Author Response:

The following is the authors' response to the original reviews.

Reply to Public Reviews:

Reply to Reviewer #1:

This is a carefully performed and well-documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.

Response: We would like to thank the reviewer for her/his time for evaluating our manuscript. We are very happy to see the reviewer for highly appreciating our manuscript.

1. Overall this is a nicely done study with nice quantitation. It remains somewhat unclear from the data and discussions in exactly what way the authors mean that FUS is an RNA chaperone: is FUS changing the structure of the repeat or does FUS binding prevent it from folding into alternative in vivo structure?

Response: We appreciate the reviewer’s constructive comments. Indeed, we showed that FUS changes the higher-order structures of GGGGCC [G4C2] repeat RNA in vitro, and that FUS suppresses G4C2 RNA foci formation in vivo. According to the established definition of RNA chaperone, RNA chaperones are proteins changing the structures of misfolded RNAs without ATP use, resulting in the maintenance of proper RNAs folding (Rajkowitsich et al., 2007). Thus, we consider that FUS is classified into RNA chaperone. To clarify these interpretations, we revised the manuscript as follows.

(1) On page 10, line 215-219, the sentence “These results were in good agreement with our previous study on SCA31 showing the suppressive effects of FUS and other RBPs on RNA foci formation of UGGAA repeat RNA as RNA chaperones …” was changed to “These results were in good agreement with … RNA foci formation of UGGAA repeat RNA through altering RNA structures and preventing aggregation of misfolded repeat RNA as RNA chaperones …”.

(2) On page 17, line 363-366, the sentence “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure, as evident by CD and NMR analyses (Figure 5), suggesting its functional role as an RNA chaperone.” was changed to “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure as evident by CD and NMR analyses (Figure 5, Figure 5—figure supplement 2), and suppresses RNA foci formation in vivo (Figures 3A and 3B), suggesting its functional role as an RNA chaperone.”

Reply to Reviewer #2:

Fuijino et al. provide interesting data describing the RNA-binding protein, FUS, for its ability to bind the RNA produced from the hexanucleotide repeat expansion of GGGGCC (G4C2). This binding correlates with reductions in the production of toxic dipeptides and reductions in toxic phenotypes seen in (G4C2)30+ expressing Drosophila. Both FUS and G4C2 repeats of >25 are associated with ALS/FTD spectrum disorders. Thus, these data are important for increasing our understanding of potential interactions between multiple disease genes. However, further validation of some aspects of the provided data is needed, especially the expression data.

Response: We would like to thank the reviewer for her/his time for evaluating our manuscript and also for her/his important comments that helped to strengthen our manuscript.

Some points to consider when reading the work:

1. The broadly expressed GMR-GAL4 driver leads to variable tissue loss in different genotypes, potentially confounding downstream analyses dependent on viable tissue/mRNA levels.

Response: We thank the reviewer for this constructive comment. In the RT-qPCR experiments (Figures 1E, 3C, 4G, 6D and Figure 1—figure supplement 1C), the amounts of G4C2 repeat transcripts were normalized to those of gal4 transcripts expressed in the same tissue, to avoid potential confounding derived from the difference in tissue viability between genotypes, as the reviewer pointed out. To clarify this process, we have made the following change to the revised manuscript.

(1) On page 30, line 548-550, the sentence “The amounts of G4C2 repeat transcripts were normalized to those of gal4 transcripts in the same sample” was changed to “The amounts of G4C2 repeat transcripts were normalized to those of gal4 transcripts expressed in the same tissue to avoid potential confounding derived from the difference in tissue viability between genotypes”.

2. The relationship between FUS and foci formation is unclear and should be interpreted carefully.

Response: We appreciate the reviewer’s important comment. We apologize for the lack of clarity. We showed the relationship between FUS and RNA foci formation in our C9-ALS/FTD fly, that is, FUS suppresses RNA foci formation (Figures 3A and 3B), and knockdown of endogenous caz, a Drosophila homologue of FUS, enhanced it conversely (Figures 4E and 4F). We consider that FUS suppresses RNA foci formation through altering RNA structures and preventing aggregation of misfolded G4C2 repeat RNA as an RNA chaperone. To clarify these interpretations, we revised the manuscript as follows.

(1) On page 10, line 215-219, the sentence “These results were in good agreement with our previous study on SCA31 showing the suppressive effects of FUS and other RBPs on RNA foci formation of UGGAA repeat RNA as RNA chaperones …” was changed to “These results were in good agreement with … RNA foci formation of UGGAA repeat RNA through altering RNA structures and preventing aggregation of misfolded repeat RNA as RNA chaperones …”.

(2) On page 17, line 363-366, the sentence “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure, as evident by CD and NMR analyses (Figure 5), suggesting its functional role as an RNA chaperone.” was changed to “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure as evident by CD and NMR analyses (Figure 5, Figure 5—figure supplement 2), and suppresses RNA foci formation in vivo (Figures 3A and 3B), suggesting its functional role as an RNA chaperone.”

Reply to Reviewer #3:

In this manuscript Fujino and colleagues used C9-ALS/FTD fly models to demonstrate that FUS modulates the structure of (G4C2) repeat RNA as an RNA chaperone, and regulates RAN translation, resulting in the suppression of neurodegeneration in C9-ALS/FTD. They also confirmed that FUS preferentially binds to and modulates the G-quadruplex structure of (G4C2) repeat RNA, followed by the suppression of RAN translation. The potential significance of these findings is high since C9ORF72 repeat expansion is the most common genetic cause of ALS/FTD, especially in Caucasian populations and the DPR proteins have been considered the major cause of the neurodegenerations.

Response: We would like to thank the reviewer for her/his time for evaluating our manuscript. We are grateful to the reviewer for the insightful comments, which were very helpful for us to improve the manuscript.

1. While the effect of RBP as an RNA chaperone on (G4C2) repeat expansion is supposed to be dose-dependent according to (G4C2)n RNA expression, the first experiment of the screening for RBPs in C9-ALS/FTD flies lacks this concept. It is uncertain if the RBPs of the groups "suppression (weak)" and "no effect" were less or no ability of RNA chaperone or if the expression of the RBP was not sufficient, and if the RBPs of the group "enhancement" exacerbated the toxicity derived from (G4C2)89 RNA or the expression of the RBP was excessive. The optimal dose of any RBPs that bind to (G4C2) repeats may be able to neutralize the toxicity without the reduction of (G4C2)n RNA.

Response: We appreciate the reviewer’s constructive comments. We employed the site-directed transgenesis for the establishment of RBP fly lines, to ensure the equivalent expression levels of the inserted transgenes. We also evaluated the toxic effects of overexpressed RBPs themselves by crossbreeding with control EGFP flies, showing in Figure 1A. To clarify them, we have made the following changes to the revised manuscript.

(1) On page 8, line 166-168, the sentence “The variation in the effects of these G4C2 repeat-binding RBPs on G4C2 repeat-induced toxicity may be due to their different binding affinities to G4C2 repeat RNA, and their different roles in RNA metabolism.” was changed to “The variation in the effects of these G4C2 repeat-binding RBPs on G4C2 repeat-induced toxicity may be due to their different binding affinities to G4C2 repeat RNA, and the different toxicity of overexpressed RBPs themselves.”.

(2) On page 29, line 519-522, the sentence “By employing site-specific transgenesis using the pUASTattB vector, each transgene was inserted into the same locus of the genome, and was expected to be expressed at the equivalent levels.” was added.

2. In relation to issue 1, the rescue effect of FUS on the fly expressing (G4C2)89 (FUS-4) in Figure 4-figure supplement 1 seems weaker than the other flies expressing both FUS and (G4C2)89 in Figure 1 and Figure 1-figure supplement 2. The expression level of both FUS protein and (G4C2)89 RNA in each line is important from the viewpoint of therapeutic strategy for C9-ALS/FTD.

Response: We appreciate the reviewer’s important comment. The FUS-4 transgene is expected to be expressed at the equivalent level to the FUS-3 transgene, since they are inserted into the same locus of the genome by the site-directed transgenesis. Thus, we suppose that the weaker suppressive effect of FUS-4 coexpression on G4C2 repeat-induced eye degeneration can be attributed to the C-terminal FLAG tag that is fused to FUS protein expressed in FUS-4 fly line. Since the caz fly expresses caz protein also fused to FLAG tag at the C-terminus, we used this FUS-4 fly line to directly compare the effect of caz on G4C2 repeat-induced toxicity to that of FUS.

3. While hallmarks of C9ORF72 are the presence of DPRs and the repeat-containing RNA foci, the loss of function of C9ORF72 is also considered to somehow contribute to neurodegeneration. It is unclear if FUS reduces not only the DPRs but also the protein expression of C9ORF72 itself.

Response: We thank the reviewer for this comment. We agree that not only DPRs, but also toxic repeat RNA and the loss-of-function of C9ORF72 jointly contribute to the pathomechanisms of C9-ALS/FTD. Since Drosophila has no homolog corresponding to the human C9orf72 gene, the effect of FUS on C9orf72 expression cannot be assessed. Our fly models are useful for evaluating gain-of-toxic pathomechanisms such as RNA foci formation and RAN translation, and the association between FUS and loss-of function of C9ORF72 is beyond the scope of this study.

4. In Figure 5E-F, it cannot be distinguished whether FUS binds to GGGGCC repeats or the 5' flanking region. The same experiment should be done by using FUS-RRMmut to elucidate whether FUS binding is the major mechanism for this translational control. Authors should show that FUS binding to long GGGGCC repeats is important for RAN translation.

Response: We would like to thank the reviewer for these insightful comments. Following the reviewer’s suggestion, we perform in vitro translation assay again using FUS-RRMmut, which loses the binding ability to G4C2 repeat RNA as evident by the filter binding assay (Figure 5A), instead of BSA. The results are shown in the figures of Western blot analysis below. The addition of FUS to the translation system suppressed the expression levels of GA-Myc efficiently, whereas that of FUS-RRMmut did not. FUS decreased the expression level of GA-Myc at as low as 10nM, and nearly eliminated RAN translation activity at 100nM. At 400nM, FUS-RRMmut weakly suppressed the GA-Myc expression levels probably because of the residual RNA-binding activity. These results suggest that FUS suppresses RAN translation in vitro through direct interactions with G4C2 repeat RNA.

Unfortunately, RAN translation from short G4C2 repeat RNA was not investigated in our translation system, although the previous study reported the low efficacy of RAN translation from short G4C2 repeat RNA (Green et al., 2017).

Author response image 1.

(A) Western blot analysis of the GA-Myc protein in the samples from in vitro translation.

(B) Quantification of the GA-Myc protein levels.

We have made the following changes to the revised manuscript.

(1) Figure 5F was replaced to new Figures 5F and 5G.

(2) On page 14-15, line 326-330, the sentence “Notably, the addition of FUS to this system decreased the expression level of GA-Myc in a dose-dependent manner, whereas the addition of the control bovine serum albumin (BSA) did not (Figure 5F).” was changed to “Notably, upon the addition to this translation system, FUS suppressed RAN translation efficiently, whereas FUS-RRMmut did not. FUS decreased the expression levels of GA-Myc at as low as 10nM, and nearly eliminated RAN translation activity at 100nM. At 400nM, FUS-RRMmut weakly suppressed the GA-Myc expression levels probably because of the residual RNA-binding activity (Figure 5F and 5G).”.

(3) On page 15, line 330-332, the sentence “Taken together, these results indicate that FUS suppresses RAN translation from G4C2 repeat RNA in vitro as an RNA chaperone.” was changed to “Taken together, these results indicate that FUS suppresses RAN translation in vitro through direct interactions with G4C2 repeat RNA as an RNA chaperone.”.

(4) On page 37, line 720-723, the sentence “For preparation of the FUS protein, the human FUS (WT) gene flanked at the 5¢ end with an Nde_I recognition site and at the 3¢ end with a _Xho_I recognition site was amplified by PCR from pUAST-_FUS.” was changed to “For preparation of the FUS proteins, the human FUS (WT) and FUS-RRMmut genes flanked at the 5¢ end with an Nde_I recognition site and at the 3¢ end with a _Xho_I recognition site was amplified by PCR from pUAST-_FUS and pUAST- FUS-RRMmut, respectively.”.

(5) On page 41, line 816-819, the sentence “FUS or BSA at each concentration (10, 100, and 1,000 nM) was added for translation in the lysate.” was changed to “FUS or FUS-RRMmut at each concentration (10, 100, 200, 400, and 1,000 nM) was preincubated with mRNA for 10 min to facilitate the interaction between FUS protein and G4C2 repeat RNA, and added for translation in the lysate.”.

5. It is not possible to conclude, as the authors have, that G-quadruplex-targeting RBPs are generally important for RAN translation (Figure 6), without showing whether RBPs that do not affect (G4C2)89 RNA levels lead to decreased DPR protein level or RNA foci.

Response: We appreciate the reviewer’s critical comment. Following the suggestion by the reviewer, we evaluate the effect of these G-quadruplex-targeting RBPs on RAN translation. We additionally performed immunohistochemistry of the eye imaginal discs of fly larvae expressing (G4C2)89 and these G-quadruplex-targeting RBPs. As shown in the figures of immunohistochemistry below, we found that coexpression of EWSR1, DDX3X, DDX5, and DDX17 significantly decreased the number of poly(GA) aggregates. The results suggest that these G-quadruplex-targeting RBPs regulate RAN translation as well as FUS.

Author response image 2.

(A) Immunohistochemistry of poly(GA) in the eye imaginal discs of fly larvae expressing (G4C2)89 and the indicated G-quadruplex-targeting RBPs.

(B) Quantification of the number of poly(GA) aggregates.

We have made the following changes to the revised manuscript.

(1) Figures 6E and 6F were added.

(2) On page 6-7, line 135-137, the sentence “In addition, other G-quadruplex-targeting RBPs also suppressed G4C2 repeat-induced toxicity in our C9-ALS/FTD flies.” was changed to “In addition, other G-quadruplex-targeting RBPs also suppressed RAN translation and G4C2 repeat-induced toxicity in our C9-ALS/FTD flies.”.

(3) On page 15, line 344-346, the sentence “As expected, these RBPs also decreased the number of poly(GA) aggregates in the eye imaginal discs (Figures 6E and 6F).” was added.

(4) On page 15, line 346-347, the sentence “Their effects on G4C2 repeat-induced toxicity and repeat RNA expression were consistent with those of FUS.” was changed to “Their effects on G4C2 repeat-induced toxicity, repeat RNA expression, and RAN translation were consistent with those of FUS.”

(5) On page 16, line 355-357, the sentence “Thus, some G-quadruplex-targeting RBPs regulate G4C2 repeat-induced toxicity by binding to and possibly by modulating the G-quadruplex structure of G4C2 repeat RNA.” was changed to “Thus, some G-quadruplex-targeting RBPs regulate RAN translation and G4C2 repeat-induced toxicity by binding to and possibly by modulating the G-quadruplex structure of G4C2 repeat RNA.”

(6) On page 19, line 417-421, the sentence “We further found that G-quadruplex-targeting RNA helicases, including DDX3X, DDX5, and DDX17, which are known to bind to G4C2 repeat RNA (Cooper-Knock et al., 2014; Haeusler et al., 2014; Mori et al., 2013a; Xu et al., 2013), also alleviate G4C2 repeat-induced toxicity without altering the expression levels of G4C2 repeat RNA in our Drosophila models.” was changed to “We further found that G-quadruplex-targeting RNA helicases, … ,also suppress RAN translation and G4C2 repeat-induced toxicity without altering the expression levels of G4C2 repeat RNA in our Drosophila models.”.

Reply to Recommendations For The Authors:

1) It is not clear from the start that the flies they generated with the repeat have an artificial vs human intronic sequence ahead of the repeat. It would be nice if they presented somewhere the entire sequence of the insert. The reason being that it seems they also tested flies with the human intronic sequence, and the effect may not be as strong (line 234). In any case, in the future, with a new understanding of RAN translation, it would be nice to compare different transgenes, and so as much transparency as possible would be helpful regarding sequences. Can they include these data?

Response: We thank the editors and reviewers for this comment. We apologize for the lack of clarity. We used artificially synthesized G4C2 repeat sequences when generating constructs for (G4C2)n transgenic flies, so these constructs do not contain human intronic sequence ahead of the G4C2 repeat in the C9orf72 gene, as explained in the Materials and Methods section. To clarify the difference between our C9-ALS/FTD fly models and LDS-(G4C2)44GR-GFP fly model (Goodman et al., 2019), we have made the following change to the revised manuscript.

(1) Schema of the LDS-(G4C2)44GR-GFP construct was presented in Figure 3—figure supplement 1.

Furthermore, to maintain transparency of the study, we have provided the entire sequence of the insert as the following source file.

(2) The artificial sequences inserted in the pUAST vector for generation of the (G4C2)n flies were presented in Figure 1—figure supplement 1—source data 1.

2) It is really nice how they quantitated everything and showed individual data points.

Response: We thank the editors and reviewers for appreciating our data analysis method. All individual data points and statistical analyses are summarized in source data files.

3) So when they call FUS an RNA chaperone, are they simply meaning it is changing the structure of the repeat, or could it just be interacting with the repeat to coat the repeat and prevent it from folding into whatever in vivo structures? Can they speculate on why some RNA chaperones lead to presumed decay of the repeat and others do not? Can they discuss these points in the discussion? Detailed mechanistic understanding of RNA chaperones that ultimately promote decay of the repeat might be of highly significant therapeutic benefit.

Response: We appreciate these critical comments. Indeed, we showed that FUS changes the higher-order structures of G4C2 repeat RNA in vitro, and that FUS suppresses G4C2 RNA foci formation. According to the established definition of RNA chaperone, RNA chaperones are proteins changing the structures of misfolded RNAs without ATP use, resulting in the maintenance of proper RNAs folding (Rajkowitsich et al., 2007). Thus, we consider that FUS is classified into RNA chaperone. To clarify these interpretations, we revised the manuscript as follows.

(1) On page 10, line 215-219, the sentence “These results were in good agreement with our previous study on SCA31 showing the suppressive effects of FUS and other RBPs on RNA foci formation of UGGAA repeat RNA as RNA chaperones …” was changed to “These results were in good agreement with … RNA foci formation of UGGAA repeat RNA through altering RNA structures and preventing aggregation of misfolded repeat RNA as RNA chaperones …”.

(2) On page 17, line 363-366, the sentence “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure, as evident by CD and NMR analyses (Figure 5), suggesting its functional role as an RNA chaperone.” was changed to “FUS directly binds to G4C2 repeat RNA and modulates its G-quadruplex structure as evident by CD and NMR analyses (Figure 5, Figure 5—figure supplement 2), and suppresses RNA foci formation in vivo (Figures 3A and 3B), suggesting its functional role as an RNA chaperone.”

Besides these RNA chaperones, we observed the expression of IGF2BP1, hnRNPA2B1, DHX9, and DHX36 decreased G4C2 repeat RNA expression levels. In addition, we recently reported that hnRNPA3 reduces G4C2 repeat RNA expression levels, leading to the suppression of neurodegeneration in C9-ALS/FTD fly models (Taminato et al., 2023). We speculate these RBPs could be involved in RNA decay pathways as components of the P-body or interactors with the RNA deadenylation machinery (Tran et al., 2004; Katahira et al., 2008; Geissler et al., 2016; Hubstenberger et al., 2017), possibly contributing to the reduced expression levels of G4C2 repeat RNA. To clarify these interpretations, we revised the manuscript as follows.

(3) On page 18, line 392-398, the sentences “Similarly, we recently reported that hnRNPA3 reduces G4C2 repeat RNA expression levels, leading to the suppression of neurodegeneration in C9-ALS/FTD fly models (Taminato et al., 2023). Interestingly, these RBPs have been reported to be involved in RNA decay pathways as components of the P-body or interactors with the RNA deadenylation machinery (Tran et al., 2004; Katahira et al., 2008; Geissler et al., 2016; Hubstenberger et al., 2017), possibly contributing to the reduced expression levels of G4C2 repeat RNA.” was added.

4) What is the level of the G4C2 repeat when they knock down caz? Is it possible that knockdown impacts the expression level of the repeat? Can they show this (or did they and I miss it)?

Response: We thank the editors and reviewers for this comment. The expression levels of G4C2 repeat RNA in (G4C2)89 flies were not altered by the knockdown of caz, as shown in Figure 4G.

5) A puzzling point is that FUS is supposed to be nuclear, so where is FUS in the brain in their lines? They suggest it modulates RAN translation, and presumably, that is in the cytoplasm. Is FUS when overexpressed now in part in the cytoplasm? Is the repeat dragging it into the cytoplasm? Can they address this in the discussion? If FUS is never found in vivo in the cytoplasm, then it raises the point that the impact they find of FUS on RAN translation might not reflect an in vivo situation with normal levels of FUS.

Response: We appreciate these important comments. We agree with the editors and reviewers that FUS is mainly localized in the nucleus. However, FUS is known as a nucleocytoplasmic shuttling RBP that can transport RNA into the cytoplasm. Indeed, FUS is reported to facilitate transport of actin-stabilizing protein mRNAs to function in the cytoplasm (Fujii et al., 2005). Thus, we consider that FUS binds to G4C2 repeat RNA in the cytoplasm and suppresses RAN translation in this study.

6) When they are using 2 copies of the driver and repeat, are they also using 2 copies of FUS? These are quite high levels of transgenes.

Response: We thank the editors and reviewers for this comment. We used only 1 copy of FUS when using 2 copies of GMR-Gal4 driver. Full genotypes of the fly lines used in all experiments are described in Supplementary file 1.

7) In Figure5-S1, FUS colocalizing with (G4C2)RNA is not clear. High-magnification images are recommended.

Response: We appreciate this constructive comment on the figure. Following the suggestion, high-magnification images are added in Figure 5—figure supplement 1.

8) I also suggest that the last sentence of the Discussion be revised as follows: Thus, our findings contribute not only to the elucidation of C9-ALS/FTD, but also to the elucidation of the repeat-associated pathogenic mechanisms underlying a broader range of neurodegenerative and neuropsychiatric disorders than previously thought, and it will advance the development of potential therapies for these diseases.

Response: We appreciate this recommendation. We have made the following change based on the suggested sentence.

(1) On page 20-21, line 455-459, “Thus, our findings contribute not only towards the elucidation of repeat-associated pathogenic mechanisms underlying a wider range of neuropsychiatric diseases than previously thought, but also towards the development of potential therapies for these diseases.” was changed to “Thus, our findings contribute to the elucidation of the repeat-associated pathogenic mechanisms underlying not only C9-ALS/FTD, but also a broader range of neuromuscular and neuropsychiatric diseases than previously thought, and will advance the development of potential therapies for these diseases.”.

Authors’ comment on previous eLife assessment:

We thank the editors and reviewers for appreciating our study. We mainly evaluated the function of human FUS protein on RAN translation and G4C2 repeat-induced toxicity using Drosophila expressing human FUS in vivo, and the recombinant human FUS protein in vitro. To validate that FUS functions as an endogenous regulator of RAN translation, we additionally evaluated the function of Drosophila caz protein as well. We are afraid that the first sentence of the eLife assessment, that is, “This important study demonstrates that the Drosophila FUS protein, the human homolog of which is implicated in amyotrophic lateral sclerosis (ALS) and related conditions, …” is somewhat misleading. We would be happy if you modify this sentence like “This important study demonstrates that the human FUS protein, which is implicated in amyotrophic lateral sclerosis (ALS) and related conditions, …”.

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

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Reply to the reviewers

Reply to the reviewers

Manuscript number: RC-2023-01932

Corresponding author(s): Dennis KAPPEI

We would like to thank all reviewers for their recognition of our approach and the quality of our work as well as their constructive criticism.

Reviewer #1

Reviewer #1: The manuscript by Yong et. al. describes a comparison of various chromatin immunoprecipitation-mass spectrometric (ChIP-MS) methods targeting human telomeres in a variety of systems. By comparing antibody-based methods, crosslinkers, dCas9 and sgRNA targeted methods, KO cells and various controls, they provide a useful perspective for readers interested in similar experiments to explore protein-DNA interactions in a locus-specific manner.

Response: We would like to thank the reviewer for the feedback and the appreciation of our work.

Reviewer #1: While interesting, I found it somewhat difficult to extract a clear comparison of the methods from the text. It was also difficult to compare as data and findings from each method was discussed in its own context. Perhaps it is not in their interest to single out a specific method and it is indeed true that there are caveats with each of the methods.

Response: Across our manuscript we have established one single workflow, for which we present some technical comparisons (e.g. using single or double cross-linking in Fig. 2a/b), technical recommendations such as the use of loss-of-function controls (e.g. Fig. 1c v. Fig. 2a and Extended Data Fig. 3g vs. 3i) and an application to unique loci using dCas9 (Fig. 3f). Based on the suggestions below, we believe that we will improve the clarity of communicating our approach.

Reviewer #1: I think the manuscript would be of interest but I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

Response: We thank the reviewer for highlighting this point. We do not think that the ChIP-MS comparison between U2OS WT and ZBTB48 KO clones (Fig. 2a) has experiment-specific caveats. Instead the KO controls as well as the dTAGV-1 degron system for MYB ChIP-MS (Extended Data Fig. 3) reveal antibody-specific off-targets, which are indeed false-positives. Please see below for further details.

Reviewer #1: Ln 57: What is "standard double cross-linking ChIP reactions" in this context? Is it the two different crosslinkers? The two proteins? The reciprocal IPs of one protein, and blotting for another? It's not clear here or from Extended Fig 1A. Upon further reading, it seems to pertain to the two crosslinkers - if so, the authors should briefly describe their workflow to help readers.

Response: As the reviewer correctly concludes, we indeed intended to highlight the use of two separate crosslinkers (formaldehyde/FA and DSP). This combination is important as illustrated in the side-by-side comparison of Fig. 2a and Fig. 2d. Here, we performed ZBTB48 ChIP-MS in five U2OS WT and five U2OS ZBTB48 KO clones. While in both experiments the bait protein ZBTB48 was abundantly enriched in the samples that were fixed with formaldehyde we lose about half of the telomeric proteins that are known to directly bind to telomeric DNA independent of ZBTB48 and all of their interaction partners. For instance, while the FA+DSP reaction in Fig. 2a enriched all six shelterin complex members, the FA only reaction in Fig. 2d only enriches TERF2. These data suggest that the use of a second cross-linker helps to stabilise protein complexes on chromatin fragments. This is a critical message of our manuscript as ChIP-MS only truly lives up its name if we can enrich proteins that genuinely sit on the same chromatin fragment without protein interactions to the bait protein. We will expand on this in both the text and our schematics in Fig. 1a and 3a to make this clearer for the readers.

Reviewer #1: Ln 95: It is surprising and quite unclear to me why it is that the WT ZBTB48 U2OS pulldown in Fig 1B shows 83 hits for the WT vs Ig control experiment but 27 hits for the WT vs KO condition in Fig 2A. The two WT experiments have the same design and reagents, shouldn't they be as close as technical replicates and provide very similar hits?

The authors seem to make the claim that most of the 'extra' proteins in WT vs Ig are abundant and false positives, but if this is so, shouldn't they bind non-specifically to the beads and be enriched equally in Ig control and ZBTB48 WT IPs?

Response: We again thank the reviewer for raising this point and the need to explain in more detail why we interpret the difference between 83 hits (anti-ZBTB48 antibody vs. IgG; Fig. 1c) and 27 hits (anti-ZBTB48 antibody used in both U2OS WT and ZBTB48 KO cells; Fig. 2a) primarily as false-positives. The KO controls in Fig. 2a allow to keep the ZBTB48 antibody as a constant variable while instead comparing the presence (WT) or absence (KO) of the bait protein. Hence, proteins that were enriched in the IgG comparison in Fig. 1c but that are lost in the WT vs. KO comparison in Fig. 2a are likely directly (or indirectly) recognised by the ZBTB48 antibody, akin to off-targets to this particular reagent. In a Western blot this would be equivalent to seeing multiple bands at different molecular weights with only the band belonging to the protein-of-interest disappearing in KO cells. To illustrate this we would like to refer to Extended Data Fig. 2, in which we have replotted the exact same data from Fig. 2a. However, in addition we have here highlighted proteins that were enriched in the IgG comparison in Fig. 1c. 46 proteins (in pink) are indeed quantified in the WT vs. KO comparison, but these proteins are found below the cut-offs (and most of them with very poor fold changes and p-values). In contrast to the other several hundred proteins common between both experiments that can be considered common background non-specifically bound to the protein G beads, these 46 proteins represent antibody-specific false-positives.

The above consideration is not unique to ChIP-MS as illustrated by the Western blot example. We also do not claim novelty on the experimental logic, e.g. pre-CRISPR in 2006 Selbach and Mann demonstrated the usefulness of RNAi controls in immunoprecipitations (IPs) (PMID: 17072306). However, our data suggests that ChIP-MS is particularly vulnerable to this type of false-positives given that the approach requires (double-)cross-linking to sufficiently stabilise true-positives on the same chromatin fragment.

To supplement the WT vs. ZBTB48 KO comparison, we had included a second experiment in the manuscript that illustrates the same point in even more dramatic fashion. First, KO controls are very clean in principle, but they themselves might come with caveats if e.g. the expression levels between WT and KO samples differ greatly. This might create a situation that the reviewer hinted to, i.e. differential expression of abundant proteins that would proportionally to their expression levels stick to the beads, resulting in “fold enrichments”. The resulting false positives could e.g. be controlled by matched expression proteomes. For ZBTB48 we have previously measured this (PMID: 28500257) and demonstrated that only a small number of genes are differentially expressed (~10) and hence we can interpret the WT vs. ZBTB48 KO comparison quite cleanly. However, for other classes of proteins such as transcription factors that regulate a large number of genes, E3 ligases etc. this might present a more serious concern. Therefore, we extended our loss-of-function comparison to such a transcription factor, MYB, by using the dTAGV-1 degron system. Importantly, the MYB antibody has been used in previous work for ChIP-seq applications (e.g. PMID: 25394790). Here, instead of 186 hits in the MYB vs. IgG comparison using the same MYB antibody in control-treated and dTAGV-1-treated cells (upon 30 min of treatment only) we only detect 9 hits. Again, similar to the WT vs. ZBTB48 KO comparison, 180 proteins are quantified in the DMSO vs. dTAGV-1 comparison, but these proteins fall below the cut-offs (Extended Data Fig. 3g vs. 3i). Again, we believe that this quite drastically illustrates how vulnerable ChIP-MS data is to large numbers of false-positives. This is not only a technical consideration as such datasets are frequently used in downstream pathway/gene set enrichment analyses etc. Such large false discovery rates would obviously lead to error-carry-forward and additional (unintended) misinterpretations. We will carefully expand our textual description across the manuscript to make these points much clearer. In addition, we will move the previous Extended Data Fig. 3 into the main manuscript to more clearly highlight this important point.

Reviewer #1: Volcano plots in Figs 1, 2, and Suppl. Tables etc: Are the plotted points the mean of 5 replicates? Was each run normalized between the replicates in each group, for e.g. by median normalization of the log2 MS intensities? This does not appear to be the case upon inspection of the Suppl Tables. Given the variability in pulldown efficiency, gel digest and peptide recovery, this would certainly be necessary.

Response: All volcano plots are indeed based on 4-5 biological replicates (most stringently in the WT vs. KO comparisons in Fig. 2 based on each 5 independent WT and ZBTB48 KO single cell clones). The x-axis of each volcano plot represents the ratio of mean MS1-based intensities between both experimental conditions in log2 scale. However, precisely to account for the variation that the reviewer highlighted we did not base our analysis on raw MS1 intensities but we used the MaxLFQ algorithm (PMID: 24942700) as part of the MaxQuant analysis software (PMID: 19029910) for genuine label-free quantitation across experimental conditions and replicates. In this context, we would also like to refer to a related comment by reviewer #2 based on which we will now addd concordance information for each replicate (heatmaps for Pearson correlations and PCA plots). We will improve this both in the text and methods section accordingly.

Reviewer #1: Ln 125: The authors make the claim that the ChIP-MS experiments are inherently noisy, with examples from WT cells, dTAG system and IgG controls. This is likely the case, yet their experiments with WT vs KO cells do not identify as many proteins overall. I find this inconsistency somewhat unclear and does not seem to match the claim of ChIP-MS experiments and crosslinking adding to non-specificity. Can the authors add the total number of identified proteins in each volcano plot, for easier reference?

Response: The number of identified proteins does not vary majorly between matched IgG and loss-of-function comparisons and for instance the single cross-linking (FA only) experiment in Fig. 2c has the largest number of quantified proteins among all ZBTB48 IPs. But we will of course add the requested information to all plots.

Reviewer #1: I think the manuscript is interest as it provides important benchmarks for ChIP-proteomics experiments. I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

Response: We would like to thank the reviewer for recognising our work as a source for important benchmarks for ChIP-MS experiments. We hope that with a more detailed description and discussion the highlighted aspects will be more clearly communicated. We originally conceived our manuscript as a short report and now realised that some of the information became too condensed and might therefore benefit from more extensive explanations.

Reviewer #2

Reviewer #2: Summary: In this manuscript, Yong and colleagues have introduced a optimized technique for studying actors on chromatin in specific regions with a localized approach thanks to revisited ChIP-mass spectrometry (MS) with label-free quantitative (LFQ). The authors exhibited the utility of their approach by demonstrating its effectiveness at telomeres from cell culture (human U2OS cells) to tissue samples (liver, mouse embryonic stem cells). As a proof of concept, this technique was tested by the authors with proteins from complex shelterin specific to telomeres (TERF2 and ZBTB48), transcription factors (MYB), and through dCas9-driven locus-specific enrichment. Notably, the authors created a U2OS dCas9-GFP clone and then introduced sgRNAs to target either telomeric DNA (sgTELO) or an unrelated control (sgGAL4). The cells expressing sgTELO exhibited a significant localization of telomeres and an enriched amount of telomeric DNA in ChIP with dCas9. They also found the proteins previously identified as known to be enriched at telomeres (for example, the 6 shelterin members).

Moreover, the authors illustrated the importance of double crosslinking (formaldehyde (FA) and dithiobis(succinimidyl propionate) (DSP) in ChIP-MS. Their data demonstrated also that ChIP-MS is inclined towards false-positives, possibly owing to its inherent cross-linking. However, by utilizing loss-of-function conditions specific to the bait, it can be tightly managed.

• Can you show the concordance between biological replicates for each ChIP with LFQ? (heatmap of Pearson correlation and PCA plot). This will confirm the robustness of the use of LFQ.

Response: We will add the requested concordance data for all volcano plots both in the form of heatmaps of Pearson correlation and PCA plots. Across our datasets, the replicates from the same experimental condition clearly cluster with each other and replicates have high concordance values of >0.9. As expected replicates for the target/bait samples have slightly higher concordance values compared to the negative controls (IgG or loss-of-function samples). We thank the reviewer for this suggestion as the new Extended Data panel will strengthen the illustration of our robust LFQ data.

Reviewer #2: You say that your technique is " a simple, robust ChIP-MS workflow based on comparably low input quantities » (line 139). What would be really interesting for a technical paper would be: a schematic and a table illustrating the differences between your method and the previously published methods (amount of material, timeline,...) to really highlight the novelty in your optimized techniques.

Response: We will add a comparison table with previous publications using ChIP-MS and for reference include some complementary approaches as requested by reviewer #3. On this note, we would like to stress that we are not “only” intending to use less material and to have an easy-to-adopt protocol. A cornerstone of our manuscript is to apply rigorous expectations to ChIP-MS experiments, in particular the ability to enrich proteins that independently bind to the same chromatin fragments as the bait protein (regardless of whether this is an endogenous protein or a exogenous, targeted bait such as dCas9). Otherwise, such experiments risk to be regular protein IPs under cross-linking conditions, which as illustrated by our loss-of-function comparisons are prone to yield particularly large fractions of false-positives.

Reviewer #2: It would be interesting to perform the dCas9 ChIP experiment in telomeric regions with and without LFQ. Since the novelty lies in this parameter, at no time does the paper show that LFQ really allows to have as many or more proteins identified but in a simpler way and with less material. A table allowing to compare with and without LFQ would be interesting.

Response: We do not fully understand what the suggestion “without LFQ” refers to exactly. We assume that this reviewer might suggest to use a different quantitative mass spectrometry approach other than LFQ, e.g. SILAC labelling, TMT labelling etc. Please note that we do not claim that LFQ quantification is per se superior to the various quantification methods that had been developed and widely used across the proteomics community especially before instrument setups and analysis pipelines were stable enough for label-free quantification (a name that is strongly owed to this historic order of development). However, a central goal of our workflow is to make robust and rigorous ChIP-MS accessible to the myriad of laboratories using ChIP-qPCR/-seq and that may not be extensively specialised in mass spectrometry. Both metabolic and isobaric labelling come not only at a higher cost but also present an experimental hurdle to non-specialists compared to performing biological replicates without any labelling, essentially the same way as for any ChIP-qPCR etc. experiment. We will further elaborate on these points in the manuscript to more clearly convey these notions.

In general, with the right effort different quantitative methods should and will likely yield qualitatively similar results. However, comparisons between LFQ approaches (MaxLFQ, iBAQ,…) and labelling approaches (SILAC, TMT, iTRAQ) have already been better explored and verbalised elsewhere (e.g. PMID: 31814417 & 29535314). Therefore, we believe that this will add relatively little value to our manuscript.

Reviewer #2: Put a sentence to explain "label free quantification". For a reader who is not at all familiar with this technique, it would be interesting to explain it and to quote the advantages compared to PLEX.

Response: Thanks for highlighting this. In line with the point above as well as a similar comment by reviewer #1 we will improve this both in the main text and manuscript to clearly explain the terminology, the MaxLFQ algorithm (PMID: 24942700) used and to highlight the advantages compared to labelling approaches.

Reviewer #2: what does the ranking on the right of each volcano plot represent (figure 1 b-e, figure 2a,d,e for example)? top of the most enriched proteins in the mentioned categories? Not very clear when we look on the volcano plot. it must be specified in the legend.

Response: The numbering these panels is meant to link protein names to the data points on the volcano plots. The order of hits is ranked based on strongest fold enrichment, i.e. from right to center. We will clarify this in the figure legends.

Reviewer #2: General assessment/Advance: The authors explain in their article that the ChIP exploiting the sequence specificity of nuclease-dead Cas9 (dCas9) to target specific chromatin loci by directly enriching for dCas9 was already published. Here, the novelty of this study lies in the use of LFQ mass spectrometry to optimize the technique and make it easier to handle. Some comparisons with previous papers or data generated by the lab will be interesting to really show the improvement and the advantage to use LFQ and therefore, to highlight better the novelty of the study.

Response: We thank the reviewer for this assessment and as mentioned above we will include such a comparison table. dCas9 has been used previously in a ChIP-MS approach termed CAPTURE (PMID: 28841410). While this is clearly a landmark paper that illustrated the dCas9 enrichment concept across multiple omics applications (i.e. not limited to proteomics) in their application to telomeres, the authors enriched only 3 out of the 6 shelterin proteins with quite moderate fold enrichments (POT1: 0.99, TERF2: 2.13, TERF2IP: 1.06; in log2 scale). Based on this alone, POT1 and TERF2IP would not have qualified for our cut-off criteria. In addition, while the authors had performed three replicates, detection is only reported in 1-2 out of 3 replicates. While it is difficult to reconstruct statistical values based on the publicly accessible data, it is therefore unlikely that even these 3 proteins would have robustly be considered hits in our datasets. Similarly, using recombinant dCas9 with a sgRNA targeting telomeres that was in vitro reconstituted with sonicated chromatin extracts from 500 million HeLa cells (CLASP; PMID: 29507191) the authors identified only up to 3 shelterin subunits (TERF2, TERF2IP and TPP1/ACD) based on 1 unique peptide each only. For comparison, in our dCas9 ChIP-MS dataset all 6 shelterin subunits are identified with 9-19 unique peptides, contributing to our robust quantification. Even when considering cell line-specific differences (HeLa cells have shorter telomeres and hence provide less biochemical material for enrichment per cell), these comparisons illustrate that prior attempts struggled to robustly replicate even the most abundant telomeric complex members.

Based on these findings, others had suggested that dCas9 “might exclude some relevant proteins from telomeres in vivo” (PMID: 32152500), implying that dCas9 ChIP-MS might inherently not be feasible including at repetitive regions such as telomeres. Therefore, we believe that our dCas9 ChIP-MS data is a proof-of-concept that the method has the genuine ability to robustly enrich key proteins at individual loci. In concordance with the comment above we will include a comparison table with previous papers and expand on these points in the discussion.

Reviewer #2: By presenting this technical paper, the authors allow laboratories across different fields to use this technique to gain insights into protein enrichment in specific chromatin regions such as the promoter of a gene of interest or a particular open region in ATACseq in a easier way and with less materials. This paper holds value in enabling researchers to answer many pertinent questions in various fields.

Response: We again thank the reviewer for this encouraging assessment and we do indeed hope that this manuscript makes a contribution to a much wider use of ChIP-MS approaches as a promising complement to existing genome-wide epigenetics analyses.

Reviewer #3

Reviewer #3: Strengths of the study:

The study is well-structured and provides a robust workflow for the application of ChIP-MS to investigate chromatin composition in various contexts.

The use of telomeres as a model locus for testing the developed ChIP-MS approach is appropriate due to its well-characterized protein composition.

The comparison of WT vs KO lines for ZBTB48 is a rigorous way to control for false-positives, providing more confidence in the results.

The direct comparison of double vs only FA-crosslinking provides valuable insights into the benefit of additional protein-protein crosslinking in ChIP-MS workflows.

Response: We thank the reviewer for this assessment and we agree that the above are several of the key features of our manuscript.

Reviewer #3: Areas for improvement: The novelty of the method is more than questionable as both ChIP-MS coupled to LFQ and dCas9 usage for locus-specific proteomics have been previously reported. The fact that the authors directly pulldown dCas9 instead of using a dCas9-fused biotin ligase and subsequent streptavidin pulldown is only a very minor change to previous methods (not even improvement). It would be more accurate for the authors to present their study as an optimization and rigorous validation of existing techniques rather than a novel approach.

Response: While we appreciate where the reviewer is coming from, it occurs to us that most of the reviewer’s comments equate ChIP approaches with other complementary methods, in particular proximity labelling. The latter is indeed a powerful experimental strategy and in fact we are ourselves avid users. As highlighted to reviewer #1 as well, our manuscript was originally conceived as a shorter report and based on the feedback we will now expand our discussion to more broadly incorporate related approaches.

However, we would like to stress that dCas9 ChIP-MS and dCas9-biotin ligase fusions are not the same thing and this is not a minor tweak to an existing protocol. While both approaches have converging aims – to identify proteins that associate with individual genomic loci – the experimental workflows differ fundamentally. Biotin ligases use a “tag and run” approach by promiscuously leaving a biotin tag on encountered proteins. Subsequently, cellular proteins are extracted and in fact proteins can even be denatured prior to enrichment with streptavidin beads. While this is an in vivo workflow that (depending on the biotin ligase used) may provide sensitivity advantages, it does not retain complex information. The latter is inherently part of ChIP workflows due to the use of cross-linkers. One obvious future application would be to maintain (= not to reverse as we have done here) the crosslink during the mass spectrometry sample preparation in order to read out cross-linked peptides to gain insights into interactions and structural features. We will now more clearly incorporate such notions into our discussion.

In addition, we would like to stress that while this reviewer focuses primarily on the dCas9 aspect of our manuscript, we believe that our general ChIP-MS workflow including the combination with label-free quantitation is useful and important already by itself as e.g. recognised by both reviewers #1 and #2.

Reviewer #3: The authors should more thoroughly discuss previous works using ChIP-MS and dCas9 for locus-specific proteomics. This would give readers a better understanding of how the current work builds on and improves these earlier methods. For a paper that aims on presenting an optimized ChIP-MS workflow it is crucial to showcase in which use cases it outperforms previously published methods.

E.g., compare locus-specific dCas9 ChIP-MS to CasID (doi.org/10.1080/19491034.2016.1239000) and C-Berst (doi.org/10.1038/s41592- 018-0006-2); how does your method perform in comparison to these?

Response: Again, while we will now incorporate more extensively comparisons with previous ChIP-MS publications (and the few prior manuscripts that included dCas9) as well as related techniques, we would like to stress that dCas9 ChIP-MS is not the same approach as CasID and C-BERST, which rely on dCas9 fusions to BirA* and APEX2, respectively. dCas9-APEX2 strategies were also published by two additional groups as CASPEX (back-to-back with the C-BERST manuscript; PMID: 29735997) and CAPLOCUS (PMID: 30805613). All of these methods target specific loci with dCas9 and promiscuously biotinylate proteins that are in proximity to the dCas9-biotin ligase fusion protein. As described above, while the application of the BioID principle (PMID: 22412018) to chromatin regions has converging aims with the dCas9 ChIP-MS part of our manuscript, they do not test the same. ChIP carries chromatin complexes through the entire workflow while the CasID approaches are independent of that. This is the same scenario if we were to compare IP-MS reactions (such as the ChIP-MS reactions presented here for endogenous proteins) and BioID-type experiments for proximity partners of the same bait proteins.

Reviewer #3: Compare likewise the described protein interactomes to previously published interactomes.

Response: We will add comparisons in form of Venn diagrams with previously published interactomes. However, we would like to stress that a key aspect of our manuscript is the smaller yet rigorous hit lists based on e.g. loss-of-function controls, higher stringencies and specificity. Simply comparing final interactomes remains reductionist relative to the importance of other variables such as experimental design, number of replicates, data analysis etc.

Reviewer #3: The authors use sgGAL4 as a control for the telomeric targeting of dCas9. The IF results (Fig3b) show that sgGAL4 barely localizes to the nucleus with very faint signals. It would be helpful to use a control with homogenous nuclear localization of dCas9 to further strengthen the author's conclusions.

Response: dCas9-EGFP in the presence of sgGAL4 localises diffusely to the nucleus as expected. We have here used a very widely used non-targeting sgRNA control that has been originally used for imaging purposes (PMID: 24360272) and has since been used in a variety of studies (e.g. PMID: 26082495, 32540968, 28427715) including a previous dCas9 ChIP-MS attempt (PMID: 28841410). In addition, to the diffuse nuclear, non-telomeric localisation we provide complementary validation of clean enrichment of telomeric DNA specifically in the sgTELO samples. Therefore, we do not see how other non-targeting sgRNAs would provide for better controls or improve our data.

Reviewer #3: The extrapolation of results from the use of telomeres as a proof-of-concept to other loci is not a given considering the highly repetitive structure of telomeric DNA. The authors should either be more cautious about generalizing the results to other loci or demonstrate that their method can also capture locus-specific interactomes at non-repetitive regions.

Response: We agree that the adoption of any locus-specific approach to single genomic loci is a steep additional hurdle and warrants rigorous data on well characterised loci with very clear positive controls. We will expand on these challenges in our discussion. However, we would like to stress that we did not make any such statement in our original manuscript apart from simply referring to our telomeric experiment as proof-of-concept evidence that locus-specific approaches are feasible by ChIP.

Reviewer #3: What are concrete biological insights from this optimized ChIP-MS workflow that previous methods failed to show?

Response: We explicitly used telomeres as an extensively studied locus with clear positive controls that at the same time allows us to evaluate likely false positives. As such the intention of the manuscript was not to yield concrete biological insights but to develop a new methodological workflow.

As also highlighted in a response to reviewer #2, based on other prior attempts to enrich telomers in ChIP-like approaches with dCas9 (PMID: 28841410 & 29507191), it had been suggested that dCas9 “might exclude some relevant proteins from telomeres in vivo” (PMID: 32152500), implying that dCas9 ChIP-MS might inherently not be feasible including at repetitive regions such as telomeres. Therefore, recapitulating the set of well-described telomeric proteins was no trivial feat and our ChIP-MS workflow (both targeted and applied to individual proteins) represents a well-validated method to in the future systematically interrogate changes in chromatin composition. As one example at telomeres, this may include chromatin changes upon the induction of telomeric fusions or general DNA damage.

Reviewer #3: For instance, the authors could compare their mouse and human TERF2 interactomes and discuss similarities and differences between both species.

Response: We thank the reviewer for this suggestion, but the comparison between mouse and human TERF2 interactomes is not suitable across the datasets that we generated. U2OS is a human osteosarcoma cell line that relies on the Alternative Lengthening of Telomeres (ALT) pathway while our mouse data is based on embryonic stem cells (mESCs) and mouse liver tissue. Even the latter, in contrast to adult human tissue, expresses telomerase. We can certainly still pinpoint (as already done in our original manuscript) individual differences among known factors, e.g. the fact that proteins such as NR2C2 are more abundantly found at ALT telomeres (PMID: 19135898, 23229897, 25723166) vs. the detection of the CST complex as telomerase terminator (PMID: 22763445) in the mouse samples. However, the TERF2 datasets contain hundreds of proteins as “hits” above our cut-offs and a key message of our manuscript is that the majority of them are likely false positives. Here, differences are likely extending to expression differences between U2OS cells, mESCs and liver samples. So while appealing in theory, this cross data set comparison would remain rather superficial and error prone at this point. As a biology focused follow-up study, this would need to be rigorously conceived based on an appropriate choice of human and murine cell line models. In addition, this would likely require the generation of FKBP12-TERF2 knock-in fusion clones to allow for rapid depletion of TERF2 for a clean loss-of-function control since sustained loss of TERF2 leads to chromosomal fusions and eventually cell death in most cell types.

Reviewer #3: The authors should also describe which interaction partners are novel and try to validate some of these using orthogonal methods.

Response: We will now highlight more explicitly two proteins, POGZ and UBTF, that are most robustly and reproducibly enriched on telomeric chromatin across datasets, including the U2OS WT vs. ZBTB48 KO comparison (Fig. 2a). However, we would like to abstain from a molecular characterization at this point. As mentioned above, the discovery of novel telomeric proteins is not the focus of this manuscript, which is primarily dedicated to method development. In addition, these type of validations in methods papers are often limited to a few assays (e.g. can 1 or 2 proteins be enriched by ChIP? Do you see some localisation by IF? etc.). However, our research group has a history of publishing in-depth mechanistic papers on the characterisation of novel telomeric proteins (e.g. PMID: 23685356, 28500257, 20639181, doi.org/10.1101/2022.11.30.518500). Therefore, a genuine validation of such factors would require functional insights and clearly warrants independent follow-up work.

Reviewer #3: Human Terf2 ChIP-MS (Fig1A) seems to be much more specific than the mouse counterpart (Fig1D) (32 TERF2 interactors out of 176 hits in human vs 12 TERF2 interactors out of 500 hits in mouse). Could the authors explain this notable difference?

Response: As eluded to above, Fig. 1A and 1D cannot be directly compared, starting with the difference in complexity in the input material – cell line vs. tissue. For comparison, the Terf2 ChIP-MS data from mouse embryonic stem cells tallies up to 19 out of 169 hits, which is much closer to the U2OS results. Again, we deem the majority of hits from the TERF2 ChIP-MS data to be false-positives and the more complex input material from mouse livers likely accounts for the difference in these numbers.

Reviewer #3: The authors used much higher cell numbers than previously published ChIP-MS experiments; while this is understandable for dCas9-based pulldowns, the cell number is expected to be down-scalable for the other IPs (TERF2, ZBTB48, MYB). Since this work primarily describes an optimized Chip-MS workflow, the authors should show that they can reasonably downscale to at least 15 Mio cells per replicate; one way of achieving this could be through digesting on the beads and not in-gel.

Response: As we will illustrate in the comparison table that was also requested by reviewer 2, our approach does not use higher cell numbers than previous ChIP-MS approaches – quite the contrary. In addition, we would like to highlight that while we state 50 million cells in Fig. 1a, we only inject 50% of our samples for MS analysis to retain a back-up sample in case of technical issues with the instruments. In other words, our workflow is already effectively based on 25 million cells and thereby pretty close to the requested 15 million cells while simultaneously requiring substantially less reagents.

Importantly, our examples are based on rather lowly expressed bait proteins such as ZBTB48 (not detected within DDA-based proteomes of ~10,000 proteins in U2OS cells). While the workflow can be applied across proteins, exact input numbers might vary depending on the bait protein, e.g. histones and its modifications would likely require less for the same absolute sample enrichment. For instance, PMID 25990348 and 25755260 performed ChIP-MS on common histone modifications but still used 300-800 million cells per replicate. Considering that we worked on substantially less abundant proteins, we here present a workflow with comparably low input samples.

Reviewer #3: It is not clear from the text or figure what the authors are trying to show in Fig2c. They should either explain this further or take the figure out.

Response: We are trying to illustrate the following: As in any IP reaction the bait protein is the most enriched protein with very high relative intensities, e.g. TERF2 in the TERF2 ChIP-MS data. Direct protein interaction partners – here the other shelterin members – follow at about 1 order of magnitude lower signal intensities. In contrast, proteins that are enriched via an interaction with the same DNA molecule (i.e. that do not physically interact with the bait protein) such as NR2C2, HMBOX1 and ZBTB48 further trail by at least 1 more order of magnitude. These are information that are not easily visualised within the volcano plots and mainly “buried” within the Supplementary Tables. However, these relative intensities displayed in Fig. 2c clearly illustrate the dynamic range challenge that ChIP-MS poses for proteins that independently bind to the same chromatin fragment. We have now modified our text to make this point more clear.

Reviewer #3: Was there any benefit in using a Q Exactive HF vs timsTOF flex?

Response: Yes, measuring the same samples (e.g. the 50% backup mentioned above) on both instruments enriches more telomeric proteins/shelterin proteins in e.g. the dCas9 ChIP-MS data set on the timsTOF fleX. However, given the difference in age of these instruments/technologies between a Q Exactive HF and a timsTOF fleX (in the context of these experiments the equivalent of a timsTOF Pro 2), this is not a fair comparison beyond concluding that a more recent instrument like the timsTOF fleX achieves better coverage and is more sensitive with otherwise comparable measurement parameters. As we did not have the opportunity to run matched samples on e.g. an Exploris 480, we would not want to make claims across vendors. As stated in the discussion we are expecting that even newer generation of mass spectrometers, such as the very recently released Orbitrap Astral or timsTOF Ultra would further improve the sensitivity and/or allow to reduce the amount of input material. Therefore, the main conclusion is that improvements in the mass spec generations improve proteomics data quality and our samples are no exception, i.e. this is not specifically pertinent to our approach.

Reviewer #3: How did the authors analyze the PTM data? This is not described in the methods section. In addition, it would be important to validate the novel PTMs described for NR2C2.

Response: We apologise for the oversight and we will add the description of PTMs as variable modifications during our MaxQuant search in the methods section. The originally deposited datasets already include this and we had simply missed this in our methods text.

While we are not 100% sure to understand the request for validation correctly, we would like to point out that the PTMs on NR2C2 have been previously reported in several high-throughput datasets and for S19 in functional work on NR2C2 (PMID: 16887930). However, the relevance in our data set is as follows: While the PTMs on TERF2 as the bait protein could occur both on telomere-bound TERF2 as well as on nucleoplasmic TERF2, NR2C2 is only enriched in the TERF2 ChIP-MS reactions due to its direct interaction with telomeric DNA. The co-detection of its modifications therefore implies that at least some of the telomere-bound NR2C2 carries these modifications. We showcase this example as an additional angle of how such ChIP-MS datasets can be analysed.

While the robust, MS2-based detection of these modified peptides in our data set and several other publicly available datasets provides strong evidence that these modifications are genuine, further functional validation would involve rather labour-intensive experiments and resource generation (e.g. phospho-site specific antibodies). We hope that the reviewer agrees with us that this would require an independent follow-up study and that this goes beyond the scope of our current manuscript.

Reviewer #3: For this kind of methods paper one would expect to see the shearing results of the ChIP-MS experiments since variations in DNA shearing can impact the detection of false-positives in the ChIP-MS experiments

Response: We will include agarose gel pictures of our sonicates, which we indeed routinely quality controlled prior to ChIP experiments as stated in our methods description.

Reviewer #3: Overall, the current state of the manuscript neither provides direct evidence that the "optimized" ChIP-MS workflow is better in certain aspects/use cases than previously published methods nor does it provide novel biological insights. At the current state it even cannot be considered as a validation of previously published methods since it does not discuss them.

Response: We politely disagree with this conclusion. Again, as mentioned above we are under the impression that this reviewer somehow equates our entire manuscript to a comparison with dCas9-biotin ligase fusions.

Instead, we here provide a workflow for ChIP-MS that incorporates label-free quantification as the experimentally easiest, most intuitive quantification method for non-mass spectrometry experts. This offers a particularly low barrier to entry aimed at making ChIP-MS more widely accessible as a complement to commonly used ChIP-seq applications. Furthermore, we showcase that as a gold standard ChIP-MS – to truly live up to its name – should have the ability to enrich proteins independently binding to the same chromatin fragment. We demonstrated that double cross-linking is critical for these assays and in return illustrate how rigorous loss-of-function controls (both KOs and degron systems) can mitigate prevalent false-positives that are exacerbated due to the cross-linking. Finally, we applied this workflow to different types of endogenous proteins (transcription factors, telomeric proteins) in cell lines and tissue and extend our work to dCas9 ChIP-MS as a targeted method.

Author Response:

The following is the authors' response to the original reviews.

We were pleased with the overall enthusiastic comments of the reviewers:

• Reviewer #1: “This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision”

• Reviewer #2: “The immediate and repeatable responses of barrier integrity changes upon light-on and light-off switches are fascinating and impressive.”

• Reviewer #3: “, these molecular tools will be of broad interest to cell biologists interested in this family of GTPases.”

We thank the reviewers for their fair and constructive comments that helped us to improve the manuscript.

Reviewer #1 (Recommendations For The Authors):

1) This paper is likely to attract a diverse audience. However, the order of data presented in this manuscript can be confusing or challenging to follow for the naive reader. This is because the tool characterization is split into two parts: before the barrier strength assay (selection of optogenetic platform and tool expression) and after (characterization of cell morphology with global and local optogenetic stimulation). Reorganizing the results such that the barrier strength results follows from an understanding of individual cell responses to stimulation may improve the ability of this readership to understand the factors at play in the changes in barrier strength observed when opto-RhoGEFs are activated.

We appreciate this idea, and we initially structured the paper in the proposed order and then decided, that we wanted to put more focus on the barrier strength results by already presenting them in the second figure. Therefore, we prefer to keep this order of figures.

2) While the description of the selection of iLID as the study's optogenetic platform is clear, a better job could be done motivating the need for engineering new optogenetic tools for the control of GEF recruitment. Given that iLID-based tools for GEFs of RhoA, Rac1, and Cdc42 already exist, some of which are cited in the introduction, more information on why these tools were not used would be helpful-were these tools tested in endothelial cells and found lacking.

The original system has the domain structure DHPH-tagRFP-SspB. But we wanted to work with a SspB-FP-GEF construct, which would allow easy exchange of the FP and the DHPH domain. This modular approach allowed us to generate and compare the mCherry, iRFP647 and HaloTag version. We don’t want to claim that we engineered an entirely new optogenetic tool but rather optimized an existing one with different tags. To make this more clear we added : ‘The membrane tag of the original iLID was changed to an optimized anchor. In addition, we modified the sequence of the domains to SspB, tag, GEF to simplify the exchange of GEF and genetically encoded tag. A set of plasmids with different fluorescent tags was created for more flexibility in co-imaging.’

3) Comment on the reason behind using DHPH vs. DH domains for each GEF is needed.

We have previously found (and this is supported by biochemical analysis of GEF activity) that the selected domains provide the best activity. We will add reference and the following to the text: ‘Their catalytic active DHPH domains were used for ITSN1 and TIAM1 (Reinhard et al., 2019).  In case of p63 the DH domain only was used, because the PH domain of p63 inhibits the GEF activity (Van Unen et al., 2015) (Fig. 1E).’

4) Since multiple Rho GTPases (e.g., RhoA, RhoB, RhoC) exist and Rho is used as the name of the GTPase family, please use RhoA where applicable for clarity.

Since the RhoGEFp63 will activate RhoA/B/C we would rather not refer to RhoA only. We will clarify this in the text: ‘Three GEFs were selected, ITSN1, TIAM1 and RhoGEFp63, which are known to specifically activate respectively Cdc42, Rac and Rho and their isoforms.’

5) A brief comment on the use of HeLa cells for protein engineering and characterization (versus the endothelial cells motivated in the introduction) may be helpful.

We added the following to the text: ‘HeLa cells were used for the tool optimization because of easier handling and  higher transfection rate in comparison to endothelial cells.’

Minor suggestions:

In figure 1C, line sections showing intensity profiles before and after protein dimerization might further emphasize the change in biosensor localization.

We are not a fan of intensity profiles as the profile depends strongly on the position of the line and it basically turns a 2D image in 1D data, for a single image. So, we prefer to stick to the quantification as shown in panel 1B (which shows data from multiple cells).

Reviewer #2 (Recommendations For The Authors):

1)The study has analyzed the effects of light-induced activation of the three optogenetic constructs in endothelial cells on their barrier function (electrical resistance) at high cell density and correlated the findings with the cellular overlap-producing effects on endothelial cells cultured at sparse cell density. It should be tried to show these effects at a cell density where these light-induced effects increase electrical resistance. Lifeact with different chromophores in adjacent cells might be useful.

We had attempted to measure the overlap in a monolayer by taking advantage of the Halotag and the variety of dyes available by staining one pool of cells red with JF 552 nm and the other far red with the JF 635 nm dye. However, the cells need at least 24 h to form a monolayer and by then they had exchanged the dye and red and far red pool could not be distinguished any longer.

Therefore, we used the Lck-mTq2-iLID construct, which already marks the plasma membrane of the cells. We created a mosaic monolayer of cells expressing mScarlet-CaaX and cells expressing Lck-mTq2-iLID + SspB-HaloTag-TIAM(DHPH). We observed and increase in the overlap between cells under this condition. The results have been added to figure 4 - figure supplement 2I&J. To the text we added:

'Additionally, cell-cell membrane overlap increased about 20 %, up on photo-activation of OptoTIAM, in a mosaic expression monolayer (figure 4 - figure supplement 2I,J, Animation 22)‘

2) The authors correctly state that some reports have shown that S1P can increase endothelial barrier function in VE-cadherin independent ways and these are related to Rac and Cdc42. This was also shown for Tie-2 in vitro and even in vitro in the absence of VE-cadherin and should also be mentioned.

We added the following to the text: ‘Not only S1P promotes endothelial barrier independent from VE-cadherin, also Tie2 can increase barrier resistance in the absence of VE-cadherin (Frye et al. 2015).’

Since a blocking antibody against VE-cadherin was used, a negative control antibody should be tested which also binds to endothelial cells.

To visualize the cell-cell junctions in the experiment shown in Supplemental Fig 3.1, we added a non-blocking VE-cadherin antibody that is directly labeled with ALEXA 647 and shows normal junction morphology. These experiments already give an indication that the live labeling antibody of VE-cadherin does not disturb the junction morphology. However, when we added the blocking antibody against VE-cadherin, known to interfere with the trans-interactions of VE-cadherin, a rapid disruption of the junctions is observed.

Additionally, previous work has shown, that VE-cadherin labeling antibody does not interfere with junction dynamics and function (see Figure 2.A, Kroon et al. 2014 ‘Real-time imaging of endothelial cell-cell junctions during neutrophil transmigration under physiological flow’, jove.). We have added the figures below, showing that addition of the control IgG and VE-cadherin 55-7H1 Abs at the timepoint where the dotted line is, did not interfere with the resistance whereas the blocking Ab drastically reduced resistance. We have added this reference to the results. ‘Previous work has shown the specific blocking effect of this antibody in comparison to the VE-cadherin (55-7H1) labeling antibody (Kroon et al., 2014).’

Author response image 1.

Reviewer #3 (Recommendations For The Authors):

Additional comments for the authors:

1) The introduction is very long and would benefit from a more concise emphasis on the information required to put the work and results in context and understand their importance.

Comment: we appreciate the comment of the reviewer. However, we wish to introduce the topic and the tools thoroughly and therefore we chose to keep the introduction as it is.

2) The N-terminal membrane-binding domain does not homogeneously translocate to the plasma membrane, since lck is a raft-associated kinase. Please comment on this.

In our hands, the Lck is among the most selective and efficient tags for plasma membrane localization (https://doi.org/10.1101/160374). We do observe homogeneous translocation, but our resolution is limited to ~200 nm and so we cannot exclude that the Lck concentrates in structures smaller than 200 nm. Given the robust performance of the lck-based iLID anchor in the optogenetics experiments, we think that the Lck anchor is a good choice.

3) Figure 1D is not very clear. What does 25 or 36% change mean? If iLID tg is conjugated to these sequences, its cytosolic localization should be reduced versus iLID alone. Is this what the graph wants to express? If so, please, label properly the ordinate axis in the graph (% of non-tagged iLID values?)

The graph is representing the recruitment efficiency of SspB to the plasma membrane for the two different membrane tags, targeting iLID to the plasma membrane. The recruitment efficiency was measured by the depletion of SspB-mScarlet intensity in the cytosol, up on light activation, and represented as a change in percentage.

We added the following to the title of the graph_: SspB recruitment efficiency for Plasma Membrane tagged iLID._

4) Supplemental figures in the main text. Fig S1D in the text refers to data in Fig S1E and Fig S1E is supposed to be Fig S1F? (page 11).

That is correct. The mistakes have been corrected (and this is now renamed to figure 1 - figure supplement 1E and 1F).

5) Figure 3. Contribution of VE-cadherin. Other junctional complexes, such as tight junctions may also intervene. However, these results would also suggest that cell-substrate adhesion rather than cell-cell junctions may modulate the barrier properties, as it has been previously demonstrated for example by imatinib-mediated activation of Rac1 (Aman et al. Circulation 2012). The ECIS system used to measure TEER in the quantitative barrier function assays can modulate these measurements and discriminate between paracellular permeability (Rb) and cell-substrate adhesion (alpha). Please, provide whether the optogenetic modulation of these GTPases does indeed regulate Rb or alpha.

The measured impedance is made up of two components: capacitance and resistance. At relatively high AC frequencies (> 32,000 Hz) more current capacitively couples directly through the plasma membranes. At relatively low frequencies (≤ 4000 Hz), the current flows in the solution channels under and between adjacent endothelial cells’ (https://www.biophysics.com/whatIsECIS.php).

Therefore, the high frequency impedance is representing cell-substrate adhesion whereas the low frequency responds more strongly to changes in cell-cell junction connections.

We only measured at 4000 Hz, representing the paracellular permeability. We chose a single frequency to maximize time resolution.

We have added this extra comment to the legend of the figure: ‘(B) Resistance of a monolayer of BOECs stably expressing Lck-mTurquoise2-iLID, solely as a control (grey), and either SspB-HaloTag-TIAM1(DHPH)(purple)/ ITSN1(DHPH) (blue) or p63RhoGEF(DH) (green) measured with ECIS at 4000 Hz, representing paracellular permeability, every 10 s.’

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Referee #1

Evidence, reproducibility and clarity

Summary:

This manuscript shows the involvement of both the proteasome and autophagy pathways in the turnover and therefore regulation of ARF7, an auxin-responsive factor involved in lateral root formation. The authors bring crucial information for the understanding of how autophagy is involved in auxin-signaling.

Major comments:

The key conclusions appear overall convincing yet this reviewer would strongly advise to take into account the following remarks for a clearer and more convincing line of inquiry. This reviewer also believes that the additional experiments could be performed relatively fast apart for the point 9) where the establishment of a homozygous line could take 6 months or more.

1. Figure 1 & Figure EV1: The nature of the loading control should be stated as it appears to be a specific protein detected by immunoblotting. Furthermore, if the authors wish to make a stronger point as to whether ARF7 is degraded by the proteasome (considering the reserves mentioned in the Discussion section), I would recommend to perform the same assays as in Figure 1 but using an alternative proteasome inhibitor such as Bortezomib and to include a proteasome subunit KO mutant such as rpt2a-2.
2. The statement "The experiment revealed that both NBR1 (Fig 2A) and ATG8a (Fig 2B), but not free YFP, co-immunoprecipitated with ARF7-Venus." Is false as the authors did not try to co-immunoprecipitate free YFP with ARF7-Venus, they used a free YFP expressing line as a negative control for their GFP-immunoprecipitation (IP). It should further be noted that although NBR1 is detected in their free YFP IP, ATG8 is at very low levels so it should be stated that they see an enrichment of ATG8 in their ARF7-Venus IP.
3. Authors state "we were unable to detect ARF7-Venus in the input of both Co-IPs which can likely be explained by the fact that ARF7-Venus is under the control of its native promoter and thus lowly expressed.", yet putative degradation products (i.e. a smear) can be observed in the input of Figure 2A, similarly to the bands observed in both IP blots. It would be interesting to repeat these co-IPs with proteolysis inhibitors such as MG132 or Pepstatin & E64-d to pinpoint the proteolytic machinery at the origin of ARF7-Venus degradation in the IPs.
4. Figure 2: The use of multicolor BiFC "mcBiFC" should be stated as such for an easier understanding of the reader. It would be helpful for the reader if the "GFP" signal resulting from the complementation would be highlights thanks to some arrows. Moreover, a western blot to verify the expression levels should be performed since every construct has an epitope tag as stated in Gehl et al. 2009.
5. General remark: all drug/chemical treatments performed in this study use a "non-treated" negative control, yet it should be pointed out that the correct corresponding negative controls should have the solvent used to dissolve the respective drug/chemical in order to exclude any effect of the solvent or vehicle.
6. Figure 4, Figure EV4: Considering the variability in size and staining of the Rubisco large-subunit in the 4 immunoblot panels, I would suggest blotting with another antibody such as anti-tubulin or anti-histone 3 as a loading control for a more convincing quantification. Moreover, the nature of the staining used to stain the Rubisco large-subunit should be stated. The authors also state "differences in ARF7 accumulation in atg5 compared to Col-0" yet no immunoblot is shown where both genotypes are present on the same membrane, in order to verify this statement.
7. Figure 5: In regards to LR density measurements, I recommend reading "Quantitative Analysis of Lateral Root Development: Pitfalls and How to Avoid Them" by Dubrovsky & Forde (Plant Cell, 2012) for a more robust method of evaluating lateral root density.
8. Discussion: The authors state that "autophagy blockage leads to increased ARF7 cytoplasmic condensates". To support this statement, I recommend crossing pARF7::gARF7-Venus into atg mutants and analysing the localization and the fluorescence intensity of ARF7-Venus in specific parts of the root, as well as performing immunoblotting in order to assess overall ARF7 accumulation in autophagy deficient genetic backgrounds.

Minor comments

1. The following statement: « In contrast, plants are able to tolerate disruption of autophagy activity without major penalties" holds true to A. thaliana of some other plants but it must be noted that in O. sativa, autophagy-deficiency may lead to male sterility, which should be considered a major penalty for evolutionary fitness. For review see Norizuki et al. 2020 (Front. Plant Sci.).
2. Figure 2: The molecular weights appear to be potentially misannotated as free YFP aligns with the 35 kDa marks although it should appear around 27 kDa.
3. Figure EV3: There are 2 merged image columns, the furthest one to the right appears to include a DIC or Trans image on top of both fluorescence channels. It would be more helpful for the reader if the DIC or Trans image was shown with the overlay of fluorescent channels in order to assess the effect of 10% 1,6-hexandiol on the plant tissue. Moreover, demonstrating the absence of tissue damage or cell-death after 1,6-hexandiol treatment would be a plus.
4. There is a typo throughout the manuscript: ZT should be "Zeitgeber" not "zeitberg".

Significance

This manuscript has the quality of describing the proteolytic balance of ARF7 and thereby, the involvement of the autophagy pathway in regulating auxin-signaling components. This research adds on to the growing interest in how autophagy participates in developmental cues, and how hormonal signaling is regulated throughout the plant.

Author Response

Reviewer #1 (Public Review):

Ichinose et al., utilize a mixture of cultured hippocampal neurons and non-neuronal cells to identify the role of the transmembrane protein teneurin-2 (TEN-2) in the formation of inhibitory synapses along the dendritic shaft. First, they identify distinct clusters of gephyrin that are either actin-rich, microtubule-rich or contain neither actin nor microtubules and find that TEN-2 is enriched in microtubule-rich gephyrin clusters. This leads the authors to hypothesize that TEN-2 recruits microtubules (MTs) through the plus end binding protein EB1 when successfully matched with a pre-synaptic partner, and perform a variety of experiments to test this hypothesis. The authors then extend this finding to state quite strongly throughout the paper, including in the title, that TEN-2 acts as a signpost for the unloading of cargo from motor proteins without providing any supporting evidence. They use previous work to justify this conclusion, but without actual experiments to back up the claim, it seems like a reach.

The strength of the paper lies in the various lines of evidence that the authors employ to assess the role of TEN-2 in MT recruitment and synaptogenesis. They have also been very thorough in validating the expression and functionality of various knock-in constructs, knock-down vectors and antibodies that were generated during the study. However, there are some discrepancies in the findings that have not been addressed satisfactorily, as well as some instances where the data presented is not of sufficient quality to support the conclusions derived from them.

Firstly, we would like to express our sincere appreciation to Reviewer #1 for providing valuable feedback. We have carefully considered Reviewer #1 suggestions and have made significant improvements to the manuscript in response. Additionally, we have conducted an additional experiment to address the missing aspects identified in the initial submission. Furthermore, we have refined the focus of our investigation by narrowing down the number of aspects we aimed to prove and instead increased the number of confirmatory experiments. Specifically, we decided to give up on two aspects: the relationship between kinesins and cargo, and the immobilization of TEN2 in synapses (i.e., extracellular binding of TEN2). Instead, we focused on emphasizing the role of TEN2 as a platform for exocytosis. These modifications have significantly enhanced the quality of our research.

1) The emphasis placed on the clustering analysis presented in figure 1 and the two associated supplementary figures is puzzling, since the conclusion derived from the results presented would be that Neuroligin 2 (NLGN2) is the strongest candidate to test for a relationship to MT recruitment at inhibitory post synapses. Instead, the authors cite prior evidence to exclude NLGN2 from subsequent analysis and choose to focus on TEN2 instead.

We fully agree on the importance of studying NLGN2, as highlighted in the DISCUSSION section (line 463-471). While the cluster analysis suggests a correlation between NLGN2 and microtubules, previous research has reported microtubule localization outside the NLGN2 region (Uchigashima et al., 2016). However, this interpretation is based on EM observations at a single time point, so it will be important to evaluate it over time. Conversely, we had believed that further investigations are needed to explore the potential interactions between TEN2 and microtubules, because of its relatively limited characterization (line 156-161).

2) It is difficult to reach the same conclusion as the authors from the images and intensity plot shown on Figure 2 E and F. While there seems to be an obvious reduction in expression levels between the TEN2N-L and TEN2TM constructs, neither seem to co-localize with EB1.

As Reviewer #1 pointed out, the previous plots on Figure 2 were of very poor quality. Due to the dynamics of microtubules, evaluating interactions using fixed cells has limitations. Therefore, we decided to shift to live-imaging. Firstly, we observed a tendency for EB3 comets to pause at inhibitory postsynapses (Figures 1D-H). This suggests the presence of a microtubule recruiter at inhibitory synapses. Next, in dendrites expressing TEN2N-L, the velocity of EB3 comets was significantly faster compared to dendrites expressing TEN2TM or TEN2N-L2mut (Figures 7A-E). This suggests that the dominant-negative effect of TEN2N-L inhibits the function of endogenous microtubule recruiters. Additionally, the interaction between TEN2 and EB1/3 has been confirmed by GST pull-down (Figure 6A). Based on these reasoning, we propose that TEN2 present in inhibitory synapses plays a role as a microtubule recruiter through its interaction with EB1/3.

3) The authors mimic the activity of TEN-2 at the inhibitory post synapse in non-neuronal cells by immobilizing HA- tagged TEN constructs in COS-7 cells as a proxy for synaptic partner matching. Using this model, they find that by immobilizing TEN2N-L, which contains EB1 binding motifs, MTs are excluded from the cell periphery (Figure 3D). This contradicts their conclusion that MTs are recruited through EB1 by TEN-2 on synaptic partner matching. Later in the paper, when they use the same TEN2N-L construct as a dominant negative in neuronal cells, they find that MTs are recruited the membrane, even if TEN2N-L is not immobilized by synaptic partner matching (Figure 6C). Taken together, these findings call into question the sequence of events driven by TEN-2 during synaptogenesis.

We believe that the differences in the results between the COS-7 and neuron experiments are influenced by variations in the intracellular protein composition and distribution between COS-7 cells and neurons. Therefore, we consider it inappropriate to directly apply the results from COS-7 to neurons. Additionally, we attempted to replicate the experiments in neurons; however, it is worth mentioning that the culture of neurons on antibodies led to a significant decrease in cell viability. As a result, we have decided to withdraw the experiment of immobilized TEN2 using antibodies.

4) It is unclear how the authors could conclude that TEN-2 is at the semi-periphery (?) of inhibitory post synapses from the STORM data that is presented in the paper. Figure 4D and 4F show comparisons of Bassoon and TEN-2 localization vs TEN-2 and gephyrin, but the image quality is not sufficient to adequately portray a strong distinction in the distance of center of mass, which is also only depicted for the TEN2-Gephyrin pair and not the TEN2-Bassoon pair in Figure 4J.

The quality limitations of attempting a three-color dSTORM of TEN2-bassoon-gephyrin were addressed by modifying it to a two-color dSTORM. To confirm this modification, a two-color STORM was performed using VGAT instead of Bassoon (Figure 3E). The statement that TEN2 localizes to half of the synapse is supported by the observation of TEN2-gephyrin in the postsynaptic area. This observation aligns with the localization of microtubules at the postsynapse as observed by electron microscopy (Gulley & Reese, 1981; Linsalata et al., 2014).

5) The authors do not satisfactorily explain why gephyrin appears to have completely disappeared in the TEN2N-L condition (Figure 6A), instead of appearing uniformly distributed as one would expect if MTs are indiscriminately recruited to the membrane by the dominant negative construct that remains unanchored.

As pointed out by Reviewer #1, it needed to be adequately proven, and we mistakenly conflated dominant-negative and gain-of-function effects. However, through the examination of live imaging of EB3, observation of the localization of gephyrin, and the additional investigation of GABAAR localization in neurons expressing partial domains of TEN2, we found that TEN2N-L functions as a dominant-negative, inhibiting the microtubule recruitment function of endogenous TEN2 (Figure 7). On the other hand, it does not exhibit a gain-of-function effect in inducing exocytosis of GABAAR because both gephyrin and GABAAR were found to be reduced in the neurons expressing TEN2N-L (Figure 7F-H). Therefore, we have corrected this point.

6) In a similar critique to that of Figure 2E and F, the distinction that the authors wish to portray between the effect of TEN2TM and TEN2N-L constructs on EGFP-TEN-2 and MAP2 colocalization (Figure 6 E and F) appear to be driven by a difference in overall expression levels of EGFP-TEN2 rather that a true difference in localization of TEN-2 and MTs.

Regarding the previous co-localization of TEN2 and microtubules after permeabilization with saponin, we have removed it from the analysis because it is not possible to perform accurate quantitative analysis in this case. We speculate that this is a combination of two factors: the variation in transfection efficiency and the inherent variability in permeabilization between neurons. Specifically, it is particularly challenging to standardize and quantify the variability in permeabilization. Instead, the current version proposes TEN2-MT interaction via EBs by live imaging of EB3 in neurons expressing each partial domain. As observed in COS-7 cells where EB was overexpressed, whether TEN2 engages in continuous binding with microtubules or if it is a transient interaction remains an interesting topic for future investigation. We have mentioned this in the DISCUSSION section as well (line 415-422).

Reviewer #2 (Public Review):

Maturation of inhibitory synapses requires multiple vital biological steps including, i) translocation of cargos containing GABAARs and scaffolds (e.g. gephyrin) through microtubules (MTs), ii) exocytosis of inhibitory synapse proteins from cargo followed by the incorporation to the plasma membrane for lateral diffusion, and iii) incorporation of proteins to inhibitory synaptic sites where gephyrin and GABAARs are associated with actin. A number of studies have elucidated the molecular mechanisms for GABAARs and gephyrin translocation in each step. However, the molecular mechanisms underlying the transition between steps, particularly from exocytosis to lateral diffusion of inhibitory proteins, still need to be elucidated. This manuscript successfully characterizes three stages of inhibitory synapses during maturation, cluster1: an initial stage that receptors are being brought in and out by the MT system; cluster2: lateral diffusion stage; cluster 3: matured postsynapses anchored by gephyrin and actin, by quantifying the abundance of MAP2 or Actin in inhibitory synapse labeled by gephyrin. Importantly, the authors' findings suggest that TEN2, a trans-synaptic adhesion molecule that has two EB1 binding motifs, plays an important role in the transition from clusters 1 to 2, and inhibitory synapse maturation. The imaging results are impressive and compelling, these data will provide new insights into the mechanisms of protein transport during synapse development. However, the present study contains several loose ends preventing convincing conclusions. Most importantly, (1) it remains more TEN2 domain characterization on inhibitory synapse maturation, (2) further validation of the HA knock-in TEN2 mouse model is required, and (3) it requires additional physiology data that complement the authors' findings.

First we would like to thank Reviewer #2 very much for the efforts and numerous suggestions. While it is highly appealing to comprehensively explain the function of a single synapse organizer in a step-by-step manner during synapse formation, we believe that it requires the identification of changing binding partners at each step, which is currently a challenging task. Therefore, in this paper, we have focused solely on the interaction between TEN2 and microtubules. As a result, we have discovered that TEN2 provides a platform for the exocytosis of GABAR, and this process relies on the interaction between TEN2 and microtubules. The analysis of the immobilization of TEN2, which was included in the previous version, will be part of a future publication. We also plan to continue detailed analysis of other domains. Thus, issues remain regarding the analysis of TEN2, but in order to confirm what is happening in just specific one step, we have made significant revisions in this revised manuscript, including analysis in HA knock-in neurons and electrophysiological analysis. We would greatly appreciate it if Reviewer #2 would reconsider the revised manuscript.

Reviewer #3 (Public Review):

In this paper, Ichinose et al. examine mechanisms that contribute to building inhibitory synapses through differential protein release from microtubules. They find that tenurin-2 plays a role in this process in cultured hippocampal neurons via EB1 using a variety of genetic and imaging methods. Overall, the experiments are generally designed well, but it is unclear whether their findings offer a significant advance. The experimental logic flow and rational difficult for readers to follow in the manuscript's current form.

Strengths:

1) The experiments are generally well designed overall, and appropriate to the questions posed.

2) Several experimental methods are combined to validate key results.

3) Use of cutting-edge technologies (i.e. STORM imaging) to help answer key questions in the paper.

We thank Reviewer #3 for reviewing our manuscript. We sincerely appreciate the valuable feedback. The previous version of the manuscript contained numerous claims, some of which were not thoroughly validated, making it prone to reader misinterpretation. Based on the results of additional experiments, we have revised the manuscript by focusing solely on the findings that were adequately confirmed, specifically highlighting the role of TEN2 in providing a platform for GABAAR exocytosis. We are grateful for your time and effort in revisiting the revised manuscript, and we believe it meets the necessary requirements.

Weakness:

1) Simplifying the text and story line would go a long way to ensure the study results are more effectively communicated. Additional specific suggestions are provided in the recommendations for the authors.

Thank you for providing valuable suggestions. Based on the results of additional experiments, we have revised our claims accordingly.

2) The introduction overall would benefit from simplification so that the reader is given only the information they need to know to understand the question at hand.

We selected essential information from previous studies that we believe readers should be aware of before reading our manuscript.

3) MT dynamics are important for paper results, but the background in the paper does not appropriately introduce this topic.

We have provided some information in lines 57-64 of the INTRODUCTION section.

4) It is a bit unclear from the abstract and introduction how the findings of this paper have significantly advanced the field or taught something fundamentally new about how inhibitory synapses are regulated.

Thank you for your valuable feedback. In the new version, we have thoroughly examined and emphasized the significance of our research findings.

5) Figure 1 - Line 109, it is obscure why "it was found appropriate" to divide the data into three clusters. This section would better justified by starting with cellular functions and then basing the clusters on these functions.

As Reviewer #3 pointed out, we have revised the classification to be based on past knowledge rather than data-driven.

6) The proteomic screen and candidate selection is not well justified and the logic steps for arriving at TEN2 are a bit weak. Again, less is more here.

As Reviewer #3 mentioned, we have made revisions in the new version. We have not completely excluded NLGN2, but rather believe that further examination and consideration of NLGN2 are necessary going forward (lines 463-471).

7) Fig. 2 - The authors should consider whether EB1 overexpression would have functional consequences that alter the results and colocalization.

The previous Figure 2, which is now Figure 6, is intended to demonstrate protein-protein interactions rather than provide functional implications. It is likely that the original function of EB1, which should be located at the plus ends of MTs, is compromised by its presence in the MT lattice. As an alternative method to demonstrate protein-protein interactions, we have also conducted GST pull-down assays (Figure 6A). From these two experimental results, we infer that the intracellular domain of TEN2 interacts with EB1. However, we have not discussed the functional implications of the TEN2-EB1 complex based on these experimental findings. The function was discussed from the results performed in Figure 7.

8) Fig. 3 - Is immobilization of COS cells using HA tag antibodies a relevant system for study of these questions?

We agree with this suggestion regarding the replication of the experimental systems to neurons, as the results have been successful in COS-7 cells. However, when we attempted to culture neurons on antibody-coated cover glass, the survival rate was significantly reduced. We were unable to directly replicate these systems to neurons. Therefore, we have decided to withdraw this claim from the publication.

9) Fig. 4 - The authors should confirm post-synaptic localization in vivo (brain).

We agree with this suggestion. Currently, our research group does not have an effective immune-labeling method for synaptic protein in the brain. This is a future challenge that we should address.

10) Figure 4D-E - The way the STORM results are presented is confusing. The authors state is shows that TEN2 is postsynaptic but before this say that the Abs are the same size as the synaptic cleft so that the results cannot be considered conclusive. This issue should be resolved.

To improve the quality of our dSTORM experiments, we abandon three color dSTORM and instead focused on two color dSTORM to draw conclusions (Figure 3E). We utilized VGAT to detect presynaptic sites. VGAT is an inhibitory presynaptic-specific molecule that is present at the center of presynaptic terminals, eliminating concerns about the size of the antibodies used.

11) Figure 5 -The authors should examine the levels of gephyrin relative to the levels of knockdown given the knockdown variability.

Thank you for your suggestion. As shown in Figure 4D of the current version, we were able to simultaneously quantify the knockdown efficiency and synaptic density. We obtained results indicating a decrease in synaptic density associated with a decrease in TEN2 expression levels.

12) Functional validation of a reduction in inhibition following TEN2 manipulation would elevate the paper.

We conducted live imaging of EBs to measure the changes when introducing the partial domain of TEN2 (Figures 7A-E). By observing the decrease in synaptic density and the impaired MT recruitment function of endogenous TEN2 due to the dominant-negative effect of TEN2N-L, we concluded that the TEN2-MT interaction serves as the platform for GABAR exocytosis.

13) Figure 6E - The expression levels of TEN2TM and TEN2NL are important to the outcome of these experiments. How did the authors ensure that the levels of two proteins were the same to begin with?

As it was also mentioned by Reviewer #1, we reply with the same answer as follows: Regarding the previous co-localization of TEN2 and microtubules after permeabilization with saponin, we have removed it from the analysis because it is not possible to perform accurate quantitative analysis in this case. We speculate that this is a combination of two factors: the variation in transfection efficiency and the inherent variability in permeabilization between neurons. Specifically, it is particularly challenging to standardize and quantify the variability in permeabilization. Instead, the current version proposes TEN2-MT interaction via EBs by live imaging of EB3 in neurons expressing each partial domain. As observed in COS-7 cells where EB was overexpressed, whether TEN2 engages in continuous binding with microtubules or if it is a transient interaction remains an interesting topic for future investigation. We have mentioned this in the DISCUSSION section as well (line 415-422).

Reviewer #3 (Public Review):

In this paper, Ichinose et al. examine mechanisms that contribute to building inhibitory synapses through differential protein release from microtubules. They find that tenurin-2 plays a role in this process in cultured hippocampal neurons via EB1 using a variety of genetic and imaging methods. Overall, the experiments are generally designed well, but it is unclear whether their findings offer a significant advance. The experimental logic flow and rational difficult for readers to follow in the manuscript's current form.

Strengths:<br /> 1) The experiments are generally well designed overall, and appropriate to the questions posed.<br /> 2) Several experimental methods are combined to validate key results.<br /> 3) Use of cutting-edge technologies (i.e. STORM imaging) to help answer key questions in the paper.

Weakness:<br /> 1) Simplifying the text and story line would go a long way to ensure the study results are more effectively communicated. Additional specific suggestions are provided in the recommendations for the authors.<br /> 2) The introduction overall would benefit from simplification so that the reader is given only the information they need to know to understand the question at hand.<br /> 3) MT dynamics are important for paper results, but the background in the paper does not appropriately introduce this topic.<br /> 4) It is a bit unclear from the abstract and introduction how the findings of this paper have significantly advanced the field or taught something fundamentally new about how inhibitory synapses are regulated.<br /> 5) Figure 1 - Line 109, it is obscure why "it was found appropriate" to divide the data into three clusters. This section would better justified by starting with cellular functions and then basing the clusters on these functions.<br /> 6) The proteomic screen and candidate selection is not well justified and the logic steps for arriving at TEN2 are a bit weak. Again, less is more here.<br /> 7) Fig. 2 - The authors should consider whether EB1 overexpression would have functional consequences that alter the results and colocalization.<br /> 8) Fig. 3 - Is immobilization of COS cells using HA tag antibodies a relevant system for study of these questions?<br /> 9) Fig. 4 - The authors should confirm post-synaptic localization in vivo (brain).<br /> 10) Figure 4D-E - The way the STORM results are presented is confusing. The authors state is shows that TEN2 is postsynaptic but before this say that the Abs are the same size as the synaptic cleft so that the results cannot be considered conclusive. This issue should be resolved.<br /> 11) Figure 5 -The authors should examine the levels of gephyrin relative to the levels of knockdown given the knockdown variability.<br /> 12) Functional validation of a reduction in inhibition following TEN2 manipulation would elevate the paper.<br /> 13) Figure 6E - The expression levels of TEN2TM and TEN2NL are important to the outcome of these experiments. How did the authors ensure that the levels of two proteins were the same to begin with?

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Reply to the reviewers

1. General Statements

__Response: __Thank you to all the reviewers for their helpful efforts on behalf of our manuscript. At current, we have addressed most of the reviewers’ major comments, including providing additional replicates for many experiments and clarifying ambiguous points in the text. Related data, figures and text have been adjusted accordingly. We believe that these changes have improved our manuscript, both strengthening our main conclusions and clarifying ambiguous text.

Several still-ongoing experiments are elaborated below. These experiments are well within the abilities of our lab and can be completed in short order.

Specific responses to the individual concerns addressed by the reviewers are outlined below.

Please feel free to contact me if I can be of any help in the decision process.

2. Description of the planned revisions

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

• *

[Reviewer 1]

Comment: Across the manuscript, NIX levels appear to be unresponsive to most treatments in the MDA-MB-231 line, including hypoxia treatment. This is an unusual result and raises questions about the role of NIX in MDA-MB-231 line, mainly that BNIP3 is the primary driver of mitophagy in this system. Indeed, Figure 7D indicates that there is very little mitophagy contribution by NIX since knockout of BNIP3 is sufficient to abolish mitophagy almost completely. Therefore, the effects seen on mitophagy following EMC3 knockout in Figure 7 might be smaller in a line that is responsive to NIX mitophagy. It would be beneficial to analyse basal mitophagy flux in an additional cell line, for example U2OS (FigS1E) in which NIX is responsive to hypoxia.

Response: Thank you for bringing this intriguing insight to our attention. We have seen that EMC3 knockout prevents lysosomal delivery of BNIP3 in U2OS cells (Fig S2D). However, we don’t know what the effects on mitophagy are in U2OS, or the extent to which mitophagy is dependent on BNIP3 and/or NIX. To test this, we will perform the suggested experiment, taking mt-Keima expressing U2OS cells testing the role of NIX and/or BNIP3 in mitophagy.

Comment: Following on from comment 1 above, Figure 7 would benefit with an analysis of hypoxia (or DFP, or cobalt chloride) stimulation of mitophagy to assess whether mitophagy levels are higher in EMC3 KOs. The authors argue that BNIP3 is trafficked to the ER during mitophagy and is not turned over by mitophagy itself, it would therefore be interesting to test if BNIP3 is prevented from being removed from mitochondria whether this would affect the rate or levels of mitophagy under stimulating conditions.

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__Response: __To address this question, we will perform mitoflux analysis on EMC3 KO cells +/- hypoxia.

Comment: Figure 4B: The localisation of tf-BNIP3 is reminiscent of ER in BTZ treated samples. How much of the protein is on mitochondria in the presence of BTZ? Does MLN4924 cause a similar issue?

__Response: __To address this question, we will perform fluorescence microscopy of tf-BNIP3 cells co-expressing mito-BFP under these treatments and utilize our Coloc2 plugin pipeline to monitor correlation.

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Comment: Can the authors assess whether BNIP3 that is on mitochondria is transferred to the ER (perhaps through photoswitchable GFP-BNIP, activated on mitos and then observe its transfer to ER)? This seems important in order to address the possibility that BNIP3 that is being turned over by the endolysosome is being delivered directly to the ER.

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__Response: __This is an interesting question and a curiosity also shared by Reviewer #2. To test this hypothesis, we will utilize a photo-switchable Dendra2 fluorophore to track BNIP3 in the cell via microscopy.

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[Reviewer #2]

Comment: How is BNIP3 inserted into the outer membrane? A previous study from the Weissman lab proposed that MTCH2 serves as insertase. The authors did not mention MTCH1 and MTCH2 in context of Fig. 2B. Were these proteins not found? Did the authors test the relevance of MTCH2 in their assay? This aspect should be addressed and mentioned.

__Response: __Thank you for the insight and suggestion. We were intrigued when the Weissman/Voorhees paper characterizing MTCH1/2 was published. Consistent with their findings, MTCH2 was found in the “suppressor” population of our tf-BNIP3 CRISPR screen, but given our 0.5-fold change threshold, the gene was not validated (fold change value = 0.46, Table S1). We suspect the lack of significance stems from the redundancy with MTCH1. Consequently, we would hypothesize that MTCH1/2 are the responsible insertases. To formally address this suggestion, we plan to genetically perturb MTCH1/2 and look at BNIP3 localization and mitophagy.

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Comment: The authors generated an interesting BNIP3 mutant with a C-terminal Fis1 anchor. This variant is constantly located in the outer membrane (which is shown here). The physiological consequence of the constitutive distribution on mitochondria is however only superficially studied. The authors should characterize this interesting mutant in some more depth.

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__Response: __In the original manuscript, we characterized BNIP3(Fis1TMD) for lysosomal delivery and mitophagy. Going forward, we will perform Seahorse oxygen consumption experiments and mitochondrial network analysis to view the physiological consequences of constitutive expression of BNIP3(Fis1TMD) on the outer membrane.

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.

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[Reviewer #1]

Comment: Continuing from comment 2, given that the authors conclude that BNIP3 is not turned over by mitophagy, can they examine whether BNIP3 is excluded from sealed mitophagosomes?

__Response: __We have softened the wording of our conclusions to reflect that the vast majority of BNIP3 lysosomal degradation is by this alternative pathway and not mitophagy. However, we do not wish to completely dismiss that BNIP3 is present on mitophagosomes. Rather, if mitophagosomes contain BNIP3, they seemingly account for only a very small portion of BNIP3 degradation in the cell, to the extent that it is not easily detectable by our assays (Lines 414-419). Definitively identifying whether BNIP3 is in sealed mitophagosomes will be part of future studies using CLEM or FIB-SEM techniques.

Comment: Is the BNIP3(FisTMD) expressed to equivalent levels to WT BFP-BNIP3? Given that theFis1 form of BNIP3 cannot traffic to endolysosomes, its levels might be higher. In addition, overexpression of the BNIP3-Fis construct was used to make the argument that dimerization is not important for mitophagy. But the authors should also take into account the possibility that with overexpression, the potential efficiency afforded to mitophagy via dimerization of endogenous proteins may be negated, and therefore hidden. Given this, I don’t think that the authors can confidently conclude that dimerization does not contribute to mitophagy, and that instead its main role is ER-endolysosomal turnover of BNIP3.

__Response: __We thank the reviewer for pointing out the possible over-interpretation of our data. Overexpression is an important caveat to consider. We would expect the Fis1 form of BNIP3 to be higher in protein levels given its deficiency in endolysosomal trafficking. Still, as the reviewer points out, over-expression could be mitigating the effect of our dimerization mutants. This caveat is now discussed in the manuscript and our interpretations regarding this fact have been greatly softened (Lines 373-376, Lines 449-462).

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Comment: Please include molecular weight markers for all western blots.

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__Response: __All western blots have now been labeled with molecular weight markers.

Comment: Figure 5A-G: These data do not make a convincing case for the role of dimerization and are very difficult to follow. Only the mislocalized S172A mutant was responsive to Baf treatment, while the LG swap mutant which is mitochondrial and cannot dimerize is unaffected by Baf treatment. Figure 5H-I utilize a construct of BNIP3 that is missing most of the protein and which has very low turnover (Figure 5B). Unfortunately these results don’t make a highly convincing case about the biology of native, full length, mitochondrial BNIP3. The authors are advised to either strengthen the dimerization argument, or perhaps lighten the language around the main conclusions from these data.

Response: __Thank you for bringing the lack of clarity to our attention. Both dimer mutants of BNIP3 (S172A and LG swap) are insensitive to Baf-A1 treatment. These results hold for full-length BNIP3 using either the tf (__Fig 5D) or IRES (Fig 5I) reporter. To demonstrate that defects in lysosomal transport were due to dimerization defects (and not other, unanticipated effects of the mutations), we looked at whether chemically induced dimerization could reverse the trafficking defects. Indeed, forced dimerization of the ER-restricted variant rescued ER-to-lysosome trafficking. From this, we conclude that that dimerization is a critical facet of BNIP3 trafficking to the lysosome.

We have re-worked the relevant text (both in results and discussion) to clarify major points and lighten the language around the conclusions from these data (described below).

First, as mentioned above, we have added a significant discussion about the limitations of our assay and of possible interpretations. (Lines 300-303, Lines 323-326, Lines 483-489).

Second, with regards to the specific construct used in this experiment, we have expanded the results section to better describe our rationale and approach (Lines 304-308). In short, because dimerization of native BNIP3 occurs within the membrane, we aimed to place the DmrB domain as close to the TM segment as possible. Due to the topology of TA proteins, a C-terminal tag isn’t possible. Therefore, we used the shortest truncation version of BNIP3 (117-end) that undergoes measurable lysosomal delivery. This was an important experimental consideration, and one we did not sufficiently rationalize in the original manuscript. We now include this point in the text.

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[Reviewer #2]

Comment: The authors show that BNIP3 on the ER is not stable but degraded by the proteasome. Does this require ERAD factors? Is the mitochondrial BNIP3 protein likewise degraded by proteasomal degradation? It is not clear whether both BNIP3 pools are constantly turned over or whether degradation exclusively/predominantly occurs on the ER surface.

Response: __These are fascinating mechanistic questions. We hope to thoroughly address these questions in a subsequent study. However, as a teaser, we have included the basic answer to these questions in __Fig 5I.

To preliminarily characterize the proteasomal degradation of ER- and mitochondrial-BNIP3, we utilized our IRES reporter system - adapted from Steve Elledge’s system for degron monitoring (Fig 5I). Strikingly, our ER-restricted BNIP3 mutation (S172A) is sensitive to inhibition of both the proteasome and the AAA-ATPase p97/VCP, a key extractase for ERAD substrates. These data tentatively suggest an ERAD-dependent degradation mechanism (although many follow-up studies will be needed to confirm the mechanistic details). In sharp contrast, our mitochondrial-restricted mutant (LG Swap) is sensitive to proteasome inhibition by Bortezomib, but it is insensitive to VCP inhibition. The differential requirement for VCP suggests that proteasomal degradation occurs on both cellular pools of BNIP3 albeit through different mechanisms.

Comment: The results of the screen shown in Fig. 2B are particularly interesting for readers. The glutathione peroxidase GPX4 was found as a top hit among the EMC components. GPX4 protects membranes (including those of mitochondria) against oxidative damage, is a major component of ferroptosis and linked to mitochondrial dysfunction and mitophagy. The authors should mention this interesting hit in the context of their discussion of the lipid-sensing properties of the dimerizing TM domains of BNIP3.

__Response: __Thank you to Reviewer #2 for bringing this to our attention. The relationship between GPX4 and BNIP3 flux is very interesting. We have incorporated GPX4 into the discussion section (Lines 457-459).

• *

[Reviewer #3]

Comment: For all of the tf-BNIP3 FACS data (all violin plots), it is unclear how many biological replicates were performed. The author only stated that at least 10,000 cells were analyzed per sample, but I believe this is for each biological replicate. To better demonstrate the biological replicates, the authors should consider using bar graphs of the medians(triplicates) with error bars.

Response: We have included biological replicates of FACS data in all primary figures (except for Fig.1C). Biological replicates, represented as medians (in triplicate), are indicated in figure legends.

Comment: In Fig 3D, it is unclear as to why there is no basal state accumulation of BNIP3 protein levels compared to Baf1A treated condition especially with USO1 and SAR1A KO samples. Is this because BNIP3 are targeted for proteasomal degradation? I think Fig 3D should include a BTZ treatment next to Baf1A to account for the lack of basal state accumulation of BNIP3.

Response: We apologize for the lack of clarity on this point. Yes, the reviewer’s interpretation of the data is correct. This point is more clearly elaborated in the text of our revised manuscript (Lines 219-223). Our results indicate that when lysosomal degradation is diminished, the expected increase in total BNIP3 protein levels is attenuated by proteasomal degradation (as evidenced by the hyperstability of BNIP3 upon Bortezomib treatment in mutant backgrounds). As requested, we have included the same knockout panel, now treated with BTZ (Fig S2E). These genetic data are further supported by Fig 3E, where a small molecule inhibitor of vesicle trafficking, Brefeldin-A, ameliorates the effect of lysosomal inhibition (BafA1) but exacerbates the effect of proteasome inhibition.

Comment: Truncation of proteins could affect their protein stability even during their synthesis. For Fig 5B and 6B, the authors should show the blots for the expression of the different truncated mutants to prove that the change in BNIP3 stability and their effect of mitoflux (or lack thereof), is not due to poor expression of these mutants.

Response: These were important potential caveats to document, and we thank the reviewer for their comment.

We note that, due to differences in transduction efficiency, western blot data is an incomplete measure for relative expression levels – it cannot distinguish between fraction of cells transduced and expression level per cell. However, RFP fluorescence (Fig 5B) and BFP fluorescence (Fig 6B) are fluorescent internal controls allowing us to assess expression levels with single cell resolution. We have provided histograms of RFP and/or BFP intensity (new Fig S4A, Fig S5B), which provides support that overall expression levels of these constructs are similar. Critically, any variation we observe does not correlate with any of the effects we report.

In addition, we have clarified the figure axis in Fig 5B to indicate that the value we are reporting is the “fold-stabilization upon BafA1 treatment”. The original figure legend wasn’t clear. Our metric (fold-stabilization) is internally normalized to compensate for differences in expression level. This is an important clarification.

Comment: For the data in Fig 7, the authors demonstrated that treating cells with proteasomal inhibitor increases mitoflux. Since the proteasome targets monomeric BNIP3 for degradation, the logical assumption is that BTZ drives dimerization of BNIP3. Can the authors demonstrate this in an approach similar to Fig 5C? This simple experiment will add significant insight into the study.

Response: __Thank you for the suggestion. As Fig 5C relied on BNIP3 over-expression, we thought it even more informative to assess the effects of BTZ on dimerization of endogenous BNIP3. Indeed, we see accumulation of an SDS-resistant BNIP3 dimer in cells treated with BTZ (__new Fig S2E, line 221). We hypothesize that BTZ indirectly drives dimerization of BNIP3 by accumulating the total levels of the protein, potentiating monomers to form additional stable dimers.

Comment: In line 168-169, "In addition, multiple suppressor genes identified from our screen had previously been reported including TMEM11..." -- Unclear what biology they are reported to be involved in

__Response: __We have clarified this line to read: "In addition, we recovered multiple known suppressors of BNIP3 flux, including outer membrane protein spatial restrictor TMEM11, mitochondrial protein import factors DNAJA3 and DNAJA11, and mitochondrial chaperone HSPA9"

Comment: Along the line with Major comment 2, the explanation for Fig 3D needs to be better elaborated, perhaps to include the role of proteasome already at this point (if the authors think this is the reason why basal BNIP3 levels remains low with USO1 and SAR1A KO).

__Response: __We have included a discussion about compensation by the proteasome in these genetic backgrounds (lines 219-226) and have referred to the newly incorporated western blot (new Fig S2E).

Comment: Line 302-304, I believe that statement only refers to Fig S4C and the statement for Fig5G is in the next sentence. Please remove Fig5G from line 304. It was confusing to read.

Response: __The reference of __Fig 5G has been removed.

Comment: Line 367, there is a reference for Fig S5C but that figure is missing.

__Response: __The spurious reference has been removed.

Comment: Line 410-411, are there any reported clinical cases of EMC mutations with phenotypes that could be explained by elevated mitophagy?

__Response: __Thank you for the suggestion. There are clinical presentations of EMC mutations and splice variants in diseases and conditions related to the central nervous system (PMID: 23105016, PMID: 26942288, PMID: 29271071). However, all characterization has been done in the clinical setting looking at clinical presentations/symptoms and not molecular or cellular characterization. We have added a line to the discussion about this speculative correlation between EMC deficiency and mitophagy (lines 516-519).

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

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

• *

[Reviewer #1]

Comment: Figure 3B: Are the red puncta observed in USO1 and SAR1A cells a product of higher levels of ER-phagy owing to BNIP3's high presence on the ER membrane?

__Response: __This is an intriguing hypothesis. We will test whether this is true using a USO1/ATG9A dual KO. However, we don’t think this result is critical to the overall arc of the manuscript and we will not include these data if they indicate otherwise.

reply to u/coachdan007 at https://www.reddit.com/r/antinet/comments/13ygoz9/comment/jn80a7z/?utm_source=reddit&utm_medium=web2x&context=3

Mostly these references were using Hypothesis, though I do have some material in Zotero. I don't use Raindrop. IIRC, I knew I'd seen the topics before and did a search for the tag bible and then narrowed it down my adding on zettelkasten and it popped up immediately. A large number of my replies here are just querying my digital ZK and spitting out pre-packaged answers or pointers to relevant material. I also occasionally do the same thing with my analog version, though with those I have to type them out. I follow roughly the same process for doing my own queries and writing. You get surprisingly good at it after a while, particularly when you know it's in there somewhere. Of course r/ has it's own internal search function too, so you could check out: - https://www.reddit.com/r/antinet/search/?q=bible&restrict_sr=1 - https://www.reddit.com/r/Zettelkasten/search/?q=bible&restrict_sr=1

and have a slightly wider net to get the fishes and loaves you're seeking. With the proper notes at hand, perhaps you'll soon be able to turn water into wine? Interestingly, I think you're the first who's ever asked this question here (or other related fora). I hope people don't think I spend all my time writing all these custom answers when I'm just tipping out my zettelkasten. (Though I do always keep my original answers too in the eventuality that I ever want to turn all of these thoughts into an article or book.)

reply to u/coachdan007 at https://www.reddit.com/r/antinet/comments/13ygoz9/comment/jn6fwzr/?utm_source=reddit&utm_medium=web2x&context=3

Thanks u/coachdan007. I've heard of the EPP, but never delved heavily into it. There's still a lot of digging I want to do into Edwards' Miscellanies, but I just haven't had the time, sadly. Perhaps I'll find it over the summer? While you're searching around you might also find it interesting/useful to have an interleaved bible as well to give you bigger "margins" to write in as you go. This may make some of the direct thinking on the page a bit easier. Don't think too hard about some super custom method, just start practicing something that makes sense and evolve it as you go and as you need to.

As for Hypothesis, following my account or reading past notes may be useful/helpful. For the day to day, I've documented pieces of it along with tips and tricks over time on my site at https://boffosocko.com/tag/hypothes.is/. Some of the older posts when I was first starting out are probably more interesting as more recent ones can be sort of meta.

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Reply to the reviewers

1. General Statements [optional]

Thank you for your letter dated on May 5, 2023 concerning our manuscript (MS# RC-2023-01906) entitled “Activation of Nedd4L Ubiquitin Ligase by FCHO2-generated Membrane Curvature.”

We thank the reviewers for their constructive comments and suggestions. We have considered all reviewers’ comments and plan to revise our manuscript accordingly.

We believe that our revision plan will greatly improve the quality of our manuscript.

1. Description of the planned revisions

__Reviewer #1 __

I enjoyed reading the paper by Sakamoto and colleagues, where they show that Nedd4L ubiquitin ligase activity is stimulated by membranes and in particular positive membrane curvature. This paper is a conceptual advance that hopefully will be extended by many other groups where membranes topology participates in the activation of associated enzymes, giving rise to added complexity but also specificity and further compartmentalization. It is an important paper for all cell biologists to understand.

1. My comments are all relatively minor and I hope can improve the readability of the paper, but will not alter the overall conclusion as this is well backed up. In general I would like to see more/better statistics/quantitation and better figure legends. I found that often one had to read the paper to understand a figure where reading the figure legend should suffice.

__Reply: __According to the reviewer’s comment, we will quantify the experiments (Fig. 1C, Fig. 2, Fig. 9B, and Fig. 10B) and add descriptions of statistics (Fig. 5, Fig. 6, B and D, and Fig. 7C). We will also write better figure legends to enable the readers to easily understand experiments.

1. This paper reminds me of a paper from Gilbert Di Paolo's lab on the activation of synaptojanin PIP2 hydrolysis by high membrane curvature. One would expect that there may be many such proteins whose activities will be dependent on their membrane environment. I find it conceptually rather likely that a protein which interacts with membranes via a C2 domain (which has membrane insertions and will thus likely be curvature sensitive) will likely show some positive curvature sensitivity. Can I suggest this paper is referenced and discussed in the light of the discussion statement "Thus, our findings provide a new concept of signal transduction in which a specific degree of membrane curvature serves as a signal for activation of an enzyme that regulates a number of substrates."

Reply: __According to the reviewer’s comment, we will cite the paper entitled “synaptojanin-1-mediated PI(4,5)P2 hydrolysis is modulated by membrane curvature and facilitates membrane fission” by Chang-Ileto et al. (Dev. Cell __20, 206–18 , 2011). We will also discuss this paper in the light of the discussion statement.

1. Where the paper could be improved (or I have not understood fully). In figure 1 there is a robust endocytosis of ENaC that is FCHo2 and Nedd4L sensitive. There is a rescue for FCHo2 in a fluorescence image (unquantified), so it would be good to have the more quantitative approach of rescue with both FCHo2 and Nedd4L in the biochemical assay.

__Reply: __Although the reviewer suggests a rescue experiment in the biochemical assay, the experiment is difficult because the transfection efficiency is low (about 50%). On the other hand, we agree with the reviewer that a quantitative approach is required in the rescue experiment (Fig. 1C). Therefore, we plan to quantify the rescue experiment for FCHO2 in the immunofluorescence assay. The reviewer also suggests a rescue experiment for Nedd4L as well as FCHO2. However, since the involvement of Nedd4L in ENaC endocytosis is well established, we do not think that the rescue experiment for Nedd4L is further required.

1. In figure 2 there is nice co-localisation between clathrin/FCHo2 and ENaC but not with Nedd4L. It would be good to have some quantitation of the co-localisation. But also one should use a Nedd4L mutant or a mutant of ENaC and so be able to visualise co-localisation between receptor and ub-ligase. I find it strange that there is no (or much less) Nedd4L-GFP visible in the cells overexpressing ENaC... Is there an explanation? Does overexpression of ENaC lead to more auto-ubiquitination of Nedd4L. Also the Nedd4L-GFP signal in other cells is punctate, while in the next figure Myc-Nedd4L is not.

__Reply: __According to the reviewer’s comment, we will perform quantitative colocalization analysis in Fig. 2.

We have found that a catalytically inactive Nedd4L mutant, C922A, co-localizes with cell-surface αENaC and FCHO2 in αβγENaC-HeLa cells. According to the reviewer’s comment, these data will be added in the revised manuscript.

In Fig. 2C, Nedd4L was transiently transfected in cells stably expressing ENaC. In Nedd4L-transfected cells, overexpression of Nedd4L stimulated ENaC internalization, resulting in the disappearance of ENaC at the cell surface. On the other hand, in non-transfected cells, cell-surface ENaC was detected. Thus, Nedd4L-negative cells are non-transfected cells (cell-surface ENaC positive cells). This explanation will be added in the revised manuscript.

The staining pattern of Nedd4L depends on what section of the cell a confocal microscope was focused on. Nedd4L-GFP signals were punctate at the bottom section of the cell in Fig. 2, whereas Myc-Nedd4L was diffusely distributed at the upper section (cytoplasm) of the cell (Fig. 3). Thus, Nedd4L shows distribution throughout the cytoplasm and punctate staining at the bottom (cell surface). The staining pattern of Nedd4L is also affected by the expression amount of Nedd4L in cells. When Nedd4L was highly expressed in COS7 and HEK293 cells in Fig. 3, the punctate staining was hardly detected. This localization pattern of Nedd4L will be clearly described in the revised manuscript.

1. In figure 3 it appears to me that there is co-localization between ENaC and amphiphysin. Is this not a positive piece of information? I am not sure that FBP17 is a good F-BAR domain to use given its oligomerization may well prevent membrane association of Nedd4L. Minor comment: I don't see tubules for amphiphysin in panel B.

__Reply: __The reviewer states that there is co-localization between Nedd4L and amphiphysin1 (Fig. 3A). However, Nedd4L was not recruited to membrane tubules generated by amphiphysin1. We will clearly show that there is no colocalization between Nedd4L and amphiphysin1.

The reviewer states that FBP17 may not be a good F-BAR domain to use because its oligomerization may well prevent membrane association of Nedd4L. However, we have shown that FCHO2 as well as FBP17 forms oligomer (Uezu et al. Genes Cells, 16, 868-878, 2011). Furthermore, we have found that FCHO2 inhibits the membrane binding and catalytic activity of Nedd4L when the PS percentage in liposomes is elevated (unpublished data and Fig. 9C). Thus, since FBP17 and FCHO2 probably have similar properties, we presume that FBP17 is a good F-BAR domain to use.

As the reviewer pointed out, membrane tubules generated by amphiphysin1 were hardly detected in HEK293 cells (Fig. 3B). It showed punctate staining, but did not co-localized with Nedd4L. This description will be added in the revised manuscript.

1. Figure 5: The affinity of Nedd4 C2 domain for calcium is quite high given we normally assume a cytosolic concentration of 100nM (approximate). The authors have rightly buffered the calcium with EGTA. Normally we would check that the buffering is sufficient by varying the protein concentration and making sure the affinity is still the same, so can I suggest the authors use 3 or 4 times the amount of C2 domain and make sure the curve does not change (provided liposomes are not limiting). Minor comment: How many experiments and what are error bars (SD?).

__Reply: __According to the reviewer’s comment, we will check that the buffering is sufficient by varying the protein concentration (Fig. 5). We will also add a description of statistics to the legend to Fig. 5.

1. Figure 6: Controls have been performed to ensure that liposomes are pelleted, according to methods. In Figure 6B can the authors show that there is the same amount of liposomes in each sample by showing more of the coomassie gel so that the reader can see the Neutravidin band is the same in each sample. Also I believe a student t-test should not be used in this experiment (but perhaps an Anova test), and in panel D there does not appear to be a description of statistics.

__Reply: __To ensure that the same amounts of liposomes were pelleted, the reviewer suggests that we show more of the Coomassie gel to present the neutravidin bands in Fig. 6B. However, as the molecular weight of neutravidin is about 15 kDa, neutravidin run out of the gel (7% SDS-PAGE gel) where Nedd4L (As the reviewer pointed out, we will use an Anova test in Fig. 6B. We will also add a description of statistics in Fig. 6D.

1. Figure 11: In panel B I note that the FCHo2 BAR domain on small liposomes appears to inhibit Ubiquitination. Is this consistent with the BAR domain not preventing Nedd4L binding?

__Reply: __The FCHO2 BAR domain enhances the liposome binding and catalytic activity of Nedd4L when the strength of interaction of Nedd4L with liposomes (20% PS) is weak. In contrast, we have also found that the FCHO2 BAR domain inhibits the membrane binding and catalytic activity of Nedd4L when the interaction of Nedd4L with liposomes is increased by elevating the PS percentage in liposomes (unpublished data and Fig. 9C). The reason for the different effects of FCHO2 on Nedd4L is considered as follows: When liposomes (20% PS) are used (the interaction of Nedd4L with PS in liposomes is weak), Nedd4L binds to liposomes mainly through ENaC (Fig. 8F). The liposome binding is hardly mediated by PS. Addition of the FCHO2 BAR domain increases the strength of interaction Nedd4L with PS by generating membrane curvature. Consequently, the FCHO2 BAR domain newly induces the PS-mediated liposome binding of Nedd4L, resulting in the enhancement of liposome binding and catalytic activity of Nedd4L. On the other hand, when the interaction of Nedd4L with PS in liposomes is increased by elevating the PS percentage in liposomes (50% PS), the liposome binding of Nedd4L is mainly mediated by PS. Addition of the FCHO2 BAR domain inhibits the PS-mediated liposome binding of Nedd4L. Since both FCHO2 and Nedd4L are PS-binding proteins, they compete with each other to bind to PS in liposomes. Therefore, the results in Fig. 11B are consistent, because the interaction of Nedd4L with PS is increased by 0.05 µm pore-size liposomes. This explanation will be added in the revised manuscript.

__Reviewer #2 __

The authors have reported the involvement of the BAR domain-containing protein FCHO2 in the Nedd4L-mediated endocytosis of ENaC. They propose a model in which the membrane curvature induced by the BAR domain-FCHO2 relieves the auto-inhibition of E3 ligase causing its activation and recruitment. The paper describes a series of in vitro reconstituted experiments that are interesting but not fully connected with the mechanism of ENaC endocytosis. Additional experiments are needed to fully support the authors' conclusions.

Major comments:

1. Although the data reported by the authors regarding FCHO2 and Nedd4L involvement in ENaC endocytosis are convincing, it is suggested that the authors perform the same ENaC endocytosis assay presented in Fig.1B under conditions of FBP17 and amphiphysin1 siRNA to formally prove the selective involvement of FCHO2 in the process among other BAR-containing proteins.

__Reply: __The reviewer suggests the same ENaC endocytosis assay presented in Fig. 1B under conditions of FBP17 and amphiphysin1 siRNA to prove the selective involvement of FCHO2 in ENaC endocytosis. There seems to be a misunderstanding. Similar to FCHO2, FBP17 and amphiphysin are well known to be involved in clathrin-mediated endocytosis. As ENaC is internalized through clathrin-mediated endocytosis, FBP17 and amphiphysin siRNA presumably inhibit ENaC endocytosis. We cannot understand the significance of FBP17 and amphiphysin1 siRNA in the ENaC endocytosis assay.

1. According to the previous point, it will be interesting to see not only a snapshot image of the internalisation assay performed by immunofluorescence (Fig.1C) but a more quantitative analysis of the different time points (as in Fig.1B) in condition of FCHO2 siRNA and eventually FBP17 and amphiphysin1 siRNA.

__Reply: __According to the reviewer’s comment, we will perform a quantitative analysis in Fig. 1C. The reviewer also suggests the immunofluorescence assay at the different time point in Fig. 1C. However, we show the time course of ENaC internalization in Fig. 1B. We do not think that the time course in the immunofluorescence assay is further required. As for FBP17 and amphiphysin siRNA, our response is the same as that to the comment 1 of this reviewer.

1. In Fig.2B, overexpression of the catalytically inactive version of Nedd4L (Nedd4L C922A) would help to see Nedd4L-ENaC co-localization.

__Reply: __This comment is the same as the comment 4 of the reviewer#1.

1. In Fig.4D, the authors need to analyse ENaC ubiquitination in the same experimental setting as Fig. 4A instead of transfecting cells with increasing amounts of Nedd4L in the presence or absence of FCHO2 BAR. It is also recommended to include Nedd4L C922A as an additional control.

__Reply: __The reviewer requests us to analyse ENaC ubiquitination in the same setting as Fig. 4A. However, an in vivo autoubiquitination assay is widely used to determine the catalytic activity of E3 Ub ligase, because the E3 activity is typically reflected in their autoubiquitination. Therefore, the autoubiquitination assay is sufficient to show that Nedd4L is specifically activated by membrane tubules generated by FCHO2 in cells. Furthermore, we have found it very difficult to compare ENaC ubiquitination among many GFP-BAR proteins (GFP alone, GFP-FCHO2, GFP-FBP17, amphiphysin1-GFP, GFP-FCHO2 mutant) in the same experimental setting as Fig. 4A. In Fig. 4A, three types of cDNAs (HA-Ub, Myc-Nedd4L, and GFP-BAR protein) were transfected in cells. The expression amounts of Myc-Nedd4L were similar among the GFP-BAR proteins. On the other hand, in Fig. 4D, four types of cDNA (HA-Ub, Myc-Nedd4L, GFP-BAR protein, and FLAG-αENaC) were transfected in cells. Under these conditions, it is very difficult to adjust the expression amounts of Nedd4L and αENaC among many GFP-BAR proteins. Even when comparing two GFP-BAR proteins (GFP alone and GFP-FCHO2), it was necessary to assess the expression amounts of Nedd4L by transfection with various cDNA amounts of Nedd4L (Fig. 4D). Moreover, as shown in Fig. 4D, enhancement of ENaC ubiquitination by FCHO2 is decreased at higher expression of Nedd4L (1.0 and 1.5 μg DNA), although the reason is unknown. Therefore, we are not sure that we will able to accurately analyse ENaC ubiquitination in the same setting as Fig. 4A instead of transfecting cells with increasing amounts of Nedd4L.

According to the reviewer’s comment, we will examine the effect of Nedd4L C922A on ENaC ubiquitination.

1. While discussing the role of hydrophobic residues in Nedd4L C2 domain,the authors never mentioned the publication by Escobedo et al., Structure 2014 (DOI:10.1016/j.str.2014.08.016), which highlighted how I37 and L38 are directly involved in Ca2+ binding. This aspect should be discussed since the authors show the importance of Ca2+ for PS binding in the sedimentation assay.

__Reply: __According to the reviewer’s comment, we will cite the reference (Escobedo et al.) and discuss the aspect (I37 and L38 are directly involved in Ca2+ binding).

1. As stated by the authors those two residues I37 and L38 are also involved in E3 enzyme activation by relieving C2-HECT interaction. It is important to further demonstrate the effect of these mutations on ENaC substrate.

__Reply: __To prove that the I37 and F38 residues are involved in E3 enzyme activation by relieving C2-HECT interaction, the reviewer requests us to further demonstrate the effect of Nedd4L I37A+F38A on ENaC ubiquitination. However, these two residues are critical noy only for Nedd4L activation but also for membrane binding and curvature sensing of Nedd4L. We also show that membrane binding of Nedd4L is critical for ENaC ubiquitination. Actually, we have found that Nedd4L I37A+F38A mutant, which loses membrane binding, shows little ENaC ubiquitination (unpublished data), whereas it enhances autoubiquitination (Fig. 4C). Thus, the effect of the I37A+F38A mutant on ENaC ubiquitination is not appropriate to prove that the two residues are involved in E3 enzyme activation.

1. There are some concerns regarding the in vitro ubiquitination assay performed in Fig.8 and following figures. The Nedd4L proteins used during the assay has been produced as His tagged at the C-terminus, it was reported (Maspero et al, Nat Struct Mol Biol 2013 DOI: 10.1038/nsmb.2566), at least for the isolated HECT domain, that modification of the C-terminal residue of the protein affects its activity. It would be important to judge the activity of the purified proteins used in the assay. Moreover, as additional control it is suggested the introduction of a mSA-ENaC PY mutant protein. The authors claimed the importance of membrane localized PY motif for recruitment and activation of Nedd4L, it would be informative to perform the experiment in presence of PY mutated ENaC.

__Reply: __The reviewer states that there are some concerns regarding His-tagged Nedd4L proteins. We have prepared Nedd4L that has no tag at its N- or C-terminus. N-terminal GST-tagged, C-terminal untagged Nedd4L was expressed in E. coli and purified by Glutathione-Sepharose column chromatography. The GST tag was cleaved off and Nedd4L was further purified by Mono Q anion-exchange column chromatography. Using this purified sample, we have examined the catalytic activity of untagged Nedd4L. We have found that concerning Ca2+-dependency, PS-dependency, and curvature-sensing, the properties of untagged Nedd4L are similar to those of C-terminal His-tagged Nedd4L (unpublished data).

According to the reviewer’s comment, we will perform the experiment in the presence of PY-mutated ENaC.

1. It is not clear why increasing the concentration of PS (from 20% to 50%) the presence of BAR domain doesn't allow ENaC ubiquitination (Fig.9C), is Nedd4L not recruited to the pellet? It would be interesting to see the sedimentation experiment of Fig.9A done in presence of 50% PS.

__Reply: __This comment is essentially the same as the comment 8 of the reviewer#1. We have found that FCHO2 BAR domain inhibits the membrane binding of Nedd4L when the PS percentage in liposomes is elevated (~50%) (unpublished data). According to the reviewer’s comment, these data will be added in the revised manuscript.

1. This reviewer is not an expert of lipids biology, thus the explanations related to the effect of FCHO2 BAR in presence of PI(4,5)P2 (Fig. 10) or 0.05 pore-size liposomes (Fig.11) were not clear. Does FCHO2 BAR have a different effect in inducing membrane tubulation in these two conditions? Is this parameter measurable by tubulation assay?

__Reply: __According to the reviewer’s comment, we will write more clearly the explanation related to the effect of FCHO2 BAR domain in the presence of PI(4,5)P2 or 0.05 μm pore-size liposomes.

Minor Comments

1. It would be appreciated if a nuclei staining panel is included in all immunofluorescence images, as it would help to identify the number of cells in the field of view (e.g., Fig. 1C, Fig. 2B).

__Reply: __According to the reviewer’s comment, we will show immunofluorescence images to identify the number of cells in Fig. 1C and Fig. 2B.

1. It would be recommended to include colocalization analysis, such as Pearson's correlation coefficient or Manders coefficient in immunofluorescence images.

__Reply: __According to the reviewer comment, we plan to perform quantitative colocalization analysis in Fig. 2.

1. It is not clear how the quantitation of mSA-ENaC ubiquitination in Fig.8D, 8C, and 9B was performed. Did the authors normalise the detected Ub signal over the amount of unmodified mSA-ENaC?

__Reply: __We did not normalize the detected Ub signals over the amount of unmodified mSA-ENaC, because the same amount of mSA-ENaC was added in each assay. The chemiluminescence intensity of Ub signals was quantified by scanning using ImageJ. According to the reviewer’ comment, we will clearly describe how the quantification of mSA-ENaC ubiquitination was performed.

__Reviewer #3 __

--- Summary ---

The manuscript by Sakamoto et al. describes how the ubiquitin ligase Nedd4L is activated by membrane curvature generated by the endocytic protein FCHO2. For their experiments, the authors use the epithelial sodium channel (ENaC) as a model Nedd4L target and CME cargo. The authors start their manuscript by showing in cells the importance of FCHo2 and Nedd4L in ENaC internalization. Using a combination of experiments in cells and biochemistry, the authors show that Nedd4L binds preferentially to membranes with the same curvature generated by FCHO2. Next, the authors show that a combination of membrane composition (PS), calcium concentration, PY domain presence and membrane curvature all act in concert to recruit Nedd4L to membranes and fully release its ubiquitination activity. Crucially, the authors show that role of FCHO2 in Nedd4L recruitment is not direct, with FCHO2 simply generating an optimal membrane curvature for Nedd4L binding. Taken together, the authors suggest a mechanism by which the curvature of early clathrin coated pits, generated by FCHO1/2 define an optimal environment for the recruitment and activation of the ubiquitin ligase Nedd4L.

The manuscript convincingly shows the membrane curvature-dependent mechanism of Nedd4L activation. The biochemistry experiments in the manuscript are well designed and the results are of clear. The quality of these experiments is very high. The experiments in cells are, however, not of the same level of quality.

--- Major comments ---

1) The results do not show convincingly that Nedd4L is recruited to CCPs. There is plenty of indirect evidence, but to support the model shown in the last figure, authors need to show more than the staining in figure 2C. Live-cell imaging showing the post-FCHo2 recruitment of Nedd4L would be required. I understand that the recruitment would possibly occur in a fraction of events and may be difficult to catch. The cmeAnalysis script from the danuser lab(https://doi.org/10.1016/j.devcel.2013.06.019 can facilitate the identification of these events.

__Reply: __According to the reviewer comment, we plan to examine by live-cell TIRF microscopy that Nedd4L is recruited to CCPs.

2) What happens to ENaC in Nedd4L and FCHO2 knockdown cells? One would expect accumulation of the receptor on the surface.

__Reply: __We have found that upon Nedd4L or FCHO2 knockdown, αENaC accumulates at the cell surface in αβγENaC-HeLa cells. According to the reviewer’s comment, we will show these data in the revised manuscript.

*3) In the experiments in figure 1, it would be important to use a standard CME cargo as an internal control (transferrin). This will serve as a functional confirmation of FCHO2 knockdown and help the reader to put the Need4L knockdown experiments into the context of CME. *

__Reply: __According to the reviewer’s comment, we will use a standard CME cargo as an internal control (transferrin).

*4) Quantification for the rescue experiment is required (figure 1C). if not possible, at least a picture where the reader can see transfected and non-transfected cells side-by-side is necessary. *

Reply: This comment is the same as those of the reviewer#1 (comment 3) and reviewer#2 (comment 2). According to the reviewer’s comment, we plan to quantify the rescue experiment (Fig. 1C).

*--- Minor comments --- *

*1) The experiments in figure 3 must be presented in order as they are in the text. For example, figure 3E is cited in the text into the context of figure 7. It is very confusing. *

__Reply: __According to the reviewer’ s comment, we will present the experiments in Fig. 3 in order they are in the text.

*2) A better explanation of the assay in 1C would facilitate its understanding for the non-specialist reader. The reader needs to read the methods section to understand how it was done. *

__Reply: __According to the reviewer’ comment, we will write a better explanation of the assay in the Fig. 1C legend to enable the readers to understand how it was done.

Thanks for sharing this very interesting and useful study! The ability to simultaneously visualize different actin isoforms with reduced effects on endogenous dynamics is fantastic and will no doubt lead to future discovery of differential functions.

The pitfalls of N-term actin tagging are well documented as you note, so strategies that allow for faithful binding to endogenous nucleators would indeed be beneficial. However, the preferred internal 229/230 tag still shows no greater co-localization (and perhaps reduced co-localization, as I am unsure of the statistical difference in figure 1C) with total f-actin/phalloidin staining relative to N-terminal tagging. This suggests that there are indeed additional effects of the internal tag on dynamics (likely driven by affected ABP binding) despite largely not identifying those defects in your assays. I would have also therefore have been interested to see the N-term tagged control for figure 3 alongside the internal tags. This control wouldn’t be quantitatively comparable of course but I can’t remember if formin binding is affected for n-term tagged actins or just getting through the formin ring.

Regardless, I’ll emphasize the importance of additional tools and information such as what you present here. The extensive interactions of actin with hundreds of binding proteins with myriad functions throughout cells highlights extensive combinatorial complexity that benefits from the availability of a full suite of actin labels so that the right labeling strategy can be selected based on application. This is therefore a very welcome addition to that suite of strategies!

Are these over-expression cells more resistant to HS than WT cells?

Not perfect, but at least that's simple enough to understand

Reviewer #1 (Public Review):

Original review:

This manuscript by Walker et. al. explores the interplay between the global regulators HapR (the QS master high cell density (HDC) regulator) and CRP. Using ChIP-Seq, the authors find that at several sites, the HapR and CRP binding sites overlap. A detailed exploration of the murPQ promoter finds that CRP binding promotes HapR binding, which leads to repression of murPQ. The authors have a comprehensive set of experiments that paints a nice story providing a mechanistic explanation for converging global regulation. I did feel there are some weak points though, in particular the lack of integration of previously identified transcription start sites, the lack of replication (at least replication presented in the manuscript) for many figures, some oddities in the growth curve, and not reexamining their HapR/CRP cooperative binding model in vivo using ChIP-Seq.

Review of revised version:

This revised manuscript by Walker et. al. addresses some of the editorial points and conceptual discussion, but in general, most of my suggestions (as the previous reviewer #1) for additional experimentation or addition were not addressed as discussed below. Therefore, my overall review has not changed.

1) For example, in point 1, the suggested analysis was not performed because it is not trivial. My reason for making this suggestion is that the original manuscript was limited to Vibrio cholerae, and the impact of the manuscript would increase if the findings here were demonstrated to be more broadly applicable. I expect papers published in eLife to have such broad applicability. But no changes were made to the manuscript in this regard. The revised version is still limited to only Vibrio cholerae.

2) For point 2, the activity of FLAG-tag luxO could have been simply validated in a complementation assay. Yes, they demonstrated DNA binding, but that is not the only activity of LuxO.

3) For point 7, the transcriptional fusions were not explored at different times or different media, which is also something that was hinted at by other reviewers. In regard to exploring expression at different time points, this seems particularly relevant for QS regulated genes.

4) For point 13, the authors express that doing an additional CHIP-Seq is outside of the scope of this manuscript. Perhaps that is the case, but the point of the comment is to validate the in vitro binding results with an in vivo binding assay. A targeted CHIP-Seq approach specifically analyzing the promoters where cooperative binding was observed in vitro could have addressed this point.

Author Response:

The following is the authors' response to the current reviews.

Reviewer #1 (Public Review):

This revised manuscript by Walker et. al. addresses some of the editorial points and conceptual discussion, but in general, most of my suggestions (as the previous reviewer #1) for additional experimentation or addition were not addressed as discussed below. Therefore, my overall review has not changed.

In our previous response, we included i) extra experimental data illustrating the reproducibility of our results and ii) added transcription start site data at the request of this reviewer. We included the information because we agreed with the reviewer that these were important points to address. For the points raised again below, we explained why the additional analysis was unlikely to add much in terms of insight or rigour. We have elaborated further below.   

1) For example, in point 1, the suggested analysis was not performed because it is not trivial. My reason for making this suggestion is that the original manuscript was limited to Vibrio cholerae, and the impact of the manuscript would increase if the findings here were demonstrated to be more broadly applicable. I expect papers published in eLife to have such broad applicability. But no changes were made to the manuscript in this regard. The revised version is still limited to only Vibrio cholerae.

Our paper is focused on the unexpected co-operative interactions between HapR and CRP. Such co-binding of two transcription factors to the same DNA site is unexpected. Consequently, it is this mode of DNA binding that is likely to be of broad interest. With this in mind, we did provide experimental, and bioinformatic, analyses for other regulatory regions and other vibrio species (Figures S3 and S6). This, in our view, is where the “broad applicability” for papers published in eLife comes from.

The analysis the reviewer suggests is not related to the main message of our paper. Instead, the reviewer is asking how many HapR binding sites seen here by ChIP-seq are also seen in other vibrio species by ChIP-seq. This is only likely to be of interest to readers with an extremely specific interest in both vibrio species and HapR. The reviewer states above that we did not make the change “because it is not trivial”. This is an oversimplification of the rationale we presented in our response. The analysis is indeed not straightforward. However, much more importantly, the outcome is unlikely to be of interest to many readers, and has no bearing on the rigour of work. With this in mind, we do not think our position is unreasonable. We also stress that, should a reader with this very specific interest want to explore further, all of our data are freely available for them to do so.

2) For point 2, the activity of FLAG-tag luxO could have been simply validated in a complementation assay. Yes, they demonstrated DNA binding, but that is not the only activity of LuxO.

DNA binding by LuxO is the only activity of the protein with which we are concerned in our paper. Furthermore, LuxO is very much a side issue; we found binding to only the known targets and potentially, at very low levels, one additional target. No further LuxO experiments were done for this reason. Indeed, even if these data were removed completely, our conclusions would not change or be supported any less vigorously. We are happy to remove the LuxO data if the reviewer would prefer but this would seem like overkill.

3) For point 7, the transcriptional fusions were not explored at different times or different media, which is also something that was hinted at by other reviewers. In regard to exploring expression at different time points, this seems particularly relevant for QS regulated genes.

In their previous review, the reviewer did not request that such experiments were done. Similarly, no other reviewer requested these experiments. Instead, this reviewer i) commented that lacZ fusions were not as sensitive as luciferase fusions ii) asked if we had done any time point experiments. We agreed with the first point, whilst also noting that lacZ is not unusual to use as a reporter. For the second point, we responded that we had not done such experiments (which by the reviewer’s own logic would have been complicated using lacZ as a reporter). This seems like a perfectly reasonable way to respond.   

We should stress that these comments all refer to Figure 2a, which was our initial screening of 23 promoter::lacZ fusions, supported by separate in vitro transcription assays. Only one of these fusions was followed up as the main story in the paper. Given that the other 22 fusions were not investigated further, and do not form part of the main story, there would seem little value in now going back to assay them at different time points.

4) For point 13, the authors express that doing an additional CHIP-Seq is outside of the scope of this manuscript. Perhaps that is the case, but the point of the comment is to validate the in vitro binding results with an in vivo binding assay. A targeted CHIP-Seq approach specifically analyzing the promoters where cooperative binding was observed in vitro could have addressed this point.

We did appreciate the original comment, and responded as such, but we do think additional ChIP-seq assays are outside the scope of this paper.

Reviewer #2 (Public Review):

This manuscript by Walker et al describes an elegant study that synergizes our knowledge of virulence gene regulation of Vibrio cholerae. The work brings a new element of regulation for CRP, notably that CRP and the high density regulator HapR co-occupy the same site on the DNA but modeling predicts they occupy different faces of the DNA. The DNA binding and structural modeling work is nicely conducted and data of co-occupation are convincing. The work seeks to integrate the findings into our current state of knowledge of HapR and CRP regulated genes at the transition from the environment and infection. The strength of the paper is the nice ChIP-seq analysis and the structural modeling and the integration of their work with other studies.

We thank the reviewer for the positive comments.

The weakness is that it is not clear how representative these data are of multiple hapR/CRP binding sites

This comment does not consider all data in our paper. We did test our model experimentally at multiple HapR and CRP binding sites. These data are shown in Figure S6 and confirm the co-operative interaction between HapR and CRP at 4 of a further 5 shared binding sites tested. We also used bioinformatics to show the same juxtaposition of CRP and HapR sites in other vibrio species (Figure S3). Hence, the model seems representative of most sites shared by HapR and CRP.

or how the work integrates as a whole with the entire transcriptome that would include genes discovered by others.

At the request of the reviewers, our revision integrated our ChIP-seq data with dRNA-seq data. No other suggestions to ingrate transcriptome data were made by the reviewers. 

Overall this is a solid work that provides an understanding of integrated gene regulation in response to multiple environmental cues.

We thank the reviewer for the positive comment.

—————

The following is the authors' response to the original reviews.

Reviewer #1 (Public Review):

This manuscript by Walker et. al. explores the interplay between the global regulators HapR (the QS master high cell density (HDC) regulator) and CRP. Using ChIP-Seq, the authors find that at several sites, the HapR and CRP binding sites overlap. A detailed exploration of the murPQ promoter finds that CRP binding promotes HapR binding, which leads to repression of murPQ. The authors have a comprehensive set of experiments that paints a nice story providing a mechanistic explanation for converging global regulation.

We thank the reviewer for their positive evaluation.

I did feel there are some weak points though, in particular the lack of integration of previously identified transcription start sites

For completeness, we have now added the position and orientation or the nearest TSSs to each HapR or LuxO binding peak in Table 1 (based on Papenfort et al.).

the lack of replication (at least replication presented in the manuscript) for many figures,

We assume that the reviewer is referring to gel images rather than any other type of assay output (were error bars, derived from replicates, are shown). As is standard, we show representative gel images. All associated DNA binding and in vitro transcription experiments have been done multiple times. Indeed, comparison between figures reveals several instances of such replication (e.g. Figures 4b & 5d, Figures 4d & 5e). We have added details of repeats done to the methods section.

some oddities in the growth curve

We do not know why cells lacking hapR have a growth curve that appears biphasic. We can only assume that this is due to some regulatory effect of HapR, distinct from the murQP locus. Despite the unusual shape of the growth curve, the data are consistent with our conclusions.

and not reexamining their HapR/CRP cooperative binding model in vivo using ChIP-Seq.

We agree that these would be interesting experiments and, in the future, we may well do such work. Even without these data, our current model is well supported by the data presented (and the reviewer seems to agree with this above).

Reviewer #2 (Public Review):

This manuscript by Walker et al describes an elegant study that synergizes our knowledge of virulence gene regulation of Vibrio cholerae. The work brings a new element of regulation for CRP, notably that CRP and the high density regulator HapR co-occupy the same site on the DNA but modeling predicts they occupy different faces of the DNA. The DNA binding and structural modeling work is nicely conducted and data of co-occupation are convincing. The work could benefit from doing a better job in the manuscript preparation to integrate the findings into our current state of knowledge of HapR and CRP regulated genes and to elevate the impact of the work to address how bacteria are responding to the nutritional environment. Importantly, the focus of the work is heavily based on the impact of use of GlcNAc as a carbon source when bacteria bind to chitin in the environment, but absent the impact during infection when CRP and HapR have known roles. Further, the impact on biological events controlled by HapR integration with the utilization of carbon sources (including biofilm formation) is not explored.

We thank the reviewer for their overall positive evaluation.

The rigor and reproducibility of the work needs to be better conveyed.

Reviewer 1 made a similar comment (see above) and we have modified the manuscript accordingly.

Specific comments to address:

1)  Abstract. A comment on the impact of this work should be included in the last sentence. Specifically, how the integration of CRP with QS for gene expression under specific environments impacts the lifestyle of Vc is needed. The discussion includes comments regarding the impact of CRP regulation as a sensor of carbon source and nutrition and these could be quickly summarized as part of the abstract.

We have added an extra sentence. However, we have used cautious language as we do not show impacts on lifestyle (beyond MurNAc utilisation) directly. These can only be inferred.

2)  Line 74. This paper examines the overlap of HapR with CRP, but ignores entirely AphA. HapR is repressed by Qrrs (downstream of LuxO-P) while AphA is activated by Qrrs. With LuxO activating AphA, it has a significant sized "regulon" of genes turned on at low density. It seems reasonable that there is a possibility of overlap also between CRP and AphA. While doing an AphA CHIP-seq is likely outside the scope of this work, some bioinformatic or simply a visual analysis of the promoters known AphA regulated genes would be interest to comment on with speculation in the discussion and/or supplement.

In short, everything that the reviewer suggests here has already been done and was covered in our original submission (see text towards the end of the Discussion). Also, we would like to point the referee to our earlier publication (Haycocks et al. 2019. The quorum sensing transcription factor AphA directly regulates natural competence in Vibrio cholerae. PLoS Genet. 15:e1008362).

3)  Line 100. Accordingly with the above statement, the focus here on HapR indicates that the focus is on gene expression via LuxO and HapR, at high density. Thus the sentence should read "we sought to map the binding of LuxO and HapR of V. cholerae genome at high density".

Note that expression of LuxO and HapR is ectopic in these experiments (i.e. uncoupled from culture density).

4)  Line 109. The identification of minor LuxO binding site in the intergenic region between VC1142 and VC1143 raises whether there may be a previously unrecognized sRNA here. As another panel in figure S1, can you provide a map of the intergenic region showing the start codons and putative -10 to -35 sites. Is there room here for an sRNA? Is there one known from the many sRNA predictions / identifications previously done? Some additional analysis would be helpful.

We have added an extra panel to Figure S1 showing the position of TSSs relative to the location of LuxO binding. We have altered the main text to accommodate this addition..

5)  Line 117. This sentence states that the CHIP seq analysis in this study includes previously identified HapR regulated genes, but does not reveal that many known HapR regulated genes are absent from Table 1 and thus were missed in this study. Of 24 HapR regulated investigated by Tsou et al, only 1 is found in Table 1 of this study. A few are commented in the discussion and Figure S7. It might be useful to add a Venn Diagram to Figure 1 (and list table in supplement) for results of Tsou et al, Waters et al, Lin et al, and Nielson et al and any others). A major question is whether the trend found here for genes identified by CHIP-seq in this study hold up across the entire HapR regulon. There should also be comments in the discussion on perhaps how different methods (including growth state and carbon sources of media) may have impacted the complexity of the regulon identified by the different authors and different methods.

We have added a list of known sites to the supplementary material (new Table S1). We were unsure what was meant by the comment “A major question is whether the trend found here for genes identified by CHIP-seq in this study hold up across the entire HapR regulon”. We have added the extra comment to the discussion re growth conditions, also noting that most previous studies relied on in vitro, rather than in vivo, DNA binding assays.

6)  The transcription data are generally well performed. In all figures, add comments to the figure legends that the experiments are representative gels from n=# (the number of replicate experiments for the gel based assays). Statements to the rigor of the work are currently missing.

See responses above. We have added a comment on numbers of repeats to the methods section.

7)  Line 357-360. The demonstration of lack of growth on MurNAc is a nice for the impact of the work. However, more detailed comments are needed for M9 plus glucose for the uninformed reader to be reminded that growth in glucose is also impaired due to lack of cAMP in glucose replete conditions and thus minimal CRP is active. But why is this now dependent of hapR? A reminder also that in LB oligopeptides from tryptone are the main carbon source and thus CRP would be active.

We find this point a little confusing and, maybe, two issues (murQP regulation, and growth in general) are being conflated. In particular, we do not understand the comment “growth in glucose is also impaired due to lack of cAMP in glucose replete conditions and thus minimal CRP is active”.

Growth in glucose should indeed result in lower cAMP levels*, and hence less active CRP, but this does not impair growth. This is simply the cell’s strategy for using its preferred carbon source. If the reviewer were instead referring to some aspect of P_murQP_ regulation then yes, we would expect promoter activity to be lower because less active CRP would be available in the presence of glucose. The reviewer also comments “why is this now dependent of hapR?”. We assume that they are referring to some aspect of growth in minimal media with glucose. If so, the only hapR effect is the change in growth rate as cells enter mid-late log-phase (i.e. the growth curve looks somewhat biphasic). A similar effect is seen in all conditions. We do not know why this happens and can only conclude this is due to some unknown regulatory activity of HapR. Overall, the key point from these experiments is that loss if luxO, which results in constitutive hapR expression, lengthens lag phase only for growth with MurNAc as the sole carbon source.

*Although in V. fischeri (PMID: 26062003) cAMP levels increase in the presence of glucose.

8)  A great final experiment to demonstrate the model would have been to show co-localization of the promoter by CRP and HapR from bacteria grown in LB media but not in LB+glucose or in M9+glycerol and M9+MurNAc but not M9+glucose. This would enhance the model by linking more directly to the carbon sources (currently only indirect via growth curves)

This is unlikely to be as straightforward as suggested. The sensitivity of CRP binding to growth conditions is not uniform across different binding sites. For instance, the CRP dependence of the E. coli melAB promoter is only evident in minimal media (PMID: 11742992) whilst the role of CRP at the acs promoter is evident in tryptone broth (PMID: 14651625). Similarly, as noted above, in Vibrio fischeri glucose causes and increase in cAMP levels. (PMID: 26062003).

9) Discussion. Comments and model focus heavily on GlcNAc-6P but HapR has a regulator role also during late infection (high density). How does CRP co-operativity impact during the in vivo conditions?

We really can’t answer this question with any certainty; we have not done any infection experiments in this work.

Does the Biphasic role of CRP play a role here (PMID: 20862321)?

Again, we cannot answer this question with any confidence as experimentation would be required. However, the suggestion is certainly plausible.

Reviewer #3 (Public Review):

Bacteria sense and respond to multiple signals and cues to regulate gene expression. To define the complex network of signaling that ultimately controls transcription of many genes in cells requires an understanding of how multiple signaling systems can converge to effect gene expression and ensuing bacterial behaviors. The global transcription factor CRP has been studied for decades as a regulator of genes in response to glucose availability. It's direct and indirect effects on gene expression have been documented in E. coli and other bacteria including pathogens including Vibrio cholerae. Likewise, the master regulator of quorum sensing (QS), HapR), is a well-studied transcription factor that directly controls many genes in Vibrio cholerae and other Vibrios in response to autoinducer molecules that accumulate at high cell density. By contrast, low cell density gene expression is governed by another regulator AphA. It has not yet been described how HapR and CRP may together work to directly control transcription and what genes are under such direct dual control.

We thank the reviewer for their assessment of our work.

Using both in vivo methods with gene fusions to lacZ and in vitro transcription assays, the authors proceed to identify the smaller subset of genes whose transcription is directly repressed (7) and activated (2) by HapR. Prior work from this group identified the direct CRP binding sites in the V. cholerae genome as well as promoters with overlapping binding sites for AphA and CRP, thus it appears a logical extension of these prior studies is to explore here promoters for potential integration of HapR and CRP. Inclusion of this rationale was not included in the introduction of CRP protein to the in vitro experiments.

We understand the reviewer’s comment. However, the rationale for adding CRP was not that we had previously seen interplay between AphA and CRP (although this is a relevant discussion point, which we did make). Rather, we had noticed that there was an almost perfect CRP site perfectly positioned to activate PmurQP. Hence, CRP was added.

Seven genes are found to be repressed by HapR in vivo, the promoter regions of only six are repressed in vitro with purified HapR protein alone. The authors propose and then present evidence that the seventh promoter, which controls murPQ, requires CRP to be repressed by HapR both using in vivo and vitro methods. This is a critical insight that drives the rest of the manuscripts focus. The DNase protection assay conducted supports the emerging model that both CRP and HapR bind at the same region of the murPQ promoter, but interpret is difficult due to the poor quality of the blot.

There are areas of apparent protection at positions +1 to +15 that are not discussed, and the areas highlighted are difficult to observe with the blot provided.

We disagree on this point. The region between +1 and +15 is inherently resistant to attack by DNAseI and there are only ever very weak bands in this region (lane 1). Other than seeing small fluctuations in overall lane intensity (e.g. lanes 7-12 have a slightly lower signal throughout) the +1 to +15 banding pattern does not change. Conversely, there are dramatic changes in the banding pattern between around -30 and -60 (again, compare lane 1 to all other lanes). That CRP and HapR bind the same region is extremely clear. Also note that this is backed up by mutagenesis of the shared binding site (Figure 4c).

The model proposed at the end of the manuscript proposes physiological changes in cells that occur at transitions from the low to high cell density. Experiments in the paper that could strengthen this argument are incomplete. For example, in Fig. 4e it is unclear at what cell density the experiment is conducted.

Such details have been added to the figure legends and methods section.

The results with the wild type strain are intermediate relative to the other strains tested.

This is correct, and exactly what we would expect to see based on our model.

Cell density should affect the result here since HapR is produced at high density but not low density. This experiment would provide important additional insights supporting their model, by measuring activity at both cell densities and also in a luxO mutant locked at the high cell density. Conducting this experiment in conditions lacking and containing glucose would also reveal whether high glucose conditions mimicking the crp results.

We agree with this idea in principle but note that the output from our reporter gene, β- galactosidase, is stable within cells and tends to accumulate. This is likely to obscure the reduction in expression as cells transition from low to high cell density. Since we have demonstrated the regulatory effects of HapR and CRP both in vivo using gene knockouts, and in vitro with purified proteins, we think that our overall model is very well supported. Further experimental additions may provide an incremental advance but will not alter our overall story. Also note the unexpected increase in intracellular cAMP due to addition of glucose, in Vibrio fischeri (PMID: 26062003).

Throughout the paper it was challenging to account for the number of genes selected, the rationale for their selection, and how they were prioritized. For example, the authors acknowledged toward the end of the Results section that in their prior work, CRP and HapR binding sites were identified (line 321-22).

This is not quite what we say, and maybe the reviewer misunderstood, which is our fault. The prior work identified CRP sites whilst the current work identified HapR sites. We have made a slight alteration to the text to avoid confusion.

It is unclear whether the loci indicated in Table 1 all from this prior study. It would be useful to denote in this table the seven genes characterized in Figure 2 and to provide the locus tag for murPQ.

Again, we are unsure if we have confused the reviewer. The results in Table 1 are all HapR sites from the current work, not a prior study. However, some of these also correspond to CRP binding regions found in prior work.

The reviewer mentions “the seven genes characterised in Figure 2” but 23 targets were characterised in Figure 2a and 9 in Figure 2b. The “VC” numbers used in Figure 2 are the same as used in Table 1 so it is easy to cross reference between the two. We have added a footnote to Table 1, also referred to in the Figure 2 legend, to allow cross referencing between gene names and locus tags (including for murQP and hapR).

Of the 32 loci shown in Table 1, five were selected for further study using EMSA (line 322), but no rationale is given for studying these five and not others in the table.

This is not quite correct, we did not select 5 from the 32 targets listed in Table 1. We selected 5 targets from Table 1 that were also targets for CRP in our prior paper. This was the rationale.

Since prior work identified a consensus CRP binding motif, the authors identify the DNA sequence to which HapR binds overlaps with a sequence also predicted to bind CRP. Genome analysis identified a total of seven sites where the CRP and HapR binding sites were offset by one nucleotide as see with murPQ. Lines 327-8 describe EMSA results with several of these DNA sequences but provides no data to support this statement. Are these loci in Table 1?

This comment is a little difficult to follow, and we may have misunderstood, but we think that the reviewer is asking where the EMSA data referred to on lines 327-328 resides. We can see that the text could be confusing in this regard. We had referred to the relevant figure (Figure S6) on line 324 but did not again include this information further down in the description of the result. We have changed the text accordingly.

Using structural models, the authors predict that HapR repression requires protein-protein interactions with CRP. Electromobility shift assays (EMSA) with purified promoter DNA, CRP and HapR (Fig 5d) and in vitro transcription using purified RNAP with these factors (Figure 5e) support this hypothesis. However, the model proports that HapR "bound tightly" and that it also had a "lower affinity" when CRP protein was used that had mutations in a putative interaction interface. These claims can be bolstered if the authors calculate the dissociation constant (Kd) value of each protein to the DNA. This provides a quantitative assessment of the binding properties of the proteins.

The reviewer is correct that we do not explicitly provide a Kd. However, in both Figures 5d and 5e, we do provide very similar quantification. In 5d, our quantification is the % of the CRP-DNA complex bound by HapR (using either wild type or E55A CRP). Since the % of DNA bound is shown, and the protein concentrations are provided in the figure legend, information regarding Kd is essentially already present. In 5e, we show the % of maximal promoter activity. This is a reasonable way of quantifying the result. Furthermore, Kd is not a metric we can measure directly in this experiment that is not a DNA binding assay.

The concentrations of each protein are not indicated in panels of the in vitro analysis, but only the geometric shapes denoting increasing protein levels.

The protein concentrations are all provided in the figure legend. It is usual to indicate relative concentrations in the body of the figure using geometric shapes.

Panel 5e appears to indicate that an intermediate level of CRP was used in the presence of HapR, which presumably coincides with levels used in lane 4, but rationale is not provided.

There was no particular rationale for this, it was simply a reasonable way to do the experiment.

How well the levels of protein used in vitro compare to levels observed in vivo is not mentioned.

The protein concentrations that we use (in the nM to low μM range) are very typical for this type of work and consistent with hundreds of prior studies of protein-DNA interactions. The general rule of thumb is that 1000 molecules of a protein per bacterial cell equates to a concentration of around 1 μM. However, molecular crowding is likely to increase the effective concentration. Additionally, in vitro, where the DNA concentration is higher.

The authors are commended for seeking to connect the in vitro and vivo results obtained under lab conditions with conditions experienced by V. cholerae in niches it may occupy, such as aquatic systems. The authors briefly review the role of MurPQ in recycling of the cell wall of V. cholerae by degrading MurNAc into GlcNAc, although no references are provided (lines 146-50). Based on this physiology and results reported, the authors propose that murPQ gene expression by these two signal transduction pathways has relevance in the environment, where Vibrios, including V. cholerae, forms biofilms on exoskeleton composed of GlcNAc.

We have added a citation to the section mentioned.

The conclusions of that work are supported by the Results presented but additional details in the text regarding the characteristics of the proteins used (Kd, concentrations) would strengthen the conclusions drawn. This work provides a roadmap for the methods and analysis required to develop the regulatory networks that converge to control gene expression in microbes. The study has the potential to inform beyond the sub-filed of Vibrios, QS and CRP regulation.

As noted above, quantification essentially equivalent to Kd is already shown (% of bound substrate is indicated in figures and all protein concentrations are given in the figure legends).

Reviewer #1 (Recommendations For The Authors):

1.  As similar experiments have been performed in other Vibrios, it would be interesting to do a more detailed analysis of the similarities and differences between the species. Perhaps a Venn diagram showing how many targets were found in all studies versus how many are species specific.

We appreciate this suggestion but would prefer not to make this change. A cross-species analysis would be very time consuming and is not trivial. The presence and absence of each target gene, for all combinations of organisms, would first need to be determined. Then, the presence and absence of binding signals for HapR, or its equivalent, would need to be determined taking this into account. For most readers, we feel that this analysis is unlikely to add much to the overall story. Given the amount of effort involved, this seems a “non-essential” change to make.

2.  Line 101-Are the FLAG tagged versions of LuxO and HapR completely functional? Can they complement a luxO or hapR deletion mutant?

The activity of FLAG tagged HapR (LuxR in other Vibrio species) has been shown previously (e.g. PMIDs 33693882 and 23839217). Similarly, N-terminal HapR tags are routinely used for affinity purification of the protein without ill effect. We have not tested LuxO-3xFLAG for “full” activity, though this fusion is clearly active for DNA binding, the only activity that we have measured here, since all know targets are pulled down.

3.  Line 106-As the authors state later that there are additional smaller peaks for HapR that could be other direct targets, I think a brief mention of the methodology used to determine the cutoff for the 5 and 32 peaks for LuxO and HapR, respectively, would be informative here.

We have added a little more text to the methods section. The added text states “Note that our cut- off was selected to identify only completely unambiguous binding peaks. Hence, weak or less reproducible binding signals, even if representing known targets, were excluded (see Discussion for further details)”.

4.  Line 118-Need a reference here to the prior HapR binding site.

This has been added.

5.  Figs. 1e-What do the numbers on the x-axis refer to? Why not just present these data as bases? The authors also refer to distance to the nearest start codon, but this is irrelevant for 4/5 of the luxO targets as they are sRNAs. They should really refer to the distance to the transcription start site. Likewise, for HapR, distance to the nearest start codon is not as informative as distance to the nearest transcription start site. A recent paper used transcriptomics to map all the transcription start sites of V. cholerae, and these results should be integrated into the author's study rather than just using the nearest start codon (PMID: 25646441).

The numbers are kilo base pairs, this has been added to the axis label. We have also changed “start codon” to “gene start” (since “gene start” is also suitable for genes that encode untranslated RNAs).

Re comparing binding peak positions to transcription start sites (TSSs) rather than gene starts, this analysis would be useful if TSSs could be detected for all genes. However, some genes are not expressed under the conditions tested by PMID: 25646441, so no TSS is found. Consequently, for HapR or LuxO bound at such locations, we would not be able to calculate a meaningful position relative to the TSS. We stress that the point of the analysis is to determine how peaks are positioned with respect to genes (i.e. that sites cluster near gene 5’ ends). Also note that nearest TSSs are now shown in the revised Table 1. In some cases, these are unlikely to be the TSS actually subject to regulation (e.g. because the regulated gene is switched off).

6.  Fig. 1e-Is there directionality to the site? In other words, if a HapR binding site is located between two genes that are transcribed in opposite directions, is there a way to predict which gene is regulated? It looks like this might be the case with the list presented in Table 1, but how such determination is made and what the various symbol in Table 1 mean are not clear to me. This also has ramifications for Fig. 2a as the direction to construct the fusion is critical for the experiment.

The site is a palindrome so lacks directionality. The best prediction re regulation is likely to be positioning with respect to the nearest TSS (which is now included in Table 1). However, this would remain just a prediction and, where TSSs are in odd locations with respect to binding sites (taking into account the caveats above) predictions would be unreliable.

We are unsure which symbol the reviewer refers to in Table 1, a full explanation of any symbols used is provided in the table footnotes.

With respect to Figure 2a, if sites were between divergent genes, and met our other criteria, we tested for regulation in both directions. For example, see the divergent genes VCA0662 (classified inactive) and VCA0663 (classified repressed).

7.  Fig. 2a-It is a little disappointing that the authors use LacZ fusions to measure transcription as this is not the most sensitive reporter gene. Luciferase gene fusions would have been much more sensitive. Also, did the authors examine multiple time points. The methods only describe "mid-log phase" but some of the inactive promoters could be expressed at other time points. Also, it would be simple to repeat this experiment in different media, such as minimal with glucose or another non- CRP carbon source, to expand which promoters are expressed.

The reviewer is correct regarding the sensitivity of β-galactosidase, which is very stable and so accumulates as cells grow. Even so, this reporter has been used very successfully, across thousands of studies, for decades. We did not examine multiple timepoints. We appreciate that the 23 promoter::lacZ fusions could be re-examined using varying growth conditions but this is unlikely to impact the overall conclusions, though it could generate some new leads for future work.

8.  Fig. 2a legend-typos

This has been corrected.

9.  Line 138-I think you mean Fig. 2a here.

This has been corrected.

10.  Fig. 2b and many additional figures quantify band intensity but do not show any replication or error. Therefore, it is impossible to gauge reproducibility of these experiments.

We have added a reproducibility statement (all experiments were done multiple times with similar results) as is standard throughout the literature. Also note that there is a lot of internal replication between figures. Figure 4d and Figure 5e lanes 1-9 show essentially the same experiment (albeit with slightly different protein concentrations) and very similar results. To the same effect, Figure 5e lanes 10-18 and lanes 19-27 show the same experiment for two different mutations of the same CRP residue. Again, the results are very similar. Also see the response to your comment 15 below.

11.  Fig. 4a-lanes 2-4-the footprint does not change with additional CRP. In other words, it looks the same at the lowest concentration of CRP versus the highest concentration of CRP. The footprints for HapR look similar. This is somewhat troubling as in these types of experiments one would like to observe a dose dependent change in the footprint correlating with more DNA occupancy.

For CRP we agree but are not concerned at all by this. The site is simply full occupied at the lowest protein concentration tested. Given that the footprint exactly coincides with a near consensus CRP site (which, when mutated, abolishes CRP binding in EMSAs, and regulation by CRP in vivo) all our results are perfectly consistent. Note that i) our only aim in this experiment was to determine the positions of CRP and HapR binding ii) our conclusions are independently backed up using gel shifts and by making promoter mutations. With respect to HapR, there are changes at the periphery of the main footprint.

12.  Fig. 4e-Why does the transcriptional activation of murQP decrease with increasing concentrations of CRP? This is also seen in Fig. 5e.

In our experience, this often does happen when doing in vitro transcription assays (with CRP and many other activators). The anecdotal explanation is that, at higher concentrations, the regulator can start to bind the DNA non-specifically and so interfere with transcription.

13. The authors demonstrate in vitro that HapR requires binding of CRP to bind the murQP promoter. It would strengthen their model if they demonstrated this in vivo. To do this, the authors only need to repeat their ChIP-Seq experiment in a delta CRP mutant and the HapR signal at murQP would be lost. In fact, such an experiment would experimentally confirm which of the in vivo HapR binding sites are CRP dependent.

We agree, appreciate the comment, and do plan to do such experiments in the future as a wider assessment of interactions between transcription factors. However, doing this does have substantial time and resource implications that we cannot devote to the project at present. We feel that our overall conclusions, regarding co-operative interactions between HapR and CRP at PmurQP, are well supported by the data already provided. This also seems the overall opinion of the reviewers.

14.  Fig. 5b-I am confused by the Venn diagram. The text states that "In all cases, the CRP and HapR targets were offset by 1 bp", but the diagram only shows 7 overlapping sites. The authors need to better describe these data.

We mean that, in all cases where sites overlap, sites are offset by 1 bp (i.e. we didn’t find any sites

overlapping but offset by 2, 3 4 bp etc).

15. Line 287-288 and Fig. 5d-The authors state that HapR binds with less affinity to the CRP E55A mutant protein bound to DNA. There does seem to be a difference in the amount of shifted bands at the equivalent concentrations of HapR, but the difference is subtle. In order to make such a conclusion, the authors should show replication of the data and calculate the variability in the results. The authors should also use these data to determine the actual binding affinities of HapR to WT and the E55A mutant CRP, along with error or confidence intervals.

All of these experiments have been run multiple times and we are absolutely confident of the result. With respect to Figure 5d, this was done many times. We note that not all experiments were exact repeats. E.g. some of the first attempts had fewer HapR concentrations. Even so, the defect in HapR binding to the CRP E55A complex was always evident. The two gels to the left show the final two iterations of this experiment (these are exact repeats). The top image is that shown in Figure 5d. The lower image is an equivalent experiment run a day or so previously. Both clearly show a defect in HapR binding to the CRP E55A complex. We appreciate that our conclusion re these experiments is somewhat qualitative (i.e. that HapR binds the CRP E55A complex less readily) but this is not out of kilter with the vast majority of similar literature and our results are clearly reproducible.

16.  Fig. 6a-It is odd that the locked low cell density mutants have such a growth defect in MurNAc, minimal glucose, and LB. To my knowledge, such a growth defect is not common with these strains. Perhaps this has to do with the specific growth conditions used here, but I can't find that information in the manuscript (it should be there). Furthermore, the growth rate of the luxO and hapR mutants appears to be similar up to the branch point (i.e. slope of the curve), but the lag phage of the luxO mutant is much longer. The authors need to address these issues in relationship to previous published literature and specify their growth conditions because the results are not consistent with their simple model described in Fig 6b.

This comment is a little difficult to pick apart as it covers several different issues. We’ll try and

answer these individually.

a)     The unusual “biphasic growth curve with hapR and hapRluxO cells: We do not know why cells lacking hapR have a growth curve that appears biphasic. We can only assume that this is due to some regulatory effect of HapR, distinct from the murQP locus. Despite the unusual shape of the growth curve, the data are consistent with our conclusions.

b)     The extended lag phase of the luxO mutant in minimal media + MurNAc: We appreciate this comment and had considered possible explanations prior to submission. In the end, we left out this speculation but are happy to include it as part of our response. The extended lag phase might be expected if CRP/HapR regulation is largely critical for controlling the basal transcription of murQP. The locus is likely also regulated by the upstream repressor MurR (VC0204) as in E. coli. So, if deprepression of MurR overwhelms the effect of HapR on murQP, we think you would expect that once the cells start growing on MurNAc, the growth rates are unchanged. But the extended lag is due to the fact that it took longer for those cells to achieve the critical threshold of intracellular MurNAc-6-P necessary to drive murR derepression. Obviously, we can not provide a definitive answer.

c)     We have added further details regarding growth conditions to the methods section and the Figure 6a legend.

17.  Fig. S6-The data to this point with murPQ suggested a model in which CRP binding then enabled HapR binding. But these EMSA suggest that both situations occur as in some cases, such as VCA0691, HapR binding promotes CRP binding. How does such a result fit with the structural model presented in Fig. 5?

This is to be expected and is fully consistent with the model. Cooperativity is a two-way street, and each protein will stabilise binding of the other. Clearly, it will not always be the case that the shared DNA site will have a higher affinity for CRP than HapR (as at PmurQP). Depending on the shared site sequence, expected that sometimes HapR will bind “first” and then stabilise binding of CRP.

18. Line 354-356-The HCD state of V. cholerae occurs in mid-exponential phase and several cell divisions occur before stationary phase and the cessation of growth, at least in normal laboratory conditions. Therefore, there is not support for the argument that QS is a mechanism to redirect cell wall components at HCD because cell wall synthesis is no longer needed.

We did not intent to suggest cell wall synthesis is not needed at all, rather that there is a reduced need. We made a slight change to the discussion to reflect this.

19. Line 357-360-Again, as stated in point 16, the statement that cells locked in the HCD are "defective for growth" is an oversimplification. The luxO mutants have a longer lag phage, but they actually outgrow the hapR mutants at higher cell densities and reach the maximum yield much faster.

In fairness, we do go on to specify that the defect is an extended lag phase. Also see our response above.

Reviewer #2 (Recommendations For The Authors):

Comments to improve the text

1)  Line 103-106, line 130, line 136, etc. Details of the methods and the text directing to presentations of figures should be in the methods and/or figure legends with (Figure x) in citation after the statement. The sentences in lines indicated can be deleted from the results. Although several lines are noted specifically here, this comment should be applied throughout the entire results section.

We appreciate this comment but would prefer not to make this change (it seems mainly an issue of personal stylistic choice). It is sometimes helpful for the reader to include such information as it avoids them having to cross reference between different parts of the manuscript.

2)  Line 115. Recommend a paragraph between content on LuxO and HapR (before "Of the 32 peaks for HapR binding")

We agree and have made this change.

3)  Line 138 and Figure 1a. I am not convinced this gel shows that VC1375 is activated by HapR. Is the arrow pointing to the wrong band? There does seem to be an induced band lower down.

We understand this comment as it is a little difficult to see the induced band. This is because this is a compressed area of the gel and the transcript is near to an additional band.

4)  Line 147. Add the VC0206-VC0207 next to murQP (and the gene name murQP into Table 1).

We have added the gene name to the figure foot note. The text has been changed as requested.

5) Methods. It is essential for this paper to have detailed methods on the bacterial growth conditions. Referring to prior paper, bacteria were grown in LB (add composition...is this high salt LB often used for vibrios or low salt LB often used for E. coli). Growth is to "mid log". Please provide the OD at collection. Is mid log really considered "high density". Provide a reference regarding HapR activity at mid log to support the method. Could the earlier collection of bacteria account for missing known HapR regulated genes? In preparing the requested ç, include growth conditions for other experiments in the legends.

Note that we have included a new supplementary table, rather than a Venn diagram. We have also added further details of growth conditions as mentioned above. Also not that, for the ChIP-seq, HapR and LuxO were expressed ectopically and so uncoupled from the switch between low and high cell density.

6)  Content of Table 1, HapR Chip-seq peaks, needs to be closely double checked to the collected data as there seems to be some errors. Specifically, VC0880 and VC0882 listed under Chromosome I are most likely VCA0880 (MakD) and VCA0882 (MakB), both known HapR induced genes on Chromosome II with VCA0880 previously validated by EMSA. This notable error suggests the table may have other errors and thus requires a very detailed check to assure its accuracy.

We appreciate the attention to detail! We have double checked, thankfully this is not an error, the table is correct (even so, we have also checked all other entries in the table). As an aside, VCA0880 is one of the locations for which we see a weak HapR binding signal below our cut-off (included in the new Table S1). In cross checking between Table 1 and all other data in the paper we noticed that we had erroneously included assay data for VC0620 in Figure 2A. This was not one of our ChIP-seq targets but had been assayed at the same time several years ago. This datapoint, which wasn’t related to any other part of the manuscript, has been removed.

If VCA0880 and VCA0882 are correctly placed on Chr. I, then add comment to text that the Mak toxin genomic island found on Chromosome II in N16961 is on Chr. I in E7946. (See recent references PMID: 30271941, 35435721, 36194176, 34799450).

See above, this is not an error.

7)  Alternatively for both comments 8 & 9, are these problems of present/missing genes or misannotations the result of the annotation of E7946 gene names not aligning with gene names of N16961? (if so, in Table 1, please give the gene name as in E7946 but include a separate column with the N16961 name for cross study comparison)

See above and below, this is not an issue.

8)  Line 126-127. Also regarding Table 1, please add a column with function gene annotation. For example, VC0916 needs to be identified as vpsU. If function is unknown, type unknown in the column. This will help validate the approach of selecting "HapR target promoters where adjacent coding sequence could be used to predict protein function."

We added an extra column to Table 1 in response to a separate reviewer request (TSS locations). This leaves no space for any additional columns. Instead, to accommodate the reviewer’s request, we have added alternative gene names to the footnote.

Not following up on VCA0880 (promoter for the mak operon) is a sad missed opportunity here as it is one of the most strongly upregulated genes by HapR (PMC2677876)

As noted above, this was not an error and VCA0880 was not one of our 32 HapR targets. As such, we would not have followed this up.

9)  Figure Legends. Add a unit to the bar graphs in Figure 1e (should be kb??) This has been corrected.

10) The yellow color text labels in figures 3c, 4a, 4c are difficult to read. Can you use an alternative darker color for CRP.

We have made this slightly darker (although to our eye it is easily reliable). We haven’t changed the colour too much, for consistency with colour coding elsewhere.

11) Figure S3. Binding is misspelled. Add units to the x-axis

This has been corrected.

12) Figure S7. The text in this figure is too small to read. Figure could be enlarged to full page or text enlarged. Are these 4 the only other known regulated promoters? Could all the known alternative promoters linked to HapR be similarly probed?

We have increased the font size and included a new Table S1 for all previously proposed HapR sites.

13) Figure S8. Original images..are any of these the replicate gels (see public comment 6)

We have added a statement regarding reproducibility, and also note the internal reproducibility between different figures in our reviewer response. The gels in Figure S8 are full uncropped versions of those shown in the main figures.

Reviewer #3 (Recommendations For The Authors):

None

Whilst there were no specific recommendations from this reviewer, we have still responded to the public review and made changes if required.

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

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Reply to the reviewers

Dear Editor and reviewers,

Thank you very much for the thorough assessment of our manuscript. We have carefully considered the comments and reflected most of them in the new version. We recognized the need to shorten and clarify the manuscript. Therefore, we have omitted particularly the less important passages concerning metabolism and the loss of genes encoding mitochondrial proteins, which cut the text by six pages in the current layout. We have also removed the text relating this model to eukaryogenesis. Finally, we have slightly changed the structure and linked the different sections to improve the flow of the story and to emphasize the key messages, which are the absence of mitochondria in a large proportion of oxymonads and the impact of this loss, loss of Golgi stacking and transformation to endobiotic lifestyle on selected gene inventories. We hope the manuscript is now clear and more concise and will be of interest to a broad readership interested in the evolution of eukaryotes, mitochondria and protists.

1. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity):

This is a very interesting paper that investigates through detailed comparative genomics the tempo and mode of the evolution of microbial eukaryotes/protists members of the Metamonada with a focus on Preaxostyla, currently the only known lineage among eukaryotes to have species that have lost, by all accounts, the mitochondria organelle all together. Notably, it includes a free-living representative of the lineage allowing potential interesting comparison between lifestyles among the Preaxostyla. This is a generally nicely crafted manuscript that presents well supported conclusions based on good quality genome sequence assemblies and careful annotations. The manuscript presents in particular (i) additional evidence for the common role of LGT from various bacterial sources into eukaryotic lineages and (ii) more details on the transition from a free-living lifestyle to an endobiotic one and (iii) the related evolution of MROs and associated metabolism.

Thank you very much for the positive assessment.

I have some comments to improve a few details:

In the introduction, lines 42-43, the last sentence should be more conservative by replacing "whole Oxymonadida" with "...all known/investigated Oxymonadida".

The sentence has been changed to: "Our results provide insights into the metabolic and endomembrane evolution, but most strikingly the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species (M. exilis, B. nauphoetae, and Streblomastix strix) extending the amitochondriate status to all investigated Oxymonadida."

Similarly on line 62, the sentence could state "... contain 140 described...".

The sentence has been changed to: "Oxymonadida contain approximately 140 described species of morphologically divergent and diverse flagellates exclusively inhabiting digestive tracts of metazoans, of which none has been shown to possess a mitochondrion by cytological investigations (Hampl 2017)."

When discussing the estimated completeness of the genome are discussed (lines 117-120) and contrasted with the values for Trypanosoma brucei and other genomes, the author should explicitly state that these genomes are considered complete, which seems is what they imply, is that the case? If so, please provide more details to support this idea.

We have elaborated on this part also in reaction to comments of other reviewers. The text now reads: "It should be noted that, despite their wide usage, BUSCO values are not expected to reach 100% in lineages distant from model eukaryotes simply due to the true absence (or high sequence divergence) of some of the assessed marker genes. For example, various Euglenozoa representatives with highly complete genome sequences, including Trypanosoma brucei, have BUSCO completeness estimates in the range of 71-88% (Butenko et al. 2020), and representatives of Metamonada fall within the range of 60-91% (Salas-Leiva et al. 2021). Specifically in the case of oxymonad M. exilis, the improvement of the genome assembly using long-read resequencing from 2092 scaffolds to 101 contigs led to only a marginal increase of BUSCO value from 75.3 to 77.5 (Treitli et al. 2021). "

Also please see the detailed table prepared in response to reviewers 2 and 3 summarizing the presence/absence of genes from BUSCO set in the selected representatives of Metamonada and Trypanosoma brucei. The table is commented in the answer to Reviewer 3 comment (page 18)

The supplementary file named "132671_0_supp_2540708_rmsn23" is listed as a Table SX? (note: I found it rather difficult to establish exactly what file corresponds to what document referred in the main text)

We apologize for this mistake. We have checked and corrected references to tables, figures and supplementary material throughout the manuscript and hope it now does not contain any errors.

Lines 243-245, where 46 LGTs are discussed, it is relevant that the authors investigate their functional annotations. Indeed, it is suggested that these could have adaptive values, hence investigating their functional annotation will allow the authors to comment on this possibility in more details and precision. When discussing LGTs it would also be very useful to cite relevant reviews on the topic - covering their origins, functional relevance when known, distribution among eukaryotes. This is done when discussing the evolution and characteristics of MROs but not when discussing LGTs, with several reviews cited and integrated in the discussion of the data and their interpretation.

Available annotations of all putative LGT genes are provided in Supplementary_file_3 and also in the Supplementary_file_6 if the gene belongs to a manually annotated cellular system. Although we agree with the reviewer that the discussion of 46 species-specific LGTs might be interesting, for the sake of conciseness and brevity of the manuscript, we have decided not to expand the discussion further. However, note that we discuss selected cases of P. pyriformis-specific LGTs in the part “P. pyriformis possesses unexpected metabolic capacities” which follows right after the lines reviewer is referring to.

The sentence, lines 263-265, where the distribution of some LGTs are discussed, needs to be made more precise. When using the work "close" the authors presumably refer to shared/similar habitat,s or else? Entamoeba is not a close relative to the other listed taxa.

The “close relatives” mentioned in the text were meant as close relatives of all p-cresol-synthesizing taxa discussed in the paragraph, including Mastigamoeba, i.e. a specific relative of Entamoeba. We have modified the text such as to make the intended meaning easier to follow.

Lines 346-348, that sentence needs to end with a citation (e.g. Carlton et al. 2007).

The citation proposed by the reviewer has been added. The sentence was changed to: " The most gene-rich group of membrane transporters identified in Preaxostyla is the ATP-binding cassette (ABC) superfamily represented by MRP and pATPase families, just like in T. vaginalis (Carlton et al. 2007). "

In the paragraph (line 580-585) discussing ATP transporters, note that Major et al. (2017) did not describes NTTs but distantly related members of MSF transporter, shared across a broader range of organisms then the NTTs. Did the authors check if the genome of interest encoded homologues of these transporters too?

The citation has been removed; we admit that it was not the most appropriate one in the given

context. Concerning the NTT-like transporters, encouraged by the reviewer we searched for them in the Preaxostyla genome and transcriptome assemblies and found no candidates. This is not explicitly stated in the revised manuscript. The paragraph now reads: “MROs export or import ATP and other metabolites typically using transporters from the mitochondrial carrier family (MCF) or sporadically by the bacterial-type (NTT-like) nucleotide transporters (Tsaousis et al. 2008). We did not identify any homolog of genes encoding proteins from these two families in any of the three oxymonads investigated. In contrast, MCF carriers, but not NTT-like nucleotide transporters, were recovered in the number of four for each P. pyriformis and T. marina (Supplementary file 6).“

Line 920-921, I don't understand how the number 30 relates to "guarantee" inferring the directionality of LGTs events. This will be very much dataset dependent, 100 sequences might still not allow to infer directionality of LGT events. The authors probably meant to "increase the possibility to infer directionality".

We agree the original wording has not been particularly fortunate, so the sentence has changed to: "Files with 30 sequences or fewer were discarded, as the chance directionality of the transfer can be determined with any confidence is low when the gene family is represented by a small number of representatives."

Reviewer #2 (Evidence, reproducibility and clarity):

Using draft genome sequencing of the free-living Paratrimastix pyriformis and the sister lineage oxymonad Blattamonas nauphoetae, Novack et al. infer the metabolic potential of the two protists using comparative genomics. The authors conclude that the common oxymonad ancestor lost the mitochondrion/mitosome and discuss general strategies for adapting to commensal/symbiotic life-style employed by this taxon. Some elaborations on pathways go on for several paragraphs and feel unnecessarily stretched, which made those sections of the paper rather difficult to digest.

Having seen reflections on the manuscript by three reviewers we carefully reconsidered its content and attempted to make it shorter and more compact by removing some of the less substantial material. Namely, we have dispensed completely with the original last section of Results and Discussion (“No evidence for subcellular retargeting of ancestral mitochondrial proteins in oxymonads”) and made various cuts throughout other sections. We hope that the revised version makes a substantially better job of delivering the key messages of our study to the readers compared to the original submission.

This might be also be because the work, and all conclusions drawn, depend entirely on incomplete (ca. 70-80%) genome data and simple similarity searches, and e.g. no kind of biochemistry or imaging is presented to underpin the manuscripts discussion.

This is a very crude and superficial assessment of our data. We have actually good reasons to believe that the genome assemblies are close to complete. Please see the discussion on this topic below and an answer to a particular comment from reviewer 3 (page 18).

This is noteworthy in light of other protist genome reports published in the last few years that differ in this respect, including previous work by this group. And for sequencing-only data, this paper - https://doi.org/10.1016/j.dib.2023.108990 - might offer an example of where we are at in 2023.

Frankly, we do not think it is fair or relevant to compare our study to the paper pointed to by the reviewer, as that paper reports on a metagenomic study that delivers a set of metagenomically assembled genomes (MAGs) of varying quality retrieved from environmental DNA samples without providing any in-depth analysis of the gene content. Our study is very different in its scope and aims, and we are not certain what lesson we should take from this reviewer’s point. We have good reasons to believe that the datasets are close to complete. Please see the discussion on this topic below and answer to comment of reviewer 3 (page 18).

With respect to previous work of the group (Karnkowska et al. 2016 and 2019), this submission is very similar (analysis pattern, even some figures and more or less the conclusion), i.e. to say, the overall progress for the broader audience is rather incremental. Then there are also some incidents, where the data presented conflicts with the author‘s own interpretation.

It was our intention to use the previous analytical experiences and approaches, which at the same time makes the new results comparable with those published before. Although the format is intentionally similar, this work is a substantial step forward because only with our present study the amitochondrial status of the large part of Oxymonadida group can be considered solidly established. This in turn allows us to estimate the timing of the loss of mitochondrion (more than 100 MYA) demonstrating that the absence of mitochondrion in this group is not an episodic transient state but a long-established status. We do not understand what exactly the reviewer had in mind when pointing to “incidents, where the data presented conflicts with the author‘s own interpretation” – we are not aware of such cases.

The text (including spelling and grammar) needs some attention and the choice of words is sometimes awkward. The overuse of quotation marks ("classical", "simple", "fused", "hits", "candidate") is confusing (e.g. was the BLAST result a hit or a "hit").

The whole text has been carefully checked and the language corrected whenever necessary by a one of the co-authors, who is a native English speaker. The use of quotation marks has been restricted as per the reviewer’s recommendation.

In its current formn the manuscript is, unfortunately, very difficult to review. This reviewer had to make considerable efforts to go through this very large manuscript, mainly because of issues affecting to the presentation and the lack of clarity and conciseness of the text. It would be greatly appreciated if the authors would make more efforts upfront, before submission, to make their work more easily accessible both to readers and facilitate the task of the reviewers.

We admit that the story we are trying to tell is a complex one, consisting of multiple pieces whose integration into a coherent whole is a challenging task. As stated above, the reports provided by the reviewers provided us with an important stimulus, leading us to substantially modify the manuscript to make it more concise, less ambiguous when it comes to particular claims, and easier to read. We hope this intention has been fulfilled to a larger degree.

About a fifth of the two genome is missing according the authors prediction (table 1). Early on they explain the (estimated) incompleteness of the genomes to be a result from core genes being highly divergent. In light of this already suspected high divergence, using (the simplest NCBI) sequence similarity approach to call out the absence of proteins (for any given lineage) may need lineage-specific optimization. The use of more structural motif-guided approaches such as hidden Markov models could help, but it is not clear whether it was used throughout or only for the search for (missing) mitochondrial import and maturation machinery. The authors state that the low completeness numbers are common among protists, which, if true, raises several questions: how useful are then such tools/estimates to begin with and does this then not render some core conclusions problematic? The reader is just left with this speculation in the absence of any plausible explanation except for some references on other species for which, again, no context is provided. Do they have similar issues such as GC-content, same core genes missing, phylogenetic relevance?, etc.. No info is provided, the reader is expected to simply accept this as a fact and then also accept the fact that despite this flaw, all conclusions of the paper that rests on the presence/absence of genes are fine. This is all odd and further skews the interpretations and the comparative nature of the paper.

The question of the completeness of the data sets was raised also by reviewer 3 and we would like to provide an explanation at this point. First, it should be stated that there is no ideal and objective way how to measure the completeness of the eukaryotic genomic assembly. In the manuscript, we have used the best established method, adopted by the community at large, which is based on the search for a set of „core eukaryotic genes“ using a standardized pipeline BUSCO or previously popular CEGMA. The pipeline uses its own tools to identify the homologues of genes/proteins which ensures standardization of the procedure. This answers the question of reviewer 2, why we have not used more sensitive tools for these searches. We did not use them, because we followed the procedure that is the gold standard for such assessments, for comparability with other genomes and to make this as clear to the reader as possible. Although the result of the pipeline is usually interpreted as the completeness of the assembly, this is a simplification. Strictly speaking, the result is a percentage of the genes from the set of 303 core eukaryotic genes (in our case) which were detected in the assembly by the pipeline. Even in complete assemblies, the value is usually below 100% because some of the genes are not present in the organism and some diverged beyond recognition. We do not see any other way how to deal with this drawback than to compare with related complete genome assemblies acting as standards. This we have done in Supplementary file 11, where we list the presence/absence of each gene for Preaxostyla species and three highly complete assemblies of Trypanosoma brucei, Giardia intestinalis and Trichomonas vaginalis. T. brucei and G. intestinalis are assembled into chromosomes. As you can see, in these three „standards“ 63, 148 and 77 genes from the core were not detected resulting in BUSCO completeness values of 79%, 51% and 75%, respectively. 18 of the non-detected genes function in mitochondria (shown in red), which are highly reduced in some of these species, so the absence of the respective genes is therefore expected. Simply not considering these genes would increase the “completeness measure” for oxymonads by 6%. The values for our standards are not higher than the values for Preaxostyla (69-82%). In summary, the BUSCO incompleteness measure is far from ideal, particularly in these obscure groups of eukaryotes. The values received for Preaxostyla give no reason for concern about their incompleteness. See also our answer to reviewer 3 (page 18).

At the same time, we admit that the BUSCO values do not confirm the high completeness of our assemblies. So, why do we think they are highly complete? One reason is that we do not see suspicious gaps in any of the many pathways which we annotated but the main reason is the high contiguity of the assemblies. Thanks to Nanopore long read sequencing, the assembly of P. pyriformis and B. nauphoetae compose of 633 and 879 scaffolds, suggesting that there are “only” hundreds of gaps. Although this may still sound too much, it is a relatively good achievement for genomes of this size and the experience shows that a further decrease in the number of scaffolds would allow the detection of additional genes but not in huge numbers. As we have shown for M. exilis (Treitli et al. 2021, doi:10.1099/mgen.0.000745) the decrease from 2 092 scaffolds to 101 contigs, i.e., filling almost 2 000 gaps, allowed the prediction of additional 1 829 complete gene models, of which 1 714 were already present in the previous assembly but only partially and just 115 were completely new. None of these newly predicted genes was functionally related to the mitochondrion. Thus, we infer the chance that all mitochondrion-related genes are hidden in the gaps of assemblies is very low.

We have provided these arguments in a condensed form in the text following the description of genome assemblies: “It should be noted that, despite their wide usage, BUSCO values are not expected to reach 100% in lineages distant from model eukaryotes simply due to the true absence (or high sequence divergence) of some of the assessed marker genes. For example, various Euglenozoa representatives with highly complete genome sequences, including Trypanosoma brucei, have BUSCO completeness estimates in the range of 71-88% (Butenko et al. 2020), and representatives of Metamonada fall within the range of 60-91% (Salas-Leiva et al. 2021). Specifically in the case of oxymonad M. exilis, the improvement of the genome assembly using long-read resequencing from 2092 scaffolds to 101 contigs led to only a marginal increase of BUSCO value from 75.3 to 77.5 (Treitli et al. 2021).”

As a side note, this will also influence the number of proteins absent in other lineages and as such has consequences on LGT calls versus de novo invention. For the cases with LGT as an explanation, it would help to briefly discuss the candidate donors and some details of the proteins in the eco-physiological context (e.g. lines 263-268 suggest that HPAD may have been acquired by EGT which was facilitated by a shared anaerobic habitat and also comment on adaptive values for acquiring this gene). Exchanging metabolic genes via LGT (Line 163) blurs the differences between roles and extent of LGT in prokaryote vs eukaryote, and therefore is exciting and could use support/arguments other than phylogenies. I guess the number of reported LGTs among protists (whatever the source) over the last decade has by now deflated the novelty of the issue in more general; a report of the numbers is expected but they alone won't get you far anymore in the absence of a good story (such as e.g. work on plant cell wall degrading enzymes in beetles).

We agree with the reviewer that the cases of LGT involving Preaxostyla would deserve more discussion in the manuscript. On the other hand, we also agree that none of them provides such a “cool” story that would deserve a special chapter or even a separate paper. Therefore, we have decided, also with regard to keeping the text in a reasonable dimension, not to expand the discussion of LGTs with the exception of HgcAB, where some new information has been included and the phylogeny of the genes updated. Please note that we had discussed in the original manuscript the donor lineages and ecological/biochemical context in the cases of GCS-L2, HPAD, UbiE, and NAD+ synthesis and this material has been kept also in the revised version.

It would help to clarify which parts of the mitochondrial ancestor were reduced during the process of reductive evolution at what time in their hypothesized trajectory. For instance, loosing enzymes of anaerobic metabolism conflicts with the argued case of an aerobic (as opposed to facultative anaerobic) mitochondrial ancestor followed by gains of anaerobic metabolism in the rest of the eukaryotes via LGT, and some papers the authors themselves cite (e.g. the series by Stairs et al.). There is no coherent picture on LGT and anaerobic metabolism, although a reader is right to expect one.

These are very interesting questions, that would fill a separate article. In the manuscript, we focus on the Preaxostyla lineage only and there the trajectory seems relatively simple: replacement of the mitochondrial ISC by cytosolic SUF in the common ancestor of Preaxostyla, loss of methionine cycle and in in consequence mitochondrial GCS and the mitochondrion itself. We have modified the first conclusion paragraph in this sense and it now reads the following:

“The switch to the SUF pathway in these species has apparently not affected the number of Fe-S-containing proteins but led to a decrease in the usage of 2Fe-2S clusters. The loss of MRO impacted particularly the pathways of amino acid metabolism and might relate also to the loss of large hydrogenases in oxymonads. “

It is not clear to us how to understand the reviewer’s remark concerning the conflict between loss of enzymes of anaerobic metabolism and the (presumed) aerobic nature of the mitochondrial ancestor. Provided that we read the reviewer’s rationale correctly, is it really so implausible that the anaerobic metabolism gained laterally by a particular lineage was then secondarily lost in specific descendant lineages? As a clear example demonstrating the feasibility of such an evolutionary pattern consider the evolution of plastids. There is no doubt these organelles move across eukaryotes by secondary or higher-order endosymbiosis or kletoplastidy, establishing themselves in lineages where there was no plastid before. Secondary simplification of such plastids, e.g. by the loss of photosynthesis, in its extreme form culminating in the complete loss of the organelle, has been robustly documented from several lineages, such as Myzozoa (e.g., https://pubmed.ncbi.nlm.nih.gov/36610734/). Hence, we see absolutely no reason to rule out the possibility that the ancestral mitochondrion was obligately aerobic and enzymes of anaerobic metabolism spread secondarily by eukaryote-to-eukaryote LGT, with their secondary loss in particular lineages. We really do not see any conflict here and we do not agree with the interpretation provided by the reviewer. That said, we admit that the discussion on the earliest stages of mitochondrial evolution is not an essential ingredient of the story we try to tell in our manuscript, so to avoid any unnecessary misunderstanding we have removed the original last sentence of Conclusions (“Thorough searches revealed …”) from the revised manuscript.

In light of their data the authors also discuss the importance of the mitochondrion with respect to the origin of eukaryotes:

First, the mitochondrion brought thousands of genes into the marriage with an archaeon, surely hundreds of which provided the material to invent novel gene families through fusions and exon shuffling and some of which likely went back and forth over the >billion years of evolution with respect to localizations. The authors look at a minor subset of proteins (pretty much only those of protein import, Fig. 6) to conclude, in the abstract no less: „most strikingly the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species." I do not question the lack of a mitochondrion here, but this abstract sentence is theatrical in nature, nothing that data on an extant species could ever proof in the absence of a time machine, and is evolutionary pretty much impossible. A puzzling sentence to read in an abstract and endosymbiont-associated evolution.

We feel that the reviewer is putting too much emphasis on an aspect of our original manuscript that is rather peripheral to its major message. Indeed, the manuscript is not, and has never been thought to be, primarily about eukaryogenesis and the exact role the mitochondrion played in it. We are, therefore, somewhat reluctant to react in full to the very long and complex argument the reviewer has raised in his/her report, so we keep our reaction at the necessary minimum. Concerning the criticized sentence in the original version of the abstract, it alluded to a section of the manuscript (“No evidence for subcellular retargeting of ancestral mitochondrial proteins in oxymonads”) that we have removed from the revised version, and hence we have modified also the abstract accordingly by removing the sentence. We still think our original arguments were valid, but apparently, much more space and more detailed analyses are required to deliver a truly convincing case, for which there is no space in the manuscript.

Second, using oxymonads as an example that a lineage can present eukaryotic complexity in the absence of mitochondria and conflating it with eukaryogenesis is a logical fallacy. This issue already affected the 2019 study by Hampl et al.. We have known that a eukaryote can survive without an ATP-synthesizing electron transport chain ever since Giardia and other similar examples and the loss of Fe-S biosynthesis and the last bit of mitosome (secondary loss) doesn't make a difference how to think about eukaryogenesis. It confuses the need and cost to invent XYZ with the need and cost of maintenance. How can the authors write "... and undergo pronounced morphological evolution", when they evidently observe the opposite and show so in their Fig. 1? The authors only present evidence for reductive evolution of cellular complexity with the loss of a stacked Golgi. What morphological complexity did oxymonads evolve that is absent in other protists? A cytosolic metabolic pathway doesn't count in this respect, because it is neither morphological, nor was it invented but likely gained through LGT according to the authors. This is quite confusing to say the least. A recent paper (https://doi.org/10.7554/eLife.81033) that refers to Hampl et al. 2019 has picked this up already, and I quote: "Such parasites or commensals have engaged an evolutionary path characterized by energetic dependency. Their complexity might diminish over evolutionary timescale, should they not go extinct with their hosts first." Here the authors raise a red flag with respect to using only parasites and commensals that rely on other eukaryotes with canonical mitochondria as examples. If we now look at Fig. 1 of this submission, Novak et al. underpin this point perfectly, as the origin of oxymonads is apparently connected to the strict dependency on another eukaryote (or am I wrong?), and they support the prediction with respect to complexity reducing after the loss of mitochondria - mitosome gone, Golgi almost gone. What's next? This is a good time to remember that extant oxymonads are only a single picture frame in the movie that is evolution, and their evolution might be a dead-end or result in a prokaryote-like state should they survive 100.000s to millions of years to come.

It seems that in this point the reviewer is particularly concerned with the following sentence that is part of the Introduction and which relates to the existence of amitochondrial eukaryotes we are studying: “The existence of such an organism implies that mitochondria are not necessary for the thriving of complex eukaryotic organisms, which also has important bearings to our thinking about the origin of eukaryotes (Hampl et al. 2018).“ Even after re-reading the sentence we confess we stay with it and find it perfectly logical. Nevertheless, we decided to omit it from the text so as not to distract from the main topic of the study.

Next, when mentioning “… pronounced morphological evolution” we mean the evolution of four oxymonad families (Streblomastigidae, Oxymonadidae, Pyrsonymphidae and Saccinobaculidae) comprising almost a hundred described species with often giant and morphologically elaborated cells that evolved from a simple Trimastix-like ancestor (Hampl 2017, Handbook of Protists, 0.1007/978-3-319-32669-6_8-1). This is a fact that can hardly be dismissed. Also, given the current oxymonad phylogenies (Treitli et al. 2018, doi.org/10.1016/j.protis.2018.06.005) and the reported absence of a mitochondrion in M. exilis, B. nauphoetae, and S. strix we can infer that the mitochondrion was lost in the common ancestor of the three species at latest. This organism must have lived more than 100 MYA, as at that time oxymonads were clearly diversified into the families (Poinar 2009, 10.1186/1756-3305-2-12). So, these organisms indeed have lived without mitochondria for at least 100 MY. We think that these facts and our inferences based on them are solid enough to keep in the conclusion the following statement: “This fact moves this unique loss to at least 100 MYA deep past, when oxymonads had been already diversified (Poinar 2009), and shows that a eukaryotic lineage without mitochondria can thrive for eons and undergo pronounced morphological evolution, as is apparent from the range of shapes and specialized cellular structures exhibited by extant oxymonads (Hampl 2017).” Furthermore, as documented in Karnkowska et al. 2019 (https://pubmed.ncbi.nlm.nih.gov/31387118/), apart the loss of the mitochondrion oxymonads are surprisingly “normal” and complex eukaryotes, in fact much less reduced than, e.g., Giardia, Microsporidia, or even S. cerevisiae (in terms of the number of genes, introns, etc.). We strongly disagree with the claim that “Golgi is almost gone” in oxymonads, and our manuscript shows exactly the opposite. Viewing oxymonads as a lineage heading towards a prokaryote-like simplicity is dogmatic and ignores the known biology of these organisms.

Some more thoughts: Line 47-52: Hydrogenosome or mitosome is a biological and established label as (m)any other and I find the use of the word "artificial" in this context strange. While the authors are correct to note that there is a (evolutionary) continuum in the reduction - obviously it is step by step - they exaggerate by referring to the existing labels as "artificial". You make Fe-S clusters but produce no ATP? Well, then you're a mitosome. It's a nomenclature that was defined decades ago and has proven correct and works. If the authors think they have a better scheme and definition, then please present one. Using the authors logic, terms such as amyloplast or the TxSS nomenclature for bacterial secretions systems are just as artificial. As is, this comes across as grumble for no good reason.

We agree that the original wording sounded like unwarranted grumbling and we have changed the sentence in the following way: "However, exploration of a broader diversity of MRO-containing lineages makes it clear that MROs of various organisms form a functional continuum (Stairs et al. 2015; Klinger et al. 2016; Leger et al. 2017; Brännström et al. 2022)."

Line 158: A duplication-divergence may also explain this since sequence similarity-based searches will miss the ancestral homologues.

We do not disagree about this, in fact, the gene the reviewer’s point is concerned with for sure is a result of duplication and divergence, as it belongs to a broader gene family (major facilitator superfamily, as stated in the manuscript) together with other distant homologs. Nevertheless, this is not in conflict with our conclusion that it “may represent an innovation arising in the common ancestor of Metamonada”.

Lines 201-202: Presence of GCS-L in amitochondriate should be explained in light of this group once having a mitochondrion, which then makes ancestral derivation and differential loss (as invoked for Rsg1) also a likely explanation along with eukaryote-to-eukaryote LGT.

Yes, this most likely holds for the standard paralogue GCS-L1 (in P. pyriformis PAPYR_5544), which has the expected distribution and phylogenetic relationships and is absent in oxymonads. The discussion is, however, mainly about the rare, divergent and until now overlooked paralogue GCS-L2 (in P. pyriformis PAPYR_1328), which we found only in three distantly related eukaryote groups, Preaxostyla, Breviatea, and Archamoebae, which strongly suggests inter-eukaryotic LGT.

Lines 356-392: Describes plenty of genomic signal for Golgi bodies but simultaneously cites literature suggesting the absence of a morphologically an identifiable Golgi in oxymonads. An explicit prediction regarding what to observe in TEM for the mentioned species might be nice to stimulate further work.

We thank the reviewer for their suggestion and are glad that they are enthusiastic about this aspect of the manuscript. Unfortunately, the morphology of unstacked Golgi ranges from single cisternae (yeast, Entamoeba), vesicles (Mastigamoeba), and a “tubular membranous structure” in Naegleria. Therefore, no strong prediction is possible of what the oxymonad Golgi might look like under light or TEM. However, the data that we have provided should lead to molecular cell biological analyses aimed at identifying the organelle, giving target proteins to tag or against which to create antibodies as Golgi markers. An additional sentence to this effect has been added to the manuscript, “They also set the stage for molecular cell biological investigations of Golgi morphological variation, once robust tools for tagging in this lineage are developed.”

Lines 414: The preceding paragraphs in this result section describes only the distribution, without mentioning origins - a sweeping one-line summary that proclaims different origin needs some context and support. Furthermore, the distribution of glycolytic enzymes might indeed be patchy, but to suggest it represents an 'evolutionary mosaic composed of enzymes of different origins' without discussing the alternative of a singular origin and different evolutionary paths (including a stringer divergence in one vs. another species) discredits existing literature and the authors own claim with respect to why BUSCO might fail in protists.

The part of the text about glycolysis the reviewer alluded to has been removed while shortening the manuscript.

Line 486: How uncommon are ADI and OTC in lineages sister to metamonada?

This is an interesting but difficult question. Firstly, we are uncertain what is the sister lineage to Metamonada. Discoba, maybe, but a recent unpublished rooting of the eukaryotic tree does not support it (https://pubmed.ncbi.nlm.nih.gov/37115919/). Generally, the individual genes of the pathway (ADI, OTC and CK) are quite common in eukaryotes, but the combination of all three is rare (Metamonada, the heterolobosean Harpagon, the green algae Coccomyxa and Chlorella, the amoebozoan Mastigamoeba, and the breviate Pygsuia), see figure 1 in Novak et al 2016, doi: 10.1186/s12862-016-0771-4.

Line 504: It might help an outside reader to include a few lines on consequences and importance of having 2Fe-S vs 4Fe-S clusters and set an expectation (if any) in Oxymonads.

We apologize for omitting this explanation. The 2Fe-2S proteins are more common in mitochondria where 2Fe-2S clusters are synthesized in the early pathway of FeS cluster assembly, while the cytosolic CIA pathways produce 4Fe-4S clusters (https://pubmed.ncbi.nlm.nih.gov/33007329/). The original expectation therefore is that species without mitochondria should not have 2Fe-2S cluster proteins. Obviously, the switch to the SUF pathway affects this expectation as we do not know, what type of cluster this pathway produces in oxymonads (https://www.biorxiv.org/content/10.1101/2023.03.30.534840v1). For the sake of brevity, we have included a short statement as the beginning of the sentence in question, which now reads as follows: “As 2Fe-2S clusters are more frequent in mitochondrial proteins, the higher number of 2Fe-2S proteins in P. pyriformis compared to the oxymonads may reflect the presence of the MRO in this organism.”

Any explanations on what unique selection pressures and gene acquisition mechanisms may be operating in P. pyriformis which might allow for the unique metabolic potential?

Every species exhibits a unique combination of traits that results from changing selection pressures imposed on historical contingency (including neutral evolutionary processes such as genetic drift). We lack real understanding of these factors for a majority of taxa including the familiar ones, so we should not expect to have a good answer to the reviewer’s question. In fact, we do not know how unique is the particular combination of P. pyriformis traits discussed in our manuscript, as there has been no comprehensive comparative analysis that would include ecologically and evolutionarily comparable taxa. We note that Paratrimastix represents only a third free-living metamonad with a sequenced genome (together with Kipferlia and Carpediemonas), so more data and additional analyses are needed to be in a position when we may start hoping answers to questions like the one posed by the reviewer are in reach.

** Referees cross-commenting** To R3: Hampl et al. 2019, to which Novak et al. refer, is about eukaryogensis and that is exactly the context in which this is discussed again and what Raval et al. 2022 had decided to touch upon. If the authors do not bring this up in light of the ability to evolve (novel) eukaryote complexity, then what else? Maybe they can elaborate, especially with respect to energetics to which they explicitly refer to in 2019 (and here). And with respect to text-book eukaryotic traits (and the evolution of new morphological ones), I do not see any new ones evolving in any oxymonad, but reduction as Novak et al. themselves picture it in this submission. Is a change in the number of flagella pronounced morphological evolution? Maybe for some, but I believe this needs to be seen in light of the context of how they discuss it. I see a reduction of eukaryotic complexity and not a gain. They have an elaborate section on the loss of Golgi characteristics (and a figure), but I fail to read something along the same lines with respect to the gain of new morphological traits. Again, novel LGT-based biochemistry does not equal the invention of a new morphology such as a new compartment. Oxymonads depend on mitochondria-bearing eukaryotes for their survival or don't they? This is the main point, and if evidence show that I am wrong, then I will be the first to adapt my view to the data presented.

While we do see the logic of the reviewer’s point, a good reply would have to be too elaborate and certainly beyond the scope of the current manuscript. As the reviewers’ reports led us to reconsider the structure of the manuscript and to make it more focused and concise, we decided to simplify the matter by removing the allusions to eukaryogenesis, realizing that it is perhaps more suitable for a different type of paper (opinion, review). The comment on the evolution of complex morphology has been answered previously (see above).

I have concerns with the presentation of a narrative that in my opinion is too one-sided and that has been has been publicly questioned in the community (in press, at meetings, personally). For the benefit of science and of the young authors on this study, this reviewer feels strongly that these issues should be taken very seriously and discussed openly in a more balanced way. . We only truly move forward on such complex topics, if we allow an open and transparent discussion.

We agree that opinions on specific details of eukaryogenesis are divided in the community and that the topic requires a nuanced discussion for which there is perhaps no place in the current manuscript. As stated in the reply to the previous point, we have removed the discussion of the implications of our current study to eukaryogenesis from the revised manuscript.

Having said that, I am happy that R3 has picked up exactly the same major concerns as I did with respect to e.g. the phrasing on mito (gene) loss and the BUSCO controversy.

We appreciate these comments and hopefully have resolved the concern in the previous answers.

Reviewer #2 (Significance):

Using draft genome sequencing of the free-living Paratrimastix pyriformis and the sister lineage oxymonad Blattamonas nauphoetae, Novack et al. infer the metabolic potential of the two protists using comparative genomics. The authors conclude that the common oxymonad ancestor lost the mitochondrion/mitosome and discuss general strategies for adapting to commensal/symbiotic life-style employed by this taxon. Some elaborations on pathways go on for several paragraphs and feel unnecessarily stretched, which made those sections of the paper rather difficult to digest. This might be also be because the work, and all conclusions drawn, depend entirely on incomplete (ca. 70-80%) genome data and simple similarity searches, and e.g. no kind of biochemistry or imaging is presented to underpin the manuscripts discussion.

We have addressed the concern about the possible incompleteness of our genome data above, demonstrating it is not substantiated ad stems from an inadequate interpretation of quality measures we provide in the manuscript. We hope that the revised manuscript, which is streamlined and more concise compared to the initial submission, conveys the key messages in a substantially more persuasive way and will be appreciated by a broad community of readers.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary: The genome sequences of two members of the protist group Preaxostyla are presented in this manuscript: Paratrimastix pyriformis and Blattamonas nauphoetae. The authors use a comparative genomics and phylogenetic approaches and compare the new genome datasets with three previously available genomes and transcriptomes from the group. The availability of genome-scale data from five Preaxostyla species is powerful to address interesting basic evolutionary questions. A substantial part of the manuscript is spent on testing the hypothesis of mitochondrial loss in the oxymonad lineage, which turns out to be supported. The datasets are also explored regarding the role of lateral gene transfer in the group, metabolic diversification and the evolution of Golgi.

Major comments: I find the manuscript very interesting with many different fascinating results presented. However, the manuscript is very long. Two genome sequences are presented and it is not clear to me what the main question was when this project was initiated and why these two species was selected to answer this question. I do not see an obvious reason for sequencing the P. pyriformis genome if the mitochondrial loss was the main question (given that a transcriptome was already available). Why not spend the time and resources on a member of Preoxystyla, which lacked previous data? The authors should more clearly state why these organisms were chosen to answer the main question or questions of the study.

We are sorry for having done a poor job when explaining the choice of the taxa for the comparison. The idea was to sample an outgroup of oxymonads (P. pyriformis) and a representative of other clades of oxymonads than M. exilis (B. nauphoetae and S. strix) for which it was feasible to obtain the data, or the data were already available. Obviously, more representatives of morphologically a probably also genetically diverse oxymonads should be investigated (e.g. Pyrsonympha, Oxymonas, Saccinobacullus) and we have such a plan but these organisms are difficult to work with. We considered it necessary to sequence the genome of P. pyriformis, and not rely on the transcriptome only, to avoid the issue of data set incompleteness (raised also by R2). Transcriptomes by nature provide an incomplete coverage of the full gene complement of the species, while our genome assemblies are close to complete, as we explain elsewhere.

The evolution of MROs have received substantial attention from the protist research community since the 1990's. During this period the mitochondrial organelle have been considered essential for eukaryotes. Therefore, the result presented in the manuscript has a high significance. However, I am not convinced that it is appropriate to use the term "evolutionary transition" for the mitochondrial loss. The loss of MRO is the endpoint of a gradual change of the internal organisation of the cell that probably started when the ancestor of these organism adapted to an anaerobic lifestyle. The last step described in the manuscript probably had little impact on how these organisms interacted with their environment. The presence or absence of biosynthesis of p-cresol by some, but not all, Preaxystyla probably is much more significant from an ecological point of view. My point is that the authors need to consider how they use the term evolutionary transition and be explicit about that.

We appreciate the comment concerning the use of the term “evolutionary transition”. Nevertheless, we believe there is no real consensus in the literature on what is and what is not an “evolutionary transition”, and the application of the term to specific cases is more or less arbitrary. For a lack of a standardized or better terminology, we have kept the term to refer to three evolutionary changes in the evolution of the Preaxostyla lineage that are particularly important from the cytological or ecological perspective, i.e. dispensing with the mitochondrion, reorganizing the Golgi apparatus by losing the stacked arrangement of the cisternae, and gaining the endobiotic life style.

In the abstract the main finding is describes as "the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species (M. exilis, B. nauphoetae, and Streblomastix strix) extending the amitochondriate status to the whole Oxymonadida.". I find this a really interesting observation, but I do find the wording a bit too bold for several reasons: • Not every protein that has participated in the mitochondrial function is known. • Mitochondrial proteins could be present in oxymonads, but divergent beyond the detection limit for existing methods. • Genes for one or several mitochondrial proteins could be present in one or more oxymonad genomes, but remain undetected due to the incomplete nature of the datasets.

Although I do think that the authors' claim very well could be true, I don't think their data fully support it. Therefore, it needs to be rephrased.

As a result of our decision to streamline the manuscript by removing the final part of Results and Discussion (“No evidence for subcellular retargeting of ancestral mitochondrial proteins in oxymonads”, the revised manuscript no longer support the statement “the data confirm the complete loss of … every protein that has ever participated in the mitochondrion function for all three oxymonad species” that is criticized by the reviewer, and hence the statement has been removed from the abstract. This addresses bullet point 1. As for bullet points 2 and 3, the proof of absence is in principle impossible to deliver, and we have been fighting with this already in the Karnkowska et al. 2016 paper. Although our certainty will never reach 100% (this is in fact impossible for a scientific, i.e., falsifiable, hypothesis), the mounting of evidence through studies gives the hypothesis on the amitochodriate status of oxymonads more and more credit. The genes for mitochondrial marker proteins have not been detected by the most sensitive methods available neither in the first genome assembly of M. exilis (Karnkowska et al. 2016), nor in the improved M. exilis genome assembly composed of only 101 contigs (Treitli et al. 2021), nor in either of the other two oxymonad species investigated here. On the other hand, they were readily detected in the data sets of P. pyriformis and T. marina. What is the probability that these genes always hide in the assembly gaps, or that they have all escaped recognition? Obviously, this probability is not zero, but we believe it is approaching so low values that it is reasonably safe to make the conclusion on the amitochondriate status of these species.

The sentence was changed to: "Our results provide insights into the metabolic and endomembrane evolution, but most strikingly the data confirm the complete loss of mitochondria for all three oxymonad species investigated (M. exilis, B. nauphoetae, and Streblomastix strix), suggesting the amitochondriate status may be common to Oxymonadida."

The third point maybe could be analysed further. BUSCO scores are reported, but also argued not being reliable for this group of organisms (which is true). Would it, for example, be useful to analyse how large fraction of the BUSCO proteins found in all non-Preoxystyla metamonada genomes that are present in the various Preoxystyla datasets?

We provide a comprehensive answer to a similar comment of reviewer 2 above (page 6-8). We performed the requested analysis and provide the result in Supplementary file 11. In this table, we record presence/absence of each gene from the BUSCO set for our data sets and the highly complete “standard” datasets of Trypanosoma brucei, Giardia intestinalis and Trichomonas vaginalis. Of the 303 genes, 117 were present in all data sets and 17 in none (see column I). 20 were present only in Trypanosoma and not in metamonads. 6 were present in all Preaxostyla and absent in other metamonads (Trichomonas and Giardia), 44 were present in all Preaxostyla and Trichomonas and absent in Giardia, suggesting high divergence of this species. Only 23 (marked by *) were present in the three “standard” genomes and absent in one or more Preaxostyla species. Of those 8 and 8 were absent specifically in S. strix and P. pyriformis, respectively, but only 1 was absent specifically in M. exilis and no such case was observed in B. nauphoetae. We conclude that this non-random pattern argues for lineage-specific divergence rather than incomplete data sets, particularly in the case of M. exilis and B. nauphoetae.

Line 160-161: 15 LGT events specific for the Preaxostyla+Fornicata clade is reported. This is an exciting finding because it supports a phylogenetic relationship between these two groups. But such an argument is only valid if the observed pattern is more common than the alternative hypotheses (Preaxostyla+Parabasalids and Fornicata+Parabasalids). How many LGT events support each of these groupings? How are these observation affected by the current taxon sampling with the highest number of datasets from Fornicata? How were putative metamonada-to-metamonada LGTs treated in this context?

19 LGT are uniquely shared between Preaxostyla+Parabasalids, which is more than the number of shared LGTs between Preaxostyla and Fornicata. No common LGT was unique to Fornicata+Parabasalids. However, the latter is a direct consequence of our investigation method, which involved reconstruction phylogenies of genes present in Preaxostyla, and not across all metamonads. So, we do not have a way to investigate LGT gene families uniquely shared between Fornicata and parabasalids.

When it comes to the effect of taxon sampling, we agree that it is possible that the number of genes of horizontal origin shared between parabasalids and Preaxostyla is underestimated because of the lower taxon sampling in parabasalids. However, it is still larger (19) than the number of LGTs shared uniquely between fornicate and Preaxostyla (15). In addition, while the taxon sampling is larger in fornicates, it also contains some representatives of closely related lineages (e.g., Chilomastix caulleryi and Chilomastix cuspidate) which, while they increase the number of fornicate representatives, does not increase the detection of shared genes between fornicates and Preaxostyla. Altogether, it's difficult to estimate how the current taxon sampling is biasing the detection of LGTs one way or another.

Regarding metamonad-to-metamonad putative LGTs: we did not consider this possibility for the sake of not overestimating the number of gene transfers for two main reasons. First of all, our LGT detection relies on the incongruence between species tree and gene tree. The closer the lineages are in the species tree, the more difficult it is to interpret any incongruence in the gene tree as single protein phylogenies are notoriously poorly resolved because they rely on the little phylogenetic signal contained in few amino-acid positions. Because of this, small incongruences with the species tree could either reflect recent LGT events between metamonads, or simply blurry phylogenetic signal. Second, we can certainly use the argument that a limited taxonomic distribution among metamonads favors an LGT event between them. However, here again, the closer the lineages involved are, the more difficult it is to distinguish a scenario where one lineage was the recipient of an LGT from prokaryote before donating it to another metamonad, from a scenario involving a single ancestral LGT from prokaryotes to metamonads, followed by differential loss, leading to a patchy taxonomic distribution. Finally, we are working with both limited taxon sampling and incomplete genomic/transcriptomic data, which makes it more difficult to identify true absences. For all these reasons, we chose to be conservative and invoke the smallest number of LGT events.

The authors have used a large-scale approach to make single-gene trees for inferences of LGT. In other parts of the manuscript inferences of evolutionary origins of single genes are made without support of phylogenetic trees. I find this inconsistent and argue that the hypothesis of the origin of a specific protein should be tested with the same rigor whether it is a putative LGT, gene duplication, gene loss or an ancestral member of LECA. Specific cases where I think a phylogenetic analysis is needed includes: • Line 222-223: It is concluded that Rsg1 is a component of LECA. • Line 307: HgcAB are argued to be acquired by LGT of a whole opeon. • Lines 350-355: It is unclear how the different numbers of transporters are interpreted (loss or expansion by duplication). This could be address with phylogenetics. • Lines 407-408: A tree should support the claim of LGT origin. (PFP) • Lines 414-415: The different origins of glycolytic enzymes should be supported by data or references. • Line 486: Trees or a reference (if available) should support the claim for LGT.

As requested, trees were constructed for HgcA, HgcB, PFP and the transporters AAAP, CTL, ENT, pATPase, and SP. Citations were added for the glycolytic enzymes and the ADI pathway. No tree for Rsg1 is needed, as this is a eukaryote-specific protein lacking any close prokaryotic relatives. The inference on its presence in the LECA is based on the phylogenetically wide, however patchy, distribution across the eukaryote phylogeny. Testing possible eukaryote-eukaryote LGTs is hampered by a limited phylogenetic signal in the short and rapidly evolving Rsg1 sequences, resulting in very poorly resolved relationships among Rgs1 sequence in a tree we attempted to make (data not shown). For this reason, we opt for not presenting any phylogenetic analysis for Rsg1.

Lines 530-531 and 773-774: "The switch to the SUF pathway in these species has apparently not affected the number of Fe-S-containing proteins but led to a decrease in the usage of 2Fe-2S clusters." I find it difficult to evaluate if the data support this because no exact numbers or identities are given for 2Fe-2S and 4Fe-4S proteins in the various genomes in Suppl. Fig. S4 or Supplementary file 4.

The functional annotation of all detected FeS clusters containing proteins is provided in Supplementary Table S8 including the types of predicted clusters (columns G or F). Basically, the only putative 2Fe2S cluster containing proteins in species of oxymonad is xanthine dehydrogenase, while Paratrimastix and Trimastix contain also 2Fe2S cluster-containing ferredoxins and hydrogenases.

The method used in the paper varies between the different parts of the paper. One example is single gene phylogenies, which are described three times in the method section [Lines 959-973, lines 1011-1034, lines 1093-1101], in addition to the automated approach within the LGT detection pipeline lines 923-926]. The approaches are slightly different with, for example, different procedures for trimming. This makes it difficult to know how the different presented analyses were done in detail. No rationale for using different approaches is given. At the least, it should be clear in the method section which approach was used for which analysis.

The reviewer is correct, and we apologize for the inconsistency. The reason is only historical –the analyses were performed by different laboratories in different periods of time. We believe this fact does not make our results less robust, although it does not “look” nice and makes the description of the methods employed longer. We have double-checked the description and introduced slight changes as to make it maximally clear which method has been used for particular analyses presented in the Results and Discussion.

Specific comments on single gene phylogenies:

• Line 966-967: Why max 10 target sequences?

The limit of 10 was applied in order to keep the datasets in manageable dimensions. The sentence has been changed to: " In order to detect potential LGT from prokaryotes while keeping the number of included sequences manageable, prokaryotic homologues were gathered by a BLASTp search with each eukaryotic sequence against the NCBI nr database with an e-value cutoff of 10-10 and max. 10 target sequences.”

• Lines 996-998: Is it a problem that these are rather old datasets?

Although the publications are slightly older the set of queries is absolutely sufficient for the purpose.

Minor comments: I appreciate that many data is included as supplementary material. However, the organisation of the data could be improved. The numbering of the files is not included in their names or within the files, as far as I could find. Descriptions of the files are often missing and information on the annotation such as colour coding is not always included. These aspects of the supplementary material needs to be strengthened in order to make it more useful. Specific comments: • Supplementary file 1, Table 1: accession numbers are missing. Kipferlia bialta appears to have a much smaller number of sequences than reported in the publication. The file consists of three tables and it would be very helpful if the reference in the main manuscript indicate the table number. • Supplementary file 4: The trees lack proper species names and a documented colour coding. There are multiple trees in the file, which make it difficult to find the correct tree. I would appreciate if the different trees were labelled A, B, C, etc., and if these were used in the main text.

Supplementary file 1: Accession numbers were added.

Supplementary file 4: Species names and alphabetical labelling were added. Colour coding was explained in the text at the first mention of the file: "(Supplementary file 4 H; Preaxostyla sequences in red)."

o There is no HPAD-AE tree (as indicated on line 258), but a HPAD tree. Which part of the tree contain the described fusion protein?

Thank you for spotting the mistake. There should have been “HPAD” instead of “HPAD-AE” indicated in the text. The sentence has been changed to:" The P. pyriformis HPAD sequence is closely related to its homolog in the free-living archamoebid M. balamuthi (Supplementary file 4 K), the only eukaryote reported so far to be able to produce p-cresol (Nývltová et al. 2017)."

o Line 280-281: "UbiE homologs occur also in some additional metamonads, including the oxymonad B. nauphoetae and certain fornicates." These sequences should be clearly highlighted in the tree.

We discovered these additional UbiE homologs only after the tree presented in the supplement had been constructed, so these sequences are missing from it. To ensure consistency we have decided to remove the remark on the presence of UbiE homologs metamonads other than P. pyriformis, so it is no longer part of the revised manuscript.

o Lines 538-544: A three-gene system is mentioned, but only two AmmoMemoRadiSam trees are found.

This part has been removed while streamlining the manuscript.

• Supplementary file 6: I find it difficult to find the proteins discussed in the text, for example "the biosynthesis of p-cresol from tyrosine (line 254-255)".

Abbreviations identifying the different enzymes have now been added to all mentions in the text, facilitating their localization in the supplementary file: "P. pyriformis encodes a complete pathway required for the biosynthesis of p-cresol from tyrosine (Supplementary file 6), only the second reported eukaryote with such capability. This pathway consists of three steps of the Ehrlich pathway (Hazelwood et al. 2008) converting tyrosine to 4-hydroxyphenyl-acetate (AAT, HPPD, ALDH) and the final step catalyzed by a fusion protein comprised of 4-hydroxyphenylacetate decarboxylase (HPAD) and its activating enzyme (HPAD-AE)."

• Supplementary file 11: Which group of species are highlighted in red? How do I know from which species these sequences are (I can make educated guesses, but prefer full species names). I do not find any reference to this file in the main manuscript.

We apologise for this inconvenience. The taxon labels in the treed in this supplementary file have been corrected to contain full species names.

Line 227-228: "630 OGs seem to be oxymonad-specific or divergent, without close BLAST hits". It is unclear if BLAST searches includes only a representative of each 630 OGs, or every single protein in these OGs.

The BLAST searches include every single protein in the investigated OGs. We clarified it in the text: “Of these, 630 OGs seem to be oxymonad novelties or divergent ancestral genes, without close BLAST hits (e-value -15) to any of these sequences.”

Line 243: I think it is five LGT mapped to internal nodes of Preoxystyla in Figure 1 (1+3+1).

You are correct, we apologize for the mistake. The sentence has been changed to: "Also, 46 LGT events were mapped to the terminal branches and 5 to internal nodes of Preaxostyla, suggesting that the acquisition of genes is an ongoing phenomenon, and it might be adaptive to particular lifestyles of the species."

Lines 325-331: The argument would be stronger with a figure showing the fusion and the alignment indicating the conserved amino acids mentioned in the text.

We agree with the reviewer but for the sake of space, we finally decided not to include a new figure.

Lines 425: "none of the species encoded" should be replaced by something like "none of the enzyme could be detected in any of the species" (the datasets are incomplete).

The sentence has been changed to: "None of the alternative enzymes mediating the conversion of pyruvate to acetyl-CoA, pyruvate:NADP+ oxidoreductase (PNO) and pyruvate formate lyase (PFL), could be detected in any of the studied species."

Line 455: "suggesting a cytosolic localization of these enzymes in Preaxostyla." The absence of a phylogenetic affiliation with the S. salmonicida homolog does not preclude a MRO localisation.

The sentence was changed to: "Phylogenetic analysis of Preaxostyla ACSs (Supplementary file 4 B) shows four unrelated clades, none in close relationship to the S. salmonicida MRO homolog, consistent with our assumption that these enzymes are cytosolic in Preaxostyla."

Lines 570-571: "Manual verification indicated that all the candidates recovered in oxymonad data sets are false positives" Using which criteria?

The manual verification was based on the annotation of predicted proteins by BLAST and InterProScan. If the annotations did not correspond to the suggested function, they were considered false positives. For example, the protein BLNAU_15573 of Blattamonas nauphoetae was detected by Sam50 HMM profile and thus was considered a candidate for Sam50 proteins. Its functional annotation from BLAST was, however, unrelated to Sam50 (“putative phospholipase B”). Therefore, this candidate was concluded as a false positive hit of the HMM search resulting from the very high sensitivity of this method.

We clarified this in the Results

“Reciprocal BLASTs indicated that all the candidates recovered in oxymonad data sets are very likely to be false positives based on the annotations of their top BLAST hits (mainly vaguely annotated kinases, peptidases and chaperones) (Fig. 6, Supplementary file 9).”.

And Material and Methods

“Any hits received by the methods described above were considered candidates and were furter inspected as follows. All candidates were BLAST-searched against NCBI-nr and the best hits with the descriptions not including the terms 'low quality protein', 'hypothetical', 'unknown', etc. were kept. For each hit, the Gene Ontology categories were assigned using InterProScan-5.36-75.0. If the annotations received from BLAST or InterProScan corresponded to the originally suggested function, the candidates were considered as verified. Otherwise, they were considered as false positives.”

Lines 743-755: "Similar observations were made in other protists with highly reduced mitochondria, such as G. intestinalis or E. histolytica,..." References are needed.

This part of the manuscript has been removed while streamlining the text.

Line 849: How was the manually curation done for the gene models in the training set?

The sentence has been changed to: "For de novo prediction of genes, Augustus was first re-trained using a set of gene models manually curated with regard to mapped transcriptomic sequences and homology with known protein-coding genes."

Lines 853-856: It is a bit unclear which dataset was used for BUSCO and downstream analysis. Was it the Augustus-predicted proteins, or the EVM polished?

The sentence has been changed to: "The genome completeness for each genome was estimated using BUSCO v3 with the Eukaryota odb9 dataset and the genome completeness was estimated on the sets of EVM-polished protein sequences as the input."

Lines 858: What is it meant that KEGG and similarity searches was used in parallel (what if both gave a functional annotation?)?

A sentence has been added for clarity: "KEGG annotations were given priority in cases of conflict."

Lines 861-862 and 1007-1008: Which genes or sub-projects does this apply to? How many genes were detected in this procedure?

The sentence has been changed to make this clear: "Targeted analyses of genes and gene families of specific interest were performed by manual searches of the predicted proteomes using BLASTp and HMMER (Eddy 2011), and complemented by tBLASTn searches of the genome and transcriptome assemblies to check for the presence of individual genes of interest that were potentially missed in the predicted protein sets (single digits of cases per set). Gene models were manually refined for genes of interest when necessary and possible."

Lines 878-879: It is not clear to me why the sum of the two described numbers should be as high as possible and would appreciate an argument or a reference.

When optimizing the inflation parameter of OrthoMCL, we reasoned that the optimal level of grouping/splitting for our purpose should result in the highest number of orthogroups containing all representatives of the groups of interest (i.e. Preaxostyla) but no other species – pan-Preaxostyla orthogroups. When going down with the values, you observe more and more groupings of pan-Preaxostyla OGs with others (indication of overgrouping) in the opposite direction you observe splitting of pan Preaxostyla OGs which indicates oversplitting. Because we were optimizing the inflation parameter for Preaxostyla and Oxymonadida at the same time, we maximized the sum of pan-Preaxostyla and pan-Oxymonadida groups.

Lines 879-881: "Proteins belonging to the thus defined OGs were automatically annotated using BLASTp searches against the NCBI nr protein database (Supplementary file 1)." Why were these annotated in a different way (compare lines 857-859).

This little inconsistency resulted from the fact that these parts of the analyses were performed by different researchers who did not cross-standardize the procedures. This inconsistency has no effect on the downstream analyses and conclusions as the annotations from Supplementary file 1 were not used in any further analyses.

Lines 894-957: "Detection of lateral gene transfer candidates": • It is not clear which sequences were tested in the procedure. All Preaxostyla, or all metamonada? I think I am confused because in the result sections you only report numbers for Preaxostyla, but in the method section metamonada is mentioned repeatedly.

Thank you for noticing. There was indeed some inconsistency in our writing.

We did an all-against-all search using all metamonads. However, we filtered out all homologous families in which Preaxostyla were not present or that had no hit against GTDB. So in the end, the LGT search was restrained to protein families containing Preaxostyla homologues. We corrected the wording in our method section.

• It would be easier to follow the procedure if numbers are provided for the different steps.

We are not sure what numbers the reviewer refers to here.

• Why was only small oxymonad proteins discarded (line 900)?

This is indeed a mistake. We meant “Preaxostyla proteins”. This is because we only considered Preaxostyla sequences with significant hits against GTDB as a starting point, so we aimed to first remove those that might be too short to yield reliable phylogenies.

• Line 911: How many sequences were collected?

Up to 10,000 hits were retained. We have added that information to the text.

• Lines 916-919: What is the difference between the protein superfamilies (line 916) and the OGs (line 919)? Are the OGs the same orthogroups that is described earlier in the method section? How are the redundancy of NCBI nr entries retrieved in different searches dealt with?

We understand the confusion here. It primarily stemmed from two different ways to establish homologous families across the manuscript because of different researchers being responsible for different parts. Protein superfamilies that were used for reconstructing the single protein trees used for the LGT analyses were assembled based on the procedure describe line 916-919 (“Protein superfamilies were assembled by first running DIAMOND searches of all metamonad sequences against all (-e 1e-20 --id 25 --query-cover 50 --subject-cover 50). Reciprocal hits were gathered into a single FASTA file, as well as their NCBI nr homologues.”). However, this was a somewhat stricter procedure than the one used to establish the OGs that are discussed in the rest of the manuscript (because of the e-value and identity cut-off used), so we eventually enriched the datasets with the putatively missing metamonad sequences that were present in the OGs but not in the initial superfamily assembly. However, since these were often more divergent sequences, we did not use these as queries for our BLAST searches against prokaryotes.

Line 987-989: "...was facilitated by Rsg1 being rather divergent from other Ras superfamily members" This statement is vague. What does it mean in practise?

The sentence has been changed to: " The discrimination was facilitated by Rsg1 having low sequence similarity to other Ras superfamily members (such as Rab GTPases)."

Lines 1037-1038: Why were these proteins re-annotated?

They were not. We are sorry for this mistake, which has been fixed in the revised manuscript.

Figures: The figures would be easier to follow if the colour coding for the five different species were consistent between the figures.

This is a good point, the colour coding has been unified across all figures.

Figure 1: It appears that the Venn diagram in C only shows the Preaxostyla-specific protein in B, not all OGs for which contain Preaxostyla proteins. This is not clear from legend or from the figure itself. The same comment applies to D.

The interpretation of the figure by the reviewer is correct; we have modified the legend to make the meaning of the figure easier to understand.

Figures 2 and 6: It would be clearer with panel labels A, B, etc, instead of "upper" and "lower" panel, as in the other figures.

This is a fair point, we have added the alphabetical labels proposed by the reviewer to the figures.

Figure 6: What is the colour code in the figure? The numbers within the boxes are not aligned.

We have added an explanation of the color code to the legend and edited the figure to make it aesthetically more pleasing.

Supplementary figures 1-3: What do green and magenta indicate in the figure?

As with the previous figure, the color code is now explained in the revised legend.

** Referees cross-commenting** I agree with the other reviewers that the discussion of the functional and ecological implications of the LGTs could be developed.

We understand the reviewers but as already explained in response to Reviewer 1, we have decided not to extend the already rather long manuscript further. We believe that the several exemplar LGT cases that we do discuss in detail provide a good impression of the significance of LGT in the evolution of Preaxostyla.

In contrast to reviewer 2, I do not see that the authors discuss their result in the context of eukaryogenesis in this manuscript. Maybe the reference reviewer 2 mention could be cited in the introduction together with Hampl et al. 2018 to acknowledge that there are different views about the importance of secondarily amitochondrial eukaryotes on our thinking about the origin of eukaryotes. I disagree with reviewer 2's objection against the wording "... and undergo pronounced morphological evolution" because I think Fig. 4 in Hampl 2017 shows a large morphological diversity among oxymonads.

We are glad to see that our perspective is not shared by other colleagues in the field. Nevertheless, having carefully considered the case we have decided to remove any mentions of eukaryogenesis from the revised manuscript, as we admit this topic is peripheral to the key message of our present study. On the other hand, we appreciate very much the note by the reviewer on the large morphological diversity among oxymonads – we have now added a similar remark to the revised manuscript (the last sentence of Conclusions).

Author Response:

The following is the authors' response to the original reviews.

We’d like to take this opportunity to thank the reviewers and editors for their consideration of our work. As detailed below, we have made the majority of the suggested corrections by the reviewers and believe these have greatly improved our manuscript. The reviewer’s comment are in blue font below and our response to each of these in black font.

Reviewer #1 (Recommendations For The Authors):

Suggestions to improve the manuscript:

-  Line 33 and 34: "This protein" is vague. Please reword to state whether you are referring to TcaA or to WTA

This has been corrected in the revised manuscript (Line 33)

-  Intro: It would be helpful to provide more rationale for testing serum as a surrogate to whole blood in the GWAS screen. Serum is obviously lacking components of the clotting cascade, and some of these components have antimicrobial functions. However, this is easily justified in the text- e.g. to avoid clumping during the screen, to focus only on serum-derived antimicrobial compounds, etc.

This has been edited in the revised manuscript (Line 84-86)

-  Line 120: Please state if the 300 clinical isolates represent 300 distinct patients, or if some of the isolates came from the same patient during sequential collections. If the latter, were there any instances in the which the tcaA SNP appeared during the course of infection?

They each came from individual patients so we were unfortunately unable to look for within host events. This information has been added to the revised manuscript (line 104).

-  Line 133: the closed parenthesis sign is missing after "CC22"

This has been corrected in the revised manuscript (Line 135)

-  Table 1a - NE1296 is misspelled as ME1296. Also there is a typo in the last entry of this table for the locus tag

This has been corrected in the revised manuscript.

-  Table 1b - the authors should comment (in the discussion) on the potential reasons why tcaA was not identified in the CC30 background.

A comment to this effect has been added to the revised manuscript (Lines 553-59)

-  Figure 2a - Why is the mutant with the empty complementation vector not significantly different from WT JE2?

The most widely used and reliable expression plasmid for complementation of mutated phenotypes in S. aureus is the pRMC2 plasmid, which requires chloramphenicol selection and anhydrotetracycline to induce expression of the cloned gene. These antibiotics, and the presence of the plasmid often affect the expression of other genes by the bacteria (as noted by this reviewer). As such, to verify complementation of a mutation the comparison we make is between the strain containing the empty plasmid induced with anhydrotetracycline with a strain with the gene containing plasmid induced with anhydrotetracycline. In that situation, the only difference between those two strains under those conditions is whether the gene is expressed or not. A comment explaining this has been added to the revised manuscript (lines 149-153).

-  Line 188: Statistical analyses should be applied to figure 3C, which also appears to be underpowered.

P values have been added to this in the revised manuscript. We present data point of three biological replicates, which are the mean of three technical replicates, which we believe is sufficiently powers for this analysis.

-  Figure 3 legend - Tecioplanin is mentioned in the title, but the data are not included here

This legend title has been the revised (Line 193).

-  Figure 4 - here is an example where testing the actual tcaA SNP could have been enlightening. For example, what if the selective pressure makes the SNP more relevant to a specific AMP or AA?

While we agree that this would be an interesting experiment to perform, the complementing vector that we would need to use to compare the wild type and SNP contains gene requires antibiotics to select for the plasmid and another to induce expression. As such it becomes quite a complex and messy experiment where synergy between the antimicrobial agents would be likely, the results of which will be difficult to interpret.

-  Lines 317-321 - Suggest moving this to discussion

We have left this here as we felt it a necessary summation/explanation of the results described in that section. It is discussed again later in the discussion section.

-  Line 341 - I believe "serum" should actually be "teicoplanin"

This has been corrected in the revised manuscript (Line 342).

-  Figure 6e - wouldn't it be more powerful to determine the WTA levels in the supernatants of these strains and conditions?

We could have done this both ways, but we focussed here only on how TcaA ligates WTA into the cell wall in the presence of serum.

-  Figure 6 - What is the explanation for the different growth yields for JE2 in tecioplanin in panel A versus panel F? Are these actually two different concentrations? If so, please update the figure legend and the methods.

The concentration used for the A was inhibitory and for F sub-inhibitory. To improve the clarity of this we have now used a table displaying the MICs for the six strains as panel A. We have also included the concentration of teicoplanin used for each experiment in the legend.

-  Line 413: Consider more precise language than "the cell wall is stronger". E.g. More crosslinks?

This has been edited in the revised manuscript (Line 421)

-  Line 415: Consider changing "altered" to a directional term such as increases. It can be difficult for the reader to follow the expected change when you are discussing how the lack of a gene versus the presence of a gene changes susceptibility in one direction and another phenotype in the opposite direction.

This has been edited in the revised manuscript (Line 423).

-  Figure 7: The conclusions made from panels A and B need to be supported by statistical analyses. It is unclear if these lines are truly different from one another.

These have been included in the revised fig 7.

-  Line 426: I believe "tcaA" is missing following "producing"

This has been corrected in the revised manuscript (Line 434).

-  Line 446: "increase" to "increases"

This has been corrected in the revised manuscript (Line 460).

-  Figure 8C: if one goal of the mouse experiment was to look at survival during transit in whole blood, earlier timepoints are indicated based on the described kinetics of bloodstream dissemination in this model.

The primary goal of this experiment was to see if TcaA contributed positively or negatively to the development of the infection. Work on this protein is ongoing, and so we hope in coming years to be able to provide more detail on its activity in vivo.

-  Line 506: "changes to the structural integrity of peptidoglycan" seems overstated without additional studies.

This has been edited in the revised manuscript (Line 524).

-  Line 564: "represents" to "represent"

This has been corrected in the revised manuscript (Line 603).

-  Line 588: The figures all refer to "100 net". Please confirm the concentration used.

This has been corrected in the revised manuscript (Line 628).

-  Line 609: This refers to capsule production? Is this a copy error from a prior paper?

Yes it is, and has been corrected in the revised manuscript (Line 650).

- Line 763: Please provide the concentrations of arachidonic acid used for each experiment.

This has been included in the revised manuscript (Line 805)

- Line 836 and 837: This mentions a time course for blood culture from the infected mice. Where are these data?

Apologies, this is another cut and paste mistake from another paper, and had been removed.

-  Line 870: please discuss how multiple comparisons testing was handled.

This has been included in the revised manuscript (Line 908).

-  Supplemental figure 5 - Please add statistical analyses to support the conclusions in the manuscript. For example, there appears to be no differences for dalbavancin. Please also italicize tcaA in the legend.

These have been included and corrected in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

Line 65 - I would suggest adding the reference (doi: 10.1128/Spectrum.00116-21), which shows increased mortality in S. aureus bacteremia patients due to agr deficient isolates.

The suggested manuscript shows this effect of Agr dysfunction to be limited to patients with moderate to severe SOFA scores. As such it would require a nuanced description here that we think will detract from the flow of the introduction.

Line 68 - Please add DOI: 10.1016/j.cmi.2022.03.015 as a reference to support the mortality rate in S. aureus bacteremia. A systematic review and meta-analysis provides the highest level of evidence, and this is a contemporary study performed in 2022

This has been included in the revised manuscript (Line 68).

Line 70 - please add supporting reference for this statement

This has been included in the revised manuscript (Line 70).

Figure 2 - This image is low quality and appears pixelated. Please revise

This has been replaced with a higher resolution image in the revised manuscript.

Figure 3c Also appears slightly pixelated

This has been replaced with a higher resolution image in the revised manuscript.

Line 173 - I think it would helpful to mention the catalytic activity encoded by tcaA (aside from mediating sensitivity to glycopeptides) is unknown.

This has been included in the revised manuscript (Line 174)

Line 174 - also confers sensitivity to vancomycin https://doi.org/10.1128/AAC.48.6.1953- 1959.2004

This has been included in the revised manuscript, albeit at a later point than suggested here (Line 406)

Line 209 - did the authors test any other antimicrobial fatty acids such as palmitoleic acid? If common mechanism would also expect decreased sensitivity to other HDFA

No, we focused on arachidonic acid as this is the most relevant antimicrobial fatty acid in serum and it is produced by neutrophils and macrophages during the inflammatory burst.

Figure 4a-D: it would be useful to know what the MIC to these different components is and how that MIC relates to the concentration in human serum

We do not have MICs for all of these compounds tested here but can confirm that the concentrations used are physiologically relevant.

Figure 4b - Can you mention in the legend how the killing assays varied for arachadonic acid versus the other AMPs? I am not immediately clear how this experiment was performed, despite referring to methods

This has been included in the text of revised manuscript (Line 211-213) and the figure legend.

Figure 5 - there is no panel D

This has been corrected in the revised manuscript.

Figure 6a: Lines 328-329 state the experiment was performed in the MIC for each strain. The legend (line 374) states 0.5 ug/ml teicoplanin was used, which is below the MIC for all of the strains tested per supp table 2. Please correct this discrepancy.

This figure has been revised and the additional information included to improve the clarity of this section in the revised manuscript.

Figure 6a: On line 328, the authors state that the tcpA knockout increases the MIC for teicoplanin in each background. Figure 6a is performed in the presence of teicoplanin at 1x the MIC of the wild type (which will be below the MIC for the knockout). Therefore, we know each tcpA mutant will be able to grow in the presence of sub-mic concentrations of teicoplanin. Would a more informative way of conveying this information be to have MIC on the Y axis and background on the X axis?

This has been corrected and clarified in the revised manuscript with a table showing the MICs (fig. 6a).

Figure 6b-c: Similarly, would it be more helpful to show how the MIC varies with the different clinical isolate tcpA mutants?

While MICs have uses in clinical setting, they are a relatively crude and binary (growth V no growth) way to measure and compare sensitivity. For these two groups of isolates the MICs did not vary, which is why we used a concentration that sat that the threshold and quantified growth of all the isolates in this. This information has been added to the legend.

Figure 6e: The figure legends instructs us to refer to supplemental figure 3 to see the densiometry results. However, Figure 6e appears to be 4 conditions (WT and mutant +/- serum) and only examines the cell wall, whereas the supplemental figure refers to two conditions (WT + mutant) and looks at the cell wall and supernatant. I would recommend providing the densitometry data associated with the conditions in figure 6e, especially as differences seem more subtle by eye.

This has been included in the revised manuscript (fig. 6f)

Line 689-691 - description of teicoplanin concentrations used in figure 2. However, no teicoplanin was used in figure 2. Assume is referring to a different figure (figure 6?)

This has been corrected and clarified in the revised manuscript. Line 724.

Please add a section in the methods describing how the MIC was determined for JE2, SH1000 and Newman. Was it performed in CA-MHB or the media that the experiment in figure 6a was performed in. Serum can alter the MIC of several antibiotics

This has been corrected and clarified in the revised manuscript. Line 724-29.

Please add a section to the methods describing the whole blood killing assay, ideally describing how the blood was not frozen and used same day as venipuncture. This is important as freeze/thaw or time periods >12 hours are likely to severely effect the function of phagocytes, especially neutrophils.

This has been corrected and clarified in the revised manuscript. Lines 635-639

Line 588: ng/ul should read ng/µl

This has been corrected in the revised manuscript too ng/ml. Line 628

Reviewer #3 (Recommendations For The Authors):

We have now included a graphical abstract (Fig. 9)

Major:

1-    Line 102: I was not able to find the accession numbers of these 300 genomes, did the authors submit it to any public repository (e.g. NCBI)?

These were submitted previously to a public repository and the associated reference cited, but we have provided these in supplementary Table 1.

Minor:

1 -    Typo in line 133. Fix parenthesis after CC22.

Corrected.

2 -    Typo: Fix figure 5 panels (5e should be 5d).

Corrected.

3 -    Line 276: It is not clear why the extract for this experiment was supplemented at 2% while the other part of the experiment was done with 10%. Clarification is needed.

The experiments at 10% was using overnight supernatant, whereas those with 2% was a purified WTA extract. This has been clarified in the revised manuscript (lines 283 and in the figure legend)

4 -    Line 278: Typo: Figure 6e should be figure 5d.

Corrected. (Line 278)

5 -    Figure 5f: There is no explanation in the text or in the figure legend what the purpose of using mprF was.

A comment has been included in the figure legend.

6 -    Line 328: It would be good if we the authors reports the CC of Newman and SH1000 for a better context for the readers.

This has been added. (Line 332)

7 -    Line 341: Did the authors mean less sensitive to teicoplanin?

Corrected. (Line 342)

8 -    Line 367: Dose dependent effect does not seem to be followed not only in panel H of Supp. Fig. 4(LL37 and EMRDA15) but also panels C, D and G.

Corrected.

9 -    Line 587: Typo: Table 2.

These have all been corrected and/or clarified in the revised manuscript.

reply to u/enabeh at https://www.reddit.com/r/Zettelkasten/comments/13s6dsg/comment/jluovm9/?utm_source=reddit&utm_medium=web2x&context=3

If you're curious, I've been collecting examples of teachers/professors who used their zettelkasten for teaching: https://hypothes.is/users/chrisaldrich?q=tag%3A%27card+index+for+teaching%27 Examples include Mario Bunge, Frederic L. Paxson, Gotthard Deutsch, Roland Barthes, and Joachim Jungius. In more recent contexts, I've seen Dan Allosso (aka u/danallosso), Mark Robertson (aka calhistorian u/calhistorian), and Sean Graham (https://electricarchaeology.ca/) using zettelkasten or linked notes using Obsidian, Roam, etc. for teaching. Perhaps we should get the group together to trade stories? Ping me with an email if you're interested.

reply to u/muhlfriedl by way of reply to u/chounosumuheya at https://www.reddit.com/r/Zettelkasten/comments/13s6dsg/comment/jlpt8ai/?utm_source=reddit&utm_medium=web2x&context=3

Examples of zettelkasten users

S.D. Goitein, Beatrice Webb, Ludwig Wittgenstein, Harold Innis, Victor Margolin, Eminem, Aby Warburg, Antonin Sertillanges, Jacques Barzun, C. Wright Mills, Gotthard Deutsch, Roland Barthes, Umberto Eco, Vladimir Nabokov, Gerald Weinberg, Michael Ende, Twyla Tharp, Hans Blumenberg, Keith Thomas, Arno Schmidt, Mario Bunge, Sönke Ahrens, Dan Allosso for a few more. If you go with those who used commonplace books and waste books, which are notebook-based instead of index card-based, there are thousands upon thousands more.

Historically the easier question might be: what creators didn't use one of these systems and was successful?!? The broad outlines of these methods go back much, much farther than Niklas Luhmann. These patterns are not new...

Personally, I've used my own slip box to write large portions of the articles on my website. I also queried it to compile this reply.

t:Rythmisierung t:Stundenplan

• "gestation rewires your brain in fundamental ways um you it rewire it primes you for caretaking as a as a mother in a way which is far more visceral and far it's it's pre-rational it's it's immensely transformative experience and it's permanent you know once you've been rewired for mummy brain you'd never really go back um and that from the point of view of raising a child that matters um because when after a baby is born it's you know as winnicott once said you know there's no such thing as a baby there's only a baby and someone there's a a baby doesn't exist as an independent entity until it's some years some years into its life arguably quite a few years into its life um and what I would say about artificial wounds is that you may be you may think that what you're doing is creating a baby without the misery of gestation but what you're doing in practice is creating a baby without creating a mother because a pregnancy doesn't just create a baby it also creates a mother"

• Comment

  • This is alluding to our altricial nature, which makes us human INTERbeings from before birth
  • https://jonudell.info/h/facet/?max=100&expanded=true&user=stopresetgo&exactTagSearch=true&tag=altricial
  • We are socially entangled even before our birth

• "I think we are very good at honing in on the ways in which the world remains imperfect and there are ways in which it is egregiously unfair today but we discount the fact that so many of the gains of the last 100 to 250 years have been enabled by the Industrial Revolution have been enabled by harnessing the hubris of harnessing fossil fuels harnessing more energy from the environment allowing us to agglomerate in cities which when you do this when you collect all of people in a room like this you're actually creating a more powerful hive mind by bringing intelligence together so that it can share ideas at closer range and it can innovate faster and through that for all the trade-offs which are undeniable there's many negatives that have come from that we're very quick to Discount when we talk about future biomedicine very quick to Discount things like polio vaccines and the virtual eradication of that disease along with smallpox of the fact that we have got so many infectious diseases under control we struggle with the big Killers like cancer and heart disease at the moment those are sort of like the biggest Global threats um but through basic Innovations through Modern Sanitation through better housing all of which the Industrial Revolution enabled we have lifted so many people out of poverty and yes we created new tears of poverty but overall fewer people are living in abject poverty today than in the past we have the higher average global life expectancies child mortality is plummeted the fact that you can give birth by cesarean section rather than in the case of my mother giving birth to a dead child which is what would have happened to me because my umbilical cord was wrapped twice around my neck the fact that technology can intervene and bring us so many of these Spoils of modernity that we readily take for granted I don't know where there's obviously attention but I don't know at what point you say we want to hit pause or indeed we want to go backwards again the challenge sort of remains like we agree we're barreling on this trajectory if we're not going to get off it then we need to think about how we manage it as well as possible and that means we need to think about how AI becomes a healthy part of our world or indeed if it can cut it can we co-exist with AI"
• Comment
  • The progress trap is the great unseen elephant in the room of progress
  • when we understand how the limits of human nature create the shadow side of progress every step of the way,
  • it is then no surprise or paradox that we arrive at this place called the Anthropocene - where the climax of technological progress and - potential for complete extinction sit side-by-side
  • https://jonudell.info/h/facet/?max=100&expanded=true&user=stopresetgo&exactTagSearch=true&tag=progress+trap

• quote

  • "it is as if man had been suddenly appointed managing director of the biggest business of all the business of evolution appointed without being asked if he wanted it and without proper warning and preparation what is more he can't refuse the job whether he wants to or not whether he is conscious of what he is doing or not he is in point of fact determining the future direction of evolution on this earth that is his inescapable Destiny and the sooner he realizes it and starts believing in it the better for all concerns"
  • Julian Huxley

• Comment

  • Huxley was prescient in making this observation,
    • as our species
      • cumulative cultural evolution, and
      • the impact of our niche construction
      • https://jonudell.info/h/facet/?max=100&expanded=true&user=stopresetgo&exactTagSearch=true&tag=cumulative+cultural+evolution
    • does now indeed more or less determine the entire course of evolution of life on earth

@Will Thanks for always keeping up with your regular threads and considerations.

I've been keeping examples of people talking about the "magic of note taking" for a bit. I appreciate your perspectives on it. Personally I consider large portions of it to be bound up with the ideas of what Luhmann termed as "second memory", the use of ZK to supplement our memories, and the serendipity of combinatorial creativity. I've traced portions of it back to the practices of Raymond Llull in which he bound up old mnemonic techniques with combinatorial creativity which goes back to at least Seneca.

A web search for "combinatorial creativity" may be useful, but there's a good attempt at what it entails here: https://fs.blog/seneca-on-combinatorial-creativity/

Visit us today!

Visit us today!

Reply to Nick at https://forum.zettelkasten.de/discussion/comment/17926/#Comment_17926

Some pieces of social media come close to the sort of sense making and cognition you're talking about, but none does it in a pointed or necessarily collaborative way. The Hypothes.is social annotation tool comes about as close to it as I've seen or experienced beyond Wikipedia and variations which are usually a much slower boil process. As an example of Hypothes.is, here's a link to some public notes I've been taking on the "zettekasten output problem" which I made a call for examples for a while back. The comments on the call for examples post have some rich fodder some may appreciate. Some of the best examples there include videos by Victor Margolin, Ryan Holiday (Robert Greene's protoge), and Dustin Lance Black along with a few other useful examples that are primarily text-based and require some work to "see".

For those interested, I've collected a handful of fascinating examples of published note collections, published zettelkasten, and some digitized examples (that go beyond just Luhmann) which one can view and read to look into others' practices, but it takes some serious and painstaking work. Note taking archaeology could be an intriguing field.

Dan Allosso's Obsidian book club has kept up with additional books (they're just finishing Rayworth's Doughnut Economics and about to start Simon Winchester's new book Knowing What We Know, which just came out this month.) Their group Obsidian vault isn't as dense as it was when they started out, but it's still an intriguing shared space. For those interested in ZK and knowledge development, this upcoming Winchester book looks pretty promising. I'd invite everyone to join if they'd like to.

Reviewer #1 (Public Review):

Specifically controlling the level of proteins in bacteria is an important tool for many aspects of microbiology, from basic research to protein production. While there are several established methods for regulating transcription or translation of proteins with light, optogenetic protein degradation has so far not been established in bacteria. In this paper, the authors present a degradation sequence, which they name "LOVtag", based on iLID, a modified version of the blue-light-responsive LOV2 domain of Avena sativa phototropin I (AsLOV2). The authors reasoned that by removing the three C-terminal amino acids of iLID, the modified protein ends in "-E-A-A", similar to the "-L-A-A" C-terminus of the widely used SsrA degradation tag. The authors further speculated that, given the light-induced unfolding of the C-terminal domain of iLID and similar proteins, the "-E-A-A" C-terminus would become more accessible and, in turn, the protein would be more efficiently degraded in blue light than in the dark.

Indeed, several tested proteins tagged with the "LOVtag" show clearly lower cellular levels in blue light than in the dark. While the system works efficiently with mCherry (10-20x lower levels upon illumination), the effect is rather modest (2-3x lower levels) in most other cases. Accordingly, the authors propose to use their system in combination with other light-controlled expression systems and provide data validating this approach. Unfortunately, despite the claim that the "LOVtag" should work faster than optogenetic systems controlling transcription or translation of protein, the degradation kinetics are not consistently shown; in the one case where this is done, the response time and overall efficiency are similar or slightly worse than for EL222, an optogenetic expression system.

The manuscript and the figures are generally very well-composed and follow a clear structure. The schematics nicely explain the underlying principles. However, limitations of the method in its main proposed area of use, protein production, should be highlighted more clearly, e.g., (i) the need to attach a C-terminal tag of considerable size to the protein of interest, (ii) the limited efficiency (slightly less efficient and slower than EL222, a light-dependent transcriptional control mechanism), and (iii) the incompletely understood prerequisites for its application. In addition, several important controls and measurements of the characteristics of the systems, such as the degradation kinetics, would need to be shown to allow a comparison of the system with established approaches. The current version also contains several minor mistakes in the figures.

Reviewer #2 (Public Review):

In this manuscript the authors present and characterize LOVtag, a modified version of the blue-light sensitive AsLOV2 protein, which functions as a light-inducible degron in Escherichia coli. Light has been shown to be a powerful inducer in biological systems as it is often orthogonal and can be controlled in both space and time. Many optogenetic systems target regulation of transcription, however in this manuscript the authors target protein degradation to control protein levels in bacteria. This is an important advance in bacteria, as inducible protein degradation systems in bacteria have lagged behind eukaryotic systems due to protein targeting in bacteria being primarily dependent on primary amino acid sequence and thus more difficult to engineer. In this manuscript, the authors exploit the fact that the J-alpha helix of AsLOV2, which unwinds into a disordered domain in response to blue light, contains an E-A-A amino acid sequence which is very similar to the C-terminal L-A-A sequence in the SsrA tag which is targeted by the unfoldases ClpA and ClpX. They truncate AsLOV2 to create AsLOV2(543) and combine this truncation with a mutation that stabilizes the dark state to generate AsLOV2*(543) which, when fused to the C-terminus of mCherry, confers light-induced degradation. The authors do not verify the mechanism of degradation due to LOVtag, but evidence from deletion mutants contained in the supplemental material hints that there is a ClpA dominated mechanism. They demonstrate modularity of this LOVtag by using it to degrade the LacI repressor, CRISPRa activation through degradation of MCP-SoxS, and the AcrB protein which is part of the AcrAB-TolC multidrug efflux pump. In all cases, measurement of the effect of the LOVtag is indirect as the authors measure reduction in LacI repression, reduction in CRISPRa activation, and drug resistance rather than directly measuring protein levels. Nevertheless the evidence is convincing, although seemingly less effective than in the case of mCherry degradation, although it is hard to compare due to the different endpoints being measured. The authors further modify LOVtag to contain a known photocycle mutation that slows its reversion time in the dark, so that LOVtag is more sensitive to short pulses of light which could be useful in low light conditions or for very light sensitive organisms. They also demonstrate that combining LOVtag with a blue-light transcriptional repression system (EL222) can decrease protein levels an additional 269-fold (relative to 15-fold with LOVtag alone). Finally, the authors apply LOVtag to a metabolic engineering task, namely reducing expression of octanoic acid by regulating the enzyme CpFatB1, an acyl-ACP thioesterase. The authors show that tagging CpFatB1 with LOVtag allows light induced reduction in octanoic acid titer over a 24 hour fermentation. In particular, by comparing control of CpFatB1 with EL222 transcriptional repression alone, LOVtag, or both the authors show that light-induced protein degradation is more effective than light-induced transcriptional repression. The authors suggest that this is because transcriptional repression is not effective when cells are at stationary phase (and thus there is no protein dilution due to cell division), however it is not clear from the available data that the cells were in stationary phase during light exposure. Overall, the authors have generated a modular, light-activated degron tag for use in Escherichia coli that is likely to be a useful tool in the synthetic biology and metabolic engineering toolkit.

Reviewer #3 (Public Review):

The authors present the mechanism, validation, and modular application of LOVtag, a light-responsive protein degradation tag that is processed by the native degradosome of Escherichia coli. Upon exposure to blue light, the c-terminal alpha helix unfolds, essentially marking the protein for degradation. The authors demonstrate the engineered tag is modular across multiple complex regulatory systems, which shows its potential widespread use throughout the synthetic biology field. The step-by-step rational design of identifying the protein that was most dark-stabilized as well as most light-responsive for degradation, was useful in terms of understanding the key components of this system. The most compelling data shows that the engineered LOVTag can be fused to multiple proteins and achieve light-based degradation, without affecting the original function of the fused protein; however, results are not benchmarked against similar degradation tagging and optogenetic control constructs. Creating fusion proteins that do not alter either of the original functions, is often difficult to achieve, and the novelty of this should be expanded upon to drive further impact.

WAT

Reviewer #1 (Public Review):

This is a generally well-written manuscript that elegantly begins to explore the molecular basis of exosome release under conditions of sheer stress or calcium influx. The authors use a sensitive luciferase assay that enables them to monitor the release of exosomes from CD63-tag-expressing cells. Upon SLO pore formation or sheer stress, cells release exosomes in a calcium-dependent manner; MVBs are (indirectly) shown to undergo calcium-dependent plasma membrane fusion in a process that depends on a set of 4 proteins that were identified by an unbiased analysis of proteins that associate with MVBs. One of these is Annexin A6, a protein shown by several other groups to participate in membrane repair. Thus, calcium triggers the binding of 4 proteins to the surface of MVBs, and likely also to the plasma membrane, driving MVB fusion at the cell surface. The authors also present a semi-intact cell system that will permit functional analysis of the MVB fusion process.

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Referee #3

Evidence, reproducibility and clarity

The authors of this study utilize a novel nanobody-based technique to specify the location of the SPT complex to either the peripheral or nuclear membrane-associated endoplasmic reticulum membranes. Considering the potential importance of sub-ER compartmentalization on metabolic enzymes of the ER, this is a novel and useful approach. The studies are, with the minor exceptions noted below, comprehensive and very well executed and documented. The authors have combined genetic, proteomic, lipidomic, and flux experimental approaches to test whether sub-ER compartmentalization affects the function and regulation of the SPT complex. The results are, for the most part, negative, although there does seem to be some effect on the overall activity of the SPT complex as measured with flux analysis. Overall, while the authors do not detect dramatic effects on SPT complex localization, the technical advance using tethered nanobodies to direct complex localization, and the complementary approaches to testing SPT function and regulation, will be useful to workers in the sphingolipid field.

Minor points:

The results with YPK1-linker-CAAX are confusing. This construct does not result in Orm2 phosphorylation with heat shock, whereas endogenous YPK1 does. Yet it can support viability even without Orm deletion. In other words, this tethered construct appears functional in viability assays, but not in a biochemical assay.This discrepancy is not discussed by the authors. The manuscript would be improved by a discussion by the authors that addresses this issue. It is not clear why the figure legend to Figure 2 suggests that Ypk1 regulates Orms mainly in the peripheral ER. Considering that WT Ypk1 is more efficient than CAAX tethered YPK1, this statement does not seem supported. Perhaps the authors can elaborate on how they came to this conclusion.

The figures depicting Orm phosphorylation (Figure 1e, f Figure 2d,e, Figure 6 b,c) should be improved. The resolution of two forms is not sufficient in Figure1 and 2. The use of Phos-Tag might solve this issue. It would be helpful to the reader to include arrows that indicate the phosphorylated and unphosphorylated forms of Orm. Quantitation of these gels is essential.

Lines 318 and 319. Figure 6e and 6f are referred to. The correct assignment is 6f and 6g.

Referees cross-commenting

I agree with Reviewer #2's assessment that some of the conclusions are over stated. While Reviewer #2 is correct that the advances in this manuscript are modest, this is principally because expected differences in the function and regulation of the SPT in different ER sub-domains did not materialize. This may be disappointing, but is still important to document

Significance

This is a very well performed study, utilizing a variety of approaches to test whether localization of the SPT complex impacts on it activity and regulation. With very minor exceptions, it is well executed and documented.

The advances reported here are two-fold. First, the authors introduce a novel approach using nanobodies that are tethered to distinct regions of the yeast endoplasmic reticulum to localize intact and unmodified complexes to distinct locations. This could be a very useful tool in other contexts to examine the role of subcellular compartmentalization in the function of enzymes and signaling components. This targeting system is well characterized in this study. The second advance, utilizing this targeting system, is that localization of the SPT complex to distinct subcompartments of the ER has minimal effects on regulation, and observable, but relatively minor effects on SPT function in terms of sphingolipid production. While a positive result would have been more exciting, negative results can be equally informative.

This study will be of interest to workers in the signaling and metabolic fields that may utilize this unique targeting strategy. It will also be of interest to the sphingolipid community.

Reviewer #3 (Public Review):

Dux (or DUX4 in human) is a master transcription factor regulating early embryonic gene activation and has garnered much attention also for its involvement in reprogramming pluripotent embryonic stem cells to totipotent "2C-like" cells. The presented work starts with the recognition that DUX contains five conserved c. 100-amino acid carboxy-terminal repeats (called C1-C5) in the murine protein but not in that of other mammals (e.g. human DUX4). Using state-of-the-art techniques and cell models (BioID, Cut&Tag; rescue experiments and functional reporter assays in ESCs), the authors dissect the activity of each repeat, concluding that repeats C3 and C5 possess the strongest transactivation potential in synergy with a short C-terminal 14 AA acidic motif. In agreement with these findings, the authors find that full-length and active (C3) repeat containing Dux leads to increased chromatin accessibility and active histone mark (H3K9Ac) signals at genomic Dux binding sites. A further significant conclusion of this mutational analysis is the proposal that the weakly activating repeats C2 and C4 may function as attenuators of C3+C5-driven activity.

By next pulling down and identifying proteins bound to Dux (or its repeat-deleted derivatives) using BioID-LC/MS/MS, the authors find a significant number of interactors, notably chromatin remodellers (SMARCC1), a histone chaperone (CHAF1A/p150) and transcription factors previously (ZSCAN4D) implicated in embryonic gene activation.

The experiments are of high quality, with appropriate controls, thus providing a rich compendium of Dux interactors for future study. Indeed, a number of these (SMARCC1, SMCHD1, ZSCAN4) make biological sense, both for embryonic genome activation and for FSHD (SMCHD1).

A critical question raised by this study, however, concerns the function of the Dux repeats, apparently unique to mice. While it is possible, as the authors propose, that the weak activating C1, C2 C4 repeats may exert an attenuating function on activation (and thus may have been selected for under an "adaptationist" paradigm), it is also possible that they are simply the result of Jacobian evolutionary bricolage (tinkering) that happens to work in mice. The finding that Dux itself is not essential, in fact appears to be redundant (or cooperates with) the OBOX4 factor, in addition to the absence of these repeats in the DUX protein of all other mammals (as pointed out by the authors), might indeed argue for the second, perhaps less attractive possibility.

In summary, while the present work provides a valuable resource for future study of Dux and its interactors, it fails, however, to tell a compelling story that could link the obtained data together.

• tag在一般情况下是由硬件自动设置和管理的，不可由软件直接修改。

如果是　２－ｗａｙ组相联或者　４－ｗａｙ组相联，其他不变，会发生啥？ 对于４－ｗａｙ组相联场景： * ３２KB的可以分成，３２KB　／　６４　＝　５１２条ｃａｃｈｅ　ｌｉｎｅ。 * 因为有４ｗａｙ，于是会每一ｗａｙ有　５１２　／　４　＝　１２８　条　ｃａｃｈｅ　ｌｉｎｅ。 * 于是每一路就有　１２８　＊　６４　＝　8192　ｂｙｔｅｓ的内存，即８ｋＢ。 为了方便索引内存地址，ｔａｇ和ｏｆｆｓｅｔ不变，只有ｉｎｄｅｘ需要调整。 * ｉｎｅｘ：内存地址后续的7个ｂｉｔｓ则是在这一ｗａｙ的是ｃａｃｈｅ　ｌｉｎｅ索引，２＾７　＝　１２８刚好可以索引１２８条ｃａｃｈｅ　ｌｉｎｅ。 对于　２－ｗａｙ场景以后补充。

Louise Bennett, a radio talkshow host for the JBC, sought to show her respect for her roots, even advocating that standard English ought to be the spoken language because of the social stigma related to speaking in the island country's dialect. She added that many people still did not accept that for many Caribbean people, there were many things best said in the language of the folk. (Davidson par.4). 

Despite being overlooked for decades Miss Lou had a following. She was featured in the Independence Anthology of Jamaican Literature in 1962, but not in the poetry section. In the 1970s Miss Lous following finally broke the stalemate that placed Louise Bennett as a guru of the Jamaican Creole receiving the Order of Jamaica. (September 7 th has been officially declared Miss Lou Day. Known as Miss Lou, that is to say the honorable Louise Bennett-Coverley who was born in Kingston Jamaica in 1919 to a widowed dressmaker. Miss Lou is highly esteemed as the queen of comedy her persona is known for highlighting, commemorating, and exploring Jamaican heritage (Davidson par 3).

September 7 th has been officially declared Miss Lou Day. Known as Miss Lou, that is to say the honorable Louise Bennett-Coverley who was born in Kingston Jamaica in 1919 to a widowed dressmaker. Miss Lou is highly esteemed as the queen of comedy her persona is known for highlighting, commemorating, and exploring Jamaican heritage (Davidson par 1-2).

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Reply to the reviewers

Reply to reviewers.

We deeply thank the reviewers for the time spent on evaluating our manuscript as well as providing comments and suggestions to improve our study.

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

*In this manuscript Lebdy et al. describe a new role of GNL3 in DNA replication. They show that GNL3 controls replication fork stability in response to replication stress and they propose this is due to the regulation of ORC2 and the licensing of origins of replication. Their data suggest that GNL3 regulates the sub nuclear localization of ORC2 to limit the number of licensed origins of replication and to prevent resection of DNA at stalled forks in the presence of replication stress.

While many of the points of the manuscript are proven and well supported by the results, there are some experiments that could improve the quality and impact of the manuscript. The main issue is that the connection between the role of GNL3 in controlling ORC2, the firing of new origins and the protection of replication forks is not clearly established. At the moment the model relies on mainly correlative data. In order to further substantiate the model, we propose to address some of the following issues:*

1. *The authors indicate that RPA and RAD51 accumulation at stalled forks is not affected by GNL3 depletion. These data should be included and other proteins should be analysed. In addition, the role of helicases could be explored through the depletion of the main helicases involved in the remodelling of the forks. * Response: As asked by the reviewer we will add the fractionation experiments that show that the level of RAD51 and RPA on chromatin is not affected by GNL3 depletion. So far, the other proteins we checked (RIF1 and BRCA1), both involved in nascent strand protection, did not show clear differences. Therefore, we concluded that depletion of GNL3 does not seem to affect the recruitment of major proteins required for protection of nascent DNA. Of course, we cannot exclude that other proteins may be affected by GNL3 depletion, but testing all the possible candidates would be time consuming with a very low chance of success. In addition, fractionation experiments are possibly not quantitative enough to uncover small differences and may be not that informative. Thus it remains possible that RPA exhaustion may be the cause of resection in absence of GNL3 as suggested by the work conducted in Lukas’ lab (Toledo et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24267891/). To test this hypothesis, we will analyze if resection in absence of GNL3 is still occurring in a well-characterized cell line that overexpress the three RPA subunits that we obtained from Lukas’ lab.

To our knowledge not many helicases have been shown to be involved in remodeling of stalled forks. The best example is RECQ1, however we feel that testing RECQ1 involvement in resection upon GNL3 depletion will complicate our story without adding much regarding the mechanism. We hope the reviewer understands our concern.

• The proposed model implies that GNL3 depletion leads to increased origin licensing. FThe authors should address if the primary effect of GNL3 depletion is on origin firing by using CDC7 inhibition in the absence of stress (Rodríguez-Acebes et al., JBC 2018). *

__Response: __This is an excellent point raised by the reviewer. To test if the primary effect of GNL3 depletion in on origin firing we will test if the defect in replication fork progression is dependent on CDC7 using DNA fibers experiments and CDC7 inhibitor.

• A way to prove that origin firing mediates the effect of GNL3 on fork protection would be to reduce the number of available origins. The depletion of MCM complexes has been shown to limit the number of back-up origins that are licensed and leads to sensitivity to replication stress (Ibarra et al., PNAS 2008). If GNL3 depletion results in increased number of origins, this effect should be prevented by the partial depletion of MCM complexes. *

__Response: __This is also an excellent point. We will test if MCM depletion decreases resection upon GNL3 depletion and treatment with HU. In addition, we will integrate in the manuscript experiments that we have done recently that show that treatment with roscovitine, a CDK inhibitor that impairs origin firing, decreases the level of resection observed in absence of GNL3. We think this experiment strengthens the results obtained with CDC7 inhibitors.

*Alternatively, the authors could try to modulate the depletion of GNL3. Origin licensing takes place in the G1 phase and thus the depletion of GNL3 by siRNA could affect the following S phase. Using an inducible degron for GNL3 depletion would allow to deplete GNL3 in G1 or S phase specifically. If the model is correct, the removal of GNL3 in S phase should not affect fork protection but removing GNL3 in the previous G2/M phase should reduce the number of licensed origins and lead to impaired fork protection. *

__Response: __This is obviously a good point given the fact that GNL3 deletion is not viable (see responses to reviewer 2). We tried to develop an auxin induced degron of GNL3, but we could not obtain homozygous clones, meaning that our clones had always an untagged GNL3 allele. Since GNL3 is essential its tagging may impair its function, explaining why we could not obtain homozygous clones. However, we are planning to optimize the design using other degrons system (for instance Halo-tag) to address the role of GNL3 specifically during S-phase. But we think this is above the scope of the present study.

*In addition to the connection GNL3-origin firing-fork protection, it is unclear how the lack of GNL3 in the nucleolus and the change in the sub nuclear localization of ORC2 controls origin firing and resection. The strong interaction observed between GNL3-dB and ORC2, and the subsequent change in ORC2 localization does not explain how origin licensing can be affected. In this sense, the authors could address: *

1. *Does the depletion of GNL3 and the expression of GNL3-dB affect the formation of the ORC complex, its subnuclear localization or its binding to chromatin? The authors have not explored if the interaction of GNL3 with ORC2 is established in the context of the ORC complex. An IF showing NOP1 with PLA data from GNL3-dB and ORC2 is needed to analyse how the expression of increasing amounts of GNL3-dB affects ORC2. * __Response: __We tested if GNL3 depletion impacts ORC2 and ORC1 recruitment on chromatin, but we could not observe significant differences. No clear differences were observed upon GNL3-dB expression either. One reason for this may be due to the excess of ORC complex on the chromatin, in addition chromatin fractionation is likely not sensitive enough to observe small differences. We think that quantitative ChIP-seq of ORC2 or other ORC subunits upon GNL3 depletion is required to visualize such differences, but this is above the scope of the study, and this constitutes the following of this project. We also tried to look at subnuclear localization of ORC2 using immunofluorescence, but the signal was not specific enough to observe differences. We think that the increased interaction (PLA) of ORC2 with GNL3-dB (Figure 5E) demonstrates a change in ORC2 subnuclear localization. To confirm this, we will perform the excellent experiment proposed by the reviewer to test if increasing level of GNL3-dB affects its interaction with ORC2 using PLA.

We do not think that the interaction between ORC2 and GNL3 is established in the context of the ORC complex since only ORC2 (and not the other ORC) was significantly enriched in the GNL3 Bio-ID experiment. The full list of proteins from the Bio-ID experiment (Figure 4A) will be provided in the revised version. Therefore, we think that either GNL3 regulates ORC2 subnuclear localization that in turns impact the ORC complex or GNL3 regulates ORC2-specific functions. More and more evidences show that ORC2 plays roles possibly independently of the ORC complex (see Huang et al. 2016 https://doi.org/10.1016/j.celrep.2016.02.091 or Richards et al. 2022 https://doi.org/10.1016/j.celrep.2022.111590 for instance). Future work should uncover how these ORC2 functions may regulate origins activity.

*In order to confirm if the mislocalization of ORC2 by the expression of GNL3-dB increases origin firing and mediates the effects on fork protection the authors could check DNA resection levels inhibiting CDC7 in high GNL3-dB conditions. Also, the levels of MCM2, phosphor-MCM2, CDC45, have not been analysed upon expression of GNL3-dB. *

__Response: __This is a good point; we will test if the resection observed upon expression of GNL3-dB is dependent on origin firing using CDC7 inhibitor. We have not measured the level of the cited proteins but instead we performed DNA combing to measure Global Instant Fork Density. We now show that expression of GNL3-WT suppresses the increased origin firing observed upon GNL3 depletion, in contrast expression of GNL3-dB does not suppress it. This important result indicates that origin firing is increased upon GNL3-dB expression, providing a link between aberrant localization and increased firing. These data will be part of the revised version of the manuscript.

The data in the paper suggest that GNL3 may affect the role of ORC2 in centromeres. Since depletion of GNL3 leads to increased levels of gH2AX, it would be interesting to address if this damage is due to incomplete replication in centromeres by analysing the co-localization of g*H2AX and centromeric markers both in unstressed conditions and upon the induction of replication stress. *

__Response: __This is indeed and interesting comment, however since it has been previously shown that gH2AX signal is rather strong upon GNL3 depletion (see Lin et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24610951/ ; Meng et al. 2013. https://pubmed.ncbi.nlm.nih.gov/23798389/) we do not think that co-localization experiments with CENP-A for instance will be informative given the high number of gH2AX foci.

*Minor points: *

1. In the initial esiRNA screen the basal levels of g*H2AX should also be shown. * Response: Our negative control is the transfection of an esiRNAs that targets EGFP (a gene that is not expressed in the tested cell line). This esiRNAs is ranked at the end of the list and therefore constitutes the basal level of gH2AX signal. In any case it is well-established that GNL3 depletion increases gH2AX signal (see Lin et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24610951/ ; Meng et al. 2013. https://pubmed.ncbi.nlm.nih.gov/23798389/).

*Figure EV1B: I think the rank needs another RS mark to see better the effect of each esiRNA on DNA lesions (high variability in all the conditions showed). *

__Response: __We understand this issue, but we cannot repeat this set of experiments for technical reasons (reagents and cost mainly). Anyway, we believe that the role of GNL3 is response to replication stress is extensively addressed by other experiments of this manuscript.

*Figure 1C and Figure EV1D/E: the quantification of the pCHK1/CHK1 levels could be included to show that there are no changes in phosphorylation upon GNL3 depletion. *

Response: it is a good point; we will put quantification in the revised version.

*In the first section of the results, at the end Figure 4B is incorrectly called for. *

__Response: __Thanks for the comment, we will modify accordingly.

The levels of GLN3 expression in 293 cells should be already included in section GNL3 interacts with ORC2.

__Response: __We will add a figure that shows the level of expression in 293 cells.

The full MS data needs to be included for both GNL3 and ORC2.

__Response: __This will be integrated in the revised version.

Figure 4B should be improved, since there is a faint band in the IgG mouse control.

__Response: __it is true that the figure is not perfect, but we believed that our Bio-ID and PLA experiments fully demonstrate the interaction between GNL3 and ORC2.

__Reviewer #1 (Significance (Required)): __

*The work is nicely written, the figures are well presented and the experiments have the necessary controls. It provides relevant information to understand how replication stress is controlled and linked to replication fork protection through origin firing. These results are relevant to the field, linking GNL3 to origin firing and with potential to help understand the role of GNL3 in cancer. They provide new information and can give rise to new studies in the future. Many of the conclusions of the manuscript are well supported. Additional support for some of the main claims would strengthen the results and also increase the impact providing a bigger conceptual advance by performing some of the suggested experiments. *

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

*This manuscript explores the role of GNL3/nucleostemin in DNA replication and specifically in the response of DNA replication to DNA damage. GNL3 is a predominantly nucleolar protein, previously characterised as a GTP-binding protein and shown to be necessary for effective recruitment of the RAD51 recombinase to DNA breaks. The entry point for this report is a mini screen, based on proteins identified previously by the authors to associate with replication forks by iPOND, for factors that increase gamma-H2Ax (an indicator of DNA damage) after treatment with the Top1 inhibitor camptothecin (CPT). In this mini-screen GNL3 emerged as the top hit.

The authors put forward the hypothesis that GNL3 is able to sequester the replication licensing factor ORC2 in the nucleolus and that failure of this mechanism leads to excessive origin firing and DNA resection following CPT treatment.*

• The model put forward is interesting, but currently rather confusing. However, for the reasons upon which I expand below, I do not believe that the data provide a compelling mechanistic explanation for the effects that are reported and I am left not being certain about some of the links that are made between the various parts of the study, even though individual observations appear to be of good quality. *

*Specific points: *

*The knockdown of GNL3 is very incomplete. In this regard, the complementation experiments are welcome and important. However, is it an essential protein? Can it be simply deleted with CRISPR-Cas9?

*__Response: __There are obviously variations between experiments but overall, the depletion of GNL3 using siRNA seems good in our opinion. Deletion of GNL3/nucleostemin leads to embryonic lethality in mouse (Beekman et al. 2006. https://pubmed.ncbi.nlm.nih.gov/17000755/ ; Zhu et al. 2006. https://pubmed.ncbi.nlm.nih.gov/17000763/). ES cells deleted for GNL3 can be obtain but do not proliferate probably because of their inability to enter in S-phase (Beekman et al. 2006. https://pubmed.ncbi.nlm.nih.gov/17000755/). We wanted to test if it was the case in our cellular model and we tried to delete it using CRISPR-Cas9. We managed to obtain few clones deleted for GNL3, but they grow really poorly prevented us to do experiments. To bypass this, and as suggested by the reviewer 1, we tried to make an auxin-induced degron of GNL3. Unfortunately, we did not manage to obtain homozygous clones, only heterozygous. One possibility could be that the tagging induced a partial loss of function of GNL3, and since GNL3 is essential, it may explain why we did not obtain homozygous clones. We may also want to use alternative degron systems such as Halo-Tag, but we believe this is out of the scope of the study.

__ __*Global instant fork density is not quite the same as actually measuring origin firing. Ideally, it would be good to see some more direct evidence of addition origin firing e.g. by EdU-seq (Macheret & Halazonetis Nature 2018) but this would be quite a significant additional undertaking. However, given the authors have performed DNA combing with DNA counterstain, they should be able to provide accurate measurements of origin density and inter-origin distance. *

__Response: __As indicated by the reviewer EdU-seq would need a lot of development since we are not using this approach in our team. In addition, this method can detect replication origins only if performed in the beginning of S-phase, meaning that only the early firing origins will be detected and not the others. GIFD measurement is actually directly linked with origin firing since it is counting the forks to duplicate the genome. The measurements of IODs have at least two main limitations: (1) there is a bias for short IODs due to the length of analyzed fibers and (2) it focuses only on origins within a cluster not globally. Overall, we believe that GIFD is the method of choice to measures origins firing. In addition, these experiments have been done by the lab of Etienne Schwob (see acknowledgments), a leader in the field.

*'Replication stress' is induced with CPT. This term is frequently used to describe events that lead to helicase-polymerase uncoupling (e.g. O'Connor Mol Cell 2015) but that is not the case with CPT, which causes fork collapse and breaks. Are similar effects seen with e.g. UV or cisplatin? Additionally, a clear statement of the authors definition of replication stress would be welcome. *

__Response: __We will better define the term ‘replication stress’ in the revised version of the manuscript. It should be understood, in our case, that any impediment that leads to replication fork stalling and measurable by DNA combing or Chk1 phosphorylation. We have not performed experiments using UV and cisplatin.

*It is really not clear how the authors explain the link between potential changes in origin firing and resection. i.e. What is the relationship between global origin firing and resection at a particular fork, presumably broken by encounter with a CPT-arrested TOP1 complex. What is the link mechanistically? This link needs elaborating experimentally or clearly explaining based on prior literature. *

• *__Response: __Most of our results on resection has been performed with hydroxyurea, but it is true that we saw resection in absence of GNL3 in response to CPT. Treatment with HU or CPT reduces fork speed and activates additional replication origins (see Ge et al. 2007 https://pubmed.ncbi.nlm.nih.gov/18079179/ for HU or Hayakawa et al. 2021 https://pubmed.ncbi.nlm.nih.gov/34818230/ for CPT ). When GNL3 is depleted, more forks are active, meaning more targets for HU and CPT. In addition, it is likely that the firing of additional origins in response to HU and CPT is stronger in absence of GNL3. Because of this we believe that factors required to protect stalled forks may be exhausted explaining why resection is observed. This is inspired by the work of Lukas’ lab (Toledo et al. 2013 https://pubmed.ncbi.nlm.nih.gov/24267891/) and is described in the figure 6. One obvious candidate that may be exhausted is RPA, to test this we will check if resection upon GNL3 depletion and treatment with HU is still occurring in cell lines provided by Lukas’ lab that overexpress RPA complex (described in Toledo et al.). We will explain our model more carefully in the revised version.

*Related to this, I remain unconvinced that the experiments in Figure 3 show that the effects of ATRi and Wee1i on origin firing and on resection are contingent on each other. I do not believe that the authors have adequately supported the statement (end of pg 9) 'We conclude that the enhanced resection observed upon GNL3 depletion is a consequence of increased origin firing.' The link between origin firing and resection needs really needs further substantiation and / or explanation.

*__Response: __Our rational was the following. Inhibition of ATR or WEE1 increase replication origin firing, a situation that may be like the one observed for GNL3 depletion. In Toledo et al, they show that inhibition of WEE1 or ATR induces exhaustion of RPA. This exhaustion is reduced in presence of CDC7 inhibitor, roscovitine (a CDK inhibitor that inhibits origin firing) or depletion of CDC45, indicating that this is due to excessive origin activation. In our case we show that the resection observed upon WEE1 or ATR inhibition is reduced upon treatment with CDC7 inhibitor. We conclude that excessive replication origin firing induces DNA resection. Since we observed the same thing upon GNL3 depletion (but not upon BRCA1 depletion) we conclude that excessive origin firing favors DNA resection likely through exhaustion of RPA. As indicated above we will test this hypothesis by overexpressing RPA. In addition, we now show that treatment with roscovitine decreases resection upon GNL3 depletion (this will be part of the revised manuscript), an experiment that we believe confirms that excessive replication origins firing is responsible for resection upon GNL3 depletion. As suggested by reviewer 1, we will also test if depletion of MCM also reduces resection observed in absence of GNL3.

*It is not clear whether the binding of ORC2 to GNL3 also sequesters other components of the origin recognition complex? Does loss of the ability of GNL3 to bind ORC2 actually lead to more ORC bound to chromatin? How does GNL3 contribute to regulation of origin firing under normal conditions? Is it a quantitatively significant sink for ORC2 and what regulates ORC2 release? *

Response: The results of GNL3 Bio-ID were extremely clear, we could not significantly detect any other ORC subunits than ORC2 (these data were not present in the manuscript but will be added in the revised version), therefore we believe that GNL3 may sequester/regulate only ORC2. We tried to see if GNL3 depletion was changing the binding of ORC1 and ORC2 to the chromatin, but we could not see any difference, one possibility may be that small differences are not detectable by chromatin fractionation. We believe that ChIP-seq or ORC2 or other ORC subunits in absence of GNL3 is required but this it out of the scope of the study. GNL3 may regulates the stability of the ORC complex on chromatin via ORC2 but GNL3 may also regulates other ORC2 functions, at centromeres for instance. It has been shown indeed that ORC2 plays roles possibly independently of the ORC complex (see Huang et al. 2016 https://doi.org/10.1016/j.celrep.2016.02.091 or Richards et al. 2022 https://doi.org/10.1016/j.celrep.2022.111590 for instance). How exactly this is affecting origin firing is still mysterious. This is something we are planning to address in the future.

We do not know if it is a quantitatively sink for ORC2 or how this is regulated, however we believe that the ability of GNL3 to accumulate in the nucleolus may sequester ORC2. Consistent with this, we show that a mutant of GNL3 (GNL3-dB) that diffuses in the nucleoplasm interacts more with ORC2 in the nucleoplasm suggesting a release. As suggested by reviewer 1 we will now test if the interaction between ORC2 and GNL3-dB is dependent on the level of expression of GNL3-dB. In addition, we now show that expression of GNL3-dB increases replication origin firing like GNL3 depletion (data that will be added in the revised version), suggesting that regulation of ORC2 is the major cause of increased firing upon GNL3 depletion.

*Minor points: *

*All blots should include size markers *

__Response: __We will add them

*Some use of language is not sufficiently precise. For instance: ** - the meaning of 'DNA lesions' at the end of the first paragraph of the introduction needs to be more explicit. *

* - the approach to measurement of these 'lesions' (monitoring gamma-H2Ax) needs to be spelled out explicitly, e.g. line 4 of the last paragraph of the introduction. *

*

• 'we observed that the interaction between GNL3-dB and ORC2 was stronger' ... I do not see how number of foci indicates necessarily the strength of an interaction. *

* - in many places throughout 'replication origins firing' should be 'replication origin firing' (or 'firing of replication origins'). *

__Response: __We will correct these language mistakes.

__Reviewer #2 (Significance (Required)): __

The model put forward here has the potential to shed light on an important facet of the cellular response to DNA damage, namely the control of origin firing in response to replication stress that will certainly be of interest to the DNA repair / replication community and possibly more widely. The roles of GNL3 are poorly understood and this study could improve this state of affairs. However, the gaps in the mechanism outlined above and somewhat confusing conclusions do limit the ability of the paper to achieve this at present.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

*In this study, Lebdy et al propose a new mechanism to regulate the resection of nascent DNA at stalled replication forks. The central element of this mechanism is nucleolar protein GNL3, whose downregulation with siRNA stimulates DNA resection in the presence of stress induced by HU (Figure 1). Resection depends on the activity of nucleases MRE11 and CtIP, and can be rescued by reintroducing exogenous GNL3 protein in the cells (Figure 1G). GNL3 downregulation decreases fork speed and increases origin activity, without any strong effect on replication timing (Figure 2). Inhibition of Dbf4-dependent kinase CDC7 (a known origin-activating factor) also restricts fork resection (Figure 3). GNL3 interacts with ORC2, one of the subunits of the origin recognition complex, preferentially in nucleolar structures (Figure 4). A mutant version of GNL3 (GNL3-dB) that is not sufficiently retained in the nucleoli fails to prevent fork resection as the WT protein (Figure 5). In the final model, the authors propose that GNL3 controls the levels of origin activity (and indirectly, stalled fork resection) by maintaining a fraction of ORC2 in the nucleoli (Figure 6). *

This model is interesting and provocative, but it also relies on a significant degree of speculation. The authors are not trying to "oversell" their observations, because the Discussion section entertains different interpretations and possibilities, and the model itself contains several interrogative statements (e.g. "ORC2-dependent?"; "exhaustion of factors?").

• While the article is honest about its own limitations, the major concern remains about its highly speculative nature. I have some questions and suggestions for the authors to consider that could contribute to test (and hopefully support) their model. *

• *If GNL3 downregulation induces an excess of licensed origins and mild replicative stress resulting in some G2/M accumulation (Figure 2), what is the consequence of longer-term GNL3 ablation? Do the cells adapt, or do they accumulate signs of chromosomal instability? (micronuclei, chromosome breaks and fusions, etc) * __Response: __This is an important point also raised by Reviewer 2: deletion of GNL3 leads to embryonic lethality in mouse and ES cells deleted for GNL3 do not proliferate and fail to enter into S-phase. Consistent with this, the clones deleted for GNL3 that we obtained using CRISPR-Cas9 grow poorly, thus preventing us to do experiments. To our knowledge micronuclei and chromosome breaks have never been analyzed upon transient depletion of GNL3 using siRNA. However, it is well established that depletion of GNL3 induces phosphorylation of H2A.X) and the formation of ATR, RPA32 and 53BP1 foci due to S-phase arrest (Lin et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24610951/ ; Meng et al. 2013. https://pubmed.ncbi.nlm.nih.gov/23798389/). DNA lesions have also been visualized by comet assay (Lin et al. 2019. https://pubmed.ncbi.nlm.nih.gov/30692636/). Consistent with this we observed a weak increased of DNA double-strand breaks upon GNL3 depletion using pulse-field gel electrophoresis as well as mitotic DNA synthesis (MiDAS). We can integrate this data in the revised version of the manuscript if required. To sum up, it is clear that GNL3 depletion is inducing problems during S-phase that may lead to possible genomic rearrangements.

• The model relies on the link between origin activity and stalled fork resection that is almost exclusively based on the results obtained with CDC7i (Figure 3). But CDC7 has other targets besides pre-RC components at the origins, such as Exo1 (from the Weinreich lab, cited in the study), MERIT40 and PDS5B (from the Jallepalli lab, also cited). The effect of CDC7i could be exerted through these factors, which are linked to fork stability and DNA resection. The loss of BRCA1 (Figure 3F) could somehow entail the loss of control over these factors. Could the authors check the possible participation of these proteins?*

__Response: __It is true that CDC7 has other targets than pre-RC components. We therefore decided to inhibit origin firing using roscovitine, a broad CDK inhibitor, a strategy previously used in Lukas lab (Toledo et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24267891/). We observed that treatment with roscovitine decreased significantly resection observed upon GNL3 depletion, confirming the link between origin activity and stalled fork resection. This will be integrated in the revised version of the manuscript. As asked by Reviewer 1, we will also perform depletion of MCM to strength our model.

Exo1 is indeed a target of CDC7 as shown by the Weinreich lab (Sasi et al. 2018. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111017/) however the authors do not formally demonstrate that Exo1 phosphorylation is required for its activity. We observed that depletion of Exo1 significantly reduced resection upon GNL3 depletion (data that will be added in the revised version), indicating that the effect of CDC7 inhibitor could be exerted via the control of Exo1. This is why our BRCA1 control is important, it is well stablished that Exo1 is required for nascent strand degradation upon BRCA1 depletion (Lemaçon et al. 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643552/) but CDC7 inhibition has no effect on resection upon BRCA1 depletion suggesting that resection by Exo1 may not be regulated by CDC7 in our context.

As stated by the reviewer MERIT40 and PDS5B are targets of DDK kinases (Jones et al. 2021 https://doi-org.insb.bib.cnrs.fr/10.1016/j.molcel.2021.01.004) and seem to be required for protection of nascent DNA and in response to HU. However, little is known about the role(s) of these proteins and we think that adding them will complicate message. We hope the reviewer understands this.

The model also relies on the fact that GNL3-dB mutant (not retained in the nucleoli) is not sufficient to counteract fork resection induced by HU (Figure 5G). The authors should test directly whether GNL3-dB induces extra origin activation, using their available DNA fibers-based technique.

__Response: __This is an excellent point. We have now GIFD (Global Instant Fork Density) data that shows that the number of active forks is increased upon dB GNL3-dB expression. It demonstrates that when GNL3 is no longer retained in the nucleolus more origins are active. These data will be integrated in the revised version of the manuscript, and we believe further support the regulation of ORC2 by GNL3.

*Finally, the model implies an exquisite regulation of the amount of ORC2 protein, which could influence the number of active origins and the extent of fork resection in case of stress. In this scenario, one could predict that ORC2 ectopic expression would have similar, or even stronger effects, than GNL3 downregulation. Is this the case? *

__Response: __We completely agree with this prediction. However, we are afraid that overexpression of ORC2 may have indirect effects due to the many described functions of ORC2, therefore it may be difficult to interpret the data. We will give a try anyway.

*Even if the connection between origins and fork resection could be firmly established, the molecular link between them remains enigmatic. The authors hint (as "data not shown") that it is neither mediated by RPA nor RAD51. Unfortunately, the reader is left without a clear hypothesis about this point. *

__Response: __We will add data that show that RPA and RAD51 recruitment is not affected by GNL3 depletion. However, the sensitivity of chromatin fractionation approach may be too weak to detect low differences. Based on the work of Lukas Lab (Toledo et al. 2013 https://pubmed.ncbi.nlm.nih.gov/24267891/) one possible mechanism may be exhaustion of the pool of RPA. This may link the excessive activation of origins observed upon GNL3 depletion and resection. To test this, we will check if resection upon GNL3 depletion and treatment with HU is still occurring in cell lines that overexpress RPA complex (described in Toledo et al.) that we obtained from Lukas’ lab.

__ __ **Referees cross-commenting**

__ __In addition to each reviewer's more specific comments, the three reviews share a main criticism: the lack of mechanistic information about the proposed link between origin activity and resection of nascent DNA at stalled forks.

__Reviewer #3 (Significance (Required)): __

In principle, this study would appeal to the readership interested in fundamental mechanisms of DNA replication and the cellular responses to replicative stress.

For the reasons outlined in the previous section, I believe that in its current version the study is not strong enough to provide a new paradigm about origins being regulated by partial ORC2 sequestering at nucleoli. The other potentially interesting advance is the connection between frequency of origin activity and the extent of nascent DNA resection at stalled forks, but the molecular link between both remains unknown.

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می تونی slot کنترل کنی

Do you think combining Ac4ManNAz and rPAL labeling could be a good way to both specifically identify Neu5Ac-ligated RNA and amplify that signal using orthogonal labels (perhaps Biotin and a FLAG tag) with different fluorophores?

This is an archived comment originally written by Peter Thuy-Boun

I've been sharing ideas via email, websites, blogs and social media since late 1990s. My goal is to motivate people to use AI and traditional search tools to look for "tutor mentor" plus words in this tag cloud where they will find much of what I have posted.

Over time links will break and in future years I won't be alive to keep sites on-line. So, will future AI tools reach into the Internet Archive to find ideas posted on sites that are no longer active? Can our ideas live longer than we do?

1.加上tag 2.我还会在这篇文章加上注释 —— 为什么我想读这篇文章？ 我想从这篇文章里得到什么？ 我会强迫自己添加一篇稍后读的文章的时候思考这个问题，并且用十几个字简单地描述。这样当我在之后读这篇文章的时候，我可以带着我的问题去阅读，这样会更有效率。

audio recordings of mbaqanga music

reply for Fidel at https://forum.eastgate.com/t/tinderbox-meetup-sunday-may-7-2023-video-connect-with-sonke-ahrens-live-the-author-of-how-to-take-smart-notes/6659

@fidel (I'm presuming you're the same one from the meetup on Sunday, if not perhaps someone might tag the appropriate person?), I was thinking a bit more on your question of using physical index cards for writing fiction. You might find the examples of both Vladimir Nabokov and Dustin Lance Black, a screenwriter, useful as they both use index card-based workflows.

Vladimir Nabokov died in 1977 leaving an unfinished manuscript in note card form for the novel The Original of Laura . Penguin later published the incomplete novel with in 2012 with the subtitle A Novel in Fragments . Unlike most manuscripts written or typewritten on larger paper, this one came in the form of 138 index cards. Penguin's published version recreated these cards in full-color reproductions including the smudges, scribbles, scrawlings, strikeouts, and annotations in English, French, and Russian. Perforated, one could tear the cards out of the book and reorganize in any way they saw fit or even potentially add their own cards to finish the novel that Nabokov couldn't. Taking a look at this might give you some ideas of how Nabokov worked and how you might adapt the style for yourself. Another interesting resource is this article with some photos/links about his method with respect to writing Lolita: https://www.openculture.com/2014/02/the-notecards-on-which-vladimir-nabokov-wrote-lolita.html

You might also find some useful tidbits on his writing process (Bristol cards/Exacompta anyone?) in: Gold, Herbert. “Vladimir Nabokov, The Art of Fiction No. 40.” The Paris Review, 1967. https://www.theparisreview.org/interviews/4310/the-art-of-fiction-no-40-vladimir-nabokov.

Carl Mydans photographed Nabokov while writing in September 1958 and some of those may be interesting to you as well.

Dustin Lance Black outlines his index card process in this video on YouTube: https://www.youtube.com/watch?v=vrvawtrRxsw

If you dig around you'll also find Michael Ende and a variety of other German fiction writers who used index cards on the Zettelkasten page on Wikipedia, but I suspect most of the material on their processes are written in German.

Index cards for fiction writing may allow some writers some useful affordances/benefits. By using small atomic pieces on note cards, one can be far more focused on the idea and words immediately at hand. It's also far easier in a creative and editorial process to move pieces around experimentally.

Similarly, when facing Hemmingway's "White Bull", the size and space of an index card is fall smaller. This may have the effect that Twitter's short status updates have for writers who aren't faced with the seemingly insurmountable burden of writing a long blog post or essay in other software. They can write 280 characters and stop. Of if they feel motivated, they can continue on by adding to the prior parts of a growing thread.

However, if you can, try to use a card catalog drawer with a rod so that you don't spill all of your well-ordered cards the way the character in Robert M. Pirsig's novel Lila (1991) did.

A hash (#) is used. A hashtag is a hash symbol prepended to a string for example: #hashtag. The 'tag' part of a 'hashtag' is the string following the hash until the next space.

Tagging and linking with AI (Napkin.one) by Nicole van der Hoeven

https://www.youtube.com/watch?v=p2E3gRXiLYY

Nicole underlines the value of a good user interface for traversing one's notes. She'd had issues with tagging things in Obsidian using their #tag functionality, but never with their [[WikiLink]] functionality. Something about the autotagging done by Napkin's artificial intelligence makes the process easier for her. Some of this may be down to how their user interface makes it easier/more intuitive as well as how it changes and presents related notes in succession.

Most interesting however is the visual presentation of notes and tags in conjunction with an outliner for taking one's notes and composing a draft using drag and drop.

Napkin as a visual layer over tooling like Obsidian, Logseq, et. al. would be a much more compelling choice for me in terms of taking my pre-existing data and doing something useful with it rather than just creating yet another digital copy of all my things (and potentially needing sync to keep them up to date).

What is Napkin doing with all of their user's data?

reply to cristinadias7 at https://forum.zettelkasten.de/discussion/comment/17804/#Comment_17804

@cristinadias7 If you're interested in the overlap of zettelkasten and art or even zettelkasten for art, I've got a small collection available digitally if you think it would be useful/helpful.

Some artworks are difficult to index on physical cards because of their physical nature, so if it helps and you don't have pictures available to file away, you can index their storage locations the way libraries would index "realia" and keep your notes on them there. Twyla Tharpe kept actual objects in larger boxes by categories which is another fascinating way of doing things.

Author Response:

Assessment note: “Whereas the results and interpretations are generally solid, the mechanistic aspect of the work and conclusions put forth rely heavily on in vitro studies performed in cultured L6 myocytes, which are highly glycolytic and generally not viewed as a good model for studying muscle metabolism and insulin action.”

While we acknowledge that in vitro models may not fully recapitulate the complexity of in vivo systems, we believe that our use of L6 myotubes is appropriate for studying the mechanisms underlying muscle metabolism and insulin action. As mentioned below (reviewer 2, point 1), L6 myotubes possess many important characteristics relevant to our research, including high insulin sensitivity and a similar mitochondrial respiration sensitivity to primary muscle fibres. Furthermore, several studies have demonstrated the utility of L6 myotubes as a model for studying insulin sensitivity and metabolism, including our own previous work (PMID: 19805130, 31693893, 19915010).

In addition, we have provided evidence of the similarities between L6 cells overexpressing SMPD5 and human muscle biopsies at protein levels and the reproducibility of the negative correlation between ceramide and Coenzyme Q observed in L6 cells in vivo, specifically in the skeletal muscle of mice in chow diet. These findings support the relevance of our in vitro results to in vivo muscle metabolism.

Finally, we will supplement our findings by demonstrating a comparable relationship between ceramide and Coenzyme Q in mice exposed to a high-fat diet, to be shown in Supplementary Figure 4 H-I. Further animal experiments will be performed to validate our cell-line based conclusions. We hope that these additional results address the concerns raised by the reviewer and further support the relevance of our in vitro findings to in vivo muscle metabolism and insulin action.

Points from reviewer 1:

1. Although the authors' results suggest that higher mitochondrial ceramide levels suppress cellular insulin sensitivity, they rely solely on a partial inhibition (i.e., 30%) of insulin-stimulated GLUT4-HA translocation in L6 myocytes. It would be critical to examine how much the increased mitochondrial ceramide would inhibit insulin-induced glucose uptake in myocytes using radiolabel deoxy-glucose.

Response: The primary impact of insulin is to facilitate the translocation of glucose transporter type 4 (GLUT4) to the cell surface, which effectively enhances the maximum rate of glucose uptake into cells. Therefore, assessing the quantity of GLUT4 present at the cell surface in non-permeabilized cells is widely regarded as the most reliable measure of insulin sensitivity (PMID: 36283703, 35594055, 34285405). Additionally, plasma membrane GLUT4 and glucose uptake are highly correlated. Whilst we have routinely measured glucose uptake with radiolabelled glucose in the past, we do not believe that evaluating glucose uptake provides a better assessment of insulin sensitivity than GLUT4.

We will clarify the use of GLUT4 translocation in the Results section:

“...For this reason, several in vitro models have been employed involving incubation of insulin sensitive cell types with lipids such as palmitate to mimic lipotoxicity in vivo. In this study we will use cell surface GLUT4-HA abundance as the main readout of insulin response...”

1. Another important question to be addressed is whether glycogen synthesis is affected in myocytes under these experimental conditions. Results demonstrating reductions in insulin-stimulated glucose transport and glycogen synthesis in myocytes with dysfunctional mitochondria due to ceramide accumulation would further support the authors' claim.

Response: We have carried out supplementary experiments to investigate glycogen synthesis in our insulin-resistant models. Our approach involved L6-myotubes overexpressing the mitochondrial-targeted construct ASAH1 (as described in Fig. 3). We then challenged them with palmitate and measured glycogen synthesis using 14C radiolabeled glucose. Our observations indicated that palmitate suppressed insulin-induced glycogen synthesis, which was effectively prevented by the overexpression of ASAH1 (N = 5, * p<0.05). These results provide additional evidence highlighting the role of dysfunctional mitochondria in muscle cell glucose metabolism.

These data will be added to Supplementary Figure 4K and the results modified as follows:

“Notably, mtASAH1 overexpression protected cells from palmitate-induced insulin resistance without affecting basal insulin sensitivity (Fig. 3E). Similar results were observed using insulin-induced glycogen synthesis as an ortholog technique for Glut4 translocation. These results provide additional evidence highlighting the role of dysfunctional mitochondria in muscle cell glucose metabolism (Sup. Fig. 5K). Importantly, mtASAH1 overexpression did not rescue insulin sensitivity in cells depleted…”

We will add to the method section:

“L6 myotubes overexpressing ASAH were grown and differentiated in 12-well plates, as described in the Cell lines section, and stimulated for 16 h with palmitate-BSA or EtOH-BSA, as detailed in the Induction of insulin resistance section.

On day seven of differentiation, myotubes were serum starved in plain DMEM for 3 and a half hours. After incubation for 1 hour at 37C with 2 µCi/ml D-[U-14C]-glucose in the presence or absence of 100 nM insulin, glycogen synthesis assay was performed, as previously described (Zarini S. et al., J Lipid Res, 63(10): 100270, 2022).”

1. In addition, it would be critical to assess whether the increased mitochondrial ceramide and consequent lowering of energy levels affect all exocytic pathways in L6 myoblasts or just the GLUT4 trafficking. Is the secretory pathway also disrupted under these conditions?

Response: As the secretory pathway primarily involves the synthesis and transportation of soluble proteins that are secreted into the extracellular space, and given that the majority of cellular transmembrane proteins (excluding those of the mitochondria) use this pathway to arrive at their ultimate destination, we believe that the question posed by the reviewer is highly challenging and beyond the scope of our research. We will add this to the discussion:

“...the abundance of mPTP associated proteins suggesting a role of this pore in ceramide induced insulin resistance (Sup. Fig. 6E). In addition, it is yet to be determined whether the trafficking defect is specific to Glut4 or if it affects the exocytic-secretory pathway more broadly…”

Points from reviewer 2:

1. The mechanistic aspect of the work and conclusions put forth rely heavily on studies performed in cultured myocytes, which are highly glycolytic and generally viewed as a poor model for studying muscle metabolism and insulin action. Nonetheless, the findings provide a strong rationale for moving this line of investigation into mouse gain/loss of function models.

Response: The relative contribution of the anaerobic (glycolysis) and aerobic (mitochondria) contribution to the muscle metabolism can change in L6 depending on differentiation stage. For instance, Serrage et al (PMID30701682) demonstrated that L6-myotubes have a higher mitochondrial abundance and aerobic metabolism than L6-myoblasts. Others have used elegant transcriptomic analysis and metabolic characterisation comparing different skeletal muscle models for studying insulin sensitivity. For instance, Abdelmoez et al in 2020 (PMID31825657) reported that L6 myotubes exhibit greater insulin-stimulated glucose uptake and oxidative capacity compared with C2C12 and Human Mesenchymal Stem Cells (HMSC). Overall, L6 cells exhibit higher metabolic rates and primarily rely on aerobic metabolism, while C2C12 and HSMC cells rely on anaerobic glycolysis. It is worth noting that L6 myotubes are the cell line most closely related to adult human muscle when compared with other muscle cell lines (PMID31825657). Our presented results in Figure 6 H and I provide evidence for the similarities between L6 cells overexpressing SMPD5 and human muscle biopsies. Additionally, in Figure 3J-K, we demonstrate the reproducibility of the negative correlation between ceramide and Coenzyme Q observed in L6 cells in vivo, specifically in the skeletal muscle of mice in chow diet. Furthermore, we have supplemented these findings by demonstrating a comparable relationship in mice exposed to a high-fat diet, as shown in Supplementary Figure 4 H-I (refer to point 4). We will clarify these points in the Discussion:

“In this study, we mainly utilised L6-myotubes, which share many important characteristics with primary muscle fibres relevant to our research. Both types of cells exhibit high sensitivity to insulin and respond similarly to maximal doses of insulin, with Glut4 translocation stimulated between 2 to 4 times over basal levels in response to 100 nM insulin (as shown in Fig. 1-4 and (46,47)). Additionally, mitochondrial respiration in L6-myotubes have a similar sensitivity to mitochondrial poisons, as observed in primary muscle fibres (as shown in Fig. 5 (48)). Finally, inhibiting ceramide production increases CoQ levels in both L6-myotubes and adult muscle tissue (as shown in Fig. 2-3). Therefore, L6-myotubes possess the necessary metabolic features to investigate the role of mitochondria in insulin resistance, and this relationship is likely applicable to primary muscle fibres”.

We will also add additional data - in point 2 - from differentiated human myocytes that are consistent with our observations from the L6 models. Additional experiments are in progress to further extend these findings.

1. One caveat of the approach taken is that exposure of cells to palmitate alone is not reflective of in vivo physiology. It would be interesting to know if similar effects on CoQ are observed when cells are exposed to a more physiological mixture of fatty acids that includes a high ratio of palmitate, but better mimics in vivo nutrition.

Response: Palmitate is widely recognized as a trigger for insulin resistance and ceramide accumulation, which mimics the insulin resistance induced by a diet in rodents and humans. Previous studies have compared the effects of a lipid mixture versus palmitate on inducing insulin resistance in skeletal muscle, and have found that the strong disruption in insulin sensitivity caused by palmitate exposure was lessened with physiologic mixtures of fatty acids, even with a high proportion of saturated fatty acids. This was associated, in part, to the selective partitioning of fatty acids into neutral lipids (such as TAG) when muscle cells are exposed to physiologic lipid mixtures (Newsom et al PMID25793412). Hence, we think that using palmitate is a better strategy to study lipid-induced insulin resistance in vitro. We will add to results:

“In vitro, palmitate conjugated with BSA is the preferred strategy for inducing insulin resistance, as lipid mixtures tend to partition into triacylglycerides (33)”.

We are also performing additional in vivo experiments to add to the physiological relevance of the findings.

1. While the utility of targeting SMPD5 to the mitochondria is appreciated, the results in Figure 5 suggest that this manoeuvre caused a rather severe form of mitochondrial dysfunction. This could be more representative of toxicity rather than pathophysiology. It would be helpful to know if these same effects are observed with other manipulations that lower CoQ to a similar degree. If not, the discrepancies should be discussed.

Response: We conducted a staining procedure using the mitochondrial marker mitoDsRED to observe the effect of SMPD5 overexpression on cell toxicity. The resulting images, displayed in the figure below (Author Response Figure 1), demonstrate that the overexpression of SMPD5 did not result in any significant changes in cell morphology or impact the differentiation potential of our myoblasts into myotubes.

Author Response Figure 1.

In addition, we evaluated cell viability in HeLa cells following exposure to SACLAC (2 uM) to induce CoQ depletion (left panel). Specifically, we measured cell death by monitoring the uptake of Propidium iodide (PI) as shown in the right panel. Our results demonstrated that Saclac-induced CoQ depletion did not lead to cell death at the doses used for CoQ depletion (Author Response Figure 2).

Author Response Figure 2.

Therefore, we deemed it improbable that the observed effect is caused by cellular toxicity, but rather represents a pathological condition induced by elevated levels of ceramides. We will add to discussion:

“...downregulation of the respirasome induced by ceramides may lead to CoQ depletion. Despite the significant impact of ceramide on mitochondrial respiration, we did not observe any indications of cell damage in any of the treatments, suggesting that our models are not explained by toxic/cell death events.”

1. The conclusions could be strengthened by more extensive studies in mice to assess the interplay between mitochondrial ceramides, CoQ depletion and ETC/mitochondrial dysfunction in the context of a standard diet versus HF diet-induced insulin resistance. Does P053 affect mitochondrial ceramide, ETC protein abundance, mitochondrial function, and muscle insulin sensitivity in the predicted directions?

Response: We would like to note that the metabolic characterization and assessment of ETC/mitochondrial function in these mice (both fed a high-fat (HF) and chow diet, with or without P053) were previously published (Turner N, PMID30131496). In addition to this, we have conducted targeted metabolomic and lipidomic analyses to investigate the impact of P053 on ceramide and CoQ levels in HF-fed mice. As illustrated in the figures below (Author Response Figure 3), the administration of P053 led to a reduction in ceramide levels (left panel) and an increase in CoQ levels (right panel) in HF-fed mice, which is consistent with our in vitro findings.

Author Response Figure 3.

We will add to results:

“…similar effect was observed in mice exposed to a high fat diet for 5 wks (Supp. Fig. 4H-I further phenotypic and metabolic characterization of these animals can be found in (41))”

We will further perform more in-vivo studies to corroborate these findings.

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Reply to the reviewers

We thank the reviewers for their comments and constructive suggestions to improve the manuscript. We are encouraged to see that both reviewers acknowledge how the results from our manuscript uses state-of-art technologies to advance molecular underpinnings of centriole length, integrity and function regulation. Both reviewers also highlighted that the manuscript is well laid out and presents clear, rigorous, and convincing data. Reviewer#1 described our manuscript of highest experimental quality and broad interest to the field of centrosome and cell biology form a basic research and genetics/clinical point of view. Here, we explain the revisions, additional experimentations and analyses planned to address the points raised by the referees. We will perform most of the experimentations and corrections requested by the reviewers. We have already made several revisions and are currently working on additional experiments.

Our responses to each reviewer comment in bold are listed below. References mentioned here are listed in the references section included at the of this document.

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

Summary: __In this manuscript, Arslanhan and colleagues use proximity proteomics to identify CCDC15 as a new centriolar protein that co-localizes and interacts with known inner scaffold proteins in cell culture-based systems. Functional characterization using state-of-the-art expansion microscopy techniques reveals defects in centriole length and integrity. The authors further reveal intriguing aberrations in the recruitment of other centriole inner scaffold proteins, such as POC1B and the SFI1/centrin complex, in CCDC15-deficient cells, and observe defects in primary cilia. __

We thank the reviewer for the accurate summary of the major conclusions of our manuscript.

Major points:

1) The authors present a high-quality manuscript that identifies a novel centriolar protein by elegantly revealing and comparing the proximity proteomes of two known centriolar proteins, which represents an important component for the maintenance of centrioles.

We thank the reviewer for highlighting that our manuscript is of high quality and presents important advances for the field.

__2) Data are often presented from two independent experiments (n = 2), which is nice, but also the minimum for experiments in biology. It is strongly recommended to perform at least three independent experiments. __

We agree with the reviewer that analysis of data form three experimental replicates is ideal for statistical analysis. We performed three replicates for the majority of experiments in the manuscript. However, as the reviewer pointed out, we included analysis from two experiments for the following figures:

• Fig. 4H: quantification of CCDC15 total cellular levels throughout the cell cycle by western blotting
• Fig. 5A: CCDC15-positive centrioles in control and CCDC15 siRNA-transfected cells
• Fig. 6B: % centriolar coverage of POC5, FAM161A, POC1B and Centrin-2 in control and CCDC15 siRNA-transfected cells
• Fig. 6C, 6E: Centrin-2 or SFI1-positive centrioles in control and CCDC15 siRNA-transfected cells
• Fig. 6J, K: normalized tubulin length and percentage of defective centrioles in cells depleted for CCDC15 or co-depleted for CCDC15 and POC1B
• Fig. 7F, H: SMO-positive cilia and basal body IFT88 levels in control and CCDC15 siRNA-transfected cells
• Fig. S3H: centriole amplification in HU-treated control and CCDC15 siRNA-transfected cells (no)
• Fig. S3A: centrosomal levels upon CCDC15 depletion There are two reasons for why we performed two experimental replicates for these experiments: 1) results from the two experimental replicates were similar, 2) quantification of data by U-ExM is laborious. To address the reviewer’s comments, we will perform the third experimental replicate for the sets of data that led to major conclusions of our manuscript, which are Figures 4H, 6C, 6E, 6J, 6K, 7F, 7H and S3A.

3) The protein interaction studies presented in Fig. 3 could be of higher quality. While it is great that the authors compared interactions to the centriolar protein SAS6, which is not expected to interact with CCDC15, the presented data raise many questions.

__a) In most cases, co-expression of tagged CCDC15 stabilizes the tested interaction partners, such that the overall abundance seems to be higher. The increase in protein abundance is substantial for Flag-FAM161A (Fig. 3D) and GFP-Centrin-2 (Fig. 3E) and is even higher for the non-interactor SAS6 (Fig. 3G), while it cannot be assessed for GFP-POC1B (Fig. 3F). Hence, the higher expression levels under these conditions make it more likely that these proteins are "pulled down" and therefore do not represent appropriate controls. __

We agree with the reviewer that the differences in protein abundance of the prey proteins upon expression of CCDC15 relative to control might impact the interpretation of the interaction data. To address this concern, we will perform the following experiments:

• To account of the potential stabilizing effects of CCDC15 expression, we will change the relative ratio of plasmids expressing proteins of interest and assess the expression of bait and prey protein levels. We will then repeat the co-immunoprecipitation experiments in conditions where prey expression levels are similar.
• To avoid the potential stabilizing effects of CCDC15 overexpression, we will perform immunoprecipitation experiments in cells expressing GFP or V5-tagged inner scaffold proteins and assess their potential physical or proximity interaction by blotting for endogenous CCDC15. __b) All Co-IP experiments are lacking negative controls in the form of proteins that are not pulled down under the presented conditions. __

For the co-IP experiments, we only included a specificity control for the interaction of the bait protein with the tag of the prey protein (i.e. GBP pulldown of GFP or GFP-CCDC15-expressing cells). As the reviewer suggested, we will also include a specificity control for the interaction of bait with the tag of the prey protein for co-immunoprecipitation experiments (i.e. GFP pulldown of cells expressing GFP-CCDC15 with V5-BirA* or V5-BirA*-FAM161A).

__c) The amounts of co-precipitation of the tested proteins appears very different. Could this reflect strong or weak interactors, or does it reflect the abundance of the respective proteins in centrioles? __

We agree with the reviewer that the quantity of the co-precipitated prey proteins might be a proxy for the interaction strength if the abundance of the bait proteins is similar. However, the expression levels of bait and prey proteins in co-transfected cells are different and thus, cannot be used to derive a conclusion on the interaction strength. For the revised manuscript, we will repeat the IP experiments and comment on this in the discussion section.

__4) The observation that IFT88 is supposedly decreased at the base of cilia in CCDC15-depleted cells requires additional experiments/evidence. Fig. 7G shows the results of n = 2 and more importantly, a similar reduction of gamma-tubulin in siCCDC15. Could the observed reduction in IFT88 be explained by a decrease in accessibility to immunofluorescence microscopy? Would the reduction in IFT88 at the base also be apparent when the signals were normalized to gamma-tubulin signals? __

To address the reviewer’s concern, we quantified the basal body gamma-tubulin and IFT88 levels in control and CCDC15-depleted cells and plotted the basal body IFT88 levels normalized to gamma-tubulin levels in Fig. 7H. Similar to the reduction in IFT88 levels, gamma-tubulin-normalized IFT88 levels was significantly less relative to control cells. Moreover, the gamma-tubulin basal body levels were similar between control and CCDC15 cells. We revised the gamma-tubulin micrographs in Fig. 7G to represent this. These results indicate that the reduction in basal body IFT88 levels upon CCDC15 depletion in specific.

__5) The observed Hedgehog signaling defects are described as follows: "CCDC15 depletion significantly decreased the percentage of SMO-positive cells". It is similarly described in the figure legend. If this was true, the simplest explanation would be that it reflects the reduction in ciliation rate (which is in a similar range). If SMO-positive cilia (instead of "cells") were determined, the text needs to be changed accordingly. __

As the reviewer pointed out, we quantified SMO-positive cilia, but not cells. We are sorry for this typo. We corrected SMO-positive cells as SMO-positive cilia in the manuscript text, Fig. 7 and figure legends.

__6) OPTIONAL: While expansion microscopy is slowly becoming one of the standard super-resolution microscopy methods, which is particularly well validated for studying centrioles, the authors should consider confirming part of their findings (as a proof of principle, surely not in all instances) by more established techniques. This could serve to convince critical reviewers that may argue that the expansion process may induce architectural defects of destabilized centrioles, as observed after disruptions of components, such as in Fig. 6. Alternatively, the authors could cite additional work that make strong cases about the suitability of expansion microscopy for their studies, ideally with comparisons to other methods. __

• SIM imaging was previously successfully applied for nanoscale mapping of other centriole proteins including CEP44, MDM1 and PPP1R35 (Atorino et al., 2020; Sydor et al., 2018; Van de Mark et al., 2015). To complement the U-ExM analysis, we have started imaging cells stained for CCDC15 and different centriole markers (i.e. distal appendage, proximal linker, centriole wall) using a recently purchased 3D-SIM superresolution microscope. We already included the SIM imaging data for CCDC15 localization in centrosome fractions purified from HEK293T cells in Fig. S5B. In the revised manuscript, we will replace confocal imaging data in Fig. 3A and 3B with SIM imaging data.
• As the reviewer noted, expansion microscopy has been successfully used for the analysis of a wide range of cellular structures and scientific questions including nanoscale mapping of cellular structures across different organisms. In particular, U-ExM of previously characterized centrosome proteins various centriole proteins have significantly advanced our understanding of centriole ultrastructure. In our manuscript, we used the U-ExM protocol that was validated for centrioles by comparative analysis of U-ExM and cryo-ET imaging by our co-authors (Gambarotto et al., 2019; Hamel et al., 2017). To clarify these points, we included the following sentence along with the relevant references in the introduction: “Application of the U-ExM method to investigate known centrosome proteins has started to define the composition of the inner scaffold as well as other centriolar sub-compartments (Chen et al., 2015; Gambarotto et al., 2021; Gambarotto et al., 2019; Kong and Loncarek, 2021; Laporte et al., 2022; Mahen, 2022; Mercey et al., 2022; Odabasi et al., 2023; Sahabandu et al., 2019; Schweizer et al., 2021; Steib et al., 2022; Tiryaki et al., 2022; Tsekitsidou et al., 2023).”

Minor points:

1) Text, figures, and referencing are clear and accurate, apart from minor exceptions.

We clarified and corrected the points regarding text, figures and references as suggested by the two reviewers.

__ 2) The title suggests a regulator role for CCDC15 in centriole integrity and ciliogenesis, which has formally not been shown. __

We revised the title as “CCDC15 localizes to the centriole inner scaffold and functions in centriole length control and integrity”.

__3) As the authors observe changes in centriole lengths in the absence of CCDC15, it would be very insightful to compare these phenotypes to other components that affect centriolar length, such as C2CD3, human Augmin complex components (as HAUS6 is identified in Fig. 1) or others. These could be interesting aspects for discussion, additional experiments are OPTIONAL. __

We agree with the reviewer that comparative analysis of centriole length phenotypes for CCDC15 and other components that regulate centriole length will provide insight into how these components work together at the centriole inner core. To this end, we phenotypically compared CCDC15 loss-of-function phenotypes to that of other components of the inner scaffold (POC5, POC1B, FAM161A) that interact with CCDC15. In agreement with their previously reported functions in U2OS or RPE1 cells, we found that POC5 depletion resulted in a 4% slight but significant increase in centriole length and POC1B depletion resulted in a 15% significant decrease. In contrast, FAM161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, our analysis of their centriolar localization dependency and regulatory roles during centriole length suggest that CCDC15 and POC1B might form a functional complex as positive regulators of centriole length. In contrast, POC5 functions as a negative regulator and might be part of a different pathway for centriole length regulation. We integrated the following sub-paragraph in the results section and also included discussion of this data in the discussion section:

“Moreover, we quantified centriole length in control cells and cells depleted for POC5 or POC1B. While POC5 depletion resulted in longer centrioles, POC1B resulted in shorter centrioles (POC5: siControl: 414.1 nm±38.3, siPOC5: 432.7±44.8 nm, POC1B: siControl: 400.6±36.1 nm, siPOC1B: 341.5±44.39 nm,). FAMA161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, these results suggest that CCDC15 might cooperate with POC1B and compete with POC5 to establish and maintain proper centriole length.”

__ 4) While the reduced ciliation rate in the absence of CCDC15 is convincing, the authors did not investigate "ciliogenesis", i.e. the formation of cilia, and hence should re-phrase. The sentence in the discussion that "CCDC15 functions during assembly" should be removed. __

To clarify that we only investigated the role of CCDC15 in the ability of cells to form cilia, we replaced sentences that indicates “CCDC15 functions in cilium assembly” with “CCDC15 is required for the efficiency of cilia formation”.

__5) The existence of stably associated CCDC15 pools with centrosomes (Fig. 2) requires further evidence. The recovery of fluorescence after photobleaching in FRAP experiments is strongly dependent on experimental setups and is only semi-quantitative. A full recovery is unrealistic, hence, it is ideally compared to a known static or known mobile component. I personally think this experiment -as it is presented now- is of little value to the overall fantastic study. The authors may consider omitting this piece of data. __

We agree with the reviewer that FRAP data by itself does not prove the existence of stably associated CCDC15 pool. As controls in these experiments, we use FRAP analysis of GFP-CCDC66, which has a 100% immobile pool at the cilia and 50% immobile pool at the centrosomes as assessed by FRAP (Conkar et al., 2019). To address these points, we toned down the conclusions derived from this experiment by revising the sentence as follows:

Additionally, we note that the following data provides support for the stable association of CCDC15 at the centrioles:

• About 49.6% (± 3.96) of the centrioles still had CCDC15 fluorescence signal at one of the centrioles upon CCDC15 siRNA treatment (Fig. 5A, 5B). The inefficient depletion of the mature centriole pool of CCDC15 is analogous to what was observed upon depletion of other centriole lumen and inner scaffold proteins including WDR90 and HAUS6 (Schweizer et al., 2021; Steib et al., 2020). __6) The data that CCDC15 is a cell cycle-regulated protein is not very convincing (see Fig. 3H), as the signals area weak and the experiment has been performed only once (n= 1). This piece of data does not appear to be very critical for the main conclusions of the manuscript and may be omitted. Otherwise, this experiment should be repeated to allow for proper statistical analysis. __

We will perform these experiments two more times, quantify cellular abundance of CCDC15 in synchronized populations from three experimental replicates and plot it with proper statistical analysis.

__7) Experimental details on how "defective centrioles" are determined are missing. __

We included the following experimental details to the methods section:

“Centrioles were considered as defective when the roundness of the centriole was lost or the microtubule walls were broken or incomplete. In the longitudinal views of centrioles, defective centrioles were visualized as heterogenous acetylated signal along the centriole wall or irregularities in the cylindrical organization of the centriole wall (Fig. 5F). We clarified these points in the methods section.

__ 8) For figures, in which the focus should be on growing centrioles (see Fig. 4), it could be helpful to guide the reader and indicate the respective areas of the micrographs by arrows. __

We added arrows to point to the respective areas of the micrographs in Fig. 4F.

__ 9) Page18: "centriole length shortening" could be changed to "centriole shortening". __

We corrected this description as suggested.

__10) It is unclear how the authors determine distal from proximal ends of centrioles in presented micrographs (see Fig. 5D). __

We determined the proximal and distal ends of the centrioles by taking the centriole pairs as a proxy. Even though we only represent a micrograph containing a single centriole in some of the U-ExM figures including Fig. 5D, the uncropped micrographs contain two centrioles, which are oriented orthogonally and tethered to each other at their proximal ends in interphase cells. We added the following sentence to the methods section to clarify this point:

*“Since centrioles are oriented orthogonally and tethered to each other at their proximal ends in interphase cells, we also used the orientation of the centriole pairs as a proxy to determine the proximal and distal ends of the centrioles.” *

__11) Fig. 7A is missing scale bars and Fig.7 overall is lacking rectangle indicators of the areas that are shown at higher magnification in the insets. __

We added scale bar to Fig. 7A and rectangle indicators for zoomed in regions in Fig. A, E, G.

12) Fig. 7C displays cilia that appear very short, especially when comparing to the micrographs and bar graphs presented. The authors may want to explain this discrepancy.

We thank the reviewer for the comment. The zoomed in representative cilia is 4.1 µM in control cells and 1.4 µM in CCDC15-depleted cells. Therefore, the representative cilia is in agreement with the quantification of cilia in Fig. 7C.

Reviewer #1 (Significance (Required)): • From a technical point of view the authors use two state-of-the-art technologies, namely proximity labeling combined with proteomics and ultrastructure expansion microscopy, that are both challenging and very well suited to address the main questions of this study. ____ • General assessment: The presented study is of highest experimental quality. Despite being very challenging, the expansion microscopy and proximity proteomics experiments have been designed and performed very well to allow solid interpretation. The results of the central data are consistent and allow strong first conclusions about the putative function of the newly identified centriolar protein CCDC15. The study presents a solid foundation for future hypothesis-driven, mechanistic analysis of CCDC15 and inner scaffold proteins in centriole length control and maintaining centriole integrity. The only limitation of the study is that the technically simpler experiments should be repeated to allow proper statistical assessment, which can be addressed easily. • Advance: This is the first study that identifies CCDC15 as a centriolar protein and localizes it to the inner scaffold. It further describes a function for CCDC15 in centriole length control and shows its importance in maintaining centriole integrity with consequences for stable cilia formation in tissue culture. The study provides further functional insights into the interdependence of inner scaffold proteins and the role of CCDC15 in the recruitment of the SFI1/centrin distal complex. • Audience: The manuscript will be of broad interest to the fields of centrosome and cell biology, both from a basic research and genetics/clinical point of view due to the association with human disorders. The state-of-the-art technologies applied will be of interest to a broader cell and molecular biology readership that studies subcellular compartments and microtubules. • Reviewer's field of expertise: Genetics, imaging, and protein-protein interaction studies with a focus on centrosomes and cilia.

We thank the reviewer for recognizing the importance of our work and for supportive and insightful comments that will further strengthen the conclusions of our manuscript. Our planned revisions will address the only major technical limitation raised by the reviewer that requires adding one more experimental replicate for analysis of the data detailed in major point#1. Notably, we also thank the reviewer to specifying the experiments that are not essential or will be out of the scope of our manuscript as “optional”.

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

Summary:

__In this study, Arslanhan et al. propose CCDC15 as a novel component of the centriole inner scaffold structure with potential roles in centriole length control, stability and the primary cilium formation in cultured epithelial cells. Using proximity labelling they explore the common interactors of Poc5 and Centrin-2, two resident molecules of the centriole inner scaffold, to hunt for novel regulators of this structure. The authors leverage expansion microscopy-based localization and siRNA-dependent loss-of-function experiments to follow up on one such protein they identify, CCDC15, with the aforementioned roles in centriole and cilia biology.

This study is designed and laid out nicely; however, to be able to support some of the important claims regarding their proximity labelling results and exploration on the roles of CCDC15, there are several major technical and reproducibility concerns that deem major revision. Similarly, the introduction (perhaps inadvertently) omits much of the recent studies on centriole size control that have highlighted the complexity of this biological problem. As such, addressing the following major points will be essential in further considering this work for publication. __

__We thank the reviewer for recognizing the importance of our work and appreciate the positive reflections on our manuscript and the feedback comments that were well thought-out and articulated and will further strengthen the conclusions of our manuscript. Our planned revisions focus on addressing the reviewer’s comments especially in further supporting our conclusions for proximity-labeling, phenotypic characterization and immunoprecipitation experiments, examining CCDC15 centriole localization in an additional cell line and investigating how CCDC15 works together during centriole length control with known components of the inner scaffold. __

Major points:

__1a) The authors use Poc5 and Centrin-2 molecules as joint baits to reveal the interactome of the centriole inner scaffold, however the work lacks appropriate experimental and analytical controls to argue that this is a proximity mapping "at the centriole inner scaffold". In its current state, it is simply an interactome of total Poc5 and Centrin-2, and it might be misleading to call it an interactome at the centriole inner scaffold (the statistical identification of shared interactors cannot do full justice to their biology at the centrosome). Appropriate expression data needed to delineate how large the centrosomal vs. cytoplasmic (or nucleoplasmic) fraction is for either of these molecules, both without and upon the addition of biotin (to see whether the bulk of interaction data stem from the cytoplasm/nucleoplasm or the centrioles themselves). The authors can test this by selectively blotting a lysate fraction containing the centrosomes after centrifugation, and compare them with the simultaneous blot of the supernatant (which were readily used for the blots presented in Fig. 1B). This experiment also becomes very relevant for the case of Centrin-2, as it also heavily localizes to the nucleoplasm as the authors found out (see Fig. 1A and Fig. S1A). __

__ Additionally, an orthogonal approach should be taken to perform bio-image analysis on their biotin/streptavidin imaging data to demonstrate the exact ratios between the centrosomal vs. cytoplasmic/nucleoplasmic biotin activation with appropriate signal normalization between the biotin/streptavidin images. This is particularly important, as although the authors claim that these cells stably express the V5BirA*, it seems that there is partial clonality to the expression. Some cells in both the Poc5 and Centrin-2 fusion constructs appear to lack the V5/Streptavidin signals upon Biotin addition (such as the two cells in the centre right in Poc5, and again a cell in the centre right for Centrin-2 images). In its current form, Fig. 1A lacks signal quantification and does not report any information about the replicates and distributions of the data. I worry that this may raise concerns on the reproducibility if published in its current form. __a) We agree with the reviewer that the proximity maps of POC5 and

a) Centrin-2 are not specific to the centriole inner scaffold and thus, do not represent the inner scaffold interactome. The proximity maps identified interactions across different pools of POC5 and Centrin-2 in nucleus, cytoplasm and centrosomes (Fig. 1, S1). To highlight these important points, we already included extensive analysis of the different cellular compartments and biological processes identified by the POC5 and Centrin-2 proximity maps in the results section (pg. 9-10).

We think that there are two reasons that caused the misinterpretation of the use of these proximity maps as the “inner scaffold interactome”: 1) the way we introduced the motivation for proximity mapping studies, 2) proposing the use of the resulting interactomes as resources for identification of the full repertoire of the inner scaffold proteins. To clarify these points, we revised the manuscript in all relevant parts that might have led to misinterpretation. Following are the specific revisions:

• To clarify that the proximity maps are not specific to the inner scaffold pools of POC5 and Centrin-2, we revised the title of the results section for Fig. 1 and 2 as follows: “Proximity mapping of POC5 and Centrin-2 identifies new centriolar proteins”.

• To indicate that POC5 and Centrin-2 localizes to the cytoplasm and/or nucleus in addition to the centrosome, we added the following sentence to the result section: “In addition to centrosomes, both fusion proteins also localized to and induced biotinylation diffusely in the cytoplasm and/or nucleus (Fig. 1A).”

• In the introduction, we revised the following sentence “Here, we used the known inner scaffold proteins as probes to identify the molecular makeup of the inner scaffold in an unbiased way.” as follows: *“Here, we used the known inner scaffold proteins as probes to identify new components of the inner scaffold”. *

• To highlight the different cellular pools of POC5 and Centrin-2 and identification of their interactors in these pools, we included the following sentence in the results section: “As shown in Fig. S1, Centrin-2 and POC5 proximity interactomes were enriched for GO categories that are relevant for their published functions during centrosomal, cytoplasmic and/or nuclear biological processes and related cellular compartments (Azimzadeh et al., 2009; Dantas et al., 2013; Heydeck et al., 2020; Khouj et al., 2019; Resendes et al., 2008; Salisbury et al., 2002; Steib et al., 2020; Yang et al., 2010; Ying et al., 2019).”

• We replaced the “interactome” statement with “proximity interaction maps” or “proximity interactors” throughout the manuscript to prevent the conclusion that the proximity maps represent the inner scaffold interactome. b) As the reviewer noted, most centrosome proteins have multiple different cellular pools including the centrosome. For most proteins like gamma-tubulin and centrin, their cytoplasmic/nucleoplasmic pools are more abundant than their centrosomal pools (Moudjou et al., 1996; Paoletti et al., 1996). For the Firat-Karalar et al. Current Biology 2015 paper, I compared the biotinylation levels of centrosomal fractions versus cytoplasmic fractions and confirmed that this is also true in cells expressing myc-BirA* fusions of CDK5RAP2, CEP192, CEP152 and CEP63 (unpublished) (Firat-Karalar et al., 2014). For the revised manuscript, we will compare the biotinylation level of centrosomal, nuclear and cytoplasmic pools of V5Bir*-POC5 and V5BirA*-Centrin-2 using the stable lines. To this end, we will use published centrosome purification protocols. We will include this data in Fig. S1 to highlight that the proximity interactomes represent the different pools of the bait proteins and to show the relative levels of the baits across their different pools.

c) BioID approach has been successfully used to probe centrosome interactions by my lab and other labs in the field. In fact, proximity interaction maps of over 50 centrosome proteins were published as resource papers by Pelletier&Gingras labs (Gheiratmand et al., 2019; Gupta et al., 2015). Analogous to our strategy in this manuscript, these studies generated proximity maps of centrosome proteins by creating cell lines that stably express BioID-fusions of centrosome proteins followed by streptavidin pulldowns from whole cell extracts and mass spectrometry analysis. Since majority of centrosome proteins also have pools in multiple cellular locations, the published BioID proximity maps for centrosome proteins are not specific to centrosomes. However, the proximity maps included all known centrosome proteins and identified new proteins, which shows that centrosome interactions are represented in pulldowns form whole cell lysates. Moreover, maps form whole cell lysates are also advantageous as they are are unbiased and can be used in future studies as resources for studying the functions and interactions of the bait proteins in different contexts.

In the Firat-Karalar et al. Current Biology 2015 paper, I combined centrosome purifications with BioID pulldowns to enrich for the centrosomal interactions in the proximity maps of centriole duplication proteins(Firat-Karalar et al., 2014). However, I started the purification with cells transiently transfected with the BioID-fusion constructs, which resulted in high ectopic expression of the fusions in the cytoplasm and/or nucleus. Therefore, centrosome enrichments were useful as an additional step before mass spectrometry. Comparative analysis of the data for proximity maps of 4 centrosome proteins generated from stable lines or centrosome fractions of transiently transfected cells substantially overlap as compared in the Gupta et al. Cell 2015 study and were more comprehensive (Table S2) (Gupta et al., 2015). Therefore, we are confident that the proximity interactomes we generated for POC5 and Centrin-2 include their centrosomal interactions.

__1b) Similarly, it is not clear whether the expression of Poc5 and Centrin-2 fusion molecules somehow interfere with their endogenous interactions or function. At least some loss-of-function (e.g., RNAi) experiments should be performed where the depletion of endogenous proteins should be attempted to rescue by the fusion constructs. This will help evaluate whether the fusion proteins can rescue the depletion of their endogenous counterparts and behave as expected from a wild-type scenario. __

The reviewer raises an important concern regarding the physiological relevance of the POC5 and Centrin-2 proximity maps. In the manuscript, we showed and discussed the validation of their proximity interactomes by two lines of evidence, which are: 1) the interactomes identified the previously described cellular compartments, biological processes or interactors of POC5 and Centrin-2, 2) the interactomes led to the identification of CCDC15 as a new inner scaffold protein.

As the reviewer indicated, stable expression of POC5 and Centrin-2 in the presence of their endogenous pools might affect cellular physiology and thereby the landscape of the interactomes. We plan to address this using the following experiments:

a) We will perform a set of functional assays to assess whether stable V5BirA*-Centrin-2 and V5BirA*-POC5 cells behaves like control cells in terms of their centrosome number, cell cycle profiles and mitotic progression. We will specifically quantify:

• centrosome number (immunofluorescence analysis for gamma-tubulin and centrin)
• their mitotic index (immunofluorescence analysis by DAPI)
• spindle polarity and percentage of multinucleation (immunofluoerescence analysis for microtubules, gamma-tubulin and DAPI)
• cell cycle profiles (flow cytometry and immunofluorescence)
• apoptosis (immunoblotting for caspase 3) Together, results from these experiments indicate that the V5BirA*-POC5 or Centrin-2-expressing stable lines do not exhibit defects associated with their stable expression.

b) We will perform expansion microscopy in V5BirA*-Centrin-2 and V5BirA*-POC5 cells to assess whether the fusion protein specifically localizes to the centriole inner scaffold, which will provide support for the presence of inner scaffold proteins in their proximity maps. Specifically, we plan to stain the fusion proteins by V5 or BirA antibodies and include the data for the antibody that works for expansion microscopy. This experiment will address whether their stable expression results in specific localization of these proteins at the centriole inner scaffold.

1c) Overall, as the entire claim around the proximity mapping revolve around its assumption about the centriole inner scaffold, these controls seem imperative to substantiate the ground truth of the biology presented in the manuscript.

In the revised manuscript, we toned down and made it clear that Centrin-2 and POC5 proximity maps are not specific to the inner scaffold and do not represent the inner scaffold interactome. Since the maps were generated from the whole cell extract, they will provide a resource for future studies aimed at studying functions and mechanisms of POC5 and Centrin-2 across their different cellular pools including the centrosome.

We would like to also highlight that the proximity maps of POC5 and Centrin-2 are not the major advances of our manuscript. The major advance of our manuscript is the identification of CCDC15 as a new inner scaffold protein that is required for regulation of centriole size and architectural integrity and thereby, for maintaining the ability of centrioles to template the assembly of functional cilia. Importantly, our results identified CCDC15 as the first dual regulator of centriolar recruitment of inner scaffold protein POC1B and the distal end SFI1/Centrin complex and provided important insight into how inner scaffold proteins work together during centriole integrity and size regulation. The new set of experiments we will perform for the revisions of the paper will strengthen these conclusions.

__2) I am curious about the choices of the cell lines in this work. The proximity mapping to reveal CCDC15 as a candidate protein for centriole inner scaffold was performed in HEK293T cells (human embryonic kidney), however its immunostaining was performed using RPE1 and U2OS cells (human retinal and osteosarcoma epithelial cells respectively). This raises questions regarding the generality of CCDC15 as a centriole inner scaffold protein. Could CCDC15 be simply unique to the centriole inner scaffold of epithelial cells such as RPE1 and U2OS cells? Or could the authors demonstrate any information/data on whether it's similarly localized to the inner scaffold in embryonic kidney cells or other cell types? If not, the claims should be moderated to reflect this fine detail. __

To test whether CCDC15 localizes to the inner scaffold in other cell types, we performed U-ExM analysis of CCDC15 localization relative to the centriolar microtubules in differentiating multiciliated epithelial cultures (MTEC). As shown in Fig. S3A, CCDC15 localized to the inner scaffold in the centrioles in MTEC ALI+4 cells. Given that the inner scaffold proteins including CCDC15 and previously characterized ones have not been studied in multiciliated epithelia, this result is important and provides support for potential role of the inner scaffold in ensuring centriole integrity during ciliary beating. Additionally, we examined CCDC15 localization by 3D-SIM in centrosomes purified from HEK293T cells, which showed that CCDC15 localizes between the distal centriole markers CEP164 and Centrin-3 and proximal centriole markers gamma-tubulin and rootletin (Fig. S3B).

3) Discussions and data on the localization of CCDC15 to centriolar satellites appear anecdotal and not fully convincing (Fig. S2D). Given that the authors test the relevance of PCM1 for CCDC15's centriolar localization, it is key to have quantitative data supporting their claim that centriolar satellites can help recruit CCDC15 to the centriole. Could the authors quantify what proportion of CCDC15 localize to the centriolar satellites? One way to do this could be to quantify the colocalization coefficience of CCDC15 and PCM1 signals.

We only observed co-localization of CCDC15 with the centriolar satellite marker PCM1 in cells transiently transfected with mNG-CCDC15. In Fig. S2E, we included the quantification of the percentage of U2OS and RPE1 cells that exhibit co-localization of PCM1 (100% of U2OS cells, about 80% of RPE1 cells). Like CCDC15, ectopic expression of WDR90 revealed its centriolar satellite localization, suggesting a potential link between centriolar satellites and inner scaffold proteins that can be investigated in future studies (Steib et al., 2020). We now included these results in the discussion section as follows:

“As assessed by co-localization with the centriolar satellite marker PCM1, mNG-CCDC15 localized to centriolar satellites in all U2OS cells and in about 80% of RPE1 cells (Fig. S2C-E). Association of CCDC15 with centriolar satellites is further supported by its identification in the centriolar satellite proteomes(Gheiratmand et al., 2019; Quarantotti et al., 2019).”

Even though endogenous staining for CCDC15 did not reveal its localization to centriolar satellites, following lines of data support the presence of a dynamic and low abundance pool of CCDC15 at the centriolar satellites: 1) CCDC15 was identified in the centriolar satellite proteome and interactome (Gheiratmand et al., 2019; Quarantotti et al., 2019). 2) CCDC15 centrosomal targeting is in part regulated by PCM1 (Fig. S2F, S2G). For majority of the proteins identified in the centriolar satellite proteome, their satellite pool can only be observed upon ectopic expression. This might be because their centriolar satellite pool is of low abundance and transient as satellite interactions are extensively identified only in proximity mapping studies, but not in traditional pulldowns

__4) Similar to above (#3), there is no quantitative information on the co-localization or partial co-localization of the signal foci in Fig. 3A and 3B. The authors readily study CCDC15's localization in wonderful detail in their expansion microscopy data, so they could actually consider taking out Fig. 3A and 3B, as the data seem redundant without any quantification. __

To address the reviewer’s concern, we included plot intensity profile analysis of CCDC15 and different centriole markers along a line drawn at the centrioles in Fig. 3A and 3B, which shows the extent of their overlap. As part of our revision plan, we will replace the confocal imaging data in Fig. 3A and 3B with 3D-SIM imaging data of CCDC15 relative to different centriole markers together with plot profile analysis. We already included 3D-SIM imaging of centrosomes purified form HEK293T cells in Fig. S3B. 3D-SIM imaging data will complement the localization data revealed by U-ExM.

__5) Do the authors also feel that CCDC15 localize to the core lumen in a somehow helical manner (Fig. 1A, Fig. 1F top and bottom panels, Fig. 5A etc.)? Le Guennec et al. 2020's helical lattice proposal for the inner scaffold further reaffirms that CCDC15 is indeed a likely major component of the inner scaffold. In my view, authors should state this physical similarity explicitly to further support their findings on CCDC15. __

As the reviewer indicated, cryo–electron tomography and subtomogram averaging of centrioles from four evolutionarily distant species showed that centriolar microtubules are bound together by a helical inner scaffold covering ~70% of the centriole length (Le Guennec et al., 2020). Although U-ExM data do not have enough resolution to show that CCDC15 localizes in a helical manner, we agree with the reviewer that the discussion of this possibility is important and thus we included the following sentence in the results:

“Longitudinal views suggest potential helical organization of CCDC15 at the inner scaffold, which is consistent with its reported periodic, helical structure (Le Guennec et al., 2020).”

__6a) The data on the link between the CCDC15 recruitment and the centriole growth (Fig. 4F) or the G2 phase of the cell cycle (Fig. 4H) are not fully convincing without quantitative data. For Fig. 4F, the authors should consider plotting the daughter centriole length vs the daughter CCDC15 intensities against each another, to see whether more elongated daughters truly tend to have more CCDC15. __

To address the reviewer’s concern, we will plot the daughter centriole length versus CCDC15 intensity at different stages of centriole duplication. In asynchronous cultures that we analyzed with U-ExM, we were not able to find enough cells to perform such quantification. To overcome this limitation, we will perform U-ExM analysis of cells fixed at different points after mitotic shake-off and stained for CCDC15 and tubulin. We will include minimum 10 different representative U-ExM data for different stages of centriole duplication in the revised manuscript along with quantification of length versus signal.

As detailed in the results section, the goal of these experiments was to determine when CCDC15 is recruited to the procentrioles during centriole duplication, but not to suggest a role for CCDC15 in centriole growth. We clarified this by including the following sentence:

“To investigate the timing of CCDC15 centriolar recruitment during centriole biogenesis, we examined CCDC15 localization relative to the length of procentrioles that represent cells at different stages of centriole duplication (Fig. 4F).”

__6b) For Fig. 4H, the argument regarding the cell cycle regulation requires quantification of the bands from several WB repeats, normalized to the expression of GAPDH within each blot (this is particularly relevant, as the bands of CCDC15 do not look dramatically different enough to draw conclusions by eye). __

We will perform these experiments two more times, quantify cellular abundance of CCDC15 in synchronized populations from three experimental replicates and plot it with proper statistical analysis.

__7a) The authors find herein that CCDC15 depletion lead to centrioles that are ~10% shorter than the controls. With the depletion of Poc5 and Wdr90 (other proposed components of the inner scaffold), the centrioles end up larger however (Steib et al., 2020). If the role of inner scaffold in promoting centriole elongation is structural, why are these two results the opposite of each other? I realize there is a brief discussion about this at the end of the paper, however, this requires a detailed discussion and speculation on the relevance of these findings. It would be key to clarify whether the inner scaffold as a structure inhibits or promotes centriole growth - or somehow both? If so, how? __

We agree with the reviewer that comparative analysis of centriole length phenotypes for CCDC15 and other components that regulate centriole length will provide insight into how these components work together at the centriole inner core. To this end, we phenotypically compared CCDC15 loss-of-function phenotypes to that of other components of the inner scaffold (POC5, POC1B, FAM161A) that interact with CCDC15. In agreement with their previously reported functions in U2OS or RPE1 cells, we found that POC5 depletion resulted in a 4% slight but significant increase in centriole length and POC1B depletion resulted in a 15% significant decrease. In contrast, FAM161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, our analysis of their centriolar localization dependency and regulatory roles during centriole length suggest that CCDC15 and POC1B might form a functional complex as positive regulators of centriole length. In contrast, POC5 functions as a negative regulator and might be part of a different pathway for centriole length regulation. We integrated the following sub-paragraph in the results section in pg. 19 and also included discussion of this data in the discussion section in pg. 23:

“Moreover, we quantified centriole length in control cells and cells depleted for POC5 or POC1B. While POC5 depletion resulted in longer centrioles, POC1B resulted in shorter centrioles (POC5: siControl: 414.1 nm±38.3, siPOC5: 432.7±44.8 nm, POC1B: siControl: 400.6±36.1 nm, siPOC1B: 341.5±44.39 nm,). FAMA161A depletion did not alter centriole length (siControl: 447.8±59.7 nm, siFAM161A 436.3±64 nm). Together, these results suggest that CCDC15 might cooperate with POC1B and compete with POC5 to establish and maintain proper centriole length.”

__7b) There might be some intriguing opposing regulatory action of Poc5 and CCDC15 as demonstrated here, where CCDC15 depletion leads to slightly over-recruitment of Poc5, and vice versa. Does this suggest that a tug-of-war going on between different molecules that localize to the inner scaffold? Does this provide some dynamicity to this structure, which might in turn regulate centriole length both positively and negatively? This may be analogous to how opposing forces of dyneins and kinesins provide robust length control for mitotic spindles. I am speculating here, but hopefully these may provide some useful grounds for further discussion in the paper. If the authors deem it interesting experimentally, they can test whether the two molecules indeed regulate centriole length by opposing each other's action, by a double siRNA of CCDC15 and Poc5 to see if this retains the centriole length at its control siRNA size (like how they do a similar test for Poc1's potential co-operativity with CCDC15 in Fig. 6J). __

We thank the reviewer for proposing excellent ideas on how inner scaffold proteins work together to regulate centriole length. As proposed by the reviewer, different proteins oppose each other analogous to how dynein and kinesin regulate mitotic spindle length. Loss-of-function and localization dependency data support that CCDC15 cooperates with POC1B, which was supported by phenotypic characterization of co-depleted cells (Fig. 6I-K).

The increase in POC5 levels and coverage at the centrioles upon CCDC15 depletion and vice versa (Fig. 7B, 7G) suggest that CCDC15 and POC5 compete with each other in centriole length regulation. As suggested by the reviewer, we attempted to test this by comparing centriole length in cells co-depleted for CCDC15 and POC5 relative to their individual depletions. Although we tried different depletion workflows, we were not able to co-deplete CCDC15 and POC5. Specifically, we tried transfecting cells with CCDC15 and POC5 siRNAs at the same time or sequentially for 48 h or 96 h. The centrioles in cells that survived co-depletion were positive for both CCDC15 and POC5. This might be because co-depletion of both proteins is toxic to cells. Since CCDC15 and POC5 are likely part of two different pathway in regulation of centrioles and also have other cellular functions, this might have caused cell death. We included the following statement in the discussion to address the excellent model proposed by the reviewer:

“Taken together, our results suggest that CCDC15 cooperates with POC1B and competes with POC5 during centriole length regulation. Moreover, they also raise the exciting possibility that centriole length can be regulated by opposing activities of inner scaffold proteins. Future studies that explore the relationship among centriole core proteins are required to uncover the precise mechanisms by which they regulate centriole integrity and size.”

__8) In their introduction section, the authors discuss how relatively little is known about the size control of centrioles, however they fail to mention a series of recent primary literature that uncover striking, new mechanisms and novel molecular players that highlight the complexity of centriole size control. This complexity appears to arise from the existence of multitude of length control mechanisms that influence the cartwheel or the microtubule length individually, or simultaneously via yet-to-be further explored crosstalk mechanisms. a. As such, when the authors talk about the procentriole size control in the introduction, they should discuss and refer to the following studies, in terms of: • How theoretical and experimental work demonstrate that procentriole length may vary dependent on the levels of its building block Sas-6 in animals (Dias Louro et al., 2021 PMID: 33970906; Grzonka and Bazzi, 2022 bioRxiv). • How a homeostatic Polo-like kinase 4 clock regulates centriole size during the cell cycle (Aydogan et al., 2018 JCB PMID: 29500190), and how biochemistry and genetics coupled with mathematical modelling unravel a conserved negative feedback loop between Cep152 and Plk4 that constitutes the oscillations of this clock in flies (Boese et al., 2018 PMID: 30256714; Aydogan et al., 2020 PMID: 32531200) and human cells (Takao et al., 2019 PMID: 31533936). __

__b. Similarly, when the authors refer to centriole size control induced by microtubule-related proteins, they should highlight the further complexity of this process by referring to: • How a molecule located at the microtubule wall, Cep295/Ana1, can regulate centriole length in flies (Saurya et al., 2016 PMID:27206860) and human cells (Chang et al., 2016 PMID:27185865) - like all the other centriolar MT molecules that the authors discuss in the manuscript. • How a crosstalk between Cep97 and Cep152 influences centriole growth in fly spermatids (Galletta et al., 2016 PMID:27185836). • How a crosstalk between CP110-Cep97 and Plk4 influences centriole growth in flies (Aydogan et al., 2022 PMID:35707992), and this molecular crosstalk is conserved, at least biochemically, in human cells (Lee et al., 2017 PMID:28562169). __

We thank the reviewer for highlighting the papers that uncovered new mechanisms and players of centriole size and integrity control as well as for the detailed explanation of how different studies led to these discoveries in different organisms. We should have discussed these proteins, functional complexes and mechanisms in our manuscript and cited the relevant literature. We inadvertently focused on literature that uncovered centriole length regulation by MAPs and the inner scaffold. In the introduction section of the revised manuscript where we introduced centriole size regulation in pg. 5, we summarized the major findings on the role of different MAPs, cartwheel and PLK4 homeostatic clock in ensuring formation of centrioles at the correct size in different organisms.

__Minor points: __

__1) Introduction section: Literature reference missing for the sentence starting with "Importantly, the stable nature of centrioles enables them to withstand...". __

We cited research articles that show the importance of centriole motility during ciliary motility and cell division.

“Importantly, the stable nature of centrioles enables them to withstand mechanical forces during cell division and upon ciliary and flagellar motility (Abal et al., 2005; Bayless et al., 2012; Meehl et al., 2016; Pearson et al., 2009).

__2) Fig. S1 legend: A typo as follows: CRAPome banalysis should read CRAPome analysis. __

We corrected this typo.

__3) Fig. S2: Info on the scale bar in the legend is missing in Fig. S2A. Scale bars for different panels are missing in general in Fig. S2A. __

We added scale bar information for Fig. S2A and to all other supplementary figure legends that lack scale bar information.

__4) Fig. 3A and 3B: When displaying the data, coloured cartoon diagrams would be beneficial to guide the reader who are not fully familiar with the spatial orientation of these proteins. __

As suggested by the reviewer, we will remove the confocal imaging data for CCDC15 localization from Fig. 3A and 3B. For the revised version, we will include 3D-SIM imaging data along with a diagram that represents the spatial orientation of CCDC15 relative to the chosen centriole markers.

__5) Fig. 3H: No information about the sample number (number of cells or technical repeats examined) reported. __

We included information on the number of experimental replicates and cells analyzed.

__6) Fig. S3B legend: A typo as follows: CCD15-depelted RPE1 cells should read CCDC15-depleted RPE1 cells. __

We corrected this typo.

__7) Fig. S3B legend: A typo as follows: cellswere fixed with should read cells were fixed with. __

We corrected this typo.

__8) There are many spelling mistakes and typos throughout the paper. I have listed a few examples above, but please carefully read through the manuscript to correct all the errors. __

Thank you for indicating the spelling mistakes we missed to correct for initial submission. In the revised manuscript, we carefully read through the manuscript to correct the mistakes.

__9) Fig. S3E: The orange columns depicting % of cells with Sas-6 dots look awkward. Why the columns look larger than the mean line? Please correct as appropriate. __

The total percentage of cells in the two categories (orange and purple) we counted is 100%, which corresponds to the column value at the y-axis. Therefore, the value for each experimental replicate for the orange category is less than 100% and is marked below the 100% line.

__10) Although authors provide microscopy information for the U-ExM and FRAP experiments, there is no information about the microscopy on regular confocal imaging experiments which should be detailed in Materials and Methods. Also, there is no information about the lenses, laser lines and the filter sets that were used in the imaging experiments. These should be provided as well. __

In the methods section, we now included detailed information for the microscopes we used and imaging setup (lenses, laser lines, filter sets, detectors, z-stack size, resolution).

11)

• __ Fig. 2A: lacks a scale bar. __
• __ Fig. 2C legend: lacks info on the scale bar length. __
• __ Fig. 5A legend: lacks info on the scale bar length. __
• __ Fig. 7A: lacks a scale bar. __
• __ Fig. 7G legend: lacks info on the scale bar length. __
• __ Fig. S2C-E: lack scale bars. __
• __ Fig. S3D, F and H: lack scale bars. (Fig. S4 in the revised manuscript)__
• __ Fig. S3J legend: lacks info on the scale bar length. (Fig. S4 in the revised manuscript)__
• __ Fig. S4A, B, D and E: lack scale bars. (Fig. S5 in the revised manuscript)__
• __ Fig. S4C legend: lacks info on the scale bar length. (Fig. S5 in the revised manuscript)__
• __ Fig. S4G legend: lacks info on the scale bar length. (Fig. S5 in the revised manuscript)__ We added the scale bars and the size information to the figures and figure legends for the above figures.

Reviewer #2 (Significance (Required)): __The findings of this study join among the relatively new literature (e.g., Steib et al., 2020 and Le Guennec et al. 2020) on the nature of centriole inner scaffold and its potential roles in centriole formation, integrity and its propensity to form the primary cilium. Therefore, it will be of interest to a group of scientists studying these topics in the field of centrosomes/cilia.

My expertise is on the biochemistry and genetics of centriole formation in animals.__

We thank the reviewer for his/her comments and constructive feedback to improve our manuscript. We are encouraged to see that the reviewer acknowledges how the results from our manuscript advances our understanding of centriole length, integrity and function regulation.

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tag line includes who but now where this is, when it was taken, context is missing from tagline, but included in paragraph beneth.

who, what, where, when in tag line

I use an app called RED, which is a widely used sharing app in China, where people can share their daily life or make recommendations. There are very detailed tag categories within the app, so it will make recommendations based on what you read regularly. At the same time, its tags are also interlinked, so it will also recommend content that you might be interested in to observe user feedback.

Which can be triggered by perhaps the most reliable Safari Extension keyboard shortcut (on iOS/iPadOS) to date: ⌘⇧E.

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Reply to the reviewers

Manuscript number: RC-2022-01723

Corresponding author(s): Daphne Avgousti, Srinivas Ramachandran

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

Summary This study by Lewis et al. examines the role of heterochromatin in the nuclear egress of herpesvirus capsids. They show that heterochromatin markers macroH2A1 and H3K27me3 are enriched at specific genome regions during the infection. They also show that when macroH2A1 is removed or H3K27me3 is depleted (both of which reduce the amount of heterochromatin at the nuclear periphery), the capsids are not able to egress as effectively. This is interesting since it could be argued that heterochromatin acts as a hindrance to the transport of viral capsids to the nuclear envelope and that the loss of it would allow capsids to reach the nuclear envelope more easily. However, this paper seems to show that heterochromatin formation, on the contrary, is necessary for efficient egress. Overall, the study seems comprehensive. The methodology is solid, and the experiments are very well controlled. However, some issues need to be addressed before publication.

Major comments

1) In line 49, the authors state, "Like most DNA viruses, herpes simplex virus (HSV-1) takes advantage of host chromatin factors both by incorporating histones onto its genome to promote gene expression and by reorganizing host chromatin during infection". In addition, HSV1 expression can be hindered by the host's interferon response via histone modifications. Ref. Johnson KE, Bottero V, Flaherty S, Dutta S, Singh VV, Chandran B. IFI16 restricts HSV-1 replication by accumulating on the HSV-1 genome, repressing HSV-1 gene expression, and directly or indirectly modulating histone modifications. PLoS Pathog. 2014 Nov 6;10(11):e1004503. doi: 10.1371/journal.ppat.1004503. Erratum in: PLoS Pathog. 2018 Jun 6;14(6):e1007113. PMID: 25375629; PMCID: PMC4223080.

We agree with the reviewer and have amended our text and added the reference. See line 57.

2) Reference 5 is misquoted in the sentence, "This redistribution of host chromatin results in a global increase in heterochromatin". In that reference, the amount of heterochromatin is not analyzed in any way. However, that particular paper shows that the transport of capsid through chromatin is the rate-limiting step in nuclear egress, which is important considering this study. Further, the article by Aho et al. shows that when the infection proceeds capsids can more easily traverse from the replication compartment into the chromatin, which means that infection can modify chromatin for easier capsid transport. For that reason, the article is an important reference, but it needs to be cited correctly.

We agree with the reviewer that this citation was misquoted and have corrected the citation. See lines 55 and 62-64.

3) The term heterochromatin channel at lines 54, 102, and 303 is misleading since the channels seen in the original referred paper are less dense chromatin areas. Also, this term is not used in the original paper where the phenomenon was first described. These less dense interchromatin channels were found by soft-X-ray tomography imaging and analyses, not by staining.

We thank the reviewer for pointing out this discrepancy and have amended the text to accurately describe the methods used in the appropriate citations. See lines 65, 115, and 383.

4) It is difficult to visualize chromatin using TEM microscopy. The values of peripheral chromatin thickness given in Figure 1e (5-15 nm) do not seem realistic given that the thickness of just one strand of histone-wrapped DNA is 11 nm. Why are the two values for WT different? If you can get so different values for WT, it is a bit worrisome (switching the WT results between the top and bottom parts of Fig. 1e would for example result in very different conclusions on the effect of macroH2A1 KO for the thickness of the chromatin layer).

*We agree with the reviewer that it is difficult to visualize chromatin by TEM. It is also important to note that comparisons can only be made between samples treated on the same day in the same way. Taking this into account, we chose to compare macroH2A1 KO cell stains to controls done at the same time, and the same for H3K27me3 depleted conditions compared to DMSO treated and prepare for EM at the same time. Visually, it is apparent that the staining in the macroH2A1 KO control cells is somewhat different than those of the H3K27me3 depleted control cells, which represents the inherent variability of this method. It is also true that one nucleosome is around 11nm, however, since the cells contain highly compacted chromatin with many other proteins present, this measurement is not appropriate to apply. Adding up the millions of nucleosomes that make up the chromosomes at 11nm each would result in a space much larger than the nucleus, therefore we focus on comparing between control and experimental conditions restricted to this assay as a relative qualitative comparison. Nevertheless, we agree with the reviewer that the notion of changing chromatin is difficult to quantify by EM and so we have taken an additional approach to test our hypothesis and confirm EM interpretations (discussed lines 391-393). We have utilized live capsid trafficking to visualize capsid movement in nuclei in the presence or absence of macroH2A1. The results from these new experiments are presented in new Figure 5 and EV5 and support our model. *

5) In lines 134-137 it says that "The enrichment of macroH2A1 and H3K27me3 was observed as large domains that were gained upon viral infection (Fig 2a), suggesting that the host landscape is altered upon infection. These gains were reflected in an increase in total protein levels measured by western blot (Fig 2b)." However, the protein levels of H3K27me3 do not seem to increase during infection. In other presented data as well (Figs. 2a, 2b, 2c, S2a) it is difficult to justify the statement that H3K27me3 is enriched in infection. When this is the case, the conclusion that the amount of heterochromatin increases in the infection (the quotation above and the one in line 315) is not supported. The statement in line 315 is also not specific since it is unclear what "newly formed heterochromatin increases" means.

We agree with the reviewer that our original description was misleading. We now have edited the text to clarify that there is redistribution of macroH2A1 and H3K27me3. In the revised manuscript, we have also included mass spectrometry data mined from Kulej et al. that show peptide counts that reflect increases in the heterochromatin markers described (see new Figure EV1a). Despite this quantitative measure, upon rigorous replicates of our western blots as requested by Reviewer 2, we concluded that the increases originally described are somewhat inconsistent by western blot. This discrepancy between mass spectrometry data and western blot is likely due to the non-linear nature of antibody binding and developing of western blots by the ECL enzymatic reaction. Therefore, our revised manuscript focuses on this redistribution as a reaction to infection and stress responses instead of a global increase as the original manuscript stated. See lines 174, 182, 196, 397 and Fig EV4d in main text and discussion sections.

• *

6) Quantitation of viral capsid location in H3K27me3-depleted cells seems somewhat arbitrary. It would have been more robust to calculate the number of capsids per unit length of the nuclear envelope with and without depletion.

We agree with the reviewer that the quantification of capsids in the H3K27me3-depleted conditions was arbitrary. In our revised manuscript, we have now repeated this quantification to accurately measure the phenotype observed, that is the chains of capsids lined up at the inner nuclear membrane. To do this, we used two measures: 1) the distance from the INM as less than 200nm and 2) the distance from other capsids as less than 300nm. Taking into account these two measures, we quantified the frequency with which multiple capsids lined up at the INM in WT and H3K27me3-depleted conditions. This is represented in the new Figure 5d. In the WT setting, we observe most often 1 single capsid at the INM, with a small fraction of 2 capsids. However, in the H3K27me3-depleted condition, we observe much greater numbers of capsids at the INM more frequently, as many as 16 at a time, leading to an average of 2-3 capsids at any single location. The source data for this figure are also provided. See lines 589 and Fig5d.

7) In lines 300-302 it says "Elegant electron microscopy work showed that HSV-1 infection induces host chromatin redistribution to the nuclear periphery2,8." However, the redistribution data in reference 8 is based on soft x-ray tomography and not on electron microscopy."

We have amended the text to accurately describe the methods used in the citations. See line 384.

8) The authors bundle together the effects of macroH2A1 removal and H3K27me3 depletion by saying that they both decrease the amount of heterochromatin at the nuclear periphery and therefore hinder capsid egress. This seems overly simplistic and macroH2A1 and H3K27me3 seem to act very differently, which is manifested in the drastic difference in nuclear capsid localization between the two cases. This difference needs to be discussed more.

We agree with the reviewer that there is a nuanced difference in the effect on nuclear egress in the absence of the two heterochromatin marks. Specifically, that macroH2A1 loss results in greater numbers of capsids dispersed throughout the nucleus, whereas depletion of H3K27me3 results in capsids reaching the INM and not escaping. To examine these differences further, we have carried out live imaging of capsid trafficking in macroH2A1 KO cells compared to control and found that capsids move much more slowly, consistent with our model, see new Figure 5h-I and EV5h-i. Conversely, H3K27me3 depletion does not prevent the capsids from reaching the INM, raising the question of whether they are successfully able to dock at the nuclear egress complex (NEC). To investigate this further, we obtained an antibody against the NEC component UL34 and probed during infection in our heterochromatin disrupted conditions. We found that UL34 levels are unchanged upon loss of macroH2A1 or depletion of H3K27me3, suggesting the levels of UL34 do not account for the decrease in titers. These data are now presented in new Figure EV3g-h. Furthermore, we have amended our model to include the two different scenarios upon loss of different types of heterochromatin (see new Figure 6) and discussion of these differences. See line 428.

Minor comments Line 45: Nuclear replicating viruses -> Nuclear-replicating viruses Line 56: is -> are Line 64: 25kDa -> 25 kDa Line 159: macroH2A1 cells -> macroH2A1 KO cells Line 289: The term gDNA is rarely used for viral DNA. Replace gDNA with viral DNA. Line 405: 8hpi -> 8 hpi Line 449: mm2 -> μm2 "Scale bar as indicated" words can be removed in the figure legends or at least should not be repeated many times within one figure legend.

We have amended the text to address these comments. See lines 52, 68, 76, 179, 334, 513, and 585.

Reviewer #1 (Significance (Required)):

These findings would appeal to a broad audience in the field of virology. Specifically, the researcher in the fields of virus-cell and virus-nucleus interactions. This manuscript analyses herpesvirus-induced structural changes in the chromatin structure and organization in the nucleus that are also likely to affect the intranuclear transport of viral capsids.

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

The manuscript "HSV-1 exploits heterochromatin for egress" describes the effects of heterochromatin at the nuclear periphery, macroH2A1 or H3K27me3 on HSV-1 replication and egress. Knocking out macroH2A1 or depleting H3K27me3 with high concentrations of tazemetostat depleted heterochromatin at the nuclear periphery, may not have affected HSV-1 protein expression and modestly inhibited the production of cell-free infectivity and HSV-1 genomes. macroH2A1 deposition was affected by infection, creating new heterochromatin domains which did not correlate directly with the levels of expression of the genes in them. The authors conclude that heterochromatin at the nuclear periphery dependent on macroH2A1 and H3K27me3 are critical for nuclear egress of HSV-1 capsids.

The experiments leading to the conclusion that HSV-1 capsids egress the nucleus through channels in the peripheral chromatin confirm previously published results (https://doi.org/10.1038/srep28844). The previously published EM micrographs show a much larger number of nuclear capsids, more consistent with the images in the classical literature, even in conditions when nuclear egress was not inhibited. Figures 1 and 4 show scarce nuclear capsids, even under the conditions when nuclear egress should be inhibited according to the model and analyses. The large enrichment in nuclear capsids in KO cells predicted by the model is not reflected in figure 4a, which shows only a modest increase in nuclear capsid density (the total number of nuclear capsids would be more informative). The number or density of nuclear capsids is not shown in H3K27 "depleted" cells. The robustness of the analyses of the number of capsids at the membrane in H3K27 "depleted" cells is unclear. For example, the analyses could be repeated with different cut offs, such as 2 or 4. If they are robust, then the conclusions will not change when the cutoff value is changed.

We appreciate the reviewer’s observation that to number of capsids we show differs from those published in the publication by Myllys et al. (Sci Rep 2016 PMID 27349677). It is important to note there are several differences between our study and that of Myllys et al. that explain the difference. First, as reviewer 1 pointed out, the Myllys et al. study used three-dimensional soft X-ray tomography combined with cryogenic fluorescence and electron microscopy to observe capsids in 3D rendered nuclei. Since our method uses only single ultrathin 50nm slices of cells, we cannot visualize the total number of capsids per nucleus, rather only per slice, which is why we have averaged slices of many nuclei to generate a statistical comparison between macroH2A1 KO or H3K27me3-depleted and control cells treated at the same time (see response to reviewer 1). Furthermore, these other methods are specialized techniques for 3D imaging that are beyond the scope of our study. Second, the Myllys et al. paper used B cells which are much smaller than HFFs, lending themselves to better tomography studies but not commonly used to study HSV-1 biology. Third, the Myllys et al. paper also used a different MOI and time point than we have. Taken together, these differences account for the disparity in visualizing capsids which is why we quantified capsid number across many images.

We agree with the reviewer that our quantification in the H3K27me3-depleted cells compared to control was somewhat arbitrary. As stated in the response to Reviewer 1 above, in our revised manuscript we have now repeated this quantification to accurately reflect the phenotype observed, that is the chains of capsids lined up at the inner nuclear membrane. To do this, we used two measures: 1) the distance from the INM as less than 200nm and 2) the distance from other capsids as less than 300nm. Taking into account these two measures, we quantified the frequency with which multiple capsids lined up at the INM in WT and H3K27me3-depleted conditions. This is represented in the new Figure 5d. In the WT setting, we observe most often 1 single capsid at the INM, with a small fraction of 2 capsids. However, in the H3K27me3-depleted condition, we observe much greater numbers of capsids at the INM more frequently, as many as 16 at a time, leading to an average of 2-3 capsids at any single location. The source data for this figure are also provided. See lines 589 and Fig 5d.

Furthermore, we have now also carried out live-imaging analysis of single capsids during infection which show the appropriate number of capsids expected when the full nucleus is visible. These results are presented in the new Figure 5 and EV5.

The quantitation of the western blots present no evidence of reproducibility and/or variability. The number of biologically independent experiments analyzed must be stated in each figure and the standard deviation must be presented. As presented, the results do not support the conclusions reached. The quality of western blots should also be improved. it is unclear why figure 2b shows viral gene expression in wild-type cells only, and not in KO or H3K27me3 depleted cells, which are only shown in the supplementary information. These blots presented in Figure S5a and S5b are difficult to evaluate as the signal is rather weak and the controls appear to indicate different loading levels. These blots do not appear to be consistent with the conclusions reached. Some blots (VP16, ICP0 in HFF) appear to indicate a delay in protein expression whereas others (VP16, ICP0 in RPE) appear to indicate earlier expression of higher levels. The claimed "depletion of H3K27me3 is not clear in in figure S5d, in which the levels appear to be highly variable in all cases, without a consistent pattern, with no evidence of reproducibility and/or variability, and using a mostly cytoplasmic protein as loading control. All western blots should be repeated to a publication level quality, the number of independent experiments must be clearly stated in each figure, and the reproducibility and/or variability must be indicated by the standard deviation.

*As reviewer 1 also pointed out, we appreciate that there is some variability with respect to the stated ‘increase’ in these heterochromatin marks during infection. As stated in response to reviewer 1, in our revised manuscript we have included a deeper analysis of these marks from global mass spectrometry that indicates an increase in total levels. Please see response to reviewer 1. *

• *

In the revised manuscript, we have now included mass spectrometry data mined from Kulej et al. that show peptide counts that reflect increases in the heterochromatin markers described (see new Figure EV1a). Despite this quantitative measure, upon rigorous replicates of our western blots as requested by Reviewer 2, we concluded that the increases originally described are somewhat inconsistent by western blot. This discrepancy between mass spectrometry data and western blot is likely due to the non-linear nature of antibody binding and developing of western blots by the ECL enzymatic reaction. Nevertheless, our genome-wide chromatin profiling showed consistent, reproducible, and statistically significant redistribution of macroH2A1 and H3K27me3 upon HSV-1 infection. Therefore, our revised manuscript now focuses on this redistribution as a reaction to infection and stress responses instead of a global increase as the original manuscript stated. See lines 174, 182, 196, 397 and Fig EV4b-c.

• *

With respect to viral protein levels, although there is slight variation in the levels of VP16 or ICP0 in RPEs compared to HFFs, we do not feel that this difference is biologically significant as several other measures of viral infection progression are unchanged (viral RNA, viral genome accumulation within infected cells). Furthermore, the significant difference in titers we observe is not explained by slight differences in ICP0 or VP16. Nevertheless, to document this variability in western blot and assuage any concern of impact infection progression, we have repeated each western blot presented in the paper three separate times and used these blots to quantify each relevant protein. Graphs of western blot quantitation can be found in each figure accompanying a western blot as follows:

Western blots:

Figures 3b-c, 4ab, EV1b, EV5a

Quantitation of western blots:

Figures 3d, 4c, EV1c, EV5b-f

• *

An enhanced analyses of the RNA-seq data, analyzing all individual genes rather than pooling them together, would provide better support to these conclusions. Then, the western blots are useful to show that the changes in mRNA result in changes in the levels of selected proteins.

• *

*We appreciate the reviewer’s interest in the RNA-seq data, however, we feel that reviewer has not understood the analysis we presented in the initial submission. To clarify, we calculated fold changes for individual genes and did not pool RNA-seq data anywhere in the manuscript. We show boxplots of log2 fold changes of individual genes. Boxplots enable summarization of the salient features of a distribution while still representing individual gene analysis. Here, the distribution being plotted is the log2 fold change of individual genes that intersect with macroH2A1 domains that change due to infection. As such, clusters 1-3 of macroH2A1 domains feature a loss in macroH2A1 due to infection and the boxplots show that the majority of genes are upregulated. To highlight this point further, in our revised manuscript we have included volcano plots of genes intersecting with each cluster also showing the split between the number of genes significantly upregulated and downregulated in each cluster at each time point (see new Figure EV3c). As expected from the boxplots, clusters 1-3 feature a much higher fraction of genes are significantly upregulated, whereas cluster 5 features a higher fraction of genes downregulated with concomitant increase in macroH2A1 due to infection. Taken together with the gene ontology analysis (new Figure Sd), these results support our model in which macroH2A1 is deposited in active regions to block transcription and promote heterochromatin formation. To further support these conclusions, we have also carried out analysis of 4sU-RNA data generated upon salt stress or heat shock and found that the regions defined by gain of macroH2A1 (i.e. clusters 5 and 6) also exhibit significant decreases in new transcription at just 1-2 hours after treatment. These data, which are presented in new Figure EV3b-c, strongly support our model in which macroH2A1 is deposited in genes downregulated upon stress response to generate new heterochromatin. *

Figure S1 raises some questions about the specificity of the macroH2A1 antibody used for CUT&Tag. As expected CUT&Tagging the cellular genome in the KO cells with the specific antibody results in lower signal than with the IgG control antibody. In contrast, viral DNA is CUT&Tagged as efficiently in the KO as in the WT cells, and in both cases significantly above the IgG controls. The simplest interpretation of these results is that the antibody cross-reacts with a protein that binds to HSV-1 genomes. The manuscript must experimentally address this possibility.

We agree with the reviewer that there is a possibility that antibodies cross react. However, we are confident that this is not the case in this scenario for the following reasons:

• *

*1 – We have carried out immunofluorescence analysis of macroH2A1 or H3K27me3 during HSV-1 infection and observe no overlap with ICP8 staining. We have included these images together with a histogram documenting the lack of overlap in the new Figure EV2f-g. *

• *

2 – CUT&Tag relies on the Tn5 transposase to insert barcodes into accessible regions of the genome. An inherent limitation of this method during viral infection is that the replicating viral genome is very dynamic and accessible, leading to easier and less specific insertion by the transposase. This is evidenced by the pattern of signal across the viral genome that is completely overlapping in the macroH2A1, H3K27me3 and IgG conditions. Snapshots of the full viral genome are now included in the new Figure EV2c-d.

• *

*Furthermore, using CUT&Tag with macroH2A1 antibody, we expect the transposition rate to be identical between WT and macroH2A1 KO conditions for the Ecoli and viral genomes. This is because we assume that the transposition in these two genomes is non-specific since there is no macroH2A1 present. Then, we expect the spike-in normalized CUT&Tag enrichment on the viral genome to be the same between WT and macroH2A1 KO conditions. Since IgG should not be affected by macroH2A1 KO, we expect the IgG enrichment to be same between WT and macroH2A1 KO conditions. Thus, non-specific background would result in higher enrichment in an apparent signal on viral genome in the macroH2A1 KO condition. *

• *

Combined with this expectation for background transposition and the following: 1) the distribution of the CUT&Tag signal across the viral genome is virtually identical between IgG, macroH2A1, and H3K27me3 CUT&Tag signal in WT and macroH2A1 KO cells (see new Figure EV2c-d), 2) that there is no colocalization between macroH2A1 or H3K27me3 with viral genomes by immunofluorescence (see new Figure EV2f-g), and 3) the whole genome correlation of the signals across CUT&Tag samples on the viral genome, but not the host, are virtually identical as presented in a heat map (see new Figure EV1g vs EV2e), we conclude that the viral CUT&Tag signal is noise. Therefore, any analysis of the signal on the viral genomes would not be biologically meaningful.

• *

Also, Figure S1 shows that the viral genome is CUT&Tag'ed with H3K27me3 antibody as efficiently in macro H2A1 WT and KO cells, and in both cases above the background signal from IgG control antibody. The authors conclude that the signal with the specific antibody "mirrors" that of the control antibody, but "mirroring" is not defined and the actual data show that there is a large increase in signal with the specific antibody. Not surprisingly, the background signal also increases, as the number of genomes increase while infection progresses. The authors conclude that "these results indicated that there was a significant background signal from the viral genome that could not be accounted for", but no evidence supporting this conclusion is presented. The data show clear signal above the background from the viral genome and that this signal is not affected by the presence or absence of macroH2A1. This section of the manuscript has to be thoroughly re-analyzed as there is clear H3K27 signal.

*We agree with the reviewer that as presented in the current manuscript it seems as though there is a real H3K27me3 signal. However, as stated in the above comment, the pattern of this signal matches that of all other conditions, including IgG, suggesting it is not a real signal, cross-reacted or otherwise, but rather an artifact of the methodology. See new Figure EV2. *

The concentration of tazemetostat used is high. Normally, concentrations of around 1µM are used in cells, and 10µM is often cytotoxic (for examplehttps://doi.org/10.1038/s41419-020-03266-3; https://doi.org/10.1158/1535-7163.MCT-16-0840). The effects on H3K27me3 presented in figure S1b appear to be normalized to mock infected treated cells. If so, they do not allow to evaluate the effectivity of the treatment. Cell viability after the four days treatment must be evaluated, the claimed "depletion" of H3K27me3 must be clearly demonstrated (the blots in figure S5 are not sufficient as presented), and levels of different histone methylations must be tested to support the claimed specificity of tazemetostat for H3K27me3 at the high concentrations used.

*While we agree with the reviewer that the cytotoxicity of any inhibitor is an important aspect to take into account, in this instance the reviewer is incorrect. The reviewer has cited papers that highlight the potential use of tazemetostat as a cancer-cell specific treatment for colorectal and B-cell cancers. In both of these cases, the primary conclusion is that tazemetostat’s cytotoxic property is largely corelated to mutation in EZH2. In fact, WT EZH2 treated cells had a more “cytostatic” response, which shows that tazemetostat is not toxic with WT EZH2 (Brach et al. Mol Cancer Ther. 2017, PMID 28835384) as is the case in our system. Furthermore, the Tan et al. study shows a non-transformed human fibroblast (CCD-18co) and embryonic colon epithelial (FHC) as “healthy controls” for their work in colorectal cancer cell lines in Figure 1D. These 2 cell lines, which are comparable to the WT HFF cells we used, show no reduction in viability at a log fold greater concentration than the 10 µM used in our paper. *

• *

*Nevertheless, we agree with the reviewer that cytotoxicity should be formally ruled out. In our original experiment, we recorded cell counts at the harvested mock, 4-, 8-, and 12 hpi and found no difference in the number of cells over the course of infection (see new Figure EV3e). We also used trypan blue staining as a measure of cell viability upon tazemetostat treatment and found no toxicity. These results are presented in new Figure EV3f. *

Furthermore, we agree with the reviewer that total H3 levels by western blot should be included in any comparison of H3 modification. While these were included in some figures, they were unintentionally omitted in others. In our revised manuscript we have now included these blots together with quantification of triplicate biological samples of H3K27me3 levels normalized to total H3. See new Figures 3, 4, EV1, and EV5.

• *

Minor comments. Reference No.27 is misquoted in lines 250-251, which state that it shows that "HSV-1 titers, but not viral replication, where reduced upon EZH2 inhibition." The reference actually shows inhibition of HSV-1 infectivity, DNA levels and mRNA for ICP4, ICP22 and ICP27. This reference uses much shorter treatments (12 h and only after infection). It also shows that inhibition of EZH2/1 up regulates expression of antiviral genes.

*We appreciate that the reviewer has pointed out a discrepancy between our results using an EZH2 inhibitor (tazemetostat) and those from reference 27 (Arbuckle et al., mBio, 2017 PMID 28811345) that requires clarification. The reviewer states that the treatments were 12 hours after infection, however, this is incorrect. In the Arbuckle et al. study, the authors used multiple different inhibitors at high doses for short treatments before infection and noted that this caused an upregulation in antiviral genes that blocked infection progression of multiple viruses including HCMV, Ad5 and ZIKA. Importantly, these genes include multiple immune signaling and interferon stimulated genes. In our study, we specifically use a much lower dose of EZH2 inhibitor, with respect to the IC50 value, and waited 3 days to ensure a steady state. In our system, any initial burst of immune response from the inhibitor would likely have subsided by the time we do our infection. Furthermore, supplemental figure EV1 from the Arbuckle et al. study states that EZH1/2 inhibitors do not affect nuclear accumulation of viral genomes and suppress HSV-1 IE expression in an MOI-independent manner (Arbuckle et al. Supplemental Figure 1). These results in fact support our conclusions that it is not any antiviral effect of inhibition of EZH2 that causes the decrease in titers that we observe. *

• *

To clarify, the IC50 value of the inhibitors used in the Arbuckle et al. study are 10 nmol/L (GSK126) and 4 nmol/L (GSK343). The IC50 is a measurement used to denote the amount of drug needed to inhibit a biological process by 50% and is commonly used in pharmacology to compare drug potency. In the Arbuckle et al. study, GSK126 was used at a concentration range of 15-30 µM, that is 1500-3000x more than the IC50 level as converted from nmol/L to µM, and GSK343 was used at a concentration range of 20-35 µM, that is 5000-8750x more than the IC50 level, to see changes in viral mRNA levels. The IC50 value for tazemetostat is 11 nmol/L which means that one would need to use a much higher molarity of tazemetostat, at least 28 µM which would be 2500x the IC50 value, to achieve the comparable biological changes as the inhibitors used in the Arbuckle et al. study. Thus, we are confident that the 10 µM concentration used in our study is an appropriate and non-toxic amount that would not impact antiviral responses at the dose and times that we used. As shown above and reported in multiple studies (for example: Knutson et al. Molecular Cancer Therapy 2014 PMID 24563539, Tan et al. Cell Death and Disease 2020 PMID 33311453 cited above, and Zhang et al. Neoplasia 2021 PMID 34246076, among others) the concentration of tazemetostat that we used is not toxic to the cells. Importantly, it was also reported that a global decrease in H3K27me3 by EZH2 inhibition using a 10 µM concentration of tazemetostat (here referred to by the identifier EPZ6438) did not impact HSV-1 RNA transcript accumulation measured by bulk sequencing (Gao et al. Antiviral Res 2020 PMID 32014498), consistent with our findings.

• *

In our revised manuscript, we have now included a discussion of these important points. See lines 409-428.

HFF are primary human cells but they are fibroblasts whereas the primary target of HSV-1 replication is epithelial cells. The wording used "they represent a common site of infection in humans" must be edited

We agree with the reviewer and have updated the text. See lines 109.

Disruption of macroH2A (1 and 2) results in general defects in nuclear architecture, not just peripheral chromatin (https://doi.org/10.1242/jcs.199216;, see also figure 1c and 5a, presenting invaginated and lobulated nuclei). The manuscript would benefit from including a broader discussion of the effects of macroH2A defects on the general nuclear architecture.

• *

We agree with the reviewer and our revised manuscript now includes a more in-depth discussion of the impact of macroH2A and other heterochromatin marks on nuclear structure. See lines 373-374 and 394.

The title should be edited, as "egress" in virology is commonly used to refer to the egress of virions from the cell, not to the nuclear egress of capsids. Adding the words nuclear and capsid should be sufficient to address this issue.

*We agree with the reviewer and will update the title to read “HSV-1 exploits host heterochromatin for nuclear egress”. Given that we are measuring multiple aspects of infection, we feel that adding the word ‘capsid’ is not necessary. *

It is unclear why preferential changes in expression of housekeeping genes would indicate "stress responses to infection". The rationale for this conclusion must be fully articulated and supported.

We agree with the reviewer that it may not be immediately clear as to why changes in house-keeping gene expression represent a stress response. In a recent study that we cite in our manuscript, Hennig et al. (PLOS Path 2018 PMID 29579120) demonstrate that changes in chromatin accessibility and gene transcription during HSV-1 infection resemble those that occur upon heat shock or salt stress. These results strongly support the model that global transcription changes caused upon stress (heat, salt, infection etc.) result in dramatic alterations to chromatin structure. In support of this notion, in our revised manuscript we now include analysis of these datasets based on our macroH2A1-defined clusters. Importantly, we found that the regions defined by gain of macroH2A1 (i.e. clusters 5 and 6) also exhibit significant decreases in new transcription at just 1-2 hours of exposure to salt and heat stress. These data, which are presented in new Figure EV3b-c, strongly support our model in which macroH2A1 is deposited on active genes to generate heterochromatin as a response to the stress of infection. We also discuss these results further in the revised manuscript, see lines 210-220, 233-236, and 424-426.

Statistical methods must be fully described in materials and methods and the number of biologically independent experiments must be stated in each figure.

*We agree with the reviewer and have included these details in each figure legend. *

Reviewer #2 (Significance (Required)):

The major strengths of the manuscript lie on the comprehensive analyses of the effects of knocking histone macroH2A in the nuclear architecture and chromatin organization. These analyses indicate that peripheral heterochromatin is defective in the KO. Another strength lies on the analyses of the news heterochromatin domains in HSV-1 infected cells. The relationship between the lack of correlation between the changes in gene expression and global heterochromatin domains defined by macroH2A1 with the main conclusion is less clear.

The major weakness is that the data presented do not strongly support the conclusions. Additional experiments are required to support the main conclusion that the effects in peripheral heterochromatin result in a biologically significant effect on capsid egress. The authors should also consider that the additional experimentation may not support the conclusion that macroH2A or H3K27me3 play critical roles in the nuclear egress of capsids.

• *

*To support our conclusions, we have carried out an entirely different set of experiments to track capsid movement. Bosse et al. PNAS 2015 PMID 26438852 and Aho et al. PLOS Path 2021 PMID 34910768 use live-imaging and single-particle tracking to characterize capsid motion relative to host chromatin. These approaches allowed the authors to discover that infection-induced chromatin modifications promote capsid translocation to the INM. They showed that 1) HSV-1 infection alters host heterochromatin such that open space is induced at heterochromatin boundaries, termed "corrals", in which viral capsids diffuse and 2) the movement of viral capsids through the host heterochromatin is the rate limiting step in HSV-1 nuclear egress. *

• *

To test our hypothesis that macroH2A1-dependent heterochromatin specifically is required, we collaborated with Dr. Jens Bosse to carry out these same experiments in our macroH2A1 KO and paired control cells. We tracked RFP-VP26 using spinning-disk confocal live imaging to track individual capsid movement within the nucleus. We found that capsids in cells lacking macroH2A1 traveled much shorter distances on average. This is represented graphically by the mean-square displacement (MSD) of capsid movement in macroH2A1 KO cells plateauing at ~0.4 µm2 vs 0.6 µm2 in WT cells, which represents the size of the “corral”, or space through which capsids diffuse. The average corral size in macroH2A1 KO cells is ~300 nm less than the average corral size in WT cells (two-thirds the size). These results are consistent with the finding that macroH2A1 limits chromatin plasticity both in vitro (Muthurajan et al. J Biol Chem 2011 PMID 21532035) and in cells (Kozlowski et al. EMBO Rep 2018 PMID 30177554). These data strongly support our hypothesis that macroH2A1-dependent heterochromatin is critical for the translocation of HSV-1 capsids through the host chromatin to reach the INM. Furthermore, these data support the model in which macroH2A1 allows for the increase of open space induced during infection. Loss of this open space restricts the movement of capsids in the nucleus, as quantified by our live-imaging experiments. These data are now included in the new Figure 5 and EV5 and described in lines 348-372 and 1011-1037.

• *

NOTE: These experiments were done in a separate lab using the same cells and MOI we used for our TEM studies. It is important to note that because this was done by live imaging where the full nucleus and cell are visible, the appropriate number of capsids is apparent.

Another major weakness is that the results of CUT&Tag of the viral genome are dismissed without proper justification. The authors conclude that the results invalidate the assays, but the results are consistent with cross-reactivity of the macroH2A1 antibody with another protein that interacts with the viral genomes and with H3K27me3 being associated with the viral genomes irrespectively of macroH2A1.

*We agree with the reviewer that as presented the viral genome reads were dismissed without thorough justification. As stated above, we are confident that the patterns we detected do not represent a biologically relevant signal but rather an artifact of the experimental set up. Furthermore, it is well known in the field that normalizing replicating viral genomes during lytic infection in any kind of chromatin profiling technique is fraught with inconsistencies as each cell may have a different copy number of viral genomes at any given time point. Therefore, we feel strongly that any analysis of the viral genome chromatin profile during a lytic replication at this point in time would require single cell sequencing which is beyond the scope of this study. We appreciate that this was not clearly presented in the original manuscript and in our revised submission we have included a full supplemental figure documenting the negative data that support our conclusions (see new Figure EV2). *

If the authors had additional data supporting the claim that these results do not reflect cross-reactivity or association with the viral genomes, these data must be presented. Without that additional data, the conclusions are not supported and these discussions must be removed from the manuscript. The authors may still opt to not analyze any association with the viral genomes, but they should not dismiss them as artifactual without actual evidence to support this claim. Previously published literature is also misquoted.

This study makes an incremental contribution to the previously published evidence showing that HSV-1 capsids egress the nucleus through channels in between the peripheral chromatin. It shows that disruption of the heterochromatin at the nuclear periphery, and the nuclear architecture in general, may have a modest effect on capsid egress. This information may be of interest mostly to a specialized audience focused on the egress of nuclear capsids.

While we agree with the reviewer on many points as stated above, we respectfully disagree that our study is merely an incremental contribution of interest only to a specialized audience focused on nuclear egress. As reviewer 2 states earlier, the strength of our study lies in the “comprehensive analyses of the effects of knocking histone macroH2A in the nuclear architecture and chromatin organization”, which would be of interest to a broad chromatin audience as well as virologists. Together with the new data presented here and a revised manuscript, we feel that our study would be of interest to a broad audience in the chromatin and virology fields as reviewers 1 and 3 also pointed out. Chromatin is generally analyzed in the context of how it might affect gene expression and the impact of chromatin on biological processes such as viral infections, and its structural role in the nucleus is not commonly considered. Here, we demonstrate an important example of the glaring effects of chromatin structure on the biological nuclear process of infection.

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

Lewis et al. reveal an unexpected role for heterochromatin formation in remodeling the nucleus to facilitate egress of the nuclear-replicating virus HSV1. By performing TEM in HSV1-infected primary human fibroblasts, the authors show that capsids accumulate at the inner nuclear membrane in regions of less densely stained heterochromatin, in agreement with studies in established cell lines. The authors go on to reveal that heterochromatin in the nuclear periphery of HSV1-infected primary fibroblasts was dependent on the histone variant macroH2A1 and is enriched with H3K27me3.CUT & Tag was used to profile macroH2A1 over time during lytic HSV1 infection and showed that both macroH2A1 and H3K27me3 were enriched over newly formed heterochromatic regions 10s-100s of Kb in length in active compartments. Remarkably, loss of macroH2A1 or H3K27me3 reduced released, cell free infection virus progeny and increased intranuclear capsid accumulation without detectably impacting the proportion of mature genome containing capsids, virus genome or protein accumulation. Their finding that newly remodeled heterochromatin forms in HSV infected cells and is a critical determinant for the association of capsids with the inner nuclear membrane is consistent with a critical role in egress.

I have only relatively minor editorial suggestions listed below to improve the manuscript:

Line 92: This subtitle should be revised to more precisely state the findings shown in the Fig 1 data. While the first part of the statement "HSV1 capsids associate with regions of less dense chromatin" is consistent with what is shown, the final phrase "...to escape the nucleus" is an interpretation of the data inferred from the static image.

We agree with the reviewer and have amended our text to more accurately describe the figure. See lines 138-139.

Line 96: I am not sure the statement that fibroblasts represent a "common" site of infection is supported by ref 15. FIbroblasts do, as indicated in ref 15, express the appropriate receptor(s) for virus entry and in culture support robust virus productive growth. However, in human tissue, infection of dermal fibroblasts appears rare, suggesting it may not be a "common" site of infection (PMCID: PMC8865408). Maybe simply revise wording to indicate fibroblasts represent "a site of infection or can be infected in tissue?".

We agree with the reviewer, as was also pointed out by reviewer 2, and have amended the text. See lines 109.

Line 126-127: As written it states that "....regions of the host genome that increase during infection", implying these genome regions are amplified (increase). I think the authors mean that infection increases binding of mH2A1 and H3K27me3 to broad regions of the host genome. Please clarify.

We agree with the reviewer that this was written ambiguously. As was pointed out by reviewers 1 and 2, the increase in these marks depends on the type of measurement. Therefore, we have modified the text in a revised manuscript to focus instead on the redistribution of these marks during infection. See line 138-139.

FIgS1, a,b,c,d: please indicate that 4,8,12 indicate hpi, correct? And indicate that in the legend M indicates Mock.

This is correct and we have updated this in the figure legend. See lines 625-627.

Line 197: "active compartments". Do the authors mean transcriptionally active compartments? Please clarify

This is correct and have clarified this in the text. See line 248.

Line 232: please replace "productive" with "infectious"

We agree with the reviewer and have amended our text. See line 295.

Line 233 - The authors conclude mH2A1 is important for egress, ruling out assembly before even bringing it up. As I read on, it is clear the authors addressed this important issue later on in the manuscript. That said, it was a bit jarring to conclude egress is important without addressing the assembly possibility at this juncture in the manuscript. One way to remedy this would be to move the Fig S6 assembly/capsid type data (lines 286-297, Fig S6) and surrounding text earlier to support the conclusion that mH2A1 did not detectably influence assembly, but is important for egress.

*We agree with the reviewer that the order of presentation makes it difficult to follow. Our revised manuscript now includes these important data within the same figure. See new Figure 5. *

Line 244: "progeny production" - it would be helpful to specify "cell free or released infectious virus progeny"

Line 248: change "produced" to released"

Line 273 replace "productive" with "infectious virus progeny released from infected cells"

Fig S5c: Was the plaque assay performed on cell free supernatants? This should be indicated.

We agree with the reviewer and have made all these changes in the text. See lines 285-287.

Reviewer #3 (Significance (Required)):

The experiments are well executed, the data are solid with appropriate statistical analysis and their analysis sufficiently rigorous, and the manuscript is clearly written. Moreover, the finding that HSV manipulates host heterochromatin marks to facilitate nuclear egress is significant and exciting. The work reveals an unexpected role for newly assembled heterochromatin in egress of nuclear replicating viruses like HSV1.

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 #3

Evidence, reproducibility and clarity

Lewis et al. reveal an unexpected role for heterochromatin formation in remodeling the nucleus to facilitate egress of the nuclear-replicating virus HSV1. By performing TEM in HSV1-infected primary human fibroblasts, the authors show that capsids accumulate at the inner nuclear membrane in regions of less densely stained heterochromatin, in agreement with studies in established cell lines. The authors go on to reveal that heterochromatin in the nuclear periphery of HSV1-infected primary fibroblasts was dependent on the histone variant macroH2A1 and is enriched with H3K27me3.CUT & Tag was used to profile macroH2A1 over time during lytic HSV1 infection and showed that both macroH2A1 and H3K27me3 were enriched over newly formed heterochromatic regions 10s-100s of Kb in length in active compartments. Remarkably, loss of macroH2A1 or H3K27me3 reduced released, cell free infection virus progeny and increased intranuclear capsid accumulation without detectably impacting the proportion of mature genome containing capsids, virus genome or protein accumulation. Their finding that newly remodeled heterochromatin forms in HSV infected cells and is a critical determinant for the association of capsids with the inner nuclear membrane is consistent with a critical role in egress.

I have only relatively minor editorial suggestions listed below to improve the manuscript:

Line 92: This subtitle should be revised to more precisely state the findings shown in the Fig 1 data. While the first part of the statement "HSV1 capsids associate with regions of less dense chromatin" is consistent with what is shown, the final phrase "...to escape the nucleus" is an interpretation of the data inferred from the static image.

Line 96: I am not sure the statement that fibroblasts represent a "common" site of infection is supported by ref 15. FIbroblasts do, as indicated in ref 15, express the appropriate receptor(s) for virus entry and in culture support robust virus productive growth. However, in human tissue, infection of dermal fibroblasts appears rare, suggesting it may not be a "common" site of infection (PMCID: PMC8865408). Maybe simply revise wording to indicate fibroblasts represent "a site of infection or can be infected in tissue?".

Line 126-127: As written it states that "....regions of the host genome that increase during infection", implying these genome regions are amplified (increase). I think the authors mean that infection increases binding of mH2A1 and H3K27me3 to broad regions of the host genome. Please clarify.

FIgS1, a,b,c,d: please indicate that 4,8,12 indicate hpi, correct? And indicate that in the legend M indicates Mock.

Line 197: "active compartments". Do the authors mean transcriptionally active compartments? Please clarify

Line 232: please replace "productive" with "infectious"

Line 233 - The authors conclude mH2A1 is important for egress, ruling out assembly before even bringing it up. As I read on, it is clear the authors addressed this important issue later on in the manuscript. That said, it was a bit jarring to conclude egress is important without addressing the assembly possibility at this juncture in the manuscript. One way to remedy this would be to move the Fig S6 assembly/capsid type data (lines 286-297, Fig S6) and surrounding text earlier to support the conclusion that mH2A1 did not detectably influence assembly, but is important for egress.

Line 244: "progeny production" - it would be helpful to specify "cell free or released infectious virus progeny"

Line 248: change "produced" to released"

Line 273 replace "productive" with "infectious virus progeny released from infected cells"

Fig S5c: Was the plaque assay performed on cell free supernatants? This should be indicated.

Significance

The experiments are well executed, the data are solid with appropriate statistical analysis and their analysis sufficiently rigorous, and the manuscript is clearly written. Moreover, the finding that HSV manipulates host heterochromatin marks to facilitate nuclear egress is significant and exciting. The work reveals an unexpected role for newly assembled heterochromatin in egress of nuclear replicating viruses like HSV1.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

The manuscript "HSV-1 exploits heterochromatin for egress" describes the effects of heterochromatin at the nuclear periphery, macroH2A1 or H3K27me3 on HSV-1 replication and egress. Knocking out macroH2A1 or depleting H3K27me3 with high concentrations of tazemetostat depleted heterochromatin at the nuclear periphery, may not have affected HSV-1 protein expression and modestly inhibited the production of cell-free infectivity and HSV-1 genomes. macroH2A1 deposition was affected by infection, creating new heterochromatin domains which did not correlate directly with the levels of expression of the genes in them. The authors conclude that heterochromatin at the nuclear periphery dependent on macroH2A1 and H3K27me3 are critical for nuclear egress of HSV-1 capsids.

The experiments leading to the conclusion that HSV-1 capsids egress the nucleus through channels in the peripheral chromatin confirm previously published results (https://doi.org/10.1038/srep28844). The previously published EM micrographs show a much larger number of nuclear capsids, more consistent with the images in the classical literature, even in conditions when nuclear egress was not inhibited. Figures 1 and 4 show scarce nuclear capsids, even under the conditions when nuclear egress should be inhibited according to the model and analyses. The large enrichment in nuclear capsids in KO cells predicted by the model is not reflected in figure 4a, which shows only a modest increase in nuclear capsid density (the total number of nuclear capsids would be more informative). The number or density of nuclear capsids is not shown in H3K27 "depleted" cells. The robustness of the analyses of the number of capsids at the membrane in H3K27 "depleted" cells is unclear. For example, the analyses could be repeated with different cut offs, such as 2 or 4. If they are robust, then the conclusions will not change when the cutoff value is changed.

The quantitation of the western blots present no evidence of reproducibility and/or variability. The number of biologically independent experiments analyzed must be stated in each figure and the standard deviation must be presented. As presented, the results do not support the conclusions reached. The quality of western blots should also be improved. it is unclear why figure 2b shows viral gene expression in wild-type cells only, and not in KO or H3K27me3 depleted cells, which are only shown in the supplementary information. These blots presented in Figure S5a and S5b are difficult to evaluate as the signal is rather weak and the controls appear to indicate different loading levels. These blots do not appear to be consistent with the conclusions reached. Some blots (VP16, ICP0 in HFF) appear to indicate a delay in protein expression whereas others (VP16, ICP0 in RPE) appear to indicate earlier expression of higher levels. The claimed "depletion of H3K27me3 is not clear in in figure S5d, in which the levels appear to be highly variable in all cases, without a consistent pattern, with no evidence of reproducibility and/or variability, and using a mostly cytoplasmic protein as loading control. All western blots should be repeated to a publication level quality, the number of independent experiments must be clearly stated in each figure, and the reproducibility and/or variability must be indicated by the standard deviation. An enhanced analyses of the RNA-seq data, analyzing all individual genes rather than pooling them together, would provide better support to these conclusions. Then, the western blots are useful to show that the changes in mRNA result in changes in the levels of selected proteins.

Figure S1 raises some questions about the specificity of the macroH2A1 antibody used for CUT&Tag. As expected CUT&Tagging the cellular genome in the KO cells with the specific antibody results in lower signal than with the IgG control antibody. In contrast, viral DNA is CUT&Tagged as efficiently in the KO as in the WT cells, and in both cases significantly above the IgG controls. The simplest interpretation of these results is that the antibody cross-reacts with a protein that binds to HSV-1 genomes. The manuscript must experimentally address this possibility.

Also, Figure S1 shows that the viral genome is CUT&Tag'ed with H3K27me3 antibody as efficiently in macro H2A1 WT and KO cells, and in both cases above the background signal from IgG control antibody. The authors conclude that the signal with the specific antibody "mirrors" that of the control antibody, but "mirroring" is not defined and the actual data show that there is a large increase in signal with the specific antibody. Not surprisingly, the background signal also increases, as the number of genomes increase while infection progresses. The authors conclude that "these results indicated that there was a significant background signal from the viral genome that could not be accounted for", but no evidence supporting this conclusion is presented. The data show clear signal above the background from the viral genome and that this signal is not affected by the presence or absence of macroH2A1. This section of the manuscript has to be thoroughly re-analyzed as there is clear H3K27 signal.

The concentration of tazemetostat used is high. Normally, concentrations of around 1µM are used in cells, and 10µM is often cytotoxic (for example https://doi.org/10.1038/s41419-020-03266-3; https://doi.org/10.1158/1535-7163.MCT-16-0840). The effects on H3K27me3 presented in figure S1b appear to be normalized to mock infected treated cells. If so, they do not allow to evaluate the effectivity of the treatment. Cell viability after the four days treatment must be evaluated, the claimed "depletion" of H3K27me3 must be clearly demonstrated (the blots in figure S5 are not sufficient as presented), and levels of different histone methylations must be tested to support the claimed specificity of tazemetostat for H3K27me3 at the high concentrations used.

Minor comments.

Reference No.27 is misquoted in lines 250-251, which state that it shows that "HSV-1 titers, but not viral replication, where reduced upon EZH2 inhibition." The reference actually shows inhibition of HSV-1 infectivity, DNA levels and mRNA for ICP4, ICP22 and ICP27. This reference uses much shorter treatments (12 h and only after infection). It also shows that inhibition of EZH2/1 up regulates expression of antiviral genes.

HFF are primary human cells but they are fibroblasts whereas the primary target of HSV-1 replication is epithelial cells. The wording used "they represent a common site of infection in humans" must be edited

Disruption of macroH2A (1 and 2) results in general defects in nuclear architecture, not just peripheral chromatin (https://doi.org/10.1242/jcs.199216;, see also figure 1c and 5a, presenting invaginated and lobulated nuclei). The manuscript would benefit from including a broader discussion of the effects of macroH2A defects on the general nuclear architecture.

The title should be edited, as "egress" in virology is commonly used to refer to the egress of virions from the cell, not to the nuclear egress of capsids. Adding the words nuclear and capsid should be sufficient to address this issue.

It is unclear why preferential changes in expression of housekeeping genes would indicate "stress responses to infection". The rationale for this conclusion must be fully articulated and supported.

Statistical methods must be fully described in materials and methods and the number of biologically independent experiments must be stated in each figure.

Significance

The major strengths of the manuscript lie on the comprehensive analyses of the effects of knocking histone macroH2A in the nuclear architecture and chromatin organization. These analyses indicate that peripheral heterochromatin is defective in the KO. Another strength lies on the analyses of the news heterochromatin domains in HSV-1 infected cells. The relationship between the lack of correlation between the changes in gene expression and global heterochromatin domains defined by macroH2A1 with the main conclusion is less clear.

The major weakness is that the data presented do not strongly support the conclusions. Additional experiments are required to support the main conclusion that the effects in peripheral heterochromatin result in a biologically significant effect on capsid egress. The authors should also consider that the additional experimentation may not support the conclusion that macroH2A or H3K27me3 play critical roles in the nuclear egress of capsids. Another major weakness is that the results of CUT&Tag of the viral genome are dismissed without proper justification. The authors conclude that the results invalidate the assays, but the results are consistent with cross-reactivity of the macroH2A1 antibody with another protein that interacts with the viral genomes and with H3K27me3 being associated with the viral genomes irrespectively of macroH2A1. If the authors had additional data supporting the claim that these results do not reflect cross-reactivity or association with the viral genomes, these data must be presented. Without that additional data, the conclusions are not supported and these discussions must be removed from the manuscript. The authors may still opt to not analyze any association with the viral genomes, but they should not dismiss them as artifactual without actual evidence to support this claim. Previously published literature is also misquoted.

This study makes an incremental contribution to the previously published evidence showing that HSV-1 capsids egress the nucleus through channels in between the peripheral chromatin. It shows that disruption of the heterochromatin at the nuclear periphery, and the nuclear architecture in general, may have a modest effect on capsid egress. This information may be of interest mostly to a specialized audience focused on the egress of nuclear capsids.

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Reviewer #1 (Public Review):

In this work, authors seek to understand how the polycomb complex may coordinate gene expression changes that occur during sequential stages of neuronal maturation. The main strengths are 1) choice of cerebellar granule neurons which mature over a protracted period during normal cerebellar development and constitute a relatively homogeneous population of neurons, 2) use of a genetic in vivo mouse model where a histone demethylase is knocked out, combined with an in vitro culture model of maturing cerebellar granule neurons in which a histone methyltransferase is inhibited, 3) use of CUT & TAG in neuronal cultures to investigate how changes in the H3K27me3 repressor chromatin modification at promoters correlate with gene expression and chromatin accessibility changes. The authors propose a bidirectional effect of the same chromatin repressor modification that is responsible, at least in part, for the timely loss of expression of early genes and the appearance of genes expressed later in maturation. This is the major impact of the work for those interested in cerebellar development. A weakness in the work lies in its narrow focus, which is on promoter regions almost exclusively.

The work is primarily bioinformatics driven and lacks physiological significance of the gene expression changes, or how the culture timing correlates with temporal regulation and chromatin changes in vivo. However, the results do support the proposal that polycomb-associated enzymatic activities play sequential roles during successive stages of cerebellar maturation.

reply to u/noobinPython at https://www.reddit.com/r/Zettelkasten/comments/12u8gbv/is_zotero_a_reliable_software_to_transcribe/

Zotero is incredibly powerful and you could use it as a full end-to-end solution if you wanted to. It's particularly good if you're also using .pdf or other digital documents as it has the ability to pull in notes you've made digitally in a variety of .pdf annotation tools including Adobe's Acrobat (free version) which includes highlighting and notes you've made. It does have its own .pdf viewer now which also allows one to read, highlight, annotate, and tag individual pieces of text and then aggregate them into a single file. In addition to pulling in all the annotations into a single note file, one could break them into smaller individual notes per document if desired and these have addressable locations within the system.

Because Zotero is so powerful and can be dovetailed with a variety of other plugins specific to it as well as with other note taking tools like Obsidian, Logseq, etc. I'd highly recommend you try using it with a single document and take some notes to see if it'll work for you. There are surely some tutorials for using it as well as other useful plugins like Zotfile, MDnotes, etc. for your note taking workflows. It's open source and been in heavy use by many academics for over a decade and is actively developed, so it's one of the more robust systems out there. There are ways to do almost anything you'd want to with it from a note taking, reading, and citation management perspective, so searching and learning a bit about its features and functionality will get you a long way. Out of the box, it's reasonably intuitive, but there are lots of advanced features internally and even more features using a variety of plugins. Just the ability to have a browser extension and a keyboard shortcut to save all the bibliographic metadata of a source in a second or less and the ability to spit out full references for sharing with others has made it a godsend for me even if it did nothing else. Searching around will provide you with a huge amount of video tutorials and ways of using it either by itself, in conjunction with Zotfile, or dovetailing it with dozens of other tools.

Personally I use it in combination with a variety of other tools including Hypothes.is and Obsidian for a comprehensive workflow, but it could do incredibly well as a note taking tool just by itself.

.

reply to u/After-Cell at https://www.reddit.com/r/antinet/comments/12noly2/rebinding_a_book_for_more_margin_space/

The historical practice of "interleaved books" was more popular in a bygone era. If you search you can find publishers that still make bibles this way, but it's relatively rare now.

Given the popularity and ease of e-books and print on demand, you could relatively easily and cheaply get an e-book and reformat it at your local print shop to either print with larger margins or to add blank sheets every other page to have more room for writing your notes. For some classic texts (usually out of copyright) you can "margin shop" for publishers that leave more marginal space or find larger folio editions (The Folio Society, as an example) for your scribbles if you like.

Writing your notes on index cards with page references is quick and simple. These also make good temporary bookmarks. Other related ideas here: https://hypothes.is/users/chrisaldrich?q=tag:%22interleaved%20books%22

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Have I just coined "margin shopping"?

Narzędzia do Hypothes.is https://jonudell.info/h/tools.html

• facet tools - wyszukiwarka
• copy annotations - kopiowanie zaznaczeń
• tag rename - zmiana tagów
• annotation powered survey - rozszerzona wyszukiwarka
• pagefit - skrypt pozwalający dostosować szerokość strony po wysunięciu panelu hypothes.is.

Przydałoby się jeszcze narzędzie pozwalające zablokować panel tak, aby nie zwijał się w momencie interakcji z elementami strony.

General comments:

This study carefully delineates the role of magnesium in cell division versus cell elongation. The results are really important specifically for rod-shaped bacteria and also an important contribution to the broader field of understanding cell shape. Specifically, I love that they are distinguishing between labile and non-labile intracellular magnesium pools, as well as extracellular magnesium! These three pools are really challenging to separate but I commend them on engaging with this topic and using it to provide alternative explanations for their observations!

A major contribution to prior findings on the effects of magnesium is the author’s ability to visualize the number of septa in the elongating cells in the absence of magnesium. This is novel information and I think the field will benefit from the microscopy data shown here.

I completely agree with the authors that we need to be more careful when using rich media such as LB. It is particularly sad that we may be missing really interesting biology because of that! It’s worth moving away from such media or at least being more careful about batch to batch variability. Batch to batch variability is not as well appreciated in microbiology as it is for growing other cell types (for example, mammalian cells and insect cells).

For me, the most exciting finding was that a large part of the cell length changes within the first 10min after adding magnesium. The authors do speculate in the discussion that this is likely happening because of biophysical or enzymatic effects, and I hope they explore this further in the future!

I love how the paper reads like a novel! Congratulations on a very well-written paper!

Kudos to the authors for providing many alternative explanations for their results. It demonstrates critical thinking and an open-mind to finding the truth.

Specific comments:

Figure 2C → please include indication of statistical significance

Figure 3C → please include indication of statistical significance

Figure 6A → please include indication of statistical significance

Figure 8B → please include indication of statistical significance

Figure S1B → please include indication of statistical significance

Figure S3B → please include indication of statistical significance

For your overexpression experiments, do the overexpressed proteins have a tag? It would be helpful to have Western blot data showing that the particular proteins are actually being overexpressed. I think the phenotypes that you observe are very compelling so I don’t doubt the conclusions. Western blot data would just provide some additional confirmation that you are actually achieving overexpression of UppS, MraY, and BcrC.

Questions:

Based on your data, there are definitely differences in gene expression when you compare cells grown in media with and without magnesium. Because the majority in cell length increase occurs in such a short time though (the first 10min), I was wondering if you think that some or most of it is not due to gene expression? Do you have any hypotheses what is most likely to be affected by magnesium? Do you think if the membrane may be affected?

Why do you think less magnesium activates this program of less division and more elongation? Additionally why is abundant magnesium activating a program of increased cell division and less elongation? Do you think there is some evolutionary advantage, especially considering how important magnesium is for ATP production?

Related to this previous question, I also wonder if this magnesium-dependent phenotype would extend to other unicellular organisms, may be protists or algae? That would be a really exciting direction to explore!

Regarding the zinc and manganese experiments, why do you think they lead to additional phenotypes compared to magnesium? Do you have any hypotheses?

Regarding your results that Lipid I availability may be a major a problem for the cell division in the absence of magnesium, do you think that is due to effects magnesium has on the enzymes directly, or do you think magnesium affects the substrate availability/conformation by coordinating the phosphate groups? Or something else, may be membrane conformation?

Reviewer #3 (Public Review):

In this manuscript, Villalobos-Cantor et al. have implemented the method for monitoring cellular proteome that their lab has established in cell culture models of Drosophila brains. The method uses a puromycin analog (O-propargyl-puromycin, OPP) that is locked by the addition of phenylacetyl group (PhAc-OPP) that can be unlocked by expression of Penicillin G acetylase (PGA) to tag the proteins translated in a specific cell type. When unlocked, OPP can get incorporated into the newly translating nascent peptide, and abort translation while allowing click chemistry addition of various tags, such as fluorophore-azide to visualize or biotin-azide to immunopurify polypeptides. The authors demonstrate the use of the method in adult drosophila brains expressing PGA in neurons or glia, showing that the addition of OPP is indeed PGA dependent and the proteins are only tagged in the cells that express PGA. The authors also show that when fluorophore azide is used to visualize the proteome and the samples are run on a gel, bands of various sizes can be observed to have incorporated OPP, arguing the method labels the proteome indiscriminately. The authors also optimized the protocol by titrating the amount of PhAc-OPP to use to minimize cellular stress. Also, they show that driving the expression of PGA with elav-Gal4 or repo-Gal4 is not toxic and does not cause phenotypes although Actin-Gal4 driven expression causes phenotypes. Finally the authors demonstrate the use of the technique to show that there is an age-induced decrease in total protein synthesis in the fly brain. This is a nice technique to implement in fly but the characterization of the technique is not complete in its current state. It is not clear what percentage of the nascent peptides are tagged, and whether the cells in the tissue are equally represented in the lysates for immunopurification.

This is a bit fey, I think. Perhaps the name is too worn to make out. The tag itself, the handover is enough.

Thank you for including this information!!! Being able to see the actual collection site / environment provides a lot of information that is often never publicly reported and gets lost with time!

Reviewer #1 (Public Review):

The study examines how hemocytes control whole-body responses to oxidative stress. Using single cell sequencing they identify several transcriptionally distinct populations of hemocytes, including one subset that show altered immune and stress gene expression. They also find that knockdown of DNA Damage Response (DDR) genes in hemocytes increases expression of the immune cytokine, upd3, and that both upd3 overexpression in hemocytes and hemocyte knockdown of DDR genes leads to increased lethality upon oxidative stress.

Strengths

1, The single cell analyses provide a clear description of how oxidative stress can cause distinct transcriptional changes in different populations of hemocytes. These results add to the emerging them in the field that there functionally different subpopulations of hemocytes that can control organismal responses to stress.<br /> 2, The discovery that DDR genes are required upon oxidative stress to limit cytokine production and lethality provides interesting new insight into the DDR may play non-canonical roles in controlling organismal responses to stress.

Weaknesses

1, In some ways the authors interpretation of the data - as indicated, for example, in the title, summary and model figure - don't quite match their data. From the title and model figure, it seems that the authors suggest that the DDR pathway induces JNK and Upd3 and that the upd3 leads to tissue wasting. However, the data suggest that the DDR actually limits upd3 production and susceptibility to death as suggested by several results:<br /> a) PQ normally doesn't induce upd3 but does lead to glycogen and TAG loss, suggesting that upd3 isn't connected to the PQ-induced wasting.<br /> b) knockdown of DDR upregulates upd3 and leads to increased PQ-induced death. This would suggest that activation of DDR is normally required to limit, rather than serve as the trigger for upd3 production and death.<br /> c) hemocyte knockdown of either JNK activity or upd3 doesn't affect PQ-induced death, suggesting that they don't contribute to oxidative stress-induced death. Its only when DDR is impaired (with DDR gene knockdown) that an increase in upd3 is seen (although no experiments addressed whether JNK was activated or involved in this induction of upd3), suggesting that DDR activation prevents upd3 induction upon oxidative stress.

2, The connections between DDR, JNK and upd3 aren't fully developed. The experiments show that susceptibility to oxidative stress-induced death can be caused by a) knockdown of DDR genes, b) genetic overexpression of upd3, c) genetic activation of JNK. But whether these effects are all related and reflect a linear pathway requires a little more work. For example, one prediction of the proposed model is that the increased susceptibility to oxidative stress-induced death in the hemocyte DDR gene knockdowns would be suppressed (perhaps partially) by simultaneous knockdown of upd3 and/or JNK. These types of epistasis experiments would strengthen the model and the paper.

3, The (potential) connections between DDR/JNK/UPD3 and the oxidative stress effects on depletion of nutrient (lipids and glycogen) stores was also not fully developed. However, it may be the case that, in this paper, the authors just want to speculate that the effects of hemocyte DDR/upd3 manipulation on viability upon oxidative stress involve changes in nutrient stores.

Reviewer #3 (Public Review):

Male infertility is an important health problem. Among pathologies with multiple morphological abnormalities of the flagellum (MMAF), only 50% of the patients have no identified genetic causes. It is thus primordial to find novel genes that cause the MMAF syndrome. In the current work, the authors follow up the identification of two patients with MMAF carrying a mutation in the CCDC146 gene. To understand how mutations in CCDC146 lead to male infertility, the authors generated two mouse models: a CCDC146-knockout mouse, and a knockin mouse in which the CCDC146 locus is tagged with an HA tag. Male CCDC146-knockout mice are infertile, which proves the causative role of this gene in the observed MMAF cases. Strikingly, animals develop no other obvious pathologies, thus underpinning the specific role of CCDC146 in male fertility.

The authors have carefully characterised the subcellular roles of CCDC146 by using a combination of expansion and electron microscopy. They demonstrate that all microtubule-based organelles, such as the sperm manchette, the centrioles, as well as the sperm axonemes are defective when CCDC146 is absent. Their data show that CCDC146 is a microtubule-associated protein, and indicate, but do not prove beyond any doubt, that it could be a microtubule-inner protein (MIP).<br /> This is a solid work that defines CCDC146 as a novel cause of male infertility. The authors have performed comprehensive phenotypic analysis to define the defects in CCDC146 knockout mice. Surprisingly, the authors provide virtually no information on the penetrance of those defects - in most cases they simply show descriptive micrographs. The message of this manuscript would have been more convincing if the key phenotypes of the CCDC146 knockout mice were quantified, in particular those shown in Fig. 2E, 7A, 11B, 13.

The manuscript text is well written and easy to follow also for non-specialists. The introduction and discussion chapters contain important background information that allow putting the current work into the greater context of fertility research. The figures could have been designed more carefully, with additional information on the genotype and other details such as the antibodies used etc. directly added to the figure panels, which would improve their readability. The author might also consider pooling small figures with complementary content into one bigger figure in order to group related information together, and again facilitate the reading of the manuscript.

Overall, this manuscript provides convincing evidence for CCDC146 being essential for male fertility, and illustrates this with a large panel of phenotypic observations, which however mostly lack quantification in order to judge their penetrance. Together, the work provides important first insights into the role of a so-far unexplored proteins, CCDC146, in spermatogenesis, thereby broadening the spectrum of genes involved in male infertility.

reply to u/bestlunchtoday at https://www.reddit.com/r/Zettelkasten/comments/12gadut/benefits_of_sharing_permanent_notes/

I love the diversity of ideas here! So many different ways to do it all and perspectives on the pros/cons. It's all incredibly idiosyncratic, just like our notes.

I probably default to a far extreme of sharing the vast majority of my notes openly to the public (at least the ones taken digitally which account for probably 95%). You can find them here: https://hypothes.is/users/chrisaldrich.

Not many people notice or care, but I do know that a small handful follow and occasionally reply to them or email me questions. One or two people actually subscribe to them via RSS, and at least one has said that they know more about me, what I'm reading, what I'm interested in, and who I am by reading these over time. (I also personally follow a handful of people and tags there myself.) Some have remarked at how they appreciate watching my notes over time and then seeing the longer writing pieces they were integrated into. Some novice note takers have mentioned how much they appreciate being able to watch such a process of note taking turned into composition as examples which they might follow. Some just like a particular niche topic and follow it as a tag (so if you were interested in zettelkasten perhaps?) Why should I hide my conversation with the authors I read, or with my own zettelkasten unless it really needed to be private? Couldn't/shouldn't it all be part of "The Great Conversation"? The tougher part may be having means of appropriately focusing on and sharing this conversation without some of the ills and attention economy practices which plague the social space presently.

There are a few notes here on this post that talk about social media and how this plays a role in making them public or not. I suppose that if I were putting it all on a popular platform like Twitter or Instagram then the use of the notes would be or could be considered more performative. Since mine are on what I would call a very quiet pseudo-social network, but one specifically intended for note taking, they tend to be far less performative in nature and the majority of the focus is solely on what I want to make and use them for. I have the opportunity and ability to make some private and occasionally do so. Perhaps if the traffic and notice of them became more prominent I would change my habits, but generally it has been a net positive to have put my sensemaking out into the public, though I will admit that I have a lot of privilege to be able to do so.

Of course for those who just want my longer form stuff, there's a website/blog for that, though personally I think all the fun ideas at the bleeding edge are in my notes.

Since some (u/deafpolygon, u/Magnifico99, and u/thiefspy; cc: u/FastSascha, u/A_Dull_Significance) have mentioned social media, Instagram, and journalists, I'll share a relevant old note with an example, which is also simultaneously an example of the benefit of having public notes to be able to point at, which u/PantsMcFail2 also does here with one of Andy Matuschak's public notes:

[Prominent] Journalist John Dickerson indicates that he uses Instagram as a commonplace: https://www.instagram.com/jfdlibrary/ here he keeps a collection of photo "cards" with quotes from famous people rather than photos. He also keeps collections there of photos of notes from scraps of paper as well as photos of annotations he makes in books.

It's reasonably well known that Ronald Reagan shared some of his personal notes and collected quotations with his speechwriting staff while he was President. I would say that this and other similar examples of collaborative zettelkasten or collaborative note taking and their uses would blunt u/deafpolygon's argument that shared notes (online or otherwise) are either just (or only) a wiki. The forms are somewhat similar, but not all exactly the same. I suspect others could add to these examples.

And of course if you've been following along with all of my links, you'll have found yourself reading not only these words here, but also reading some of a directed conversation with entry points into my own personal zettelkasten, which you can also query as you like. I hope it has helped to increase the depth and level of the conversation, should you choose to enter into it. It's an open enough one that folks can pick and choose their own path through it as their interests dictate.

要发一篇笔记之前，搜索下类似的内容，看看小红书官方推荐什么 tag，另外可以看看相同领域的博主使用什么 tag。

Resources

Links and resources from the second half of the chat and Q&A:

• TAG Feedback Protocol via https://learninginhand.com/blog/feedbackchat
• Robin DeRosa's OER pedagogical endeavor
• Annotation Rubrics
• Faculty Focus Live - A Hyflex Course: Bridging the Gap Between Online and In-person Students
• Bringing Theories to Practice: Universal Design Principles and the Use of Social Annotation to Support Neurodiverse Students by Christian Aguiar, Fatma Elshobokshy, and Amanda Huron
• Compass Points rubric

Video

<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/KkgXkJZXa0E" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

Watch on YouTube

Author Response

Reviewer #1 (Public Review):

The authors start the study with an interesting clinical observation, found in a small subset of prostate cancers: FOXP2-CPED1 fusion. They describe how this fusion results in enhanced FOXP2 protein levels, and further describe how FOXP2 increases anchorageindependent growth in vitro, and results in pre-malignant lesions in vivo. Intrinsically, this is an interesting observation. However, the mechanistic insights are relatively limited as it stands, and the main issues are described below.

Main issues:

1) While the study starts off with the FOXP2 fusion, the vast majority of the paper is actually about enhanced FOXP2 expression in tumorigenesis. Wouldn't it be more logical to remove the FOXP2 fusion data? These data seem quite interesting and novel but they are underdeveloped within the current manuscript design, which is a shame for such an exciting novel finding. Along the same lines, for a study that centres on the prostate lineage, it's not clear why the oncogenic potential of FOXP2 in mouse 3T3 fibroblasts was tested.

We thank the reviewer very much for the comment. We followed the suggestion and added a set of data regarding the newly identified FOXP2 fusion in Figure 1 to make our manuscript more informative. We tested the oncogenic potential of FOXP2 in NIH3T3 fibroblasts because NIH3T3 cells are a widely used model to demonstrate the presence of transformed oncogenes2,3. In our study, we observed that when NIH3T3 cells acquired the exogenous FOXP2 gene, the cells lost the characteristic contact inhibition response, continued to proliferate and eventually formed clonal colonies. Please refer to "Answer to Essential Revisions #1 from the Editors” for details.

2) While the FOXP2 data are compelling and convincing, it is not clear yet whether this effect is specific, or if FOXP2 is e.g. universally relevant for cell viability. Targeting FOXP2 by siRNA/shRNA in a non-transformed cell line would address this issue.

We appreciate these helpful comments. Please refer to the "Answer to Essential Revisions #1 from the Editors” for details.

3) Unfortunately, not a single chemical inhibitor is truly 100% specific. Therefore, the Foretinib and MK2206 experiments should be confirmed using shRNAs/KOs targeting MEK and AKT. With the inclusion of such data, the authors would make a very compelling argument that indeed MEK/AKT signalling is driving the phenotype.

We thank the reviewer for highlighting this point and we agree with the reviewer’s point that no chemical inhibitor is 100% specific. In this study, we used chemical inhibitors to provide further supportive data indicating that FOXP2 confers oncogenic effects by activating MET signaling. We characterized a FOXP2-binding fragment located in MET and HGF in LNCaP prostate cancer cells by utilizing the CUT&Tag method. We also found that MET restoration partially reversed oncogenic phenotypes in FOXP2-KD prostate cancer cells. All these data consistently supported that FOXP2 activates MET signaling in prostate cancer. Please refer to the "Answer to Essential Revisions #2 from the Editors” and to the "Answer to Essential Revisions #7 from the Editors” for details.

4) With the FOXP2-CPED1 fusion being more stable as compared to wild-type transcripts, wouldn't one expect the fusion to have a more severe phenotype? This is a very exciting aspect of the start of the study, but it is not explored further in the manuscript. The authors would ideally elaborate on why the effects of the FOXP2-CPED1 fusion seem comparable to the FOXP2 wildtype, in their studies.

We thank the reviewer very much for the comment. We had quantified the number of colonies of FOXP2- and FOXP2-CPED1-overexpressing cells, and we found that both wildtype FOXP2 and FOXP2-CPED1 had a comparable putative functional influence on the transformation of human prostate epithelial cells RWPE-1 and mouse primary fibroblasts NIH3T3 (P = 0.69, by Fisher’s exact test for RWPE-1; P = 0.23, by Fisher’s exact test for NIH3T3). We added the corresponding description to the Results section in Line 487 on Page 22 in the tracked changes version of the revised manuscript. Please refer to the "Answer to Essential Revisions #5 from the Editors” for details.

5) The authors claim that FOXP2 functions as an oncogene, but the most-severe phenotype that is observed in vivo, is PIN lesions, not tumors. While this is an exciting observation, it is not the full story of an oncogene. Can the authors justifiably claim that FOXP2 is an oncogene, based on these results?

We appreciate the comment, and we made the corresponding revision in the revised manuscript. Please refer to the "Answer to Essential Revisions #3 from the Editors” for details.

6) The clinical and phenotypic observations are exciting and relevant. The mechanistic insights of the study are quite limited in the current stage. How does FOXP2 give its phenotype, and result in increased MET phosphorylation? The association is there, but it is unclear how this happens.

We appreciate this valuable suggestion. In the current study, we used the CUT&Tag method to explore how FOXP2 activated MET signaling in LNCaP prostate cancer cells, and we identified potential FOXP2-binding fragments in MET and HGF. Therefore, we proposed that FOXP2 activates MET signaling in prostate cancer through its binding to MET and METassociated gene. Please refer to the "Answer to Essential Revisions #2 from the Editors” for details.

Reviewer #2 (Public Review):

1) The manuscript entitled "FOXP2 confers oncogenic effects in prostate cancer through activating MET signalling" by Zhu et al describes the identification of a novel FOXP2CPED1 gene fusion in 2 out of 100 primary prostate cancers. A byproduct of this gene fusion is the increased expression of FOXP2, which has been shown to be increased in prostate cancer relative to benign tissue. These data nominated FOXP2 as a potential oncogene. Accordingly, overexpression of FOXP2 in nontransformed mouse fibroblast NIH-3T3 and human prostate RWPE-1 cells induced transforming capabilities in both cell models. Mechanistically, convincing data were provided that indicate that FOXP2 promotes the expression and/or activity of the receptor tyrosine kinase MET, which has previously been shown to have oncogenic functions in prostate cancer. Notably, the authors create a new genetically engineered mouse model in which FOXP2 is overexpressed in the prostatic luminal epithelial cells. Overexpression of FOXP2 was sufficient to promote the development of prostatic intraepithelial neoplasia (PIN) a suspected precursor to prostate adenocarcinoma and activate MET signaling.

Strengths:

This study makes a convincing case for FOXP2 as 1) a promoter of prostate cancer initiation and 2) an upstream regulator of pro-cancer MET signaling. This was done using both overexpression and knockdown models in cell lines and corroborated in new genetically engineered mouse models (GEMMs) of FOXP2 or FOXP2-CPED1 overexpression in prostate luminal epithelial cells as well as publicly available clinical cohort data.

Major strengths of the study are the demonstration that FOXP2 or FOXP2-CPED1 overexpression transforms RWPE-1 cells to now grow in soft agar (hallmark of malignant transformation) and the creation of new genetically engineered mouse models (GEMMs) of FOXP2 or FOXP2-CPED1 overexpression in prostate luminal epithelial cells. In both mouse models, FOXP2 overexpression increased the incidence of PIN lesions, which are thought to be a precursor to prostate cancer. While FOXP2 alone was not sufficient to cause prostate cancer in mice, it is acknowledged that single gene alterations causing prostate cancer in mice are rare. Future studies will undoubtedly want to cross these GEMMs with established, relatively benign models of prostate cancer such as Hi-Myc or Pb-Pten mice to see if FOXP2 accelerates cancer progression (beyond the scope of this study).

We appreciate these positive comments from the reviewer. We agree with the suggestion from the reviewer that it is worth exploring whether FOXP2 is able to cooperate with a known disease driver to accelerate the progression of prostate cancer. Therefore, we are going to cross Pb-FOXP2 transgenic mice with Pb-Pten KO mice to assess if FOXP2 is able to accelerate malignant progression.

2) Weaknesses: It is unclear why the authors decided to use mouse fibroblast NIH3T3 cells for their transformation studies. In this regard, it appears likely that FOXP2 could function as an oncogene across diverse cell types. Given the focus on prostate cancer, it would have been preferable to corroborate the RWPE-1 data with another prostate cell model and test FOXP2's transforming ability in RWPE-1 xenograft models. To that end, there is no direct evidence that FOXP2 can cause cancer in vivo. The GEMM data, while compelling, only shows that FOXP2 can promote PIN in mice and the lone xenograft model chosen was for fibroblast NIH-3T3 cells.

To determine the oncogenic activity of FOXP2 and the FOXP2-CPDE1 fusion, we initially used mouse primary fibroblast NIH3T3 for transformation experiments, because NIH3T3 cells are a widely used cell model to discover novel oncogenes2,3,10,11. Subsequently, we observed that overexpression of FOXP2 and its fusion variant drove RWPE-1 cells to lose the characteristic contact inhibition response, led to their anchorage-independent growth in vitro, and promoted PIN in the transgenic mice. During preparation of the revised manuscript, we tested the transformation ability of FOXP2 and FOXP2-CPED1 in RWPE1 xenograft models. We subcutaneously injected 2 × 106 RWPE-1 cells into the flanks of NOD-SCID mice. The NODSCID mice were divided into five groups (n = 5 mice in each group): control, FOXP2overexpressing (two stable cell lines) and FOXP2-CPED1- overexpressing (two cell lines) groups. The experiment lasted for 4 months. We observed that no RWPE-1 cell-injected mice developed tumor masses. We propose that FOXP2 and its fusion alone are not sufficient to generate the microenvironment suitable for RWPE-1-xenograft growth. Collectively, our data suggest that FOXP2 has oncogenic potential in prostate cancer, but is not sufficient to act alone as an oncogene.

3) There is a limited mechanism of action. While the authors provide correlative data suggesting that FOXP2 could increase the expression of MET signaling components, it is not clear how FOXP2 controls MET levels. It would be of interest to search for and validate the importance of potential FOXP2 binding sites in or around MET and the genes of METassociated proteins. At a minimum, it should be confirmed whether MET is a primary or secondary target of FOXP2. The authors should also report on what happened to the 4-gene MET signature in the FOXP2 knockdown cell models. It would be equally significant to test if overexpression of MET can rescue the anti-growth effects of FOXP2 knockdown in prostate cancer cells (positive or negative results would be informative).

We appreciate all the valuable comments. As suggested, we performed corresponding experiments, please refer to the " Answers to Essential Revisions #2 from the Editors”, to the "Answer to Essential Revisions #6 from the Editors”, and to the "Answer to Essential Revisions #7 from the Editors” for details.

Reviewer #3 (Public Review):

1) In this manuscript, the authors present data supporting FOXP2 as an oncogene in PCa. They show that FOXP2 is overexpressed in PCa patient tissue and is necessary and sufficient for PCa transformation/tumorigenesis depending on the model system. Overexpression and knock-down of FOXP2 lead to an increase/decrease in MET/PI3K/AKT transcripts and signaling and sensitizes cells to PI3K/AKT inhibition.

Key strengths of the paper include multiple endpoints and model systems, an over-expression and knock-down approach to address sufficiency and necessity, a new mouse knock-in model, analysis of primary PCa patient tumors, and benchmarking finding against publicly available data. The central discovery that FOXP2 is an oncogene in PCa will be of interest to the field. However, there are several critically unanswered questions.

1) No data are presented for how FOXP2 regulates MET signaling. ChIP would easily address if it is direct regulation of MET and analysis of FOXP2 ChIP-seq could provide insights.

2) Beyond the 2 fusions in the 100 PCa patient cohort it is unclear how FOXP2 is overexpressed in PCa. In the discussion and in FS5 some data are presented indicating amplification and CNAs, however, these are not directly linked to FOXP2 expression.

3) There are some hints that full-length FOXP2 and the FOXP2-CPED1 function differently. In SF2E the size/number of colonies between full-length FOXP2 and fusion are different. If the assay was run for the same length of time, then it indicates different biologies of the overexpressed FOXP2 and FOXP2-CPED1 fusion. Additionally, in F3E the sensitization is different depending on the transgene.

We appreciate these valuable comments and constructive remarks. As suggested, we performed the CUT&Tag experiments to detect the binding of FOXP2 to MET, and to examine the association of CNAs of FOXP2 with its expression. Please refer to the " Answer to Essential Revisions #2 from the Editors" and the " Answer to Essential Revisions #4 from the Editors" for details. We also added detailed information to show the resemblance observed between FOXP2 fusion- and wild-type FOXP2-overexpressing cells. We added the corresponding description to the Results section in Line 487 on Page 22 in the tracked changes version of the revised manuscript. Please refer to the “Answer to Essential Revisions #5 from the Editors” for details.

2) The relationship between FOXP2 and AR is not explored, which is important given 1) the critical role of the AR in PCa; and 2) the existing relationship between the AR and FOXP2 and other FOX gene members.

We thank the reviewer very much for highlighting this point. We agree that it is important to examine the relationship between FOXP2 and AR. We therefore analyzed the expression dataset of 255 primary prostate tumors from TCGA and observed that the expression of FOXP2 was significantly correlated with the expression of AR (Spearman's ρ = 0.48, P < 0.001) (Figure 1. a). Next, we observed that both FOXP2- and FOXP2-CPED1overexpressing 293T cells had a higher AR protein abundance than control cells (Figure 1. b). In addition, shRNA-mediated FOXP2 knockdown in LNCaP cells resulted in a decreased AR protein level compared to that in control cells (Figure 1. c). However, we analyzed our CUT&Tag data and observed no binding of FOXP2 to AR (Figure 1. d). Our data suggest that FOXP2 might be associated with AR expression.

Figure 1. a. AR expression in a human prostate cancer dataset (TCGA, Prostate Adenocarcinoma, Provisional; n = 493) classified by FOXP2 expression level (bottom 25%, low expression, n = 120; top 25%, high expression, n = 120; negative expression, n = 15). P values were calculated by the MannWhitney U test. The correlation between FOXP2 and AR expression was evaluated by determining the Spearman's rank correlation coefficient. b. Immunoblot analysis of the expression levels of AR in 293T cells with overexpression of FOXP2 or FOXP2-CPED1. c. Immunoblot analysis of the expression levels of AR in LNCaP cells with stable expression of the scrambled vector or FOXP2 shRNA. d. CUT&Tag analysis of FOXP2 association with the promoter of AR. Representative track of FOXP2 at the AR gene locus is shown.

Reference

1. Mayr C, Bartel DP. Widespread shortening of 3'UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell. 2009 Aug 21;138(4):673-84.
2. Gara SK, Jia L, Merino MJ, Agarwal SK, Zhang L, Cam M et al., Germline HABP2 Mutation Causing Familial Nonmedullary Thyroid Cancer. N Engl J Med. 2015 Jul 30;373(5):448-55.
3. Kohno T, Ichikawa H, Totoki Y, Yasuda K, Hiramoto M, Nammo T et al., KIF5B-RET fusions in lung adenocarcinoma. Nat Med. 2012 Feb 12;18(3):375-7.
4. Chen F, Byrd AL, Liu J, Flight RM, DuCote TJ, Naughton KJ et al., Polycomb deficiency drives a FOXP2-high aggressive state targetable by epigenetic inhibitors. Nat Commun. 2023 Jan 20;14(1):336.
5. Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG et al., CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun. 2019 Apr 29;10(1):1930.
6. Spiteri E, Konopka G, Coppola G, Bomar J, Oldham M, Ou J et al., Identification of the transcriptional targets of FOXP2, a gene linked to speech and language, in developing human brain. Am J Hum Genet. 2007 Dec;81(6):1144-57.
7. Lai CS, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP. A forkhead-domain gene is mutated in a severe speech and language disorder. Nature. 2001 Oct 4;413(6855):519-23.
8. Hannenhalli S, Kaestner KH. The evolution of Fox genes and their role in development and disease. Nat Rev Genet. 2009 Apr;10(4):233-40.
9. Shu W, Yang H, Zhang L, Lu MM, Morrisey EE. Characterization of a new subfamily of winged-helix/forkhead (Fox) genes that are expressed in the lung and act as transcriptional repressors. J Biol Chem. 2001 Jul 20;276(29):27488-97.
10. Wang C, Liu H, Qiu Q, Zhang Z, Gu Y, He Z. TCRP1 promotes NIH/3T3 cell transformation by over-activating PDK1 and AKT1. Oncogenesis. 2017 Apr 24;6(4):e323.
11. Suh YA, Arnold RS, Lassegue B, Shi J, Xu X, Sorescu D et al., Cell transformation by the superoxide-generating oxidase Mox1. Nature. 1999 Sep 2;401(6748):79-82.

An annotated list of collaborative scholarly projects in the Humanities may look like existing curated catalogues of digitale editions.

reply to u/iamharunjonuzi at https://www.reddit.com/r/Zettelkasten/comments/12bvcmn/how_do_i_store_when_coming_across_an_actual_fact/

How central is the fact to what you're working at potentially developing? Often for what may seem like basic facts that are broadly useful, but not specific to things I'm actively developing, I'll leave basic facts like that as short notes on the source/reference cards (some may say literature notes) where I found them rather than writing them out in full as their own cards.

If I were a future biologist, as a student I might consider that I would soon know really well what a cell was and not bother to have a primary zettel on something so commonplace unless I was collecting various definitions to compare and contrast for something specific. Alternately as a non-biologist or someone that doesn't use the idea frequently, then perhaps it may merit more space for connecting to others?

Of course you can always have it written along with the original source and "promote" it to its own card later if you feel it's necessary, so you're covered either way. I tend to put the most interesting and surprising ideas into my main box to try to maximize what comes back out of it. If there were 2 more interesting ideas than the definition of cell in that documentary, then I would probably leave the definition with the source and focus on the more important ideas as their own zettels.

As a rule of thumb, for those familiar with Bloom's taxonomy in education, I tend to leave the lower level learning-based notes relating to remembering and understanding as shorter (literature) notes on the source's reference card and use the main cards for the higher levels (apply, analyze, evaluate, create).

Ultimately, time, practice, and experience will help you determine for yourself what is most useful and where. Until you've developed a feel for what works best for you, just write it down somewhere and you can't really go too far wrong.

Reviewer #3 (Public Review):

Bacteria sense and respond to multiple signals and cues to regulate gene expression. To define the complex network of signaling that ultimately controls transcription of many genes in cells requires an understanding of how multiple signaling systems can converge to effect gene expression and ensuing bacterial behaviors. The global transcription factor CRP has been studied for decades as a regulator of genes in response to glucose availability. It's direct and indirect effects on gene expression have been documented in E. coli and other bacteria including pathogens including Vibrio cholerae. Likewise, the master regulator of quorum sensing (QS), HapR), is a well-studied transcription factor that directly controls many genes in Vibrio cholerae and other Vibrios in response to autoinducer molecules that accumulate at high cell density. By contrast, low cell density gene expression is governed by another regulator AphA. It has not yet been described how HapR and CRP may together work to directly control transcription and what genes are under such direct dual control.

Using both in vivo methods with gene fusions to lacZ and in vitro transcription assays, the authors proceed to identify the smaller subset of genes whose transcription is directly repressed (7) and activated (2) by HapR. Prior work from this group identified the direct CRP binding sites in the V. cholerae genome as well as promoters with overlapping binding sites for AphA and CRP, thus it appears a logical extension of these prior studies is to explore here promoters for potential integration of HapR and CRP. Inclusion of this rationale was not included in the introduction of CRP protein to the in vitro experiments.

Seven genes are found to be repressed by HapR in vivo, the promoter regions of only six are repressed in vitro with purified HapR protein alone. The authors propose and then present evidence that the seventh promoter, which controls murPQ, requires CRP to be repressed by HapR both using in vivo and vitro methods. This is a critical insight that drives the rest of the manuscripts focus.

The DNase protection assay conducted supports the emerging model that both CRP and HapR bind at the same region of the murPQ promoter, but interpret is difficult due to the poor quality of the blot. There are areas of apparent protection at positions +1 to +15 that are not discussed, and the areas highlighted are difficult to observe with the blot provided.

The model proposed at the end of the manuscript proposes physiological changes in cells that occur at transitions from the low to high cell density. Experiments in the paper that could strengthen this argument are incomplete. For example, in Fig. 4e it is unclear at what cell density the experiment is conducted. The results with the wild type strain are intermediate relative to the other strains tested. Cell density should affect the result here since HapR is produced at high density but not low density. This experiment would provide important additional insights supporting their model, by measuring activity at both cell densities and also in a luxO mutant locked at the high cell density. Conducting this experiment in conditions lacking and containing glucose would also reveal whether high glucose conditions mimicking the crp results.

Throughout the paper it was challenging to account for the number of genes selected, the rationale for their selection, and how they were prioritized. For example, the authors acknowledged toward the end of the Results section that in their prior work, CRP and HapR binding sites were identified (line 321-22). It is unclear whether the loci indicated in Table 1 all from this prior study. It would be useful to denote in this table the seven genes characterized in Figure 2 and to provide the locus tag for murPQ. Of the 32 loci shown in Table 1, five were selected for further study using EMSA (line 322), but no rationale is given for studying these five and not others in the table.

Since prior work identified a consensus CRP binding motif, the authors identify the DNA sequence to which HapR binds overlaps with a sequence also predicted to bind CRP. Genome analysis identified a total of seven sites where the CRP and HapR binding sites were offset by one nucleotide as see with murPQ. Lines 327-8 describe EMSA results with several of these DNA sequences but provides no data to support this statement. Are these loci in Table 1?

Using structural models, the authors predict that HapR repression requires protein-protein interactions with CRP. Electromobility shift assays (EMSA) with purified promoter DNA, CRP and HapR (Fig 5d) and in vitro transcription using purified RNAP with these factors (Figure 5e) support this hypothesis. However, the model proports that HapR "bound tightly" and that it also had a "lower affinity" when CRP protein was used that had mutations in a putative interaction interface. These claims can be bolstered if the authors calculate the dissociation constant (Kd) value of each protein to the DNA. This provides a quantitative assessment of the binding properties of the proteins. The concentrations of each protein are not indicated in panels of the in vitro analysis, but only the geometric shapes denoting increasing protein levels. Panel 5e appears to indicate that an intermediate level of CRP was used in the presence of HapR, which presumably coincides with levels used in lane 4, but rationale is not provided. How well the levels of protein used in vitro compare to levels observed in vivo is not mentioned.

The authors are commended for seeking to connect the in vitro and vivo results obtained under lab conditions with conditions experienced by V. cholerae in niches it may occupy, such as aquatic systems. The authors briefly review the role of MurPQ in recycling of the cell wall of V. cholerae by degrading MurNAc into GlcNAc, although no references are provided (lines 146-50). Based on this physiology and results reported, the authors propose that murPQ gene expression by these two signal transduction pathways has relevance in the environment, where Vibrios, including V. cholerae, forms biofilms on exoskeleton composed of GlcNAc.

The conclusions of that work are supported by the Results presented but additional details in the text regarding the characteristics of the proteins used (Kd, concentrations) would strengthen the conclusions drawn. This work provides a roadmap for the methods and analysis required to develop the regulatory networks that converge to control gene expression in microbes. The study has the potential to inform beyond the sub-filed of Vibrios, QS and CRP regulation.

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This is interesting to think about such a low amount of media consumption. I always imagined that on top of outdoor activities and activities without media, there would also be a decent amount of time spent consuming media, even if that was radio or magazines.