authors address this? Alternatively, to show differences in expression levels between these three cells in that panel and others, authors could use a heatmap LUT of the V5 signal to differentiate expression levels more clearly in different cells.
9. Line 139. How was the ectopic expression 'suppressed to endogenous levels'? The panels in Suppl Fig 1 of 'low expression' clearly show increased MTCL2 signal when compared to non-transfected cells in the same panel still. This would suggest ectopic expression is still above endogenous levels.
10. Figure 5C. The label for MTCL2 construct should read mMTCL2 ΔC-MTBD to clarify the expression construct used.
11. In Figures 6A and 6C the label shows a-tubulin, but the staining is of a Golgi marker.
12. In Figures 6B and 6D the different conditions should be separated more in the graph, the datapoints overlap.
13. Lines 246-7. The authors claim the Golgi-associated and centrosomal MTs can be easily distinguished in MTCL2 knockdown cells. They should include annotation in the corresponding figures to highlight these different populations.
14. Figure 8A. A horizontal line is missing in the panel showing MTCL/a-tub merge.
15. Figures 8C and 8D. The acetylated tubulin staining in control cells (control RNAi and GFP) in these panels vary greatly. Can the authors comment on this? Additionally, there appears to be an increase in acetylated tubulin on the Western Blot (8E) shown in cells expressing GFP-MTCL2 CMTB that is not reflected in the image in Figure 8D. Since a significant population of GFP-MTCL2 CMBT localizes to the nucleus, it is possible that the functional population of GFP-MTCL2 CMBT that can stabilize MTs is much lower than GFP-MTCL1 CMBT despite showing equal levels in the Western Blot. The author should compare signal intensity in the cytosol of GFP-expressing cells and base their analysis of acetylated tubulin levels on cells where cytosolic levels are comparable.

Significance

This work describes a novel MT crosslinking protein, MTCL2. The authors show that MTCL2 may function predominantly on non-centrosomal MTs associated with the Golgi and suggest a function in linking the centrosome and Golgi in polarized, migrating cells. However, the manuscript is highly descriptive as the authors do not uncover a mechanism for how MTCL2 stabilizes and crosslinks MTs and do not address potential functional interactions between MTCL1 and MTCL2. Additionally, there are some contradictory findings that are not addressed in the current manuscript.

This work adds a new factor to an expanding list of proteins that regulate non-centrosomal MTs (reviewed in Meiring et al., 2019, Current Opinion in Cell Biology, and Sanders and Kaverina, 2015, Frontiers in Neuroscience), and would be of interest to those interested in cell biology of MT organization and function.

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Referee #1

Evidence, reproducibility and clarity

Summary:

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

In this manuscript, the authors identify MTCL2, a paralog of the MTCL1 protein and study its interaction with the Golgi complex and with microtubules. A shorter version of this protein was identified before and named SOGA (suppressor of glucose from autophagy). A role of MTCL2 in regulating the polymerization of Golgi associated microtubules is reported as well as an implication in cell polarity and migration.

Major comments:

- Are the key conclusions convincing?

Most of the key conclusions are valid but the main one should be either reinforced or tuned down. In particular, the authors tend to give central role to MTCL2 in regulating the formation and organization of Golgi-associated MT network, and conversely in organizing Golgi elements, without considering the other factors identified (the authors cite relevant papers though but do not discuss this). They should analyze the function of MTCL2 in relation to the role of CLASP2, AKAP450, Golgi-g-Tubulin, or even EB proteins (like EB3). I also do not think that carrying out super resolution microscopy is enough to "reveal the possibility that MTCL2 mediates the association of the Golgi membrane with stabilized MTs". More generally, the authors cannot conclude that MTCL2 preferentially associated to Golgi-MT only from their immunofluorescence and KD experiments. The centrosome (the main MTOC) is indeed also localized in the perinuclear area. Easy to do additional experiments may help to confirm these conclusions (see below). Also, the authors could strengthen the way the study how MTCL1 and MTCL2 binds to microtubules and Golgi (see below). The localization or interaction of MTCL2 with Golgi-associated MT is not directly shown. The title should be changed also. I am not sure I understand what an asymmetric microtubule network means in this context. I guess that the authors mean non-centrosomal microtubule network.

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

The authors also state that tubulin acetylation is induced by MTCL1 C-MTBD but it may simply be stabilized. They should also clarify if MTCL2 regulates Golgi-dependant nucleation microtubules

- 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. To demonstrate that MTCL2 associated to Golgi-MT, microtubule regrowth experiments following nocodazole treatment have to be conducted (time course). Another efficient way to analyze such events, as shown by the Kaverina and the Akhmanova labs for example, is to use fluorescent EB proteins (e.g. EB3) to image microtubule plus ends and back-track them to identify nucleation points. Carrying out such an experiment (nocodazole way-out and EB tracking) in the presence or absence of MTCL2 would allow to confirm, or not, the functional hypothesis of the authors. Additionally, carrying electron microscopy analysis would be important to qualify better the effects observed on Golgi complexes upon depletion. The authors mention the effects on the "morphology of Golgi ribbon" but it is rather unclear. Last, because the authors compare the way MTCL1 and MTCL2 bind microtubules, and suggest intriguing differences, domain swapping experiments between these two isoforms would be important to carry out.

- 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 proposed experiments to study Golgi-based nucleation are easy and inexpensive, as are domain swapping experiments. Electron microscopy on the other hand is quite expert and requires either internal knowledge, access to a facility or setting-up a collaboration. A few months, 3-4, would be needed.

- 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?

I am not a statistician but I was not convinced by the use of the quantification of "skewness", in particular in figure 5B. Whether a Wilcoxon test is adequate is unclear to me.

Minor comments:

-Specific experimental issues that are easily addressable.

Yes

-Are prior studies referenced appropriately?

Some studies are referred but the published data not actually used (with the exception of the final scheme). The authors should comment on the fact that other Golgi-associated MT binding proteins have been shown to be involved in the mechanisms highlighted here. Why they would not take over in the absence of MTCL2 should be properly discussed. Similarly, in the discussion, the authors indicate that SOGA has been found as an interacting partner of CLASP2. As CLASP2 is a microtubule binding protein also localized at the Golgi complex and binding to acetylated microtubules, the authors should at least comment on the putative role of the interaction between MTCL2 and CLASP2 in the phenotypes they described. The role of the interaction between CLASP2 and MTCL2 should be discussed and ideally tested.

-Are the text and figures clear and accurate? In general, yes. There are however quite a few problems:

• In the introduction, page 3 line 74-77, the authors wrote « The resultant N-terminal fragment is released into the cytoplasm to suppress autophagy by interacting with the Atg12/Atg5 complex, whereas the C-terminal fragment is secreted after further cleavage (see Fig. 1A, boxed illustration). » while on the Fig1 the boxed area indicates that SOGA bears Atg16 and Rab5 binding domains. Please double check the interacting partners of SOGA1.

• Figure 1 B and C are not cited in the main text. • Figure 1E: a loading control is needed to evaluate the expression level of SOGA/MTCL2 in the mouse tissues. In the liver, the size of the bands is different than in other tissues (smaller size). The authors might comment if these smaller bands correspond to the cleaved version of SOGA that was previously described in mouse hepatocyte.

• Figure 2A: single color picture for the anti-tubulin immunolabeling would help to see the distribution of microtubules in the perinuclear area. The perinuclear region is a crowded area with many intracellular compartments accumulating there as well as cytoskeleton elements. • Figure 2C: same comment as above, a single-color picture for the anti-MTCL2 and anti-GM130 immunolabeling are required.

• page 7, line132-134: the authors state: « Close inspection using super-resolution microscopy further revealed the possibility that MTCL2 mediates the association of the Golgi membrane with stabilized MTs (Fig. 2D, arrows). » To my opinion, the data are over-interpreted. The signals partially co-localize but this does not indicate a function of MTCL2 in mediating the interaction.

• Figure 3: Another way of merging the anti MTCL2 and GS28 pictures have to be provided. The pictures are difficult to interpret with the current display.

• Figure 4C: please indicate the meaning of « ppt »

• Figure 5B and C: for easier reading of the figure, it would be useful to annotate with MTCL2 construct is overexpressed following doxycycline treatment (MTCL2 WT (A) and MTCL2 delta C-MTBD (C)).

• Figure 6 A and C: the labels are wrong. Bottom pictures correspond to anti-GM130 immunostaining not anti-tubulin. If I am not mistaken, it is MTCL2 delta C which is studied in panel C.

• Page 11, line 212: Supplementary Figure 2 (knockdown in RPE1 cells) is intended to be cited not Supplementary Figure 3.

• Figure 8A: single color pictures are needed to appreciate the distribution of the signals

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

Yes, see above

Significance

- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

It adds a new player in the machinery involved in the interplay between the Golgi complex and microtubules for their mutual organization. To me a key observation is the unlinking between the Golgi complexes and the centrosome but this observation is not really used and studied (here again, may be a nocodazole wash-out experiment and real-time analysis may help)

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

A large number of studies, cited by the authors, have identified proteins involved in mutual organization of Golgi membranes and microtubules. Identification and study of MTCL1 and 2 are important in this context. It also questions the role and function of the initially identified SOGA.

- State what audience might be interested in and influenced by the reported findings.

This is a pure cell biology study that will primarily interest people studying the Golgi complex and micrutubules. People interested by the internal organization of the cell, the interaction between the centrosome and the Golgi and intracellular polarity would also be interested, as well as people studying migration.

- 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 studied Golgi dynamics and function as well as microtubule dynamics. I have no expertise in statistical analysis.

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Reply to the reviewers

Reviewer 1 (Evidence, reproducibility and clarity):

The main message of this paper, as far as I understood since I am not a molecular bioinformatician but I am certainly interested in mtDNA variations especially related to disease, is that there is a very obvious bias among synonymous changed in the ORF of human mtDNA, more frequent for aminoacids with 4 variants, more frequent in P position, and much more frequently characterized by transversion rather than transition substitutions. This survey is well written and, although edited in a rather technical language, the message is reachable and interesting. I also agree on the conclusions of the Author concerning the considerations that this set of new data should prompt one to draw also considering non-synonymous, potentially pathogenic mutations. The only contribution I feel I can provide to this manuscript is to invite the Authors to consider the possibility that the selection may be due to a preferred codon bias, linked to the higher or lower compliance of different codon to be translated by the translational in situ machinery of mitochondria. I am not sure that this applies also for mitochondrial mitochondria and related factors (you may want to ask Aleksey Amunts in Stockholm or Bob Lightowlers or Zoscha Lightowlers in Newcastle on this matter). I do know that this is certainly a problem for recombinant proteins containing, for instance, mammalian MTS fused with a bacterial restriction enzyme; in most of the cases the bacterial sequence has to be recoded using the preferred codon for mammalian system in order to increase translation by an eukaryotic (mammalian) translation machinery. I wonder whether you could discuss this possibility in your paper and maybe perform some further comparative measurement to test it.

I appreciate the supportive comments of Reviewer 1 regarding the accessibility of our manuscript, and I address comments related to codon bias below.

Reviewer 1 (Significance):

The paper provides novel information on the structure and constrains of mtDNA variants in humans, opens an area of investigation which is new and potentially relevant, with some possible implications also on pathogenic mtDNA mutations in humans.

I thank Reviewer 1 for their positive comments about the novelty of this work and the important implications of our study.

Reviewer 1 (Referee Cross-commenting):

I said in my first comment that I am not a bioinformatician, but Referee 2 made a great job in identifying some critical points and suggest the Authors how to cope with them. I maintain my opinion, that I think it's shared by referee 2, that the paper conveys an interesting and rather unexpected message, and that if the Authors are able to answer properly to the points raised by referee 2 the paper should be published.

We are quite glad to hear that Reviewer 1 would like to see this manuscript published, provided that the items noted by the reviewers are properly addressed.

Response to Reviewer 1:

R1Q1 (Continuation from Referee Cross-commenting): I confirm that the only contribution I feel I can provide to this manuscript is to invite the Authors to consider the possibility that the selection may be due to a preferred codon bias, linked to the higher or lower compliance of different codons to be translated by the translational in situ machinery of mitochondria. I wonder whether the Authors could consider this possibility in the Discussion and possibly perform some further comparative measurement to test it.

R1A1: My manuscript takes into consideration the possibility that codon-specific preferences would determine the frequency of mtDNA variants. Findings that argue against codon bias as a strong source of selection include:

1) At two-fold degenerate P3s, nearly every site (> 97%) harbored at least one HelixMTdb sample associated with a non-reference base. It is worth noting that HelixMTdb is not enriched for known mitochondrial disease variants.

2) SSNEs are very tightly associated with transversions from the human reference sequence, implicating mutational biases as a cause of any limited diversity in the HelixMTdb.

3) Every possible base can be found at 99% of >500 analyzed I-P3 positions (those P3s at which the base at codon positions one and two is identical throughout the alignment), arguing against the idea that codon bias plays a significant role in controlling variant frequency across mammals. The only exception that I identified in my extensive analysis is the P3 found within the first methionine codon of COX3.

4) Earlier, more limited studies of mitochondrial codon choice (citations of these earlier studies can be found in the manuscript) also argue against substantial selection based upon codon choice.

5) Finally, I would note that the set of tRNAs encoded by vertebrate mtDNAs is quite limited, with only one tRNA linked to each codon family defined by codon positions P1 and P2. There is no evidence, to my knowledge, that nucleus-encoded tRNAs enter human mitochondria. Therefore, the scope of potential selection linked to, for example, translation speed and protein folding seems particularly limited at vertebrate mitochondria.

While most evidence does not support strong selection on mtDNA codon choice in vertebrates, I do report divergence in TSS distributions obtained from the I-P3s of different amino acids within the same degeneracy class (eg. two-fold purine, two-fold pyrimidine, four-fold), hinting at some minimal role for codon preferences at P3. However, on the whole, mutational propensities are likely to be the predominant factor controlling synonymous variation.

Reviewer 2 (Evidence, reproducibility and clarity):

The manuscript explores a large database of human mtDNA sequences and performs some comparative analysis across mammals to characterise the profile of mtDNA mutations. It finds that some variants are surprisingly poorly represented in human mtDNA and suggests that mutational bias rather than selection is the dominant driver of this heterogeneity.

This is an interesting message and an efficient and interpretable of a large-scale dataset to shed light on biological mechanisms, which is a highly desirable philosophy. The factors shaping human mtDNA heterogeneity are of immense interest for several fields from population genetics to medicine, making this a valuable perspective. My comments are mainly quite fine-grained and reflect instances where I think the argument could be tighter, rather than fundamental flaws in the approach. In the cases where these points are due to my own naivety, I apologise and suggest that more explanation of these points could help other readers like me!

I am happy to read that Reviewer 2 (Dr. Iain Johnston) finds my approach to be fundamentally sound, and I certainly appreciate the insightful comments and suggestions that he has provided.

Reviewer 2 (Significance):

I wrote the above review without realising the reviewer interface would be categorised in this way. Here's a repeat of my "significance" comments

The manuscript explores a large database of human mtDNA sequences and performs some comparative analysis across mammals to characterise the profile of mtDNA mutations. It finds that some variants are surprisingly poorly represented in human mtDNA and suggests that mutational bias rather than selection is the dominant driver of this heterogeneity.

This is an interesting message and an efficient and interpretable of a large-scale dataset to shed light on biological mechanisms, which is a highly desirable philosophy. The factors shaping human mtDNA heterogeneity are of immense interest for several fields from population genetics to medicine, making this a valuable perspective.

I am very pleased that the reviewer appreciates the importance and potential impact of my analysis. We agree that mtDNA heterogeneity is likely to be of high medical relevance.

Response to Reviewer 2:

R2Q1: The first paragraph is focused on humans without explicitly saying so; missing heritability is less of an issue in, for example, plants [Brachi et al., 2011. Genome biology, 12(10), pp.1-8]. This focus should be clearer (or the differences across kingdoms mentioned!). It's also worth noting that the argument about pathogenic variants being infrequent because of selection can only address missing heritability in pathogenic variants, and cannot (directly) inform the missing heritability in traits like height etc. Also, the whole motivation with respect to missing heritability currently comes across as a bit of a non sequitur. An introduction section could be used to help describe how the analysis of the provenance of mtDNA mutations contributes to the missing heritability question.

R2A1: I agree that beginning the manuscript with a discussion of genome-side association studies may distract the reader from the main topic at hand: the utility of variant frequency when predicting pathogenicity in humans. I have changed the Introduction accordingly.

R2Q2: I also suggest that such an introduction section introduces the (later cited) previous work from Reyes and others on mutational profiles in mtDNA to set the scene.

R2A2: I now provide these citations in the second paragraph of the Introduction. However, I do not expand further upon mutational propensities in that section, with an eye toward minimizing manuscript length toward publication as a short report.

R2Q3: An early result, that 35% of possible synonymous mutations do not appear in a dataset, lacks a null hypothesis. Depending on the size of the dataset this may be very surprising or very unsurprising : an order of magnitude estimate of what proportion would be expected under uniform mutation and zero selection would help comparison here. I guess this can be as simple as 16k/3*4 R2A3: The reviewer raises an excellent point regarding how 'surprising' it should be to the reader, previous to downstream analyses revealing transition/transversion biases, that so many synonymous substitutions are lacking within this dataset. While the authors of the HelixMT study removed mtDNA from highly related individuals from the analysis, the vast majority of the mtDNAs analyzed (91.2%) were from haplogroup N and of inferred European ancestry (doi.org/10.1101/798264). The authors of the HelixMTdb study do note that nearly all mtDNA lineages were present in the study, presumably encompassing roughly 100,000 years of human mtDNA evolution. That said, how this information alone may be used to quantitatively model expectations under zero selection is unclear.

To address this question of whether sample diversity might be very limited in the HelixMTdb study, I have carried out additional analyses on this dataset. I now assess, for third codon positions allowing two-fold synonymous change (serine and leucine not included, due to their decoding by two different tRNAs), how often only one nucleotide was found at that position. For two-fold degenerate P3s, > 97% (n=1604) harbored both nucleotide possibilities within the database. This result strongly suggests that mtDNA diversity was well sampled in the HelixMTdb study, since a database consisting of highly related samples would presumably be characterized by a greater number of sites showing total identity. Moreover, when considering analyzed four-fold degenerate P3s (again, leucine and serine codons were omitted), only a very small number of sites showed no diversity (1%), with more than half of sites harboring at least three different bases. My interpretation is that the HelixMTdb authors have successfully sampled a very diverse set of human mitochondrial genomes. I have added these new analyses to the manuscript as Fig. 2a and 2b.

I have also changed the word 'surprising' to 'noteworthy' within the relevant portion of my manuscript text.

R2Q4: I think some comments and additional framing of the diversity in the central database would be valuable and important for interpretation. I believe it has, for example, rather more European rows than African ones, thus (to take a very basic view) sampling a less diverse population more than a more diverse one.

R2A4: I now state explicitly that the vast majority of the mtDNAs analyzed (91.2%) were from haplogroup N and of inferred European ancestry. Also, please see point R2A3 for further discussion of the human mtDNA diversity reflected within HelixMTdb.

R2Q5: Another rhetorically important number lacking a comparison with a null is that guanine was detected at >3000 P3 positions accepting synonymous purine substitutions. This is cited as evidence that nucleotide frequencies at P3s don't reflect selection inherent to translation. But this link isn't clear -- if such selection was present, how different from 3000 would Iexpect this number to be? Isn't there a continuum of possibilities? Is the key idea that 3000 is greater than some other number, and if so, what is that?

R2A5: The purpose of this figure is simply to demonstrate that no nucleotide is ruled out when considering silent substitutions at the P3 of any amino acid. This is consistent with (although does not prove, and I believe that the I-P3 analysis provides stronger evidence on this point) a minimal role for mitochondrial codon preference in mtDNA evolution. To reflect that my point is more general, and not to be taken as a quantitative comparison, I changed my text to: 'However, even considering the relative depletion of guanine from all four-fold degenerate P3s and two-fold degenerate purine P3s, guanine was nonetheless detected at thousands of P3 positions (Fig. 3b)'.

R2Q6: I also wasn't clear whether/how the finding that little selection inherent to translation was implicitly extended to suggest little general selection overall. The following section only considers selection acting at specific P3 sites, thus implicitly discarding other hypotheses about general selection based on nucleotide content but not inherent to translation. Perhaps I am misunderstanding this translation link, but selection based on general nucleotide profiles (for example, due to thermodynamic stability [Samuels, Mech. Ageing Dev. 2005; 126: 1123-1129] or availability of nucleotides [Aalto & Raivio, Mech. Ageing Dev. 2005; 126: 1123-1129; Ott et al., Apoptosis. 2007; 12: 913-922]) would seem to still be on the table?

R2A6: I would argue against selection upon nucleotide choice linked to local changes to mtDNA thermodynamic stability. Most prominently, when considering two-fold degenerate sites, nucleotide differences from the reference sequence were identified within the HelixMTdb at almost every analyzed position (Fig. 2a), even though hydrogen bond strength between opposing bases would be affected in every case (AT>GC or vice versa). Of course, my argument here applies generally, and there may be a small subset of sites for which nucleotide substitutions can cause a pronounced functional defect because of a change to local mtDNA structure.

I would also argue against mitochondrial nucleotide availability as a source of selective pressure within the human population. When considering the entire L-strand sequence (NC_012920.1), nucleotide counts are as follows:

A 5124

C 5181

G 2169

T 4094

And when considering both strands, nucleotide counts and frequencies are as follows:

A 9218 (27.8%)

C 7350 (22.2%)

G 7350 (22.2%)

T 9218 (27.8%)

One nucleotide substitution would lead to a change in nucleotide frequencies by less than 0.02%. While the formal possibility exists that mitochondrial nucleotide availability lies exquisitely close to an important threshold, there is no current evidence to support this proposition. And here again, the diversity of P3 nucleotide choice found among the HelixMTdb samples would argue against this possibility.

That said, it is worth noting that nucleotide frequencies, and mtDNA mutation rates relative to nuclear mutation rates do appear to differ among clades (PMID: 8524045 and 28981721). Therefore, while selection related to nucleotide availability seems an unlikely explanation for the variant frequencies that I have recovered at degenerate sites among human samples, I certainly would not rule out taxon-specific dietary, environmental, or physiological factors that, over longer evolutionary timescales, might shape mtDNA nucleotide frequencies.

I would like to raise the possibility of another source of selection upon nucleotide choice. Specifically, one might propose that synonymous mtDNA substitutions could affect the binding of proteins controlling the replication, compaction, or expression of mtDNA. Indeed, an intriguing study has reported that human cells manifest a mtDNA footprinting pattern (PMID: 30002158), suggestive of regulatory sites bound to protein or sites of transcriptional pausing. However, Blumberg et al. found no statistically significant difference in human synonymous change at footprinted sites, arguing against a strong selective pressure on nucleotide choice at footprinted P3s. Moreover, footprinting sites identified in the above-mentioned study are conserved in mouse and human, but I have shown that all four nucleotides are acceptable at all four-fold degenerate sites (n=252), all two-fold degenerate pyrimidine sites (n=157), and 99% of two-fold degenerate purine sites (n=152) within the mammalian I-P3 set, again arguing against general limitations on nucleotide choice caused by protein association. These analyses cannot, however, totally rule out the possibility that a subset of individual P3s are under some selection due to their role in binding or traversal of proteins.

R2Q7: A reptile is chosen as an outgroup for a comparative analysis of mammals. As always when a choice is made, the question arises: what if that choice was different? Perhaps the corresponding figures can be presented for two other choices of outgroup to demonstrate that there's nothing particularly unrepresentative about this reptile?

R2A7: While preparing this revised manuscript, I have performed an updated analysis using the most current mammalian mtDNA dataset available on RefSeq. For these new tests, I used Iguana iguana, rather than Anolis punctatus, as an outgroup. The new results are essentially indistinguishable from my previous findings. Importantly, when old TSS values and new TSS values for I-P3 sites were compared by linear regression, the R-squared value is 0.9955, with a p-value of

R2Q8: Another analysis involves classifying variant frequency into discrete groups based on percentage appearance, then seeking links with the TSS statistic. First, it is not clear why discretisation is needed here. A statistical model embracing the continuous nature of variant frequency requires fewer arbitrary choices (e.g. of numbers and boundaries of classes).

R2A8: A primary audience of this manuscript will certainly be the human genetics community, which commonly speaks in terms of variant classes (eg. 'common', 'rare', 'ultra-rare'). Therefore, I prefer to also use such classifications when analyzing the relationship between TSS and mtDNA variant frequency. I took advantage of the following references when generating frequency classifications:

Bomba L, Walter K, Soranzo N. 2017. The impact of rare and low-frequency genetic variants in common disease. Genome Biol 18:77.

McInnes G, Sharo AG, Koleske ML, Brown JEH, Norstad M, Adhikari AN, Wang S, Brenner SE, Halpern J, Koenig BA, Magnus DC, Gallagher RC, Giacomini KM, Altman RB. 2021. Opportunities and challenges for the computational interpretation of rare variation in clinically important genes. Am J Hum Genet 108:535–548.

R2Q9: Second, an interpretation point here is in danger of equating absence of evidence with evidence of absence. Without an estimate of statistical power, an absence of a significant relationship cannot suggest that anything is likely or unlikely, only that there may not be sufficient power to detect an effect.

R2A9: To address this point, I have changed my text as follows:

Old: 'However, I detected no significant relationship between TSS and variant frequency for four-fold degenerate I-P3s (Fig. 2d), indicating that the highly elevated SSNE abundance at four-fold degenerate P3s is unlikely to be due to selection.'

New: 'However, I detected no significant relationship between TSS and variant frequency for four-fold degenerate I-P3s (Fig. 2d), consistent with the idea that the highly elevated SSNE abundance at four-fold degenerate P3s is unlikely to be due to selection.'

R2Q10: Figs 1a and 1e have a log vertical axis but I think the lowest points actually corresponds to zero? This is not compatible with a log axis and the zero position should be explicitly labelled with its own tick (perhaps in parentheses to highlight the discontinuity).

R2A10: Quite correct, and I had neglected to clarify those details in the previous version of the manuscript. I now designate the samples with zero counts in the population using a smaller dot size, and I describe this approach in the figure legend.

R2Q11: The methods are presented in an interesting way, with specific filenames for the code associated with each part of the pipeline explicitly provided. This is (very!) nice but it would also be good to describe in words what each piece of code does (e.g. "this was used as input for x.py, which counts the mutations and outputs a profile" or some such). This is indeed sometimes written but some parts lack an explanation.

R2A11: I have now expanded my description of several scripts within the Methodology section.

R2Q12: I could do with an additional sentence or two on the statistical analysis. As Kolmogorov-Smirnov tests examine differences between distributions, it's not immediately unambiguous how they are applied to total count statistics. Are count distributions with respect to variant frequency analysed for each amino acid separately? Or are the amino acids somehow ordered and the distributions across them compared? Or something else?

R2A12: TSS distributions are held for each individual amino acid, which are then compared by Kolmogorov-Smirnov testing only within a given degeneracy category (four-fold degenerate, two-fold degenerate purine, two-fold degenerate pyrimidine). I have now elaborated upon this statistical test selection, and other details of the analysis, in the Methodology section.

Reviewer 2 (Referee Cross-commenting):

I agree that codon bias is an interesting potential axis of selection. Even if the analysis rejects the hypothesis of selective effects inherent to translation, it is conceivable that codon bias could be shaped by selection in other indirect ways (depending on how "inherent" is defined, these could include tRNA/nucleotide availability, GC content and thermodynamic stability, etc). I think this aligns with my suggestion that modes of selection that are not directly linked to translation could be explored in more depth before discounting selective effects overall. IJ

I hope that I have now successfully addressed points related to codon bias, GC content, and thermodynamic stability in the manuscript, as well as here in this response to the reviewers.

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Referee #2

Evidence, reproducibility and clarity

The manuscript explores a large database of human mtDNA sequences and performs some comparative analysis across mammals to characterise the profile of mtDNA mutations. It finds that some variants are surprisingly poorly represented in human mtDNA and suggests that mutational bias rather than selection is the dominant driver of this heterogeneity.

This is an interesting message and an efficient and interpretable of a large-scale dataset to shed light on biological mechanisms, which is a highly desirable philosophy. The factors shaping human mtDNA heterogeneity are of immense interest for several fields from population genetics to medicine, making this a valuable perspective. My comments are mainly quite fine-grained and reflect instances where I think the argument could be tighter, rather than fundamental flaws in the approach. In the cases where these points are due to my own naivety, I apologise and suggest that more explanation of these points could help other readers like me!

The first paragraph is focused on humans without explicitly saying so; missing heritability is less of an issue in, for example, plants [Brachi et al., 2011. Genome biology, 12(10), pp.1-8]. This focus should be clearer (or the differences across kingdoms mentioned!). It's also worth noting that the argument about pathogenic variants being infrequent because of selection can only address missing heritability in pathogenic variants, and cannot (directly) inform the missing heritability in traits like height etc. Also, the whole motivation with respect to missing heritability currently comes across as a bit of a non sequitur. An introduction section could be used to help describe how the analysis of the provenance of mtDNA mutations contributes to the missing heritability question. I also suggest that such an introduction section introduces the (later cited) previous work from Reyes and others on mutational profiles in mtDNA to set the scene.

An early result, that 35% of possible synonymous mutations do not appear in a dataset, lacks a null hypothesis. Depending on the size of the dataset this may be very surprising or very unsurprising : an order of magnitude estimate of what proportion would be expected under uniform mutation and zero selection would help comparison here. I guess this can be as simple as 16k/34 << 200k. Also the ancestry of the dataset is important here: if all samples are highly related then a more homogenous mutational profile is unsurprising. Perhaps one could assign a quantity like an effective population size to the database and compare this to 16k/34? I think some comments and additional framing of the diversity in the central database would be valuable and important for interpretation. I believe it has, for example, rather more European rows than African ones, thus (to take a very basic view) sampling a less diverse population more than a more diverse one.

Another rhetorically important number lacking a comparison with a null is that guanine was detected at >3000 P3 positions accepting synonymous purine substitutions. This is cited as evidence that nucleotide frequencies at P3s don't reflect selection inherent to translation. But this link isn't clear -- if such selection was present, how different from 3000 would we expect this number to be? Isn't there a continuum of possibilities? Is the key idea that 3000 is greater than some other number, and if so, what is that?

I also wasn't clear whether/how the finding that little selection inherent to translation was implicitly extended to suggest little general selection overall. The following section only considers selection acting at specific P3 sites, thus implicitly discarding other hypotheses about general selection based on nucleotide content but not inherent to translation. Perhaps I am misunderstanding this translation link, but selection based on general nucleotide profiles (for example, due to thermodynamic stability [Samuels, Mech. Ageing Dev. 2005; 126: 1123-1129] or availability of nucleotides [Aalto & Raivio, Mech. Ageing Dev. 2005; 126: 1123-1129; Ott et al., Apoptosis. 2007; 12: 913-922]) would seem to still be on the table?

A reptile is chosen as an outgroup for a comparative analysis of mammals. As always when a choice is made, the question arises: what if that choice was different? Perhaps the corresponding figures can be presented for two other choices of outgroup to demonstrate that there's nothing particularly unrepresentative about this reptile?

Another analysis involves classifying variant frequency into discrete groups based on percentage appearance, then seeking links with the TSS statistic. First, it is not clear why discretisation is needed here. A statistical model embracing the continuous nature of variant frequency requires fewer arbitrary choices (e.g. of numbers and boundaries of classes). Second, an interpretation point here is in danger of equating absence of evidence with evidence of absence. Without an estimate of statistical power, an absence of a significant relationship cannot suggest that anything is likely or unlikely, only that there may not be sufficient power to detect an effect.

Figs 1a and 1e have a log vertical axis but I think the lowest points actually corresponds to zero? This is not compatible with a log axis and the zero position should be explicitly labelled with its own tick (perhaps in parentheses to highlight the discontinuity).

The methods are presented in an interesting way, with specific filenames for the code associated with each part of the pipeline explicitly provided. This is (very!) nice but it would also be good to describe in words what each piece of code does (e.g. "this was used as input for x.py, which counts the mutations and outputs a profile" or some such). This is indeed sometimes written but some parts lack an explanation.

I could do with an additional sentence or two on the statistical analysis. As Kolmogorov-Smirnov tests examine differences between distributions, it's not immediately unambiguous how they are applied to total count statistics. Are count distributions with respect to variant frequency analysed for each amino acid separately? Or are the amino acids somehow ordered and the distributions across them compared? Or something else?

Iain Johnston

Significance

I wrote the above review without realising the reviewer interface would be categorised in this way. Here's a repeat of my "significance" comments

The manuscript explores a large database of human mtDNA sequences and performs some comparative analysis across mammals to characterise the profile of mtDNA mutations. It finds that some variants are surprisingly poorly represented in human mtDNA and suggests that mutational bias rather than selection is the dominant driver of this heterogeneity.

This is an interesting message and an efficient and interpretable of a large-scale dataset to shed light on biological mechanisms, which is a highly desirable philosophy. The factors shaping human mtDNA heterogeneity are of immense interest for several fields from population genetics to medicine, making this a valuable perspective.

Referee Cross-commenting

I agree that codon bias is an interesting potential axis of selection. Even if the analysis rejects the hypothesis of selective effects inherent to translation, it is conceivable that codon bias could be shaped by selection in other indirect ways (depending on how "inherent" is defined, these could include tRNA/nucleotide availability, GC content and thermodynamic stability, etc). I think this aligns with my suggestion that modes of selection that are not directly linked to translation could be explored in more depth before discounting selective effects overall. IJ

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Referee #1

Evidence, reproducibility and clarity

The main message of this paper, as far as I understood since I am not a molecular bioinformatician but I am certainly interested in mtDNA variations especially related to disease, is that there is a very obvious bias among synonymous changed in the ORF of human mtDNA, more frequent for aminoacids with 4 variants, more frequent in P position, and much more frequently characterized by transversion rather than transition substitutions. This survey is well written and, although edited in a rather technical language, the message is reachable and interesting. I also agree on the conclusions of the Author concderning the considerations that this set of new data should prompt one to draw also considerin g non-synonymous, potentially pathogenic mutations. The only contribution I feel I can provide to this manuscript is to invite the Authors to coinsider the possibility that the selection may be due to a preferred codon bias, linked to the higher or lower campliance of different codon to be translated by the translational in situ machinery of mitochondria. I am not sure that this applies also for mitochondrial mitochondria and related factors (you may want to ask Aleksey Amunts in Stockholm or Bob Lightowlers or Zoscha Lightowlers in Newcastle on this matter). I do know that this is certainly a problem for recombinant proteins containing, for instance, mammalian MTS fused with a bacterial restriction enzyme; in most of the cases the bacterial sequence has to be recoded using the preferred codon for mammalian syste in orderr to increase translation by an eukaryotic (mammalian) translation machinery. I wonder whether you could discuss this possibility in your paper and maybe perform some further comparative measurement to test it.

Significance

The paper provides novel information on the structure and constrains of mtDNA variants in humans, opens an area of investigation which is new and potentially relevant, with some possible implications also on pathogenic mtDNA mutations in humans.

Referee Cross-commenting

I said in my first comment that I am not a bioinformatician, but Referee 2 made a great job in identifying some critical points and suggest the Authors how to cope with them. I maintain my opinion, that I think it's shared by referee 2, that the paper conveys an interesting and rather unexpected message, and that if the Authors are able to answer properly to the points raised by referee 2 the paper should be published. I confirm that the only contribution I feel I can provide to this manuscript is to invite the Authors to consider the possibility that the selection may be due to a preferred codon bias, linked to the higher or lower compliance of different codons to be translated by the translational in situ machinery of mitochondria. I wonder whether the Authors could consider this possibility in the Discussion and possibly perform some further comparative measurement to test it.

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Reply to the reviewers

Reply to Reviewers:

## Comments by Reviewer 1

For the sake of clarity, it may help to provide some table illustrating the proportion of gastruloids behaving precisely as the best example shown

We thank the reviewer for raising this point. For the expression patterns of each of the main endodermal markers analyzed, at the timepoints considered, we will provide additional context on the variability among Gastruloids. We envisage to provide such results in a format similar to what used e.g. in Supplementary Figure 4 of https://doi.org/10.1038/s41467-021-23653-4 [Xu, Peng-Fei, et al. "Construction of a mammalian embryo model from stem cells organized by a morphogen signalling centre." (2021)]; i.e. categorization of the phenotypes observed among gastruloids, and quantification of their proportions.

It would also strengthen the message even more to provide some quantitation of co-expression for the main markers. As the behaviour seems very consistent, it is likely that such quantification would not be very arduous, and it would show the strength of the model.

We thank again the reviewer for highlighting the value of co-localisation data, and in fact this is a suggestion also put forward by our other reviewer. We are currently developing a pipeline to segment the nuclei on the DAPI channel of each immunostained Gastruloid, and extract marker intensities within each cell. We envisage the results to be presented in a format similar to what shown e.g. in Fig. 2 of https://doi.org/10.1242/dev.159103 [Mulas, Carla, et al. "Oct4 regulates the embryonic axis and coordinates exit from pluripotency and germ layer specification in the mouse embryo." (2018)]. Such data would undoubtedly strengthen the reliability of the claims we here draw mostly from a qualitative inference of colocalisation.

The introduction could be shortened, and the results a bit more to the point.

As this is also a point raised by both reviewers, we will make sure to go back over the entirety of the manuscript and do the necessary adjustments, especially for what concerns the Results section. We would however like to point out that the unusual length of our Introduction section is to be understood as a deliberate departure from the limits and conventions prescribed by traditional publishing formats. In line with our intentional choice of a journal-independent publication platform (i.e. a preprint server), and of journal-independent review process, we also presented and contextualized our research in a journal-independent format. We see this as an opportunity to present research in a voice and in a form truer to that of the researchers that carried it out. We thank both reviewers for their feedback on the format and will certainly still make sure to minimize redundancy of information throughout our manuscript.

## Comments by Reviewer 2

The following conclusions were not warranted by the findings:

Endoderm emergence can occur in the absence of extraembryonic tissues and embryonic architecture: It is unclear if extraembryonic tissues (e.g., primitive endoderm and visceral endoderm-like cells) are absent in the early phase of gastruloid development.

The reviewer raises an important point regarding the presence/absence of cells with extraembryonic endoderm identity within the early developing gastruloid. Our claim of absence of these cell types is grounded on previous gastruloid research (Turner, David A., et al. "Anteroposterior polarity and elongation in the absence of extra-embryonic tissues and of spatially localised signalling in gastruloids: mammalian embryonic organoids." (2017); van den Brink, Susanne C., et al. "Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids." Nature 582.7812 (2020): 405-409.), which has indeed found no evidence of extraembryonic endoderm in Gastruloids. While other datasets do not include early Gastruloid stages where such cells may instead be detected, transcriptional analyses of later timepoint Gastruloids [Rossi, Giuliana, et al. "Capturing cardiogenesis in gastruloids." (2021)] also do not seem able to uniquely define extraembryonic endoderm signatures. Accordingly, we did not see this claim as having to be further substantiated.

In light of most recent reports (see https://doi.org/10.1038/s41467-021-23653-4 [Xu, Peng-Fei, et al. "Construction of a mammalian embryo model from stem cells organized by a morphogen signalling centre." (2021)]), the necessary absence of extraembryonic endoderm types within self-organising embryonic models may appear to need to be at least revisited. Xu et al indeed report extraembryonic endoderm cells in an embryoid model in many ways analogous to the Gastruloid system. These claims are mainly based on the recovery of DBA (Dolichos Biflorus Agglutinin) signal, a marker traditionally associated with extraembryonic endoderm in vivo. Yet, and based on our own unpublished exploration of the topic, we presently cannot confirm DBA lectins to be an exclusive marker of extraembryonic endoderm in an in vitro setting (i.e. in the cell types obtained from and amongst differentiating stem cells), as not only we detect DBA positivity in wide cellular domains that are incompatible with any realistic estimate of the extraembryonic makeup of Gastruloids, but we also see this marker decorating the membrane of 2D colonies of pluripotent mouse embryonic stem cells maintained under 2iLIF conditions. When counterstaining for Ttr (Transthyretin; aka prealbumin) (a major discriminant of extraembryonic vs embryonic endoderm, as recovered from single-cell datasets; https://endoderm-explorer.com/), we further cannot detect positivity in either DBA+ or DBA- cells.

To the best of the knowledge and evidence available to us, we thus still consider the absence of extraembryonic endoderm in Gastruloids to be a substantiated claim. We thank however the reviewer for raising this important point and agree that dedicated characterization of extraembryonic endoderm signatures/markers in the developing Gastruloid could certainly help to support the validity of the claims made in previous Gastruloid literature, from which we draw the premise that no extraembryonic endoderm is present. To facilitate this process, and in fact to better contextualize our results in the light of what now published in Xu et al, we will now include detected DBA and Ttr (absent) patterns in early Gastruloids, as well as the expression patterns of extraembryonic endoderm markers for the timepoints for which single cell transcriptomics data is available (i.e. 96h onwards).

While the gastruloid does not replicate the morphological feature of the post-gastrula embryo, it nevertheless has a certain degree of tissue organization. Perhaps the emergence of DE-like cells in 2-D culture would be a more convincing model for "the absence of extraembryonic tissues and embryonic architecture".

The observation is correct: gastruloids do not replicate the architecture of the peri gastrulation mouse embryo. Concomitantly, they display a striking degree of tissue (re-/)organization as they proceed throughout differentiation. We were deliberate in the presentation of our data, and as such we always refer to “absence of embryonic architecture” rather than “absence of architecture” (i.e. of any architecture), as the latter assertion would in fact be contrary to a main finding of our investigation.

Inde ed, the observation that Gastruloids, and specifically endodermal cells within them, can give rise to such developed tissue architectures, by self-organisation and without the need of externally-supplied matrices, is a major focus of our manuscript. Since Gastruloids start as an unstructured, epithelioid, cluster of stem cells, the architectural rearrangements observed in the mature Gastruloid highlight an intrinsic propensity of endodermal cells to forming epithelia. Key aspect to this point, is that these architectural rearrangements are carried out by cells in a landscape that is not architecturally similar to that of the embryo (specifically, where endoderm progenitors do not and cannot arise from a columnar epithelium and do not and cannot have a visceral-endoderm-like destination in which to intercalate).

Considering what discussed in this and in the previous point, we struggle to frame the Gastruloid as not a “convincing model for the absence of extraembryonic tissues and embryonic architecture", given that it satisfies both criteria. More complete and articulated discussions and appraisals of the value of Gastruloids and other 3D in vitro models towards uncovering fundamental features of embryonic development are available elsewhere, including in their comparison to 2D differentiation assays (van den Brink, Susanne C., and Alexander van Oudenaarden. "3D gastruloids: a novel frontier in stem cell-based in vitro modeling of mammalian gastrulation." (2021); Simunovic, Mijo, and Ali H. Brivanlou. "Embryoids, organoids and gastruloids: new approaches to understanding embryogenesis." (2017); Turner, David A., Peter Baillie‐Johnson, and Alfonso Martinez Arias. "Organoids and the genetically encoded self‐assembly of embryonic stem cells." (2016)). We refer readers to these reviews to help inform their own assessment on the matter.

Of course, this is not to say that 2D culture models are not an equally valuable system to study peri-gastrulation development (if only as exemplified by https://doi.org/10.1186/s12915-014-0063-7 [Turner, David A., et al. "Brachyury cooperates with Wnt/β-catenin signalling to elicit primitive-streak-like behaviour in differentiating mouse embryonic stem cells." (2014)] and by the numerous studies on micropatterned stem cells). To the best of our knowledge, however, no 2D model of spontaneous, undirected endodermal differentiation has been investigated in detail and from a developmental perspective. We share the reviewer’s interest on the insight that this kind of approach could provide. Still, claims of absence of some degrees of architectural organisation or of extraembryonic tissues are not straightforward in self-organising 2D systems either.On the other hand, 2D approaches that consist in directed differentiation of stem cells to specific endodermal fates are clearly not the type of investigation we were interested within the scope of our experimental questions. We also believe that e.g. differentiation of 2D epithelia that then bud to form 3D spheres are generally contexts that are too decoupled from embryonic modes of development to provide the same degree of developmental insight than e.g. Gastruloids. Having both 2D and 3D platforms at our availability, we opted for latter.

The following conclusions were not warranted by the findings: […]

The FoxA2+/Sox17+ endoderm progenitors never transitioning through the mesenchymal intermediates and never leaving the epithelial compartment that they arise: In view of that the stereotypic morphogenetic activity was not documented during the development of the gastruloid, it is not possible to exclude the possibility of the progenitors undergo a partial EMT (loss of epithelial feature and cellular polarity and display of morphogenetic movement, as in vivo) in the transition from progenitor to the epithelial endoderm cells. The DE-like cells when first discerned in the gastruloid are apparently epithelialized. In the absence of lineage tracing results, It is not clear whether they are still residing in the "epithelial compartment that they arise".

We agree with the reviewer’s comment: the ability to trace endodermal cells throughout their journey in the Gastruloid and throughout differentiation, specifically in conjunction with a live monitor of their epithelial status (e.g. overlayed with a Cdh1 reporter) would provide clear and definitive insight on the endodermal and epithelial transitions taking place in this system. Our conclusions are based on timed immunostaining showing that in early Gastruloids all cells are epithelioid. Finding FoxA2+ (and Sox17+) cells consistently within a Cdh1+ context, while also having necessarily emerged within an epithelioid context, suggests that these cells never leave an epithelial compartment. Within the text, we also put forwards alternative hypotheses equally consistent with our observations: namely that these cells would indeed leave the epithelial compartment (still, through incomplete EMT processes not relying on Snai1 programmes), but reintegrate it at short timescales. We do not find FoxA2+ cells within the mesodermal compartment, as one would expect from comparison with the embryo, and we do not see Snai1 expression within Cdh1+ cells. We would like a live tracing system whereas we could track endodermal identity (e.g. a FoxA2 reporter) while being also able to track its epithelial (E-cadherin, Cdh1) status. We do not foresee to be able to perform such experiment in the near future. Live tracking of Cdh1+ cells in Gastruloids has been described in [Hashmi, Ali, et al. "Cell-state transitions and collective cell movement generate an endoderm-like region in gastruloids." BioRxiv (2020).].

We use the term “mesenchymal” to signify “not-epithelial”, and as such “Cdh1-negative”. When we hypothesise endoderm cells not to go through EMT, we imply a classical, complete, Snai1-mediated EMT. By “leaving the epithelial compartment” we mean “losing Cdh1 expression” (and as such not being associated anymore with the epithelial/epithelioid compartment). As such, we are not excluding the possibility put forwards by the reviewer: i.e. endodermal progenitors going through a partial EMT with loss of epithelial architecture, but not epithelial markers, and movement in this epithelioid state. In fact, this is the interpretation we are favoring in our report. We will clarify our use of each term within the text of the preprint and provide more clarity to these points. Accordingly, we will rephrase each of the terms above (“mesenchymal”, “absence of EMT”, and “epithelial compartment”) in terms of the Cdh1 and Snai1 status of the cells.

The mature endoderm cells are patterned segmentally in the gastruloid. The findings that the molecular phenotype (marker expression) of the mature endoderm cells "aligns with (cellular) identities along the entire length of the embryonic gut tube" are not sufficient evidence of spatial A-P patterning of endoderm cells. The expression pattern of Foxa2/Cdh1 (Fig 5d) was not informative of tissue patterning.

We share the reviewer’s point that alignment of the molecular phenotype (transcript expression) of Gastruloid cells with that of cells along varying position of the gut tube of the embryo does not in fact necessarily imply that these cells are spatially patterned within the Gastruloid. We were deliberate in our presentation of the data on this point, and in fact explicitly presented to readers the equally probable possibility that “the variety of cell identities uncovered in the single cell dataset are intermingled throughout the core of the Gastruloid” (rather than being spatially patterned; lines 787-789). This possibility is in fact provided as the rationale to highlight the need for alternative investigations able to provide spatial information (provided in the following section).

Promisingly, the markers we did investigate (Pax9 for anterior endoderm; Cdx2 and TBra for posterior identities) were found to not be intermingled throughout the primordium but correctly expressed at anterior and posterior domains (as already known for TBra and Cdx2 from previous Gastruloid literature). We do agree that showing the distribution of AP markers within a same sample would provide a more immediate and compelling visual of the AP patterning of anterior and posterior markers. We also agree that the number of markers we investigated spatially is still restricted, and further characterisation, specifically of middle markers, would provide a more complete picture of the extent to which the Gastruloid primordium is effectively patterned. We would like however to point out that we do not make claims of spatial patterning beyond those supported by the markers we did confirm by immunostaning/HCR; i.e. we only claim spatial patterning of anterior and posterior domains (“Gastruloid endoderm contains patterned anterior and posterior endodermal types”; line 663).

We agree with the reviewer that the expression pattern of Foxa2/Cdh1 is not informative of tissue patterning. When using these markers on Gastruloids, we indeed use them as “pan-endodermal” markers to identify the general cellular domain at the core of the Gastruloid.

[…] Whether endoderm cells are patterned or not is, however, irrelevant for the understanding of the mode of endoderm formation, unless the timing and the mechanism of allocation of endoderm cells of specific segmental property has been studied in the gastruloid.

The relevance of the specific set of results indicated by the reviewer (i.e. the topic of patterning of endodermal cells in the Gastruloid) is maybe better appreciated in the context of understanding the modes of later development and maturation of endoderm in vitro, and its self-organising and self-patterning abilities after it has formed, rather than provide direct insights into the formation of the germ layer itself. In the preprint, we indeed present this investigation as a segue to the first set of experiments that instead focus on the formation of the endoderm itself. As the reviewer points out, we have not investigated the specific aspect of timing and mechanisms of allocation of endoderm cells to specific segmental identities as these cells are emerging within the Gastruloid. Our reporting that Gastruloids contain endodermal identities that do end up specified to different segmental identities in the first place sets the basis for the kind of investigation the reviewer suggests.

it is also unclear if a structure reminiscent of the embryonic gut (closed or partly open) was formed (or self-organised) in the gastruloid.

We thank the reviewer for raising this point. The structures we describe are initially multi-branched and whisk-shaped (120h), and in turn resolve a single rod-like tissue that follows the outer geometry of the Gastruloid (144h), interfacing with an outer envelope of mesenchymal (non-endodermal) cells. We provide depth-coded maximal intensity projection of these structures (under Cdh1 immunostaining) to try to best convey images of their three-dimensional shape (Figure 4A, Figure 5C). While we believe this epithelial primordium to be fully closed and non-hollow, in fact quite different than the folding/folded epithelium of the post-gastrulating mouse gut endoderm, a better idea of the 3D organisation of this primordium could certainly be provided by outputs from light sheet imaging and series of optical sections along the z-axis of our immunostained samples. We plan to include these alternative visualisations and in the meantime refer to already published light-sheet data as found in [Rossi, Giuliana, et al. "Capturing cardiogenesis in gastruloids." (2021)]. Here too, the endodermal primordium appears as a dense, closed mass of cells. To contextualise these structures with respect to most recent literature, we do not see the kind of vacuolated and segmented structures described instead in https://doi.org/10.1038/s41467-021-23653-4 [Xu, Peng-Fei, et al. "Construction of a mammalian embryo model from stem cells organized by a morphogen signalling centre." (2021)]

The information regarding the spatial localization of specific germ layer markers in the gastruloids at different timepoints would be important to understand how the morphology progresses and how it is comparable to the developing embryo itself. How is the organisation of the mesoderm and endoderm layers in comparison to embryo in the early timepoints and later timepoints of gastruloids?

We agree with the reviewer about the helpfulness of such characterisations. While a temporal characterisation of the evolution of the endoderm compartment in relationship to the other cell types is provided in Figure 4A, cells of the other two germ layers are not explicitly labelled. We will provide analogous immunostaining series across Gastruloid development (early to late timepoints), choosing markers able to highlight cells of each of the three germ layers. Given the difficulty of finding specific germ layer markers, and the often-unforgiving limitations on the markers that can be chosen for simultaneous staining due to host-species antibody cross-reactivity, we also find useful to piece together relative germ layer localisation from the much wider imaging data now widely available in previous Gastruloid literature. We thus point interested readers to complement the endoderm descriptions from our preprint with dedicated characterisations of the axial organisation and distribution of the other two germ layers in Gastruloids published in https://doi.org/10.1038/s41586-018-0578-0 [Beccari, Leonardo, et al. "Multi-axial self-organization properties of mouse embryonic stem cells into gastruloids." Nature 562.7726 (2018): 272-276.], which also specifically discusses these aspects in relation to the germ layer organisation of the embryo proper.

Clarify if Foxa2 and Sox17 double positive cells exist in the Cdh1 patches (Fig 3a). In Fig 4, authors have demonstrated the development of epithelial primordium with overlaying mesodermal wings, however it is important to show if Foxa2, Sox17, or other definitive endoderm markers co-express in these cells.

We thank the reviewer for highlighting the value of co-localisation data, and in fact this is a suggestion also put forward by our other reviewer. We are currently developing a pipeline to segment the nuclei on the DAPI channel of each immunostained Gastruloid, and extract marker intensities within each cell. We envisage the results to be presented in a format similar to what shown e.g. in Fig. 2 of https://doi.org/10.1242/dev.159103 [Mulas, Carla, et al. "Oct4 regulates the embryonic axis and coordinates exit from pluripotency and germ layer specification in the mouse embryo." (2018)]. Such data would undoubtedly strengthen the reliability of the claims we here draw mostly from a qualitative inference of colocalisation. When showing the distribution of markers within the 120h epithelial primordium, the assumption was that since the entire primordium is FoxA2+, any other marker would colocalise with a subset of them, when expressed within the primordium. We do note the importance of colocalisation data for a more exact description of the cell type identities contained within the primordium.

It was suggested that E-Cadherin is maintained during endoderm differentiation. N-cadherin expression may be examined to determine if N-cad is expressed in the other region of gastruloids.

We share the interest in describing N-cadherin expression patterns, especially in the context of EMT development and endoderm epitheliality, and thank the reviewer for highlighting the value of this marker. To the time of publication, we had been unfortunately unable to source a N-cadherin antibody giving good signal quality in our hands. We are planning further staining of early and late Gastruloids for this marker. Given what recently reported in https://doi.org/10.1038/s41556-021-00694-x [Scheibner, Katharina, et al. "Epithelial cell plasticity drives endoderm formation during gastrulation." (2021)] we expect endodermal cells to display double cadherin expression (Cdh1+/Cdh2+), and the mesodermal compartment to display N-cadherin instead only.

In Fig 6, FACS quantification is not proportional to the expression of the TBra:GFP as shown in the microscopic images at 96 hr, 120hr. Fig 6D does not show the TBra:GFP positive cells on the y -axis in the top-left quadrant, even though it is quite visible in microscopy - at 96, 120 hr. Microscopic images suggest TBra signal is almost completely lost at 128hr whereas FACS does not represent that. Infact, at 120 hours, the plot shows opposite of what microscopy shows.

The reviewer is correct in pointing out these discrepancies and we will make sure to flag them explicitly in the main body of our report. It appears that the trend in reporter expression highlighted by FACS data is delayed with respect to what visible by live imaging (where TBra loss of expression can already be seen starting at t=120h). The decrease in TBra expression does not instead seem to be recovered even by the last FACS timepoint. Concomitantly, the TBra signal detected during time-lapse seems to decrease to abnormally low level (indeed, be lost), especially if compared to equivalent timepoints processed for FACS. We will investigate whether TBra reporter signal is particularly vulnerable to sustained illumination (i.e. timelapse conditions), as it appears to be still be present at later timepoints in both FACS and end-imaged Gastruloids (see clear posterior expression in Fig 6B). Maintained presence of TBra expression is also what expected and detected by immunostaining in both our data and in general Gastruloid literature. In light of the possible effects of sustained imaging on our reporters, we only use timelapse data to describe global cell movement (and of the FoxA2+ cells, rather than changes in reporter expression), and instead rely on the FACS data for claims based on intensity values. While we resolve the effect of timelapse imaging on TBra reporter detection, we will explicitly highlight the discrepancy between the two investigative approaches within the Results section, and thank the reviewer for bringing this point to our attention.

Gastruloids were sampled at 96-168 hours for single cell transcriptome analysis. However, the specimens documented in this study were those only up to 144 hours. How does the gastruloid morphology look at 168 hours? It is essential to show the morphology and characterise the further development from 144 to 168 hours, to compare the single cell RNA seq data with the morphology of the gastruloid.

The reviewer is correct in pointing out the absence of imaging data showing the internal endodermal primordium at t=168h. Examples of 168h Gastruloids (but not of the internal primordium) are shown in Figure 8D, indeed when we compare transcriptomics data with spatial patterning and verify the spatial distribution of late gut markers. At this stage, the internal endodermal primordium is mostly similar to its configuration at t=144h. We will incorporate additional morphological data for Gastruloids at 168h to show the organisation of the endodermal primordium at this later stage and to facilitate morphologytranscriptome comparisons.

In Fig 7, it is surprising to see that the proportion of cells in the two clusters 13, 4 that mark endoderm are a minor portion of the whole dataset collected, whereas the microscopic images suggest that the majority of the gastruloid structure from 120hr onwards is marked by Foxa2 and shows the epithelial primordium morphology as claimed.

Microscopic images are optical sections through the midplane of each Gastruloid as to capture the full extent of the internal Gastruloid epithelial primordium. We believe that these pictures fail to accurately convey the degree to which this primordium is in fact completely surrounded by a thick and dense layer of mesenchymal cells, not only in the lateral dimension as can be appreciated e.g. in Figure 4B, but also above and below the plane of the microphotographs. A better idea of the volume occupied by such cells, and the proportion of endodermal vs non-endodermal cells, could be provided by lightsheet imaging. At later timepoints, and as hinted by e.g. panel 4B [144h], the volume occupied by non-endodermal cells can be considerable. Linking back to a previous suggestion from the reviewer, we believe documentation of Gastruloid morphology at 168h could help to further clarify the relationship between data coming from the single cell dataset and morphological data as seen from the Gastruloid themselves. This might also be a further opportunity to underscore that the single-cell dataset accessed for our endoderm-targeted analysis was produced in the context of a different study https://doi.org/10.1016/j.stem.2020.10.013 [Rossi, Giuliana, et al. "Capturing cardiogenesis in gastruloids." (2021)], in which Gastruloids made with this same cell line were additionally treated with cardiogenesis-inducing factors. The proportion of cells classified as cardiac mesoderm and associated mesodermal types is thus likely much over-represented compared to what present in non-induced Gastruloids (i.e. those considered in this report), and as in fact illustrated by the imaging data presented in the paper.

The single-cell RNA-seg data should be analysed for the co-expression of multiple segment-specific cell markers to ensure that the mature endoderm cells align with high-confidence with the known cell types in different segments of the embryonic gut, and that the localization of representative cell types can be validated spatially along an endoderm structure in single gastruloids.

We will analyze the single-cell RBAseq dataset to show the co-expression of segment specific markers. As the reviewer points out we have only shown the patterns of expression of single markers at a time. As mentioned in a previous point, we also agree that the number of markers we verified to be spatially pattern is still restricted, and further characterisation, specifically of middle markers, would provide a more complete picture of the extent to which the Gastruloid primordium is effectively patterned. We do also agree that showing the distribution of AP markers within a same sample would provide a more immediate and compelling visual of the AP patterning of anterior and posterior markers.

It is not clearly indicated how many replicates were performed to assure consistency/reproductivity of the gastruloid results. Statistical results were not provided for most of the immunostaining experiments, either in the main text or in the figure legends.

Both reviewers highlight the qualitative nature of much of the data that is presented. Accordingly, we will more clearly and more consistently indicate the number of samples analysed and the number of replicates considered. For what concerns the “statistical results” of immunostaining experiments, and as indicated in response to a previous comment, we envisage to provide such results in a format similar to what used e.g. in Supplementary Figure 4 of https://doi.org/10.1038/s41467-021-23653-4 [Xu, Peng-Fei, et al. "Construction of a mammalian embryo model from stem cells organized by a morphogen signalling centre." (2021)]; i.e. categorization of the phenotypes observed among gastruloids, and quantification of their proportions. To convey statistics on the variability among Gastruloids, our current pipeline can output scatterplots like the one presented in Figure 4C, where the datapoint spread informs about the variability of the data. We will provide statistics on these plots in a numerical format.

Majority of the images presented in the manuscript are shown as Maximum Intensity Projections, and it is not clearly stated if the localisation of the cells expressing specific protein markers are present on the surface or in the internal layers of the gastruloids. Optical slices of the gastruloid images may be presented as supplementary information.

Most of the images presented in the manuscript are presented as optical cross-section through the midplane of the Gastruloid. We can make this clearer in the text. Indeed, and since this report focuses on on the spatial distribution of markers along the AP axis of the Gastruloids (and not DV or LR axes), we found midplane optical sections to best capture the entirety of the axis and thus the full extent of marker pattern (where those patterns are not asymmetric along the DV axis). All cells positive for immunostained markers are thus to be interpreted to be within the midplane of the Gastruloid, and as such internal to the Gastruloid if falling internally to the midplane, and external to the Gastruloid when falling towards the edges. As suggested by the reviewer we will acquire optical sections along the z-stack of our immunostained samples and provide them as supplementary information. Maximum intensity projections were only shown when wanting to better show the 3D structure of the internal epithelial primordium, and were always depth-coded to aid visualisation (Figure 4A, rightmost panel, Figure 5C).

- Are prior studies referenced appropriately?

Yes, except for the study on endoderm formation by lineage tracing in vivo, high-resolution single-cell analytics and functional analysis of genetic mutant embryos.

We thank the reviewer for pointing out inappropriate referencing of studies on these three topics, yet given the breadth of each of these topics and the absence of any more specific information we remain unsure on how to address this comment appropriately. We would thus like to warn readers that this comment might have remained unaddressed.

Results may be presented with reference to the data figures in the appropriate sequential order, or the figures may be re-organised to match the presentation in the Results.

We thank the reviewer for bringing this to our attention. The figures are automatically organised in the order they are presented in the Results, yet since most of the figures are page-long their placement may appear odd. We will look into the matter and readjust figure positioning where possible, and/or reduce mentions of data shown in previous figures.

Reduce the verbosity throughout the manuscript, especially the Results and Discussion.

As this is also a point raised by both reviewers, we will make sure to go back over the entirety of the manuscript and do the necessary adjustments, especially for what concerns the Results section. We would however like to point out that the unusual length of our Introduction section is to be understood as a deliberate departure from the limits and conventions prescribed by traditional publishing formats. In line with our intentional choice of a journal-independent publication platform (i.e. a preprint server), and of journal-independent review process, we also presented and contextualized our research in a journal-independent format. We see this as an opportunity to present research in a voice truer to that of the researchers that carried it out. We thank both reviewers for their feedback on the format and will certainly still make sure to minimize redundancy of information throughout our manuscript.

This study on endoderm development, however, is confounded by the inherent limitation of the experimental model: lack of extraembryonic tissue components, the atypical morphological structure and the deviation from the in vivo schedule of development and morphogenesis. This may raise doubt of the relevance of the findings to the

We find difficult to share the view expressed by the reviewer here. The “lack of extraembryonic tissue components, the atypical morphological structure, and the deviation from the in vivo schedule of development and morphogenesis”, which they identify here as the inherent limitations of the model, represent in fact its value for us and for most in the Gastruloid field. For more complete and articulated discussion on how it is precisely the differences with the embryo proper that provides insights when studying in vitro models of embryonic development, we refer interested readers to dedicated discussions on the topic [van den Brink, Susanne C., and Alexander van Oudenaarden. "3D gastruloids: a novel frontier in stem cell-based in vitro modeling of mammalian gastrulation." (2021); Simunovic, Mijo, and Ali H. Brivanlou. "Embryoids, organoids and gastruloids: new approaches to understanding embryogenesis." (2017); Turner, David A., Peter Baillie‐Johnson, and Alfonso Martinez Arias. "Organoids and the genetically encoded self‐assembly of embryonic stem cells." (2016)).]. In fact, we share the reviewer’s concern about the relevance of these findings to “understanding of the morphogenetic activity and molecular control of endoderm formation during gastrulation in the embryo”. The constant questioning of such relevance is in fact a central point in Gastruloid research, where insight comes from a dialectic comparison between what observed in vitro and what known to happen in vivo. The reviewer questions the relevance of the findings “to the understanding of the morphogenetic activity and molecular control of endoderm formation during gastrulation in the embryo”. The relevance of our findings might be better framed in that they provide a better “understanding of the morphogenetic activity and molecular control of endoderm formation” tout court. In this case, outside of the embryo (and inside a self-organising developmental system), and reflections on this inform better understanding of how endoderm might be developing in vivo.

Our findings in the Gastruloid open lines of inquiry to be verified and tested in the embryo. Both similarity and differences being equally informative on the intrinsic and extrinsic elements of endoderm behaviour. In fact, where some of the aspects we describe have been investigated in the embryo proper (see [Scheibner, Katharina, et al. "Epithelial cell plasticity drives endoderm formation during gastrulation." (2021)]) many of the same themes have emerged.

Knowledge gleaned from the present study on the gastruloid study added little to that of a recent study of the morphogenetic program of endoderm formation in the mouse embryo and the ESC differentiation model (https://doi.org/10.1038/s41556-021-00694-x ) .

The reviewer is absolutely correct in mentioning [Scheibner, Katharina, et al. "Epithelial cell plasticity drives endoderm formation during gastrulation." (2021)] to the readers of this public review. Given that these results were published posteriorly to our preprint, we could only reference them in our later versions, and we did this extensively throughout the text. We consider the paper mentioned by the reviewer [Scheibner, Katharina, et al. "Epithelial cell plasticity drives endoderm formation during gastrulation." (2021)] to represent a major contribution to the topic of (mouse) endoderm development, specifically in its investigation of endoderm EMT mechanisms as they take place within the mouse embryo itself. In relationship with what we describe here in Gastruloids, we see what reported by Scheibner’s et al extremely validating and as a very strong example of the investigative validity of in vitro models of development. Here is the in vivo exploration of the same topic highlighting many of the endoderm features we had inferred from in vitro observations, or that our observations further supported (specifically, incomplete EMT, tight association with epithelioid character, low evidence for mesendodermal intermediates etc..).

To say that our study added very little to what now available in [Scheibner, Katharina, et al. "Epithelial cell plasticity drives endoderm formation during gastrulation." (2021)] represents however an inaccurate view of our study as being a study of endoderm development in vivo (see also answer to previous point). We would also want to point out that a major portion of this preprint describes the self-organisation of endoderm in vitro, the emergence and development of almost all AP-endoderm identities by self-organisation, the effective spatial patterning of at least some of these (waiting further characterisation), and the description of an accessible, tractable, reproducible in vitro model system to study endoderm development and provide populations of interest for culture. Our study also provides further insight on the necessary inputs to endoderm development and patterning, and whether extracellular matrices and extraembryonic tissues are part of such necessary inputs. Sharing the view expressed by the other reviewer, we see great insight from all of these aspects, and these are certainly not the topic or focus of the study referenced by this reviewer.

As we do throughout the preprint, we strongly encourage readers interested in the topic to refer to [Scheibner, Katharina, et al. "Epithelial cell plasticity drives endoderm formation during gastrulation." (2021)] for insights coming from the embryo proper. We also take the opportunity to stress the value of all types of science, be it incremental, consolidating, or complementary.

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Referee #2

Evidence, reproducibility and clarity

• Are the key conclusions convincing?

The following conclusions were not warranted by the findings: Endoderm emergence can occur in the absence of extraembryonic tissues and embryonic architecture: It is unclear if extraembryonic tissues (e.g., primitive endoderm and visceral endoderm-like cells) are absent in the early phase of gastruloid development. While the gastruloid does not replicate the morphological feature of the post-gastrula embryo, it nevertheless has a certain degree of tissue organization. Perhaps the emergence of DE-like cells in 2-D culture would be a more convincing model for "the absence of extraembryonic tissues and embryonic architecture".<br> The FoxA2+/Sox17+ endoderm progenitors never transitioning through the mesenchymal intermediates and never leaving the epithelial compartment that they arise: In view of that the stereotypic morphogenetic activity was not documented during the development of the gastruloid, it is not possible to exclude the possibility of the progenitors undergo a partial EMT (loss of epithelial feature and cellular polarity and display of morphogenetic movement, as in vivo) in the transition from progenitor to the epithelial endoderm cells. The DE-like cells when first discerned in the gastruloid are apparently epithelialized. In the absence of lineage tracing results, It is not clear whether they are still residing in the "epithelial compartment that they arise".

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

The mature endoderm cells are patterned segmentally in the gastruloid. The findings that the molecular phenotype (marker expression) of the mature endoderm cells "aligns with (cellular) identities along the entire length of the embryonic gut tube" are not sufficient evidence of spatial A-P patterning of endoderm cells. Only the spatial regionalization of Pax6-expressing cells (Fig. 8) and Cdx2-expressing cells (Fig 4C) were shown on different gastruloid specimens. The expression pattern of Foxa2/Cdh1 (Fig 5d) was not informative of tissue patterning. It is also unclear if a structure reminiscent of the embryonic gut (closed or partly open) was formed (or self-organised) in the gastruloid. Whether endoderm cells are patterned or not is, however, irrelevant for the understanding of the mode of endoderm formation, unless the timing and the mechanism of allocation of endoderm cells of specific segmental property has been studied in the gastruloid.

• 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.

Specific points:

1. The information regarding the spatial localization of specific germ layer markers in the gastruloids at different timepoints would be important to understand how the morphology progresses and how it is comparable to the developing embryo itself. How is the organisation of the mesoderm and endoderm layers in comparison to embryo in the early timepoints and later timepoints of gastruloids?
2. Clarify if Foxa2 and Sox17 double positive cells exist in the Cdh1 patches (Fig 3a). In Fig 4, authors have demonstrated the development of epithelial primordium with overlaying mesodermal wings, however it is important to show if Foxa2, Sox17, or other definitive endoderm markers co-express in these cells.
3. It was suggested that E-Cadherin is maintained during endoderm differentiation. N-cadherin expression may be examined to determine if N-cad is expressed in the other region of gastruloids.
4. In Fig 6, FACS quantification is not proportional to the expression of the TBra:GFP as shown in the microscopic images at 96 hr, 120hr. Fig 6D does not show the TBra:GFP positive cells on the y -axis in the top-left quadrant, even though it is quite visible in microscopy - at 96, 120 hr. Microscopic images suggest TBra signal is almost completely lost at 128hr whereas FACS does not represent that. Infact, at 120 hours, the plot shows opposite of what microscopy shows.
5. Gastruloids were sampled at 96-168 hours for single cell transcriptome analysis. However, the specimens documented in this study were those only up to 144 hours. How does the gastruloid morphology look at 168 hours? It is essential to show the morphology and characterise the further development from 144 to 168 hours, to compare the single cell RNA seq data with the morphology of the gastruloid.
6. In Fig 7, it is surprising to see that the proportion of cells in the two clusters 13, 4 that mark endoderm are a minor portion of the whole dataset collected, whereas the microscopic images suggest that the majority of the gastruloid structure from 120hr onwards is marked by Foxa2 and shows the epithelial primordium morphology as claimed.
7. The single-cell RNA-seg data should be analysed for the co-expression of multiple segment-specific cell markers to ensure that the mature endoderm cells align with high-confidence with the known cell types in different segments of the embryonic gut, and that the localization of representative cell types can be validated spatially along an endoderm structure in single gastruloids.

• 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 suggested experiments may be accomplished in a few months.

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

Yes for the methods. It is not clearly indicated how many replicates were performed to assure consistency/reproductivity of the gastruloid results.

• Are the experiments adequately replicated and statistical analysis adequate?

Statistical results were not provided for most of the immunostaining experiments, either in the main text or in the figure legends.

Minor comments:

• Specific experimental issues that are easily addressable.

Majority of the images presented in the manuscript are shown as Maximum Intensity Projections, and it is not clearly stated if the localisation of the cells expressing specific protein markers are present on the surface or in the internal layers of the gastruloids. Optical slices of the gastruloid images may be presented as supplementary information.

• Are prior studies referenced appropriately?

Yes, except for the study on endoderm formation by lineage tracing in vivo, high-resolution single-cell analytics and functional analysis of genetic mutant embryos.

• Are the text and figures clear and accurate?

Results may be presented with reference to the data figures in the appropriate sequential order, or the figures may be re-organised to match the presentation in the Results.

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

While taking into consideration the limitation of this embryo model for studying morphogenesis, highlight the interesting/unique findings of the gastruloid study, albeit they may have been discovered in the study of embryos in vivo.

Reduce the verbosity throughout the manuscript, especially the Results and Discussion.

Significance

• Describe the nature and significance of the advance (e.g., conceptual, technical, clinical) for the field.

This study demonstrated that definitive endoderm (DE)-like cells can be generated in the stem cell-derived embryo-like structure, the gastruloid. This observation is significant for that gastruloids can serve as an amenable experimental model, in comparison to other 2D invitro differentiation models, for elucidating the requisite cellular process in endoderm differentiation and the acquisition of cell identity. It was inferred that, in the gastruloid, epithelial-mesenchyme transition may not be a requisite cellular process for the formation of the DE-like cells and that endoderm formation may not involve progression through an intermediate mesenchymal state. The progenitor cells may have acquired and retained the attribute of epithelization during differentiation and organization the DE-like cells into an endoderm layer.<br> This study on endoderm development, however, is confounded by the inherent limitation of the experimental model: lack of extraembryonic tissue components, the atypical morphological structure and the deviation from the in vivo schedule of development and morphogenesis. This may raise doubt of the relevance of the findings to the understanding of the morphogenetic activity and molecular control of endoderm formation during gastrulation in the embryo.

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

Knowledge gleaned from the present study on the gastruloid study added little to that of a recent study of the morphogenetic program of endoderm formation in the mouse embryo and the ESC differentiation model (https://doi.org/10.1038/s41556-021-00694-x ) . This recent study has advanced the understanding of the functional attributes that segregate the endoderm progenitors and mesoderm progenitors in the primitive streak and the posterior epiblast, and has characterised the role of epithelial plasticity and the modulation of EMT activity under the control of Forkhead box transcription factor A2 and modulation of WNT signalling in the formation of the definitive endoderm.

• State what audience might be interested in and influenced by the reported findings.

Developmental biologists, stem cell scientists and embryo modellers

• 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.

Mouse embryogenesis, gastrulation, in vitro stem cell differentiation, advanced microscopy, single cell transcriptomics.

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Referee #1

Evidence, reproducibility and clarity

The study adresses a long-standing question in mouse gastrulation: the existence of a mesendodermal progenitor, similar to other species. Recent data in the mouse point towards a direct transition from epiblast to endoderm without going through a bipotent progenitor, and without loss of epithelial characteristics (notably Probst 2021). The authors take advantage of the gastruloid system to follow the emergence of endoderm cells. Through co-staining with markers of various germ layers at different timepoints, live imaging, and reanalysis of single cell RNASeq data, they propose a model in which endoderm cells differentiate from E-cadherin positive cells without going through a bipotent stage. Interestingly, they then organise a rod like structure along the anterior-posterior axis of the gastruloid, that display some polarity illustrated by a marker of anterior gut.

The data present show very high reproducibility, and authors fully exploit the organoid system by analysing a large amount of samples and showing very similar results. The results are qualitatively very convincing. For the sake of clarity, it may help to provide some table illustrating the proportion of gastruloids behaving precisely as the best example shown. It would also strengthen the message even more to provide some quantitation of co-expression for the main markers. As the behaviour seems very consistent, it is likely that such quantification would not be very arduous, and it would show the strength of the model.

The manuscript is pleasantly written and all statements are clearly explained. It is a bit long though, in particular the introduction, some of which might read more like a review of the field. It is less striking in the Results part, but there is some level of repetition that is a bit distracting.

Significance

The question is important and timely, and the data are clear and convincing. There have been a number of publications addressing it in the last years, either in the embryo or in gastruloids (notably Hashmi 2021). All data appear to point in the same direction, and this study is certainly an important contribution. An original aspect to my knowledge is the self organisation of the central rod. The system is simple, reproducible, and opens novel possibilities to dispose of a large number of cells to explore the emergence of endoderm subpopulations.

The fact that several studies converge is rather an advantage as they all have specificities, and I believe they are adequately cited here.

In summary this is an important and well conducted study that may just benefit from some additional quantification to prove robustness. In terms of writing, it is pleasant and quite literary, but perhaps a little bit too much so. The introduction could be shortened, and the results a bit more to the point.

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Reply to the reviewers

Reply to the Reviewers

We would like to thank the reviewers for their thoughtful comments and efforts towards improving our manuscript. Based on the reports, we have revised the manuscript entitled “Bi-phasic effect of gelatin in myogenesis and skeletal muscle r__egeneration__ (RC-2021-00854 )”. We have addressed all the concerns, revised the text and figures. Modified parts are in red, and line numbers are tracked in this response letter. Our detailed point-by-point responses to the reviewers’ comments are listed below. We believe that these changes strengthen the manuscript and are grateful to the reviewers for all of their suggestions.

Point-by-point description of the revisions

R__eviewer #2 (Evidence, reproducibility and clarity (Required)):__

\*Summary:***

The manuscript by Xiao Ling Liu and colleagues titled "Bi-phasic effect of gelatin in myogenesis and skeletal muscle regeneration" deals with the effect of gelatin on differentiation of myoblast cell line, in vitro, and on skeletal muscle regeneration upon muscle injury, in vivo. In vivo, the gelatin is a product of collagen breakdown associated with skeletal muscle regeneration upon acute or chronic muscle damage.

Specifically, the authors define a dose-dependent effect of gelatin, beneficial at low dose and detrimental at high dose. This effect is mediated by the level of ROS accumulation leading to the induction of different cytokines with opposite effects on skeletal muscle regeneration.

**Major comments:**

The experimental purpose is well tackled from both biochemical and functional point of view, and the proposed experiments are quite exhaustive.

Response: We are grateful for the encouraging comments from this reviewer.

However, I would suggest some additional experimental analyses to improve the robustness and quality of the study, as well as text and figure editing, as reported below.

Regarding the additional experiments/analyses/images:

1. • Figure 5: I would suggest to add an image of C2C12 cells in GM (growth medium), as representative images of proliferation analysis upon LCG/HCG/NAC treatment.* Response: We appreciate this suggestion by the reviewer. New images of C2C12 cells upon LCG/HCG/NAC treatment have now been added as in Supplementary Figure 4B. The new results are described in main text Lines 237-239.

• Figure 5: I would suggest to repeat the main si-NOX2 experiments with an alternative siRNA to rule out off target effects.*

Response: We thank the reviewer for this suggestion. We have now added a new siRNA targeting NOX2 with an independent sequence and shown the results in Figure 5F-J and Supplementary Figure 4H-J. The sequences of si-NOX2 and si-NC are shown in Materials and Methods Lines 681-687. The newly added siRNAs confirmed results with the previous siRNA, suggesting unlikely off-target effects. This result is described in the main text Lines 246-256.

• In vivo experiments could be improved by adding DHE or DCFH staining on muscle TA cryosections to quantify the level of oxidative stress.*

Response: We appreciate this suggestion by the reviewer. We have now stained DCFH-DA in TA cryosections to quantify oxidative status in situ and show the results in Supplementary Figure 8A-B. Indeed, low- and high-dose gelatin injections both triggered ROS production, and high-dose injection resulted in a high accumulation of ROS. The new result is described in the main text Lines 318-321.

• The proposed model could be better tackled by additional in vivo treatment with Ab anti IL-6 or anti TNFalpha in combination with CTX and LCG or HCG, followed by H/E staining at 14 dpi.*

Response: We appreciate this suggestion by the reviewer. We have now added in vivo treatment with IL-6 or TNFα neutralizing antibody (Ab) in combination with CTX and LCG or HCG. The procedure is illustrated in Figure 8J and described in main text Lines 522-525. H&E staining showed that anti-IL-6 Ab injection significantly reduced the beneficial effect of LCG, but had no effect on HCG-treated mice. By contrast, anti-TNFα Ab injection significantly suppressed infiltration of macrophages into the injury site upon HCG, reversed the deleterious effect of HCG on muscle repair, resulting in myofibers with higher CSA and more myofibers with central nuclei. The new results are described in Figure 8J-K and main text Lines 331-346.

**Minor comments:**

1. Each acronym should be indicated in full in the main text at the first mention (for instance BHP, NAC and others). Moreover, I would suggest to add an acronym list for reagents and factors Response: We thank the reviewer for this suggestion and have now added an acronym list in Material and Methods, Lines 466-504.

Experimental methods should be better detailed; for instance I would suggest:

1. • Add a detailed description of the quantification of differentiation indexes* Response: We thank the reviewer for this suggestion. We have now added a detailed description on the quantification method of myogenesis and differentiation to Material and Methods, Lines 577-582.

• Explain how cell growth (OD 450nm) and optical density (570nm) assays have been performed*.

Response: We thank the reviewer for pointing this out. The cell growth was examined by measuring dehydrogenase activities that generate a soluble formazan dye, whose OD 450nm value was measured as a proportional value to the number of viable cells in the sample (CCK-8 kits according to manufacturer’s instructions).

The transwell cell migration ratio was determined by measuring the optical density of crystal violet staining (570 nm) of migrated cells on the bottom of the transwell filters (transwell filters have 8 μm pores to allow cells to pass through). The non-migrated cells on the top of the transwell filter were scraped away.

Detailed descriptions have now been added to Material and Methods, Lines 593-609.

• Explain how ROS species and antioxidant enzymes have been measured (Fig 4C and 4D)*

Response: We thank the reviewer for pointing this out. The levels of ROS species (O2.- , OH· and H2O2) and antioxidant enzymes in Figure 4C and 4D were examined using commercial kits according to the manufacturer’s instructions (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Briefly, cells were lysed in RIPA lysis buffer and protein concentrations were determined using BCA method. The O2.- level and SOD activity were measured by adding electron transfer substances that reduce azo blue tetrazole to blue methionine. The activity of SOD was evaluated by the absorption of methionine. GSH-PX facilitates the reaction between H2O2 and GSH to produce H2O and oxidized glutathione (GSSG). The activity of GSH-PX can thus be obtained by measuring the consumption of GSH in this enzymatic reaction. OH· was measured using Fenton reaction. The level of H2O2 was determined according to the reaction with molybdic acid. We have now added the corresponding information to Figure. 4 legend and main text Lines 1066-1067. Detailed protocols can be found in Material and Methods, Lines 704-714.

Figures and figure legends:

1. Please add in the figures the figure number in order to facilitate the reading of the pdf file Response: We thank the reviewer for this suggestion and have added figure numbers to the PDF file.

The sequence of the panels should be coherent with the alphabet and reading left to right and up to down

Response: Yes, we have rearranged the figure layouts to be coherent.

• In the Figure 1, I would suggest to add the whole TA sections for H/E staining in order to appreciate the overall beneficial or detrimental effect of LCG and HCG, respectively.*

Response: We thank the reviewer for this suggestion and have added the whole-section view of TA with H&E staining as in Supplementary Figure 1C. The results are described in the main text Lines 132-136.

• I would suggest to show Supplementary figures 1C and 1D in the Figure 1*.

Response: We thank the reviewer for this suggestion and have now moved Supplementary Figures 1C and 1D to Figure 1F and 1G.

In the Supplementary Fig 2A, I would suggest to the authors to show and comment only the data about proliferation: the earlier orientation, fusion and differentiation of C2C12 exposed to LCG are a consequence of the positive effect of LCG on proliferation

Response: We thank the reviewer for this suggestion and have removed data and comments except for proliferation of C2C12 in Supplementary Figure 2A.

• The quality of representative images of western blot is not always high: the bands are fuse and, consequently, the quantification is not reliable. For instance Fig S4 B, S4 G; in Fig 2 the representative image does not really represent the reported quantification.*

Response: We thank the reviewer for this suggestion and have replaced western blot images in Supplementary Figure 4B, 4G and Figure 2.

• Please specify in the figure legends the meaning of the acronym in the axis title (for instance RFU, MFI or DCF) and in the axis title the unit of measure (for instance Count (?)).*

Response: We thank the reviewer for this suggestion and have spelt out “RFU” as “Relative fluorescence intensity”, changed “MFI” to “Relative fluorescence intensity of NOX2” in Figure 4H and Figure 8H. The unit of measurement is the fluorescence intensity. The related modifications have been described in the legend of Figure 4H and Figure 8H, Lines 1071, 1125-1126. We have now included “DCF: 2',7'-dichlorofluorescein” in the acronym list in Material and Methods, Line 473. DCF positive ratio is now used to reflect ROS level in the population. We have explained “Count” in the legend as “Cell counts”.

The authors always wrote "filed" in place of "field"**.

Response: We apologize for this typo and have corrected it with others throughout the text.

• In the figures 7 and 8 the letters for densitometry panel are missed.*

Response: We could not identify those missing labels and letters in the densitometry panel. We suspect this issue could be from the soft wares opening the documents.

• In the figure legend 8 the panel letters do not match the panels in the figure*.

Response: We thank the reviewer for pointing this mistake out and have corrected Figure legend 8.

• Figure 3I: replace "nucleis" with "nuclei"*.

Response: We thank the reviewer for pointing out this mistake and have modified Figure 3I.

Text editing:

1. • Line 64: Satellite cells (SCs) would be more appropriate than myoblasts* Response: We thank the reviewer’s suggestion and have replaced myoblasts with satellite cells in the main text Line 60.

• Line 64: Please define more carefully the location of SCs*

Response: We thank the reviewer for this suggestion. SCs are underneath the basal laminin and myofiber plasma membrane in the resting skeletal muscle. The results are described in the main text Lines 60-65.

• Line 67: MyoD+/Myog+ would be more appropriate than Pax7+/Myog+*

Response: We thank the reviewer’s suggestion and have changed pax7+/MyoG+ into MyoD+/MyoG+ in the main text Line 64.

• Line 67: (Pax7+)/Myog+ "myocytes" in place of myotubes ... and fuse with each other to generate myotubes*

Response: We thank the reviewer for pointing this out and have modified the sentence in Lines 64-65.

• Line 69: Please add Myf6/MRF4 to MRFs list*

Response: We thank the reviewer for pointing out and have added Myf6/MRF4 to MRFs list in the main text Line 66.

• Line 112: replace "its" with "their"*

Response: We thank the reviewer for pointing out this mistake and have replaced “its” with “their”.

• Lines 330-331: "promoted IL-6" "enhanced TNF", please insert secretion/production*

Response: We thank the reviewer’s suggestion and have inserted “production and secretion” behind IL-6 and TNFa in the main text Lines 310-312. .

• Line 352-353: this sentence is not necessary*

Response: We have deleted this sentence.

Reviewer #2 (Significance (Required)):

The study reports robust and interesting data applicable to both basic research and translational research, such as tissue engineering applications.

Response: We thank the reviewer for sharing this positive and important opinion on our work. We are delineating the biological pathways of gelatin treatments, motivated by the application of this biocompatible and industrial material for treating disease and aging-related skeletal muscular dystrophies

Keywords for field of expertise of this reviewer:

Skeletal muscle regeneration

Duchenne Muscular Dystrophy

Inflammation

Macrophages

Oxidative stress

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

**Summary**

The Review Commons submission by Liu and colleagues entitled "Biphasic effect of gelatin in myogenesis and skeletal muscle regeneration" provide a systematic in vitro and in vivo evaluation of the effects of myogenic cell exposure to low or high dose gelatin. Through these analyses they uncover pro- and anti-regenerative effects of gelatin that are dose dependent and through a series of cell and molecular studies, they attribute these dual effects to a ROS-IL6/TNFa signaling axis. The study is well designed and executed. For the most part the figures and experimental details are clear and transparent, and most of the conclusions are supported by the data. Specific comments follow.

Response: We are grateful for the reviewer’s encouraging comments.

**Major Comments**

1. • Study framing in the Introduction is mis-aligned with the research conducted. Currently the Introduction sets the stage for an in vivo exploration of the effects of endogenous gelatin produced in the course of muscle regeneration. However, there are no experiments in this paper investigating the presence or effects of endogenous gelatin.* Response: We thank the reviewer for pointing this out and apologize for the misleading parts in our abstract and introduction. The reported phenomenon of a temporary breakdown of collagen after skeletal muscle injury has inspired us to apply gelatin for achieving a pro-regenerative effect. Although it will be a very interesting biological pathway to study, no adequate research tools are available for measuring endogenous gelatin in vivo. We have re-written parts in abstract and introduction to avoid confusion, see Lines 34-35, 78-93.

1.a. The impact of the study would indeed be increased by including a systematic characterization of endogenous gelatin levels during muscle regeneration in healthy mice as compared to in those where fibrosis is prevalent. A demonstration that ROS-IL6/TNFa levels align with patterns seen in the in vitro studies, and pharmacological manipulations to 'rescue' would all provide a demonstration of a hormesis gelatin response in vivo. Meaning, is this process something that naturally occurs in the physiological context, or is it one that is possible, but only be supraphyiological gelatin injections?

Response: We resonate with the reviewer and would have loved to investigate whether what we have observed is a fundamental mechanism of the natural healing process. But we are currently limited by the lacking of adequate tools for endogenous gelatin quantification. We appreciate the reviewer’s suggestion to compare normal and aberrant repairing processes such as fibrosis and would like to explore the possibility in a separate future study.

1.b. Alternatively to 1.a., the authors should reframe the Introduction to focus on understanding the effects of gelatin as a biomaterial that is being used in regenerative medicine applications. In this case, the authors should delete/edit/reframe lines 74-102 and instead use lines 103 on to motivate the study so as to be consistent.

Response: We have rephrased sections of Abstract and Introduction to focus on gelatin as a biomaterial. Please find the changes in the main text Lines 34-35, 78-93.

• Satellite cell conclusions in Figures 2C-D that are based upon representative images provided in 2A, are questionable. Pax7 staining in mouse tissue sections is notoriously difficult and the antibodies can have dramatic lot to lot variability. The immunostaining provided in the representative images is not convincing, and hence, draws into question the conclusions based upon them.*

Response: We thank the reviewer for the suggestion and have optimized Pax7 staining based on the published protocol by Feng et al., 2018 in JoVE. The new images can be found in Figure 2A.

2.a. If the authors wish to leave the satellite cell conclusions in their study, they will need to optimize their Pax7 staining and repeat this study. They should focus on Pax7+ objects that contain a nucleus and are located below the basal lamina. Also, the word 'activation' in line 180 should be edited to 'expansion' as the histological analysis and study design preclude an evaluation of satellite cell activation.

Response: Our new results after optimizing staining protocol support that low-dose gelatin injection causes more Pax7+ cells (green) underneath the basal lamina (red) in injured TA muscle and high-dose gelatin injection suppressed the number of Pax7+ SCs at 7 D.P.I. The new results were shown in Figure 2A and described in the main text Lines 152-155.

We have changed the word “activation” into “expansion” according to reviewer’s suggestion in the main text Line 161.

2.b. Alternatively to 2.a., it would not diminish the impact of this study to remove the 'satellite cell' findings in their entirety from the manuscript.

Response: Please see above. We hope the new images are convincing to this reviewer.

• It is surprising that the molecular hallmarks of low vs high gelatin injection shown in Fig. 8 would still be present at a time point 2-weeks after the initial injection.*

Response: Please see below 3.b.

3.a. It would increase the impact of the study to better understand the basis of this surprising observation. This point links to point 1.a. as one would ideally need to quantify baseline gelatin levels pre-injury and post-injury. For example, is the injected gelatin still present 14-days after injection? Or is the MMP profile altered in a way that sustains these levels one direction or the other? Etc.

Response: Please see below 3.b.

3.b. Alternatively to 3.a., the authors should use the Discussion to note this point and speculate on the significance.

Response: We thank the reviewer for making this important point. The sustained effect of gelatin materials has been reported in several previous studies, and we now have discussed possible mechanisms such as MMP expression profiles and lasting interplays between SCs, macrophages, ECM, and myoblasts. See the main text Lines 437-459.

**Minor Comments**

1. • The authors should conduct a careful review of the manuscript to address minor typos and grammatical errors.* Response: We thank the reviewer for pointing this out and have carefully reviewed the manuscript and corrected minor typos and grammatical errors.

• It is unclear from the Figure Legends, Results, or Methods what the 'PBS' condition in Figure 1 refers to. Is this the uninjured control? If so, consider using 'Cntrl' as the label and then defining it in the figure legend for clarity.*

Response: We thank the reviewer for pointing this out. Yes, PBS/phosphate-buffered saline is the vehicle for delivering CTX thus representing the uninjured model. In the revised manuscript, we have replaced “PBS: phosphate-buffered saline” with “Ctrl” in Figures, Figure Legends, Results and Methods.

• It is unclear from the Figure Legends, Results, or Methods what is quantified to read-out the 'myogenesis index' and 'fusion index' that is reported in Fig. 5D & E. Please reconcile.*

Response: This point has been addressed in the previous section. Myotube formation was quantified using myogenesis and fusion index. Myogenesis index (% nuclei within MyHC-stained myocytes/total nuclei) and fusion index (% nuclei in myotubes with >5 nuclei/total nuclei in MyHC-stained cells) are now explained both in legends and in Materials and Methods. Please see the main text Lines 577-582.

Reviewer #3 (Significance (Required)):

The manuscript constitutes a technical advance, and offers a molecular mechanism, in support of the notion that intramuscular injection of low dose gelatin serves to expedite the process of skeletal muscle regeneration. This study has translational implications for a regenerative medicine application of this knowledge. It is my opinion that this aspect of the study is well supported by the results and requires Response: We thank the reviewer for sharing this positive and important comment. Indeed, the motivation of this work was to delineate biological pathways of gelatin treatments in myogenesis and muscle regeneration for potential therapeutic applications.

The manuscript would constitute both a technical and a conceptual advance by addressing Major Point 1a as the authors would show, for the first time, that low and high dose gelatin levels naturally exist in vivo to mediate the process of muscle endogenous repair. This would be highly significant, because as the authors rightly point out in Lines 74-76 of their manuscript "...by what mechanisms ECM regulates the functional, morphological, and molecular events of skeletal muscle regeneration remain poorly understood". It is my opinion that making this latter point would require 1-3 months of additional studies.

Response: We thank the reviewer for pointing to this important scientific direction. But regrettably, missing adequate tools for quantifying endogenous gelatin in vivo is currently prohibitive.

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

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Referee #2

Evidence, reproducibility and clarity

Summary

The Review Commons submission by Liu and colleagues entitled "Biphasic effect of gelatin in myogenesis and skeletal muscle regeneration" provide a systematic in vitro and in vivo evaluation of the effects of myogenic cell exposure to low or high dose gelatin. Through these analyses they uncover pro- and anti-regenerative effects of gelatin that are dose dependent and through a series of cell and molecular studies, they attribute these dual effects to a ROS-IL6/TNFa signaling axis. The study is well designed and executed. For the most part the figures and experimental details are clear and transparent, and most of the conclusions are supported by the data. Specific comments follow.

Major Comments

1. Study framing in the Introduction is mis-aligned with the research conducted. Currently the Introduction sets the stage for an in vivo exploration of the effects of endogenous gelatin produced in the course of muscle regeneration. However, there are no experiments in this paper investigating the presence or effects of endogenous gelatin.

1.a. The impact of the study would indeed be increased by including a systematic characterization of endogenous gelatin levels during muscle regeneration in healthy mice as compared to in those where fibrosis is prevalent. A demonstration that ROS-IL6/TNFa levels align with patterns seen in the in vitro studies, and pharmacological manipulations to 'rescue' would all provide a demonstration of a hormesis gelatin response in vivo. Meaning, is this process something that naturally occurs in the physiological context, or is it one that is possible, but only be supraphyiological gelatin injections?

1.b. Alternatively to 1.a., the authors should reframe the Introduction to focus on understanding the effects of gelatin as a biomaterial that is being used in regenerative medicine applications. In this case, the authors should delete/edit/reframe lines 74-102 and instead use lines 103 on to motivate the study so as to be consistent.

1. Satellite cell conclusions in Figures 2C-D that are based upon representative images provided in 2A, are questionable. Pax7 staining in mouse tissue sections is notoriously difficult and the antibodies can have dramatic lot to lot variability. The immunostaining provided in the representative images is not convincing, and hence, draws into question the conclusions based upon them.

2.a. If the authors wish to leave the satellite cell conclusions in their study, they will need to optimize their Pax7 staining and repeat this study. They should focus on Pax7+ objects that contain a nucleus and are located below the basal lamina. Also, the word 'activation' in line 180 should be edited to 'expansion' as the histological analysis and study design preclude an evaluation of satellite cell activation.

2.b. Alternatively to 2.a., it would not diminish the impact of this study to remove the 'satellite cell' findings in their entirety from the manuscript.

1. It is surprising that the molecular hallmarks of low vs high gelatin injection shown in Fig. 8 would still be present at a time point 2-weeks after the initial injection.

3.a. It would increase the impact of the study to better understand the basis of this surprising observation. This point links to point 1.a. as one would ideally need to quantify baseline gelatin levels pre-injury and post-injury. For example, is the injected gelatin still present 14-days after injection? Or is the MMP profile altered in a way that sustains these levels one direction or the other? Etc.

3.b. Alternatively to 3.a., the authors should use the Discussion to note this point and speculate on the significance.

Minor Comments

1. The authors should conduct a careful review of the manuscript to address minor typos and grammatical errors.
2. It is unclear from the Figure Legends, Results, or Methods what the 'PBS' condition in Figure 1 refers to. Is this the uninjured control? If so, consider using 'Cntrl' as the label and then defining it in the figure legend for clarity.
3. It is unclear from the Figure Legends, Results, or Methods what is quantified to read-out the 'myogenesis index' and 'fusion index' that is reported in Fig. 5D & E. Please reconcile.

Significance

The manuscript constitutes a technical advance, and offers a molecular mechanism, in support of the notion that intramuscular injection of low dose gelatin serves to expedite the process of skeletal muscle regeneration. This study has translational implications for a regenerative medicine application of this knowledge. It is my opinion that this aspect of the study is well supported by the results and requires <1month of additional edits to finalize the manuscript.

The manuscript would constitute both a technical and a conceptual advance by addressing Major Point 1a as the authors would show, for the first time, that low and high dose gelatin levels naturally exist in vivo to mediate the process of muscle endogenous repair. This would be highly significant, because as the authors rightly point out in Lines 74-76 of their manuscript "...by what mechanisms ECM regulates the functional, morphological, and molecular events of skeletal muscle regeneration remain poorly understood". It is my opinion that making this latter point would require 1-3 months of additional studies.

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

Learn more at Review Commons

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Referee #1

Evidence, reproducibility and clarity

Summary:

The manuscript by Xiao Ling Liu and colleagues titled "Bi-phasic effect of gelatin in myogenesis and skeletal muscle regeneration" deals with the effect of gelatin on differentiation of myoblast cell line, in vitro, and on skeletal muscle regeneration upon muscle injury, in vivo. In vivo, the gelatin is a product of collagen breakdown associated with skeletal muscle regeneration upon acute or chronic muscle damage.

Specifically, the authors define a dose-dependent effect of gelatin, beneficial at low dose and detrimental at high dose. This effect is mediated by the level of ROS accumulation leading to the induction of different cytokines with opposite effects on skeletal muscle regeneration.

Major comments:

The experimental purpose is well tackled from both biochemical and functional point of view, and the proposed experiments are quite exhaustive. However, I would suggest some additional experimental analyses to improve the robustness and quality of the study, as well as text and figure editing, as reported below.

Regarding the additional experiments/analyses/images:

• Figure 5: I would suggest to add an image of C2C12 cells in GM (growth medium), as representative images of proliferation analysis upon LCG/HCG/NAC treatment.
• Figure 5: I would suggest to repeat the main si-NOX2 experiments with an alternative siRNA to rule out off target effects.
• In vivo experiments could be improved by adding DHE or DCFH staining on muscle TA cryosections to quantify the level of oxidative stress.
• The proposed model could be better tackled by additional in vivo treatment with Ab anti IL-6 or anti TNFalpha in combination with CTX and LCG or HCG, followed by H/E staining at 14 dpi.

Minor comments:

Each acronym should be indicated in full in the main text at the first mention (for instance BHP, NAC and others). Moreover, I would suggest to add an acronym list for reagents and factors

Experimental methods should be better detailed; for instance I would suggest:

• Add a detailed description of the quantification of differentiation indexes
• Explain how cell growth (OD 450nm) and optical density (570nm) assays have been performed
• Explain how ROS species and antioxidant enzymes have been measured (Fig 4C and 4D)

Figures and figure legends:

• Please add in the figures the figure number in order to facilitate the reading of the pdf file
• The sequence of the panels should be coherent with the alphabet and reading left to right and up to down
• In the Figure 1, I would suggest to add the whole TA sections for H/E staining in order to appreciate the overall beneficial or detrimental effect of LCG and HCG, respectively.
• I would suggest to show Supplementary figures 1C and 1D in the Figure 1
• In the Supplementary Fig 2A, I would suggest to the authors to show and comment only the data about proliferation: the earlier orientation, fusion and differentiation of C2C12 exposed to LCG are a consequence of the positive effect of LCG on proliferation
• The quality of representative images of western blot is not always high: the bands are fuse and, consequently, the quantification is not reliable. For instance Fig S4 B, S4 G; in Fig 2 the representative image does not really represent the reported quantification.
• Please specify in the figure legends the meaning of the acronym in the axis title (for instance RFU, MFI or DCF) and in the axis title the unit of measure (for instance Count (?)).
• The authors always wrote "filed" in place of "field"
• In the figures 7 and 8 the letters for densitometry panel are missed.
• In the figure legend 8 the panel letters do not match the panels in the figure
• Figure 3I: replace "nucleis" with "nuclei"

Text editing:

• Line 64: Satellite cells (SCs) would be more appropriate than myoblasts
• Line 64: Please define more carefully the location of SCs
• Line 67: MyoD+/Myog+ would be more appropriate than Pax7+/Myog+
• Line 67: (Pax7+)/Myog+ "myocytes" in place of myotubes ... and fuse with each other to generate myotubes
• Line 69: Please add Myf6/MRF4 to MRFs list
• Line 112: replace "its" with "their"
• Lines 330-331: "promoted IL-6" "enhanced TNF", please insert secretion/ production
• Line 352-353: this sentence is not necessary

Significance

The study reports robust and interesting data applicable to both basic research and translational research, such as tissue engineering applications.

Keywords for field of expertise of this reviewer:

Skeletal muscle regeneration

Duchenne Muscular Dystrophy

Inflammation

Macrophages

Oxidative stress

Transparent Peer Review

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Reply to the reviewers

Firstly, we would like to thank the reviewers for their helpful and insightful comments.

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

In the manuscript of Ramadan et al. , authors use the ex vivo organoid approach to compare gene expression in organoids derived from adult type stem cells when these organoids are grown using different matrices. The presence of Collagen type I induces the emergence of cells with a transcriptome similar to fetal progenitors. In contrast, laminin the main component of matrigel, induces an organoid-protruded phenotype with transcriptome of stem cell type. Then, they correlate these data with expression of collagens and laminins from data publicly available. They show by qRT -PCR that laminins are more expressed in mesenchymal versus epithelial fractions postnatally. They hypothesize on this basis that the remodeling at postnatal stage is likely only dependent on the mesenchymal compartment and it involves interaction of laminins with integrity a6.

It seems that some of the presented data have already been described and could not be considered as « novel ».

For some of the statements, like this one « the basement-membrane produced by the epithelium is not sufficient to increase stem cell numbers and induce a morphological crypt formation », the conclusion is not sustained by provided experiments. To draw definitive conclusion on this particular point, authors could reproduce the experiment presented in Fig. 4d but using Cre recombinases specific for mesenchymal and epithelial compartments rather than the ubiquitous Cre line. It would be interesting to investigate if organoids grown from lamc1-/- mice can generate protruded organoids or not.

In addition, how interpret the fact that fetal organoids up is associated with « laminin interactions » in fig. 1c?

The statement that the epithelium-produced basement membrane is not sufficient to increase stem cell numbers is based on our in vitro observations. Analysis of the RNAseq data shows that the expression of several laminins is increased on collagen (see heatmap of laminin interactions below, which will be added to the manuscript). This is also the reason why ‘laminin interactions’ is highly significant in the gene set enrichment analysis (Fig. 1C). Despite this upregulation, we never observed morphological changes (or expression changes) as when laminin is added to the collagen-hydrogel. In addition, we showed that the vast majority of ECM components is produced by the mesenchyme in vivo, in line with previous literature as cited in the manuscript. The mentioned Cre lines to address the question in vivo are unfortunately not available to our collaborators with the Lamc1 k.o. mice and it would therefore take too long to perform these experiments.

However, to address this point in vitro we will grow organoids from Lamc1 fl/fl mice and induce loss of laminin in the pure epithelial cell culture. Organoids will then be analysed for morphological changes, as well as proliferation and gene expression changes.

One major point to address regards statistics. In material and methods, the paragraph describing statistical analyses is missing. Moreover, in the figures presenting qPRC data ( figs 1g 3b 3D 3g 4c and f), no statistic analysis is provided; and the number of samples for some conditions is extremely limited (n=2). In general, the term « independent experiment « should be clarified : does it correspond to one organoid line for which the experiment was repeated or one single experiment using different organoid lines?

In fig 4c , all collagen conditions are set to 1.

The avoidance of statistical inference for most of the experiments was a deliberate choice. In line with several comments (e.g. 1. Vaux, D. L. (2012) Know when your numbers are significant. Nature. 492, 180–181), we chose to show all individual data points (with exception of Fig. 3D, n=5, to ease interpretation) without statistics. In addition, for most expression data, we have data from RNAseq, single-cell RNAseq and qPCRs repeated at different hydrogel concentrations to obtain reliable results. Further, the in vivo mesenchymal qPCR expression data was validated with RNA in situ hybridization showing the mainly mesenchymal expression.

The term independent experiment was used mainly for repeated experiments with the same organoid lines (exception RNAseq data, different organoids derived from individual mice). While conducting these experiments, we realised that the variability of these experiments comes from time in culture, density of cells and even Matrigel variation. The experiment in Fig. 4c (n=4, each time with the all controls) was performed with longer intervals in between, and showed variation in the absolute levels of expression. However, relative to each control we believe the effect is clear.

As we will perform additional experiments for the revision of this paper, we will then perform statistical tests in the key experiments (e.g. Itga6 experiment) to alleviate any concerns regarding significance.

Regarding the experiment presented in fig 4c, authors should include additional control conditions : anti-a6 integrity antibody in matrigel and use of an isotype antibody.

We will conduct additional experiments regarding the Itga6. In addition to including the mentioned controls for the neutralizing antibody, we will genetically inactivate Itga6 via an inducible Crispr/Cas9. This should enable us to delete Itga6 when the cells are grown on collagen, and hence reduce the possibility of compensation in matrigel derived organoids.

Another point regards RNAscope data presented in Fig 4b, it is surprising to observe such difference in terms of expression between E19 and P0. Does this mean that birth dramatically unregulates Itga6 expression in few hours? Authors should comment this point if verified.

We do believe that birth is a timepoint where a dramatic change in the ECM and their receptors can be observed. The epithelial RNAseq data would already indicate that at 18.5 there is an increase in expression compared to E16. This upregulation of the receptor is in line with the dramatic remodelling of the ECM at birth, as is shown by the expression of the basement membrane components in Fig. 3d.

Authors should avoid the word « signaling » for laminin-integrin interactions as they do not study this aspect at all in their experiments.

The word signaling was used for the protein:receptor interaction and to distinguish it from changes to the physical characteristics of the hydrogel. But we agree with the reviewer, that we did not study laminin signaling per se and therefore will change the wording accordingly.

Regarding Col1a1, authors cannot claim that it's expression only slightly changed (fig 3d) as it is clearly upregulated between E17 and P0.

The reviewer is right, and we apologise for the misleading sentence. The contrast was meant to the basement membrane components that are very lowly expressed at E17 and then suddenly show the burst of expression at birth, whereas collagen seems to be continuously expressed with a peak at P7. We will rephrase the sentence.

Reviewer #1 (Significance (Required)):

Overall, the methodology used for the asked questions is accurate.

One potential problem for publication comes from the fact that some of the findings are already reported and hat the present data do not provide further advances.

for example, collagen and fetal-like expression profile, Ly6a sorting and replating in culture-Yui et al, 2018, Jabaji et al, 2013.

We obviously do not agree with the reviewer on this point. We build upon the work of Jabaji et al. 2013, and Wang 2017 to characterise the specific effect of collagen on the intestinal epithelium compared to a pure Matrigel culture. The emergence of Ly6a cells was nicely shown by Yui et al., however it was unclear if collagen changes the fate of all intestinal cells or only a few. We strongly feel that our data extends these findings as it associates the changes we observe in vitro to the development of the crypt morphology and intestinal stem cells.

The phenotype of Lamc1-/- mice and the observed reduced stem cell marker expression are also reported by Fields et al, 2019.

Indeed, as we cited this paper. However we predicted based on our in vitro model, that deletion of laminin would result in this specific fetal-like gene expression and hence were happy to include these findings in our manuscript.

Infine, authors do not interpret their ex vivo data in the context of fetal progenitors which grow as spheres in matrigel (containing laminin)?

Our ex-vivo (in vitro) data would suggest that adult epithelial cells express some genes that are characteristic for fetal organoids, however we do not think these cells completely revert back to a fetal stage. Regarding the comment of spheres, it is noteworthy that fetal cells from E14-16 stay as spheres in Matrigel, whereas fetal cultures from E19 initially grow as spheres and then develop into organoids within 30 days in vitro (M. Navis et al., “Mouse fetal intestinal organoids: new model to study epithelial maturation from suckling to weaning,” EMBO Rep., vol. 20, no. 2, pp. 1–12, 2019.).

In figure 5, should we interpret that there is no laminin at all in the fetal mesenchyme?

We now see how the image is a bit misleading for that stage. The levels of laminin are lower in the fetal stage as can be seen by the IF image in Fig.3f and the image will be updated. We also apologise for the lack of labeling in Figure 3f, which should be E19, P7 and adult.

Also, authors do not cite a paper reporting on the role of the epithelium ( stem cells) in regulating its own extracellular matrix composition, this process modulating the stem cell number and fate (Fernandez-Vallone et al. 2020). As this is contradictory with the claim that only mesenchyme impacts on crypt morphogenesis, authors could discuss on this point.

In the paper by Fernandez-Vallone, deletion of Lgr5 in E16.5 embryos resulted in a decrease expression of several ECM genes. Further, the authors could show that the fetal epithelium does express for example Col1a1 at this point, which decreases with maturation. However even for the example of Col1a1 it is evident in their paper that the mesenchyme expresses Col1a1 at much higher levels. Our proposed experiments with Lamc1 k.o. In organoids will show if the produced laminins of the epithelium are essential.

This manuscript could be interesting for an audience in the stem cell and developmental fields ( my field of expertise).

**Referee Cross-commenting**

Considering the pertinent and sometimes overlapping comments of the two other reviewers, the estimated time is revised to 3-6 months.

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

**Summary:**

In this manuscript, Ramadan and colleagues demonstrate that depending on the extracellular matrix (ECM) composition in which mouse intestinal organoids and/or 2D intestinal epithelial cells are grown in, cellular composition of the epithelium changes. Organoids plated on 2D collagen layers show a unique cell cluster characteristic of fetal-like genes, while organoids plated with increased amount of Matrigel in 2D or in 3D exhibit a shift towards higher stem cell abundance and the absence of the fetal-like gene cluster. Specifically, the ECM component Laminin supports acquisition of stem cells identities in small intestinal epithelial cells, correlating with a transient increase in expression levels of collagen and laminin genes in vivo spanning time points of crypt formation. The authors reported the functional contribution of laminin signaling (Lamc1 KO) via integrin alpha 6 (antibody-blocking) to intestinal stem cell acquisition in vitro and in vivo.

There are a handful of comments/concerns that would need to be addressed before publication.

Major points:

1. The author claimed: "the effect of ECM components on gene expression is not due to difference in morphology (2D collogen vs. 3D Matrigel)". The conclusion of the 2D vs. 3D experiment should be toned down to that organoid morphology (2D vs. 3D) does not directly impact on the expression of fetal-like genes. Otherwise more analysis of RNAseq data with different group of genes (e.g., in different mechanosensing pathways) should be provided with Fig. S1D. Also, would it be technically feasible to perform experiments of SI in collagen (3D) in all group of experiments? Directly comparing 3D Matrigel with 3D collagen avoids the concern of the 2D vs. 3D effect.

We apologise for the too strong claim of structure of growth vs. signalling of the ECM and its effect on the transcriptome. Indeed, the main message is that a changed morphology from 3D to 2D is not responsible for the expression of fetal-like genes. The paragraph will be rephrased.

Also, would it be technically feasible to perform experiments of SI in collagen (3D) in all group of experiments? Directly comparing 3D Matrigel with 3D collagen avoids the concern of the 2D vs. 3D effect.

To address this point, we want to refer to the excellent idea of growing established organoids in collagen (3D) vs. Matrigel (3D) as suggested by this reviewer (and reviewer #3) in Minor points #5. This circumvents the need for Wnt3a addition, which affects stem cell and Paneth cell gene expression.

1. For Fig. 1f, the authors should include overlapping stainings of Lyz (or Olfm4, CD44 etc.) and Adolase B signal, or they could perform Aldolase B staining in Lgr-5-DTR-GFP and/or Lyz-RFP organoid line. From the current data provided one cannot draw clear conclusions on the crypt morphology as claimed by the authors. Additionally, when talking about crypt morphology and apical accumulation of Actin specifically in the Lyz+ cells, the authors should show a higher zoom in of the picture and either add an orthogonal slice to see apical and basal side and the specific accumulation in one of the cell types, or also co-label with apical polarity markers.

We will perform additional co-stainings to further highlight the differences in the spatial distribution of differentiated cells and undifferentiated-crypt-like cells. Further we will provide higher magnification images highlighting the apical accumulation of Actin in the crypt-like structures, which can also be seen in mature organoids.

1. Authors referred the organoid transient change to fetal-like state. To exam the similarity of ECM-induced reprogramming with the regenerative-type of reprogramming, it would be essential to compare the expression of the selected fetal-like genes (Anxa3, Ly6a/Sca1, Msln, Col4a2 et al.), as well as bulk and single-cell (if applicable) RNA-seq data.

Here, we would like to refer to the excellent study by Yui et al. ([S. Yui et al., “YAP/TAZ-Dependent Reprogramming of Colonic Epithelium Links ECM Remodeling to Tissue Regeneration,” Cell Stem Cell, vol. 22, no. 1, pp. 35-49.e7, 2018.). In this study the authors detected the same gene signature in the repairing epithelium. We can provide a GSEA for the Ly6a+ signature that was derived from this paper, if necessary.

1. For in vivo data, authors were looking at the normal development of intestine. Following the point of organoid culture recapitulates regeneration, it would be relevant to check the in vivo ECM change by staining in the process of intestinal regeneration or discuss would the fetal-like genes be involved in regeneration.

We will address this point in the discussion as it also involves the study by Yui et al.

1. For Fig2.d and e, it would be important to measure compactness vs. the emergence/probability of Ly6+ cells to see if there is correlation.

If we understand the reviewer correctly, this would address the important relationship between cell shape and cell fate/type. However, this is a topic that needs more attention than a simple correlation and would exceed the scope of this manuscript as we are not able to modulate cell shape to make any further points about its effect on the fetal gene expression program.

1. In Fig.2d, Ly6a expression is very obscure, and it would be important to show control staining for cell boundaries (eg. Phalloidin, PM) to visualize which nuclei show Ki67 staining and are high or low in Ly6a (plus quantification).

We will improve the image in Fig.2d and include the mentioned Actin staining. In addition we will perform an analysis via Flow Cytometry to quantify the level of Ly6a staining and EdU positivity.

1. In Fig. 2f-g, FACS-ed Ly6a+ and Ly6- cells embedded in Matrigel can grow into organoids with crypts. Here the imaging of Paneth cell staining is not clear, and a quantification on number of Paneth cells per crypt would be very helpful to confirm the phenotype. Also, authors should either provide data on the initial size of seeded cell clusters and report organoid growth and cell type composition in more detail when plating from Ly6a+ and Ly6- cells or report the variation in the respective populations.

This comment suggests that we may not have described the experimental settings properly. The sorted cells were embedded as single cells, not as clusters, in drops of matrigel (10k cells/25ul Matrigel). The emergence of Paneth cells together with a normal organoid architecture grown from Ly6a+ cells shows their stem cell capacity, as has been shown by Yui et al. before from the regenerating colon. In addition, organoids from both cell populations (Ly6a+ and Ly6a-) could be passaged, indicating presence of intestinal stem cells.

1. The authors could also test whether Ly6+ cells have any advantages over Ly6- cells when grown on collagen I instead of Matrigel.

We will sort Ly6a+ and Ly6- negative cells and plate them on collagen I. It will be interesting to see if the Ly6a+ cells can give rise to the other cell types when plated on collagen or if they stay Ly6+ cells. This will also answer whether Ly6a+ need the presence of Ly6a- cells in the cultures. In addition, the experiment proposed in #6 will also highlight any proliferative advantage of Ly6a cells compared to Ly6-negative cells on collagen.

1. In Fig.3f, a control of membrane protein staining should be added for the experiment. The increased Laminin signal can be caused by the global increase of protein when there are more cells, or tissues are more compact. When authors make conclusion of "Dramatic remodelling of ECM during crypt formation ", the experiment should also count cell numbers vs. Laminin (intensity). The phenotype can come from increased area of interface between epithelium and mesenchyme instead of active remodelling.

We agree with the reviewer that by itself the IF images are not enough to make such a claim. However, we would point to the qPCR data and RNA in situ, that can be more easily normalised and shows the dramatic increase in expression of all laminins at birth. To show that laminin protein is increasing is more difficult than we initially anticipated. However, in the study by De Arcangelis (A. De Arcangelis et al., “Hemidesmosome integrity protects the colon against colitis and colorectal cancer,” Gut, vol. 66, no. 10, pp. 1748–1760, 2017.) the authors use an EDTA assay to show that the epithelium detaches easily when Itga6 is deleted. Within the figure, it seems also that the epithelium detaches easily at P2, compared to P14. As EDTA is disrupting laminin polymerisation, this would further indicate increased laminin protein deposition after birth.

1. The authors claim that intestinal stem cells in vivo are controlled by Laminin signalling that goes via Integrin alpha 6. However, there is no evidence provided that supports the contribution of ITGA6 in the in vivo setting. So, the authors should either tone down on that point or show a convincing in vivo experiment (e.g., inhibit ITGA6 in vivo by inhibitor injections or by extracting the ECM of a wild-type mouse and seeding intestinal epithelial cells without vs. with ITGA6 blocking antibody which should recapitulate the phenotype in Fig. 4 c.

We apologise for this confusion. We are well aware about the limitations of our Itga6 blocking experiment in vitro and its relevance in vivo. We tried to get material of the inducible VilCreER Itga6 mouse as referenced in the discussion of the manuscript, without any luck so far. Therefore we will highlight further that any claims about the laminin:Itga6 interaction can only be made in vitro.

1. Fig. 4: For the data of ITGA6 expression and all sorts of analysis on protein expression with staining, normalization with cell numbers should be performed.

The RNAseq data that shows the upregulation of Itga6 in the epithelium at E18 is normalized within. Our RNAscope only further validates these expression changes and highlights the specific enriched expression at the bottom of the nascent crypts. We can add quantification of the RNAscope if required.

1. Two questions on mechanisms:

2. What is the mechanism from ITGA signaling to Ly6a+ cell fate?

3. And would/how Laminin induce ITGA expression? Depends on how much the authors would like to go deep with the project, could be addressed further with functional studies, or touch on the topics with discussion.

These are important questions, however we do agree that this will go to deep for the scope of this manuscript. We will address these open questions in the discussion and leave the experimental part for a follow-up study.

**Minor points:**

1. Text in Fig.S1d regarding 'in' or 'on' collagen, could be clearer by changing the terms to 2D and 3D correspondingly.

We agree and the text will be changed accordingly.

1. Fig. S1a, it is great the authors showed that similar stiffness in Matrigel and collagen I. It would be important to check the concentration of collagen I vs. stiffness (also for increasing concentrations of Laminin in Fig. 3b), since this is also the type of ECM change that might lead to the change of cell status in cancer progression or collective cell migration.

We will perform further stiffness measurements of the hydrogels and update the Fig. S1a.

1. When plating intestinal epithelial cells on collagen I, is the Ly6+ phenotype altered upon Wnt addition? This is not so clear from the RNAseq data Fig S1d., so authors should provide antibody stainings (stem cells/Paneth cells). This could give insight whether Ly6+ cells are still able to convert into stem cells/ Paneth cells by changing morphogen concentration vs. ECM composition.

We will reanalyse the RNaseq dataset further, specifically analysing the ratio of stem cell and Paneth cell gene expression. However, as mentioned before, Wnt3a specifically does reduce the expression of Paneth cell markers.

Similar to this point, also enteroendocrine cell fate is absent in collagen I condition (Fig2.ab), the authors could address this point by medium induced EE cell fate.

Due to the reduced number of secretory cells the clustering in Fig2 a/b does not separate all the different cell lineages. However, EE cells are present in the collagen cultures as characterised by expression of Chga, just reduced in their number (see Supl Fig. 2B).

1. It would be more informative to indicate the thickness of ECM layer in culture of 2D collagen I, as well as the image of the whole well, demonstrating the morphological variation in the middle and peripheral of the ECM layer.

The thickness of the collagen layer is about 1mm in a 6well plate and we do not observe any morphological differences in the cells between the periphery and center of the well.

1. After the formation of PC/SC clusters, would ECM contribute to maintenance? Putting mature organoids from Matrigel to Collagen I 3D would help to clarify.

This is an interesting experiment that we will conduct, we thank the reviewer for this suggestion. Indeed, established organoids should be able to grow in collagen I without Wnt3a addition. The paper by Sachs et al. (N. Sachs, Y. Tsukamoto, P. Kujala, P. J. Peters, and H. Clevers, “Intestinal epithelial organoids fuse to form self-organizing tubes in floating collagen gels,” Development, vol. 144, no. 6, pp. 1107–1112, 2017. et al) used extensive washing with PBS to remove Matrigel from the organoids. We will go one step further and trying to completely remove laminin specifically by EDTA incubation, as has been shown recently (J. Y. Co et al., “Controlling Epithelial Polarity: A Human Enteroid Model for Host-Pathogen Interactions,” Cell Rep., vol. 26, no. 9, pp. 2509-2520.e4, 2019.). This should then also answer whether disruption of laminin signalling is sufficient to induce fetal-gene expression without the addition of collagen I in a 3D setting.

1. Check secretome and individual culture of Mesenchyme, see if the increase of Laminin is epithelium independent.

We agree that the mesenchyme is key for laminin production, therefore these are important questions. Our prediction would be that epithelium from birth (P0) versus adult might result in different responses on the mesenchyme. However, we feel these experiments are better suited for a follow-up study.

1. In general, the authors should look at cell polarity markers to check the ECM contribution to cell polarity in different cell types.

We thank the reviewer for the suggestions and as mentioned above, we will perform additional stainings.

Reviewer #2 (Significance (Required)):

**Significance:**

The work highlights the role of ECM on stem cell niche and is of great interest to the organoid and stem cell community.

Our field of expertise is image- and seq-technology-based quantitative biology, regeneration and mechanics in organoid.

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

**Summary:**

Ramadan et al present a highly informative paper detailing how the Extracellular matrix influences the development of the intestine. Specifically, the authors provide a thorough analysis of how manipulating the components of the ECM can affect organoid growth, morphology, and gene expression of the organoids. Most importantly, the authors isolate laminin as a critical component of the ECM which impacts the development of fetal-like epithelium. While the in vitro work is generally compelling and of interest to the field, the in vivo data is someone lacking in depth and novelty. Particularly, these conclusions from the abstract could be much better supported: "This laminin:ITGA6 signalling is essential for the stem cell induction and crypt formation in vitro. Importantly, deletion of laminin in the adult mouse results in a fetal-like epithelium with a marked reduction of adult intestinal stem cells." The in vivo work was largely published previously and has caveats noted below, while the in vitro association of ITGA6 signaling with crypt formation is over-interpreted based upon an antibody blocking experiment and a lack of statistical rigor. Despite these concerns, this reviewer finds the work of considerable interest in an important area of the field (epithelial/stromal interactions of the intestine).

**Major Concerns:**

The use of the Ubc-Cre Lamc1-flox mouse model is an interesting way to test the impact of loss Lamc1 on intestinal development. However, with the Ubc-Cre, does the mouse model have other deleterious effects on the mouse beyond the intestine?

Can the authors use a more localized Cre to observe specifically the impacts of Lamc1 loss in the intestine? What is the fate of these mice? Can the authors show swiss roll, low mag sections to let the reader know the extent of this phenotype? OLFM4 and Ki67 IHC should be conducted over a timecourse to show how the changes occur over time after loss of Lamc1. How long does Lamc1's protein product perdure after tamoxifen treatment? More details of this exciting, in vivo validation of the authors' in vitro studies are key to elevating the impact of this work. However, it appears that much of this mouse model was previously published, but the previous findings are not well summarized in the current manuscript.

We will describe the model in more detail and refer readers to the excellent study of our collaborators which answers most of the raised questions. It is interesting to note that although a ubiquitous Cre was used to delete Lamc1 in adult mice, a phenotype was only observed in the intestine indicating a specific role for continuous laminin production here.

Can the authors show that ITGA6 loss has functional consequences in vivo with an epithelial knockout or via an organoid knockdown? A more rigorous genetic test of this proposed function would be important for substantiating the claims made in the abstract.

As referenced in the discussion of the manuscript, there is a VilCreER Itga6 mouse described in the literature (A. De Arcangelis et al., “Hemidesmosome integrity protects the colon against colitis and colorectal cancer,” Gut, vol. 66, no. 10, pp. 1748–1760, 2017.), which mainly focus on the colon. However, the authors use an EDTA assay in the small intestine to show that the epithelium detaches easily when Itga6 is deleted (Fig. 1J). Within the figure, it seems also that the epithelium detaches easily at P2, compared to P14. As EDTA is disrupting laminin polymerisation, this would further indicate increased laminin protein deposition after birth which is dependent on Itga6.

We tried to get material of the inducible VilCreER Itga6 mouse however without any luck so far.

We will conduct additional experiments regarding the Itga6 in vitro. In addition to including additional controls for the neutralizing antibody, we will genetically inactivate Itga6 via an inducible Crispr/Cas9. This should enable us to delete Itga6 when the cells are grown on collagen, and hence reduce the possibility of compensation in matrigel derived organoids.

The authors state that the changes in gene expression are not due to differences in morphology, but rather are specific to the components of the environment. While the authors show that organoids treated with Wnt3a "in Matrigel" and "CollagenI" appear to have similar morphologies and yet still result in a different gene expression profiles, it would be of great interest to see whether that difference persists without Wnt3a when organoids are "in Matrigel" and "in CollagenI". While the reviewer understands the difficulties of culturing organoids in 3D Collagen without Wnt3a, organoids can be indeed be cultured in 3D using "floating collagenI rings" (Sachs et al 2017).

This is an interesting experiment that we will conduct, we thank the reviewer for this suggestion. Indeed, established organoids should be able to grow in collagen I without Wnt3a addition. The paper by Sachs et al. (N. Sachs, Y. Tsukamoto, P. Kujala, P. J. Peters, and H. Clevers, “Intestinal epithelial organoids fuse to form self-organizing tubes in floating collagen gels,” Development, vol. 144, no. 6, pp. 1107–1112, 2017. et al) used extensive washing with PBS to remove Matrigel from the organoids. We will go one step further and trying to completely remove laminin specifically by EDTA incubation, as has been shown recently (J. Y. Co et al., “Controlling Epithelial Polarity: A Human Enteroid Model for Host-Pathogen Interactions,” Cell Rep., vol. 26, no. 9, pp. 2509-2520.e4, 2019.). This should then also answer whether disruption of laminin signalling is sufficient to induce fetal-gene expression without the addition of collagen I in a 3D setting.

Similarly, while the authors indicate that increasing Matrigel concentrations altered the gene expression patterns in a dose-dependent manner, it is unknown whether this can fully be attributed to the Matrigel composition, or whether the layer of Matrigel is providing the capability to transition from 2D to 3D culture.

We are not entirely sure, we understood this point. The Matrigel and the collagen I are mixed before they solidify, therefore enabling a homogenous hydrogel. The different hydrogels are not layered (if that is what the reviewer is referring to).

The authors cultured organoids in different concentrations of Laminin/CollagenIV when mixed with CollagenI. Can organoids be sustained only on a matrix of CollagenIV and/or Laminin? Would this show more direct differences between CollagenI vs. Laminin cultured organoids?

Organoids cannot be grown in pure Collagen IV, but pure laminin should be feasible. We did initial experiments with 3-5mg/ml laminin in PBS and that was sufficient to allow organoid growth. We will perform additional experiments with pure laminin and show the impact on organoid growth.

With the reduction in stem-cell and Paneth cells in the Lamc1-KO mice, it would also be of interest to determine what cell types are now prominent within the heavily elongated intestinal "crypt" structures seen in the Lamc1-KO mice and whether populations are more TA-cells or enterocytes to consider differentiation status of the cells. Additionally, it would also of interest to see if the Itga6 expression is significantly altered in the absence of Lamc1.

We will test expression changes for Itga6 in the Lamc1-KO mice, in the epithelium via qPCR. Additionally we can stain tissue from these mice for Sox9, Ki67 and differentiated markers eg. CD44, AldolaseB, Villin etc .to determine whether the elongated, hyperproliferative crypts contain progenitor cells or secretory enterocytes.

**Minor concerns:**

Matrigel is a complex matrix derived from mouse tumors. In many instances in the manuscript, the authors portray it as a more simpler mix of laminin/Collagen4 (fig 3a). It should be made clearer to the reader that Matrigel is not a mix of recombinant proteins, but a more clear depiction of how Matrigel is derived will be critical for this study, given the focus on specific ECM components and how they affect intestinal epithelial growth.

We agree and will change the oversimplified view of Matrigel.

Some labels of specific conditions would be appreciated in the figures as opposed to only the figure legends (ie. Fig. 1b and 1d should be labeled with comparisons; Fig. 3f labels of fluorescence, Fig. 4b label of itga6 staining).

We apologise for this and the Figure labels will be updated.

With the light staining of Lamc1 in-situ, it is hard to appreciate the expression of laminin within the stroma of the intestine when compared to Col4. This reviewer is also curious of the biological relevance of the concentrations of Laminin/CollagenIV when culturing organoids in Fig. 4a.

Indeed, the Lamc1 due to its lower expression than Col4a1 is more difficult to see. Maybe the reviewer overlooked Suppl.Fig.4A, where the blue channel of these in situ images show more contrast. If required, we can try to optimise the hybridisation times to increase the signal a bit further.

When culturing the organoids with the mixture of collagen and laminin or collagen IV, the concentrations of the two single components were selected similar to their concentrations in Matrigel. Regarding the absolute concentrations of laminin/collagen IV in vitro versus their “concentration” in vivo is much harder to answer. In addition to the unknown concentrations in vivo, there are many more Laminin types present with specific localisation and even specific receptor interactions. For our in vitro studies we relied on the Laminin present in EHS tumours, which is Laminin alpha 1 beta 1 gamma 1. We are currently investigating decellularization protocols to purify the ECM from mouse intestinal tissue, but again this would be more suited for a follow up study.

It would be appreciated if gene expression analyses presented in figures would include p-values to provide context for differences in gene expression.

The avoidance of statistical inference for most of the experiments was a deliberate choice. In line with several comments (e.g. 1. Vaux, D. L. (2012) Know when your numbers are significant. Nature. 492, 180–181), we chose to show all individual data points (with exception of Fig. 3D, n=5, to ease interpretation) without statistical testing. For most expression data, we have data from RNAseq, single-cell RNAseq and qPCRs repeated at different hydrogel concentrations to obtain reliable results. Further, the in vivo mesenchymal qPCR expression data was validated with RNA in situ hybridization showing the mainly mesenchymal expression.

As we will perform additional experiments for the revision of this paper, we can perform statistical tests in the key experiments (e.g. Itga6 experiment) to alleviate any concerns regarding significance.

In figure 3f, the authors report "immunofluorescence of laminin". How is this measured? Can more details be given about the antibody in the text and figure legend? Laminins are a family of genes, and it's not clear what's being demonstrated in this figure panel. Developmental stages of the samples are also not clear.

We apologise for the lack of labeling in Fig.3f. The details of the antibody were hidden in the Materials and Methods of the manuscript (Slides were incubated with Laminin Polyclonal Antibody (1/200, Thermo Fisher #PA5-22901) overnight at 4C ). This pan-laminin antibody reacts with most Laminin isoforms alpha1, alpha2, beta1, gamma1. We will declare it as a pan-laminin antibody in the Figure legend to help future readers.

Reviewer #3 (Significance (Required)):

This work is in an exciting "hot" area of research to understand the role of non-epithelial cells in intestinal epithelial development and function. The audience would be those in the GI field and those studying tissue-tissue interactions.

There's some concern that the in vivo portion of the manuscript (4th figure) uses a model that was previously characterized and published by this group, and that isn't clearly disclosed. The manuscript would benefit from more disclosure and detail about the in vivo phenotype. Such changes would substantially increase the impact and novelty of the study.

We would like to point out that we cited the paper of the original study that uses the model throughout the manuscript. We will disclose in more detail that this group did the study and that the reduction in stem cell genes was already mentioned in the original publication.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Ramadan et al present a highly informative paper detailing how the Extracellular matrix influences the development of the intestine. Specifically, the authors provide a thorough analysis of how manipulating the components of the ECM can affect organoid growth, morphology, and gene expression of the organoids. Most importantly, the authors isolate laminin as a critical component of the ECM which impacts the development of fetal-like epithelium. While the in vitro work is generally compelling and of interest to the field, the in vivo data is someone lacking in depth and novelty. Particularly, these conclusions from the abstract could be much better supported: "This laminin:ITGA6 signalling is essential for the stem cell induction and crypt formation in vitro. Importantly, deletion of laminin in the adult mouse results in a fetal-like epithelium with a marked reduction of adult intestinal stem cells." The in vivo work was largely published previously and has caveats noted below, while the in vitro association of ITGA6 signaling with crypt formation is over-interpreted based upon an antibody blocking experiment and a lack of statistical rigor. Despite these concerns, this reviewer finds the work of considerable interest in an important area of the field (epithelial/stromal interactions of the intestine).

Major Concerns:

The use of the Ubc-Cre Lamc1-flox mouse model is an interesting way to test the impact of loss Lamc1 on intestinal development. However, with the Ubc-Cre, does the mouse model have other deleterious effects on the mouse beyond the intestine? Can the authors use a more localized Cre to observe specifically the impacts of Lamc1 loss in the intestine? What is the fate of these mice? Can the authors show swiss roll, low mag sections to let the reader know the extent of this phenotype? OLFM4 and Ki67 IHC should be conducted over a timecourse to show how the changes occur over time after loss of Lamc1. How long does Lamc1's protein product perdure after tamoxifen treatment? More details of this exciting, in vivo validation of the authors' in vitro studies are key to elevating the impact of this work. However, it appears that much of this mouse model was previously published, but the previous findings are not well summarized in the current manuscript.

Can the authors show that ITGA6 loss has functional consequences in vivo with an epithelial knockout or via an organoid knockdown? A more rigorous genetic test of this proposed function would be important for substantiating the claims made in the abstract. The authors state that the changes in gene expression are not due to differences in morphology, but rather are specific to the components of the environment. While the authors show that organoids treated with Wnt3a "in Matrigel" and "CollagenI" appear to have similar morphologies and yet still result in a different gene expression profiles, it would be of great interest to see whether that difference persists without Wnt3a when organoids are "in Matrigel" and "in CollagenI". While the reviewer understands the difficulties of culturing organoids in 3D Collagen without Wnt3a, organoids can be indeed be cultured in 3D using "floating collagenI rings" (Sachs et al 2017). Similarly, while the authors indicate that increasing Matrigel concentrations altered the gene expression patterns in a dose-dependent manner, it is unknown whether this can fully be attributed to the Matrigel composition, or whether the layer of Matrigel is providing the capability to transition from 2D to 3D culture.

The authors cultured organoids in different concentrations of Laminin/CollagenIV when mixed with CollagenI. Can organoids be sustained only on a matrix of CollagenIV and/or Laminin? Would this show more direct differences between CollagenI vs. Laminin cultured organoids? With the reduction in stem-cell and Paneth cells in the Lamc1-KO mice, it would also be of interest to determine what cell types are now prominent within the heavily elongated intestinal "crypt" structures seen in the Lamc1-KO mice and whether populations are more TA-cells or enterocytes to consider differentiation status of the cells. Additionally, it would also of interest to see if Itga6 expression is significantly altered in the absence of Lamc1.

Minor concerns:

Matrigel is a complex matrix derived from mouse tumors. In many instances in the manuscript, the authors portray it as a more simpler mix of laminin/Collagen4 (fig 3a). It should be made clearer to the reader that Matrigel is not a mix of recombinant proteins, but a more clear depiction of how Matrigel is derived will be critical for this study, given the focus on specific ECM components and how they affect intestinal epithelial growth.

Some labels of specific conditions would be appreciated in the figures as opposed to only the figure legends (ie. Fig. 1b and 1d should be labeled with comparisons; Fig. 3f labels of fluorescence, Fig. 4b label of itga6 staining).

With the light staining of Lamc1 in-situ, it is hard to appreciate the expression of laminin within the stroma of the intestine when compared to Col4. This reviewer is also curious of the biological relevance of the concentrations of Laminin/CollagenIV when culturing organoids in Fig. 4a. It would be appreciated if gene expression analyses presented in figures would include p-values to provide context for differences in gene expression.

In figure 3f, the authors report "immunofluorescence of laminin". How is this measured? Can more details be given about the antibody in the text and figure legend? Laminins are a family of genes, and it's not clear what's being demonstrated in this figure panel. Developmental stages of the samples are also not clear.

Significance

This work is in an exciting "hot" area of research to understand the role of non-epithelial cells in intestinal epithelial development and function. The audience would be those in the GI field and those studying tissue-tissue interactions.

There's some concern that the in vivo portion of the manuscript (4th figure) uses a model that was previously characterized and published by this group, and that isn't clearly disclosed. The manuscript would benefit from more disclosure and detail about the in vivo phenotype. Such changes would substantially increase the impact and novelty of the study.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, Ramadan and colleagues demonstrate that depending on the extracellular matrix (ECM) composition in which mouse intestinal organoids and/or 2D intestinal epithelial cells are grown in, cellular composition of the epithelium changes. Organoids plated on 2D collagen layers show a unique cell cluster characteristic of fetal-like genes, while organoids plated with increased amount of Matrigel in 2D or in 3D exhibit a shift towards higher stem cell abundance and the absence of the fetal-like gene cluster. Specifically, the ECM component Laminin supports acquisition of stem cells identities in small intestinal epithelial cells, correlating with a transient increase in expression levels of collagen and laminin genes in vivo spanning time points of crypt formation. The authors reported the functional contribution of laminin signaling (Lamc1 KO) via integrin alpha 6 (antibody-blocking) to intestinal stem cell acquisition in vitro and in vivo.

There are a handful of comments/concerns that would need to be addressed before publication.

Major points:

1. The author claimed: "the effect of ECM components on gene expression is not due to difference in morphology (2D collogen vs. 3D Matrigel)". The conclusion of the 2D vs. 3D experiment should be toned down to that organoid morphology (2D vs. 3D) does not directly impact on the expression of fetal-like genes. Otherwise more analysis of RNAseq data with different group of genes (e.g., in different mechanosensing pathways) should be provided with Fig. S1D. Also, would it be technically feasible to perform experiments of SI in collagen (3D) in all group of experiments? Directly comparing 3D Matrigel with 3D collagen avoids the concern of the 2D vs. 3D effect.
2. For Fig. 1f, the authors should include overlapping stainings of Lyz (or Olfm4, CD44 etc.) and Adolase B signal, or they could perform Aldolase B staining in Lgr-5-DTR-GFP and/or Lyz-RFP organoid line. From the current data provided one cannot draw clear conclusions on the crypt morphology as claimed by the authors. Additionally, when talking about crypt morphology and apical accumulation of Actin specifically in the Lyz+ cells, the authors should show a higher zoom in of the picture and either add an orthogonal slice to see apical and basal side and the specific accumulation in one of the cell types, or also co-label with apical polarity markers.
3. Authors referred the organoid transient change to fetal-like state. To exam the similarity of ECM-induced reprogramming with the regenerative-type of reprogramming, it would be essential to compare the expression of the selected fetal-like genes (Anxa3, Ly6a/Sca1, Msln, Col4a2 et al.), as well as bulk and single-cell (if applicable) RNA-seq data.
4. For in vivo data, authors were looking at the normal development of intestine. Following the point of organoid culture recapitulates regeneration, it would be relevant to check the in vivo ECM change by staining in the process of intestinal regeneration or discuss would the fetal-like genes be involved in regeneration.
5. For Fig2.d and e, it would be important to measure compactness vs. the emergence/probability of Ly6+ cells to see if there is correlation.
6. In Fig.2d, Ly6a expression is very obscure, and it would be important to show control staining for cell boundaries (eg. Phalloidin, PM) to visualize which nuclei show Ki67 staining and are high or low in Ly6a (plus quantification).
7. In Fig. 2f-g, FACS-ed Ly6a+ and Ly6- cells embedded in Matrigel can grow into organoids with crypts. Here the imaging of Paneth cell staining is not clear, and a quantification on number of Paneth cells per crypt would be very helpful to confirm the phenotype. Also, authors should either provide data on the initial size of seeded cell clusters and report organoid growth and cell type composition in more detail when plating from Ly6a+ and Ly6- cells or report the variation in the respective populations.
8. The authors could also test whether Ly6+ cells have any advantages over Ly6- cells when grown on collagen I instead of Matrigel.
9. In Fig.3f, a control of membrane protein staining should be added for the experiment. The increased Laminin signal can be caused by the global increase of protein when there are more cells, or tissues are more compact. When authors make conclusion of "Dramatic remodelling of ECM during crypt formation ", the experiment should also count cell numbers vs. Laminin (intensity). The phenotype can come from increased area of interface between epithelium and mesenchyme instead of active remodelling.
10. The authors claim that intestinal stem cells in vivo are controlled by Laminin signalling that goes via Integrin alpha 6. However, there is no evidence provided that supports the contribution of ITGA6 in the in vivo setting. So, the authors should either tone down on that point or show a convincing in vivo experiment (e.g., inhibit ITGA6 in vivo by inhibitor injections or by extracting the ECM of a wild-type mouse and seeding intestinal epithelial cells without vs. with ITGA6 blocking antibody which should recapitulate the phenotype in Fig. 4 c.
11. Fig. 4: For the data of ITGA6 expression and all sorts of analysis on protein expression with staining, normalization with cell numbers should be performed.
12. Two questions on mechanisms: a) What is the mechanism from ITGA signaling to Ly6a+ cell fate? b) And would/how Laminin induce ITGA expression? Depends on how much the authors would like to go deep with the project, could be addressed further with functional studies, or touch on the topics with discussion.

Minor points:

1. Text in Fig.S1d regarding 'in' or 'on' collagen, could be clearer by changing the terms to 2D and 3D correspondingly.
2. Fig. S1a, it is great the authors showed that similar stiffness in Matrigel and collagen I. It would be important to check the concentration of collagen I vs. stiffness (also for increasing concentrations of Laminin in Fig. 3b), since this is also the type of ECM change that might lead to the change of cell status in cancer progression or collective cell migration.
3. When plating intestinal epithelial cells on collagen I, is the Ly6+ phenotype altered upon Wnt addition? This is not so clear from the RNAseq data Fig S1d., so authors should provide antibody stainings (stem cells/Paneth cells). This could give insight whether Ly6+ cells are still able to convert into stem cells/ Paneth cells by changing morphogen concentration vs. ECM composition. Similar to this point, also enteroendocrine cell fate is absent in collagen I condition (Fig2.ab), the authors could address this point by medium induced EE cell fate.
4. It would be more informative to indicate the thickness of ECM layer in culture of 2D collagen I, as well as the image of the whole well, demonstrating the morphological variation in the middle and peripheral of the ECM layer.
5. After the formation of PC/SC clusters, would ECM contribute to maintenance? Putting mature organoids from Matrigel to Collagen I 3D would help to clarify.
6. Check secretome and individual culture of Mesenchyme, see if the increase of Laminin is epithelium independent.
7. In general, the authors should look at cell polarity markers to check the ECM contribution to cell polarity in different cell types.

Significance

Significance:

The work highlights the role of ECM on stem cell niche and is of great interest to the organoid and stem cell community.

Our field of expertise is image- and seq-technology-based quantitative biology, regeneration and mechanics in organoid.

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Referee #1

Evidence, reproducibility and clarity

In the manuscript of Ramadan et al. , authors use the ex vivo organoid approach to compare gene expression in organoids derived from adult type stem cells when these organoids are grown using different matrices. The presence of Collagen type I induces the emergence of cells with a transcriptome similar to fetal progenitors. In contrast, laminin the main component of matrigel, induces an organoid-protruded phenotype with transcriptome of stem cell type. Then, they correlate these data with expression of collagens and laminins from data publicly available. They show by qRT -PCR that laminins are more expressed in mesenchymal versus epithelial fractions postnatally. They hypothesize on this basis that the remodeling at postnatal stage is likely only dependent on the mesenchymal compartment and it involves interaction of laminins with integrity a6. It seems that some of the presented data have already been described and could not be considered as « novel ». For some of the statements, like this one « the basement-membrane produced by the epithelium is not sufficient to increase stem cell numbers and induce a morphological crypt formation », the conclusion is not sustained by provided experiments. To draw definitive conclusion on this particular point, authors could reproduce the experiment presented in Fig. 4d but using Cre recombinases specific for mesenchymal and epithelial compartments rather than the ubiquitous Cre line. It would be interesting to investigate if organoids grown from lamc1-/- mice can generate protruded organoids or not. In addition, how interpret the fact that fetal organoids up is associated with « laminin interactions » in fig. 1c?

One major point to address regards statistics. In material and methods, the paragraph describing statistical analyses is missing. Moreover, in the figures presenting qPRC data ( figs 1g 3b 3D 3g 4c and f), no statistic analysis is provided; and the number of samples for some conditions is extremely limited (n=2). In general, the term « independent experiment «  should be clarified : does it correspond to one organoid line for which the experiment was repeated or one single experiment using different organoid lines? In fig 4c , all collagen conditions are set to 1.

Regarding the experiment presented in fig 4c, authors should include additional control conditions : anti-a6 integrity antibody in matrigel and use of an isotype antibody.

Another point regards RNAscope data presented in Fig 4b, it is surprising to observe such difference in terms of expression between E19 and P0. Does this mean that birth dramatically unregulates Itga6 expression in few hours? Authors should comment this point if verified. Authors should avoid the word « signaling » for laminin-integrin interactions as they do not study this aspect at all in their experiments.

Regarding Col1a1, authors cannot claim that it's expression only slightly changed (fig 3d) as it is clearly upregulated between E17 and P0.

Significance

Overall, the methodology used for the asked questions is accurate.

One potential problem for publication comes from the fact that some of the findings are already reported and hat the present data do not provide further advances.

for example, collagen and fetal-like expression profile, Ly6a sorting and replating in culture-Yui et al, 2018, Jabaji et al, 2013.

The phenotype of Lamc1-/- mice and the observed reduced stem cell marker expression are also reported by Fields et al, 2019.

Infine, authors do not interpret their ex vivo data in the context of fetal progenitors which grow as spheres in matrigel (containing laminin)? In figure 5, should we interpret that there is no laminin at all in the fetal mesenchyme?

Also, authors do not cite a paper reporting on the role of the epithelium ( stem cells) in regulating its own extracellular matrix composition, this process modulating the stem cell number and fate (Fernandez-Vallone et al. 2020). As this is contradictory with the claim that only mesenchyme impacts on crypt morphogenesis, authors could discuss on this point.

This manuscript could be interesting for an audience in the stem cell and developmental fields ( my field of expertise).

Referee Cross-commenting

Considering the pertinent and sometimes overlapping comments of the two other reviewers, the estimated time is revised to 3-6 months.

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Reply to the reviewers

Reviewer #1

This manuscript by Gulyrutlu and co-workers addresses the role of CUG expanded repeat RNA associated with DM1 in regulating the formation of higher order RNP assemblies such as stress granules and P-bodies in the cell. The authors used lens epithelial cells (hLECs) derived from a DM1 patient

We used cell lines from several patients and age-matched controls to avoid effects of individual cell-line variation. We will make sure that this is clear in the text.

or a HeLa cell inducible model of DM1 to investigate whether expression of the CUG repeat-associated protein MBNL1 and CUGBP1 affected the formation and dispersal of stress granules and P-bodies. The authors show that MBNL1 and CUGBP1 are components of SGs and PBs in hLECs and HeLa cells. In cells expressing the CUG repeat, there are minor alterations in the dispersal of stress granules as well as in the formation of P-bodies.

The alterations in the formation and dispersal of stress granules are not minor. For example, in the HeLa cell model, stress granules take more than twice as long to form in cell expressing the CUGexp repeats associated with DM1 and disperse in half the time. These data are already in the results section, but we will highlight them in a revision and have included an additional representation of the data to the figure, using graphs of ‘proportion of cells with stress granules’ against time. The changes we see are as large, or larger, then results published elsewhere (see appendix below)

MBNL1 could affect the formation and dispersal of SGs independent of the CUG repeat.

In fact, we present data in HeLa cells with MBNL1 almost completely removed by shRNA revealing that this has a much smaller effect on stress granules than does the expression of CUGexp RNA. This is an important point, as it is widely assumed that most of the cellular defects in DM1 are caused by the ‘sequestration’ of MBNL1 in the CUGexp foci. Since only . This is not what our data show. In the hexanediol experiments, both cell lines over-express MBNL1 in similar amounts. The difference between them is that one cell line expresses a DMPK1 mini-gene with a CUG expansion and the other expresses a mini-gene without the expansion. Again, our results show that the alteration to P-body responses to 1,6-hexanediol can be attributed to the presence of the CUGexp RNA, rather than altered levels of MBNL1. We will revise the results and discussion to further emphasise this point.

Finally, in HeLa cells, overexpression of MBNL1 can reduce the dispersal of P-bodies upon 1,6-hexanediol treatment.

This is not what our data show. In the hexanediol experiments, both cell lines over-express MBNL1 in similar amounts. The difference between them is that one cell line expresses a DMPK1 mini-gene with a CUG expansion and the other expresses a mini-gene without the expansion. Again, our results show that the alteration to P-body responses to 1,6-hexanediol can be attributed to the presence of the CUGexp RNA, rather than altered levels of MBNL1. We will revise the results and discussion to further emphasise this point.

Major comments:

One limitation of the work is that the perturbations seen with stress granules or P-bodies are all relatively small, and no evidence for a functional consequence on gene expression is demonstrated. Specifically, the authors observe only minor alterations in the formation or disaggregation of PBs and SGs in these DM1 models. Further, some of the effects observed are independent of the CUG repeat expression, suggesting that MBNL1 and CUGBP1 might have independent roles in modulating some properties of SG and PB formation or dispersal.

As above, the changes we see in SG formation and dispersal are not small. There are already numerous studies of the effects of DM1 on gene expression and mRNA splicing. This is not what we set out to study: we are interested in perturbations to the organisation of cellular structures associated with the expression of the CUGexp repeat RNA characteristic of DM1. We do show some data relating specifically to the proteins MBNL1 and CUGBP1 in the paper. shRNA resulting in almost complete loss of these proteins has much smaller effects that the expression of CUGexp RNA, suggesting that the major part of the effects caused by expression of the CUGexp RNA is not mediated through changes in MBNL1 or CUGBP1 levels. MBNL1 and CUGBP1 levels may well contribute to alterations in SG dynamics, but our data suggest that they are minor contributors. This is an advance in our current knowledge

1. The authors could investigate whether the CUG repeat RNA itself is localized to SGs or PBs in their models, and whether the presence of the repeat RNA is absolutely necessary for regulating the dynamics of SG or PB formation.

We have now done this. The CUG repeat RNA is not localised in stress granules or PBs to a detectable extent. This suggests that the effect we see on these structures by expression of the expanded RNA occurs despite the absence of the RNA from the structures. This is similar to the effects of the ALS-associated paraspeckle protein FUS, which can affect the integrity of nuclear LLPS structures (gems) despite not co-localising with them https://doi.org/10.1016/j.celrep.2012.08.025 We have added these data to the manuscript, as part of draft figure 8, and will add text emphasising this as an additional example of disease-causing macromolecules affecting the structure of LLPS domains in which they are not found.

1. The authors use 1,6-hexanediol to suggests that PBs and SGs in HeLa cells show behavior analogous to LLPS. However, the use of 1,6,-hexanediol to establish an assembly as a LLPS is a relatively limited analysis (despite its widespread use in the field), since this compound can affect the formation of multiple cellular substructures that are not always LLPS (for example, see Wheeler et al, 2016, eLife).

We are aware of and have cited this publication. Our comments about LLPS structures are measured, as there is still controversy about how to definitively identify them in cells. SGs and PBs have, however, previously been widely published to be formed by LLPS. The rapid exchange of SG and PB components during FRAP and the ability of SGs to both fuse and bud (seen in our supplementary movies) are also supportive of these structures behaving as LLPS structures in our models. Wheeler et showed that, in yeast, the nuclear pore complex and some cytoskeletal structures were affected by 1,6-hexanediol but membrane-bound structures such as the ER and mitochondria were not. The disruption of the nuclear pore complex is not unexpected, since phase separation is involved in cargo shuttling through the NPC (reviewed in https://doi.org/10.1016/j.devcel.2020.06.033). We will revise our discussion to make it more clear that we are not relying only on the use of 1,6-hexanediol to define SGs and PBs as LLPS structures but also on other aspects of their dynamic behaviour and on extensive prior literature.

Significance

This study would be of interest to the field if the impact of the DM! repeat RNAs on PB and SG were more substantial...

As above, the effects we see on SG formation and loss are substantial. Tissue types affected in DM1 are prone to stress, particularly the lens of the eye, so alterations to cellular response to stress associated with the presence of CUG repeats are of key importance to understanding the cellular pathology of DM1.

...and if some functional consequences were demonstrated.

In terms of function, we show altered responses to stress caused by the expression of CUGexp RNA and probably mediated through alterations in the propensity of LLPS cytoplasmic structures (SGs and PBs) to form and be resolved. Additionally, we can now show that SGs in HeLa cells expressing CUGexp RNA contain less total polyA RNA than is seen in controls, and that ‘docking’ events between SGs and PBs are compromised in cells with CUGexp RNA. These docking events are proposed to mediate transfer of RNA from SGs to PBs (reviewed in https://doi.org/10.1007/978-1-4614-5107-5_12). These new data demonstrate functional impairment of SGs and PBs associated with DM1. We have included this as an additional draft figure 8.

The lack of a strong effect on SG or PB formation in the DM1 models, along with the CUG repeat-independent effect of MBNL1 on the formation and dispersal of these complexes, argues that MBNL1/CUGBP1 may not significantly affect the formation or dispersal of SGs and PBs.

We are actually not arguing that MBNL1 and CUGBP1 are the main effectors in the changes we see to SGs and PBs, but that the CUGexp RNA is the key player, so are a little confused by this comment.

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Reviewer #2:

In the current study, the authors compared the dynamics of P-bodies (PBs) and stress granules (SGs) between control and several DM1 cell lines. They found that MBNL1 and CUGBP1, two CUG repeat RNA-binding proteins that are primarily nuclear, could also co-localize with PBs in the cytoplasm and re-localize to SGs under stress. Small differences were observed in SG assembly and disassembly dynamics between control and DM1 HLECs, between HeLa cells expressing either CTG12 or CTG960, and between HeLa cells with and without shRNAs targeting CUGBP1 or MBNL1.

As detailed above, the alterations in SG assembly and disassembly in cells expressing CUGexp RNA are not small, in contrast to those in cells will lowered expression of MBNL1 and CUGBP1, which are much smaller suggesting that the changes caused by CUGexp RNA largely do not result from loss of MBNL1 (or CUGBP1). We have inserted additional graphs of ‘proportion of cells with stress granules’ against time' and will modify the text to emphasise both of these points.

Overall, the experiments were clearly described and the results properly presented. However, critical controls, as detailed below, are missing in multiple analyses. The mechanisms underlying these apparent differences are also unknown.

We do not consider that any ‘critical controls’ are missing, but can supply all of the additional analysis of our data that the reviewer requests below. We can also now provide additional mechanistic insight and will add an additional figure showing lowered amount of polyA RNA in stress granules in cells expressing CUGexp RNA and compromised docking events between stress granules and P-bodies, suggesting impaired communication between them.

Major concerns:

1. Throughout the study, the authors compared MBNL1 and CUGBP1 association with PBs and SGs without considering the potential differences in their cytoplasmic abundance between control and DM1 cell lines, which seems to be case for MBNL1 abundance in CTG960-expressing HeLa cells (Fig. 3). Provided that PBs and SGs exchange components with the cytosol at an equilibrium, if the cytoplasmic abundance of, for example, MBNL1 is decreased in DM1, one would expect the equilibrium being shifted resulting in less MBNL1 associated with PB/SG. Therefore, before measuring the association or the assembly/disassembly kinetics of PB and SG, the authors should first test whether MBNL1 and CUGBP1 abundance may be different between control and DM cell lines.

There is, in fact, no difference in the relative cytoplasmic abundance of GFP-MBNL1 between CTG12 and CTG960- expressing HeLa cells. Each has approximately a 50/50 split between nucleus and cytoplasm, with <3% of nuclear GFP-MBNL1 found in nuclear CUGexp foci when they are present. We have added a graph demonstrating this to the supplementary data. The abundance of total endogenous MBNL1 is also not altered in DM1 patient-derived lens cell lines compared to controls, as shown by semi-quantitative western blot analysis, which we have also added to the supplementary data. However, if the expression of CUGexp RNA did cause a major loss of cytoplasmic MBNL1, this change would be reflective of the situation seen in DM1 and would not invalidate our results or conclusions.

The same caveat applies to MBNL1/CUGBP1 knockdown experiments, where knocking down one may change the abundance of the other.

To carry out FRAP experiments or live cell analysis of SG formation and loss, it is necessary to over-express a tagged version of the protein being studied. For the knockdown experiments shown in figure 6, therefore, when we knocked down MBNL1, CUGBP1 was present in excess as a GFP-tagged protein and when we knocked down CUGBP1, MBNL1 was present in excess as a GFP-tagged protein. Thus, any effects of the knockdowns on expression of the endogenous proteins being analysed would be highly unlikely to influence the results.

1. Similarly, the authors did not consider the possibility that changes in SG/PB dynamics may be due to changes in the abundance/availability of essential SG/PB components such as GE1 and G3BP1.

From our immunofluorescence experiments, there was certainly no obvious reduction in GE1 or TIA1 abundance (we did not assess G3BP1). We have quantitative proteomic analysis (unpublished) from a similar pair of cell lines, expressing CUGexp RNA alongside GFP rather than GFP-MBNL1. This shows no change in GE1 or G3BP1, so we would not expect to see any here either. We can easily carry out a quantitative western blot analysis to confirm and will add this to the supplementary data

1. Most of the observed differences between control and DM cell lines were modest, leaving one wonder whether it could be simply due to cell line-to-cell line variability. Whenever possible, the authors should present results for each individual lines. For example, in Fig.2, 3 DM1 lines and 2 control lines were used. Was the difference in SG disassembly (Fig. 2B) observed in each of the 3 lines?

Some of the alterations were modest and there is cell line-to-cell line variability in the lens cell lines. This is why we pooled the data: on average, DM1 cells disperse their SGs more quickly than control cell lines do on average. This is not an unusual way to present data from patient cell lines of diverse genetic background. We have added data for stress granule loss in the individual cell lines to the supplementary data. These data show a consistent trend towards quicker dispersal of stress granules in patient cell lines. The variability between the patient lens cell lines was also the primary reason for us to develop the inducible system in HeLa cells, on a fixed genetic background, as explained in the manuscript.

Minor points:

1. Western blot in Fig. 3 shows two protein products from both endogenous and overexpressed MBNL1. Please explain.

Many of the commercially available anti-MBNL1 antibodies show this double-band in some cell lines as evidenced in numerous publications and on manufacturers’ websites (for example https://abclonal.com/catalog-antibodies/MBNL1RabbitmAb/A5149, https://www.ptglab.com/products/MBNL1-Antibody-66837-1-Ig.htm). We haven’t analysed the two bands in detail, but assume this to be a result of a post translational modification of some sort. Since GFP-MBNL1 and endogenous MBNL1 show the same thing, we do not consider it to be a major concern. We do mention the double-band as ‘characteristic’ in the figure legend for figure 3 so are not seeking to conceal anything here.

1. No data were shown to substantiate the statement that "MBNL1 localises to CUGexp foci and CUGBP1 does not" (page 6).

This has been published many times and is shown in figure 1A. However, we will add in a citation for this and have added an additional supplementary figure showing the lack of co-localisation in the foci from figure 1A more clearly together with separate data confirming that MBNL1 and CUGexp RNA do not co-localise with CUGBP1 in the nuclei of line HeLa_CTG960_GFPMBNL1.

1. The y-axis of Fig. 4D should not go beyond 1.

We will trim the axis. There are no data points above 1.0, just the indicator of statistical significance

Significance:

The nature of the current study is highly descriptive with little mechanistic insights.

Our work is not descriptive, as we observed a change in stress granules in patient cells, which we could then replicate (and enhance) in a novel inducible model of DM1 designed to abrogate the unavoidable variation in patient-derived cell lines. We also now have additional mechanistic insights (see above) and have added an additional figure (draft figure 8) detailing these.

For the subtle differences observed between control and DM1 cell lines, it remains unclear whether it may be due to cell line-to-cell line variation (see above).

We cannot completely rule out an influence of cell line-to-cell line variation in the patient-derived lens cell lines (see above), though we think this unlikely as we saw the same effect repeated and amplified in the inducible HeLa-derived cell model, which was designed to minimise this concern. Furthermore, for stress granule loss, we see a larger effect in the HeLa cell model after 72hrs of induction than after 24hrs (figure 5C). This argues strongly that the effects seen are due to the expression of CUGexp RNA and we will emphasise this point more strongly.

Some difference appear to be specific to one model but not the others (e.g., SG formation is slower in HeLa-CTG960 cells but not in DM1 HLECs).

Even for the observations that seem consistent between models, the current results yielded little novel biological insights into whether and how these subtle differences in PB/SG dynamics may relate to DM1 pathogenesis. Collectively, these weaknesses render the current study incremental at best.

The key biological insight the results provide is that the presence of the CUGexp repeat RNA results in defects in LLPS structures that are largely separable from any sequestration of MBNL1 in nuclear foci. With many researchers attributing the cellular defects in DM1 simply to the loss of MBNL1 by sequestration into nuclear foci, both this separation of altered stress response from MBNL1 levels and the involvement of altered LLPS formation (evidenced by the changes in PB behaviour on 1,6-hexanediol treatment) are novel biological insights into the cellular pathology of DM1. Additionally, our results shift the emphasis from nuclear effects to those seen in the cytoplasm.

In terms of specific DM1 pathogenesis, the eye lens is subject to constant repeated stress and is subject to continued growth throughout the life span, relying on lens epithelial cells as a stem cell pool. Epithelial cells are also vital to the homeostatic regulation of ions, growth factor and nutrient flow from the aqueous humor to the underlying fibre cells. Any alterations in the response of lens epithelial cells, in particular, to stress is highly relevant to the pathology of cataract seen in DM1. We will revise our discussion to emphasise these key points more strongly.

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Reviewer #3

The manuscript entiled "Phase-separated stress granules and processing bodies are compromised in Myotonic Dystrophy Type 1" by Gulyurtlu et al., characterizes the composition and ydnamics of stress granules and P-bodies in two Myotonic Dystrophy Type 1 (DM1) cell models, human lens epithelial cells from DM1 patients and age-matched controls and HeLa_CTG12_GFPMBNL1 and HeLa_CTG960GFPMBNL1 cell lines. The manuscript is somewhat descriptive with lack of functional data and some discrepancies. For example, in the discussion section, the authors conclude that "MBNL1 appears to be absent from P-bodies in cells with CUGexp foci in their nuclei. This observation suggests that the role of MBNL1 in P-bodies may be disrupted by the presence of CUGexp RNA." Figure 4A shows that "P-bodies in the DM1 model line, HeLa_CTG960GFPMBNL1 do not contain detectable amounts of GFPMBNL1". However, Figure 4E shows similar levels of total cellular MBNL1 per PB between the control CTG12 and mutated CTG960 lines.

There is no discrepancy here. The reviewer has misinterpreted our data. PBs in the HeLa CTG960 cell line do not contain detectable amounts of GFP-MBNL1 under normal growth conditions, as shown in figure 4A. The data shown in figure 4E concern arsenite-treated cells, where some PBs in the CTG960 line do contain detectable levels of GFP-MBNL1, but significantly less than in the control CTG12 cells. We will reword these sections to make sure this is clear.

Most importantly, in Figure S3 the authors show that CUGexp foci are present in 1-2 % of the cells. The claim appears to be too strong for the data presented in the manuscript.

This is not what is shown in figure S3. The reviewer has misinterpreted our data. Figure S3 shows that in cells from line CTG960, only 1-2% of the total nuclear GFP-MBNL1 signal is found in the CUGexp foci, despite the intensity of the signal within them. Virtually all of the cells from the CTG960 cell line contain CUGexp foci (>95%). We will add a statement to this effect into the results section. We would not have continued working with a cell line in which only 1-2% of cells showed the DM1 phenotype of nuclear CUGexp RNA foci.

Although the findings are interesting and of potential impact for a better understanding of the implications of RNA-protein condensate dynamics in the pathogenesis of DM1, the work presented here is still descriptive and preliminary in my opinion. In summary, the conclusions are not so convincing and additional experiments are essential to support the authors claims.

This reviewer seems to have misinterpreted several of our data sets, including the specific points above, leading to the assertion that our conclusions are not convincing.

Several months of works will be required to consolidate data and reorganize and ameliorate the manuscript, including the way data are presented and quantified.

We already have data with which to address the majority of the queries posed, so should be able to make the adjustments relatively quickly.

Specific comments:

"On removal of stress, clearance of stress granules is mediated largely by a form of autophagy." This statement is not correct since the majority of stress granules disassemble and are not targeted to autophagy; in healthy cells only 5 % (or less) of the total SGs tend to persist in presence of autophagy or lysosome inhibitors, while the vast majority disassembles. Please rephrase carefully.

The degree and manner of dispersal of stress granules in healthy cells on removal of stress is not well understood, but is known to differ depending on the type and duration of the stress (DOI: 10.1126/science.abj2400). We do not yet know how this may be altered in DM1, however, compromised autophagy is implicated in cataract formation, which is of relevance to our study. We will re-phrase this section of the discussion carefully to reflect the complex situation.

Figure 1: RNA-protein complexes have heterogeneous composition. In HLECs, do all PBs colocalize with MBNL1 and CUGBP1 or only a fraction of them?

We do not routinely see PBs without MBNL1 or CUGBP1 in the HLECs, in contrast to the situation in the HeLa CTG960 line. We have data available in order to quantify this and will add the numbers to the text of the results.

Figure 2: Stress granules and P-Bodies are known to touch each-other, a process referred to as a "kissing event". The authors have studied the mobility of GFP-MBNL1 inside these two types of assemblies. It would be important also to quantify the "kissing" events. Is this altered in DM1 cells?

We couldn’t find reference in the literature to ‘kissing events’ between SGs and PBs, but found several references to ‘docking’ events. We have noticed such interactions between PBs and SGs in our models. We are currently quantifying this and our first experiments (one in the HeLa cell model and one comparing one of the patient-derived lens cell lines to a control) suggest that there is a change in the frequency and/or size of such interactions in the HeLa CTG960 cell line compared to the CTG12 control and in the DM1 lens cell line derived to control. If this holds true in our repeat experiments (currently in progress), this would also provide the mechanistic insight requested by reviewer 2. We have included this, together with our data showing a decrease in total polyA RNA in stress granules in HeLa cells expressing CUGexp RNA, as an additional draft figure 8.

Figure 3: In HeLa cells overexpressing CTG960_GFPMBNL1, beside the accumulation of one bright CUGexp puncta, several intranuclear GFPMBNL1 protein foci are visible. This subcellular distribution is different from the one observed in the control HeLaCTG12 GFPMBNL1. Can the author describe what these intranuclear GFPMBNL1 protein foci are?

In most cells expressing CUGexp RNA, several nuclear foci form (usually one large and several smaller) and all of them contain MBNL1 (or GFP-MBNL1 in the HeLa_CTG960_GFPMBNL1 cell line). Figure S3 shows object identification using MBNL1 in this cell line, with two clear foci detected as the reviewer points out. We have added an additional panel to supplementary figure 1 to confirm that the additional foci are also CUGexp RNA foci and will clarify in the text of the results that there is not a single focus of CUGexp RNA in each nucleus.

Is GFPMBNL1 accumulating at the level of splicing speckles? Or paraspeckles? Or other types on intranuclear condensates such as e.g. PML nuclear bodies? The different intranuclear distribution of GFPMBNL1 should be better characterized.

The sub-nuclear distribution of MBNL1 is, indeed, very complex. MBNL1 also sometimes co-localises to splicing speckles/interchromatin granule clusters as we have previously reported in lens epithelial cell lines (DOI: 10.1042/BJ20130870 ) . The details of differences in the nuclear distribution of MBNL1, beyond its accumulation in CUGexp RNA foci, in DM1 cells compared to controls is the subject of another manuscript we have in preparation but are beyond the scope of the current study.

Moreover, the % of cells expressing CTG960_GFPMBNL1 and forming intranuclear CUGexp foci is only mentioned in the discussion (Figure S3); for clarity it should be reported in the main text when describing Figure 3.

The number of cells forming nuclear CUGexp foci on expression of CTG960_GFP-MBNL1 is >95% and we will add this to the text of the results section.

"Figure S2: Quantitation of GFPMBNL1 in P-bodies in HeLa cell model of DM1." The authors report in the legend "Some, but not all, of these P-bodies contain detectable amounts of GFPMBNL1". However, the figure only shows a representative image of cells without quantification. Quantitation should be provided.

We have data available to provide this simple quantitation. Approximately 38% of PBs in arsenite-treated cells from line HeLa_CTG960_GFPMBNL1 contain detectable levels of GFPMBNL1 using a manually-assigned cut-off intensity. We will add this to the relevant figure legend (now figure S5). However, this method of analysis requires an intensity to be manually set above which GFP-MBNL1 signal is considered ‘detectable’. This is hugely subjective, and in our opinion, the automatically generated quantitative comparison of “% total cellular MBNL1 per P-body” as shown in figure 4E is a more experimentally robust way to demonstrate a small loss of MBNL1 from P-bodies in cells from line HeLa_CTG960_GFPMBNL1 treated with arsenite compared to the relevant control.

The authors report "a subtle change in stress granule architecture associated with the presence of CUGexp RNA". This statement is not supported by experimental data and should be omitted.

We will qualify this statement to make it clear that we are referring to a subtle alteration in the co-localisation between CUGBP1 and MBNL1 specifically in the SGs, as our experimental data shown in figure 4D clearly support that, showing a statistically significant increase in the Pearson’s co-efficient of cololcalisation between MBNL1 and CUGBP1 in cell containing CUGexp RNA compared to the relevant control (0.90+/-0.05 for CTG960; 0.87+/-0.07 for CTG12).

Figure 4. MBNL1 and CUGBP1 co-localise in P-bodies. What is the % of colocalization?

We’re not sure exactly what is being requested here or what biological question the reviewer is asking us to address. MBNL1 and CUGBP1 co-localise in virtually all PBs (except in the HeLa CTG960 line where MBNL1 is undetectable in PBs under normal growth conditions). Figure 4E shows that, in cells with PBs upregulated by sodium arsenite, the mean amount of total cellular MBNL1 per PB is 0.1%, so it will be similar in cells grown under normal conditions as the PB sizes are similar and they appear to be of similar brightness by immunofluorescence. Again, this would be straightforward to quantify with our existing data if this is, indeed what the reviewer is requesting, but we question the biological significance. We would be reluctant to derive a Pearson’s co-efficient for the degree of co-localisation between CUGBP1 and MBNL1 in P-bodies as the structures are too small in size for this to be meaningful within the limits of imaging capabilities. We could, however, provide this if this is a specific request.

Figure 5: "Treatment with sodium arsenite was then carried out under time-lapse microscopy, with Z-stacks of images taken every 4 minutes until stress granule formation was clearly seen (Fig.5A). This revealed a pronounced delay in formation of stress granules in cells containing CUGexp foci (HeLa CTG960 GFPMBNL1, 36 min +/- 12) compared to those without (HeLa CTG12 GFPMBNL1, 15 min +/- 2) (Fig.5B)." Data representation in Figure 5 is unclear and the pronounced delay in stress granule formation is not appreciated. Since the authors performed a live imaging taking pictures every 4 minutes, it would me more informative to plot the data and show the assembly and disassembly kinetics over time for both control and CTG960_ GFPMBNL1 cell lines (similar to what shown in e.g. Gwon et al., Science 2021, Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner, Figure 2G).

The bar graph in figure 5B shows that cells from the CTG960 line take more than twice as long to form SGs compared to controls and are lost in half the time, with the precise numbers given in the text. A simple bar graph seemed the clearest way to present this. However, we have plotted our existing data in a similar manner similar to that in the cited reference and added this to figure 5. These graphs clearly show that the differences we see are at least as great as in other published literature, including the reference given by the reviewer (see below).

Concerning Figure 1, the authors report no difference in the kinetic of stress granule formation in HLECs. However, they only report data after 45 and 60 min of arsenite treatment; at these time-points the assembly step is maximal. Thus, for consistency, the authors should include earlier time-points to the analysis of stress granule assembly also in HLECs, similar to what done in HeLa cells in Figure 5.

The assembly step is not ‘maximal’ in these cell lines after 45 minutes. Figure 2A clearly shows that only ~30% of cells have SGs after 45 minutes of treatment, compared with 100% of cells after 90 minutes shown in figure 2B. We have additional data at 10, 20 and 30 minutes all showing no significant differences. We had omitted them to keep the graph simple, but have now included them as a graph of ‘% of cells with stress granules against time’ in figure 2.

"Having established that MBNL1 and CUGBP1 co-localise closely in stress granules": the authors investigated the colocalization of each of these two proteins with stress granule markers but they did not verify whether MBNL1 and CUGBP1 co-localise.

In figure 1B we show that endogenous CUGBP1 and endogenous MBNL1 both co-localise with the stress granule marker TIA1 in stress granules in lens epithelial cells. It would, therefore, be highly unlikely that CUGBP1 and MBNL1 would not co-localise with each other in stress granules. We have also previously verified that GFPMBNL1 behaves identically to its endogenous counterpart (Coleman et al, 2014). Furthermore, in figure 4C and 4D, we show close co-localisation between endogenous CUGBP1 and GFPMBNL1 in stress granules in our HeLa cell model, using high-resolution AiryScan microscopy for which we provide detailed quantitation.

This aspect should be addressed experimentally since the authors also conclude that "a complex relationship between MBNL1 and CUGBP1 in stress granules" exists. Thus, the authors need to assess the colocalization of GFPMBNL1 with endogenous CUGBP1 in stress granules and the one of GFPCUGBP1 with endogenous MBNL1.

The complex relationship we propose is based on the effects of CUGBP1 or MBNL1 knockdown on the dynamic behaviours of each other by FRAP assay and not solely on their co-localisation, although we have already analysed their co-localisation in detail as above.

Figure 6: Please add antibody labeling to microscopy panels A and B.

Certainly, this was an accidental omission and has been added

Moreover, specify is the numbers refer to minutes in panel F. The data representation is also unclear - see comment above, Figure 5.

As stated in the figure legend and on the graph axes, these numbers have been normalised to the mean time taken for SG formation/loss in the control CTG12 cell line (set at 100%). The precise numbers in minutes for mean and SD are given in the text. We have added additional graphs of ‘% of cells with stress granules against time’ to this figure, with the values in minutes given to clarify the exact time-scale.

Figure 7: was 1,6-hexanediol added in presence of arsenite? Or was arsenite removed?

Arsenite was not removed (neither was Doxycycline) as we wanted to examine the effect of 1,6-hexanediol on SGs and PBs without the added complication of the effects of stress removal. We will clarify this point in the methods/results.

Aberrant persistent stress granules have been implicated in age-related (Mateju et al., 2017) and neurodegenerative diseases (Protter and Parker, 2016), such as ALS and FTD (Jain et al., 2016; Markmiller et al., 2018; Zhang et al., 2018). These are proposed to result from increased liquid-to-solid phase transitions within the stress granules (Mateju et al., 2017)." The authors should better define what are aberrant stress granules (e.g. see Ganassi et al., 2016; Turakhiya et al., 2018, PMID: 29804830).*

We will expand on this subject in the discussion

"Persistent stress granules have long been associated with degenerative conditions, notably ALS (Li et al., 2013)". I suggest updating the reference adding a more recent one.

We selected this 2013 review to emphasise that there is a long history of association of persistent stress granules with degenerative conditions. We will add in an additional, more recent review.

Significance

The work is descriptive; thus, in this form I do not consider that it is strongly advancing the field.

Having noted alterations to stress granule disassembly in lens epithelial cells from DM1 patients, we went on to develop a novel inducible model in which we replicated and enhanced these effects by expressing the large CUGexp RNA that causes DM1 as part of a DMPK mini-gene mimicking the genetic mutation seen in DM1 patients. This is not purely descriptive. Furthermore, we are now in a position to add an additional figure showing two pieces of evidence for functional defects in stress granules associated with CUGexp RNA expression 1) reduced accumulation of total PolyA RNA in stress granules indicating compromised function and 2) compromised ‘docking’ events between stress granules and P-bodies, a process proposed to be integral to the function of both structures.

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Referee #3

Evidence, reproducibility and clarity

The manuscript entiled " Phase-separated stress granules and processing bodies are compromised in Myotonic Dystrophy Type 1" by Gulyurtlu et al., characterizes the composition and ydnamics of stress granules and P-bodies in two Myotonic Dystrophy Type 1 (DM1) cell models, human lens epithelial cells from DM1 patients and age-matched controls and HeLa_CTG12_GFPMBNL1 and HeLa_CTG960_GFPMBNL1 cell lines. The manuscript is somewhat descriptive with lack of functional data and some discrepancies. For example, in the discussion section, the authors conclude that "MBNL1 appears to be absent from P-bodies in cells with CUGexp foci in their nuclei. This observation suggests that the role of MBNL1 in P-bodies may be disrupted by the presence of CUGexp RNA." Figure 4A shows that "P-bodies in the DM1 model line, HeLa_CTG960_GFPMBNL1 do not contain detectable amounts of GFPMBNL1". However, Figure 4E shows similar levels of total cellular MBNL1 per PB between the control CTG12 and mutated CTG960 lines. Most importantly, in Figure S3 the authors show that CUGexp foci are present in 1-2 % of the cells. The claim appears to be too strong for the data presented in the manuscript.

Although the findings are interesting and of potential impact for a better understanding of the implications of RNA-protein condensate dynamics in the pathogenesis of DM1, the work presented here is still descriptive and preliminary in my opinion. In summary, the conclusions are not so convincing and additional experiments are essential to support the authors claims. Several months of works will be required to consolidate data and reorganize and ameliorate the manuscript, including the way data are presented and quantified.

Specific comments:

"On removal of stress, clearance of stress granules is mediated largely by a form of autophagy." This statement is not correct since the majority of stress granules disassemble and are not targeted to autophagy; in healthy cells only 5 % (or less) of the total SGs tend to persist in presence of autophagy or lysosome inhibitors, while the vast majority disassembles. Please rephrase carefully.

Figure 1: RNA-protein complexes have heterogeneous composition. In HLECs, do all PBs colocalize with MBNL1 and CUGBP1 or only a fraction of them?

Figure 2: Stress granules and P-Bodies are known to touch each-other, a process referred to as a "kissing event". The authors have studied the mobility of GFP-MBNL1 inside these two types of assemblies. It would be important also to quantify the "kissing" events. Is this altered in DM1 cells?

Figure 3: In HeLa cells overexpressing CTG960_GFPMBNL1, beside the accumulation of one bright CUGexp puncta, several intranuclear GFPMBNL1 protein foci are visible. This subcellular distribution is different from the one observed in the control HeLaCTG12 GFPMBNL1. Can the author describe what these intranuclear GFPMBNL1 protein foci are? Is GFPMBNL1 accumulating at the level of splicing speckles? Or paraspeckles? Or other types on intranuclear condensates such as e.g. PML nuclear bodies? The different intranuclear distribution of GFPMBNL1 should be better characterized. Moreover, the % of cells expressing CTG960_GFPMBNL1 and forming intranuclear CUGexp foci is only mentioned in the discussion (Figure S3); for clarity it should be reported in the main text when describing Figure 3.

"Figure S2: Quantitation of GFPMBNL1 in P-bodies in HeLa cell model of DM1." The authors report in the legend "Some, but not all, of these P-bodies contain detectable amounts of GFPMBNL1". However, the figure only shows a representative image of cells without quantification. Quantitation should be provided.

The authors report "a subtle change in stress granule architecture associated with the presence of CUGexp RNA". This statement is not supported by experimental data and should be omitted.

Figure 4. MBNL1 and CUGBP1 co-localise in P-bodies. What is the % of colocalization?

Figure 5: "Treatment with sodium arsenite was then carried out under time-lapse microscopy, with Z-stacks of images taken every 4 minutes until stress granule formation was clearly seen (Fig.5A). This revealed a pronounced delay in formation of stress granules in cells containing CUGexp foci (HeLaCTG960 GFPMBNL1, 36 min +/- 12) compared to those without (HeLaCTG12 GFPMBNL1, 15 min +/- 2) (Fig.5B)." Data representation in Figure 5 is unclear and the pronounced delay in stress granule formation is not appreciated. Since the authors performed a live imaging taking pictures every 4 minutes, it would me more informative to plot the data and show the assembly and disassembly kinetics over time for both control and CTG960_ GFPMBNL1 cell lines (similar to what shown in e.g. Gwon et al., Science 2021, Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner, Figure 2G). Concerning Figure 1, the authors report no difference in the kinetic of stress granule formation in HLECs. However, they only report data after 45 and 60 min of arsenite treatment; at these time-points the assembly step is maximal. Thus, for consistency, the authors should include earlier time-points to the analysis of stress granule assembly also in HLECs, similar to what done in HeLa cells in Figure 5.

"Having established that MBNL1 and CUGBP1 co-localise closely in stress granules": the authors investigated the colocalization of each of these two proteins with stress granule markers but they did not verify whether MBNL1 and CUGBP1 co-localise. This aspect should be addressed experimentally since the authors also conclude that "a complex relationship between MBNL1 and CUGBP1 in stress granules" exists. Thus, the authors need to assess the colocalization of GFPMBNL1 with endogenous CUGBP1 in stress granules and the one of GFPCUGBP1 with endogenous MBNL1.

Figure 6: Please add antibody labeling to microscopy panels A and B. Moreover, specify is the numbers refer to minutes in panel F. The data representation is also unclear - see comment above, Figure 5.

Figure 7: was 1,6-hexanediol added in presence of arsenite? Or was arsenite removed?

Minor comments:

Aberrant persistent stress granules have been implicated in age-related (Mateju et al., 2017) and neurodegenerative diseases (Protter and Parker, 2016), such as ALS and FTD (Jain et al., 2016; Markmiller et al., 2018; Zhang et al., 2018). These are proposed to result from increased liquid-to-solid phase transitions within the stress granules (Mateju et al., 2017)." The authors should better define what are aberrant stress granules (e.g. see Ganassi et al., 2016; Turakhiya et al., 2018, PMID: 29804830).

"Persistent stress granules have long been associated with degenerative conditions, notably ALS (Li et al., 2013)". I suggest updating the reference adding a more recent one.

Significance

The work is descriptive; thus, in this form I do not consider that it is strongly advancing the field.

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Referee #2

Evidence, reproducibility and clarity

In the current study, the authors compared the dynamics of P-bodies (PBs) and stress granules (SGs) between control and several DM1 cell lines. They found that MBNL1 and CUGBP1, two CUG repeat RNA-binding proteins that are primarily nuclear, could also co-localize with PBs in the cytoplasm and re-localize to SGs under stress. Small differences were observed in SG assembly and disassembly dynamics between control and DM1 HLECs, between HeLa cells expressing either CTG12 or CTG960, and between HeLa cells with and without shRNAs targeting CUGBP1 or MBNL1. Overall, the experiments were clearly described and the results properly presented. However, critical controls, as detailed below, are missing in multiple analyses. The mechanisms underlying these apparent differences are also unknown.

Major concerns:

1. Throughout the study, the authors compared MBNL1 and CUGBP1 association with PBs and SGs without considering the potential differences in their cytoplasmic abundance between control and DM1 cell lines, which seems to be case for MBNL1 abundance in CTG960-expressing HeLa cells (Fig. 3). Provided that PBs and SGs exchange components with the cytosol at an equilibrium, if the cytoplasmic abundance of, for example, MBNL1 is decreased in DM1, one would expect the equilibrium being shifted resulting in less MBNL1 associated with PB/SG. Therefore, before measuring the association or the assembly/disassembly kinetics of PB and SG, the authors should first test whether MBNL1 and CUGBP1 abundance may be different between control and DM cell lines. The same caveat applies to MBNL1/CUGBP1 knockdown experiments, where knocking down one may change the abundance of the other.
2. Similarly, the authors did not consider the possibility that changes in SG/PB dynamics may be due to changes in the abundance/availability of essential SG/PB components such as GE1 and G3BP1.
3. Most of the observed differences between control and DM cell lines were modest, leaving one wonder whether it could be simply due to cell line-to-cell line variability. Whenever possible, the authors should present results for each individual lines. For example, in Fig.2, 3 DM1 lines and 2 control lines were used. Was the difference in SG disassembly (Fig. 2B) observed in each of the 3 lines?

Minor points:

1. Western blot in Fig. 3 shows two protein products from both endogenous and overexpressed MBNL1. Please explain.
2. No data were shown to substantiate the statement that "MBNL1 localises to CUGexp foci and CUGBP1 does not" (page 6).
3. The y-axis of Fig. 4D should not go beyond 1.

Significance

The nature of the current study is highly descriptive with little mechanistic insights. For the subtle differences observed between control and DM1 cell lines, it remains unclear whether it may be due to cell line-to-cell line variation (see above). Some difference appear to be specific to one model but not the others (e.g., SG formation is slower in HeLa-CTG960 cells but not in DM1 HLECs). Even for the observations that seem consistent between models, the current results yielded little novel biological insights into whether and how these subtle differences in PB/SG dynamics may relate to DM1 pathogenesis. Collectively, these weaknesses render the current study incremental at best.

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Referee #1

Evidence, reproducibility and clarity

This manuscript by Gulyrutlu and co-workers addresses the role of CUG expanded repeat RNA associated with DM1 in regulating the formation of higher order RNP assemblies such as stress granules and P-bodies in the cell. The authors used lens epithelial cells (hLECs) derived from a DM1 patient or a HeLa cell inducible model of DM1 to investigate whether expression of the CUG repeat-associated protein MBNL1 and CUGBP1 affected the formation and dispersal of stress granules and P-bodies. The authors show that MBNL1 and CUGBP1 are components of SGs and PBs in hLECs and HeLa cells. In cells expressing the CUG repeat, there are minor alterations in the dispersal of stress granules as well as in the formation of P-bodies. MBNL1 could affect the formation and dispersal of SGs independent of the CUG repeat. Finally, in HeLa cells, overexpression of MBNL1 can reduce the dispersal of P-bodies upon 1,6-hexanediol treatment.

Major comments:

One limitation of the work is that the perturbations seen with stress granules or P-bodies are all relatively small, and no evidence for a functional consequence on gene expression is demonstrated. Specifically, the authors observe only minor alterations in the formation or disaggregation of PBs and SGs in these DM1 models. Further, some of the effects observed are independent of the CUG repeat expression, suggesting that MBNL1 and CUGBP1 might have independent roles in modulating some properties of SG and PB formation or dispersal.

1. The authors could investigate whether the CUG repeat RNA itself is localized to SGs or PBs in their models, and whether the presence of the repeat RNA is absolutely necessary for regulating the dynamics of SG or PB formation.
2. The authors use 1,6-hexanediol to suggests that PBs and SGs in HeLa cells show behavior analogous to LLPS. However, the use of 1,6,-hexanediol to establish an assembly as a LLPS is a relatively limited analysis (despite its widespread use in the field), since this compound can affect the formation of multiple cellular substructures that are not always LLPS (for example, see Wheeler et al, 2016, eLife).

Significance

This study would be of interest to the field if the impact of the DM! repeat RNAs on PB and SG were more substantial, and if some functional consequences were demonstrated. The lack of a strong effect on SG or PB formation in the DM1 models, along with the CUG repeat-independent effect of MBNL1 on the formation and dispersal of these complexes, argues that MBNL1/CUGBP1 may not significantly affect the formation or dispersal of SGs and PBs.

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Reply to the reviewers

Reviewer #1:

This paper puts together a nice set of data showing that a specific gene called Resf1 when deleted effects the ability of ESCs to self-renew and proceed to germline fates. I believe the data are sound and that they provide the evidence needed for the authors to make their conclusions.While I think the data are presented well and the manuscript is well-written, the "modest" functional results suggest this work would be more suited for a specialized journal.

We thank Reviewer 1 for their supportive comments.

• *

Reviewer #2:

1. In the presence of LIF, there is no difference between Resf1 knockout mESCs and WT mESCs except the expression of Esrrb, Nanog and Pou5f1. What about other genes? RNA-seq is needed to distinguish the two cell lines.

Fukuda et al. have shown that deletion of Resf1 leads to misregulation of ~1000 genes (adj. p-value 2) in presence of LIF. This highlights large differences between transcriptomes of Resf1 KO and WT cells that occur despite only a marginal difference in self-renewal efficiency between Resf1 KO cells and WT in the presence of LIF. It is therefore questionable whether the time and resources required to perform the requested RNA-seq would produce data that could unambiguously identify the potential causative effector difference downstream of Resf1.

As an alternative approach, we have reanalysed the Fukuda et al RNA-seq data. We find that Esrrb is significantly downregulated (in agreement with our Q-RT-PCRs), as are Klf4 and LifR (FDR 1.5). However, our meta-analysis of the Fukuda et al data did not show Pou5f1 and Nanog to be differentially expressed (FDR 1.5). This is in line with the lower level of downregulation of Pou5f1 and Nanog, compared to Esrrb in our Q-RT-PCR data. Notably, our gene expression analyses were performed in 5 biological replicates, whereas Fukuda et al. performed RNA-seq in two biological replicates. We can include the meta-analysis of the Fukuda et al data in our submission. As the change in ESC self-renewal that we see at low LIF concentrations could result from a decrease in Lifr expression, we will verify the change in expression of Lifr by Q-RT-PCR. Importantly, we will do this in a way that discriminates between expression of the transmembrane Lifr and soluble LifR, since the latter acts antagonistically (PMID: 9396734, Chambers, BJ, 1997).

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1. The authors showed Resf1 is not required for Nanog function, so how does Resf1 regulate the expression of pluripotency genes? Through epigenetic modifications or signaling pathways? The authors should design experiments to explain the detailed mechanisms.

The strength of the immunoblot signal for RESF1 is low, even when Resf1 is expressed episomally. Therefore, although we could try to co-immunoprecipitate with the Resf1-v5 cell line and endogenous Nanog, the expression level of RESF1 may mean this effort is unsuccessful. Given the fact that the result will not affect the conclusions of our study, we do not think this effort is justifiable.

1. The authors showed that Resf1 interacts with Nanog, but they used forced expressed proteins. Does the endogenous Resf1 interacts with endogenous Nanog? Do they bind to some same DNA sequences?

This are important questions to answer. However, many more experiments would be required to reach firm conclusions. The reviewer is right to say that the mechanisms by which Resf1 affects pluripotency are unknown and remain to be answered in future. We therefore propose to improve the text discussing similarities in pluripotency phenotype between deletions of Trim28, SETDB1, YTHDC1 and RESF1. As deletion of RESF1 partner SETDB1 or other proteins involved in repression of retrotransposons lead to downregulation of pluripotency genes and in some cases collapse of ESCs (e.g. PMID: 19884255, Bilodeau et al. 2009; PMID: 19884257, Yuan et al. 2009), we hypothesise that the RESF1 phenotype may be explained by affecting SETDB1 chromatin binding and therefore repression of SETDB1 targets. The mild phenotype of RESF1 KO indicates that RESF1 would not be an essential component of this repressor complex but rather “a modulatory protein”.

It is also worth noting that the meta-analysis of the RNA-seq data from Fukuda et al. suggests that Resf1-null ESCs may express reduced levels of LifR mRNA, and this is something we plan to investigate.

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1. In figure 5C, some Resf1 positive cells showed Nanog negative. Are these Nanog negative cells pluripotent?

Nanog-null ESCs are pluripotent (PMID: 18097409, Chambers et al., 2007). In addition, NANOG-negative cells in FCS/LIF cultures can retain pluripotency. Our purpose in this figure was therefore not to say whether NANOG-negative:RESF1-positive cells are pluripotent but to draw attention to the broader expression of RESF1 in FCS/LIF compared to NANOG. Such broader expression has also been noted for other heterogeneously expressed factors (PMID: 31582397, Pantier et al. 2019).

1. In figure 6A, the naïve mESCs are induced to EpiLCs. Is the transition efficiency of Resf1 knockout cells the same with WT mESCs? The finally obtained PGCLCs should be identified.

We show that the key TFs of EpiLC state are expressed similarly in WT and Resf1 KO cells (Supplementary figure 4) and we have data showing that WT and Resf1KO EpiLCs have a similar morphology. Together this suggests an efficient transition to an EpiLC state. Our analysis has identified expression of Blimp1/Ap2g/Prdm14 in Resf1-null cultures. Compared to wild-type cells these levels are reduced up to 3-fold. As this is from an unsorted population and the number of SSEA1/CD61-positive cells is decreased around 2x, this suggests that the PGCLC population formed by Resf1-null cells is reduced in proportion but is otherwise normal.

We will add photographs of EpiLC colonies formed by Resf1 KO and WT cells.

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1. in figure 5c, the scale bar is missing.

We will add missing scale bars in the figure 5C.

Reviewer #3:

1. What was less clear was an explanation of why colonies 4 and 24 were chosen. Were there other colonies with the desired expression? Was this amount of expression repeated in replicative experiments with approximately 2 colonies only available to be selected?

Approximately 30 colonies were selected for analysis. Of these, only 2 had deletion of both Resf1 alleles. We will make this point clearer in the text.

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1. Figure 1C, 5C and S2B with microscopic images should include a scale bar.

Missing scalebars in the Figure 1C will be added. Unfortunately the microscopy setup used to collect the images in Figures 5C and S2B did not allow scalebars to be added at the time of imaging and these cannot be added retrospectively. However, we do not think that inclusion of scalebars, even were it possible would affect the conclusions of our manuscript.

1. Figure 1E needs a better explanation of the significance, "less clear cut" is not adequate. Reporting statistics, or lack of significance, on the graph would help.

We will update the manuscript and the Figure 1E to include results of a statistical analysis (Wilcoxon-rank sum test) comparing formation of AP+ colonies between Resf1 KO and WT cells at different LIF concentrations. These results show that both Resf1 KO cell lines have lower median number of AP+ colonies than WT cells at LIF concentrations 0 and 1 (p.adj. *

1. It's translatability to medicine, although perhaps that is not the intention, is somewhat lacking. Is there a naturally occurring situation where LIF is absent that would require this pathway to be used? These were mouse ESC's, perhaps this study could incorporate information about relevant translation to a human condition to aid in the significance. This manuscript suggests a mechanistic evaluation by which self-renewal can occur other than the canonical pathway, which is interesting and can inform the field.

Our results suggest that RESF1 directly or indirectly supports self-renewal of ESCs. Interestingly, Human cell atlas identified RESF1 expression as a negative predictor of survival of renal cancer and was found to be expressed in testis cancer cells and other cancer tissues. Therefore, RESF1 could promote self-renewal of cancer cells similarly to ESCs. However, this is speculative and needs further studies. As this is both outside of the scope of this manuscript and our expertise, we do not think it prudent for us to pursue this line of inquiry. However, we agree that further studies could evaluate RESF1 function in human tissues, especially pluripotent cells and germ cells. As we show that RESF1 deletion leads to reduced induction of PGCLCs and previous studies showed infertility of Resf1 KO mice, investigating link between human fertility and RESF1 could have implications in reproductive medicine.

We will improve our discussion to highlight the possible significance of RESF1 function in human fertility.

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Referee #3

Evidence, reproducibility and clarity

The authors aimed to study RESF1 in ESC's to understand it's role in germ cell specification and PGCLC differentiation under in vitro experimental challenges.

The experiments performed and reported were thorough and convincing. Data and methods were clearly explained.

What was less clear was an explanation of why colonies 4 and 24 were chosen. Were there other colonies with the desired expression? Was this amount of expression repeated in replicative experiments with approximately 2 colonies only available to be selected?

Figure 1C, 5C ad S2B with microscopic images should include a scale bar.

Figure 1E needs a better explanation of the significance, "less clear cut" is not adequate. Reporting statistics, or lack of significance, on the graph would help.

Significance

Understanding the specific interactions and suggested role of RESF1 in self-renewal is informative on a molecular biology and developmental biology level.

It's translatability to medicine, although perhaps that is not the intention, is somewhat lacking. Is there a naturally occurring situation where LIF is absent that would require this pathway to be used? These were mouse ESC's, perhaps this study could incorporate information about relevant translation to a human condition to aid in the significance. This manuscript suggests a mechanistic evaluation by which self-renewal can occur other than the canonical pathway, which is interesting and can inform the field.

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

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Referee #2

Evidence, reproducibility and clarity

The authors uncovered the new roles of Resf1 in mESC self-renewal and germline entry. They showed that Resf1 deletion reduced mESC self-renewal, and it's not required for Nanog function. In addition, the efficiency of PGCLC specification of Resf1 knockout mESC is less than WT mESC. However, these conclusions are too preliminary and the underlying mechanism is missing.

Major comments:

1. In the presence of LIF, there is no difference between Resf1 knockout mESCs and WT mESCs except the expression of Esrrb, Nanog and Pou5f1. What about other genes? RNA-seq is needed to distinguish the two cell lines.
2. The authors showed Resf1 is not required for Nanog function, so how does Resf1 regulate the expression of pluripotency genes? Through epigenetic modifications or signaling pathways? The authors should design experiments to explain the detailed mechanisms.
3. The authors showed that Resf1 interacts with Nanog, but they used forced expressed proteins. Does the endogenous Resf1 interacts with endogenous Nanog? Do they bind to some same DNA sequences?
4. In figure 5C, some Resf1 positive cells showed Nanog negative. Are these Nanog negative cells pluripotent?
5. In figure 6A, the naïve mESCs are induced to EpiLCs. Is the transition efficiency of Resf1 knockout cells the same with WT mESCs? The finally obtained PGCLCs should be identified.

Minor comments:

1. in figure 5c, the scale bar is missing.

Significance

The authors uncovered the new roles of Resf1 in mESC self-renewal and germline entry.

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Referee #1

Evidence, reproducibility and clarity

This paper puts together a nice set of data showing that a specific gene called Resf1 when deleted effects the ability of ESCs to self-renew and proceed to germline fates. I believe the data are sound and that they provide the evidence needed for the authors to make their conclusions.

Significance

While I think the data are presented well and the manuscript is well-written, the "modest" functional results suggest this work would be more suited for a specialized journal.

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Reply to the reviewers

First of all, we would like to thank the each of the expert reviewers for their effort in evaluating our study. We are confident that we can positively address each of the issues and queries raised by the reviewers.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** This study investigates the functions of the tricalbin proteins in S. cerevisiae, which are homologs extended synaptotagmins in mammals. It suggests the tricalbins modulate plasma membrane phospholipid composition and are particularly important for surviving shift to elevated temperature. The tricalbins are proposed to directly or indirectly promote phosphatidylserine transport from the ER to the plasma membrane during heat stress. They also promote the localization of the kinase Pkh1 to the plasma membrane during heat stress.

**Major comments:**

General Response: We thank the reviewer for the comments and opinions. While they are starkly different from the other two reviewers’, they have caused us to consider how we can add additional support for our conclusions and to consider alternative possibilities.

1. To determine whether the tricalbins and other tethering proteins play a role in phospholipid homeostasis, lipid distribution is measured using biosensors (FLARES) and phospholipid levels are determined by mass spec. The experiments are well done but say little about what role the tricalbins or other tethering proteins play in homeostasis. There are no measurements of lipid transport or rates of phospholipid production, degradation or modification. It is reasonable to propose the tricalbins transport lipids, since other proteins with SMP domains do, but this study does not present any evidence that they do.

Response: We thank the reviewer for stating that the quantitative microscopy and lipidomics experiments in our study are well done. However, we strongly disagree that the results do not provide new information on the roles of the tricalbins or other tethering proteins in membrane lipid homeostasis. In fact, the lipidomics results definitively show that Ist2 and Scs2/22 control phosphatidylserine (PS) levels. In contrast, loss of the tricalbins does not significantly affect PS levels. We will provide a new figure to make this even more clear to expert and non-expert readers.

While the tricalbins do not regulate PS levels, the data clearly show that the tricalbins control PS acyl chain saturation and cellular distribution. A PS reporter is reduced at the plasma membrane (PM) upon loss of the tricalbins and we have now confirmed increased localization at the endoplasmic reticulum (ER). These new results will be included in a revised manuscript. As the lipid desaturase Ole1 is localized in the ER, the lipidomics are consistent with increased ER residency of PS species. Thus, the data indicate that while the tricalbins do not regulate PS levels, they control either delivery of PS from the ER to the PM, and/or they may control the organization and stability of PS at the PM which would be a novel finding on its own.

The reviewer must be aware that there are currently no in vivo cell assays that directly (only) measure lipid transport. These experiments are subject to several factors including lipid metabolism, anterograde transport rates, bilayer organization, lipid accessibility, and retrograde transport rates. While our findings clearly show that the Tcb proteins do not control PS levels, we agree that there are alternative possibilities to explain the changes in PS distribution. In our revised manuscript, we will perform additional experiments to distinguish between these possibilities. New experiments will include mutant forms of Tcb3 bearing substitutions in the SMP domain. We will also examine whether the Tcb proteins control PS organization, availability/accessibility, and stability at the PM (also see Reviewer #3, comment 2). This latter possibility may reveal a novel concept regarding Tcb/E-Syt protein function that goes beyond their proposed conventional role as lipid transfer proteins. Based on the outcome of these experiments, we shall adjust the final cartoon model and conclusions in the discussion accordingly.

The study convincingly demonstrates that there are fewer Pkh1 puncta formed after temperature shift in cells lacking tricalbins than in wild-type cells (Fig. 6 C,D). However, there is no demonstration that this change in localization alters Pkh1 function or signaling.

Response: Regulation of Pkh1 by lipids is outside the scope of our current study that is focused on providing new understanding of Tcb protein function. However, the decrease in heat-induced Pkh1 puncta may provide insight into the PM integrity defects in cells lacking Tcb1/2/3 (as Pkh1 is required for PM integrity). To test whether Pkh1 function is compromised in the tcb1/2/3 mutant cells, we can test whether constitutively active Ypk1 (which acts downstream of Pkh1) rescues the PM integrity defects in tcb1/2/3 mutant cells.

There is no demonstration that association of tricalbins and Skh1 (Fig. 4) has any functional significance or affects phosphoinositide metabolism.

Response: We thank the reviewer for raising this issue. If the association of Tcb3 and Sfk1 has functional significance, then one would expect that loss of the proteins should phenocopy one another. Deletion of the Sfk1 cytoplasmic domain necessary for co-localization with Tcb3 should also phenocopy loss of Tcb3. This is exactly what we find. Localization of the PS probe is decreased at 42oC upon loss of Sfk1 or truncation of the Sfk1 cytoplasmic tail, similar to cells lacking Tcb3. Furthermore, we find that Tcb3 regulates sterol homeostasis at the PM (using the D4H probe), as has been recently reported for Sfk1 (Kishimoto et al, 2021). Thus, Tcb3 and Sfk1 not only co-localize, but they also share common functions in PM lipid organization. These new results will be presented in our revised manuscript.

The reviewer also inquired about potential roles for Tcb3 and Sfk1 in phosphoinositide lipid homeostasis, as Sfk1 has reportedly been implicated in heat-induced PI(4,5)P2 synthesis. However, while we find clear roles for Sfk1 and the tricalbins in PS and sterol homeostasis, we did not find a requirement for Sfk1 or the tricalbins in PI(4,5)P2 homeostasis upon heat stress conditions. These findings will be included in our revised manuscript. Importantly, our results indicate that the tricalbins and Sfk1 primarily control PS and sterol homeostasis at the PM, and may regulate phosphoinositides indirectly, and thus provide new insight into the key role of these proteins.

The study proposes the tricalbins directly or indirectly promote phosphatidylserine transport after temperature shift, but transport has not measured and other possibilities are not ruled out.

Response: While the Tcb3 SMP domain has been shown to transfer phospholipids in vitro (Qian et al., 2021), we agree that a role in PS transfer in vivo should be examined in more detail and that other possible roles in PS homeostasis should also be considered (also see responses to Reviewer #3, comment 2).

Upon heat shock, we not only observe a decrease in relative levels of the PS reporter at the PM in the tcb1/2/3 mutant cells (as shown in our original manuscript), but also a corresponding increase in relative levels of the PS probe at the ER and vacuole membrane (also see response to Reviewer #3, comment 5). This could reflect impaired delivery of PS from the ER to the PM and reduced stability of PS at the PM (i.e. increased internalization of PS into the cell).

In our original manuscript, we showed that deletion of the SMP domain (the lipid transfer domain), phenocopies deletion of the full-length Tcb3 protein in terms of PS distribution and PM integrity following heat shock. To more rigorously test whether lipid transport activity of the SMP domain is responsible for these phenotypes, we will generate amino acid substitutions within the SMP domain of Tcb3 that maintains its overall structure but impairs its ability to transport lipids (by targeting conserved key residues identified in Saheki et al., 2016). We will then assess whether SMP-mediated lipid transfer is necessary for PS homeostasis and PM integrity under heat stress.

We also agree that other possibilities should be examined. First, to rule out a defect in PS production upon heat stress, we are performing new mass spectrometry lipidomics experiments to measure levels of individual phospholipid species in the tcb1/2/3 mutant and wild type cells after heat stress.

Second, we have considered whether the Tcb proteins control phospholipid bilayer distribution (e.g. flip and flop). However, cells lacking the Tcbs are not hypersensitive to duramycin (Omnus et al. 2016) and thus phosphatidylethanolamine exposure on the extracellular leaflet is not increased. Moreover, cells lacking the Tcbs (and Scs2/22 and Ist2) are not impaired in the uptake of exogenous NBD-labelled phospholipids (and thus flip across the PM bilayer is not impaired). Possibly, there may be increased lipid ‘flop’ in the mutant cells at high temperature. We can test whether there is increased phospholipid exposure in the extracellular leaflet at high temperature, but our results thus far indicate accumulation of PS on internal membrane compartments (the ER and vacuole membrane).

Another potentially exciting possibility is that the tricalbin proteins bind and stabilize PS within the cytosolic leaflet of the PM and prevent its internalization by endocytosis or non-vesicular transfer. This mechanism would be completely independent of lipid transport to the PM and would constitute new mechanistic insight into Tcb function. We will test whether PS (and sterol) becomes more accessible (less stable or reduced sequestered pools at the PM) and internalized into the cell, upon removal of the tricalbin proteins. For example, we will monitor PS distribution in cells where endocytosis is blocked with latrunculin A.

As mentioned, there currently no cellular lipid transport assay that directly (only) measure anterograde transport. However, if the Tcb3 SMP domain mutants are impaired in PS homeostasis and PM integrity, then we can consider monitoring PS transfer in vivo. By performing the experiments outlined here, we will have thoroughly characterised the roles of the tricalbin proteins in PS homeostasis at the PM. Moreover, the new findings may even reveal novel roles that are independent of transport.

Reviewer #1 (Significance (Required)): While this study is likely to be of interest to those studying the tricalbins or phospholipid homeostasis, it is incremental and provides little conceptual advance on what is already known about the tricalbins and extended synaptotagmins. They have already been implicated in lipid homeostasis in the plasma membrane and this study provides no new mechanistic insight into how this occurs. Similarly, it has already been shown that the tricalbins play a role in maintaining cell integrity during heat stress and there is little new insight into what role the tricalbins play. Perhaps the most notable part of the study is the idea that tricalbins are necessary for phosphatidylserine transport during stress, but considerable additional work is necessary to make a strong case for this claim.

Response: We strongly disagree with the reviewer’s opinions. Indeed, Reviewer #2 found our study “novel and detailed” and Reviewer #3 found the results in our study to be “highly valuable” and “interesting”.

In contrast to the reviewer’s claims, there are certainly novel findings in our study. Foremost, this is the first study that demonstrates a role of the tricalbins in PS homeostasis. Previous studies have implicated E-Syt family members in diacylglycerol and phosphoinositide regulation. Our results indicate that the tricalbins and Sfk1 primarily control PS and sterol homeostasis at the PM, not phosphoinositides, and thus provide new insight into the key role of these proteins. Second, while a previous study by Collado et al reported a role of the tricalbins in PM integrity upon heat stress, this work did not provide mechanistic insight into this process. We performed the PM integrity assays for the Collado et al study (as co-authors). Our current study now shows that Tcb function is needed for PS homeostasis and Pkh1 recruitment at the PM upon heat stress; both factors are needed for PM integrity under these conditions. As such, our current study does provide new insight into roles of the Tcbs in PM integrity. Finally, we are exploring roles of the Tcb proteins in PS homeostasis that go beyond their proposed functions as lipid transfer proteins. We are convinced that our study will provide novel and deep mechanistic understanding of the Tcb/E-Syt protein family.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): The study examines the roles of tricalbins in signalling in yeast. By using several mutations they are able to evaluate whether the interactions are caused by tethering the PM and ER or by other mechanisms. Particualrly powerful is their use of cryo-EM tomography and shot-gun mass spectrometery. They look at all the major lipids of the ER and PM and consider their interconversions. They identify large changes in lipid species with one double bond and with two, particularly for PS.

Reviewer #2 (Significance (Required)): There is little information about membrane physical properties and how it changes as a result of changes in lipid molecular species. Nevertheless, the information provided is novel and detailed. The topic of ER-PM contact sites is new and evolving and this paper advances our understanding of the yeast system considerably. It also looks at protein-protein interactions by fluorescence methods and studies the consequences of heat shock.

Response: We are pleased that the reviewer concluded that our study on the Tcb proteins “advances our understanding of the yeast system considerably” and found our use of lipidomics to be “powerful”.

This reviewer only had only one critique; there “is little information about membrane physical properties and how it changes as a result of changes in lipid molecular species”. In our revised manuscript, we will provide new data showing changes in levels of sterol (ergosterol) accessibility/availability at the PM in cells lacking the tricalbin proteins. Sterol lipids exist in distinct pools in the PM bilayer (extracellular vs. cytoplasmic leaflet, accessible vs. sequestered) that control the biophysical and mechanical properties of the PM (packing order, permeability, etc.). Moreover, PS and sterol lipids are proposed to undergo mutual associations whereby PS controls sterol accessibility (the ‘umbrella’ model) and sterol in turn stabilizes PS in the cytoplasmic leaflet of the PM. Our findings demonstrate that the primary function of the Tcb proteins is PS and sterol organization in the PM, providing new mechanistic insight into regulatory mechanisms for membrane homeostasis. We will attempt to further characterize changes in PM mechano-chemical and biophysical properties to further understand how changes in membrane lipid composition affect membrane integrity.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): The Tcb/ESyt proteins play important role in contact site formation and non-vesicular lipid transport. However, their exact functions remain highly controversial. This study by Stefan and colleagues revealed a new role for the yeast Tcb proteins, especially Tcb3, in regulating plasma membrane phosphatidylserine, as well as PIP2. These results are highly valuable to people working on membrane contact sites and lipid trafficking. Overall, the results are fairly convincing and interesting. There are however some concerns and suggestions:

Response: We are pleased that the reviewer stated that our study has “revealed a new role for the yeast Tcb proteins” and that the findings are “fairly convincing and interesting”. We also thank the reviewer for providing constructive criticisms and helpful suggestions.

1. Fig. 4, for Tcb3 and Sfk1 interaction, what about Tcb1/2, which would be good controls for the specificity of the interaction.

Response: We agree that it would be useful to determine if the Tcb3-Sfk1 interaction is specific. We will perform additional BiFC experiments to address whether Tcb1 and Tcb2 associate with Sfk1. Our previous work suggested that Tcb3 forms heterodimers with Tcb1 and Tcb2 necessary for PM integrity (Collado et al., 2019). However, the functional association between Sfk1 and Tcb3 may be specific to Tcb3, and we will test this possibility. It would be interesting to identify a function specific to an individual Tcb protein.

A central question is whether Tcb3 transfers PS by itself through the SMP domain or requires other lipid transfer proteins. One possibility is that the transfer is mediated by Osh6 and Osh7. Did the expression level of Osh6/7 change between the delta tether and the ist2scs2/22 null strain? Under normal and stressful conditions?

Response: We agree that it is important to address whether Tcb3 transfers PS via its SMP domain (an issue also raised by Reviewer #1) or whether this activity is carried out by another lipid transfer protein, such as Osh6 and Osh7. We have considered several alternative possibilities, as well as designed and performed new experiments, as described below (two key experiments are in italics).

To rigorously test whether SMP domain-mediated lipid transport activity is required for PS homeostasis at the PM under heat stress, we will generate amino acid substitutions within the Tcb3 SMP domain that maintain its overall structure but impair its ability to transport lipids (targeting conserved key residues described in Saheki et al., 2016 & Bian et al., 2018). We will then assess whether SMP-mediated lipid transfer is necessary for PS homeostasis and PM integrity under heat stress.

As suggested by the reviewer (and discussed in our original manuscript), the tricalbins might serve as scaffolds for PS transfer proteins, including the Osh6 and Osh7 proteins, under stress conditions. Osh6 and Osh7 are recruited to ER-PM contacts through interactions with the Ist2 tether protein where they mediate PS delivery to the PM under non-stress conditions (D’Ambrosio et al., 2020). However, strong lines of evidence suggest that Ist2, Osh6, and Osh7 are not required for PS homeostasis at the PM under stress conditions. First, loss of Ist2 has no impact on the PS probe under heat stress conditions (this result will be included in the revised manuscript). Therefore, the Ist2-Osh6/7 interaction is not required for PS homeostasis upon heat stress. Ist2 is not required for PM integrity upon heat stress either (Omnus et al., 2016).

These results do not rule out the possibility that the tricalbins serve as scaffolds for other PS transfer proteins, such as Osh6 and Osh7, under stress conditions. In this scenario, a switch between Osh6/7 tethering proteins may occur: from Ist2 under normal growth conditions to the Tcb proteins during stress conditions. However, our findings also suggest that Osh6 and Osh7 function is impaired upon stress conditions, including heat and nutrient starvation. Notably, Osh6 and Osh7 become mis-localized from the PM under upon heat stress (we can provide this data in the revised manuscript). The mechanisms for Osh6/7 attenuation upon stress conditions is outside the scope of our current study, but our preliminary results suggest that changes in cytoplasmic pH and ion homeostasis are involved; this will be a focus of a future study. This is also in line with results from a previous study (Omnus et al., 2020) that showed Osh3 forms intracellular aggregates in response to heat stress. The activity of the Osh proteins and other lipid transfer proteins, in general, may be impaired upon stress conditions (see below).

To directly determine whether Osh6 and Osh7 are required for PS homeostasis at the PM under heat stress conditions, we will monitor the distribution of the PS probe in cells lacking the Osh6 and Osh7 (osh6 osh7 double mutant cells) upon heat stress. This is a key experiment that will directly address the reviewer’s concerns.

As suggested by the reviewer, we can also examine the expression and localization of GFP-tagged Osh6 and/or Osh7 in tcb1/2/3, scs2/22 ist2, and ‘delta tether’ mutant cells. However, we have not observed any changes in other Osh proteins, including Osh2 and Osh3, in the ‘delta tether’ mutant strain.

Finally, we have considered yet another possibility. PS transfer to the PM may be generally attenuated under heat stress conditions and a key role of the Tcb proteins may be to bind and stabilize PS at the PM. In other words, the Tcb proteins may act as a ‘buttress’ to stabilize and maintain pre-existing pools of PS at the PM under stress conditions. Consistent with this idea, the Tcb3 C2 domains are required for PS homeostasis and PM integrity upon heat stress. If the Tcb3 SMP domain mutants are not impaired in PS homeostasis or PM integrity, then this alternative mechanism may be very relevant to PM quality control and organization in response to membrane stress. To address this possibility, we will address whether PS is internalized or removed (extracted) from the PM upon heat stress in cells lacking the Tcb proteins. This may uncover a novel role of the Tcb proteins that is independent of SMP domain-mediated lipid transfer.

Figure 5A. It is not obvious that the intensity of C2 decreases in tcb null cells at 42 degree. Perhaps there is more internal staining.

Response: We thank the reviewer for pointing out this issue. We think Figures 5A and 5B together convincingly show increased cytoplasmic localization of the PS reporter in the tcb1/2/3 mutant cells upon heat stress. More importantly, we thank the reviewer for pointing out the increased localization at internal membrane compartments. We realized it would be important to identify the PS-containing membrane compartments in the tcb1/2/3 mutant cells upon heat stress. We have now confirmed that the PS probe localizes at the ER and vacuole membrane (to be included in the revised manuscript). The example in Figure 5A also shows accumulation of the PS probe at both the nuclear ER and vacuole membrane. Thus, whilst wild type cells show little PS reporter localization at the ER or vacuole membrane, loss of the tricalbin proteins leads to an increase in ER and vacuole membrane PS probe localization after heat shock. Accumulation at the ER may reflect impaired PS delivery from the ER to the PM, and possibly rerouting to the vacuole membrane. Alternatively, as discussed above, vacuole membrane localization may be due increased PS removal from the PM and delivery to the vacuole membrane in tcb1/2/3 cells upon heat stress.

It is important to determine the primary function of Tcb3 since both defects in both PIP2 and PS were observed. If the change in PIP2 is due to a lack of PS, can overexpressing Osh6/7 rescue the PIP2 defect in the tetherless mutant?

Response: We agree that is important to determine whether the primary function of the Tcb proteins is regulation of PS or PI(4,5)P2 homeostasis. Our new findings definitively indicate that their primary function is PS regulation, not PI(4,5)P2 regulation. A clear effect in the distribution of the PS reporter was observed following heat shock in the tcb1/2/3 mutant cells. In contrast, there is no difference in the localization of the PI(4,5)P2 reporter in tcb1/2/3 cells compared to wild type after heat shock (also see response to reviewer #1, comment 3). In addition, cells lacking the Tcb proteins were not impaired in heat-induced PI(4,5)P2 synthesis, as assessed by metabolic labelling and HPLC analysis. These findings will be included in our revised manuscript, as they indicate that the Tcb proteins primarily control PS at the PM, not phosphoinositides, and thus provide new insight into the main role of these proteins.

Both PS and sterol are required for the proper recruitment of type I PIP5K to the PM (Nishimura et al., 2019). Therefore, defects in PS distribution could be responsible for the PI(4,5)P2 effects observed in Figure 2. However, overexpression of Osh7 in the ‘delta tether’ mutant did not significantly rescue localization of the PI(4,5)P2 reporter (included in the revision plans). Sterol organization is also perturbed in the ‘tether’ mutant cells (Quon et al., 2018), and this may explain why Osh7 expression did not rescue. Accordingly, Osh2/3/4 (sterol transfer proteins) rescue the PI(4,5)P2 effects.

The detection of PS by LactC2 has been well-established. However, an alternative approach would be to use the 2XPH in permeabilized cells. See PMID: 33929485 for some detailed discussions on the techniques. It is not a requirement for the authors to adopt the 2XPH.

Response: We thank the reviewer for suggesting another technique to confirm the results of the LactC2 domain as a PS probe. In this study, we have primarily used a genetically encoded LactC2 probe to observe PS distribution within live, intact cells. Whilst this approach was sufficient to identify accumulation of PS on cytosolic membrane leaflets of the ER and vacuole (see above), the addition of an exogenous probe to permeabilized cells may allow the detection of PS on luminal and extracellular membrane leaflets. Therefore, we plan to repeat our heat shock experiments using permeabilized cells and a purified tagged form of the LactC2 protein. This may allow for improved imaging of intracellular PS localisation and bilayer distribution. However, these experiments are technically challenging, and fixation and permeabilization conditions have not yet been optimized for yeast cell experiments. It is not yet known whether we will be able to optimize these protocols in a reasonable amount of time while completing revisions to the manuscript.

**Minor:**

1. the discussion seems to be a bit long

Response: We will shorten the discussion and modify our final conclusions based on the results from the new experiments.

Reviewer #3 (Significance (Required)): These proteins are highly important in cell biology/contact sites. The redundancy made it difficult to pinpoint their function. Previous studies have had a number of models. The current study proposed a new function of these proteins, i.e. PS transfer, and this is very interesting and valuable. There will be a good audience for this work. I specialize in lipid storage and trafficking, lipid droplets, cholesterol and phosphatidylserine.

Response: We are pleased that this expert reviewer found our study to be “very interesting and valuable”.

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Referee #3

Evidence, reproducibility and clarity

The Tcb/ESyt proteins play important role in contact site formation and non-vesicular lipid transport. However, their exact functions remain highly controversial. This study by Stefan and colleagues revealed a new role for the yeast Tcb proteins, especially Tcb3, in regulating plasma membrane phosphatidylserine, as well as PIP2. These results are highly valuable to people working on membrane contact sites and lipid trafficking. Overall, the results are fairly convincing and interesting. There are however some concerns and suggestions:

Major:

1.Fig. 4, for Tcb3 and Sfk1 interaction, what about Tcb1/2, which would be good controls for the specificity of the interaction.

1. A central question is whether Tcb3 transfers PS by itself through the SMP domain or requires other lipid transfer proteins. One possibility is that the transfer is mediated by Osh6 and Osh7. Did the expression level of Osh6/7 change between the delta tether and the ist2scs2/22 null strain? Under normal and stressful conditions?
2. Figure 5A. It is not obvious that the intensity of C2 decreases in tcb null cells at 42 degree. Perhaps there is more internal staining.
3. It is important to determine the primary function of Tcb3 since both defects in both PIP2 and PS were observed. If the change in PIP2 is due to a lack of PS, can overexpressing Osh6/7 rescue the PIP2 defect in the tetherless mutant?
4. The detection of PS by LactC2 has been well-established. However, an alternative approach would be to use the 2XPH in permeabilized cells. See PMID: 33929485 for some detailed discussions on the techniques. It is not a requirement for the authors to adopt the 2XPH.

Minor:

1. the discussion seems to be a bit long

Significance

These proteins are highly important in cell biology/contact sites. The redundancy made it difficult to pinpoint their function. Previous studies have had a number of models. The current study proposed a new function of these proteins, i.e. PS transfer, and this is very interesting and valuable. There will be a good audience for this work. I specialize in lipid storage and trafficking, lipid droplets, cholesterol and phosphatidylserine.

Referee Cross-commenting

I was asked to include my expertise in the Significance part. Other reviewers did not include it. Maybe my last sentence should be removed?

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Referee #2

Evidence, reproducibility and clarity

The study examines the roles of tricalbins in signalling in yeast. By using several mutations they are able to evaluate whether the interactions are caused by tethering the PM and ER or by other mechanisms. Particualrly powerful is their use of cryo-EM tomography and shot-gun mass spectrometery. They look at all the major lipids of the ER and PM and consider their interconversions. They identify large changes in lipid species with one double bond and with two, particularly for PS.

Significance

There is little information about membrane physical properties and how it changes as a result of changes in lipid molecular species. Nevertheless, the information provided is novel and detailed. The topic of ER-PM contact sites is new and evolving and this paper advances our understanding of the yeast system considerably. It also looks at protein-protein interactions by fluorescence methods and studies the consequences of heat shock.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This study investigates the functions of the tricalbin proteins in S. cerevisiae, which are homologs extended synaptotagmins in mammals. It suggests the tricalbins modulate plasma membrane phospholipid composition and are particularly important for surviving shift to elevated temperature. The tricalbins are proposed to directly or indirectly promote phosphatidylserine transport from the ER to the plasma membrane during heat stress. They also promote the localization of the kinase Pkh1 to the plasma membrane during heat stress.

Major comments:

1. To determine whether the tricalbins and other tethering proteins play a role in phospholipid homeostasis, lipid distribution is measured using biosensors (FLARES) and phospholipid levels are determined by mass spec. The experiments are well done but say little about what role the tricalbins or other tethering proteins play in homeostasis. There are no measurements of lipid transport or rates of phospholipid production, degradation or modification. It is reasonable to propose the tricalbins transport lipids, since other proteins with SMP domains do, but this study does not present any evidence that they do.
2. The study convincingly demonstrates that there are fewer Pkh1 puncta formed after temperature shift in cells lacking tricalbins than in wild-type cells (Fig. 6 C,D). However, there is no demonstration that this change in localization alters Pkh1 function or signaling.
3. There is no demonstration that association of tricalbins and Skh1 (Fig. 4) has any functional significance or affects phosphoinositide metabolism.
4. The study proposes the tricalbins directly or indirectly promote phosphatidylserine transport after temperature shift, but transport has not measured and other possibilities are not ruled out.

Significance

While this study is likely to be of interest to those studying the tricalbins or phospholipid homeostasis, it is incremental and provides little conceptual advance on what is already known about the tricalbins and extended synaptotagmins. They have already been implicated in lipid homeostasis in the plasma membrane and this study provides no new mechanistic insight into how this occurs. Similarly, it has already been shown that the tricalbins play a role in maintaining cell integrity during heat stress and there is little new insight into what role the tricalbins play. Perhaps the most notable part of the study is the idea that tricalbins are necessary for phosphatidylserine transport during stress, but considerable additional work is necessary to make a strong case for this claim.

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Reply to the reviewers

1. General Statements We are grateful to all reviewers for the critical comments and valuable suggestions that have helped improve our paper. We provide point-to-point answers to the comments and added detailed explanations in the preliminary revised manuscript. We put the comments made by the reviewer in italics with our responses below.

Description of the planned revisions

Reviewer #1

\*Major comments:***

- The authors thought almost all iRFP forms a holo-complex with BV when HO1 is expressed in fission yeast. This should be proved by calculating the percentage of BV-iRFP in yeast. It is meaningful to compare the percentage of BV-iRFP and PCB-iRFP in vitro and in yeast.

We would like to thank the reviewer for this valuable comment. We plan to quantify the percentage of iRFP holo-complex in vitro and in yeast by using fluorescence correlation spectroscopy (FCS). In FCS, the fluctuation of fluorescence emitted from a very tiny space (i.e., confocal volume, ~ 1 fL) in solution is measured and statistically analyzed by autocorrelation, providing the average number of fluorescent molecules in the confocal volume (Krichevsky and Bonnet, 2002, Rep. Prog. Phys.). The fusion proteins of iRFP with mNeonGreen (mNG) are purified or expressed in yeast, and thus the stoichiometry of iRFP to mNG must be 1:1. The numbers of iRFP and mNG are measured by FCS in vitro or in yeast. If iRFP forms 100% holo complex with BV or PCB, the number of fluorescent iRFP is equal to that of mNG. We have used FCS to measure the concentration of fluorescent protein in mammalian cells (Sadaie et al., 2014, MCB; Komatsubara et al., 2020, JBC), and therefore have no technical difficulties. One possible limitation is the maturation efficiency of mNG in vitro and in yeast, although it would not disturb at least the qualitative comparison of holo-complex formation between iRFP-BV and iRFP-PCB. In this preliminary version of the revised manuscript, we added the data of FCS measurement in yeast, but not yet in vitro, in Supplementary Figure S5A. The numbers of fluorescent iRFP molecules relative to the numbers of mNG were comparable between iRFP-BV (HO1 expressing cell) and iRFP-PCB (SynPCB2.1 expressing cell). Of note, the number of fluorescent iRFP molecules in cells treated with external BV was significantly lower than that in cells expressing HO1, suggesting the low permeability of BV.

In addition to the FCS measurement, we prepare the following backup experiments; recombinant iRFP proteins are mixed with sufficient amounts of BV or PCB, and used them as a reference. Then, the iRFP proteins are expressed and purified from yeast, and subjected to Zinc blot with the reference iRFP proteins to compare the percentage of iRFP holo-complex in vitro and in yeast. This method is feasible, but we are not able to quantitatively determine the percentage of iRFP holo-complex with BV or PCB.

-The brightness of fluorescent proteins in organisms often depends on the molecular brightness (fluorescence quantum yield and extinction coefficient) and the amounts of fluorescent proteins. The authors indicated that iRFP-PCB is brighter than iRFP-BV at the molecular level. To calculate the amounts of iRFP-PCB and iRFP-BV when different proteins are expressed in yeast, it is better to explain the results that phycocyanobilin was better than biliverdin for the imaging in fission yeast.

We agree with the reviewer’s comment. We will quantify the amounts of iRFP in the fission yeast cells expressing iRFP, iRFP/HO1, and iRFP/SynPCB2.1 by western blot analysis and fluorescent imaging. With regard to western blot analysis, the iRFP proteins are normalized by Tubulin in western blot analysis, which has been used as the reference protein.

During this preliminary revision period, we assessed the expression levels by fluorescence microscopy. The fusion proteins of iRFP fused with mNG were expressed in yeast and the expression levels were quantified by the fluorescence signals of mNG. In this preliminary version of the revised manuscript, we only added fluorescence imaging data in Supplementary Figure S4. The fluorescence intensities of mNG among cells expressing iRFP, iRFP/HO1, and iRFP/SynPCB2.1 were comparable as well as cells treated with BV or PCB. We will further confirm this result by western blotting analysis.

To test the application of PCB as chromophore in mammalian cells, a HO1 gene knock out mammalian cells should be used.

We would like to thank the reviewer for this excellent suggestion. It is indeed interesting to examine the effect of an HO1 gene knock-out on the iRFP fluorescence and the application of PCB as a chromophore in mammalian cells. We plan to knock out the HO1 gene in HeLa and HeLa/BVRA KO cells by conventional CRISPR/Cas9 genome editing techniques. After the establishment of HO1-KO HeLa cell lines, iRFP fluorescence is assessed in the same way as we did in Figure S9. In addition, we will carefully investigate the effect of BV in serum on iRFP fluorescence.

The authors may use the all-in-one plasmids carrying SynPCB2.1 and iRFP fusion protein genes to image the target proteins in mammalian cells.

According to the reviewer’s suggestion, we will construct an all-in-one plasmid carrying SynPCB2.1 and an iRFP fusion protein gene for iRFP imaging in mammalian cells.

To this end, the IRES and iRFP genes are inserted downstream of the SynPCB2.1 gene cassette, and iRFP fluorescence is confirmed in mammalian cells.

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

Reviewer #1

\*Minor comments:***

- In line 35. iRFP is derived from bacteriophytochromes.

We have replaced “phytochrome” with “bacteriophytochrome”.

- In line 62. The reference of Rodriguez et al. deals with allophycocyanin instead of bacteriophytochromes.

We have included the following note in the revised manuscript.

(Page 3, line 56)

“Near-infrared fluorescent proteins have been developed through the engineering of phytochromes, which are photosensory proteins of plants, bacteria, and fungi (Chernov et al., 2017), or allophycocyanin, which is a light-harvesting phycobiliprotein of cyanobacteria (Rodriguez et al., 2016). “

- In lines 65-66. Bacteriophytochromes bind BV, phycocyanin, allophycocyanin and cyanobacterial phytochromes bind PCB, and plantal phytochromes bind PΦB.

Thank you for the notification. We have added the explanation in the revised manuscript as follows.

(Page 3, line 65)

“Unlike the canonical fluorescent proteins derived from jellyfish or coral, phytochromes and allophycocyanin require a linear tetrapyrrole as a chromophore such as biliverdin IXα (BV), phycocyanobilin (PCB), or phytochromobilin (PΦB); bacteriophytochromes bind to BV, allophycocyanin and cyanobacterial phytochromes bind to PCB, and plantal phytochromes bind to PΦB.”

- In lines 67-71. The authors described the biosynthesis of BV, PCB and PΦB. But it missed many references.

We agree with this reviewer’s comment, and have included the references in the revised manuscript as follows.

(Page 3, line 70)

“These linear tetrapyrroles are produced from heme (Terry and Lagarias 1991; Beale 1993). Heme-oxygenase (HO) catalyzes oxidative cleavage of heme to generate BV with the help of ferredoxin (Fd), an electron donor, and ferredoxin-NADP+ reductase (Fnr) (Cornejo, Willows, and Beale 1998). In cyanobacteria, PCB is produced from BV through PcyA, Fd, and Fnr, while in plants PΦB is synthesized from BV using HY2, Fd, and Fnr (Muramoto et al. 1999; Frankenberg et al. 2001; Kohchi et al. 2001).“

- In line 270. One full stop should be deleted in "cells.. Fission".

We have corrected this mistake.

- In line 272. How did the authors add the high concentration of PCB (625 microM) into the culture? PCB is insoluble.

As the reviewer pointed out, 625 microM PCB seem to be insoluble in PBS solution and fission yeast culture medium, because insoluble PCB debris was observed in the medium. We have included the following note in Materials and Methods of the revised manuscript.

(Page 20, line 505)

“Of note, 625 μM PCB or 625 μM BV is insoluble in PBS solution and fission yeast culture medium, and 25 μM PCB or 25 μM BV is insoluble in mammalian cell culture medium, because insoluble PCB or BV debris is observed.”

- In line 401. smURFP is derived from allophycocyanin instead of cyanobacteriochromes.

We have corrected this mistake.

- In line 474. As BV and PCB are insoluble, how do the authors add the pigments into DMEM?

We directly added BV or PCB dissolved in DMSO into the culture medium of HeLa cells, i.e., DMEM, 10% FBS, and maintained cells for 3 hours. We have included the following note in the figure legend of the revised Supplementary Information.

Fig. S9 legend

“Of note, 25 μM BV or PCB remained unsolved in the cultured medium for mammalian cells, and therefore chromophores in the medium could be saturated under these conditions.”

- In line 509. In which solvent are BV and PCB dissolved?

BV or PCB dissolved in DMSO was added into His-iRFP PBS solution. We have added the explanations as follows.

(Page 22, line 555)

“Purified His-iRFP in PBS solution was mixed with an excess amount (1:5 molar ratio) of BV or PCB dissolved in DMSO, followed by size exclusion chromatography with NAP-5 Columns (Cytiva, 17085301) to remove free BV or PCB.”

- In lines 546, 547. What is ROI?

We apologize for the reviewer’s confusion. ROI is the abbreviation of “region of interest.” We have included the full expression in the revised manuscript.

- The authors should indicate whether codon optimization was necessary when HO1, PcyA, Fd and Fnr were expressed in yeast or in mammalian cells.

We would like to thank the reviewer for pointing out this issue. The codon optimization for expressing SynPCB genes was needed in mammalian cells (Uda et al., PNAS, 2017). In fission yeast, we used the same SynPCB gene optimized for human codon usage, but not for fission yeast codon usage. As far as we used, there have been no problems with PCB synthesis in fission yeast. We have included this important note in the revised manuscript as follows.

(Page 20, line 483)

“The nucleotide sequence of these genes and SynPCB were optimized for human codon usage (see Benchling link; Table S1). ”

- Figure S1 should show the software and algorithm for the construction of phylogenetic.

Thank you for pointing out the ambiguity in our statement. We have not constructed the species tree on our own; instead, we have manually drawn the tree by Adobe Illustrator, based on the latest genome-scale phylogeny of fungi (Yuanning Li et al. 2021), where results of multiple algorithms were compared to evaluate the reliability of phylogenies. To make it explicit, we revised the manuscript as follows.

(Page 24, line 627)

“We have manually drawn the evolutionary relationship among representative species (Nguyen et al. 2017) based on a recent genome-scale phylogeny (Yuanning Li et al. 2021), which is consistent with the current consensus view of the fungal tree of life (James et al. 2020).”

Here, to validate that the tree in Figure S1 is consistent with the current consensus phylogeny, we have added a reference to a recent review on the fungal tree of life (Timothy Y. James et al., Ann Rev Microbiol, 2020). Additionally, we modified the tree in Figure S1 to represent the remaining ambiguity among subdivisions in Basidiomycota (Yuanning Li et al. 2021, Figure 4G).

- In Figure 1C and Figure 2C. The authors should explain the reasons why the fluorescence of iRFP was lower when yeast was treated with excessive BV and PCB.

The decrease in iRFP intensity under 625 μM PCB or 625 μM BV could be due to cell death and/or toxicity by the excess DMSO. We have included the explanation in the revised manuscript.

Figure 1 legend

“The decrease in iRFP intensity under 625 μM BV could be due to cell death and/or toxicity by the excess DMSO”

Figure 2 legend

“The decrease in iRFP intensity under 625 μM PCB or BV could be due to cell death and/or toxicity by the excess DMSO”

- In Supplementary Information, line 51. What is "teh"?

We have corrected this mistake.

Reviewer #2

\*Minor comments:***

- In the paragraph headed "iRFP brightens iRFP more efficiently..." there is mention of "NLS-iRFP-NLS". Please introduce the abbreviation (nuclear localisation sequence?) and, if possible, why the sequence is present N- and C-terminally.

We would like to thank the reviewer for this comment. The abbreviation of NLS (nuclear localization signal) has been already included in the previous version of manuscript (page 5, line 113). Two NLSs are fused with iRFP because the addition of a single NLS does not sufficiently localize the protein at the nucleus. We have included this note in the revised manuscript (page 5, line 114).

- The fluorescence intensity increase upon PCB compared to BV treatment of S. pombe cultures expressing iRFP (Fig. 2C,D) appears about 7-fold, which is far more than the factor of 2 measured on the level of fluorescence quantum yield, and the protein levels were determined to be comparable. Are there any factors conceivable that further enhances the signal of PCB-bound iRFP in S. pombe?

As we have discussed in the previous version of the manuscript, we presume the three factors enhancing fluorescence of iRFP-PCB in fission yeast; (1) the increase in quantum yield (~ 1.61-fold, Fig. 3F), (2) blue-shifted excitation and emission spectrum of iRFP-PCB, which are beneficial for our microscopic setup, and (3) the efficiency of chromophore formation (~ 1.75-fold) (Rumyantsev et al. 2015). During this revision, we calculated to what extent the second factor potentially enhances iRFP-PCB fluorescence compared to iRFP-BV. Based on the emission spectrum (Fig. S3C, left), iRFP-PCB is approximately 1.3-fold more effectively excited by 640 nm of excitation laser than iRFP-BV. Similarly, the detection of iRFP-PCB fluorescence is about 2.0-fold more efficient than that of iRFP-BV with our emission filter (665-705 nm emission filter). Based on these data, the rough estimation yields 1.61*1.3*2*1.75 = 7.3-fold increase, which is comparable with the experimental results showing 5~10-fold increase in iRFP-PCB (Figs. 2C, 2D, 3G). We have included this additional discussion in the revised manuscript as follows:

(page 17, line 429)

“Based on the emission and excitation spectrum (Fig. S3C), iRFP-PCB is approximately 1.3-fold more effectively excited by 640 nm of the excitation laser, and detected about 2.0-fold more efficiently with our emission filter (665-705 nm emission filter) in comparison to iRFP-BV.”

(page 17, line 433)

“Based on these data, the rough estimation yields 1.61*1.3*2*1.75 = 7.3-fold increase, which is comparable with the experimental results showing the 5~10-fold increase in iRFP-PCB fluorescence compared to iRFP-BV (Figs. 2C, 2D, 3G).”

- In the spectral data of Fig. 3D,E, there appears to be a spectrometer artifact at around 450 nm in all spectra, which is not commented on. It is certainly not part of the cofactor / holoprotein spectra.

The peaks around 450 nm in spectra are the artifact of our spectrometer for unknown reasons. We have added an explanation of this artifact in the revised manuscript.

(page 10, line 287,290)

“Of note, there is a spectrometer artifact at around 450 nm in all spectra.”

- Lines 292/296: "Fig. 3H" should read "Fig. 2G".

We have corrected these mistakes.

- The reader may wonder whether it is a common or rather rare phenomenon that the exchange of BV by PCB or some other bilin as (usually covalently bound) cofactor in iRFPs (or, more general, also in the parental phytochromes from different branches of the tree of Life) can be performed. Are there comparable studies available in the literature in addition to the work of Rumyantsev et al. (2015)?

According to the reviewer’s suggestion, we examined the fluorescence of miRFP670 and miRFP703 in fission yeast cells under the conditions of DMSO, BV, or PCB treatment, or SynPCB2.1 expression. We found that the fluorescence intensities of miRFP670 and miRFP703 with PCB treatment or SynPCB2.1 expression showed much higher values than those with BV treatment as observed in iRFP fluorescence in this study (Figure S7). These data indicate that the replacement of BV with PCB is beneficial for enhancing the fluorescence intensity of other iRFPs. We have included the data in the revised manuscript as follows:

(page 12, line 340)

“To explore the generality of the application of PCB and SynPCB2.1 system to other near-infrared fluorescent proteins, we measured fluorescence intensities of miRFP670 and miRFP703, which are derived from a different branch of bacteriophytochrome RpBphP1 (Shcherbakova et al. 2016), in fission yeast treated with BV or PCB or expressing SynPCB2.1 (Fig. S7). The fluorescence intensities of both miRFP670 and miRFP703 were enhanced by the addition of PCB and the expression of SynPCB2.1 compared to the addition of BV in a similar manner iRFP (Fig. S7). From these data, we concluded that PCB biosynthesis by SynPCB2.1 is suitable for imaging with near-infrared fluorescent proteins in fission yeast.”

Reviewer #3

(1) In the Fig. 1D, authors tried to measure the BV incorporation into fission yeast cells. How about the time after 180 min, is it still a increase for the fluorescence?

According to the reviewer’s suggestion, we did the same experiments in Figure 1D for up to 24 hours. As the reviewer expected, iRFP fluorescence still increased gradually for up to 24 hours. We replaced Figure 1D with the new data. We do not have any good idea why iRFP fluorescence gradually increases in the presence of BV.

(2) The authors used the Fig. 3H in the manuscript, but I did not find the H in the Fig. 3.

We apologize for the reviewer’s confusion. We have corrected these mistakes.

(3) The information for the BVRA KO HeLa cells need to be provided.

We have included the information of the BVRA KO HeLa cells in the materials and methods.

(page 21, line 519)

“BVRA KO HeLa cells have been established previously (Uda et al. 2017)”

Description of analyses that authors prefer not to carry out

Reviewer #1

- What is the affinity (KD) of BV and PCB to iRFP? Whether is this related to the fluorescence intensity of iRFP in yeast?

The attachment of BV or PCB to iRFP is an irreversible reaction, i.e., BV or PCB is covalently attached to iRFP. For this reason, it is technically impossible to measure equilibrium dissociation constant (Kd) values to evaluate the affinity of BV or PCB to iRFP.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, entitled "Near-infrared imaging in fission yeast by genetically encoded biosynthesis of phycocyanobilin", Sakai and co-workers report that phycocyanobilin (PCB) could function as a brighter chromophore for iRFP than BV, and biosynthesis of PCB allows live-cell imaging with iRFP in the fission yeast. They first found that fission yeast cells did not produce BV and therefore did not show any iRFP fluorescence due to the lack of BV and HO gene. Upon the addition of external BV, the iRFP fluorescence signal could be recovered. In addition, expression of HO1 in fission yeast cells also resulting in the iRFP fluorescence. Next, they found that PCB brightens iRFP more efficiently than BV in fission yeast and in vitro, while the fluorescence excitation and emission spectra were 10 nm blue-shifted in iRFP-PCB compared to iRFP-BV. Finally, they introduced a system named SynPCB2.1 for efficient PCB biosynthesis in fission yeast and concluded that PCB biosynthesis by SynPCB2.1 is ideal for iRFP imaging in fission yeast based on the experiments data. They also developed all-in-one plasmids carrying SynPCB2.1 and iRFP fusion protein genes to image the target proteins in fission yeast. In the final part of this manuscript, PCB was tested as an iRFP chromophore in HeLa cells, however it does not offer significant advantage over BV. Overall, this work provides a system for efficient iRFP imaging in fission yeast. Yet, several issues have been identified and need to be addressed in order to strengthen or even validate some of the conclusions made by the authors.

(1) In the Fig. 1D, authors tried to measure the BV incorporation into fission yeast cells. How about the time after 180 min, is it still a increase for the fluorescence?

(2) The authors used the Fig. 3H in the manuscript, but I did not find the H in the Fig. 3.

(3) The information for the BVRA KO HeLa cells need to be provided. To test the application of PCB as chromophore in mammalian cells, a HO1 gene knock out mammalian cells should be used. The authors may use the all-in-one plasmids carrying SynPCB2.1 and iRFP fusion protein genes to image the target proteins in mammalian cells.

Significance

This work provides new information regarding the chromophore of iRFP. It shows that PCB brightens iRFP more efficiently than BV in fission yeast and in vitro. The all-in-one plasmids system developed in this work supplies a tool for iRFP imaging in the organisms without BV production. Although the iRFP-PCB produces a brighter fluorescence compared with iRFP-BV, the fluorescence excitation and emission spectra were ~15 nm blue-shifted compared to that of iRFP-BV, which might restrict its applications in tissues imaging. As the abundant of BV exist in the mammalian cells, iRFP-PCB does not offer significant advantage over iRFP-BV.

Referee Cross-commenting

All the reviewers gave reasonable suggestions.

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Referee #2

Evidence, reproducibility and clarity

Summary:

iRFPs have been engineered from the chromophore-binding domains of bacterial phytochromes to expand the wavelength range of genetically-encoded markers for fluorescence imaging and sensing applications into the far-red or near-infrared. In contrast to fluorescent proteins from jellyfish or corals, in which the chromophore is formed autocatalytically, iRFPs require a bilin chromophore for fluorescence such as biliverdin IX alpha (BV), phycocyanobilin (PCB) or phytochromobilin (P[phi]B). While available in some, these molecules may be not available in sufficient amounts in the particular cell type under study, and, therefore, frequently requires the introduction of at least one further gene such as heme oxygenase (HO, to generate BV from heme) or in addition PcyA (which produces PCB from BV). In this study, the authors show that one of the previously described iRFPs (denoted as iRFP713 in the original studies), which is non-fluorescent upon expression in the fission yeast Schizosaccharomyces pombe due to the lack of an endogenous HO gene, is able to complement with PCB in S. pombe when added externally (to the culture medium) or if produced intracellularly with the help of a plasmid developed for PCB synthesis (termed SynPCB2.1). It is shown that iRFP with PCB bound as cofactor exhibits brighter fluorescence than the BV-bound original iRFP713, which is due to a nearly two-fold increased fluorescence quantum yield as shown by in vitro reconstituted and in vivo generated iRFP with bound PCB. S. pombe cells harbouring the SynPCB2.1 system for PCB synthesis even release ("leak") PCB into the surrounding medium to be taken up by cells not producing PCB. Furthermore, the authors report the generation of a plasmid for C-terminal tagging of a protein of interest by iRFP, novel genome integration vectors and all-in-one plasmids harbouring the genes for PCB synthesis and iRFP-fused marker proteins. The genome integration vectors ensure that stable one-copy integration into the genome occurs at different uncritical loci on each of the chromosomes (different from the common Z-locus), which are in gene-free regions and distant enough for crossing strains, and at sites that do not affect the auxotrophy characteristics of cells. The plasmid system, termed pSKI, harbours elements for propagation in E. coli, constitutive or inducible promoters, a multiple cloning site, a selection marker cassette and homology arms separated by a unique restriction enzyme cutting site for plasmid linearization. The viability of the C-terminal fluorescence tagging system relying on PCB-bound iRFP in S. pombe is shown with 10 different proteins of variant and highly specific intracellular location, which all show the expected cellular distribution pattern by fluorescence imaging. Multi-spectral fluorescence labelling schemes (manifold of five) were successfully tested in S. pombe with four specifically localized proteins harbouring different conventional FP tags, and, in addition, one labeled with iRFP(PCB). Finally, it was tested whether PCB can be exploited to yield a brighter iRFP fluorophore also in a mammalian model cell line, HeLa cells. Here, treatment of cells with externally added BV or PCB produced a similar increase in iRFP fluorescence and also knockout cells devoid of the BV breakdown enzyme BVRA did not show different fluorescence levels upon BV or PCB treatment. Therefore, it is concluded that PCB is applicable to iRFP imaging in mammalian cells though offering no advantage, which to some extent limits the advantages to cells which naturally do not produce BV from heme.

Major comments:

The claim that the developed plasmid system for multi-spectral fluorescence imaging applications in fission yeast S. pombe, which relies on PCB synthesis and incorporation of PCB into iRFP, is fully justified by the data shown in the manuscript. Interpretation and the derived conclusions are put forward in a decent and balanced way. No additional experiments are required to justify the claims.

Minor comments:

• In the paragraph headed "iRFP brightens iRFP more efficiently..." there is mention of "NLS-iRFP-NLS". Please introduce the abbreviation (nuclear localisation sequence?) and, if possible, why the sequence is present N- and C-terminally.
• The fluorescence intensity increase upon PCB compared to BV treatment of S. pombe cultures expressing iRFP (Fig. 2C,D) appears about 7-fold, which is far more than the factor of 2 measured on the level of fluorescence quantum yield, and the protein levels were determined to be comparable. Are there any factors conceivable that further enhances the signal of PCB-bound iRFP in S. pombe?
• In the spectral data of Fig. 3D,E, there appears to be a spectrometer artifact at around 450 nm in all spectra, which is not commented on. It is certainly not part of the cofactor / holoprotein spectra.
• Lines 292/296: "Fig. 3H" should read "Fig. 2G".
• The reader may wonder whether it is a common or rather rare phenomenon that the exchange of BV by PCB or some other bilin as (usually covalently bound) cofactor in iRFPs (or, more general, also in the parental phytochromes from different branches of the tree of Life) can be performed. Are there comparable studies available in the literature in addition to the work of Rumyantsev et al. (2015)?

Significance

The observation of an increased fluorescence quantum yield upon PCB insertion into iRFP is a technical advance with value as such, which will stimulate more detailed studies by spectroscopists (fluorescence, IR, Raman in particular) to clarify the principles underlying the increased quantum yield. While the findings and the developments of this study present a clear advance for imaging applications in fission yeast (available for cell biologists), at least the data on HeLa cells show that the method may not present an advance in terms of delivering a better (i.e. brighter) near-infrared chromophore for mammalian cells. It seems that the ability of a cell line to produce BV (from heme with the help of a HO) limits the potential of PCB addition to iRFP-expressing cells, since BV is readily taken up by the protein shortly after protein biosynthesis. Thus, more elaborate interventions in heme metabolism may be required to fully exploit the potential of the findings in mammalian cell systems and to fulfil the claim that also the optogenetics toolbox may benefit from the findings in the future.

My own expertise relates to spectroscopic analyses (fluorescence, IR, Raman) of iRFP and mutant variants thereof in search of the determinants for increased fluorescence quantum yield, fluorescence lifetime analyses and photoreceptor research.

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Referee #1

Evidence, reproducibility and clarity

• This paper provided a method for near-infrared imaging using iRFP and proved that phycocyanobilin was better than biliverdin for the imaging in fission yeast.

Major comments:

• What is the affinity (KD) of BV and PCB to iRFP? Whether is this related to the fluorescence intensity of iRFP in yeast?
• The authors thought almost all iRFP forms a holo-complex with BV when HO1 is expressed in fission yeast. This should be proved by calculating the percentage of BV-iRFP in yeast. It is meaningful to compare the percentage of BV-iRFP and PCB-iRFP in vitro and in yeast. -The brightness of fluorescent proteins in organisms often depends on the molecular brightness (fluorescence quantum yield and extinction coefficient) and the amounts of fluorescent proteins. The authors indicated that iRFP-PCB is brighter than iRFP-BV at the molecular level. To calculate the amounts of iRFP-PCB and iRFP-BV when different proteins are expressed in yeast, it is better to explain the results that phycocyanobilin was better than biliverdin for the imaging in fission yeast.

Minor comments:

• In line 35. iRFP is derived from bacteriophytochromes.
• In line 62. The reference of Rodriguez et al. deals with allophycocyanin instead of bacteriophytochromes.
• In lines 65-66. Bacteriophytochromes bind BV, phycocyanin, allophycocyanin and cyanobacterial phytochromes bind PCB, and plantal phytochromes bind PΦB.
• In lines 67-71. The authors described the biosynthesis of BV, PCB and PΦB. But it missed many references.
• In line 270. One full stop should be deleted in "cells.. Fission".
• In line 272. How did the authors add the high concentration of PCB (625 microM) into the culture? PCB is insoluble.
• In line 401. smURFP is derived from allophycocyanin instead of cyanobacteriochromes.
• In line 474. As BV and PCB are insoluble, how do the authors add the pigments into DMEM?
• In line 509. In which solvent are BV and PCB dissolved?
• In lines 546, 547. What is ROI?
• The authors should indicate whether codon optimization was necessary when HO1, PcyA, Fd and Fnr were expressed in yeast or in mammalian cells.
• Figure S1 should show the software and algorithm for the construction of phylogenetic.
• In Figure 1C and Figure 2C. The authors should explain the reasons why the fluorescence of iRFP was lower when yeast was treated with excessive BV and PCB.
• In Supplementary Information, line 51. What is "teh"?

Significance

• near-infrared fluorescent protein broadens the spectrum of fluorescent protein and facilitates deep tissue imaging. Based on iRFP, this work successfully constructed the near-infrared fluorescent protein with PCB as chromophore in fission yeast by using the method of synthesizing PCB, which has been realized in mammalian cells by this group. The method of synthesizing PCB in yeast not only facilitates fluorescence imaging, but also meaningful for optogenetics experiments in yeast. This work is useful for the study of fluorescence imaging, optogenetics and biosynthesis when using yeast as a model organism.

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Reply to the reviewers

Manuscript number: RC-2021-00831

Corresponding author(s): Lu, Gan

Reviewer comments are in regular font. Our rebuttal is in bolded font. Experiments that we plan for the full revision are preceded with “FULL:”. In the revision files, the changes are highlighted in yellow.

General Statements

We thank the reviewers for their detailed feedback. There are two major concerns. First, the manuscript lacks functional analysis of the meiotic triple helices (MTHs). Second, the manuscript makes claims about the properties of synaptonemal complexes (SCs) and MTHs that are inadequately supported. In order to address the first concern, we would need extensive experiments to first identify and then perturb the genes associated with the MTH. Such experiments are beyond the scope of this manuscript and are the focus of future studies. We will address the second concern with mostly text revisions. We will also improve some of the imaging analysis with new experiments that can be done in a few months’ time.

2. Description of the planned revisions

We will acquire new cryo-ET data of pachytene-arrested cell cryolamellae using our new K3-GIF camera. These new data have higher signal-to-noise ratios and allow us to generate a higher-resolution subtomogram average of the MTHs. The achievable resolution will depend on the conformational homogeneity of the MTH segments and on the number of cryotomograms we can capture. If we are able to achieve a subnanometer-resolution reconstruction, we will narrow down the possible identities of the subunits. Even if we cannot achieve subnanometer resolutions, the new data will allow us to test if ladder-like densities were missed in our lower-resolution older data, thereby improving our understanding of the SC’s structure. We will also perform subtomogram analysis of purified ribosomes as a control to strengthen our handedness determination.

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

The Reviewers’ original comments are reproduced in regular font. Line numbers refer to the preliminary revision.

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

Ma and coworkers report studies of budding yeast undergoing meiosis by cryo-ET. They fail to detect structures interpretable as synaptonemal complex, and instead detect feather-like bundles of what appears to be a triple helix. These structures do not appear to be related to the synaptonemal complex, as spo11 mutants that do not initiate recombination, red1 mutants that lack axial elements, and zip1 mutants that lack the central element of the SC still make these bundles. These structures are absent from cells treated with latrunculin A, which depolymerizes actin filaments, but expected structures are not visible in light microscopy of cells treated with two different F-actin-staining reagents. However, it should be noted that another study (Takagi et al, 2021, bioRxiv) did detect actin associated with these structures by immunogold labeling. The structures are also reversibly dissolved in 7% hexanediol.

This part of the paper's findings is well supported by data and is certainly of interest, although interest is somewhat limited by the unknown nature of these structures-what they contain, let alone their function, remains to be determined-in fact, it is not even determined whether or not they are made of protein. However, as an initial report of a previously unknown phenomenon, the paper is of some value.

__Thank you for raising the issue of whether MTHs are composed of protein or not. Aside from the proteins, the only other materials capable of forming large bundles of linear polymers are polysaccharides and DNA. Yeast polysaccharides are found in the cell wall, so they are unlikely to be a candidate for the MTHs. In the nucleus, DNA is abundant. While we favor that MTHs are composed of protein, we cannot rule out that the MTHs are non-chromatin DNA-protein complexes. Depending on the resolution of future subtomogram averages, we may get a better idea of the MTH’s composition.__

There is, unfortunately, a second aspect of the paper that cannot be supported. Although it is clear that synaptonemal complex is present in the cells examined (by standard cytological methods) the authors cannot detect structures consistent with SC in their cryo-ET images. Unfortunately, authors then extrapolate from their inability to detect SC to conclusions about SC, such as that it is not crystalline, and even go so far as to suggest that their failure to detect SC invalidates two models for crossover interference, and that the ladder-like structure reported for SC in many organisms using many difference approaches may be a fixation artifact. Authors show remember that the absence of evidence is not evidence for absence; the speculation described above should be removed from both the abstract and discussion.

We have removed the speculation about crossover interference and limited the scope of our discussion on SCs to budding yeast only.

The differences between traditional EM and cryo-EM are not trivial. In the introduction, we added more details to explain the differences in both the sample preparation and contrast generation:

Lines 79-87: “Meiotic nuclei have been studied for decades by traditional EM (Fawcett, 1956; Moses, 1956), but not by cryo-ET. Cryo-ET can reveal 3-D nanoscale structural details of cellular structures in a life-like state because the samples are kept unfixed, unstained, and frozen-hydrated during all stages of sample preparation and imaging (Ng and Gan, 2020). The densities seen in cryo-ET data come from electron scattering of the biological macromolecules. In comparison, the densities seen in traditional EM from electron scattering of heavy metals such as uranium, tungsten, and osmium, which have adhered to a subset of biological macromolecules that were not extracted in earlier steps.”

We have also removed the term ‘artifact’ from lines 201-202:

Original: “The ladder model is based on images of traditional EM samples, which are vulnerable to fixation and staining artifacts.”

Revised: “The ladder model is based on images of traditional EM samples.”

Reviewer #1 (Significance (Required)):

This paper reports a previously unreported structure in the nuclei of yeast cells undergoing meiosis. The composition and function of this structure remain to be determined. This considerably limits the significance of the paper.

**Referee Cross-commenting**

I agree with the concerns of the other reviewers. I also agree with reviewer 3 that to raise the significance of the paper would require much work. But I think that the raw observation is of value, albeit in a journal of record. So I would stick with my recommendation of text changes, keeping in mind that there may not be a suitable journal in LSA's portfolio.

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

This work describes helical filamentous structures observed in budding yeast nuclei that were cryosectioned and imaged using cryo-electron tomography (cryoET).

The goal of this work seems to have been to conduct an ultrastructural analysis of the synaptonemal complex (SC), a meiosis-specific protein structure that holds chromosomes together during meiosis and is thought to regulate meiotic recombination. In conventional TEM images of fixed, embedded, and stained sections obtained from pachytene nuclei, SCs usually appear as long, thin, transversely striated structures. At pachytene, SCs extend along the full length of a thin (100-nm) gap between paired chromosomes (typically 1-6 µm long in yeast cells). Surprisingly, the authors did not observe SCs, perhaps because these structures do not produce much contrast in cryo-EM images. Instead, they observe abundant triple helical structures in the nucleoplasm, which they designate as "meiotic triple helices" (MTHs). The authors report that these triple helices assemble at the same time as but independently of the SC. They publised a preprint in which they indicated that these structures were somehow related to SCs, but this the revised version reports that they appear independently of SCs. They further report that treatment of cells with the F-actin depolymerizing drug Latrunculin-A (LatA) resulted in a lack of detectable triple helical structures in the nucleus, suggesting that these structures may be a form of actin or, alternatively, that they may require F-actin for their assembly.

While the work is technically mostly sound, its significance is unclear because the reported structures have no known function. Many, if not most proteins will form helical structures if their concentration rises above a threshold defined by their binding affinity for themselves (see https://doi.org/10.1186/1741-7007-11-119 and references therein), so this may simply be an example of an abundant nuclear protein that polymerizes to form helical filaments under the conditions that trigger yeast sporulation.

Thank you for raising the interesting possibility that MTHs form helices because their subunits have exceeded a critical concentration threshold. In the revised text, we discuss the possibility that the MTH is simply a protein that is highly expressed in meiotic cells and polymerize either due to exceeding a critical concentration, or having undergone a biochemical change like a post-translational modification:

Lines 408-415: “Note it is possible that the MTHs may not be directly involved in meiosis, but are instead a protein that is at a sufficient concentration or has the right biochemical modifications to form helices in pachytene because it is known that many proteins can form a helix under the right conditions {Crane, 1950; Pauling, 1953; Theriot, 2013}. These MTHs also have lateral interactions that allow them to pack with crystalline density. Their sensitivity to 1,6-hexanediol suggests that the polymerization both within and between MTHs are based on hydrophobic interactions. Further work will be needed to determine the identity of the MTH’s subunits and their potential function.”

The authors perform cryosectioning and cryoEM on yeast cells undergoing meiosis to show that the assembly and disassembly of MTHs follow a similar time course as that of the SC. The observation of these triple-helical filaments in meiotic nuclei has also been reported in another study (https://www.biorxiv.org/content/10.1101/778100v2.full.pdf+html), which proposed, based on immuno-EM labeling, that they may be actin cables. This study reports that the structures are not detected using phalloidin or Lifeact-mCherry. However, treatment with LatA did eliminate detection of MTH structures, suggesting that they may be comprised of actin.

In my view, there are a number of issues that should be addressed before publication. Many of these relate to the presentation of the findings. Detailed comments below:

1. The presentation of the work is very confusing. The authors clearly expected to observe SC structures, but did not. They conclude that MTHs are not SCs, since they do not depend on molecular components required for SC assembly. They should describe their findings in a more straightforward way rather than veering from introducing the SC to describing the MTHs.

We have restructured the manuscript to tone down the discussion about the SC. However, we have to start with the SC because it is the most iconic feature of pachytene cells and a major organizer of chromatin in meiosis. Furthermore, its presence, as indicated by Zip1-GFP signals, was key to establishing that our cells were indeed arrested in pachytene. It would have been confusing to then overlook the absence of structural features as conspicuous and prevalent as what was expected of SCs. The other sections did have room for improvement. In the sections below, we describe the changes point by point.

Similarly, the discussion section on "recombination and chromosome segregation" seems inappropriate and irrelevant, since no data are presented in this study regarding the functions of the MTHs, and there is no reason to think that they contribute to crossover interference, chromosome segregation, or other aspects of meiosis. Additionally, most of the ideas presented in this section seem very muddled. I recommend deleting this section.

We have deleted the section entitled "Recombination and chromosome segregation".

Throughout the text, we have also changed the term “meiosis-specific” to “meiosis-related” when describing MTHs. Doing so allows for the possibility that MTHs might just form as a consequence of being expressed to a high enough concentration as discussed above.

Along the same lines as comment #1: The title should be changed - absence of evidence for "ladders" is not evidence of absence. Prior work using TEM and superresolution fluorescence microscopy has clearly shown that ladder-like SC structures exist in pachytene nuclei of budding yeast and many other organisms, although they apparently cannot be visualized using the methods described here.

We have changed the title to be less forceful, yet report what we see and don’t see by cryo-ET:

“Cryo-ET detects bundled triple helices but not ladders in meiotic budding yeast”

The authors should clarify whether cryo-sectioning was performed through the full volume of pachytene nuclei.

This comment refers to our attempt at serial cryosectioning, as shown in Fig. S8. We have revised the text in lines 332-334 to reflect the estimated volume covered:

“We successfully reconstructed six sequential sections from one ndt80Δ cell (Figure S8), which represents approximately one third of a nucleus (assuming a spherical shape).”

We also changed Fig. S8’s title so that it doesn’t sound like we reconstructed an entire nucleus:

“MTH bundles are extensive throughout the cell nucleus.” → “MTH bundles are extensive.”

It is not clear from the manuscript which camera/microscope configuration was used to acquire the cryoET data that were used for sub-tomogram averaging. The authors state in the methods that Falcon II and K3-GIF was used for projection images, but it's not clear if this applies to all images. These technical details should be clarified.

We have added a new column to Table S4 that reports the camera used for all the projection imaging and tomography experiments. In the original MS, all of the subtomogram averaging was done using Falcon II data.

FULL: In the full revision, we plan to incorporate new subtomogram averages of MTHs in situ, using K3-GIF data of cryolamellae.

The analysis of the handedness of the helices seems to be questionable as the resolution of the reconstructions for 80S are also quite low. I am uncertain whether this can be used to state with confidence that the MTH are right-handed.

FULL: In the full revision, we will use purified 70S ribosomes, imaged on the same K3-GIF camera and using the same software workflow as for the new subtomogram averaging of MTHs in situ. We expect higher resolution for both ribosomes and MTHs, which will make the handedness assignment unambiguous.

The authors claim that treatment with Latrunculin-A (LatA) leads to disappearance of MTHs. However, they support this with projection images of cells treated with LatA. The projection images are of poor quality and the vitrification in these cells (as well as the DMSO treated cells) do not look appropriate. They should present data for LatA-treated cells and DMSO-treated controls obtained using the same approach and ideally imaged in parallel with untreated cells. They should also quantify the number of sections and cells imaged for all conditions.

Once we realized that the MTH bundles were visible in projections, we chose to report detections of MTH bundles by projection imaging instead of the costly tilt series. The apparent poor quality and questionable vitrification comes from the fact that the projection images show the cryosection’s crevasses and knife marks, which reside on opposite cryosection surfaces. These sectioning artifacts are computationally excluded from tomographic slices. The following line was added to the figure caption to explain this:

“These image features are not devitrification artifacts; they are absent from the tomographic slices in other figures because they can be computationally excluded.”

The quantification of the MTHs in Lat-A vs control cells are in Table 2. We have now added these numbers to both the text and the figure caption.

The similarities between the MTHs and SCs - that both are present in meiotic nuclei and sensitive to hexanediol - seem unlikely to be functioanlly relevant. Again, I think the presentation suffers from being focused on the SC which was not seen, rather than on the MTHs.

We have toned down the discussion on SCs throughout the manuscript. We have retained the motivation for using 1,6-hexanediol to probe the MTHs physico-chemical properties and the fact the previous work on SCs provided motivation for this perturbation experiment. However, we removed the comparison of their relative sensitivities to 1,6-hexanediol (see reply to point #10 below).

The absence of 100-nm-wide zones containing nucleosomes is again not evidence for lack of SCs. SCs are ribbon-like structures - they are about 100 nm in one dimension but the thickness has not been characterized reliably; even if SCs do exclude nucleosomes (which is not certain) the excluded volume might be much smaller than the authors imagine.

We did not argue for “lack of SCs”; these structures clearly exist in our cells given the fluorescent linear structures seen in Zip1-GFP expressing cells. We only say that the textbook portrayal of SCs needs revision, though we should have restricted our statement to yeast. In the literature and textbooks: whenever the SC’s central element is drawn, it is depicted without internal nucleosomes and being densely packed with SC proteins.

Does Lifeact-mCherry enter the nucleus? This information is important in interpreting the failure to detect MTHs using this probe.

While Lifeact-mCherry is small enough to passively diffuse through the nuclear pore, our data cannot rule out that this molecule is excluded from the nucleus. We added the following sentence as a caveat:

Lines 244-245 “Note that we cannot rule out that Lifeact-mCherry is excluded from the nucleus.”

The sensitivity of the MTH structures to 1,6-hexanediol treatment is potentially interesting, but it does not reveal anything about their structure or function, only that their assembly likely depends in part on hydrophobic interactions. Caution should be used in interpreting these findings.

We have toned down the discussion about the meaning of the MTH bundles’ 1,6-hexanediol sensitivity by removing this line from the original Results:

“MTH bundles are therefore sensitive to a slightly higher concentration of 1,6-hexanediol than SCs are and reform when 1,6-hexanediol is removed.”

We have also added the following line to more clearly say what sensitivity to 1,6-hexanediol means:

Lines 412-414: “Their sensitivity to 1,6-hexanediol suggests that the polymerization both within and between MTHs are based on hydrophobic interactions”

The figure legends and/or Methods sections should clarify what is represented in each figure, and how the data were acquired. In particular, cryotomographic slices of varying dimensions (6nm, 10 nm or 12 nm or 70 nm) are mentioned in the captions of several figures (2-6, and S1, S2, S3). However, is often unclear whether these represent physical or computational sections.

“Tomographic slice” refers to a rendering of a computationally extracted slice from a reconstructed tomogram. To make it clearer, we have added the term “computational” to describe the tomographic slices in each figure caption.

Page 23 has a supplemental figure but no captions. Is this the same as Fig. S8?

Yes, this is a copy of Fig. S8 that appeared due to MS Word’s jumping-figure bugs. We will manually edit the PDF in the future revision.

I do not find the model figure (Figure 7) to be helpful. Additionally, the failure to detect SCs and the presence of MTHs do not warrant a "revised model of the meiotic yeast nucleus."

We now call panel A and B the “Traditional EM” and the “Cryo-EM” models, respectively. The figure therefore reports the large nuclear bodies seen by the two methods and no longer implies correctness.

We also changed the related sentence in the Introduction:

Original: “Our work strongly suggests that current models of pachytene nuclear cytology need revision.”

Revised: “Our analysis shows that MTHs coexist with SCs, which have an unknown cryo-ET structure.”

The absence of MTHs in haploid cells induced to undergo meiosis should perhaps be studied in more detail. Even SCs are present in haploid meiotic cells, so the absence of these structures may be informative as to their function. Haploid cells should also be stained for SCs and imaged by immunofluorescence to verify that they are in meiosis.

The haploid strain that was treated with sporulation media cannot enter meiosis. Haploid cells that are capable of entering meiosis need to be disomic for chromosome III, with each copy having a different mating type at the Mat locus. We believe that the construction and studies of such strains would be more meaningful after we identify the MTH’s subunits and determine its function in diploid cells.

The yeast strain is SK1, not SK-1.

Thank you. This mistake is corrected.

What are "self-pressurized-frozen samples" (p.2)?

Self-pressurized-frozen samples are generated by an alternative to the conventional machine-based high-pressure-freezing method. We have added more details in the new lines 135-141:

“Self-pressurized freezing is a simpler and lower-cost alternative to conventional high-pressure freezing, which requires a dedicated machine that consumes large amounts of cryogen. In the self-pressurized freezing method, the sample is sealed in a metal tube and rapidly cooled in liquid ethane. The material in direct contact with the metal cools first and expands by forming crystalline ice, which exerts pressure on the material in the center of the tube (Leunissen and Yi, 2009; Yakovlev and Downing, 2011; Han et al., 2012).”

Reviewer #2 (Significance (Required)):

The observation of MTHs is novel but (as stated above) of unclear significance, given that their molecular identity and function are unknown.

This work may be of interest to the meiosis field, with the caveats described above that the functional relevance is currently unclear.

This review was co-written by referees with expertise in meiosis, chromosome organization, SC structure and function, and cryoEM.

**Referee Cross-commenting**

The reviews are strikingly concordant so I don't think much needs to be added.

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

The authors report the observation of filamentous structures (further termed meiotic triple helices MTH) in the nucleoplasm during meiosis in yeast cells. Those structures are visualized using Cryo-ET. While the authors initially seem to assume an association of those structures with synaptonemal complexes, they discover that those structures rely on filamentous actin and are not affiliated with synaptonemal complexes after all.

While I think that the observation of those MTH by Cryo-ET is interesting, the overall structure of the paper and presentation of the data are not very well done. As the authors find throughout their experiments that the MTHs are not associated with synaptonemal complexes the strong focus in the first figures on synaptonemal complexes as well as the title of the paper are very mis-leading. The authors try to initially make the point that other labs have observed ladder like structures by transmission electron microscopy and want to make the claim that those observed structures might be an artifact of sample preparation, hence the title: Meiotic budding yeast assemble bundled triple helices but not ladders. However, at the end those structures seem unrelated to synaptonemal complexes.

In addition, several labs have reported the presence of nuclear actin in meiosis and mitosis and have even succeeded to show those structures by transmission electron microscopy, questioning the "artifact" argument.

Presumably, the Reviewer means intranuclear actin, as opposed to perinuclear (cytoplasmic) actin. If so, then we have only seen one paper, the one from the Takagi et at 2021 (bioRxiv) that has reported seeing structures associated with nuclear actin. Note however, that the revised Takagi bioRxiv paper is very careful in saying that the filament bundles contain actin, which is not the same as saying that the filaments are polymers of actin.

Our “artifact” argument – now removed – referred to SCs, not to nuclear actin.

In the revised manuscript, we use the term “intranuclear” to make it clear that we are referring to structures inside the nucleus. We also use the term F-actin, where appropriate, to refer to the best-studied actin polymer, which resembles a double helix. Doing so eliminates confusion about other forms of actin: G-actin, which cryo-ET cannot yet directly visualize due to its small size; and non-canonical actin polymers, for which there are no previous experimental X-ray or cryo-EM structures for comparisons.

The story line of the paper is weak and I think the authors would have been better of reporting their cryo-ET structures and making a better link to actin or determining what else they think might be a component of those structures. Immuno-EM (as actually shown in Reference 41) of actin would have been much more convincing. The authors could also use the power of Cryo-ET and the achievable higher resolution to describe those filaments in much more detail. In my opinion this would have been a much better and more exciting paper.

We disagree that “making a better link to actin” is the right approach because doing so presupposes that the structures are composed of actin, for which the present evidence is inadequate. We do agree that determining what the MTHs are (or are not) would be valuable.

FULL: Now that we have better access to a K3-GIF camera and a cryo-FIB-SEM, we will attempt higher-resolution subtomogram averaging analysis. If we are able to achieve subnanometer resolution, we will attempt to narrow down the fold of the MTH subunit. Note that this goal will require that the MTHs are conformationally homogenous and that we can image sufficient copies of the MTHs.

In summary: While I think the Cryo-ET images of those structures could be very exciting the paper unfortunately does not do a very good way in presenting this data and is at times misleading trying to proof or rather dis-proof a connection to synaptonemal complexes. Based on this I think that the paper can not be published in the current form and needs major revisions that would require a significant amount of time.

**Minor comments:**

Figure 1: The choice of timepoints is confusing and makes it hard to compare. While wild type is shown at 0,1,3,4,5,8h, the mutant is shown at 0,2,3,4,5,6,7h. It would be appropriate to select the same timpepoints for both conditions.

FULL: We will recollect fluorescence images of the mutant cells at the same time points as the wild type.

Figure 3 and 4 need a quantification of the number of observed MTHs, in particular as only selected regions of the nuclei are shown.

These images were taken from single cryosections instead of serial cryosections, which would have been too difficult to do for multiple conditions and multiple cells. Therefore, quantification would be obfuscated by the fact each cryosection samples a small fraction of the nuclear volume. We believe that reporting the number of cell cryosections that are MTH-positive (Table 2) is at present the best way to characterize their abundance and ability to polymerize. Once we are able to identify the MTH gene products, we will be able to perform GFP tagging experiments and thereby get a much better estimate of the polymer mass as a function of biochemical perturbations.

Fig 7. The data certainly does not support a "REVISED" model of the yeast nuclear organization.

We have changed the Figure title to “A cryo-ET model of the meiotic yeast nucleus.” We now also refer to panels A and B as “Traditional EM model” and “Cryo-ET model”, respectively.

Reviewer #3 (Significance (Required)):

Several publications have already shown the presence of actin in meiotic and mitotic nuclei and have even succeeded in observing those structures by transmission electron microscopy. Based on this it is not clear why the authors have not tried to put their work in context to all these observations and used their technology to obtain novel information on the structure, which might be helpful to identify which proteins compose the MTHs. Based on how the data is presented I do not think that this paper contributes anything new to the field.

Presumably, the reviewer means that F-actin has been imaged in yeast cells, because for actin to exist in nuclei in mitosis/meiosis, the organism would have to undergo closed mitosis/meiosis. Furthermore, for actin to be observable by transmission electron microscopy, it would have to be in the filamentous (F-actin) form. We could not find any publications that report transmission electron microscopy of F-actin in yeast cells. We therefore cannot relate our results to F-actin in the meiotic nucleus.

My field of expertise is meiosis and mitosis as well as imaging (light and electron microscopy).

**Referee Cross-commenting**

All reviewers seem to agree that the general observation of these structures is interesting but that there is a reduced significance as the function and identity of these structures remains unknown.

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

The main unanswered question is: what is the function of the MTH bundles? To address this question, we would first need to identify the gene products that are needed for MTH assembly. Next, we would need to do genetic perturbation experiments to actually determine the MTHs’ function. These experiments would constitute a complete study, which is better suited for a separate, future manuscript.

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Referee #3

Evidence, reproducibility and clarity

The authors report the observation of filamentous structures (further termed meiotic triple helices MTH) in the nucleoplasm during meiosis in yeast cells. Those structures are visualized using Cryo-ET. While the authors initially seem to assume an association of those structures with synaptonemal complexes, they discover that those structures rely on filamentous actin and are not affiliated with synaptonemal complexes after all. While I think that the observation of those MTH by Cryo-ET is interesting, the overall structure of the paper and presentation of the data are not very well done. As the authors find throughout their experiments that the MTHs are not associated with synaptonemal complexes the strong focus in the first figures on synaptonemal complexes as well as the title of the paper are very mis-leading. The authors try to initially make the point that other labs have observed ladder like structures by transmission electron microscopy and want to make the claim that those observed structures might be an artifact of sample preparation, hence the title: Meiotic budding yeast assemble bundled triple helices but not ladders. However, at the end those structures seem unrelated to synaptonemal complexes. In addition, several labs have reported the presence of nuclear actin in meiosis and mitosis and have even succeeded to show those structures by transmission electron microscopy, questioning the "artifact" argument. The story line of the paper is weak and I think the authors would have been better of reporting their cryo-ET structures and making a better link to actin or determining what else they think might be a component of those structures. Immuno-EM (as actually shown in Reference 41) of actin would have been much more convincing. The authors could also use the power of Cryo-ET and the achievable higher resolution to describe those filaments in much more detail. In my opinion this would have been a much better and more exciting paper. In summary: While I think the Cryo-ET images of those structures could be very exciting the paper unfortunately does not do a very good way in presenting this data and is at times misleading trying to proof or rather dis-proof a connection to synaptonemal complexes. Based on this I think that the paper can not be published in the current form and needs major revisions that would require a significant amount of time.

Minor comments:

Figure 1: The choice of timepoints is confusing and makes it hard to compare. While wild type is shown at 0,1,3,4,5,8h, the mutant is shown at 0,2,3,4,5,6,7h. It would be appropriate to select the same timpepoints for both conditions.

Figure 3 and 4 need a quantification of the number of observed MTHs, in particular as only selected regions of the nuclei are shown.

Fig 7. The data certainly does not support a "REVISED" model of the yeast nuclear organization.

Significance

Several publications have already shown the presence of actin in meiotic and mitotic nuclei and have even succeeded in observing those structures by transmission electron microscopy. Based on this it is not clear why the authors have not tried to put their work in context to all these observations and used their technology to obtain novel information on the structure, which might be helpful to identify which proteins compose the MTHs. Based on how the data is presented I do not think that this paper contributes anything new to the field. My field of expertise is meiosis and mitosis as well as imaging (light and electron microscopy).

Referee Cross-commenting

All reviewers seem to agree that the general observation of these structures is interesting but that there is a reduced significance as the function and identity of these structures remains unknown.

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Referee #2

Evidence, reproducibility and clarity

This work describes helical filamentous structures observed in budding yeast nuclei that were cryosectioned and imaged using cryo-electron tomography (cryoET).

The goal of this work seems to have been to conduct an ultrastructural analysis of the synaptonemal complex (SC), a meiosis-specific protein structure that holds chromosomes together during meiosis and is thought to regulate meiotic recombination. In conventional TEM images of fixed, embedded, and stained sections obtained from pachytene nuclei, SCs usually appear as long, thin, transversely striated structures. At pachytene, SCs extend along the full length of a thin (100-nm) gap between paired chromosomes (typically 1-6 µm long in yeast cells). Surprisingly, the authors did not observe SCs, perhaps because these structures do not produce much contrast in cryo-EM images. Instead, they observe abundant triple helical structures in the nucleoplasm, which they designate as "meiotic triple helices" (MTHs). The authors report that these triple helices assemble at the same time as but independently of the SC. They publised a preprint in which they indicated that these structures were somehow related to SCs, but this the revised version reports that they appear independently of SCs. They further report that treatment of cells with the F-actin depolymerizing drug Latrunculin-A (LatA) resulted in a lack of detectable triple helical structures in the nucleus, suggesting that these structures may be a form of actin or, alternatively, that they may require F-actin for their assembly.

While the work is technically mostly sound, its significance is unclear because the reported structures have no known function. Many, if not most proteins will form helical structures if their concentration rises above a threshold defined by their binding affinity for themselves (see https://doi.org/10.1186/1741-7007-11-119 and references therein), so this may simply be an example of an abundant nuclear protein that polymerizes to form helical filaments under the conditions that trigger yeast sporulation.

The authors perform cryosectioning and cryoEM on yeast cells undergoing meiosis to show that the assembly and disassembly of MTHs follow a similar time course as that of the SC. The observation of these triple-helical filaments in meiotic nuclei has also been reported in another study (https://www.biorxiv.org/content/10.1101/778100v2.full.pdf+html), which proposed, based on immuno-EM labeling, that they may be actin cables. This study reports that the structures are not detected using phalloidin or Lifeact-mCherry. However, treatment with LatA did eliminate detection of MTH structures, suggesting that they may be comprised of actin.

In my view, there are a number of issues that should be addressed before publication. Many of these relate to the presentation of the findings. Detailed comments below:

1. The presentation of the work is very confusing. The authors clearly expected to observe SC structures, but did not. They conclude that MTHs are not SCs, since they do not depend on molecular components required for SC assembly. They should describe their findings in a more straightforward way rather than veering from introducing the SC to describing the MTHs. Similarly, the discussion section on "recombination and chromosome segregation" seems inappropriate and irrelevant, since no data are presented in this study regarding the functions of the MTHs, and there is no reason to think that they contribute to crossover interference, chromosome segregation, or other aspects of meiosis. Additionally, most of the ideas presented in this section seem very muddled. I recommend deleting this section.
2. Along the same lines as comment #1: The title should be changed - absence of evidence for "ladders" is not evidence of absence. Prior work using TEM and superresolution fluorescence microscopy has clearly shown that ladder-like SC structures exist in pachytene nuclei of budding yeast and many other organisms, although they apparently cannot be visualized using the methods described here.
3. The authors should clarify whether cryo-sectioning was performed through the full volume of pachytene nuclei.
4. It is not clear from the manuscript which camera/microscope configuration was used to acquire the cryoET data that were used for sub-tomogram averaging. The authors state in the methods that Falcon II and K3-GIF was used for projection images, but it's not clear if this applies to all images. These technical details should be clarified.
5. The analysis of the handedness of the helices seems to be questionable as the resolution of the reconstructions for 80S are also quite low. I am uncertain whether this can be used to state with confidence that the MTH are right-handed.
6. The authors claim that treatment with Latrunculin-A (LatA) leads to disappearance of MTHs. However, they support this with projection images of cells treated with LatA. The projection images are of poor quality and the vitrification in these cells (as well as the DMSO treated cells) do not look appropriate. They should present data for LatA-treated cells and DMSO-treated controls obtained using the same approach and ideally imaged in parallel with untreated cells. They should also quantify the number of sections and cells imaged for all conditions.
7. The similarities between the MTHs and SCs - that both are present in meiotic nuclei and sensitive to hexanediol - seem unlikely to be functioanlly relevant. Again, I think the presentation suffers from being focused on the SC which was not seen, rather than on the MTHs.
8. The absence of 100-nm-wide zones containing nucleosomes is again not evidence for lack of SCs. SCs are ribbon-like structures - they are about 100 nm in one dimension but the thickness has not been characterized reliably; even if SCs do exclude nucleosomes (which is not certain) the excluded volume might be much smaller than the authors imagine.
9. Does Lifeact-mCherry enter the nucleus? This information is important in interpreting the failure to detect MTHs using this probe.
10. The sensitivity of the MTH structures to 1,6-hexanediol treatment is potentially interesting, but it does not reveal anything about their structure or function, only that their assembly likely depends in part on hydrophobic interactions. Caution should be used in interpreting these findings.
11. The figure legends and/or Methods sections should clarify what is represented in each figure, and how the data were acquired. In particular, cryotomographic slices of varying dimensions (6nm, 10 nm or 12 nm or 70 nm) are mentioned in the captions of several figures (2-6, and S1, S2, S3). However, is often unclear whether these represent physical or computational sections.
12. Page 23 has a supplemental figure but no captions. Is this the same as Fig. S8?
13. I do not find the model figure (Figure 7) to be helpful. Additionally, the failure to detect SCs and the presence of MTHs do not warrant a "revised model of the meiotic yeast nucleus."
14. The absence of MTHs in haploid cells induced to undergo meiosis should perhaps be studied in more detail. Even SCs are present in haploid meiotic cells, so the absence of these structures may be informative as to their function. Haploid cells should also be stained for SCs and imaged by immunofluorescence to verify that they are in meiosis.
15. The yeast strain is SK1, not SK-1.
16. What are "self-pressurized-frozen samples" (p.2)?

Significance

The observation of MTHs is novel but (as stated above) of unclear significance, given that their molecular identity and function are unknown.

This work may be of interest to the meiosis field, with the caveats described above that the functional relevance is currently unclear.

This review was co-written by referees with expertise in meiosis, chromosome organization, SC structure and function, and cryoEM.

Referee Cross-commenting

The reviews are strikingly concordant so I don't think much needs to be added.

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Referee #1

Evidence, reproducibility and clarity

Ma and coworkers report studies of budding yeast undergoing meiosis by cryo-ET. They fail to detect structures interpretable as synaptonemal complex, and instead detect feather-like bundles of what appears to be a triple helix. These structures do not appear to be related to the synaptonemal complex, as spo11 mutants that do not initiate recombination, red1 mutants that lack axial elements, and zip1 mutants that lack the central element of the SC still make these bundles. These structures are absent from cells treated with latrunculin A, which depolymerizes actin filaments, but expected structures are not visible in light microscopy of cells treated with two different F-actin-staining reagents. However, it should be noted that another study (Takagi et al, 2021, bioRxiv) did detect actin associated with these structures by immunogold labeling. The structures are also reversibly dissolved in 7% hexanediol.

This part of the paper's findings is well supported by data and is certainly of interest, although interest is somewhat limited by the unknown nature of these structures-what they contain, let alone their function, remains to be determined-in fact, it is not even determined whether or not they are made of protein. However, as an initial report of a previously unknown phenomenon, the paper is of some value.

There is, unfortunately, a second aspect of the paper that cannot be supported. Although it is clear that synaptonemal complex is present in the cells examined (by standard cytological methods) the authors cannot detect structures consistent with SC in their cryo-ET images. Unfortunately, authors then extrapolate from their inability to detect SC to conclusions about SC, such as that it is not crystalline, and even go so far as to suggest that their failure to detect SC invalidates two models for crossover interference, and that the ladder-like structure reported for SC in many organisms using many difference approaches may be a fixation artifact. Authors show remember that the absence of evidence is not evidence for absence; the speculation described above should be removed from both the abstract and discussion.

Significance

This paper reports a previously unreported structure in the nuclei of yeast cells undergoing meiosis. The composition and function of this structure remain to be determined. This considerably limits the significance of the paper.

Referee Cross-commenting

I agree with the concerns of the other reviewers. I also agree with reviewer 3 that to raise the significance of the paper would require much work. But I think that the raw observation is of value, albeit in a journal of record. So I would stick with my recommendation of text changes, keeping in mind that there may not be a suitable journal in LSA's portfolio.

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Reply to the reviewers

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

Saha and colleagues investigated the functions of the long non-coding RNA (lncRNA) DRAIC in malignant glioma. They find that DRAIC expression decreases cell migration/invasion and tumorsphere/colony formation in vitro, and tumor growth in vivo using established cell lines. Mechanistically, DRAIC is known to inhibit NF-kB signaling and the authors demonstrate that DRAIC activates AMPK leading to repression of mTOR, which decreases protein synthesis and increases autophagy. This is a solid study highlighting a potentially interesting pathway of tumor growth and invasion in brain tumors.

Answer: We appreciate Reviewer 1 for the positive feedback of our study

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Major Comments:

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1) It is unclear whether the presented values (mean +/- SD) in the histograms refer to repeat measurements (in which case n = 1) or independent experiments (n>1). The number of replicate experiments is not stated in the methods or figure legends. This must be included.

Answer: We want to thank Reviewer 1 for pointing out this omission. We have now included this information in the Materials and methods and in figure legends section.

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2) I don't think the immunoblot for p62 in Fig. 5C shows a convincing increase following DRAIC knockout, so the statement on p.8 should be revised.

Answer: We have revised the statement to say: Consistent with DRAIC decrease being associated with a decrease in autophagic flux, and despite a decrease in p62 mRNA, the level of P62 protein is increased in three of the DRAIC KO prostate cancer cells (Fig. 5C, KO1, KO2, KO4 compared to WT) and unchanged in the other two.

3) On p.8/Fig.5 the authors make a case that increased DRAIC levels increase lysosomal degradation of autophagosome core proteins LC3 II / p62 (resulting in decreased protein levels of both), while simultaneously increasing gene expression of LC3B and p62 (causing increased mRNA levels). The data for DRAIC overexpression fit this logic fairly well (even though I think more work is needed to fully support this claim), but I am finding it difficult to reconcile the DRAIC knockout data with this scenario - here, loss of DRAIC results in increased protein levels to decreased autophagy, but also decreased gene expression. To fully support this argument, rescue experiments would be needed using FoxO3a knockout/overexpression.

Answer: Note that the mRNA level is not always correlated with protein expression. This is particularly true for LC3 and p62, whose protein levels are significantly affected by the extent of fusion of autophagosomes and lysosomes and subsequent degradation in autophagolysosomes. Thus, although the mRNA of these genes is decreased in DRAIC KO cells (Fig. 5E), the proteins are increased (Fig. 5C) because of decrease of autophagic flux (and decrease of degradation in the autophagolysosomes).

The overexpression of FoxO3a in the DRAIC KO cells will not restore mRNA levels of LC3 or p62, because we show in Fig. 4H that FoxO3 phosphorylation by AMPK is suppressed by DRAIC KO. This phosphorylation is important for the induction of LC3 or p62 mRNA by FoxO3.

FoxO3 knockout or knockdown in DRAIC OE cells should decrease LC3B or p62 mRNA in Fig. 5D, but it is already known from the Literature that FoxO3a is necessary for inducing LC3B or p62 mRNA. Cell Metab. 2007 Dec;6(6):458-71. doi: 10.1016/j.cmet.2007.11.001.PMID: 18054315.

4) Similarly, the data supporting increased autophagy following DRAIC overexpression (Fig. 5F/G) are a bit weak and lack controls (is the LC3B-GFP overlapping with endogenous LC3B and autophagosomes? Was the transfection efficiency comparable? Is there fusion with lysosomes?). In the absence of stronger data, the authors should temper their claims that DRAIC increases autophagy.

Answer: LC3B of fusion protein LC3B-GFP is known to overlap with the p62 puncta (similar to endogenous LC3B). This result is in Fig. 4A of the citation that we have now added (Proc Natl Acad Sci U S A. 2016 Nov 22;113(47): E7490-E7499. doi: 10.1073/pnas.1615455113. Epub 2016 Oct 17)

To support our hypothesis that DRAIC OE induces more autophagy compared to empty vector, we used Bafilomycin A1 in Figure 5B to inhibit the autophagosome and lysosome fusion. We see the accumulation of more LC3B upon treatment with Bafilomycin A1 in the DRAIC OE cells (compared to EV containing U251 cells), consistent with the idea that autophagosome-lysosome fusion is increased by DRAIC OE.

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5) No information is provided on animal numbers used in this study. How many mice were used per cohort? Were male and female mice used? Authors should follow ARRIVE guidelines in reporting animal experiments. The method for calculating tumor volume needs to be specified.

Answer: We have included the details about the animal study in methods section of our modified manuscript .

6) Student's T-test is inappropriate for comparisons of more than two groups (i.e. all experiments using DRAIC knockout cells) - for these experiments a Kruskal Wallis test or ANOVA should be used. Did the authors test for normal distribution of their data? This may affect statistical testing and should be taken into consideration.

Answer: We have now modified our statistical calculation and included in the statistical analysis section in our modified manuscript.

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Minor Comments:

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7) Authors mention that DRAIC expression is undetectable in immortalized astrocytes and GBM cancer stem cells (Fig. S1). What is the source of these cells and how were they cultured?

Answer: The immortalized astrocytes and GBM stem cells and their culture conditions is now described.

8) The immunoblot in Fig. 3D could be replaced with a slightly lower exposure to make the difference between WT and DRAIC KO more obvious.

Answer: We have now replaced the immunoblot with lower exposure.

9) Some immunoblots in Fig. 3 (panel E, p-S6K and S6K; panel H, actin) are not of the best quality and an effort should be made to replace them.

Answer: We have now replaced the immunoblot p-S6K as reviewer mentioned.

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10) Why are different loading controls used in Fig. 3 (a-Tubulin v actin)?

Answer: We use multiple loading control to make sure that we are not underestimating or overestimating changes in the experimental protein because of unexpected changes in the loading controls.

11) Compared to other blot images in the same figure (e.g. Fig. 3E), the bands for p-mTOR and mTOR in Fig. 3F look compressed and should be shown appropriately sized.

Answer: We have modified the Figure as reviewer suggested.

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12) The layout of Fig. 4 is somewhat confusing. I would suggest organizing this according to DRAIC overexpression in A172 and U373 cells versus DRAIC knockout in LNCaP cells. Each immunoblot should be clearly labelled with the corresponding cell line, and it should be clearly explained why p-FoxO3a was tested in U251 cells, rather than A172/U373 as in the rest of the figure.

Answer: We thank the reviewer for the constructive criticism. We have labeled all the cell lines in the Figure as reviewer suggested. We have now systematically alternated the prostate cancer cells (for KO) and the GBM cells (for OE), as we looked at each relevant marker. We have now included the western blot for p-FoxO3a from another glioblastoma cell line U373. Please find the modified Figure 4K for p-FoxO3a.

13) Labelling of immunoblot in Fig. 5B is confusing and should be improved.

Answer: We have modified the Fig. 5B to make the label clearer.

14) Changes in GLUT1 expression (Fig. 7A) should be validated on the protein level.

Answer: We have included the immunoblot for GLUT1 from DRAIC KO cells in Figure 7B. GLUT1 protein is increased upon DRAIC KO.

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Reviewer #1 (Significance (Required)):

The authors describe a novel link between the lncRNA DRAIC and AMPK activation through inhibition of NF-kB-mediated regulation of GLUT1. This study extends their previous work on DRAIC inhibition of NF-kB in prostate cancer (Saha et al. Cancer Res 2020). There is one study describing DRAIC effects on growth and invasion in glioma cell lines (Li et al. Eur Rev Med Pharmacol Sci 2020), but the work presented by Saha and colleagues contains stronger experimental data and a more detailed and previously undescribed mechanism.

The current study presents a mechanistic advance that increases the understanding of tumor growth and protein synthesis in cancer cells. The data presented in the study are not supported by in vivo experiments (other than suppression of tumor growth by DRAIC overexpression), validation in human tissue and/or primary patient-derived human glioblastoma cells, or even substantial rescue experiments. This limits the influence of the work on the field. I'm also not sure how transferable findings from DRAIC knockout in prostate cancer cell lines are to glioma, although the results are mostly complementary to the data from glioma cell lines. This is particularly relevant to the proposed mechanism of GLUT1 regulation by NF-kB, as the bulk of experimental data in Figures 6 and 7 was generated in prostate cancer cell lines and is only poorly validated in glioma cells. The study results will be most relevant for researchers investigating cell signaling pathways and autophagy in cancer.

Answer: We like to thank reviewer for the positive comments on our study. The DRAIC KO experiments of Fig. 6 and 7 cannot be done in glioma cells, because as we show if Supp. Fig. S1, there are no glioma cells or GSC that express DRAIC to levels comparable to LnCaP. We have shown that GLUT1 mRNA decreases in the glioma cells when DRAIC is overexpressed (Supp. Fig. S4. We also show in Fig. 7G that AMP levels increase when DRAIC is overexpressed in glioma cells.__

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

In this manuscript, the authors describe DRAIC as a lncRNA downregulated in prostate cancer. They postulate that DRAIC expression surpasses invasion, migration and growth. Mechanistically, the authors show that DRAIC activates AMPK by suppressing NFkB target gene TOR and indirectly impacting translation and autophagy. Collectively the observation is interesting and robust. However, I have several technical requests, particularly regarding the mechanistic part of the paper.

Answer: We appreciate the positive feedback. We have addressed the reviewer’s concerns in our modified manuscript.

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Major Comments:

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1) The authors should rescue Ko phenotypes by over expressing DRAIC to consider potential off target effects.

Answer: DRAIC OE alone is sufficient to have exactly the opposite effect as DRAIC KO in protein translation (Fig. 3C-F), so DRAIC OE will rescue the effect of DRAIC KO. We make a similar argument for all the phenotypes, including mTOR, S6K and ULK1(S757) phosphorylation (Fig. 3G-J), AMPK and FoxO3a phosphorylation (Fig. 4B-C; J-L), autophagic flux (Fig. 5B, C) and effects on LC3B and p62 mRNAs (Fig. 5D, E). The same is true for our published phenotypes of DRAIC KO on invasion, migration and NF-kB activity (Saha, Cancer Research, 2020)

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2) The blots showing TOR and ULK1 phosphorylation need to be repeated. This is an important part of the paper and I feel that these blots are hard to interpret. p-S6K typically run a bit higher in gels. there may be a technical problem.

Answer: We are not sure which specific blots the reviewer is referring to, and it is possible that the blots the other reviewers pointed to are the ones under question. We have changed those blots so that the results are clear.

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3) GLUT1-related results are interesting, but the authors should provide genetic evidence that the effects are mediated by GLUT1. How do we know that glucose uptake is indeed upregulated upon knockout?

Answer: In Fig. 7 C-F we show that the effects of DRAIC KO on invasion, protein translation, AMP levels and AMPK activity are reversed by the GLUT1 inhibitor Bay-876. This is a cleaner result than using siRNA to knockdown GLUT1. siRNAs can have off-target activity and sometimes cannot decrease a protein sufficiently below the threshold necessary to see reversal of action.

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Minor Comments:

4) The figures need to be updated. FOnts are all different, lots of unaligned graphs, quality of the blots are poor.

Answer: We have updated the Figures and changed fonts as reviewer mentioned.

Reviewer #2 (Significance (Required)):

The observation is interesting, but the mechanism is incompletely understood. This is a nice addition to the literature, even without the mechanism.

Answer: We want to thank the reviewer for the constructive criticism.

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

**Summary:**

Shaha and colleagues present a study demonstrating the tumor suppressive role of DRAIC, a long non-coding RNA transcript, through transmission of the signal from IKK/NF-kB to the AMPK/mTOR pathway via regulation of GLUT1 expression. The inhibition of mTOR by this pathway results in the reduction of protein translation, cellular invasion and activation of autophagy. Several diseases and models as well as multiple genetic and pharmacological manipulations were used to investigate the mechanisms at play. The manuscript is well written and the experiments are well designed. The conclusions are supported by the results. The following major and minor comments should be addressed:

Answer: We appreciate the reviewer for the positive comments on our study.

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Major Comments:

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1) In addition to reporting the effect of DRAIC overexpression on tumor volume, the authors should present survival studies with one or more models.__

Answer: __We thought of doing the survival study in our glioblastoma model but unfortunately, the tumor growth is very rapid (exceeding the size permitted by our IACUC in 2-3 weeks). The animal ethics welfare committee did not allow us to keep the mice for a longer time to perform the survival study.

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2) Since the authors study metabolic energy sensor pathways, related to glycolysis, it would be important to perform some of the key experiments in physiological level of glucose: e.g., pmTOR, pAMPK, LC3-II expression level in DRAIC overexpressing and deficient cells.

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Answer: The concentration of glucose in plasma is 1G/L, while that of the RPMI medium is 2G/L. We do not think we are too far from the physiological levels of glucose.

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3) In addition to RT-PCR data, GLUT1 protein levels should be investigated in the different DRAIC expressing cells.

Answer: We have incorporated the GLUT1 protein expression data from DRAIC KO cells in Figure 7B and DRAIC overexpressing cells in supplementary Figure 4G-H. The blots from the same gels were split into different panels, the loading control GAPDH remain same in Figure 4K and Supplementary Figure S4H.

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4) The effect of DRAIC on GLUT1 expression is also measured in condition of glucose saturation, which does not reflect disease state. The decrease of GLUT1 in response to DRAIC overexpression and the increased GLUT1 level in DRAIC deficient cells should be investigated in physiological levels of glucose.

Answer Same as above. We are near physiological levels of glucose.

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__Minor Comments:

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5) All the data are generated with established cell lines (e.g., U87) but more clinically relevant models, such as patient-derived primary cells like the ones used in Fig. S1, could be used to replicate some of the key findings.

Answer: As we showed in Fig. S1 that DRAIC is not expressed in glioma stem cells, and so knockout experiments are not possible. We believe that the knockout experiments are the most relevant to this paper because they do not run the risk of artefacts from overexpression of an RNA far beyond physiological levels.

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6) Also please provide further details about the patient-derived cells from Fig. S1.

Answer: We have mentioned the details of the cell lines in our modified manuscript.

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7) The statistical analysis section states that the number of measurements is indicated however I don't see the sample size of the experiments.

Answer: We have now incorporated the number the experiments in our modified text.

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Reviewer #3 (Significance (Required)): The study reports a new model of regulation of tumor via long non-coding RNA. This article adds to the growing literature The topic and content of the article is relevant and significant to the field of tumor research but the significance and impact could be enhanced with the use of more physiologically relevant models and conditions as pointed in the major comments.

Answer: We want to thank the reviewer for the positive feedback on our study.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Shaha and colleagues present a study demonstrating the tumor suppressive role of DRAIC, a long non-coding RNA transcript, through transmission of the signal from IKK/NF-kB to the AMPK/mTOR pathway via regulation of GLUT1 expression. The inhibition of mTOR by this pathway results in the reduction of protein translation, cellular invasion and activation of autophagy. Several diseases and models as well as multiple genetic and pharmacological manipulations were used to investigate the mechanisms at play. The manuscript is well written and the experiments are well designed. The conclusions are supported by the results. The following major and minor comments should be addressed:

Major comments:

1. In addition to reporting the effect of DRAIC overexpression on tumor volume, the authors should present survival studies with one or more models.
2. Since the authors study metabolic energy sensor pathways, related to glycolysis, it would be important to perform some of the key experiments in physiological level of glucose: e.g., pmTOR, pAMPK, LC3-II expression level in DRAIC overexpressing and deficient cells.
3. In addition to RT-PCR data, GLUT1 protein levels should be investigated in the different DRAIC expressing cells.
4. The effect of DRAIC on GLUT1 expression is also measured in condition of glucose saturation, which does not reflect disease state. The decrease of GLUT1 in response to DRAIC overexpression and the increased GLUT1 level in DRAIC deficient cells should be investigated in physiological levels of glucose.

Minor comments:

1. All the data are generated with established cell lines (e.g., U87) but more clinically relevant models, such as patient-derived primary cells like the ones used in Fig. S1, could be used to replicate some of the key findings.
2. Also please provide further details about the patient-derived cells from Fig. S1.
3. The statistical analysis section states that the number of measurements is indicated however I don't see the sample size of the experiments.

Significance

The study reports a new model of regulation of tumor via long non-coding RNA. This article adds to the growing literature The topic and content of the article is relevant and significant to the field of tumor research but the significance and impact could be enhanced with the use of more physiologically relevant models and conditions as pointed in the major comments.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors describe DRAIC as a lncRNA downregulated in prostate cancer. They postulate that DRAIC expression surpasses invasion, migration and growth. Mechanistically, the authors show that DRAIC activates AMPK by suprressing NFkB target gene TOR and indirectly impacting translation and autophagy. Collectively the observation is interesting and robust. However, I have several technical requests, particularly regarding the mechanistic part of the paper.

• The authors should rescue Ko phenotypes by over expressing DRAIC to consider potential off target effects.
• The blots showing TOR and ULK1 phosphorylation need to be repeated. This is an important part of the paper and I feel that these blots are hard to interpret. p-S6K typically run a bit higher in gels. there may be a technical problem.
• GLUT1-related results are interesting but the authors should provide genetic evidence that the effects are mediated by GLUT1. How do we know that glucose uptake is indeed upregulated upon knockout?

Minor:

The figures need to be updated. FOnts are all different, lots of unaligned graphs, quality of the blots are poor.

Significance

The observation is interesting, but the mechanism is incompletely understood. This is a nice addition to the literature, even without the mechanism.

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Referee #1

Evidence, reproducibility and clarity

Saha and colleagues investigated the functions of the long non-coding RNA (lncRNA) DRAIC in malignant glioma. They find that DRAIC expression decreases cell migration/invasion and tumorsphere/colony formation in vitro, and tumor growth in vivo using established cell lines. Mechanistically, DRAIC is known to inhibit NF-kB signaling and the authors demonstrate that DRAIC activates AMPK leading to repression of mTOR, which decreases protein synthesis and increases autophagy. This is a solid study highlighting a potentially interesting pathway of tumor growth and invasion in brain tumors.

Major comments:

• It is unclear whether the presented values (mean +/- SD) in the histograms refer to repeat measurements (in which case n = 1) or independent experiments (n>1). The number of replicate experiments is not stated in the methods or figure legends. This must be included.
• I don't think the immunoblot for p62 in Fig. 5C shows a convincing increase following DRAIC knockout, so the statement on p.8 should be revised.
• On p.8/Fig.5 the authors make a case that increased DRAIC levels increase lysosomal degradation of autophagosome core proteins LC3 II / p62 (resulting in decreased protein levels of both), while simultaneously increasing gene expression of LC3B and p62 (causing increased mRNA levels). The data for DRAIC overexpression fit this logic fairly well (even though I think more work is needed to fully support this claim), but I am finding it difficult to reconcile the DRAIC knockout data with this scenario - here, loss of DRAIC results in increased protein levels to decreased autophagy, but also decreased gene expression. To fully support this argument, rescue experiments would be needed using FoxO3a knockout/overexpression.
• Similarly, the data supporting increased autophagy following DRAIC overexpression (Fig. 5F/G) are a bit weak and lack controls (is the LC3B-GFP overlapping with endogenous LC3B and autophagosomes? Was the transfection efficiency comparable? Is there fusion with lysosomes?). In the absence of stronger data, the authors should temper their claims that DRAIC increases autophagy.
• No information is provided on animal numbers used in this study. How many mice were used per cohort? Were male and female mice used? Authors should follow ARRIVE guidelines in reporting animal experiments. The method for calculating tumor volume needs to be specified.
• Student's T-test is inappropriate for comparisons of more than two groups (i.e. all experiments using DRAIC knockout cells) - for these experiments a Kruskal Wallis test or ANOVA should be used. Did the authors test for normal distribution of their data? This may affect statistical testing and should be taken into consideration.

Minor comments:

• Authors mention that DRAIC expression is undetectable in immortalized astrocytes and GBM cancer stem cells (Fig. S1). What is the source of these cells and how were they cultured?
• The immunoblot in Fig. 3D could be replaced with a slightly lower exposure to make the difference between WT and DRAIC KO more obvious.
• Some immunoblots in Fig. 3 (panel E, p-S6K and S6K; panel H, actin) are not of the best quality and an effort should be made to replace them.
• Why are different loading controls used in Fig. 3 (a-Tubulin v actin)?
• Compared to other blot images in the same figure (e.g. Fig. 3E), the bands for p-mTOR and mTOR in Fig. 3F look compressed and should be shown appropriately sized.
• The layout of Fig. 4 is somewhat confusing. I would suggest organizing this according to DRAIC overexpression in A172 and U373 cells versus DRAIC knockout in LNCaP cells. Each immunoblot should be clearly labelled with the corresponding cell line, and it should be clearly explained why p-FoxO3a was tested in U251 cells, rather than A172/U373 as in the rest of the figure.
• Labelling of immunoblot in Fig. 5B is confusing and should be improved.
• Changes in GLUT1 expression (Fig. 7A) should be validated on the protein level.

Significance

The authors describe a novel link between the lncRNA DRAIC and AMPK activation through inhibition of NF-kB-mediated regulation of GLUT1. This study extends their previous work on DRAIC inhibition of NF-kB in prostate cancer (Saha et al. Cancer Res 2020). There is one study describing DRAIC effects on growth and invasion in glioma cell lines (Li et al. Eur Rev Med Pharmacol Sci 2020), but the work presented by Saha and colleagues contains stronger experimental data and a more detailed and previously undescribed mechanism.

The current study presents a mechanistic advance that increases the understanding of tumor growth and protein synthesis in cancer cells. The data presented in the study are not supported by in vivo experiments (other than suppression of tumor growth by DRAIC overexpression), validation in human tissue and/or primary patient-derived human glioblastoma cells, or even substantial rescue experiments. This limits the influence of the work on the field. I'm also not sure how transferable findings from DRAIC knockout in prostate cancer cell lines are to glioma, although the results are mostly complementary to the data from glioma cell lines. This is particularly relevant to the proposed mechanism of GLUT1 regulation by NF-kB, as the bulk of experimental data in Figures 6 and 7 was generated in prostate cancer cell lines and is only poorly validated in glioma cells. The study results will be most relevant for researchers investigating cell signaling pathways and autophagy in cancer.

Reviewer keywords:

neurooncology, cancer stem cells, signaling pathways in cancer

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Reply to the reviewers

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

In the study, the authors found that cancer cells including breast, bladder, lung, neuroglioma, and prostate display an increase of Lipid Droplet (LD) after 6 Gy x-ray (Fig.1). And, the cells containing high LDs showed more radioresistance than the cells with low LDs after irradiated with a dose Gy x-ray (Fig.2). Ferritin Heavy Chain (FTH1), the main intracellular iron storage protein, is found to be upregulated after 6 Gy exposure or in the LDs high cells. FTH1 knockdown decreases LD accumulation and increases radiation sensitivity (Fig.3). Overexpression of FTH1 or DFO (an iron chelator agent) treatment in shFTH1 cells rescue the LD accumulation and cancer radioresistance (Fig.4)

Major comments:

1. The conclusion has some conflict with some publication (PMC5928893) which shows fatty acid oxidation not LDs lead to cancer radioresistance. So the authors should rule out this possibility through knockdown of CPT1.
2. Lipid droplets are dynamic in cells. The sorted cells (10% highest or lowest LD-expressing cells) in Fig.3 may not stand for subpopulation, so the authors should add exogenous lipids or cholesterol to test cancer cell radioresistance.
3. It is impossible to overexpress FTH1 in shFTH1 cells (the stable shRNA will target all mRNA of FTH1) (Fig.4 and methods section: cell culture and FTH1 Reconstitution).
4. The relationship between the free cytoplasmic iron and LD accumulation is not so convincing. Add exogenous iron to test LD accumulation.

Minor comments:

1. Remove fig.1C which is not related to the conclusion;
2. Fig.2 label error: LD520 should be LD540;
3. Fig.3, A: change loading control HSC70 which not so stable in the cells; D: add quantification of LD number.
4. Fig.4, A: change loading control HSC70, and repeat western of MCF7 shFTH1/pcDNA3
5. Line 111, "...that general ROS levels resulted not altered..." should be "...that general ROS levels were not altered..."
6. Fig.S4 legend: "Figure S2" should be "Figure S4".

Significance

The FTH1 affects Lipid Droplets is novel (some results in this study have published: radiation led to LD accumulation (PMC5928893) and an increase of FTH1 (PMC4688087 and PMID:32937103).

The finding is helpful to improve radiation therapy which may combine with drugs targeting FTH1 or iron metabolism.

The researchers who worked in cancer treatment are interested in this finding.

My expertise is cancer lipid metabolism and cancer therapy.

Referee Cross-commenting

I completely agree the comments from the other Reviewer. The authors need enhance the correlation between the lipid droplets genes (e.g., DGAT1/2, SOAT1, ACAT1) and the iron metabolism (e.g., FTH1), and improve data quality as suggested by reviewers.

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Referee #1

Evidence, reproducibility and clarity

In this work Tirinato and co-authors used different experimental approaches to trace correlations between cancer cell stemness, lipid droplets, iron homeostasis and radiation resistance. Their findings were acquired by using different cancer cell lines from different origin, and using diverse techniques, including cytometry, microscopy, clonogenic assays and shRNAi. In general, the manuscript is well written and organized. It is also easy to be followed and the authors managed to convince the readers about the importance of this important aspect of cancer metabolism. The fact that cell lipid droplets content might condition cancer survival to radiotherapy poses novelty and therefore it deserves attention in the delimitation of new anticancer therapies or protocols. There are however some issues that should be amended to improve the quality of the manuscript.

Major comments:

The main concern is that all the work is based on cancer cell lines. The use of some cell lines derived from clinical samples or analysis of the clinical data already deposited in bank webs could be useful to support their conclusions. This last could be easy. Exploration of this depositories could help the author to reinforce the correlation between the expression of the lipid droplets genes, as well as that related with the iron metabolism, with the radiotherapy efficacy in patients.

When analyzed in detail I have some comments regarding specific sections:

Lipid droplets detection images (Figs 1, 3 y 4). Why are the nuclei size look so different in both conditions? FTH1 expression. Fig 3A vs 3C and 4A. As depicted is difficult to have a clear picture of the variations in expression in FHT1 in the different cell lines. Why the authors evaluate the success of shRNAi by PCR if the wb works so well? Is there any correlation between the RR and the expression of FTH1 intra cell lines? What happen when FTH1 is downregulated in the rest of the cell lines? More than restore, the authors overexpressed FTH1, what is the result when FTH1 is overexpressed in the different cell lines?

Minor comments:

The correlation between nile red staining and ROS is not clear (Fig S2). The authors may try to graphic the ROS mfi of different subpopulations (lineal in X) vs the mfi of red nile (y, in log scale). Abreviation in introduction ER, is endoplasmic reticulum? A picture with a putative model could be helpful to summarize the findings.

Significance

As stated above the idea that lipid droplets and iron metabolism might be determinants in the cancer survival to radiotherapy poses novelty and therefore it deserves attention in the delimitation of new anticancer therapies or protocols.

Although I have experience in cancer lipid metabolism I am not an expert in the field of lipid droplets.

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Referee #4

Evidence, reproducibility and clarity

Summary:

In this study, the investigators describe two new Trypanosoma brucei brucei strains wheich have been subjected to minimal passage in rodents or in culture. In addition to characterizing some of the phenotypic and genetic characteristics of these isolates and lines derived from them the authors also characterize the changes in gene copy numbers occurring during in vitro passage. While this study illuminated the impact of in vitro culturing for adaptation that might have been overlooked for years in unicellular eukaryotes, the experimental design and method description make their conclusions less convincing.

Major comments:

My main discomfort with this work is that it provides just enough information to be interesting and perhaps important, but not enough to be definitive. Part of this is not the fault of the authors - especially in dealing with various "lab" strains and their genomes - over which they have no control in terms of where and how they were isolated, maintained and investigated. But the authors seem to add to this problem by not determining the clonality of their new strains, failing to provide a full genome sequence (although they seem to have the data they need - including long-read sequencing - to do so), and with often vague descriptions of the analyses they performed as part of this report. My biggest concern has to do with the population structure of the beginning parasites set and to what extent the cultivation - even though limited - is selecting for variants within an existing non-clonal population rather than showing evidence of evolution of a (mostly"??) clonal population. The authors note that they have not cloned so as to "minimize manipulation" - which is sensible. But the provided references (particularly Ref 21) use microsatellite markers to establish wide clonality in populations in individuals. Although the authors have not indicated it here, one would presume that that "evolution" being observed in their lines over time (in animal or in culture) have not altered the microsatellite signature of the lines (thus are still clonal based upon standards of Ref 21), but has resulted in genetically and biologically altered lines - i.e. clonal variants. It seems without knowing the complexity of the beginning populations (granted - not easy), it is hard to say whether the changes over time are due to selection of a subset of the initial population, genetic changes in an initially clonal population, or a combination of the two. Indeed, a selection process would explain the unexpected differences found in early sampled (low parasitemic but high stumpy) and late sampled (high parasitemic but low stumpy) lines as well as the erratic growth in culture. The authors seem to go back and forth on this issue but the key problem is that there is not enough information here to understand this clearly, and for that reason, the work, although considerable, adds mostly anecdotal observations but relatively little definitively. The authors attribute gene/chromosome ploidy changes under in vitro culturing as adaptation of parasites due to chromosome replication and/or segregation. But this conclusion can not be reached unless the populations were clonal at the beginning. Related to this point, it is surprising that there is not a comparison between the A and B lines of each isolate to each other (e.g. 65A/65B). If the isolates are clonal to begin with then one would expect little difference between the 2 harvested 1 day apart from different rats.

Overall the manuscript is difficult to follow and interpret, very light on details and Figure 2 is very confusing and is not particularly helped by the author's summary of them. A little more detail on how the experiment was done might help. It should be made clear and explicitly stated that the cultures were split (by how much/to what level - seems like it is 0.5 x 10e6?) once cultures reached what level (there doesn't appear to be any pattern there - so are they split every 24hrs unless they have declined?). Readers should not have to go to a previous publication to find these types of details needed to interpret the results. The text refers to "diluting them" but presumably a portion of the culture was reseeded/cultured - or was there a constant dilution of the culture over time? The authors refer to a "slowed growth" after 1-2 days, but 3 of the 4 cultures also declined again between 6 and 11 days. This is not consistent with the hypothesis that the rebound after 2-3 days was the result of an adaptation to a depleted component (if so, why would an additional "adaptation" be necessary?). 2E seems to have been handled differently from A-D. The growth curves in S2 fig do not exhibit the same drops as evident in the Fig 2 cultures.

A fundamental finding of this research is detecting the gene copy number variation from the sequencing results of the strains after in vitro passages. However, the description of how to processing the sequencing reads was used to determine the gene copy number is vague. The description lacks basic information like the analysis workflow, software packages, parameters, and other important details. These details are the keys to support their conclusion.

According to the authors, only one representative was considered for multicopy genes. But what is the criteria to call 'multicopy genes' here? How homologous these multicopy genes are set to be? The authors also mentioned that reads may also map to homologous genes, how is this issue dealt with during the analysis?

Overall, the study seems rushed and too preliminary; much of the writing is loose and lacking in details. The authors mention that they have long read nanopore sequence but don't appear to use it to appropriately address some of the questions (e.g. Line 195 "In the meantime..."). As a result, although the results are intriguing (but not totally surprising - this work uncovers a bit of the dirty little secret most scientist realize underlies the pathogen strains that we often present as stable entities while knowing that they have and are changing over time), the study presents more questions than it answers - as the authors seem to acknowledge in their final paragraph.

Minor comments:

Line 79-83 - statements require some references The strain "A/B" nomenclature comes into use in the manuscript (line 129 and Fig 1 legend) without defining what the difference between A and B is (apparently this is the samples from 2 different cultures (Line 187) or from different rats - but it takes a bit of work to figure that out. - 1C could be interpreted to mean that the 2 rats got infected by either the A or B lines - or that the resulting lines were named based on the rat they came from, or they are just different cultures of the same stabilate).

Line 141 "firmly confirmed" sounds a bit off - just "confirmed" or "firmly supported", perhaps better. Line 140/141 - S fig 2 a bit hard to understand - as is the remaining paragraphs on culture outcomes. As this is expressed as a ratio, could it not be argued that polyA+ enriches for (or ribo-minus "depletes" - using the author's treminology) short RNAs (or that ribo-minus enriches for longer)? It does seem likely that the relatively rare extra-long RNAs are underrepresented in the poly A+ method but not clear that this shows anything other than that the different techniques have different strengths/weaknesses.

Line 156 ."... had run out of a nutrient..."

Line 170: "several additional attempts ..." these were from the mouse blood "stabilates"?

Line 188 and 189, figure S1 is figure S2, not S1.

Line 192, comma is in the wrong place.

Line 225: do you mean 'figure 4E' instead of 'figure 4B'. It would be informative if the authors could provide a statistic comparison of frequency of copy number alteration between tandem repeated genes and those that are not in tandem to support their statement in line 293-294.

For the figures with chromosomes displayed, are these data points from the copy number of each unique gene? Or are they the average gene copy number per region? The chromosome number should be in order, Chr10 and Chr11 should be after Chr9, not the Chr1.

Significance

There is a lot of good information here, but it could be more clearly presented and better detailed. Placement in the literature is fine - although comparison to long-read sequenced T. brucei or T. cruzi genomes might deserve mention.

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Referee #3

Evidence, reproducibility and clarity

Summary

Most laboratory research using T. b. brucei has made use of two strains, the monomorphic Lister 427 strain and the pleomorphic EATRO1125 strain. These strains have been passaged in vitro for decades in separate laboratories, thus selecting for populations that differ from both the original isolate and between laboratories.

In this study, Mulindwa et al. describe the isolation of two new T. b. brucei strains from cattle in Uganda, MAK65 and MAK98, which show differences in virulence and propensity for stumpy form differentiation in rodents. To assess the effect of culture adaptation on trypanosomes, the researchers compared the gene copy number of the MAK65 and MAK98 isolates before and after culture adaptation. The study found that isolates cultured for as little as a week already demonstrated a broader gene copy number distribution than those that were not culture adapted. Broader gene copy number distributions, compared to the non-culture-adapted isolates, were also observed for a number of routinely-passaged Lister 427 and EATRO1125 cultures. The researchers observed reproducible increases in copy number for certain genes, such as those encoding histones, HSP70 and PFR proteins. The study postulated that changes in gene copy number observed across the genome increased bias towards rapid proliferation and stress tolerance upon culture adaptation.

Major comments

I have some concerns about the method that was used for the copy-number calculations. Firstly, I can imagine that a non-uniform distribution of the reads across the genome for any of the datasets could influence the results. Was this checked? Secondly, in the methods section lines 394/395 it is said that the modal RPKM for each dataset was 'adjusted slightly' to get a symmetrical distribution. Upon checking the modal RPKMs and the adjusted values used for the calculations, the adjusted values appear to have been adjusted to different extents between the different datasets to fit the authors assumptions. Do the authors believe that this could perhaps account for some of the subtle gene copy number changes observed (as is discussed in lines 287-290)? Next, why were the reads aligned 20 times? I think in general the method needs to be explained far more clearly so that the audience can understand what you did.

Minor comments

Additional experiments:

Would it be possible to generate a phylogenetic tree comparing these new isolates with the Lister 427, TREU927 and EATRO1125 isolates in circulation to get an idea of how these strains may have evolved? I this could add to the strength of the manuscript.

Title: Since no other unicellular eukaryotes are mentioned throughout the text, I think a more appropriate title for this study might be 'Adaptation of Trypanosoma brucei brucei bloodstream forms to in vitro culture results in gene copy-number changes.'

Line 59: There have been more recent studies than the referenced paper (Cross et al., 2014) that quote far higher numbers of alternative VSG genes or pseudogenes in the Lister 427 strain. In Müller et al (2018, doi: 10.1038/s41586-018-0619-8) over 2500 VSGs are quoted and in Cosentino et al (2021, doi: 10.1101/2021.04.13.439624) 2872 VSGs are identified.

Line 109/110: The dates of isolation written here, Feb 1st and July 30th 2016, are different to what is written under the Date of Isolation column in Supplementary text 1, table 1- 25/5/2016 and 30/7/2017. Do these two dates represent different things?

Line 233: Should Figure 5 A, B, C and E (rather than A, B, D and E) be referenced here to show all the initial, not cultured results?

Line 270/271: PIP39 does not promote differentiation to stumpy forms, but instead contributes towards the efficient differentiation of stumpies to procyclic forms (Szoor et al, 2010, doi: 10.1101/gad.570310).

Discussion:

It should be discussed in the text that changes in gene copy number have also been observed upon Leishmania culture adaptation (see, for e.g., Gerald Späth's work). This will strengthen the authors' conclusions. Furthermore, similar observations of aneuploidy and triploidy in some T. brucei Lister 427 strains have recently been reported in Cosentino et al (2021, doi: 10.1101/2021.04.13.439624). This could also be referenced somewhere in the text.

Line 364: I think the Nijuru et al (2005, doi: 10.1007/s00436-004-1267-5) paper would be the correct reference here since it describes the use of the ITS-1 PCR. Reference 13 is actually referring to another paper, I cannot find Nijuru et al in the references list.

Line 379: PAD staining should be referenced to be more informative and allow reproducibility.

Figure 1, Line 580: How many cells were counted for determining the % PAD positivity?

Figure 1, Line 581: Scale bar should be included in D.

Supplement Text 1:

I found Supplement Text 1 a little confusing for two reasons. Firstly, I was never sure if the tables or references being referenced were from the main text or from the supplement text 1, perhaps this could be made a little clearer to aid the reader. Secondly, the names of the isolates switched between, for e.g., MAK 65 and Tb065. For simplicity it could help to try and stick to one naming system.

It might be worth adding a sentence about why Tb236B was not followed up. Was this because it could not easily be distinguished from MAK65 by microsatellite analysis?

S3 Figure: Legend describes red bars but there are none in the figure.

Table 2: In the legend for Sheet 2, the cut-off for the increase is missing.

Significance

Though the T. b. brucei Lister 427 and EATRO1125 strains are used most commonly for laboratory-based research, they have been extensively passaged in vitro without characterisation of the changes that have occurred between them and the original isolate, or indeed, between laboratories.

Mulindwa et al. demonstrated that changes to gene copy number occur rapidly upon adaptation to culture of field isolates, and that different cultures of the same isolate can furthermore have different ploidies. This is an important advance and raises awareness a) that trypanosomes undergo changes upon laboratory adaptation, b) of the nature of some of these changes and c) that the changes can occur rapidly. Changes to gene copy number have also been shown to effect Leishmania donovani upon culture adaptation (Prieto Barja et al, 2017, doi: 10.1038/s41559-017-0361-x).

This study will be of interest to the trypanosome community in general, but particularly those who work on biology that we already know is impacted by prolonged in vitro passage-differentiation, virulence and antigenic variation.

Reviewer expertise: BSF differentiation, antigenic variation

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Referee #2

Evidence, reproducibility and clarity

Summary:

Trypanosoma brucei brucei is a protozoan parasite studied both because it is the cause of Human African Trypanosomiasis, and as a unicellular eukaryotic model organism. However, only few isolates are lab-adapted and most literature focuses on derivatives of Lister 427 and EATRO1125, which have both been cultured under in vitro conditions for several decades. In this manuscript, Mulindwa and colleagues describe and characterise two novel T. b. brucei strains (MAK65 and MAK98) isolated from the field with minimal passage through rodents. These new strains are clearly more closely related to trypanosomes in the field, compared to the aforementioned in vitro models. The authors investigate infection dynamics in murine models, in particular to assess differentiation capabilities of the parasites. Furthermore, optimisation of in vitro adaptation is also attempted successfully. Finally, the authors show, through genome sequencing, that adaptation of both strains to laboratory-based culture affects gene ploidy, and several examples of interest are discussed. A key conclusion is that cultured lines from the same original isolate can rapidly (in a matter of weeks) develop karyotype differences, which could carry implications for the study, as well as genetic manipulation of these organisms. The availability of novel laboratory strains for the study of T. b. brucei is a significant outcome for the trypanosome research community.

Major comments:

The key conclusions, in particular that adaptation to in vitro culture results in reproducible genomic alterations are convincing. In my opinion, this is a descriptive study, in that the underlying causes (and consequences) of the findings are not investigated further.

The authors state several hypotheses based on their results in the 'Outlook' section. The majority of these hypotheses are valid points to make, but I feel the following should be toned down: Lines 321-323 - These hypotheses are speculative, based on available data, and should be revised. There are likely many factors that will impact parasite growth in initial stages of culture adaptation, but most commonly there is a selection bottleneck effect that will impact initial growth.

Line 344 - With the data presented, it is difficult to say whether tissue distribution is different between the two trypanosome strains. I would suggest removing this claim, unless work was carried out to investigate differences in tissue distribution.

Changes in gene copy number is discussed, but one interesting aspect is whether this translates to changes at the protein level (e.g. are there increases in HSP70 mRNA or protein levels after in vitro adaptation?). Whilst not essential, carrying out qPCRs or Western blots to confirm this would enhance the impact of this study. If these experiments are not carried out, this point should be added to the discussion.

It would be very interesting to further analyse the genomics data to assess whether there are further changes to metabolic gene copy number, perhaps as a result of in vitro adaptation. These changes could be linked to the high levels of nutrients available in HMI-9 medium. Currently, only a pteridine transporter is mentioned, although the tables list several genes such as glycerol kinase and an arginine transporter. Are there changes in glucose transporter copy number after culturing these strains? Copy number of this array can often vary in field isolates.

The arginine transporter, AAT5 was previously shown to be an essential transporter (PMID: 28045943). Loss of copy numbers could reflect reduced need in a nutrient-rich environment such as in vitro culture medium. I feel this may be worth mentioning, but not investigating further.

More detail is required in the Materials and Methods section, in particular the "sequence analysis" section: Which Illumina kits were used? What tool/software was used for genome alignment? Which reference genome was used (if TREU 927, what version)? What parameters were used for tryprnaseq and DeSeqU1 (if default settings were used, please mention this)? What tool/software was used for gene copy analysis (i.e. the alignment that was restricted to 20 alignments)? How was modal RPKM value adjusted to obtain a symmetrical distribution? These details are essential for this work to be reproducible. In addition, they would help establish a pipeline that can be used to directly compare other novel field isolates.

In the RNA preparation section (line 372), please indicate replicate number for each sample group used for transcriptomics analysis, as well as parasitaemia or cell number used for extractions. In addition, if RNA extraction was carried out according to the Trifast manual, please state this.

The protocol for staining with PAD1 requires more detail (line 380). If this protocol is available in a previous study, it is sufficient to reference this.

The study mentions attempts to adapt cells using methylcellulose (line 179). It would be helpful for the reader to know what percentage (w/v) was attempted. In addition, did the authors consider using serum supplements from different host sources (e.g. goat or adult bovine?). If only FBS supplementation was attempted, it would be worthwhile mentioning this.

This reviewer is not an expert on statistical analysis of genomics data, but it may be of interest to carry out statistical analysis of the differences in copy number between non-culture-selected and culture-selected parasites (figure 6) to determine whether these differences are significant.

In my opinion, experiments are adequately replicated.

Minor comments:

Line 136 - What is the rationale of calculating Log2 fold changes of 65A/98A to compare to log2 fold change of ST/SL? Given the authors are in possession of normalised read count data for all 4 sample groups, it would also be interesting to show correlation between the individual datasets (e.g. 65A vs 98A, 65A vs ST, 65A vs SL, etc). Perhaps this would be more informative, and give an idea whether, for example, 65A is more similar to previously published datasets derived from stumpy or slender parasites.

Prior studies are referenced appropriately, with the following exception: Line 70 - note T. b. gambiense as an example of African trypanosome that does not undergo sexual recombination (reference PMID: 26809473)

To make the text and legends clearer, it would be beneficial to add commas to numbers >1,000. In addition, text would be more legible if spaces are added between numbers and units (e.g. 480 bp, instead of 480bp), with the exception of temperature and percentages. There are also inconsistencies in spacing around the multiplication symbol when stating cell densities (line 170: 1.5 x106/ml; line 176: 5x 105/ml) - please add spacing on both sides.

In figures 1A and 1B, y-axis numbering is not consistent (i.e. 1A is linear, 1B is logarithmic) I would recommend maintaining consistency between these two experiments.

In figure 1C, the 'A' and 'B' variants of the MAK strains are not defined in the legend, nor main text. It would be helpful for the reader to know what is meant by'98A' vs '98B'.

In figure 1E, statistical test used to generate R and R2 are not indicated.

In figure 2, please make y-axis scale consistent (e.g. 2A consistent with 2B, 2C consistent with 2D) as this makes it much easier for the reader to compare and contrast the data2.

In all figures showing gene copy number overviews (4, 5 and S3), the chromosomes not in order (i.e. chromosomes 10 and 11 should come after 9). I feel these figures would be improved by keeping chromosomes in increasing number, left to right.

In figures 5A and 5B, does the 'c' refer to culture or clone? This should be clarified. In addition, it is difficult to tell which colour represents which culture/clone in panel A in particular.

Line 308 - Could the authors explain why the translation factor eIF3c result (significant decrease in copy number) is an "odd" one?

In the legends for Tables 1 and 2 it is worth mentioning that the "Tb927" prefix has been removed for legibility.

Several genes/proteins are discussed in the study (e.g. HSP70, histones). However, the implications of these changes, and their potential significance, are not discussed. A paragraph on what these changes could mean for the biology of the parasite would be of interest.

As mentioned above, lack of PAD1 staining in MAK98 does not necessarily mean differences in tissue distribution compared to MAK65 and this sentence should be revised.

Can the authors comment on expected differences in drug sensitivity in these strains compared to Lister 427 and EATRO1125? For example, changes in pteridine transporter copy number, if reflected at the protein level, could impact anti-folate uptake.

A previous study investigating transcriptomics of culture-derived vs. in vivo derived T. brucei concluded that in vitro-cultured cells can be used as alternatives to animal-derived parasites (PMID: 29606092). How do the results presented here impact the findings of this previous study?

Significance

The availability of new strains to study African trypanosomes is significant. Furthermore, low passage number means that these strains more accurately reflect naturally occurring strains in the field, compared to the commonly used EATRO1125 and Lister 427, both of which have been adapted for laboratory use for decades.

The finding that adaptation to laboratory culture has a significant effect on gene copy number is also of importance, and suggests variation in karyotype between strains in different labs is common, even if they are derived from the same original isolate. The logical next step is to find out how these karyotype differences impact cell biology.

Few trypanosome strains are routinely used in a laboratory setting, in particular the monomorphic Lister 427 and the pleomorphic EATRO1125 (AnTat1.1). The adaptation of two novel strains with minimal passage and differing virulence is a significant outcome. In the past, adaptation involved growing parasites on feeder layer cells but addition of cysteine removes this requirement (PMID: 4045385). Comparison of culture- and animal-derived T. brucei has been carried out before (for example, PMID 29606092), and in addition, genomic analysis has previously suggested aneuploidy does not occur (PMID: 30256189). Therefore, the finding that adaptation to in vitro culture can result in changes in gene copy number is novel, and significant. It is unknown whether these changes are reflected at the mRNA or protein level and this is an important next step.

The main audience for these findings is the trypanosome research community, especially groups working on the genomics of African trypanosomes. In addition, the findings are of significance (as mentioned in the manuscript) to researchers designing genetic manipulation experiments with culture-derived trypanosomes.

The background of this reviewer is in biochemical parasitology, with work focusing on livestock trypanosomes (Trypanosoma congolense, Trypanosoma vivax). Key words: Metabolism, Metabolomics, Transcriptomics, Drug mode of action, Drug resistance.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The authors present an analysis of gene copy number in Trypanosoma brucei lines based on resequencing. The analysis includes 2 strains isolated very recently, which are sequenced straight from passage in animals and following culture in flasks. They then compare gene copy number in these strains to T. brucei lines commonly used in the lab and reference DNA probably isolated after multiple passages through animals, concluding that there are some genes that consistently change copy number on adaptation to culture. There is some overlap with the authors' previous work [PMID: 26715446] comparing isolates of T. b. rhodesiense (data also included here), but here they have the addition of lines pre and post adaptation to culture, and focus more on gene copy number over transcriptomic changes.

The question is a really interesting one: what are labs selecting for when they adapt these parasites to culture? The authors generate some useful data on this subject and suggest some of the additional analysis that could be done with these data in future in the Outlook section. However, I have 2 major comments about the analysis presented that I believe affect whether it can be used to support the conclusions made in this manuscript.

Major comments:

1) In the analysis, the authors' discuss variation in measured copy number as if representing genuine changes in gene number. However, variation can also result from differences in sequencing depth or be introduced during DNA isolation, processing, sequencing and/or mapping. In my opinion, this variation is not adequately dealt with in the analysis presented and as a result at least some of the subsequent interpretations are likely to be incorrect. While array copy numbers in particular would be expected to become more heterogeneous as a population ages, this is unlikely to affect the majority of diploid genes and should not greatly influence the distribution around copy number of 2. Moreover, where average diploid genes do change, they are expected to do so across whole chromosomes or large sections of chromosomes, which does not appear to be the pattern seen in S3 Fig. Moreover, heterogeneity in the population of chromosomes present in the culture resulting from changes in replication/segregation [as suggested in the text] would affect the spread of copy numbers between chromosomes, but not the distribution on each chromosome (as the genes are still linked even when the average chromosome copy number is non-integer). For example, for the distribution broadening seen in MAK98_cA (which appears to be broadening of the distribution at all positions on all chromosomes) to be the result of a biological process would require widespread and extreme fragmentation of the chromosomes. I would suggest this is very unlikely against the alternative of variation introduced during DNA processing, sequencing and/or mapping - and indeed, this is the likely the cause of for the majority of observed difference in the overall distribution around 2 copies.

This source of variation in copy number estimates needs to be accounted for in testing for differences as the certainty for each estimate will vary with sample preparation and also gene length. In addition, the distributions for Lister427 1313 and MAK98_cA probably suggest that these should be removed from the analysis.

2) The lines being analysed are not phylogenetically independent, meaning that similarity can reflect shared ancestry rather than selection. The 3 lines derived from Lister 427 are obviously expected to be very closely related, and the authors have previously shown that the 4 T. b. rhodesiense isolates are highly similar (suggesting recent common ancestor) [PMID: 26715446]. In addition, although EATRO1125 was derived independently, this too might be more closely related to 427 than to the new lines. As such, copy number in each cannot be taken as an independent measurement. I'm afraid I think this makes the statistical approach taken to identify 'reproducible' gene copy number changes invalid. For example, the monooxygenase Tb927.9.1400 in Fig. 6E is present as 2 copies in T. b. rhodesiense isolates and 3 copies in EATRO1125/Lister427 lines, but this probably only represents a single difference between 2 clades, not 8 independent measurements. On adaptation to culture this gene is unchanged in MAK98 (excluding cA) and goes from 2→3 in MAK65. This last observation is interesting (especially if the 4 MAK65 lines are completely independent, which wasn't clear from the text), but whether such change is statistically significant across the whole genome needs testing.

Notwithstanding the above, unique to this study are the copy number estimates for the same populations pre and post adaptation. I think if the authors could apply a method that tests for directional changes in these while accounting for effects of gene size and the heterogeneity due to sequencing discussed above, then an analysis of which of these were observed in both MAK65 and MAK98 - and which also shared by Lister427/EATRO1125 - would be very interesting and potentially very informative, but I don't think the current analysis is sufficient to support the claims made. The authors may want to consult a statistician, but I suspect biological replicates of the sequencing samples will be required.

Minor comments:

The concept of "gene ploidy" is rather unhelpful and I would suggest that the authors make a more strict distinction between changes in ploidy (caused by duplication/loss of chromosomes) and changes in gene copy number (predominantly due to array expansion/contraction). For example, a gene with copy number of 4 can exist as 2 copies on each of 2 homologous diploid chromosomes - and an expansion of one array that increases the copy number to 5 is not a change in ploidy.

The purpose of Fig 1A/B is to compare the infections between 2 T.b. isolates (MAK65/98), but the parasitaemias are displayed on different scales and with different transforms making such comparison extremely difficult. Same for Fig 2A-D and F-G - text discusses growth rate, but can't be judged on linear scale.

Very difficult to see PAD1 staining when overlaid with transmitted light (Fig1D) - could this be separated?

Discussion is made comparing the proportion of cells with PAD1 staining, but it is unclear how many cells and replicates this is based on - percentages should be given with estimate of SEM, confidence intervals or similar.

I found naming of the cultures derived from MAK65 and MAK98 confusing and counter-intuitive. I believe the following to be correct:

MAK65 cA/cB are independent cultures with a total of 30 days in culture and the doubling time ~7h (after ~day 20) MAK65 cC/cD have only 17 days in culture but achieved a similar doubling time to cA/B after ~day 8. MAK98 cA has 33 days in culture, but grew slowly throughout (doubling time ~13h). MAK98 cB has 24 days in culture including a freezing step, but doubling time dropped to 10h after ~day 9. This really needs to be clearer in the manuscript and it would be extremely useful to have some indication of the number of generations each line has been through in culture. To me, it was confusing that MAK65 cA/cB have been longer in culture than cC/cD.

"Our observations on copy number are the tip of the iceberg: a survey of just a single ~10 kb region revealed selection for smaller insertions, deletions and point mutations (S4 Fig).". S4 Fig shows a region around a repeated HSP70 gene, with a large number of SNP/INDELs versus reference. As would be expected, some of these are haplotype-specific, but changes in the ratio of the sequencing reads between the haplotypes should not be presented as evidence for selection without a statistical test for enrichment.

The Supplemental data are an important resource here. They are too large to check extensively, but in trying to reproduce the copy number estimates for the unique genes, I found that the read counts for MAK98 and EATRO1125 in Sheet 5 to be identical except for some NA values in EATRO1125. This is presumably an error. I was also surprised that no genes have 0 count when DNA was mapped to 927 reference - except for DNA from 927 itself which is the one sample I'd expect not to have this behaviour. Is there an explanation for this?

Significance

Combined with evidence, reproducibility and clarity.

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Reply to the reviewers

Response to Reviewers

We thank the reviewers for their valuable and pertinent suggestions that helped us immensely in revising our manuscript. Listed below are the responses to reviewers’ comments where reviewer’s comments are highlighted in bold while the responses are in normal font.

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

As HIV-1 bnAbs show extensive somatic mutations and have several unique properties such as long CDR and ability to recognize glycopeptides, there is a possibility that some of them can cross-react with SARS-CoV-2 and a fraction of these could even neutralize the virus. To explore if this is true, Mishra et al from the Luthra Lab study investigated the cross-reactivity of existing HIV-1 neutralizing antibodies with purified and stabilized SARS-CoV-2 trimeric ectodomain S2P and receptor binding domain. Indeed, they find that antibodies that recognize the CD4 binding site and MPER region of HIV-envelope show fairly strong reactivity to SARS-CoV-2 RBD and S2P like the CoV-1 cross reactive antibody CR3022. They further go on to test neutralization using pseudoviruses decorated with SARS-CoV-2 spike and find that one of the several binding antibodies, N6 can indeed neutralize, though not potently as expected. They also assessed the cross-reactivity and neutralization of plasma from children with chronic HIV-1 infection to SARS-CoV-2 pseudoviruses. Some sample of plasma indeed showed neutralization of SARS-CoV-2 pseudoviruses. Based on these observations, the authors propose cross-reactive epitopes such as the N6 epitope can be used as a blueprint to engineer variant super-antibodies that can be effective against several viral pathogens including SARS-VoV-2. Overall, the study is well conducted and detailed with clarity.

Q1. The authors should include an authentic SARS-CoV-2 neutralizing antibodies in the study as a positive control. There are several RBD as well as NTD binding nAbs that have been identified. This will allow one to assess the extent of cross-reactivity and neutralization exhibited by HIV-1 bnAbs and gauge the extent of practical utility. CR3022 while important, is not enough.

R1. We have now included an Anti-RBD nAb, CC12.1, as positive control for both binding assay and neutralization assays (line numbers 73 – 77).

Q2. Some plasma of HIV-1 infected children showed neutralizing activity against SARS-CoV-2. However, the neutralization seen may not be solely due to HIV-1 bnAbs in plasma. Antibodies to other human coronaviruses likely contributes to this neutralization. The authors should also test reactivity/neutralization to 229E, NL63, OC43, HKU1, MERS spike/pseudoviruses to support their claim. In its absence, the authors should tone down their interpretation and provide alternate explanation.

R2. We have now performed neutralization assays with the plasma from all the HIV-1 infected children against all seven coronaviruses (SARS-CoV-1, SARS-CoV-2, HKU1, NL63, MERS, OC43, and 229E) (line numbers 150 – 160 and 551 – 564).

Q3. The authors start out with the following premise "Given the unique nature of HIV-1 bnAbs and their ability to recognize and/or accommodate viral glycans, we reasoned that the glycan shield of SARS-CoV-2 spike protein can be targeted by HIV-1 specific bnAbs". While HIV-1 envelope is heavily glycosylated, it is interesting to note that the cross-reactive HIV-1 bnAbs target RBD of SARS-CoV-2 spike protein which is not heavily glycosylated (2 sites). In absence of glycosylations-probing experiments, it is difficult to justify this premise. Rather the extensive SHM and self-reactivity (MPER-directed antibodies) could be the likely reason for cross-reactivity.

R3. We have discussed the polyreactivity and/or autoreactivity of HIV-1 bnAbs as one of the plausible reasons for the observed cross-reactivity with SARS-CoV-2 (line numbers 166 – 180).

Minor

Q4. The color profile of in figure 3 needs to be revised or symbols added to allow the curves to be distinguished from each other.

R4. We have changed the color profile of figure 3 and have added symbols as well (line number 527__). Figure 3 from the original manuscript is now figure 4 in the revised manuscript.

Reviewer #2 (Significance (Required))

This is a significant study that highlights the concept of developing a broadly cross-reactive antibodies that may be effective against multiple pathogens, a concept not usually ascribed to antibodies that are known for their specificity unlike drugs that can be repurposed. In-depth study of epitopes that can elicit multi-pathogen cross-reactive/neutralizing antibodies can on the other hand allow generation of antigenic templates that can be effective as vaccines. This study is suitable for broader audiences to disseminate above-mentioned concepts.

**Referee Cross-commenting**

Q1. There is a lot of evidence now that lenti-pseudoviruses are a good surrogate for identifying SARS-CoV-2 neutralizing antibodies. That said the authors may want to ensure the data is reproducible even with authentic SARS-CoV-2 viruses as reviewer 2 suggests to avoid surprises.

R1. We have now tested the ability of N6 to neutralize authentic SARS-CoV-2 virus using a cytopathic effect (CPE) based neutralization assay. N6 failed to show any reduction in CPE upto a concentration of 20 µg/ml (line numbers 135 – 141).

We have now discussed N6’s inability to neutralize authentic virus in discussion section (line numbers 181 – 187).

Q2. I also agree with the Reviewer 2 that structural analyses of the cross-neutralizing antibody with SARS-CoV-2 spike will strengthen the manuscript.

R2. While we do agree with both the reviewer’s suggestion that a structural analysis of N6 binding with SARS-CoV-2 will provide details into the exact nature of epitopes-paratopes that are responsible for the cross-reactivity observed, we believe it to be out of scope for the current work. The current work was designed to show the ability of polyreactive and somatically hypermutated antibodies generated in chronic HIV-1 infection to bind to new and emerging pathogens such as SARS-CoV-2. While only N6 showed neutralization of SARS-CoV-2, it is also noteworthy that several HIV-1 bnAbs showed significant binding to both purified RBD as well as surface expressed spike from SARS-CoV-2. As all these distinct bnAbs have different paratopes, a detailed structural analysis to understand the exact nature by which several distinct bnAbs cross-reacted with SARS-CoV-2 is not feasible. We are optimistic that our results will provide that impetus for future detailed structural studies (line numbers 194 – 198__).

Q3. Also, the authors may want to avoid 293T-ACE2 cells as their targets for carrying out neutralization assays. There is data now from Davide Corti's group that indicate ACE2 overexpression can significantly underestimate the neutralizing capacity of antibodies especially those that bind to RBDs (bioRxiv 2021.04.03.438258; doi: https://doi.org/10.1101/2021.04.03.438258). This may be particularly true for N6. A cell line like Vero E6 that endogenously express ACE2 would be the way to go. If the authors decide to use Lenti pseudoviruses as the first step in Vero cells, please make sure to use G89V mutated HIV gag-pol to avoid TRIM5alpha restriction.

R3. Based on reviewer’s suggestion, we have now performed neutralization assays for all bnAbs using both HEK293T/ACE2 cells and Vero-E6 cells. We have used an HIV-1 proviral backbone that does contain G89V mutation in the gag-pol region (line numbers 96 – 121).

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

The authors report that HIV-1 sp-bnMAb cross-neutralized SARS-CoV-2 pseudotyped virus in vitro. The finding that N6 can block HIV-based-SARS-CoV-2 pVs from entering 293T-ACE2 cells is intriguing and requires further investigation before the manuscript can be considered for publication.

Major comments

Q1. The authors must demonstrate that N6 is capable of neutralizing authentic SARS-CoV-2.

R1. We have now tested the ability of N6 to neutralize authentic SARS-CoV-2 virus using a cytopathic effect (CPE) based neutralization assay. N6 failed to show any reduction in CPE upto a concentration of 20 µg/ml (line numbers 135 – 141).

We have now discussed N6’s inability to neutralize authentic virus in discussion section (line numbers 181 – 187).

Q2. The authors should also demonstrate it works in another system i.e., recombinant VSV/SARS-CoV-2 (used by Naveenchandra Suryadevara et al., 2021 "Neutralizing and protective human monoclonal antibodies 1 recognizing the N2 terminal domain of the SARS-CoV-2 spike protein" https://doi.org/10.1016/j.cell.2021.03.029) which is a well validated system for SARS-CoV-2 neutralization activity.

R2. We have additionally used a MLV based pseudovirus system to further increase the robustness of neutralization assays. In addition, we have now used three distinct cell lines (HEK293T/ACE2, Vero-E6 and Huh7 cells) to exclude any bias in neutralization assays due to endogenous overexpression of ACE2 on HEK293T cells (line numbers 96 – 121).

Q3. Please, reference the HIV-1 based SARS pVs neutralization assay used in this work if it has been used earlier, either by your team or others. Details regarding the performance of this particular assay for SARS-CoV-2 Nab detection will aid the reader to understand its robustness and weaknesses.

R3. We have provided several references for both the HIV-1 lentiviral and MLV retroviral pseudovirus neutralization assays. Furthermore, we have added detailed results for optimization of neutralization assays that we performed (line numbers 96 – 121 and figure 3). In addition, we have discussed the limitation and weaknesses of pseudovirus neutralization assays in comparison to authentic virus neutralization assays (line numbers 183 – 187).

Q4. In the method section, a list of all controls used to guarantee neutralization assay performance must be included (i.e., positive and negative mAb controls, virus controls, etc.); additionally, it might be necessary to show raw data of all control results.

R4. We have provided the detail of all controls that were used to guarantee the robustness of neutralization assay in the methodology section of “neutralization assay.’ Furthermore, we have now added an entire section in the results titled, ‘Optimized conditions for neutralization of pseudotyped coronaviruses,’ where we now provide all the technical details that were optimized to ensure the robustness of the neutralization assay (line numbers 96 – 121).

Q5. Control description are missing in the method section for all or most of the assays (some descriptions are found in the result section, but definitely they should be found in M&M, for each technique).

R5. We have now provided the description for all the controls in methodology section too.

Q6. The authors draw some similarities between HIV-1 Env and SARS-CoV-2 Spike proteins from structure and glycosylation point of view, but the fact that N6 binds to ENV-CD4bs is beyond these pictures and for that reason a detailed structural analysis MUST be performed to show the mechanism of cross-neutralization and corroborate it exists.

R6. While we do agree with both the reviewer’s suggestion that a structural analysis of N6 binding with SARS-CoV-2 will provide details into the exact nature of epitopes-paratopes that are responsible for the cross-reactivity observed, we believe it to be out of scope for the current work. The current work was designed to show the ability of polyreactive and somatically hypermutated antibodies generated in chronic HIV-1 infection to bind to new and emerging pathogens such as SARS-CoV-2. While only N6 showed neutralization of SARS-CoV-2, it is also noteworthy that several HIV-1 bnAbs showed significant binding to both purified RBD as well as surface expressed spike from SARS-CoV-2. As all these distinct bnAbs have different paratopes, a detailed structural analysis to understand the exact nature by which several distinct bnAbs cross-reacted with SARS-CoV-2 is not feasible. We wish that our results will provide that impetus for future detailed structural studies (line numbers 194 – 198).

Q7. As the authors may realize, based on previous comments, any false positive results that could have biased the main conclusion of this article, must be excluded in order to claim such intriguing finding.

Wherever applicable, we have tried to use proper positive and negative control and have tried to tone down the inferences.

Q8. Regarding BINDING AND NEUTRALIZATION OF SARS-COV-2 WITH PLASMA FROM HIV-1 INFECTED PAEDIATRIC PATIENTS, how can you discard previous exposure to actual SARS-CoV-2 in such population, and/or previous infection with other common hu-CoVs, which has been shown to elicit cross reactive SARS-CoV-2 Abs.

R8. All the pediatric plasma samples used in the binding and neutralization assays were pre-pandemic (2013-2014). While we do agree that exposure to common endemic coronaviruses might have elicited some degree of cross-reactive SARS-CoV-2 antibodies, we have now performed neutralization assay for all seven coronaviruses (including common endemic coronaviruses HKU1, OC43 and 229E) using the plasma that showed SARS-CoV-2 neutralization (line numbers 150 – 160 and figure 6__).

Minor comments can be addressed once these main concerns are solved.

Reviewer #3 (Significance (Required)):

Q9. The fact that N6 (and plasma from HIV-1 infected patients) can neutralize SARS-CoV-2 is novel and intriguing; however, these observations must be supported with new experiments. Showing that N6 neutralizes authentic SARS-CoV-2 would be a key point to address. Moreover, the mechanism for such interaction should be explored using a detailed structural analysis.

R9. Please see response to query 6.

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Referee #2

Evidence, reproducibility and clarity

The authors report that HIV-1 sp-bnMAb cross-neutralized SARS-CoV-2 pseudotyped virus in vitro. The finding that N6 can block HIV-based-SARS-CoV-2 pVs from entering 293T-ACE2 cells is intriguing and requires further investigation before the manuscript can be considered for publication.

Major comments:

The authors must demonstrate that N6 is capable of neutralizing authentic SARS-CoV-2.

The authors should also demonstrate it works in another system i.e., recombinant VSV/SARS-CoV-2 (used by Naveenchandra Suryadevara et al., 2021 "Neutralizing and protective human monoclonal antibodies 1 recognizing the N2 terminal domain of the SARS-CoV-2 spike protein" https://doi.org/10.1016/j.cell.2021.03.029) which is a well validated system for SARS-CoV-2 neutralization activity.

Please, reference the HIV-1 based SARS pVs neutralization assay used in this work if it has been used earlier, either by your team or others. Details regarding the performance of this particular assay for SARS-CoV-2 Nab detection will aid the reader to understand its robustness and weaknesses.

In the method section, a list of all controls used to guarantee neut assay performance must be included (i.e., positive and negative mAb controls, virus controls, etc.); additionally, it might be necessary to show raw data of all control results.

Control description are missing in the method section for all or most of the assays (some descriptions are found in the result section, but definitely they should be found in M&M, for each technique).

The authors draw some similarities between HIV-1 Env and SARS-CoV-2 Spike proteins from structure and glycosylation point of view, but the fact that N6 binds to ENV-cd4bs is beyond these picture and for that reason a detailed structural analysis MUST be performed to show the mechanism of cross-neutralization and corroborate it exists.

As the authors may realize, based on previous comments, any false positive result that could biased the main conclusion of this article, must be excluded in order to claim such intriguing finding.

Regarding BINDING AND NEUTRALIZATION OF SARS-COV-2 WITH PLASMA FROM HIV-1 INFECTED PAEDIATRIC PATIENTS, how can you discard previous exposure to actual SARS-CoV-2 in such population, and/or previous infection with other common hu-CoVs, which has been shown to elicit cross reactive SARS-CoV-2 Abs.

Minor comments can be addressed once these main concerns are solved.

Significance

The fact that N6 (and plasma from HIV-1 infected patients) can neutralize SARS-CoV-2 is novel and intriguing; however, these observations must be supported with new experiments. Showing that N6 neutralizes authentic SARS-CoV-2 would be a key point to adress. Moreover, the mechanism for such interaction should be explored using a detailed structural analysis.

Referee Cross-commenting

I am happy to realize that there is an agreement in the major points observed by both Reviewers. These comments Must be answered properly by the authors now.

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Referee #1

Evidence, reproducibility and clarity

As HIV-1 bNabs show extensive somatic mutations and have several unique properties such as long CDR and ability to recognize glycopeptides, there is a possibility that some of them can cross-react with SARS-CoV-2 and a fraction of these could even neutralize the virus. To explore if this is true, Mishra et al from the Luthra Lab study investigated the cross-reactivity of existing HIV-1 neutralizing antibodies with purified and stabilized SARS-CoV-2 trimeric ectodomain S2P and receptor binding domain. Indeed they find that antibodies that recognize the CD4 binding site and MPER region of HIV-envelope show fairly strong reactivity to SARS-CoV-2 RBD and S2P like the CoV-1 cross reactive antibody CR3022. They further go on to test neutralization using pseudoviruses decorated with SARS-CoV-2 spike and find that one of the several binding antibodies, N6 can indeed neutralize, though not potently as expected. They also assessed the cross-reactivity and neutralization of plasma from children with chronic HIV-1 infection to SARS-CoV-2 pseudoviruses. Some sample of plasma indeed showed neutralization of SARS-CoV-2 pseudoviruses. Based on these observations, the authors propose cross-reactive epitopes such as the N6 epitope can be used as a blueprint to engineer variant super-antibodies that can be effective against several viral pathogens including SARS-VoV-2. Overall the study is well conducted and detailed with clarity.

1. The authors should include an authentic SARS-CoV-2 neutralizing antibodies in the study as a positive control. There are several RBD as well as NTD binding nABs that have been identified. This will allow one to assess the extent of cross-reactivity and neutralization exhibited by HIV-1 bNAbs and guage the extent of practical utility. CR3O22 while important, is not enough.
2. Some plasma of HIV-1 infected children showed neutralizing activity against SARS-CoV-2. However the neutralization seen may both be solely due to HIV-1 bnAbs in plasma. Antibodies antibodies to other human coronaviruses likely contributes to this neutralization. The authors should also test reactivity/neutralization to 229E, NL63, OC43, HKU1, MERS spike/peusdoviruses to support their claim. In its absence, the authors should tone down their interpretation and provide alternate explanation.
3. The authors start out with the following premise "Given the unique nature of HIV-1 bnAbs and their ability to recognize and/or accommodate viral glycans, we reasoned that the glycan shield of SARS-CoV-2 spike protein can be targeted by HIV-1 specific bnAbs". While HIV-1 envelope is heavily glycosylated it is interesting to note that the cross-reactive HIV-1 bnAbs target RBD of SARS-CoV-2 spike protein which is not heavily glycosylated (2 sites). In absence of glysosylation-probing experiments, it is difficult to justify this premise. Rather the extensive SHM and self-reactivity (MPER-directed antibodies) could be the likely reason for cross-reactivity.

Minor:

1. The color profile of in figure 3 needs to be revised or symbols added to allow the curves to be distinguished from each other.

Significance

This is a significant study that highlights the concept of developing a broadly cross-reactive antibodies that may be effective against multiple pathogens, a concept not usually ascribed to antibodies that are known for their specificity unlike drugs that can be repurposed. In-depth study of epitopes that can elicit multi-pathogen cross-reactive/neutralizing antibodies can on the other hand allow generation of antigenic templates that can be effective as vaccines. This study is suitable for broader audiences to disseminate above-mentioned concepts.

Referee Cross-commenting

1.There is a lot of evidence now that lentipseudoviruses are a good surrogate for identifying SARS-CoV-2 neutralizing antibodies. That said the authors may want to ensure the data is reproducible even with authentic SARS-CoV-2 viruses as reviewer 2 suggests to avoid surprises.

1. I also agree with the Reviewer 2 that structural analyses of the cross-neutralizing antibody with SARS-CoV-2 spike will strengthen the manuscript.
2. Also the authors may want to avoid 293T-ACE2 cells as their targets for carrying out neutralization assays. There is data now from Davide Corti's group that indicate ACE2 overexpression can significantly underestimate the neutralizing capacity of antibodies especially those that bind to RBDs (bioRxiv 2021.04.03.438258; doi: https://doi.org/10.1101/2021.04.03.438258). This may be particularly true for N6. A cell line like Vero E6 that endogenously express ACE2 would be the way to go. If the authors decide to use Lenti pseudoviruses as the first step in Vero cells, please make sure to use G89V mutated HIV gagpol to avoid TRIM5alpha restriction.

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Reply to the reviewers

Manuscript number: RC-2021-00733

Corresponding author(s): Haiyun Gan

Sara Monaco, PhD Managing Editor Review Commons

Dear Dr. Monaco,

We would like to thank all the reviewers for their insightful and constructive comments, which have helped us to improve our work. Please find below our responses to each of the concerns raised by the reviewers.

Sincerely

Haiyun

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

**Summary:**

• In this article, Li and colleagues demonstrate the utility of a novel proximity labeling-based strategy, which they term AMAPEX (antibody-mediated protein A APEX) for the proteomic characterization of the protein environment of specific histone modifications. They apply this new methodology in mouse embryonic fibroblasts for subsequent purification of biotinylated proteins and mass-spectrometric identification. **Major comments:**

• The authors present a high quality, descriptive manuscript that introduces a novel proximity labeling approach from a mostly technical point of view. The presentation of the data and methods are mostly clear, such that they should be easy to reproduce.

  We thank the reviewer for the positive view of our work.

• The biggest shortcoming of the study in its current form seems to be the lack of a proper assessment of the method's sensitivity AND -most importantly also- specificity. The authors do not validate potentially new interactors of the modified histones experimentally, which would highlight their technology as a discovery tool. Without the assessment of newly identified proteins, these could simply represent false positives, which would point towards an additional requirement for optimization of the experimental setups. In that light the newly identified proteins are merely potential histone interactors. Validation would require establishment or purchase of additional antibodies, or alternatively cloning and transfection of respective candidates, which will probably take an extra 2-3 months of work. While the study may be publishable in its current form, this validation seems a valuable investment to strengthen the preliminary conclusions.

  We agree with the reviewer that lack of validation of our approach is the major shortcoming of our study. As described in our manuscript, we could identify most of the histone interacting proteins found by other methods in the published data(Vermeulen et al, 2010; Villasenor et al, 2020) (fig. 2A). We also provide evidence that the H3K27me3 proteins (NSD1, Brd9) identified by H3K27me3 AMAPEX are co-localized with H3K27me3 at the same genomic regions (fig. 2C). In addition, as we observed the enrichment of splicing proteins of the H4K5ac interacting proteins, we purified nucleosomes and performed SF3B1 IP and confirmed the interaction of SF3B1 with H4K5ac. We also performed H3K27me3 mono-nucleosome IP and confirmed the interaction between H3K27me3 and NSD2. Together, these results suggested that AMAPEX is a reliable method to map histone proximal proteins. We have added the SF3B1/H4K5ac, H3K27me3/NSD2 binding data to the revised manuscript (Fig. 2E and Fig. 7C).

• With two biological replicates the study fulfills the minimum requirements for reproducibility, however, it would benefit from an additional replicate.

  We added extra replicates in the assays for H3K27me3 and H3K4me3 (Fig. 2A and Fig. 6A ).

**Minor point:**

• The sentences in lines 22f (and 44f) could be misunderstood. Please rephrase statements to be unambiguous that it specifies the proteomic surrounding of post-translationally modified proteins. The proximity labeling technologies may themselves be limited in identifying post-translationally modified proteins, as they label reactive side-chains that are often targeted by PTMs. Post-translationally modified lysine residues cannot be targeted by biotin ligase-based methodologies, as tyrosine phosphorylations cannot be assessed by peroxidase-based labeling.

  We agree that these sentences are confusing. We rephrased the sentence to “cannot be applied to identify proteins surrounding of post-translationally modified proteins” and “mapping the biomolecules that are proximal to post-translationally modified (PTM) proteins like histone modifications is complex” in our revised manuscript.

Reviewer #1 (Significance (Required)):

• From a technical point of view, most of the key conclusions of this paper are convincing. Assessing the proteomic environment of post-translationally modified proteins is of extremely high interest and the methodology seems broadly applicable to address such questions. It should be noted that proximity labeling has not originally been utilized to map protein-protein interactions, as the enzymatic activities available probe their surroundings, this could be an over-interpretation. Nonetheless, the "proteomic surrounding" of target proteins, which would also include hard to identify transient interactions is of high general interest for molecular biologists.

  Thanks so much, we have rephrased our sentences in the revised manuscript.

• Although it can be generally agreed that having to express an exogenous fusion protein is a limitation of current proximity labeling setups, the methodology presented here in turn has the limitation that it cannot be performed in living cells, which is a significant disadvantage.

  We agree that it is a disadvantage that our assay cannot be done in living cells, as most of the antibody-based assays have these limitations. However, we have successfully applied the AMAPEX to the cell samples under native conditions and we have included the results in the revised manuscript (Fig. 8)

Moreover, our method working under fixation conditions can be potentially applied to FFPE samples. We will add discussion to our revised manuscript.

• My lab is establishing and constantly improving various proximity labeling methodologies in combination with mass spectrometry for sub-organellar proteomics. While the technology is sound and well executed, I am not an expert in the biology of histone modifications and the proteins involved and cannot assess the novelty nor the actual value of the generated datasets. It appears to me though that additional validation by independent experiments would strengthen the manuscript. The authors describe the usefulness of the technology mainly in confirming known interactors of modified histones, however, it would be nice (for non-specialists) to explicitly state how high the coverage of the known interactors is and discuss why some of them might have been missed.

  Thanks for the suggestion. We have added the statement of the coverage of the known interactors in our revised manuscript and we also include discussions.

• The manuscript in its current form completely lacks a discussion of the presented data. Even if the focus will remain on the technical aspects, the findings should be properly discussed by comparison to other proteomics approaches studying histones. Ideally the data should also be discussed in the light of current proximity labeling technologies and potential future directions.

  We have added the discussion part to our revised manuscript.

• While I cannot assess the value for histone research, the manuscript will be very interesting for experts focusing on proximity labeling technologies and subcellular proteomics. Reviewer #2 (Evidence, reproducibility and clarity (Required)):

• This work describes interesting approach for mapping interactome of specifically modified histone protein using antibody-based APEX2. Although it contains interesting results and useful techniques for biological community, I found that it requires revision and addition for the publication.

• I could understand the reason using antibody-based proximity labeling approach for mapping the biomolecules that interact with post-translationally modified histone because it should be complicated to map it with exogenous protein expression approach. However, the introduction of antibody-conjugated APEX2 should require fixation and permeabilization steps that can usually compromise ultrastructure. The authors should comment whether these procedures can affect protein composition and structures of nucleosome in the Discussion part of this manuscript.

  We agree with the reviewer. To address the concerns raised here, we have performed AMPEX under native conditions (H3K37me3). Our results showed that AMPEX under native conditions can still identify most of the surrounding proteins identified with crosslink approach. We have added the data to the revised manuscript.

There are also advantages to perform the AMAEX with fixed samples, which may help capture the temporary events. We have added this to the discussion of the revised manuscript.

Formaldehyde crosslinking may cause undesired effect. For instance, the nuclear proteins/loci are not equally efficiently cross-linked; cross-linking may trigger the DNA damage response; crosslinking can also mask epitopes of some antibodies, affecting antibody/antigen binding. However, most of the approaches studying chromatin/nucleosome in cells requires crosslinking and permeabilization. We have included discussion about whether fixation and permeabilization affects protein composition and structures of nucleosome.

• For generation of biotin-phenoxyl radical, HRP-conjugated antibody can be utilized as shown in BAR method (Daniel Z Bar et al. Nat Methods, 2018, 15, 127-133). Since secondary-HRP antibody is commercially available, one cannot make an effort to express and purify pA-APEX2 for this approach. The authors should clearly explain why they selected APEX2 and what is an expected advance(s) using APEX2 in their approach

  We have compared pA-APEX2 with secondary-HRP and we found that pA-APEX2 can give better specificity as there more proteins identified by H3K27me3 mediated APEX2 located in the nuclear than that of HRP. We found that while only 33.31% of proteins identified by HRP based method locates in the nuclear, 69.4% of that identified by pA-APEX2 locates in nuclear, which implicates the increased specificity of the pA-APEX2 method compared to HRP conjugated 2nd antibody. We have added to data to revised manuscript (Fig. 3C, D).

• For the detection of the APEX-mediated biotinylated proteins, direct mass identification of tyrosine-modified peptides with chemical probes can tell the most correct information of the proximal proteins (see Lee SY et al. J. Am. Chem. Soc. 2017, 139, 3651-3662; Namrata D Udeshi et al. Nature Methods 2017, 14, 1167-1170). Thus, if the authors can obtain the biotin-modified peptide information from each antibody-conjugated APEX2, the quality of their interactome results should be much improved. If authors may be under the situation that cannot conduct further mass experiments, it might be required to check whether their important finding molecules (e.g. Arid2, Brd7, Nsd2) are really "biotinylated" by conducting Streptavidin-HRP western blot experiment after enrichment of those proteins by using primary antibody. If biotinylation is specifically conducted by pA-APEX2 with H3K27me3 antibody, the authors can observe SA-HRP blot signal on the enriched protein band on the membrane. Negative controls should be the samples omit pA-APEX2, H3K27me3 antibody, biotin-phenol, H2O2, respectively or using different PTM targeted primary antibody. This result can confirm that their findings are enriched from proximity-dependent biotinylation of APEX2, not from spurious binding events to the other biotinylated proteins or self-labeled bait proteins.

  We thank the reviewer’s suggestion. We agree that finding the biotinylated peptides of the discovered proteins in our experiments could make the results more convincing. A variable modification of biotin-phenol on tyrosine was added to the search setting of MaxQuant (version 1.6.10.43), which indeed led to the identification of very few (only ten) biotinylated peptides without the ones from Arid2, Brd7, or Nsd2. However, these results were not surprising. Since, we pulled down the low abundant target proteins based on the strong binding of biotin-streptavidin (Kd ≈ 10−14–10−16 M ) (Laitinen et al, 2007)., which is also unusually stable against heat, denaturants, extremes of pH and proteolytic enzymes(Wilchek et al, 2006). These were considered as the obvious advantages of the technology. Thereafter, to obtain the better peptide coverage, on-bead tryptic digestion was performed under a relatively mild condition (50mM HEPES pH8.0, 1μM CaCl2, and 2% ACN). Therefore, it is highly likely that, the biotinylated peptides were still trapped on the streptavidin beads but not detectable in the samples.

We indeed performed the Streptavidin-HRP western blot experiment and compared to the samples omit H3K27me3 antibody, H2O2, or samples of IgG, there is increased SA-HRP blot signal in the samples conducted by pA-APEX2 with H3K27me3 antibody (Supplementary Fig. 1F).

Reviewer #2 (Significance (Required)):

• For antibody-binding APEX2 strategy, this work is not the first one and the authors should mention the precedent work in the manuscript: Jisu Lee et al. Chem. Commun., 2015, 51, 10945-10948. And the author also commented the antibody-based proximity labeling mapping works including Daniel Z Bar et al. Nat Methods, 2018, 15, 127-133 in the manuscript.

  We are sorry for the oversight. We have rephrased our sentences in the revised manuscript. In addition, we have also compared our method with the one published in Nat Methods.

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

**Summary:**

• The manuscript by Li and coworkers describe an approach for detecting the PTM specific interacting proteomes through Proximity labeling strategy. In this method, cellular proteomes were first cross-linked by formaldehyde and then incubated with PTM specific antibodies. Afterwards, a protein A-APEX2 fusion protein was added to target the antibody and label the proteins in proximity with biotin. The biotinylated proteins can be enriched and subsequently identified by LC-MS/MS. The authors claim that these proteins are PTM-specific interacting proteins because they are close to the PTM-recognition antibody. They mainly applied the method to the histone marks and identified the interacting proteins for several histone modifications. They found that the identified proteins overlap partially with the published CHIP-seq data and drew complicated interaction networks based the proteomic data using the STRING database. **Major comments:**

• The APEX technology has been widely used for capturing subcellular proteome or proteome proximal to the target protein of interest. Here the authors aimed to use antibodies specific for detecting certain histone PTMs to guide the APEX enzyme for proximal labeling. While the idea is interesting, the actual applicability is rather limited as APEX does not enable identification of direct interactions. With the current methodology design, the reviewer did not see its advantage over a formaldehyde crosslinking followed by a conventional immunoprecipitation using the PTM-specific antibody. The authors are suggested to compare the head-to-head performance of these two approaches.

  We agree with the reviewer that APEX2 is for proximal labeling and we have rephrased our statement from “binding” or “interaction’ to “proximal”.

We have compared with the data of published work. (Vermeulen et al., 2010; Villasenor et al., 2020) (Fig. 2A) and we were able to identify most of the proteins identified in the published work. To improve our method, we performed AMPEX under native conditions and still got very robust labeling of nearby proteins, which is an improvement compared to the formaldehyde crosslinking followed by a conventional immunoprecipitation using the PTM-specific antibody method.

• From the methodological point of view, it is unclear why formaldehyde crosslinking is included here. Neither did the authors justify its necessity for the whole workflow. With treatment with 0.1% formaldehyde for proteome crosslinking, how about physiological status of the cells? Authors should assess survival of cells under formaldehyde treatment. After a long labeling time, will the labeling results still reflect the physiological status of the protein interaction networks in living cells?

  We agree with the reviewer regarding the role of crosslinking in our method. To this point, we have performed AMPEX under native conditions and still got very robust labeling of nearby proteins. On the other hand, crosslinking allows our method to be potentially used for FFPE samples.

• Overall, the manuscript lacks sufficient description on how the pipeline was optimized. For example, does it matter where the APEX2 is fused to protein A? The authors need to do a better job to present their optimization process.

  We found the activity is robust with current pA-APEX2. There is published data that there is only minimal effect on enzymatic activity when APEX2 is fused to Tn5 at different positions, which suggested APEX2’s enzymatic activity is unlikely to be affected by the location of fusion proteins.

• Crosslinking will result in high background in proximity labeling, in figure 1 D, the signal from IgG is obviously high. Strangely, the background signals completely disappeared in figure 2A. What are the differences between these two experiments? Similarly, in figure S4, the IgG lanes in A, B and C looked quite different from each other. The results suggested that the workflow might not be as robust as the authors have claimed.

  The high background of IgG in Fig. 4A in very possibly caused by the relatively less effective labeling by H3K4me3. As we responded before, we have included news results obtained under native conditions, which reduced background.

• The authors drew complicated interaction networks for each histone PTMs, however, a comparison between the networks for a modified histone versus the unmodified one is missing. Such data would be more valuable and informative as they can provide new clues on how the PTM mediates different interaction networks for cellular signaling.

  We have performed AMPEX with different Histone modification antibodies, while there are overlaps between these modifications, we could find proximal proteins specific identified by different histone modifications.

• The manuscript lacks the experimental validation of the identified interaction protein partners. The authors used the published CHIP-seq data, however, the cell lines of CHIP-seq are different from the one they used for APEX labeling. In this regard, an side-by-side comparison of this method with the CHIP-seq results should be performed, in terms of both purification efficiency and specificity.

  To validate our findings, we have purified nucleosomes and performed SF3B1 IP and confirmed the interaction of SF3B1 with H3K5ac. We have also performed mono-nucleosome H3K27me3 IP and confirmed the binding between H3K27me3 and NSD2. We have included these validated results in our revised manuscript.

• For the proteomic data, reproducibility calculation should use intensity ratio instead of intensity. The high dynamic of signal intensity will mislead the audience. P values between replicates should be calculated, a cutoff of

  We have calculated P values between replicates by t-test and added them in supplementary table s1.

**Minor comments:**

• Reference format should be checked. eg. redundant citation, incorrect format etc.

  We have checked our references for the errors and corrected them as many as we can find.

• Loading controls should be attached along with western blotting results (figure 1d, figure 2A).

  We have included the Ponceau S staining as loading controls of these western blotting results in our revised manuscript.

• This manuscript lacks clear conclusion or discussions. Subtitles are needed to delineate results. The overall logical organization of this manuscript should be strengthened. Currently, it is quite hard to follow and appreciate which part of the data is more technically novel and biologically significant.

  We have added subtitles and discussion in the revised manuscript.

• The title is too broad as the authors only showed the application on histone PTMs.

  We have changed our title to “Defining proximity proteomics of Histone modifications by antibody-mediated protein A-APEX2 labeling”.

• The introduction lacks proper description of other strategies for mapping PTM specific interactome, especially those by photo-affinity peptides or photo-affinity unnatural amino acids.

  We have added the other strategies for mapping PTM specific interactome in the introduction in the revised manuscript.

Reviewer #3 (Significance (Required)):

• It is more technical improvement by fusing protein A with APEX2 so that the proximity labeling can be guided around a specific PTM using the proper antibody. Researchers in the field of histone modifications will be interested in the current technique. The reviewer is with expertise in proteomics with focus on PTM analysis. References

Laitinen OH, Nordlund HR, Hytoenen VP, Kulomaa MSJTiB (2007) Brave new (strept)avidins in biotechnology. 25: 269-277

Vermeulen M, Eberl HC, Matarese F, Marks H, Denissov S, Butter F, Lee KK, Olsen JV, Hyman AA, Stunnenberg HGJC (2010) Quantitative interaction proteomics and genome-wide profiling of epigenetic histone marks and their readers. 142: 967-980

Villasenor R, Pfaendler R, Ambrosi C, Butz S, Giuliani S, Bryan E, Sheahan TW, Gable AL, Schmolka N, Manzo M et al (2020) ChromID identifies the protein interactome at chromatin marks. Nat Biotechnol 38: 728-736

Wilchek M, Bayer EA, Livnah OJIL (2006) Essentials of biorecognition: the (strept)avidin-biotin system as a model for protein-protein and protein-ligand interaction. 103: 27-32

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Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript by Li and coworkers describe an approach for detecting the PTM specific interacting proteomes through Proximity labeling strategy. In this method, cellular proteomes were first cross-linked by formaldehyde and then incubated with PTM specific antibodies. Afterwards, a protein A-APEX2 fusion protein was added to target the antibody and label the proteins in proximity with biotin. The biotinylated proteins can be enriched and subsequently identified by LC-MS/MS. The authors claim that these proteins are PTM-specific interacting proteins because they are close to the PTM-recognition antibody. They mainly applied the method to the histone marks and identified the interacting proteins for several histone modifications. They found that the identified proteins overlap partially with the published CHIP-seq data and drew complicated interaction networks based the proteomic data using the STRING database.

Major comments:

1. The APEX technology has been widely used for capturing subcellular proteome or proteome proximal to the target protein of interest. Here the authors aimed to use antibodies specific for detecting certain histone PTMs to guide the APEX enzyme for proximal labeling. While the idea is interesting, the actual applicability is rather limited as APEX does not enable identification of direct interactions. With the current methodology design, the reviewer did not see its advantage over a formaldehyde crosslinking followed by a conventional immunoprecipitation using the PTM-specific antibody. The authors are suggested to compare the head-to-head performance of these two approaches.
2. From the methodological point of view, it is unclear why formaldehyde crosslinking is included here. Neither did the authors justify its necessity for the whole workflow. With treatment with 0.1% formaldehyde for proteome crosslinking, how about physiological status of the cells? Authors should assess survival of cells under formaldehyde treatment. After a long labeling time, will the labeling results still reflect the physiological status of the protein interaction networks in living cells?
3. Overall, the manuscript lacks sufficient description on how the pipeline was optimized. For example, does it matter where the APEX2 is fused to protein A? The authors need to do a better job to present their optimization process.
4. Crosslinking will result in high background in proximity labeling, in figure 1 D, the signal from IgG is obviously high. Strangely, the background signals completely disappeared in figure 2A. What are the differences between these two experiments? Similarly, in figure S4, the IgG lanes in A, B and C looked quite different from each other. The results suggested that the workflow might not be as robust as the authors have claimed.
5. The authors drew complicated interaction networks for each histone PTMs, however, a comparison between the networks for a modified histone versus the unmodified one is missing. Such data would be more valuable and informative as they can provide new clues on how the PTM mediates different interaction networks for cellular signaling.
6. The manuscript lacks the experimental validation of the identified interaction protein partners. The authors used the published CHIP-seq data, however, the cell lines of CHIP-seq are different from the one they used for APEX labeling. In this regard, an side-by-side comparison of this method with the CHIP-seq results should be performed, in terms of both purification efficiency and specificity.
7. For the proteomic data, reproducibility calculation should use intensity ratio instead of intensity. The high dynamic of signal intensity will mislead the audience. P values between replicates should be calculated, a cutoff of < 0.05 or < 0.01 is mostly used in proteome data.

Minor comments:

1. Reference format should be checked. eg. redundant citation, incorrect format etc.
2. Loading controls should be attached along with western blotting results (figure 1d, figure 2A).
3. This manuscript lacks clear conclusion or discussions. Subtitles are needed to delineate results. The overall logical organization of this manuscript should be strengthened. Currently, it is quite hard to follow and appreciate which part of the data is more technically novel and biologically significant.
4. The title is too broad as the authors only showed the application on histone PTMs.
5. The introduction lacks proper description of other strategies for mapping PTM specific interactome, especially those by photo-affinity peptides or photo-affinity unnatural amino acids.

Significance

It is more technical improvement by fusing protein A with APEX2 so that the proximity labeling can be guided around a specific PTM using the proper antibody. Researchers in the field of histone modifications will be interested in the current technique. The reviewer is with expertise in proteomics with focus on PTM analysis.

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

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---

Referee #2

Evidence, reproducibility and clarity

This work describes interesting approach for mapping interactome of specifically modified histone protein using antibody-based APEX2. Although it contains interesting results and useful techniques for biological community, I found that it requires revision and addition for the publication.

1. I could understand the reason using antibody-based proximity labeling approach for mapping the biomolecules that interact with post-translationally modified histone becuase it should be complicated to map it with exogenous protein expression approach. However, the introduction of antibody-conjugated APEX2 should require fixation and permeabilization steps that can usually compromise ultrastructure. The authors should comment whether these procedures can affect protein composition and structures of nucleosome in the Discussion part of this manuscript.

2. For generation of biotin-phenoxyl radical, HRP-conjugated antibody can be utilized as shown in BAR method (Daniel Z Bar et al. Nat Methods, 2018, 15, 127-133). Since secondary-HRP antibody is commercially available, one cannot make an effort to express and purify pA-APEX2 for this approach. The autrhos should clearly explain why they selected APEX2 and what is an expected advance(s) using APEX2 in their approach.

3. For the detection of the APEX-mediated biotinylated proteins, direct mass identification of tyrosine-modified peptides with chemical probes can tell the most correct information of the proximal proteins (see Lee SY et al. J. Am. Chem. Soc. 2017, 139, 3651-3662; Namrata D Udeshi et al. Nature Methods 2017, 14, 1167-1170). Thus, if the authors can obtain the biotin-modified peptide information from each antibody-conjugated APEX2, the quality of their interactome results should be much improved. If authors may be under the situation that cannot conduct further mass experiments, it might be required to check whether their important finding molecules (e.g. Arid2, Brd7, Nsd2) are really "biotinylated" by conducting Streptavidin-HRP western blot experiment after enrichment of those proteins by using primary antibody. If biotinylation is specifically conducted by pA-APEX2 with H3K27me3 antibody, the authors can observe SA-HRP blot signal on the enriched protein band on the membrane. Negative controls should be the samples omit pA-APEX2, H3K27me3 antibody, biotin-phenol, H2O2, respectively or using different PTM targeted primary antibody. This result can confirm that their findings are enriched from proximity-dependent biotinylation of APEX2, not from spurious binding events to the other biotinylated proteins or self-labeled bait proteins.

Significance

For antibody-binding APEX2 strategy, this work is not the first one and the authors should mention the precedent work in the manuscript: Jisu Lee et al. Chem. Commun., 2015, 51, 10945-10948. And the author also commented the antibody-based proximity labeling mapping works including Daniel Z Bar et al. Nat Methods, 2018, 15, 127-133 in the manuscript.

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:

In this article, Li and colleagues demonstrate the utility of a novel proximity labeling-based strategy, which they term AMAPEX (antibody-mediated protein A APEX) for the proteomic characterization of the protein environment of specific histone modifications. They apply this new methodology in mouse embryonic fibroblasts for subsequent purification of biotinylated proteins and mass-spectrometric identification.

Major comments:

• The authors present a high quality, descriptive manuscript that introduces a novel proximity labeling approach from a mostly technical point of view. The presentation of the data and methods are mostly clear, such that they should be easy to reproduce.

• The biggest shortcoming of the study in its current form seems to be the lack of a proper assessment of the method's sensitivity AND -most importantly also- specificity. The authors do not validate potentially new interactors of the modified histones experimentally, which would highlight their technology as a discovery tool. Without the assessment of newly identified proteins, these could simply represent false positives, which would point towards an additional requirement for optimization of the experimental setups. In that light the newly identified proteins are merely potential histone interactors. Validation would require establishment or purchase of additional antibodies, or alternatively cloning and transfection of respective candidates, which will probably take an extra 2-3 months of work. While the study may be publishable in its current form, this validation seems a valuable investment to strengthen the preliminary conclusions.

• With two biological replicates the study fulfills the minimum requirements for reproducibility, however, it would benefit from an additional replicate.

Minor point:

• The sentences in lines 22f (and 44f) could be misunderstood. Please rephrase statements to be unambiguous that it specifies the proteomic surrounding of post-translationally modified proteins. The proximity labeling technologies may themselves be limited in identifying post-translationally modified proteins, as they label reactive side-chains that are often targeted by PTMs. Post-translationally modified lysine residues cannot be targeted by biotin ligase-based methodologies, as tyrosine phosphorylations cannot be assessed by peroxidase-based labeling.

Significance

• From a technical point of view, most of the key conclusions of this paper are convincing. Assessing the proteomic environment of post-translationally modified proteins is of extremely high interest and the methodology seems broadly applicable to address such questions. It should be noted that proximity labeling has not originally been utilized to map protein-protein interactions, as the enzymatic activities available probe their surroundings, this could be an over-interpretation. Nonetheless, the "proteomic surrounding" of target proteins, which would also include hard to identify transient interactions is of high general interest for molecular biologists.

• Although it can be generally agreed that having to express an exogenous fusion protein is a limitation of current proximity labeling setups, the methodology presented here in turn has the limitation that it cannot be performed in living cells, which is a significant disadvantage.

• My lab is establishing and constantly improving various proximity labeling methodologies in combination with mass spectrometry for sub-organellar proteomics. While the technology is sound and well executed, I am not an expert in the biology of histone modifications and the proteins involved and cannot assess the novelty nor the actual value of the generated datasets. It appears to me though that additional validation by independent experiments would strengthen the manuscript. The authors describe the usefulness of the technology mainly in confirming known interactors of modified histones, however, it would be nice (for non-specialists) to explicitly state how high the coverage of the known interactors is and discuss why some of them might have been missed.

• The manuscript in its current form completely lacks a discussion of the presented data. Even if the focus will remain on the technical aspects, the findings should be properly discussed by comparison to other proteomics approaches studying histones. Ideally the data should also be discussed in the light of current proximity labeling technologies and potential future directions.

• While I cannot assess the value for histone research, the manuscript will be very interesting for experts focusing on proximity labeling technologies and subcellular proteomics.

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Reply to the reviewers

Dear reviewers,

We thank all reviewers for and their appreciation of our work and even more so for their constructive comments and suggestions, which will significantly improve the quality of the manuscript. We were able to complete the revision and address all reviewer comments. Aside a more stringent discussion of the literature, and rewording of certain paragraphs for clarity, we also generated additional experimental data.

More importantly, to address the concern that we did not provide a positive marker for the intranuclear compartment, we present new images. We attempted to label gamma-Tubulin by generating new antibodies, GFP-tagged strains, and trying multiple commercial antibodies since the beginning of the project. Only recently we found an antibody providing a more specific signal at the expected location, although with some likely cross-reactivity with alpha- and beta-tubulin, and now show these data in the supplements. Additionally, we generated expansion microscopy samples stained with a fluorophore-coupled NHS-Ester, a bulk protein label. These data show that the centrosome contains an exceptionally protein dense hourglass-shaped region, which spans from the extranuclear to the intranuclear compartment, as revealed by centrin and tubulin co-staining. This fortifies our claims about the distinct nature of the intranuclear centrosome compartment containing the microtubule nucleation sites.

Further, we add images of 5-SiR-Hoechst, SPY555-Tubulin, Centrin1-GFP triple labelling live cells to demonstrate the specificity of the microtubule dye and to underline that we are indeed acquiring the dynamics from the first nuclear division on.

In terms of formatting we added line numbers and uploaded high quality figures separately. Due to the added data and panels we needed to split Fig. 1 into two separate figures, rewrote the figure legends and moved them to the end of the document.

Please find below a point-by-point response to the comments.

Best regards,

Julien Guizetti

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

The manuscript by Simon and collaborators addresses the dynamic changes of spindle and hemi-spindle microtubules occurring along schizogony in Plasmodium falciparum. The work explores the temporal correlation of the changes observed in intranuclear spindles with changes at the level of the centriolar plaque; the nuclear microtubule organizing center of these parasites, using centrin as a bona fide marker of the structure. The study shows that spindle microtubules organize from an intranuclear region, devoid of chromatin, distinct from the centrin region which had not been observed or described before. It further shows that centrin does not localize at the nuclear envelope, but it is actually extranuclear.

This work significantly expands on previous knowledge regarding the functional and spatial organization of the nucleus in P. falciparum, and the structure once defined as "an electron dense mass on the nuclear envelope." It uses state of the art microscopy approaches such as STED, UExM and CLEM, in combination with immunolabeling, dyes and parasites over expressing fluorescent protein fusions, to address these questions.

**Major comments:**

• Are the key conclusions convincing?

I find the manuscript successfully addresses the posed questions. The data presented supports the conclusions.

• 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.

No

• 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.

N/A

• 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.

On the data shown in Figure 1, it is unclear to me what elements are taken into account to define "anaphase." Anaphase could be defined by using chromatin markers - such as CenH3- which have been identified in Plasmodium and the authors make use of in Figure 1F.

We acknowledge that the term anaphase is ill-defined here. Further it suggests a mitotic morphology analogous to the one observed in “classical” models (prophase, metaphase, anaphase,…), which is not fully appropriate. In line with the comments by Reviewer 3 we, therefore, decided to use the term “extended spindle” instead (Fig. 1 & 2). This better reflects the morphological criterion on which we based the stage definition.

• Are prior studies referenced appropriately?

The authors state that "with the exception of centrins and gamma tubulins" few canonical centrosome components are conserved in Plasmodium. These parasites are in fact able to assemble a more or less canonical centriole for microgamete basal body formation. Widely conserved centriolar components such as Sas6 are coded by the malaria genome, and have been characterized previously. This work is neither referenced nor discussed in the manuscript.

The reviewer is right to point out this omission. We were too much focussed on the blood stage centriolar plaques while writing this section, where centrioles are not observed. Of course centriole-like structures are relevant in other life cycle stages, such as microgametes, and should be discussed (line 104). Some previous attempts to endogenously tag Sas6 to verify its localization in blood stages were unfortunately not successful.

• Are the text and figures clear and accurate?

I find the timings shown in Figure 1A, with respect to the schematic quantification shown in Figure 1B, confusing. Shown as it is, one naturally correlates the images on Fig1A above with the cell cycle progression timing shown on Fig1B, below. However, by time 260min, for example, two somewhat adjacent centrin signals can be observed. Though this is defined as anaphase- by an unspecified criterium- this could very well be representative of metaphase. Nonetheless, the timing shown on Figure 1B for "anaphase" onset is 170min, which is inconsistent with the images above. I suggest that either, the quantification is shown in a different format (ex. bar plots) which could then better reflect the cell to cell variations observed (by use of error bars, for example) or that the figure explanation in the results section clarifies this issue.

We understand how this representation is misleading and have adjusted the figure and text accordingly. We modified the time stamps in Fig. 1A (now Fig. 1C) to the scale used in Fig. 1B (now Fig. 1D) i.e. collapse of the hemispindle is t=0 and explain this in the text (line 158). Since we feel that Fig. 1B (now Fig. 1D) is a good and compact visual representation of progression through the first division we kept the bar plots in the supplements (Fig. S1), but added a title clarifying that average duration between multiple movies are shown.

As presented, the data in Figure 1C is rather uninformative. A pattern could be more immediately extracted if dots corresponding to subsequent appearance of centrin dots in the same nucleus were connected to each other.

Concerning the appearance of the centrin signals we adopted the good suggestion by the reviewer and connected “paired” centrin signals by lines (Fig. 1E).

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

There are a number of edits required on the text. Row numbers would have been helpful in pointing these out. I point some edits below, but thorough revision of the manuscript for grammatical and synthetic errors would be beneficial.

• Cytokinetic segmeter - please replace with "segmented"

• Please refer to Figure 1D when appropriate - there is quite an extensive paragraph describing the results shown on this figure, but it is only referenced at the start.

• "..., as did the and the number of branches per nucleus,..." please rewrite as appropriate.

We apologize for not providing line numbers, but have corrected the addressed points and applied a grammatical check throughout the manuscript. We have added additional references to Figure 1D (now Fig. 2A) in the text.

Reviewer #1 (Significance (Required)):

This manuscript could be interested to a wide audience interested in cell cycle, cell division, cell organization and organelle positioning, infectious diseases and microscopy. However, the introduction assumes that readers are somewhat experts in the malaria field. I suggest the authors include a brief introduction of the malaria life cycle, and a schematic representation of the division mode. This will help non-experts follow the narrative more easily.

We are happy to read that the reviewer sees value of this study for a broader audience. Following the suggestion, we added a small schematic (Fig. 1A, lines 54, 62) highlighting the relevant steps of schizogony and expanded the introduction of the life cycle (line 46).

This work rectifies long-standing inconsistencies observed by different experimental approaches in the nuclear organization of malaria parasites during schizogony. However, what the functional consequences of the alternative modes of spindle organization in malaria could be, are not clearly stated or discussed. In this respect, as it stands, the manuscript is rather descriptive and lacks mechanistic insight. Nonetheless, the data presented are of superb quality, and the manuscript represents a tremendous leap in structural insight and imaging resolution for the field of malaria. I find the data is suitable for publication albeit minor adjustments are made (specially to Figure 1 and/or the description of the results shown in Figure 1, for consistency).

We agree that the value of this manuscript lies in the clarification of conflicting data, unprecedented structural insight, and providing a useful working model for the malaria parasite centrosome. Although this study is ultimately descriptive it forms the indispensable basis to generate more meaningful functional insight about centrosome biology and nuclear division. Some of the functional consequences worth considering are: i) The (at least) bipartite composition indicating that centrosome functionality is spatially spread throughout the nucleoplasm/cytoplasm boundary. ii) The delayed appearance of the centrin signal after tubulin signal allows the prediction that centrosome assembly is a staged process occurring over an elongated period of time. iii) The generally amorphous structure of the compartment predicts the involvement of yet to be uncovered matrix-like proteins harbouring microtubule nucleation sites. iv) Lastly, our model has important implications for the mechanism of centrosome duplication. In a centrosome containing centrioles (like in vertebrates), the duplication event can easily be explained by physical separation of the daughter and mother centrioles. Spindle pole body duplication in yeasts is achieved by de novo formation of a new one, which remains connected by a half bridge until it is split. The centriolar plaque organization revealed here suggests that we need an entirely new model of centrosome duplication (or splitting) to describe and understand this process in malaria parasites. We now address those points more explicitly in the discussion section (e.g. lines 375, 443, 467).

**Referee Cross-commenting**

I agree with all the other reviewer's comments. I'm glad the reviewers seem to be experts in the field of malaria cell division and have pointed out previous studies which were not appropriately referenced. I second those comments.

---

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

**Summary:**

The manuscript by Simon et al have used advance cell biology technology like STED, expansion and live cell imaging to decipher the configuration of microtubules, centrin and nuclear pore during unconventional cell division process in malaria parasite. They have shown the dynamics of centrin and its localisation with respect to centriolar plaque that is characteristic of these parasite cell during schizogony> They also implicate from their studies that there is extended intranuclear compartment which is devoid of chromatin

**Major Comments**

• Are the key conclusions convincing? Yes to some extent

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

*Some part are preliminary and speculative as there is no solid data supporting it. Please see below

• 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.

*Yes to substantiate their claim

• 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.

*They can do these quite quickly less than a month

• 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

The authors present beautiful imaging and some in depth structure using tomography and CLEM to show the location of centrin which is generally considered the marker for centrosome or Microtubule organising centre in malaria parasite. These approaches are still not been applied in Plasmodium and hence very informative. Though they present some advance microscopy but a lot of these concept for hemispindle were shown earlier in many light and super resolution microscopy studies. Authors claim that they are first to show that there is space between centrin and nucleus but it has been show previously in centrin studies in Plasmodium berghei using super resolution microscopy (Roques et al 2019 Fig1 and supplementary videos1&2) as well as expansion microscopy recently by group of Brochet etal 2021.

We thank the reviewer for the appreciation of our work. We are, indeed, not the first to describe the gap between centrin and tubulin or the nucleus. We just aimed to reiterate this finding, also visible in our data, in order to transition to the analysis of nuclear pore positioning to clarify whether centrin is actually extranuclear. Nevertheless, we should have cited the Roques and Bertiaux et al. studies again in this context, which we have now rectified (line 252).

In addition the microtubule dynamics was also recently shown with Kinesin5 live cell imaging for schizogony in Plasmodium berghei (PMID: 33154955) which author have omitted in their manuscript.

We thank the reviewer for pointing out the Kinesin-5 study by Zeeshan et al., which we failed to cite and discuss. We now state the findings of this publication and put it into the context of our work (see also answer to next point). Microtubule associated proteins, such as the microtubule plus end tracking EB1 and the aforementioned Kinesin-5, are indeed useful markers to investigate microtubule dynamics leading to the interesting results shown by Zeeshan et al. Nevertheless, we want to point out that labelling microtubule associated proteins (MAPs) remains an approximation of the underlying microtubule organization. As the authors in Zeeshan et al. indicate by themselves, Kinesin-5 does not decorate axonemal microtubules or the membrane-associated microtubule structure formed during cytokinesis in very late schizont stages. Further, colocalization between alpha-tubulin and kinesin-5 in schizont-stage parasites is not complete indicating a preferential decoration of certain sections of the microtubule structures (possibly the microtubule ends), which could only be resolved by super-resolution microscopy. Using a live cell dye, such as SPY555-tubulin, which directly binds to microtubules will provide a uniform labelling of any microtubule species and hopefully prove useful to the field in the future. Lastly, we present time-lapse microscopy analysis of blood stage cells, contrary to single time point images of live cells, providing a quantified chronology of microtubule reorganization at single cell level (with time stamps). Therefore, we feel that our claim, although it should be relativized, is formally speaking accurate.

It is also important that authors give valid discussion about previous studies on hemispindle, microtubule dynamics with respect to schizogony (PMID: 18693242; PMID: 11606229; PMID: 33154955) rather than giving the impression that they have given this concept first time on hemispindle dynamics and centrin location during schizogony.

We agree that those studies should be discussed in more detail. We are grateful to the reviewer for pointing out the Fowler et al. 2001 (PMID: 11606229) study. They use an antibody against gamma-tubulin to demonstrate its presence at the apical pole of subpellicular microtubules (f-MAST) in the merozoite and cytokinetic stages (line 102). However, we were unable to reveal a specific gamma-tubulin staining using the antibody used by them in the preceding schizont stage. After trying many different commercial gamma-tubulin antibodies and attempting to generate our own we now finally observe a gamma tubulin localization at the poles of intranuclear spindles in schizont stage, although the only successful antibody still displays some background staining, possibly including cross-reactivity with alpha or beta-tubulin (Fig. S4, line 237).

The highly insightful study by Mahajan et al. 2008 (PMID: 18693242) indeed suggests that centrin localizes away from the DNA and demonstrate the distinct localization from tubulin. They, however, likely due to the resolution limit of their microscopy techniques, speculate that the centrin signal is embedded in the membrane, while we could show by super-resolution and nuclear pore staining that centrin is distinct from the membrane (now Fig. 2A; line 257). The work done by Zeeshan et al. 2020 (PMID: 33154955) nicely shows dynamics of kinesin-5 in nuclear division. In schizont stages Kinesin-5 signal elongates and splits alongside the mitotic spindle with which it overlaps for the most part. Colocalization with centrin is less strong although the authors note some overlap. Our data suggest that centrin and tubulin are clearly distinct. In male gametes the authors show nicely time-resolved data of kinesin spreading along the elongating spindle, although hemispindles are not observed at this stage. We introduce and discuss these findings (lines 123, 432).

The concept of bipartite centrosome is already been discussed in Toxoplasma and the claim by authors in Plasmodium presented here is not substantiated experimentally. They showed that centrin is part of outer region while they do not show with any marker for the inner region. It will be very helpful if the authors use gamma tubulin or MORN1 to show the location with respect to centrin and microtubule. In the absence of this localisation the claims are preliminary and speculative. If the centrosomal protein complex is not involved in microtubule nucleation, then how the nucleation is happening. What are the molecules present in this amorphous matrix? It will be great to check the location of gamma-tubulin or some inner centrosome molecules described in Toxoplasma that is deemed to be MTOC.

We share the opinion that our Plasmodium data should be compared to Toxoplasma, while still being assessed independently. Despite Toxoplasma belonging to the apicomplexan the conclusion that their centrosomes should be organized in a similar fashion is by no means self-evident considering for example their significant evolutionary distance. Actually, several noteworthy morphological differences have already been well documented. i) Toxoplasma MTOC does contain centrioles in the outer core which is coherent with the centrin and gamma-tubulin localization in this region. ii) Toxoplasma MTOC contains an additional nuclear membrane protrusion enclosing the inner core. iii) mitotic microtubules in Toxoplasma are thought to penetrate the nuclear membrane to connect to centromeres. iv) the inner and the outer core are both extranuclear and therefore not to be equated with the intranuclear compartments. We now expand a bit on the discussion of the aforementioned differences (line 382). Nevertheless, we thank the reviewer for making us realize that the term “bipartite” is a poor choice to describe the centriolar plaque organization in this context. Therefore, we replaced it in the abstract (line 29) and the main text (line 375).

We acknowledge the fact that it would be desirable to show a marker localizing to the intranuclear compartment, and not only through visualizing the microtubule nucleation complex (Fig. 4A-B) and the positioning of the microtubule ends in this region (Fig. 3A). Concerning MORN1 we found no indication in the published localization data that it is, like in Toxoplasma, associated with the nucleus in Plasmodium species, where it is only found associated with the budding complex (and we are currently unable to procure an antibody) (line 422). We have attempted gamma-tubulin visualization on many occasions throughout the project (transgenic parasite lines, commercial antibodies, self-made antibodies) and only recently found an antibody revealing some specific signal. Indeed, we found localization at the poles of the spindles i.e. the intranuclear compartment (line 237). Unfortunately, this “best-possible staining” still showed some unspecific spindle staining likely resulting from cross-reactivity with alpha- or beta-tubulin causing us to put these data into the supplements (Fig. S4).

We had more luck with attempting a “new” type of staining, recently used in Plasmodium (Bertiaux & Balestra et al. 2021) using a fluorophore-coupled NHS-Ester in expanded samples. This chemical unspecifically stains proteins and revealed that the centrosomal region contains an exceptionally protein dense “hourglass-shaped” structure (Fig. 3F-H). Since the outer part of this structure colocalizes with centrin and the inner part overlaps with microtubules we assume that the centrosomal complex stretches throughout the nucleo-cytoplasmic boundary and fills part of the intranuclear compartment (line 320). Especially the highly protein dense region at the neck of the “hourglass” seems very coherent with the nuclear membrane embedded electron dense region which can be seen in electron microscopy (e.g. Fig. 3E & 4B). We feel that this staining strongly supports the presence of this novel intranuclear compartment.

The expansion microscopy is very nice and some of it presented in supplementary can be moved to main section.

Thanks for sharing our enthusiasm about this imaging technique. We have now selected a representative image of a hemispindle and mitotic spindle stage nucleus imaged by U-ExM and added it to the main section (Fig. 2B, line 231).

The localisation CenH3 is bit puzzling as it has been shown that centromere/ kinetochore cluster and are present during early and mid schizogony. The various foci with respect to nuclei are not what has been seen previously. Please discuss the difference in these two findings.

The localization pattern can easily be explained by the increased resolution of STED nanoscopy used in this study. Previous studies (e.g. Hoeijmakers et al. 2012 and Zeeshan et al. 2020) used classical confocal microscopy. Under those imaging conditions the individual foci seen here can´t be resolved and would, in accordance with the other studies, appear as one cluster. We slightly modified the text for more clarity (line 247).

Reviewer #2 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

* This is more technical advancement on the subject of centrin by using STED, tomography and CLEM.

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

* This work has relevance relation to cell division during schizogony in asexual stages in par with Toxoplasma or in Apicomplexa in general

• State what audience might be interested in and influenced by the reported findings.

Working with Apicomplexa, Protist, cell division and mitosis.

• 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.

Working on Cell division in Plasmodium.

**Referee Cross-commenting**

I agree with the reviewers and some of the experiment suggested and the minor details have to be addressed. There are some loose ends and these suggestions will enhance clarity of the data. It is a very nice study and some of the comments suggested by reviewers will improve the manuscript. __

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

**Summary:**

The centrosome is the primary microtubule-organizing center (MTOC) in eukaryotic cells that nucleate spindle microtubules necessary for chromosome segregation. In most eukaryotic cells, the canonical centrosome is composed of centrioles surrounded by an electron-dense proteinaceous matrix named the pericentriolar matrix (PCM) competent for microtubule nucleation mitotic spindle assembly. Following the breakdown of the nuclear envelope breakdowns, the mitotic spindle microtubules gain access to the kinetochores of the condensed mitotic chromosomes. Once the mitotic spindle is fully developed, centrosomes are at opposite poles of the cells, and chromosomes are pulled toward opposite poles. Cell division completes with cytokinesis resulting in the active formation of two nuclei within two daughter cells. Interestingly, during its asexual replication cycle, the malaria parasite Plasmodium falciparum undergoes multiple asynchronous rounds of mitosis with segregation of uncondensed chromosomes followed by nuclear division within an intact nuclear envelope. The multi-nucleated cell is then subjected to a single round of cytokinesis that produces dozen of daughter cells. We know about the Plasmodium centrosome is that it is made of an acentriolar structure embedded in the nuclear envelope and serves as MTOC during cell division. However, the biogenesis and regulation of the Plasmodium centrosome are poorly understood. Given the peculiarity of the cell division in Plasmodium parasites, understanding the molecular mechanisms that drive and regulate MTOC duplication and maturation could unveil novel targets for the treatment of malaria. In this study, Simon et al. successfully applied challenging and cutting-edge microscopy techniques to monitor the dynamic formation of the spindle microtubules and MTOC during Plasmodium intraerythrocytic mitosis. In addition, they remarkably combined stimulated emission depletion (STED) with ultrastructure expansion microscopy to define an uncharacterized intranuclear compartment devoid of chromatin as the nucleation site of nuclear microtubules. And lastly, the authors adapted an in-resin correlative light and electron microscopy (CLEM) approach to define the centriolar plaque position in a novel intranuclear compartment with centrosomal function.

**Major comments:**

1. In the methods section, it is stated that across this study, three different anti-tubulin antibodies (alpha-tubulin B-5-1-2, alpha-tubulin TAT1, beta-tubulin KMX1) were used, and two anti-centrin antibodies (TgCentrin1 and PfCentrin3) were used, one of which seems to have been generated in this study (anti-PfCentrin3). It is unclear in the figures or results section when each of these antibodies was used, and the authors should give a rationale for using multiple antibodies in combination.

To label microtubules we used the mouse anti-alpha-tubulin B-5-1-2 (Sigma, T5168) antibody throughout the study. Except for U-ExM were we added two additional primary antibodies against tubulin. Due to the expansion of the samples the antibody binding epitopes are stretched out in space. This causes a significant reduction of local epitope concentration (expansion factor 4.5 in all directions results in ~ 80-fold increase in the volume), which can reduce the signal intensity. Adding multiple antibodies binding different epitopes of tubulin can compensate for this dilution effect to some degree, as has been shown before by Gao et al. 2018. At the same time the expansion contributes to the accessibility of the usually densely packed tubulin epitopes within the microtubule polymer, which certainly adds to the success of U-ExM. What the respective contributions of those effects are is not clear, but we found superior signal-to-noise ratios when combining three tubulin antibodies instead of using one. The TgCentrin1 antibody was only used in Fig. 2C (now Fig. 3B) and validated the localization pattern of our new PfCentrin3 antibody we used in the other pictures. We now provide clearer description of antibody usage in the methods section and a new supplemental table.

The anti-PfCentrin3 antibody seems to have been generated for this study. If this is the case, the authors should provide evidence that this antibody binds to the recombinant PfCentrin3 it was raised against and binds PfCentrin3 in parasite lysates.

The anti-PfCentrin3 antibody was, indeed, produced for this study and we should have provided our western blot data right away. We now show the requested blot, which shows bands at the appropriate size in parasite lysate as well as for the recombinant protein, in the supplements (Fig. S2, line 178).

In the first paragraph of the Results section, the authors' remark of centrin foci that they are "...only detectable later (Mov. S2) or sometimes not at all." In Figure 1 A-C, it is implied that the first observed division is the first nuclear division of that parasite. Given that some nuclei do not have a visible centrin focus, it cannot be concluded with certainty that these parasites only contain a single nucleus and that this is their first division. The authors would need to include a quantifiable DNA stain to show this unequivocally to show a single nucleus. It has undergone DNA replication, similar to Klaus et al., 2021 BioRxiv paper. In the absence of a DNA stain, the authors should reword to clarify that this is the first observed division and speculate that it is the first division of that nucleus, but the authors should draw no firm conclusions about the first division.

Indeed the variability in protein levels that can result from exogenous expression can lead to some cells not showing clear Centrin1-GFP foci. Although this is a rare event we wanted to acknowledge this observation. The live cell microtubule staining using Spy555-Tubulin we use is, however, highly specific and sensitive and would stain any nucleus undergoing division including the first one. If there would be more than one nucleus in the observed cell it would unequivocally show two clearly separated tubulin signals (hemispindle or mitotic spindle). To illustrate this we added Fig. 1B (line 148) showing two live parasites stained with SPY555-Tubulin plus a Hoechst-based dye showing one or two nuclei alongside the corresponding tubulin signal. We modified the text to clarify how we stage the parasite for time-lapse acquisition (line 154). We already extensively experimented with state of the art fluorogenic live cell DNA dyes (e.g. from Spirochrome and the Johnsson group) to visualize the nuclei directly in time lapse microscopy, but even at minimal concentrations they all significantly inhibit mitotic progression. We also add this information in the main text (line 150).

In the first paragraph of the Results section, the authors write: " We quantified the duration of hemispindle, accumulation and anaphase stages ...." Anaphase spindle fibers means that the sister chromatids are separated. In the absence of a centromeric marker like NDC80, it doesn't seem easy to claim the anaphase stage. The authors should write " extended spindle." The authors might also consider using the term collapsed spindle instead of accumulation to reflect the dynamic of the intranuclear microtubules during the blood-stage replication. The same modification should be made for Figure 1B, so we read " hemispindle, collapsed spindle and extended spindle."

We thank the reviewer for this suggestion, which is very much in line with a comment by Reviewer 1 on the definition of anaphase. We acknowledge that the term is ill-defined here. Further, it suggest a mitotic morphology analogous to the one observed in “classical” models (prophase, metaphase, anaphase,…), which is not fully appropriate. Consequently, we decided to adapt the suggested terminology in Fig. 1 (and also new Fig. 2) and in the text (line 160).

Based on the evidence in this study, it cannot be stated unequivocally that the centrosome is entirely extranuclear, at least not as it is implied in Figure 3C. In Supplementary Figure 4, the microtubules appear to be extruding from a circular structure that may either be intranuclear or span the nuclear envelope. In Supplementary Figure 6, the structure pointed to as the centrosome appears to be embedded within the nuclear membrane with a top structure on the cytosolic side of the nuclear envelope. Thus, the best support for an extranuclear centrosome comes from the CLEM images. Still, it is noteworthy that the double membrane of the nuclear envelope is not visible on this slice in the region where the centrin fluorescence is found. Considering some of the fluorescence pixels for centrin are outside the parasite plasma membrane, and some of the Hoechst pixels are outside the nuclear envelope, this data does not show unequivocally that centrosomes are entirely extranuclear. However, this argument would be strengthened if the authors performed a proteinase K protection assay (or something similar) to determine if Centrin1 and Centrin3 are exposed to the cytosol. However, in the absence of that or further evidence, the authors should dampen their claims about the centrosome being exclusively extranuclear, as represented in the schematic in figure 3C.

We thank the reviewer for this comment, which highlights an issue in our communication of our working model of the centriolar plaque. At no point we intended to claim that the centrosome is exclusively extranuclear. Rather, centrins, which are currently the only reliable marker proteins, localize to a subcompartment of the extranuclear region of the centriolar plaque. Additionally, the centrosome clearly contains an intranuclear region. The composition of this intranuclear compartment is elusive, except that it harbors microtubule nucleation sites. Indeed, our model in Fig. 3C (now Fig. 4C) is misleading and not well annotated. The newly added NHS-Ester staining fortifies this claim (Fig. 3F-H. Consequently, we corrected our working model by adding an explicit figure labelling (now Fig. 4C).

We apologize for the misleading labelling in Fig. S6 (now Fig. S7). The green arrow was intended to point out the electron dense region associated with the nuclear membrane, which has been seen in previous studies, and was not intended to represent the entire extended centriolar plaque. If anything, this smaller region might provide the link between the intra and extranuclear compartments that the reviewer also identified in Suppl. Fig. 4 (now Fig. 2D). We modified the annotation of the Fig. S7 and Fig. 4A-B accordingly, labelling it the “electron dense region”. More importantly, we hope that our newly added data using NHS-Ester staining of protein dense regions (Fig. 3F-H) highlights the spread of the centrosome across the nucleo-/cytoplasmic boundary more clearly.

Considering whether centrin is actually extranuclear, we feel that the data shown in Fig. 2A (now 3A) is convincing. We have, however, added two panels of the relevant regions showing centrin localization respective to the nuclear pore and adjusted the contrast as we acknowledge the limited “visibility” within the unadjusted panels. The fact, that the centrin signal slightly overlaps with the nuclear envelope in CLEM images can be explained by the relatively poor resolution of the widefield microscope we had to use to image the sections. From the other super-resolution images in the manuscript, we know that the perimeter of the better resolved centrin signal is significantly smaller. Otherwise one had to assume from the CLEM data that centrin is also in the cytosol of the red blood cell and that DNA is localized outside the nucleus. On a similar note the fluorescence image is, contrary to the tomography image, a single slice since the thickness of the sample section (about 200nm) is significantly below the z-resolution (about 500nm) of a fluorescence microscope.

Throughout the study, the level of biological replication is unclear. The authors rigorously include all the data points for each of their graphs and the total number of images/videos quantified. And what needs to be added, in either the figure legends or a methods section, is the number of biological replicates for each of these measures came from.

We have added the number of replicas in the figure legends.

**Minor comments:**

STED is present as an acronym in the abstract and should be spelled out in full and clarified that it is a super-resolution microscopy technique.

We opted to remove STED from the abstract (leaving it at super-resolution, which includes expansion microscopy) to avoid disrupting the “flow” of the abstract and now spell out the acronym at the first mention in the introduction (line 127).

The second paragraph of the Results section states that ring and early trophozoite stage parasites do not express tubulin or centrin. Still, only an early trophozoite is shown in Supplementary Figure 2. Therefore, the authors should either include a similar image of a ring-stage parasite or remove ring-stage parasites from that statement.

We have removed the ring stages from the statement.

The second paragraph of the Results section contains the sentence, "At which point tubulin is reorganized into the bipolar microtubule array, which then forms the mitotic spindle cannot be resolved here." The authors are implying that the point at which tubulin is reorganized into the microtubule array, which goes on to form the mitotic spindle, cannot be resolved here. This is not particularly clear, though, and this sentence could be reworded for clarity.

We reformulated the sentence to clarify the point we failed to make with the previous wording (line 188).

The second paragraph of the Results section contains some statements about the results without referencing the figures that these statements come from. The authors should clarify this to make clear which figures each statement refers to.

We added more references to the appropriate figure throughout the paragraph (lines 188, 219, 223).

In the third paragraph of the introduction section, the authors write, " Centriolar plaques seem partially embedded in the nuclear membrane, but their positioning relative to the nuclear pore-like "fenestra" remains unclear." Unfortunately, the lack of reference did not allow me to understand if the authors state literature or comment on past published results.

We added the reference which was incorrectly positioned before the sentence instead of at the end (line 82).

the authors could add some references:

• Second section of the introduction: " the 8-28 nuclei are packaged into individual daughter cells, called merozoites ( Rudlaff et al. 2019 PMID: 31097714)

• Third section of the introduction: " The centrosome of P.falciparum is called centriolar plaque" ( Arnot et al. 2011, Sinden 1991a); " the nuclear pore-like "fenestra" remains unclear (Wall et al. 2018; Zeeshan et al. 2020).

• Fourth section of the introduction: " tubulin antibody staining are extensive structures measuring around 2-4um ( Ref?)

• When the authors introduce subpellicular microtubules of segmented schizonts, a reference to a study that shows these structures should be included.

• A previous study that shows the distinct structure of microtubule minus ends should be cited when this structure is described.

• Third section of the results, the authors should cite Bertiaux et al. 2021 with the Gambarotto et al. 2019 paper regarding U-ExM.

We apologize for missing some important references or putting them in the wrong position. We now added all the references or cite them again at the appropriate locations throughout the text.

Figure 1E shows hemispindle and mitotic spindle lengths of U-ExM expanded parasites, but the position within the figure and figure legend implies that these lengths were determined unexpanded parasites. Therefore, it should be stated in the figure legend that these measurements come from U-ExM expanded parasites. Moreover, I encourage the authors to include U-ExM images in the main figures. The images are beautiful, represent a significant technical achievement, and directly relate to Figure 1E. To the best of my knowledge, this is only the second study to perform expansion microscopy on Plasmodium and the first to use PFA-fixed parasites and a nuclear stain. It would be valuable for the Plasmodium and ExM communities to see this technical advancement represented in the main text.

We thank the reviewer for the appreciation of our ExM data and added it to Fig. 2B before the quantification of the microtubule length and number and added the information to the legend.

In the second paragraph of the Results section, the authors write, " but clearly display the microtubule cytoskeleton associated with the inner membrane complex." It would bring clarity to define in few words what the IMC is.

We included a short definition of the IMC (line 223).

The methods section details that the length of microtubules was determined by dividing the observed values by an expansion factor of 4.5. If the authors recorded the expansion factors of their gels, this data should be included, and how it was recorded should be stated in the methods. If not, the authors should include the rationale of using an expansion factor of 4.5 as this is slightly different from the previously published expansion factor of P. falciparum of 4.3.

We recorded the expansion factor by measuring the gel size pre and post expansion with a ruler and found a factor of 4.5 on average. We added this information in the methods (line 688).

There are several parasite lines used in this study, and some figures are not clear what parasite line was used. Could the authors please include the parasite lines in the figure legends of Figure 1 D-F, Figure 3, Supplementary Figures 1-2, and Supplementary Figures 4-7?

We added the parasite line information in the legends as requested.

Nuclear pore complexes, of which Nup313 is a component, can have cytoplasmic, integral, and nuclear-facing components. If it has been shown previously that PfNup313 is the homolog of Nup214 in vertebrates present on the cytosolic side of NPC, this should be stated. If not, then it should be clarified that it is unknown whether Nup313 faces the cytoplasm, nucleus, or is embedded in the NE, as this has implications for the colocalization of Nup313 and Centrin.

Nuclear pore proteins are very poorly conserved in P. falciparum and Nup313 has only been recently identified as such (Kehrer et al. 2018) mainly by the presence of FG-repeats (as for all the other newly defined proteins). The only related ortholog that can be found through BLAST search against humans, yeasts, and Arabidopsis is Nup100 from S. cerevisiae. ScNup100 is a central pore localizing protein but the sequence similarity to Nup313 is low. We are not aware of any findings showing relatedness to vertebrate Nup214, while sequence analysis rather indicates the absence of orthology. To clearly demonstrate the individual positioning of the few known Nups within the parasite´s nuclear pore complex would require a dedicated long-term project. However, due to the presence of FG-repeats one can assume that it is part of the central FG-Nups layer rather than of the intranuclear basket or the cytoplasmic filaments (line 255). Therefore it would localize more closely to the nuclear envelope than the latter. Either way, a clear gap between centrin and Nup313 signal can be identified and colocalization has not been observed. These data indicate that the exact position of Nup313 on the cytoplasmic, integral or nuclear-facing site is not decisive for the conclusions made in this study and our observations preclude scenarios where centrin is not extranuclear.

It seems from the image in Figure 2C that DRAQ5 and Hoechst have at least visually indistinguishable localizations. Have the authors taken any STED deconvolved images of nuclei stained with both Hoechst and DRAQ5? Considering the striking increase in detail of the Hoechst signal in STED deconvolved images, it may be informative both to this study and to people who work on chromatin organization what the chromatin staining looks like in the absence of bias towards chromatin state.

It would, indeed, be interesting to analyse chromatin organization by those means, but DRAQ5 is not a STED compatible dye, highly prone to bleaching, and therefore not suitable for such analysis. Being an infrared dye DRAQ5 is compared to the UV excited Hoechst also yielding a reduced spatial resolution, which is limited by the emitted wave length.

For the tomography and TEM images, the centrosome is indicated with an arrow, but it isn't entirely clear what that arrow is pointing to for some images. It would be clearer if the centrosome were outlined in green, like the NE, rather than just an arrow. This is particularly important for Supplementary Figure 4, where to my eye, it appears that the microtubules inside the chromatin-free region are coming directly out of a circular structure, which could be interpreted as the centriolar plaque.

The reviewer is right to point out the use of arrows for centrosome annotation. It was intended for orientation of readers to indicate the “likely position of the centriolar plaque” since a clear boundary around the centrosome can´t be defined. It would have been more precise to indicate that the arrow is pointing at the electron dense region associated with the nuclear membrane, which is of course only one of the sub-regions of the centrosome. This is particularly important since we want to emphasize the extended dimensions of the centrosome. Consequently, we modified the annotation to “electron dense region” in all concerned figures and corresponding legends.

The ordering of Figure 2A-C seems to imply that the DNA-free region was measured in the STED deconvolved images, but the methods imply that it was in the confocal images. The authors should clarify this in the figure legend or by rearranging B and C's order.

Hoechst signal was indeed acquired and measured in confocal mode and to avoid confusion we have changed the order of the figures (now Fig. 3B-C) as suggested.

The authors should provide some more detail on how the DNA-free zone was measured. For example, was it measured on single slices or maximum intensity projections? Was it measured from the middle, far, or near side of the centrin focus? Etc.

The measurement was carried out in the slice where the DNA-free zone was in focus. Depth was measured from below the centrin signal until the “bottom” of the DNA-free zone. We hoped that the little schematic above the figure would clarify this question, but acknowledge the need to more clearly explain the measurement method, which we now do in the corresponding figure legend (Fig. 3C).

The methods state that the mCherry signal in figure 2C was detected using a mCherry nanobody. This should be clarified in the figure legends as it currently seems as if we see endogenous mCherry fluorescence.

The visible signal is certainly a combination of the mCherry plus the “boosting” effect from the Atto594-coupled nanobody that we added. Clearly, this should be mentioned in the figure legend, which we now do.

The data in Supplementary Figure 4 seems vital to the interpretation of the study. Therefore, for clarity, I encourage the authors to include Supplementary Figure 4 in Figure 2.

We share the reviewers view on these data and moved them to the main figures (now Fig. 3D).

In the last sentence of the discussion, it is unclear what the authors mean by how the nuclear compartment "splits," could they please clarify?

We were referring to the event of centrosome duplication, which has to occur during nuclear division. In a structure without centrioles or a spindle pole body structure forming a half bridge we therefore need a new model to explain how the two poles of the spindle are formed. Potential modes are splitting or de novo assembly. This aspect, as also pointed out by other reviewer, warrants a bit more explanation, which can now be found in the discussion (line 468).

If the pArl-PfCentrin3-GFP plasmid or pDC2-cam-coCas9-U6.2-hDHFR have been published previously, the respective studies should be cited. If not, the study where the vector backbones were first established should be cited.

We have now cited the original studies publishing the vector backbones for the first time in the methods (lines 490, 501).

From the current text, it is not clear that the Nup313 tagged parasites also had a GlmS ribozyme. It is shown in Supplementary Figure 3, but the authors should clarify either in the text of the results, or figure legends, that this parasite line was Nup313_3xHA_GlmS

The Nup313-tagged line indeed has a glms ribozyme after the HA-tag, which we now mention in the figure legends.

In the plasmid constructs section of the methods, the authors list several primers by number but not by sequence. Instead, the authors should include the sequence and orientation of each of the primers mentioned in a table as supplementary data.

This is a good suggestion. We have generated a table at the end of the supplementary data file and on this occasion we also added tables of all the antibodies and dyes used in this study.

The authors should cite the study where the TgCentrin1 antibody was generated and provide the Rat anti-HA 3F10 antibody catalog number, as catalog numbers are provided for other commercial primary antibodies.

We now provide the missing catalog numbers in the supplemental data table.

There is an issue with the formatting of the journal-title in the Kukulski et al. reference.

Thank you for noticing this error, which we now corrected.

Reviewer #3 (Significance (Required)):

The genome of P. falciparum is fully sequenced; however, over 50% of encoded proteins are of unknown function, with many of these proteins unique to Plasmodium parasites. By identifying and characterizing essential biological processes, especially those divergent from human host cell processes, we will formulate ways to interfere with them by developing novel antimalarial drugs. The process of Plasmodium cell division differs from the classical cell cycle of its human host. In the study led by Caroline Simon, authors successfully utilized recent developments of super-resolution microscopies on expanded parasites to identify novel features of cell division machinery of the malaria blood-stage parasite.

Simon et al.'s work highlight the growing interest in the diversity of cell division mode of Apicomplexan parasites, which will likely contribute to a deeper understanding of the origin and functional role of the centrosome in eukaryotic life. In 2020, the Open Biology journal published a unique article collection named Focus on Centrosome Biology showcasing research that advanced our knowledge on centrosome function, evolution and abnormalities. In addition, the reported findings will interest research groups studying cell cycle regulation and evolution beyond the field of parasitology.

Our lab studies the peculiar cell cycle of Plasmodium falciparum to gain a functional understanding of mechanistic principles of nuclear envelope assembly and integrity during the cell division of the human malaria parasite.

**Referee Cross-commenting**

It is a wonderful study, and once all reviewer's comments are addressed, the manuscript should be in excellent shape for publication.