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Welcome back and this is going to be a super quick lesson where I just want to discuss cost allocation tags. So this is something you'll use in normal operations when you manage AWS accounts. But for the exam, there are a number of key points that you need to be aware of. So let's keep this brief and just jump in and get started.

Cost allocation tags are things that you can enable to provide additional information for any billing reports available within AWS. So cost allocation tags need to be enabled individually. And this is either on a per account basis for standard accounts or something that's performed in the organizational master account if you use AWS Organizations.

Now cost allocation tags come in two different forms. You have AWS generated ones. You can always start with AWS: and two very common ones are AWS:createdBy or AWS:cloudformation:stack-name. And if you enable cost allocation tags, then these tags are added to AWS resources automatically by AWS.

Now I always see questions in the exam which do mention AWS:createdBy. Now this details which identity created a resource as long as cost allocation tags are enabled. So this is not something that can be added retroactively. You need to make sure that this is enabled on an account or for an organization. And from that point onward, AWS will automatically add this cost allocation tag to any resource or any supported resource within the account.

There are also user defined tags which can be enabled. So you can create these—for example, maybe you wanted to have department tags or cost center tags or tags that indicated whether environments were production or development. And you can enable these and use them as cost allocation tags and these will be visible in any AWS cost reporting.

Now both of these—so user defined and AWS defined or AWS generated—they're going to be visible once enabled within AWS cost reports and these can be used as a filter. So you're able to determine which resources were created by a user or which resources belong to certain departments or cost centers. And you can use this as part of your organizational finance systems to correctly allocate AWS costs to specific areas of your business.

Now enabling these and having them so they're visible within cost reports can take up to 24 hours. So this is something that you need to plan in advance. None of these are retroactive. So keep that in mind for the exam and real world usage.

Now to illustrate how this works and what better way than to use some obnoxiously large graphics. Let's take a simple example: two EC2 instances for the category application. Let's say that in advance I create or enable two different cost allocation tags, AWS:createdBy and a user defined tag called app. This is what you might see.

Resources created will automatically be tagged with these two different tags. So the AWS generated AWS:createdBy tag, which allows you to see which identity created that resource. And then the user defined tag user:application and the two different current values for this tag are Categorim-prod and Categorim-dev.

Now any reporting which is generated from this point onward will include these tags. So we could split out the costs for our finance team detailing which costs are allocated for the Categorim production and the Categorim development application. And then we could also produce isolated costs for resources created by specific AWS users.

So by using cost allocation tags effectively, we can feed these costs into our organizational finance processes.

Now that's pretty much all you need to know for the exam. Just the format of these tags—pay specific attention to AWS:createdBy because that's what I see in the exam all the time. Just know that these need to be enabled. They are not retroactive. And once you've enabled them, it can take up to 24 hours for these to be visible and used by AWS.

So that being said, that's everything I wanted to cover in this lesson. Go ahead and complete the video. And when you're ready, I'll look forward to you joining me in the next.

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

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Reply to the reviewers

Manuscript number: RC-2024-02465

Corresponding author(s): Saravanan, Palani

1. General Statements

We would like to thank the Review Commons Team for handling our manuscript and the Reviewers for their constructive feedback and suggestions. In our revised manuscript, we have addressed and incorporated all the major suggestions of the reviewers, and we have also added new significant data on the role of Tropomyosin in regulation of endocytosis through its control over actin monomer pool maintenance and actin network homeostasis. We believe that with all these additions, our study has significantly gained in quality, strength of conclusions made, and scope for future work.

2. Point-by-point description of the revisions

Reviewer #1

Evidence, reproducibility and clarity

There are 2 Major issues -

Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.

Response: We would like to clarify that all mNG-Tpm constructs used in our study contain a 40 amino-acid (aa) flexible linker between the N-terminal mNG fluorescent protein and the Tpm protein as per our earlier published study (Hatano et al., 2022). During initial optimization, we have also experimented with linker length and the 40aa-linker length works optimally for clear visualization of Tpm onto actin cable structures in budding yeast, fission yeast (both S. pombe and S. japonicus), and mammalian cells (Hatano et al., 2022). These constructs have also been used since in other studies (Wirshing et al., 2023; Wirshing and Goode, 2024) and currently represents the best possible strategy to visualize Tpm isoforms in live cells. In our study, we characterized these proteins for functionality and found that both mNG-Tpm1 and mNG-Tpm2 were functional and can rescue the synthetic lethality observed in Dtpm1Dtpm2 cells. During our study, we observed that mNG-Tpm1 expression from a single-copy integration vector did not restore full length actin cables in Dtpm1 cells (Fig. 1B, 1C). We hypothesized that this could be a result of reduced binding affinity of the tagged tropomyosin due to lack of normal N-terminal acetylation which stabilizes the N-terminus. The 40aa linker is unstructured and may not be able to neutralize the charge on the N-terminal Methionine, thus, we tried to insert -Ala-Ser- dipeptide which has been routinely used in vitro biochemical studies to stabilize the N-terminal helix and impart a similar effect as the N-terminal acetylation (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) by restoring normal binding affinity of Tpm to F-actin (Monteiro et al., 1994; Greenfield et al., 1994). We observed that addition of the -Ala-Ser- dipeptide to mNG-Tpm fusion, indeed, restored full length actin cables when expressed in Dtpm1 cells, performing significantly better in our in vivo experiments (Fig. 1B, 1C). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may stabilize the N-terminus of Tpm and allow normal head-to-tail dimer formation (Greenfield et al., 1994; Monteiro et al., 1994; Frye et al., 2010). We have discussed this in our new Discussion section (Lines 350-372). Since, the addition of -AS- dipeptide was referred to as "acetyl-mimic (am)" in a previous study (Alioto et al., 2016), we continued to use the same nomenclature in our study. Now as per your suggestions and to be more accurate, we have renamed "mNG-amTpm" constructs as "mNG-ASTpm" throughout the study to not confuse or claim that -AS- addition mimics acetylation. In any case, we have not seen any other ill effect of -AS- dipeptide introduction in addition to our 40 amino acid linker suggesting that it can also be considered part of the linker. Although, we agree with the reviewer that biochemical characterization of the effect of linker would be important to determine, we strongly believe that it is currently outside the scope of this study and should be taken up for future work with these proteins. Our study has majorly aimed to understand the functionality and utility of these mNG-Tpm fusion proteins for cell biological experiments in vivo, which was not done earlier in any other model system.

My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.

Response: We agree with the reviewer that N-terminal tagging of tropomyosin may have effects on its function, but these constructs represent the only fluorescently tagged functional tropomyosin constructs available currently while C-terminal fusions are either non-functional (we were unable to construct strains with endogenous Tpm1 gene fused C-terminally to GFP) or do not localize clearly to actin structures (See Figure R1 showing endogenous C-terminally tagged Tpm2-yeGFP that shows almost no localization to actin cables). To our knowledge, our study represents a first effort to understand the question of spatial sorting of Tpm isoforms, Tpm1 and Tpm2, in S. cerevisiae and any future developments with better visualization strategies for Tpm isoforms without compromising native N-terminal modifications and function will help improve our understanding of these proteins in vivo. We have also discussed these possibilities in our new Discussion section (Lines 391-396).

Significance

This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly. The significance of this study, given the above, and the concerns raised is not clear to this reviewer.

Response: __Our study explores multiple facets of Tropomyosin (Tpm) biology. The lack of functional tagged Tpm has been a major bottleneck in understanding Tpm isoform diversity and function across eukaryotes. In our study, we characterize the first functional tagged Tpm proteins (Fig. 1, Fig. S1) and use them to answer long-standing questions about localization and spatial sorting of Tpm isoforms in the model organism S. cerevisiae (Fig. 2, Fig. 3, Fig. S2, Fig. S3). We also discover that the dual Tpm isoforms, Tpm1 and Tpm2, are functionally redundant for actin cable organization and function, while having gained divergent functions in Retrograde Actin Cable Flow (RACF) (Fig. 4, Fig. 5A-D, Fig. S4, Fig. S5, Fig. S6). We have now added new data on role of global Tpm levels controlling endocytosis via maintenance of normal linear-to-branched actin network homeostasis in S. cerevisiae (Fig. 5E-G)__. We respectfully differ with the reviewer on their assessment of our study and request the reviewer to read our revised manuscript which discusses the significance, limitations, and future perspectives of our study in detail.

Reviewer #2

Evidence, reproducibility and clarity

This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.

Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.

1. Functionality of the acetyl-mimic tagged tropomyosin constructs: The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.

__Response: __We would like to thank the reviewer for his feedback and suggestions. Based on the suggestions, we have now more accurately described the growth rescue observed by expression of mNG-ASTpm1 in Dtpm1 cells in the revised text. We have also removed the use of "completely functional" to describe mNG-Tpm functionality and corrected any errors in Figure citations in the revised manuscript.

As per reviewers' suggestion, we have now tested rescue of synthetic lethality of Dtpm1Dtpm2 cells by expression of all mNG-Tpm variants and we find that all of them are capable of restoring the viability of Dtpm1Dtpm2 cells when expressed under their native promoters via a high-copy plasmid (pRS425) (Fig. S1E) but only mNG-Tpm1 and mNG-ASTpm1 restored viability of Dtpm1Dtpm2 cells when expressed under their native promoters via an integration plasmid (pRS305) (Fig. S1F). These results clearly suggest that while both mNG-Tpm1 and mNG-Tpm2 constructs are functional, Tpm1 tolerates the presence of the N-terminal fluorescent tag better than Tpm2. These observations now enhance our understanding of the functionality of these mNG-Tpm fusion proteins and will be a useful resource for their usage and experimental design in future studies in vivo.

It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.

Response: __We agree with the reviewer's observation and for the sake of clarity and accuracy, we have now renamed "mNG-amTpm" with "mNG-ASTpm". The use of -AS- dipeptide is very routine in studies with Tpm (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) and its addition restores normal binding affinities to Tpm proteins purified from E. coli (Monteiro et al., 1994). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may help neutralize the impact of a freely protonated Met on the alpha-helical structure and stabilize the N-terminus helix of Tpm and allow normal head-to-tail dimer formation (Monteiro et al., 1994; Frye et al., 2010; Greenfield et al., 1994). Consistent with this, we also observe a highly significant improvement in actin cable length when expressing mNG-ASTpm as compared to mNG-Tpm in Dtpm1 cells, suggesting an improvement in function probably due to increased binding affinity (Fig. 1B, 1C). We have also discussed this in our answer to Question 1 of Reviewer 1 and the revised manuscript (Lines 350-372)__.

__ Localization of Tpm1 and Tpm2:__Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.

Response: __We thank the reviewer for this observation and their suggestions. We agree that relative concentrations of functional Tpm1 and Tpm2 in cells may influence the extent of their localizations. As per the reviewer's suggestion, we have now conducted our quantitative analysis in cells lacking endogenous Tpm1 and only expressing mNG-ASTpm1 from an integrated plasmid copy at the leu2 locus and the data is presented in new __Figure S3. We compared Tpm-bound cable length (Fig. S3A, S3B) __and Tpm-bound cable number (Fig. S3A, S3C) along with actin cable length (Fig. S3D, S3E) and actin cable number (Fig. S3D, S3F) in wildtype, Dbnr1, and Dbni1 cells. Our analysis revealed that mNG-ASTpm1 localized to actin cable structures in wildtype, Dbnr1, and Dbni1 cells and the decrease observed in Tpm-bound cable length and number upon loss of either Bnr1 or Bni1, was accompanied by a corresponding decrease in actin cable length and number upon loss of either Bnr1 or Bni1. Thus, this analysis reached the same conclusion as our earlier analysis (Fig. 2) that mNG-ASTpm1 does not show preference between Bnr1 and Bni1-made actin cables. mNG-ASTpm2 did not restore functionality, when expressed as single integrated copy, in Dtpm1Dtpm2 cells (new results in __Fig. S1E, S1F, S5A) thus, we could not conduct a similar analysis for mNG-ASTpm2. This suggests that use of mNG-ASTpm2 would be more meaningful in the presence of endogenous Tpm2 as previously done in Fig. 2D-F.

We have now also performed additional yeast mating experiments with cells lacking bnr1 gene and expressing either mNG-ASTpm1 or mNG-ASTpm2 and the data is shown in new Figure 3. From these observations, we observe that both mNG-ASTpm1 and mNG-ASTpm2 localize to the mating fusion focus in a Bnr1-independent manner (Fig. 3B, 3D) and suggests that they bind to Bni1-made actin cables that are involved in polarized growth of the mating projection. These results also add strength to our conclusion that Tpm1 and Tpm2 localize to actin cables irrespective of which formin nucleates them. Overall, these new results highlight and reiterate our model of formin-isoform independent binding of Tpm1 and Tpm2 in S. cerevisiae.

In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.

Response: __We thank the reviewer for pointing this out. Our data and analysis do not suggest that Tpm1 and Tpm2 show any preference for decoration of cables in either mother or bud compartment. As per the reviewer's suggestion, we have now quantified the ratio of mean mNG fluorescence in the bud to the mother (Bud/Mother) and the data is shown in __Figure. S2G. The bud-to-mother ratio was similar for mNG-ASTpm1 and mNG-ASTpm2 in wildtype cells, and the ratio increased in Dbnr1 cells and decreased in Dbni1 cells for both mNG-ASTpm1 and mNG-ASTpm2 (Fig. S2G). __This is consistent with the decreased actin cable signal in the mother compartment in Dbnr1 cells and decreased actin cable signal in the bud compartment in Dbni1 cells (Fig. S2A-D). Thus, our new analysis shows that both mNG-ASTpm1 and mNG-ASTpm2 have similar changes in their concentration (mean fluorescence) upon loss of either formins Bnr1 and Bni1 and show similar ratios in wildtype cells as well, suggesting no preference for binding to actin cables in either bud or mother compartment. The preference inferred by the reviewer seems to be a bias of the current representative images and thus, we have replaced the images in __Fig. 2A, 2D to more accurately represent the population.

The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?

__Response: __We did not use LifeAct for our analysis as LifeAct is known to cause expression-dependent artefacts in cells (Courtemanche et al., 2016; Flores et al., 2019; Xu and Du, 2021) and it also competes with proteins that regulate normal cable organization like cofilin. Use of LifeAct would necessitate standardization of expression to avoid such artefacts in vivo. Also, phalloidin staining provides the best staining of actin cables and allows for better quantitative results in our experiments. The use of LifeAct along with mNG-Tpm would also require optimization with a red fluorescent protein which usually tend to have lower brightness and photostability. However, during the revision of our study, a new study from Prof. Goode's lab has developed and optimized expression of new LifeAct-3xmNeonGreen constructs for use in S. cerevisiae (Wirshing and Goode, 2024). Thus, a similar strategy of using tandem copies of bright and photostable red fluorescent proteins can be explored for use in combination with mNG-Tpm in the future studies.

__ Complementation of tpm1∆ by Tpm2:__

I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue.

The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.

Response: __We thank the reviewer for pointing this out. We would like to clarify that in our RT-qPCR experiments, the primers were chosen within the Tpm1 and Tpm2 gene and do not distinguish between transcripts from endogenous or plasmid copy. We have now mentioned this in the Materials and Methods section of the revised manuscript. So, they represent a relative estimate of the total mRNA of these genes present in cells. We were consistently able to detect ~19 fold increase in Tpm2 total mRNA levels as compared to wildtype and ∆tpm1 cells (Fig. S4D) when tpm2 was expressed from a high-copy plasmid (pRS425). This increase in Tpm2 mRNA levels was accompanied by a rescue in growth (Fig. S4A) and actin cable organization (Fig. S4B) of ∆tpm1 cells containing pRS425-ptpm2TPM2. When tpm2 was expressed from a low-copy number centromeric plasmid (pRS316), we detected a ~2 fold increase in Tpm2 transcript levels when using the tpm1 promoter and no significant change was detected when using tpm2 promoter (Fig. S4E)__. We have made sure that these results are accurately described in the revised manuscript.

As per the reviewer's suggestion, we have now conducted a more extensive analysis to ascertain the expression levels of Tpm2 in our experiments and the data is now presented in new Figure S5. We used mNG-ASTpm1 and mNG-ASTpm2 to rescue growth of ∆tpm1 (Fig. S5A) and correlated growth rescue with protein levels using quantified fluorescence intensity (Fig. S5B, S5C) and western blotting (anti-mNG) (Fig. S5D, S5E). We find that ∆tpm1 cells containing pRS425-ptpm1mNG-ASTpm1 had the highest protein level followed by pRS425-ptpm2 mNG-ASTpm2, pRS305-ptpm1mNG-ASTpm1, and the least protein levels were found in pRS305-ptpm2 mNG-ASTpm2 containing ∆tpm1 cells in both fluorescence intensity and western blotting quantifications (Fig. S5C, S5E). Surprisingly, we were not able to detect any protein levels in ∆tpm1 cells containing pRS305-ptpm2 mNG-ASTpm2 with western blotting (Fig. S5D) which was also accompanied by a lack of growth rescue (Fig. S5A). This most likely due to weak expression from the native Tpm2 promoter which is consistent with previous literature (Drees et al., 1995). Taken together, this data clearly shows that the rescue observed in ∆tpm1 cells is caused due to increased expression of mNG-ASTpm2 in cells and supports our conclusion that increase in Tpm2 expression leads to restoration of normal growth and actin cables in ∆tpm1 cells.

__ Specific function of Tpm2:__

The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.

Response: __We agree with the reviewer and as per the reviewer's suggestion, we have performed another experiment which include wildtype, ∆tpm2 cells containing empty pRS316 vector or pRS316-ptpm2TPM1 or pRS316-ptpm1TPM1. We find that RACF rate increased in ∆tpm2 cells as compared to wildtype and was restored to wildtype levels by exogenous expression of Tpm2 but not Tpm1 (Fig. S6E, S6F). Since, actin cables were not detectable in ∆tpm1 cells, we measured RACF rates in ∆tpm1 cells expressing Tpm1 or Tpm2 from a plasmid copy, which restored actin cables as shown previously in __Fig. 5A-C. We observed that RACF rates were similar to wildtype in ∆tpm1 cells expressing either Tpm1 or Tpm2 (Fig. S6E, S6F), suggesting that Tpm1 is not involved in RACF regulation. Taken together, these results suggest a specific role for Tpm2, but not Tpm1, in RACF regulation in S. cerevisiae, consistent with previous literature (Huckaba et al., 2006).

Minor comments: __1.__The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).

Response: We thank the reviewer for pointing this out. We have now repeated the drop test multiple times (Fig. R2), but we see similar growth rates as the drop test already presented in Fig. S4A. __At this point, it would be difficult to ascertain the basis of this difference observed at 23{degree sign}C and 30{degree sign}C, but a recent study that links leucine levels to actin cable stability (Sing et al., 2022) might explain the faster growth of these ∆tpm1 cells containing a leu2 gene carrying high-copy plasmid. However, there is no effect on growth rate at 37{degree sign}C which is consistent with other spot assays shown in __Fig. S1D, S4F, S5A.

Significance

I am a cell biologist with expertise in both yeast and actin cytoskeleton.

The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.

We thank the reviewer for their positive assessment of our work and the constructive feedback that has greatly improved the quality of our study. After addressing the points raised by the reviewer, we believe that our study has significantly gained in consolidating the major conclusions of our work.

**Referees cross-commenting**

Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.

Response: __We acknowledge the reviewer's point about the effect of Ala-Ser dipeptide and would request the reviewer to refer to our response to Reviewer 1 (Question 1) for a more detailed discussion on this. We have also extensively addressed the question of Tpm2 expression levels as suggested by the reviewer (new data in __Figure S5) which has further strengthened the conclusions of our study.

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

Summary:__ The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.

Major and Minor Comments: 1. The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.

Response: __We agree with the reviewer's suggestions and have now updated our Materials and Methods section to describe the image analysis pipelines used in more detail. We have also added examples of quantification procedure for actin cable length/number and mitochondrial morphology as an additional Supplementary __Figure S7. Briefly, the following pipelines were used:

• Actin cable length and number analysis: This was done exactly as mentioned in McInally et al., 2021, McInally et al., 2022. Actin cables were manually traced in Fiji as shown in __ S7A__, and then the traces files for each cell were run through a Python script (adapted from McInally et al., 2022) that outputs mean actin cable length and number per cell.
• Mitochondria morphology: Mitochondria Analyzer plug-in in Fiji was used to segment out the mitochondrial fragments. The parameters used for 2D segmentation of mitochondria were first optimized using "2D Threshold Optimize" to find the most accurate segmentation and then the same parameters were run on all images. After segmentation of the mitochondrial network, measurements of fragment number were done using "Analyze Particles" function in Fiji. An example of the overall process is shown in __ S7B.__ As per the reviewer's suggestion, we have now included the description of the statistical test used in the Figure Legends of each Figure in the revised manuscript. We have used One-Way Anova with Tukey's Multiple Comparison test, Kruskal-Wallis test with Dunn's Multiple Comparisons, and Unpaired Two-tailed t-test using the in-built functions in GraphPad Prism (v.6.04).

**Referees cross-commenting**

I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.

Response: __We acknowledge the reviewer's concern about the Ala-Ser dipeptide and would request them to refer our earlier discussion on this in response to Reviewer 1 (Question 1) and Reviewer 2 (Question 2). We would also request the reviewer to refer to our answer to Reviewer 2 (Question 6) where we have extensively addressed the question of Tpm2 expression levels and their effect on rescue of Dtpm1 cells. This data is now presented as new __Figure S5 in our revised manuscript.

Reviewer#3 (Significance (Required)):

The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.

The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.

As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.

We thank the reviewer for their positive outlook and assessment of our study. We have incorporated all their suggestions, and we are confident that the revised manuscript has significantly improved in quality due to these additions.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

There are 2 Major issues:

1. Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.
2. My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.

Significance

This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly.

The significance of this study, given the above, and the concerns raised is not clear to this reviewer.

Reviewer #2 (Public review):

Summary

In this paper, the function of trpγ in lipid metabolism was investigated. The authors found that lipid accumulation levels were increased in trpγ mutants and remained high during starvation; the increased TAG levels in trpγ mutants were restored by the expression of active AMPK in DH44 neurons and oral administration of the anti-diabetic drug metformin. Furthermore, oral administration of lipase, TAG and free fatty acids effectively restored survival of trpγ mutants under starvation conditions. These results indicate that TRPv plays an important role in the maintenance of systemic lipid levels through the proper expression of lipase. Furthermore, authors have shown that this function is mediated by DH44R2. This study provides an interesting finding in that the neuropeptide DH44 released from the brain regulates lipid metabolism through a brain-gut axis, acting on the receptor DH44R2 expressed in gut cells.

Strengths

Using Drosophila genetics, careful analysis of which cells express trpγ regulates lipid metabolism is performed in this study. The study supports its conclusions from various angles, including not only TAG levels, but also fat droplet staining and survival rate under starved conditions, and oral administration of substances involved in lipid metabolism.

Weaknesses

The function of lipases, as well as identification of cell types, in the DH44R2-expressing cells in the gut can be investigated.

Reviewer #3 (Public review):

In this manuscript, the authors demonstrated the significance of the TRPγ channel in regulating internal TAG levels. They found high TAG levels in TRPγ mutant, which was ascribed to a deficit in the lipolysis process due to the downregulation of brummer (bmm). It was notable that the expression of TRPγ in DH44+ PI neurons, but not dILP2+ neurons, in the brain restored the internal TAG levels and that the knockdown of TRPγ in DH44+ PI neurons resulted in an increase in TAG levels. These results suggested a non-cell autonomous effect of Dh44+PI neurons. Additionally, the expression of the TRPγ channel in Dh44 R2-expressing cells restored the internal TAG levels. The authors, however, did not provide an explanation of how TRPγ might function in both presynaptic and postsynaptic cells in the non-cell autonomous manner to regulate the TAG storage. The authors further determined the effect of TRPγ mutation on the size of lipid droplets (LD) and the lifespan and found that TRPγ mutation caused an increase in the size of LD and a decrease in the lifespan, which were reverted by feeding lipase and metformin. These were creative endeavors, I thought. The finding that DH44+ PI neurons have non-cell autonomous functions in regulating bodily metabolism (mainly sugar/lipid) in addition to directing sugar nutrient sensing and consumption is likely correct, but the paper has many loose ends.

Comments on revisions:

The authors have addressed nearly all of my concerns with additional experiments and explanations.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This research article by Nath et al. from the Lee Lab addresses how lipolysis under starvation is achieved by a transient receptor potential channel, TRPγ, in the neuroendocrine neurons to help animals survive prolonged starvation. Through a series of genetic analyses, the authors identify that TRPγ mutations specifically lead to a failure in lipolytic processes under starvation, thereby reducing animals' starvation resistance. The conclusion was confirmed through total triacylglycerol levels in the animals and lipid droplet staining in the fat bodies. This study highlights the importance of transient receptor potential (TRP) channels in the fly brain to modulate energy homeostasis and combat metabolic stress. While the data is compelling and the message is easy to follow, several aspects require further clarification to improve the interpretation of the research and its visibility in the field.

Strengths:

This study identifies the biological meaning of TRPγ in promoting lipolysis during starvation, advancing our knowledge about TRP channels and the neural mechanisms to combat metabolic stress. Furthermore, this study demonstrates the potential of the TRP channel as a target to develop new therapeutic strategies for human metabolic disorders by showing that metformin and AMPK pathways are involved in its function in lipid metabolisms during starvation in Drosophila.

Weaknesses:

Some key results that might strengthen their conclusions were left out for discussion or careful explanation (see below). If the authors could improve the writing to address their findings and connect their findings with conclusions, the research would be much more appreciated and have a higher impact in the field.

Here, I listed the major issues and suggestions for the authors to improve their manuscript:

(1) Are the increased lipid droplet size and the upregulated total TAG level measured in the starved or sated mutant in Figure 1? This information might be crucial for readers to understand the physiological function of TRP in lipid metabolism. In other words, clarifying whether the upregulated lipid storage is observed only in the starved trp mutant will advance our knowledge of TRPγ. If the increase of total TAG level is only observed in the starved animals, TRP in the Dh44 neurons might serve as a sensor for the starvation state required to promote lipolysis in starvation conditions. On the other hand, if the total TAG level increases in both starved and sated animals, activation of Dh44 through TRPγ might be involved in the lipid metabolism process after food ingestion.

We measured total TAG level in Figure 1 and LD sizes in Figure 2 under sated condition. We inserted “under sated condition” to clarify it. lines 97 and 147-148.

Thanks for your suggestions.

(2) It is unclear how AMPK activation in Dh44 neurons reduces the total triacylglycerol (TAG) levels in the animals (Figure 3G). As AMPK is activated in response to metabolic stress, the result in Figure 3G might suggest that Dh44 neurons sense metabolic stress through AMPK activation to promote lipolysis in other tissues. Do Dh44 neurons become more active during starvation? Is activation of Dh44 neurons sufficient to activate AMPK in the Dh44 neurons without starvation? Is activation of AMPK in the Dh44 neurons required for Dh44 release and lipolysis during starvation? These answers would provide more insights into the conclusion in Lines 192-193.

In our previous study, we demonstrated that trpγ mutants exhibited lower levels of glucose, trehalose and glycogen level (Dhakal et al. 2022), and in the current study, we observed excessive lipid storage in the trpγ mutant, indicating imbalanced energy homeostasis. Given the established role of AMPK in maintaining energy balance (Marzano et. al., 2021, Lin et al 2021), we employed the activated form of AMPK (UAS-AMPK^TD) in our experiments. Our result showed that expression of activated AMPK in Dh44 neurons led to a reduction in total TAG levels, suggesting that AMPK activation in these neurons can promote lipolysis even in the absence of starvation. Regarding the activation of Dh44 neurons, Dus et al in 2015 reported that Dh44 cells in the brain are activated by nutritive sugars especially in starvation conditions. In addition, another report showed a role of Dh44 neuron in regulating starvation induced sleep suppression (Oh et. al., 2023) which may imply that these neurons become more active under starved conditions. We did not directly assess whether Dh44 neuron activity increases during starvation or whether AMPK activation in these neurons is required for DH44 release and subsequent lipolysis, our finding support the notion that AMPK activation in Dh44 neuron is sufficient to reduce TAG levels, potentially by metabolic stress response typically observed during starvation. We explained it like the following: “Dh44 neurons regulate starvation-induced sleep suppression (Oh et. al., 2023), which implies that these neurons become more active under starved conditions.” lines 190-191.

(3) It is unclear how the lipolytic gene brummer is further downregulated in the trpγ mutant during starvation while brummer is upregulated in the control group (Figure 6A). This result implies that the trpγ mutant was able to sense the starvation state but responded abnormally by inhibiting the lipolytic process rather than promoting lipolysis, which makes it more susceptible to starvation (Figure 3B).

Thanks for your suggestions. We explained it like the following: “The data indicates that the trpg mutant can sense the starvation state but responds abnormally by suppressing lipolysis instead of activating it. This dysregulated lipolytic response likely increases the mutant's vulnerability to starvation, as it cannot effectively mobilize lipid stores for energy during periods of nutrient deprivation.” lines 251-254.

(4) There is an inconsistency of total TAG levels and the lipid droplet size observed in the Dh44 mutant but not in the Dh44-R2 mutant (Figures 7A and 7F). This inconsistency raises a possibility that the signaling pathway from Dh44 release to its receptor Dh44-R2 only accounts for part of the lipid metabolic process under starvation. Adding discussion to address this inconsistency may be helpful for readers to appreciate the finding.

Thanks for your suggestion. We included the following in the Discussion: “There is an inconsistency of total TAG levels and the LD size observed in the Dh44 mutant. This inconsistency raises a possibility that the signaling pathway from DH44 release to its receptor DH44R2 only accounts for part of the lipid metabolic process under starvation. While Dh44 mutant flies displayed normal internal TAG levels, Dh44R2 mutant flies exhibited elevated TAG levels. This suggested that the lipolysis phenotype could be facilitated by a neuropeptide other than DH44. Alternatively, a DH44 neuropeptide-independent pathway could mediate the lipolysis.” lines 429-436.

Reviewer #2 (Public Review):

Summary:

In this paper, the function of trpγ in lipid metabolism was investigated. The authors found that lipid accumulation levels were increased in trpγ mutants and remained high during starvation; the increased TAG levels in trpγ mutants were restored by the expression of active AMPK in DH44 neurons and oral administration of the anti-diabetic drug metformin. Furthermore, oral administration of lipase, TAG, and free fatty acids effectively restored the survival of trpγ mutants under starvation conditions. These results indicate that TRPv plays an important role in the maintenance of systemic lipid levels through the proper expression of lipase. Furthermore, authors have shown that this function is mediated by DH44R2. This study provides an interesting finding in that the neuropeptide DH44 released from the brain regulates lipid metabolism through a brain-gut axis, acting on the receptor DH44R2 presumably expressed in gut cells.

Strengths:

Using Drosophila genetics, careful analysis of which cells express trpγ regulates lipid metabolism is performed in this study. The study supports its conclusions from various angles, including not only TAG levels, but also fat droplet staining and survival rate under starved conditions, and oral administration of substances involved in lipid metabolism.

Weaknesses:

Lipid metabolism in the gut of DH44R2-expressing cells should be investigated for a better understanding of the mechanism. Fat accumulation in the gut is not mechanistically linked with fat accumulation in the fat body. The function of lipase in the gut (esp. R2 region) should be addressed, e.g. by manipulating gut-lipases such as magro or Lip3 in the gut in the contest of trpγ mutant. Also, it is not clarified which cell types in the gut DH44R2 is expressed. The study also mentioned only in the text that bmm expression in the gut cannot restore lipid droplet enlargement in the fat body, but this result might be presented as a figure.

We appreciate the reviewer’s insightful suggestions. Unfortunately, due to the unviability of the reagent (UAS-Lip3), we were unable to manipulate gut lipase in trpy mutants as proposed. However, we additionally performed immunostaining to examine the co-expression of trpγ and Dh44R2 in the gut, and our results indicate that both trpγ and Dh44R2 are co-expressed in the R2 region of the gut (Figure 7O and P). Furthermore, we have updated our figures to address the point that bmm expression in the gut does not restore lipid droplet enlargement in the fat body, with the revised version (Figure 5I and J).

Reviewer #3 (Public Review):

In this manuscript, the authors demonstrated the significance of the TRPγ channel in regulating internal TAG levels. They found high TAG levels in TRPγ mutant, which was ascribed to a deficit in the lipolysis process due to the downregulation of brummer (bmm). It was notable that the expression of TRPγ in DH44+ PI neurons, but not dILP2+ neurons, in the brain restored the internal TAG levels and that the knockdown of TRPγ in DH44+ PI neurons resulted in an increase in TAG levels. These results suggested a non-cell autonomous effect of Dh44+PI neurons. Additionally, the expression of the TRPγ channel in Dh44 R2-expressing cells restored the internal TAG levels. The authors, however, did not provide an explanation of how TRPγ might function in both presynaptic and postsynaptic cells in the non-cell autonomous manner to regulate the TAG storage. The authors further determined the effect of TRPγ mutation on the size of lipid droplets (LD) and the lifespan and found that TRPγ mutation caused an increase in the size of LD and a decrease in the lifespan, which were reverted by feeding lipase and metformin. These were creative endeavors, I thought. The finding that DH44+ PI neurons have non-cell autonomous functions in regulating bodily metabolism (mainly sugar/lipid) in addition to directing sugar nutrient sensing and consumption is likely correct, but the paper has many loose ends. I would like to see a revision that includes more experiments to tighten up the findings and appropriate interpretations of the results.

(1) The authors need to provide interpretations or speculations as to how DH44+ PI neurons have non-cell autonomous functions in regulating the internal TAG stores, and how both presynaptic DH44 neurons and postsynaptic DH44 R2 neurons require TRPγ for lipid homeostasis.

In Discussion, we had mentioned our previous finding. “ We previously proposed that TRPg holds DH44 neurons in a state of afterdepolarization, thus reducing firing rates by inactivating voltage-gated Na+ channels (Dhakal et al., 2022). At the physiological level, this induces the consistent release of DH44 and depletion of DH44 stores, resulting in nutrient utilization and storage malfunctions.”

We also included the following: “TRPg in DH44 neurons may influence the release of metabolic signals or hormones that act on postsynaptic DH44R2 cells. These postsynaptic cells could, in turn, modulate lipid storage and metabolism in a non-cell autonomous manner. However, the mechanism by which TRPg functions in DH44R2 cells remains unclear. One possible explanation is that TRPg in the gut may be activated by stretch or osmolarity (Akitake et al. 2015).” lines 439-440.

This interaction between presynaptic and postsynaptic cells may ensure a coordinated response to metabolic changes and maintain lipid homeostasis. Thus, both Dh44-expressing and Dh44-R2-expressing cells are crucial for the proper functioning of TRPγ in regulating internal TAG levels and lipid storage.

(2) The expression of TRPγ solely in DH44 R2 neurons of TRPγ mutant flies restored the TAG phenotype, suggesting an important function mediated by TRPγ in DH44 R2 neurons. However, the authors did not document the endogenous expression of TRPγ in the DH44R2+ gut cells. This needs to be shown.

We appreciate the reviewer’s suggestion. To address this, we performed immunostaining to examine the expression of TRPγ in the DH44R2+ gut cells. Our results, as shown in Figure 7 O and P, confirm that TRPγ is co-expressed in the Dh44R2+ cells in the gut. We also found that Dh44R2 is expressed in the brain as well. We documented this part like the following: “Given that Dh44R2 is predominantly expressed in the intestine, we performed immunostaining to examine whether Dh44R2 co-localizes with trpg in gut cells. Our results confirmed that Dh44R2 and trpg are co-expressed in intestinal cells (Figure 7O and P). Additionally, we analyzed Dh44R2 expression in the brain and found that two Dh44R2-expressing cells are co-localized with Dh44-expressing cells in the PI region (Figure 7Q). To further delineate whether Dh44R2-mediated fat utilization is specific to the brain, gut, or fat body, we knocked down Dh44R2^RNAi using Dh44-GAL4, myo1A-GAL4, and cg-GAL4, respectively (Figure 7–figure supplement 1E). Notably, knockdown of Dh44R2 with Myo1A-GAL4 resulted in elevated TAG levels, indicating that DH44R2 activity in lipid metabolism is specific to the gut.” lines 375-384.

(3) While Dh44 mutant flies displayed normal internal TAG levels, Dh44R2 mutant flies exhibited elevated TAG levels (Figure 7A). This suggested that the lipolysis phenotype could be facilitated by a neuropeptide other than Dh44. Alternatively, a Dh44 neuropeptide-independent pathway could mediate the lipolysis. In either case, an additional result is needed to substantiate either one of the hypotheses.

The Dh44 mutant flies exhibited normal TAG levels, whereas Dh44R2 mutant flies showed elevated TAG levels. However, when we examined the lipid droplets in the fat body, both Dh44 mutant and Dh44R2 mutant flies displayed larger lipid droplets, indicating a disruption in lipid metabolism. Additionally, we assessed starvation survival time and found that both Dh44 and Dh44R2 mutant flies exhibited reduced survival under starvation conditions compared to controls. Supplementation with lipase (Figure 7–figure supplement 1A), glycerol (Figure 7–figure supplement 1B), hexanoic acid (Figure 7–figure supplement 1C), and mixed TAGs (Figure 7–figure supplement 1D) improved starvation survival time, further supporting that the lipid metabolism pathway was impaired in both mutants. These observations highlight the role of Dh44 in regulating lipolysis. We included related Discussion: “There is an inconsistency of total TAG levels and the LD size observed in the Dh44 mutant. This inconsistency raises a possibility that the signaling pathway from DH44 release to its receptor DH44R2 only accounts for part of the lipid metabolic process under starvation. While Dh44 mutant flies displayed normal internal TAG levels, Dh44R2 mutant flies exhibited elevated TAG levels. This suggested that the lipolysis phenotype could be facilitated by a neuropeptide other than DH44. Alternatively, a DH44 neuropeptide-independent pathway could mediate the lipolysis.” lines 429-436.

(4) While the authors observed an increased area of fat body lipid droplets (LD) in Dh44 mutant flies (Figure 7F), they did not specify the particular region of the fat body chosen for measuring the LD area.

We have chosen the 2-3 segment in the abdomen for all fat body images, which we already mentioned in Nile red staining in the Method section line 630-631.

(5) The LD area only accounts for TAG levels in the fat body, whereas TAG can be found in many other body parts, including the R2 area as demonstrated in Figure 5A-D using Nile red staining. As such, measuring the total internal TAG levels would provide a more accurate representation of TAG levels than the average fat body LD area.

We have measured total internal TAG level in whole body throughout the experiments (Figure 1F, 2C, 2E, 3C, 3G, 4A, 4B, 7A, 7I, and many Supplementary Figures) except bmm expression using GAL4/UAS system. Now we include this new data in Figure 5–figure supplement 1) which is the same conclusion with LD analysis.

(6) In Figure 5F-I, the authors should perform the similar experiment with Dh44, Dh44R1, and Dh44R2 mutant flies.

We did the experiments with Dh44, Dh44R1, and Dh44R2 mutant flies and we found that Dh44 and Dh44R2 mutant flies showed reduced starvation survival time than control and which was increased after supplementation of lipase, glycerol, hexanoic acid and TAG (Figure 7– figure supplement 1A–D). lines 361-372.

(7) The representative image in Figure 6B does not correspond to the GFP quantification results shown in Figure 6C. In trpr1;bmm::GFP flies, the GFP signal appears stronger in starved conditions than in satiated conditions.

We updated it with new images. We quantified GFP intensity level using image J and found that GFP intensity level was significantly lower in starved condition in trpγ¹;bmm::GFP flies than sated condition.

(8) In Figure 6H-I, fat body-specific expression of bmm reversed the increased LD area in TRPγ mutants. The authors also showed that Dh44+PI neuron-specific expression of bmm yielded a similar result. The authors need to provide an interpretation as to how bmm acts in the fat body or DH44 neurons to regulate this.

We first inserted the following in results: “Furthermore, the expression of bmm in the fat body, as well as Dh44 neurons in the PI region, can promote lipolysis at the systemic level.” lines 276-277.

Additionally, we discussed it in the Discussion: “Brummer lipase is essential for regulating lipid levels in the insect fat body by mediating lipid mobilization and energy homeostasis. In Nilaparvata lugens, it facilitates triglyceride breakdown (Lu et al., 2018), while studies in Drosophila show that reduced Brummer lipase expression decreases fatty acids and increases diacylglycerol levels, highlighting its role in lipid metabolism (Nazario-Yepiz et al., 2021). Here, we additionally demonstrate that bmm expression in DH44 neurons within the PI region can systemically regulate TAG levels. Cell signaling or energy status in DH44 neurons may contribute to hormonal release that targets organs such as the fat body.” lines 451-459.

(9) The authors should explain why the DH44 R1 mutant did not represent similar results as the wild type.

We added “In addition, bmm levels in Dh44R1^Mi under starved condition did not increase as significantly as in the control. This suggests a unique role of DH44 and its receptors in regulating lipid metabolism and response to nutritional status in Drosophila.” lines 358-360.

(10) It would be good to have a schematic that represents the working model proposed in this manuscript.

We updated the schematic model in revised version (Figure 8).

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

This paper characterized the function of trpγ in Dh44-expressing PI neurons for lipid metabolism and lipolysis induced by prolonged starvation. The authors applied a series of lipolytic genetic manipulation and lipid/lipid metabolism supplements to rescue the trpγ deficits in lipolysis: the expression of active AMPK in the DH44-expressing PI neurons or brummer, a lipolytic gene, in the trpγ-expressing cells, and oral administration of the anti-diabetic drug metformin, lipase, TAG and free fatty acids. Despite this exhaustive characterization of the defective lipolysis in the trpγ mutants, there remain puzzles in inconsistent defects of Dh44 and DH44R2 in the total TAG levels and in the expression and functions of the receptor in the gut. Clarification of these points and other issues raised by the reviewers should improve the mechanisms of lipid metabolism through Dh44 signalling.

Reviewer #1 (Recommendations For The Authors):

(1) It might be worth introducing Dh44 in the introduction section as it is unclear to readers how the authors hypothesized the site-of-action of TRPγ in Dh44 neurons for lipid metabolism after reading the introduction.

We introduced the following: “We found that TRPg expression in Dh44 neuroendocrine cells in the brain is critical for maintaining normal carbohydrate levels in tissues (Dhakal et al. 2022). Building on this, we hypothesized that TRPg in Dh44 cells also regulates lipid and protein homeostasis.” lines 69-71.

(2) Providing a summary model in the end to integrate the present findings and their previous publication about TRPγ functions in Drosophila sugar selection would greatly help readers understand and appreciate the general role of TRPγ in balancing energy homeostasis.

We made a schematic model in Figure 8.

(3) Swapping the order of Figures 5 and 6 might be a better way to tell the story without logic gaps. The results addressing the mechanisms of metformin and TRPγ in promoting lipolysis under starvation are interrupted by the lipid storage data in the R2 cells in the current Figure 5A-5E. In addition, presenting Figure 5A-5E before or together with Figure 7 will help readers appreciate the expression of Dh44-R2 and its function in regulating lipid metabolism in Figure 7.

We did.

(4) It might be misleading to use the word "sated" for the condition of 5-hour mild starvation. The word "mild starvation" or the equivalents might be a better word choice.

We appreciate the reviewer’s concern. As hemolymph sugar level does not drop down significantly in 5 hr starvation, the previous papers (Dus et al 2015, Dhakal et al 2022) indicated it as sated condition. To use the word consistently, we prefer using “sated” instead of “mild starvation”.

(5) It is unclear what the white arrows are pointing at in Figures 7O and 7P. Some of those seem to be non-specific signals, so it is hard to connect the figure to the conclusion in Lines 351-353. It would be helpful to add some explanations to help readers interpret Figures 7O and 7P.

In the previous version, Figure 7O and 7P white arrows represented the expression of Dh44R2 in the SEZ region of the brain and R2 region of the gut. In revised version, to make clear, we performed additional immunostaining for the co-expression of trpγ and Dh44R2 in the gut. We found that trpγ and Dh44R2 co-expressed at the R2 region of the gut specifically (Figure 7O and P). Similarly, we found that two cells of Dh44R2 co-expressed in Dh44 cells in the PI region of the brain (now Figure 7Q). We updated this part. lines 375-380.

(6) The figure legend for the (G) panel in Figure 2-figure Supplement 1 was mislabeled as (F).

We corrected it.

(7) In Line 85, the authors might want to write "… among these mutants, only trpγ mutant displayed reduced carbohydrate levels, suggesting …". Please confirm the information for the sentence. lines 87-88.

We clarified it.

Reviewer #2 (Recommendations For The Authors):

(1) The trpγ[G4] would be difficult for non-Drosophila researchers to understand; it would be better to use trpγ-Gal4.

We got the mutant line from Dr. Craig Montell who named it. We explained it like the following in the main text: “controlled by GAL4 knocked into the trpg locus (trpg^G4 flies; +)” line 109.

(2) The arrows in Figures 7O and 7P need to be explained in the figure legends.

We did.

Reviewer #3 (Recommendations For The Authors):

(11) Lines 95-96 should have a reference.

We did.

(12) Lines 129-130: It should read "TRPγ expressed in DH44 cells is sufficient for the regulation of lipid levels."

We changed it as suggested.

(13) Figure 5E needs to be repeated with more trials.

We increased the n numbers. Previously (Figure 5E) we included area of 10 LDs from 3 samples, and in revised figure (Figure 6I) we have included 28 LDs from 10 samples.

(14) Figures 5F-I, bold lines are not too visible and therefore, dotted lines could be used.

We changed it as suggested.

(15) Line 356: It is not true that D-trehalose or D-fructose is commonly detected by DH44 neurons. These sugars at concentrations much higher than the physiological concentration range stimulate DH44 neurons (see Dus et al., 2015).

We removed it.

(16) Lines 362-363: It should read "Expression of TRPγ in DH44 neurons was necessary and sufficient to regulate the carbohydrate and lipid levels.".

We changed it.

(17) Lines 369-370: The authors need to consider removing the possible role of CRF in regulating lipid homeostasis. It could be considered to be far-fetched.

We removed it.

(18) Line 407-408: the sentence "Nevertheless, it is also known that DH44 neurons mediate the influence of dietary amino acids on promoting food intakes in flies (37)" needs to be removed. They used amino acid concentrations that were far greater than the physiological levels observed in the internal milieu of flies. Still, many laboratories cannot reproduce the result of using the high AA concentrations.

We removed it.

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Reviewer #1 (Public review):

Summary:

Inhibitory hM4Di and excitatory hM3Dq DREADDs are currently the most commonly utilized chemogenetic tools in the field of nonhuman primate research, but there is a lack of available information regarding the temporal aspects of virally-mediated DREADD expression and function. Nagai et al. investigated the longitudinal expression and efficacy of DREADDs to modulate neuronal activity in the macaque model. The authors demonstrate that both hM4Di and hM3Dq DREADDs reach peak expression levels after approximately 60 days and are stably expressed for a period of at least 1.5 years in the macaque brain. During this period, DREADDs effectively modulated neuronal activity, as evidenced by a variety of measures, including behavioural testing, functional imaging, and/or electrophysiological recording. Notably, some of the data suggest that DREADD expression may decline after two years. This is a novel finding and has important implications for the utilization of this technology for long-term studies, as well as its potential therapeutic applications. Lastly, the authors highlight that peak DREADD expression may be significantly influenced by the choice of viral titer and the expressed protein tag, emphasizing the importance of careful design and selection of viral constructs for neuroscientific research. This study represents a critical step in the field of chemogenetics, setting the scene for future development and optimization of this technology.

Strengths:

The longitudinal approach of this study provides important preliminary insights into the long-term utility of chemogenetics, which has not yet been thoroughly explored.

The data presented are novel and inclusive, relying on well-established in vivo imaging methods, as well as behavioral and immunohistochemical techniques. The conclusions made by the authors are generally supported by a combination of these techniques. In particular, the utilization of in vivo imaging as a non-invasive method is translationally relevant and likely to make an impact in the field of chemogenetics, such that other researchers may adopt this method of longitudinal assessment in their own experiments. Rigorous standards have been applied to the datasets, and the appropriate controls have been included where possible.

The number of macaque subjects (20) from which data was available is also notable. Behavioral testing was performed in 11 subjects, FDG-PET in 5, electrophysiology in 1, and [11C]DCZ-PET in 15. This is an impressive accumulation of work that will surely be appreciated by the growing community of researchers using chemogenetics in nonhuman primates.

The implication that chemogenetic effects can be maintained for up to 1.5-2 years, followed by a gradual decline beyond this period, is an important development in knowledge. The limited duration of DREADD expression may present an obstacle in the translation of chemogenetic technology as a potential therapeutic tool, and it will be of interest for researchers to explore whether this limitation can be overcome. This study therefore represents a key starting point upon which future research can build.

Weaknesses:

Overall, the conclusions of the paper are mostly supported by the data but may be overstated in some cases, and some details are also missing or not easily recognizable within the figures. The provision of additional information and analyses would be valuable to the reader and may even benefit the authors' interpretation of the data.

The conclusion that DREADD expression gradually decreases after 1.5-2 years is only based on a select few of the subjects assessed; in Figure 2, it appears that only 3 hM4Di cases and 2 hM3Dq cases are assessed after the 2-year timepoint. The observed decline appears consistent within the hM4Di cases, but not for the hM3Dq cases (see Figure 2C: the AAV2.1-hSyn-hM3Dq-IRES-AcGFP line is increasing after 2 years.)

Given that individual differences may affect expression levels, it would be helpful to see additional labels on the graphs (or in the legends) indicating which subject and which region are being represented for each line and/or data point in Figure 1C, 2B, 2C, 5A, and 5B. Alternatively, for Figures 5A and B, an accompanying table listing this information would be sufficient.

While the authors comment on several factors that may influence peak expression levels, including serotype, promoter, titer, tag, and DREADD type, they do not comment on the volume of injection. The range in volume used per region in this study is between 2 and 54 microliters, with larger volumes typically (but not always) being used for cortical regions like the OFC and dlPFC, and smaller volumes for subcortical regions like the amygdala and putamen. This may weaken the claim that there is no significant relationship between peak expression level and brain region, as volume may be considered a confounding variable. Additionally, because of the possibility that larger volumes of viral vectors may be more likely to induce an immune response, which the authors suggest as a potential influence on transgene expression, not including volume as a factor of interest seems to be an oversight.

The authors conclude that vectors encoding co-expressed protein tags (such as HA) led to reduced peak expression levels, relative to vectors with an IRES-GFP sequence or with no such element at all. While interesting, this finding does not necessarily seem relevant for the efficacy of long-term expression and function, given that the authors show in Figures 1 and 2 that peak expression (as indicated by a change in binding potential relative to non-displaced radioligand, or ΔBPND) appears to taper off in all or most of the constructs assessed. The authors should take care to point out that the decline in peak expression should not be confused with the decline in longitudinal expression, as this is not clear in the discussion; i.e. the subheading, "Factors influencing DREADD expression," might be better written as, "Factors influencing peak DREADD expression," and subsequent wording in this section should specify that these particular data concern peak expression only.

Reviewer #3 (Public review):

Summary

This manuscript, from the developers of the novel DREADD-selective agonist DCZ (Nagai et al., 2020), utilizes a unique dataset where multiple PET scans in a large number of monkeys, including baseline scans before AAV injection, 30-120 days post-injection, and then periodically over the course of the prolonged experiments, were performed to access short- and long-term dynamics of DREADD expression in vivo, and to associate DREADD expression with the efficacy of manipulating the neuronal activity or behavior. The goal was to provide critical insights into the practicality and design of multi-year studies using chemogenetics and to elucidate factors affecting expression stability.

Strengths are systematic quantitative assessment of the effects of both excitatory and inhibitory DREADDs, quantification of both the short-term and longer-term dynamics, a wide range of functional assessment approaches (behavior, electrophysiology, imaging), and assessment of factors affecting DREADD expression levels, such as serotype, promoter, titer (concentration), tag, and DREADD type.

Minor weaknesses are related to a few instances of suboptimal phrasing, and some room for improvement in time course visualization and quantification. These would be easily addressed in a revision.

These findings will undoubtedly have a very significant impact on the rapidly growing but still highly challenging field of primate chemogenetic manipulations. As such, the work represents an invaluable resource for the community.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Overall, the conclusions of the paper are mostly supported by the data but may be overstated in some cases, and some details are also missing or not easily recognizable within the figures. The provision of additional information and analyses would be valuable to the reader and may even benefit the authors' interpretation of the data.

We thank the reviewer for the thoughtful and constructive feedback. We are pleased that the reviewer found the overall conclusions of our paper to be well supported by the data, and we appreciate the suggestions for improving figure clarity and interpretive accuracy. Below we address each point raised:

The conclusion that DREADD expression gradually decreases after 1.5-2 years is only based on a select few of the subjects assessed; in Figure 2, it appears that only 3 hM4Di cases and 2 hM3Dq cases are assessed after the 2-year timepoint. The observed decline appears consistent within the hM4Di cases, but not for the hM3Dq cases (see Figure 2C: the AAV2.1-hSyn-hM3Dq-IRES-AcGFP line is increasing after 2 years.)

We agree that our interpretation should be stated more cautiously, given the limited number of cases assessed beyond the two-year timepoint. In the revised manuscript, we will clarify in both the Results and Discussion that the observed decline is based on a subset of animals. We will also state that while a consistent decline was observed in hM4Di-expressing monkeys, the trajectory for hM3Dq expression was more variable—with at least one case showing increased in signal beyond two years.

Given that individual differences may affect expression levels, it would be helpful to see additional labels on the graphs (or in the legends) indicating which subject and which region are being represented for each line and/or data point in Figure 1C, 2B, 2C, 5A, and 5B. Alternatively, for Figures 5A and B, an accompanying table listing this information would be sufficient.

We thank the reviewer for these helpful suggestions. In response, we will revise the relevant figures as noted in the “Recommendations for the authors”, including simplifying visual encodings and improving labeling. We will also provide a supplementary table listing the animal ID and brain regions for each data point shown in the graphs.

While the authors comment on several factors that may influence peak expression levels, including serotype, promoter, titer, tag, and DREADD type, they do not comment on the volume of injection. The range in volume used per region in this study is between 2 and 54 microliters, with larger volumes typically (but not always) being used for cortical regions like the OFC and dlPFC, and smaller volumes for subcortical regions like the amygdala and putamen. This may weaken the claim that there is no significant relationship between peak expression level and brain region, as volume may be considered a confounding variable. Additionally, because of the possibility that larger volumes of viral vectors may be more likely to induce an immune response, which the authors suggest as a potential influence on transgene expression, not including volume as a factor of interest seems to be an oversight.

We thank the reviewer for raising this important issue. We agree that injection volume is a potentially confounding variable. In response, we will conduct an exploratory analysis including volume as an additional factor. We will also expand the Discussion to highlight the need for future systematic evaluation of injection volume, especially in relation to immune responses or transduction efficiency in different brain regions.

The authors conclude that vectors encoding co-expressed protein tags (such as HA) led to reduced peak expression levels, relative to vectors with an IRES-GFP sequence or with no such element at all. While interesting, this finding does not necessarily seem relevant for the efficacy of long-term expression and function, given that the authors show in Figures 1 and 2 that peak expression (as indicated by a change in binding potential relative to non-displaced radioligand, or ΔBPND) appears to taper off in all or most of the constructs assessed. The authors should take care to point out that the decline in peak expression should not be confused with the decline in longitudinal expression, as this is not clear in the discussion; i.e. the subheading, "Factors influencing DREADD expression," might be better written as, "Factors influencing peak DREADD expression," and subsequent wording in this section should specify that these particular data concern peak expression only.

We appreciate this important clarification. In response, we will revise the title to “Factors influencing peak DREADD expression levels”, and we will specify that our analysis focused on peak ΔBP_ND values around 60 days post-injection. We will also explicitly distinguish these findings from the later-stage changes in expression seen in the longitudinal PET data in both the Results and Discussion sections.

Reviewer #2 (Public review):

Weaknesses

This study is a meta-analysis of several experiments performed in one lab. The good side is that it combined a large amount of data that might not have been published individually; the downside is that all things were not planned and equated, creating a lot of unexplained variances in the data. This was yet judiciously used by the authors, but one might think that planned and organized multicentric experiments would provide more information and help test more parameters, including some related to inter-individual variability, and particular genetic constructs.

We thank the reviewer for bringing this important point to our attention. We fully agree that the retrospective nature of our dataset, compiled from multiple studies conducted within a single laboratory, introduces variability due to differences in constructs, injection sites, and timelines. While this reflects the real-world constraints of long-term NHP research, we acknowledge the need for more standardized approaches. We will add a statement in the revised Discussion emphasizing that future multicenter and harmonized studies would be valuable for systematically examining specific parameters and inter-individual variability.

Reviewer #3 (Public review):

Minor weaknesses are related to a few instances of suboptimal phrasing, and some room for improvement in time course visualization and quantification. These would be easily addressed in a revision.

These findings will undoubtedly have a very significant impact on the rapidly growing but still highly challenging field of primate chemogenetic manipulations. As such, the work represents an invaluable resource for the community.

We thank the reviewer for the positive assessment of our manuscript and for the constructive suggestions noted in the “Recommendations for the authors”. In response, we will carefully review and revise the manuscript to improve visualization and quantification.

Author response:

The following is the authors’ response to the original reviews

Recommendations for the authors:

Reviewer #1 (Recommendations for the Authors):

The interpretation of results obtained with opto-Treacle (related to Figure 2C) may be expanded.

We thank the reviewer for their insightful comment regarding the interpretation of the results obtained with opto-Treacle. We understand the concern that the difference in the size of the condensates formed by opto-Treacle (Figure 2C) compared to Treacle-2S or other constructs may raise questions about the role of tetramerization in driving condensate formation, as 2S is known to tetramerize while FusionRed is not susceptible to multimerization.

To address this concern, we emphasize that we have demonstrated that overexpressed Treacle forms large condensates even in the absence of any fluorescent protein, as included in the revised manuscript. This observation supports the conclusion that Treacle's ability to form condensates is intrinsic and does not depend on the multimerization capacity of the fluorescent tag.

We believe that the observed difference in condensate size between opto-Treacle and Treacle-2S, Treacle-GFP, or untagged Treacle arises primarily from the time available for condensate assembly. Opto-Treacle condensation occurs rapidly, within approximately 10 seconds of blue light illumination, whereas Treacle-2S, Treacle-GFP, or untagged Treacle undergo condensation over the extended period of 24–48 hours of protein overexpression. This temporal difference likely accounts for the disparity in condensate size, as longer assembly times allow for larger and more mature condensates to form.

Given this reasoning, we consider it unnecessary to further emphasize the size differences in the main text of the article, as we believe the underlying explanation is clear and supported by the data. Nonetheless, we are open to incorporating additional clarifications if the reviewer deems it necessary.

The authors might reconsider referring to Treacle as a scaffold. Ultimately, the scaffold for the nucleolus is the rDNA with its bound proteins. Scaffold proteins, by definition, bind multiple protein partners and facilitate the formation of multiprotein complexes, a role not really attributed to homotypic LLPS.

We thank the reviewer for raising this important point regarding the use of the term "scaffold" in relation to Treacle. We fully acknowledge that rDNA, along with its associated protein complexes, serves as the primary structural scaffold for the nucleolus. However, we believe that referring to Treacle as a scaffold is appropriate and justified within the specific context of our study.

First, we emphasize that we describe Treacle as a scaffold specifically for nucleolar fibrillar centers (FCs), rather than for the nucleolus as a whole. This distinction is important, as our work focuses on the role of Treacle in organizing FC components, rather than the broader structural organization of the nucleolus.

Second, as the reviewer notes, scaffold proteins are defined by their ability to bind multiple protein partners and facilitate the formation of multiprotein complexes. Our findings demonstrate that Treacle's condensation properties promote the binding and retention of key rDNA-associated protein partners, including RPA194, UBF, and Fibrillarin, within the FCs. This activity aligns with the functional definition of a scaffold protein, as Treacle supports the spatial organization and cooperative interactions of FC components essential for rRNA transcription and processing. Therefore, while we appreciate the reviewer's observation regarding the central role of rDNA as a nucleolar scaffold, we maintain that the use of the term "scaffold" to describe Treacle's role in organizing FCs is consistent with its demonstrated functional properties.

If authors decide to add the "Ideas and Speculation" subsection to their Discussion, it may be interesting to discuss the following outstanding questions: does Treacle undergo homotypic or heterotypic LLPS? Does its overexpression favor homotypic interactions? How does it segregate FC and DFC compartments -by exclusion? How does phase-separated Treacle interact with other proteins?

We thank the reviewer for these insightful questions. While we believe that adding a dedicated "Ideas and Speculation" subsection would be redundant, we have already addressed the questions regarding Treacle’s homotypic or heterotypic LLPS and its interactions with other proteins in the revised "Discussion" section. Additionally, we have included a new section in the manuscript specifically focused on investigating the role of Treacle condensation in its interactions with protein partners, further expanding on these points.

In Materials and Methods, smFISH section -"probes were designed as described (Yao et al, 2019) and labeled with FITS on the 3'ends" - was it meant to say FITC (i.e. Fluorescein)?

We thank the reviewer for catching this error. This was indeed a typo, and we have corrected it to "FITC (i.e., Fluorescein)" in the revised text.

Reviewer #2 (Recommendations for the Authors):

Regarding recombinant Treacle, the main concern is that the authors may not be observing the condensation of Treacle itself. The quality of the purchased recombinant Treacle is unclear (this reviewer could not find Treacle listed on the vendor website despite using the supplied catalog number or vapors search terms). Furthermore, it is not clear if the condensates observed are Treacle or potentially the Dextran crowder. Only small percentages (>1%-5%) of either Dextran or PEG are needed to induce phase separation in two-component mixtures of these polymers. PEG may be in the Treacle storage butter. In addition to clarifying the State of recombinant Treacle, these concerns could be further assuaged by direct visualizing of Treacle forming condensates (via fluorescent n-terminal tagging) and filling in more of the phase space to observe the loss of condensates at a threshold concentration of Treacle. In general, the gold standard for establishing condensation of a given protein is mapping the full binodal phase diagram diagram of the protein. Understanding that protein is a limited resource, most groups simply map the lower concentration arm of the binodal, and this is sufficient to characterize a protein as having intrinsic condensation behavior. A similar mapping effort of Treacle would be welcomed. 

We thank the reviewer for their thoughtful comments and for highlighting concerns regarding the interpretation of our experiments with commercial recombinant Treacle. We recognize the importance of ensuring that the observed condensation properties are intrinsic to Treacle and not influenced by potential contaminants, storage buffer components, or tags on the protein.

To address these concerns, we have re-evaluated the condensation properties of Treacle using a recombinant fragment independently purified in our laboratory. Specifically, we expressed and purified a Treacle fragment (amino acids 291–426), which includes two S/E-rich low-complexity regions (LCRs) and two linker regions, in E. coli. The protein was expressed as a TEV-cleavable maltose-binding protein (MBP) fusion, purified under native conditions via amylose resin, and subjected to TEV cleavage. This was followed by ion-exchange chromatography and extensive dialysis to remove any remaining impurities. These additional steps ensured that the purified Treacle fragment was of high purity and free from confounding components, such as polyethylene glycol (PEG). We have included detailed descriptions of this protocol in the revised manuscript.

Using this purified Treacle fragment, we confirmed its intrinsic condensation behavior in vitro. In the presence of 5% PEG8000 as a crowding agent, the fragment formed liquid-like condensates that exhibited spherical morphology and dynamic fusion events, key hallmarks of liquid-liquid phase separation (LLPS). Additionally, we demonstrated that the condensation of this Treacle fragment was sensitive to changes in pH and salt concentration but unaffected by 1,6-hexanediol treatment, suggesting that the condensates are stabilized predominantly by electrostatic interactions (Fig. 4B of the revised manuscript). Importantly, these findings provide robust evidence that Treacle possesses intrinsic phase-separation properties. All results from the commercial Treacle protein used in the initial version of the manuscript have been replaced with data obtained using this independently purified recombinant fragment.

We undestand that the condensation behavior of the fragment may not fully capture the behavior of full-length Treacle. Nevertheless, the in vitro experiments provide valuable mechanistic insights into the biophysical properties of Treacle. Furthermore, as emphasized in the revised manuscript, our study primarily focuses on understanding the condensation and functional role of Treacle in a cellular context, where we observe its critical involvement in organizing nucleolar structure and regulating rRNA transcription. These cellular experiments highlight the biological relevance of Treacle’s condensation behavior.

With regard to mapping the binodal phase diagram of Treacle, we concur with the reviewer that such an effort would be ideal for a more comprehensive characterization of Treacle’s condensation properties. However, the limited availability of purified protein currently precludes a detailed mapping effort. Despite this limitation, we believe the qualitative assessments of Treacle’s condensation under varying conditions, now included in the revised manuscript, sufficiently demonstrate its intrinsic ability to phase-separate.

In conclusion, we are grateful for the reviewer’s feedback, which has allowed us to refine our methodology and strengthen the evidence supporting the intrinsic condensation properties of Treacle. We are confident that the revised manuscript provides a robust and thorough characterization of Treacle’s phase-separation behavior and its functional role in the cell, addressing the reviewer’s concerns. Thank you for your constructive recommendations, which have significantly improved the quality of our work.

Replacing 'liquid-phase' and 'liquid' with 'liquid-like' would make the language consistent with other papers in the field and more accurately reflect the degree of material state analysis carried out in the study.

We thank the reviewer for this insightful recommendation. In response to the suggestion, we have revised the manuscript to replace the terms "liquid-phase" and "liquid" with "liquid-like" throughout the text. This change ensures consistency with terminology commonly used in the field and more accurately reflects the degree of material state analysis performed in our study. We believe this adjustment improves the clarity and precision of our findings, aligning the manuscript with standard practices in the field. Thank you for helping us enhance the quality of the presentation.

The 'unclear' nature of the condensation behavior of the FC phase of the nucleolus is listed as a motivation for carrying out the study in the introduction; the authors could note here two recent papers that have investigated the nature of FC condensation: Jaberi-Lashkari et al. 2023 and King et al. 2024. The reviewer notes that while these were both pre-printed in late 2022, they were only recently published.

We thank the reviewer for bringing these recent studies to our attention. In response to the suggestion, we have cited the papers by Jaberi-Lashkari et al. (2023) and King et al. (2024) in both the introduction and discussion sections of the revised manuscript. These references are highly relevant to the context of our study and provide valuable insights into the condensation behavior of the FC phase of the nucleolus. We agree that incorporating these works strengthens the framing of our study and situates it more effectively within the broader field. Thank you for this constructive recommendation.

The statement that Treacle is "the main molecule present in the FC" is a substantial claim that does not need to be made to promote the author's case, nor is it well supported by the provided reference (Gal et al., 2022).

We thank the reviewer for pointing out this overstatement in our original manuscript. In response, we have revised the text to provide a more accurate and well-supported description. Specifically, we have replaced the claim that Treacle is "the main molecule present in the FC" with a statement highlighting its direct interactions with UBF and RNA Pol I, as well as its colocalization with these proteins within the FC. This revision ensures alignment with the provided references and more accurately reflects the current understanding of Treacle's role in the FC. We appreciate the reviewer's attention to this detail, which has helped us improve the clarity and accuracy of our manuscript.

The statement that "Treacle is one of the most intrinsically disordered proteins" is vague and unnecessarily grand. Treacle is a fully intrinsically disordered protein; these comprise 5% of the human proteome (Tsang et al. 2020), so Treacle is, indeed, unusual in that regard.

We thank the reviewer for highlighting the vague and unnecessarily broad nature of the original statement. In response, we have revised the text to provide a more precise and accurate description of Treacle's structural properties. Specifically, we replaced the claim that "Treacle is one of the most intrinsically disordered proteins" with the statement that "According to protein structure predictors (e.g., AlphaFold, IUPred2, PONDR, and FuzDrop), Treacle is a fully intrinsically disordered protein." This wording reflects the unique nature of Treacle while remaining scientifically accurate and supported by reliable computational predictions. We appreciate the reviewer's feedback, which has allowed us to improve the rigor and clarity of our manuscript.

A comment on the implications of the immobile pool of Treacle (which appears to be ~50% in WT and across a range of mutants) would be welcome. Additionally, the limitations of FRAP for interrogating material properties of condensed material in living systems are provided in Goetz and Mahamid, 2020. In this paper, the authors review instances where the ultrastructure of condensate is known and where FRAP data is available. They show that crystalline assemblies can recover faster than apparently liquid, spherical assemblies. A comment in the text about how these limitations apply to this study would be welcome.

We appreciate the reviewer’s insightful comments regarding the interpretation of the immobile pool of Treacle and the limitations of FRAP for characterizing material properties in living systems. As noted in our response to the public review, we believe the ~50% recovery rate after photobleaching observed in our experiments is best explained by the redistribution of Treacle molecules within the condensate, rather than significant exchange with the surrounding phase. This interpretation is strongly supported by the full- and half-FRAP analyses included in the revised manuscript, which demonstrated internal mixing dynamics within the condensates.

There appears to be a typo in the following sentence: "The highly positively charged CD serves as the nucleation center for RD but exhibits ambivalent phase properties, transitioning from LLPS to LSPS in the absence of rRNA." The LLPS to LSPS behavior was observed for mutants to the central domain (RD), not the c-terminal domain (CD).

Throughout the authors report single snapshots of representative cells and single line traces. Analysis of the key morphological feature across the population of cells would help the reader understand how widespread the observed phenotype is.

We thank the reviewer for raising this important point regarding the representation of morphological features across the cell population. To address this concern, we have included widefield micrographs of cell fields in the revised figures to provide a more comprehensive view of the phenotypes observed.

The statement that "The phase behavior of polymers is determined by interactions through associative motifs, referred to as stickers, separated by spacers, which are not the primary driving forces for phase separation" could be improved by pointing out that this is potentially incomplete for describing the kind of condensation that highly charged polymers undergo. The high charge and charge segregation of Treacle suggest that it is a blocky polyampholyte and that it condenses by coacervation. Models of associative polymers can be useful for describing coacervation, however, the driving forces for coacervation are less understood and have been proposed to include an entropic component (see Sathyavageeswaran et al. 2024, Sing and Perry 2020 and work from their groups as well as the Obermayer (Columbia) and Terrell (U. Chicago) Groups).

We thank the reviewer for highlighting this important aspect of the phase behavior of charged polymers and for suggesting relevant references. In response, we have revised the discussion section of the manuscript to include a more nuanced explanation of the condensation mechanisms for highly charged polymers such as Treacle. Specifically, we now describe Treacle as a blocky polyampholyte, suggesting that its condensation behavior may be driven by coacervation mechanisms.The relevant references have been added to the discussion section of the revised manuscript.

In addition to the above, the authors may consider citing two recent publications from the Pappu group (King et al. Cell 2024 and King et al. Nucleus 2024) that directly investigate the condensation potential of K-rich and E/D-rich' grammars' on nucleolar proteins and show that, like the authors, the K-rich region is essential for localization and is conserved across nucleolar proteins.

We thank the reviewer for bringing these relevant publications to our attention. The suggested references from the Pappu group (King et al., Cell 2024, and King et al., Nucleus 2024) have been added to the introduction and discussion sections of the revised manuscript, and their findings have been appropriately integrated into our analysis.

The authors could consider replacing the use of LLPS with a more generic term such as "condensation" or "biomolecular condensation." LLPS of polymers is a segregative transition driven by its incompatibility with the surrounding solvent. As indicated, Treacle is likely to be undergoing some form of coacervation (which is predominantly an associative tradition), which can be genetically described as condensation. See Pappu et al. 2023 for more details.

We thank the reviewer for their insightful suggestion. Following the reviewer's recommendation, we have replaced the term "LLPS" with "condensation" or "coacervation" throughout the manuscript, where appropriate. Additionally, we have referenced Pappu et al. (2023) and other to provide further context and clarity regarding the distinctions between these terms.

The authors cite Yao et al. 2019, but do not cite the follow-up study (Wu et al. 2021) or provide a statement on how the Chan group finds a role for the RGG domain of FBL in keeping the certain canonical markers of the FC and DFC de-mixed.

We thank the reviewer for pointing out these important references. The relevant citations, including Wu et al. (2021), have been added to the manuscript.

Reviewer #3 (Recommendations for the Authors):

The following comment is true but could be broadened to include examples of structured regions promoting biomolecular condensation. "In biological systems, phase separation is mainly a characteristic of multivalent or intrinsically disordered proteins (Banani et al, 2017; Shin & Brangwynne,2017; Uversky, 2019)."

We have expanded the statement as recommended by the reviewer: "In biological systems, phase separation is facilitated by a combination of multivalent interactions mediated by intrinsically disordered proteins and site-specific interactions that drive percolation."

Related to Figure 1.

The authors report Treacle-dependent EU incorporation (Figure 1D), but are there any changes more broadly to nucleolar number or size as a consequence? How do the authors interpret that the quantitative effect of AMD treatment is more extreme than Treacle depletion (Figure 1E).

We thank the reviewer for raising these important points. Regarding nucleolar number and morphology, we did not observe a change in the number of nucleoli upon Treacle depletion. However, nucleoli appeared more regularly rounded under these conditions, which we interpret as a consequence of the decreased rDNA transcription activity caused by Treacle depletion. A similar rounding of nucleoli is also observed upon actinomycin D (AMD) treatment, which is consistent with reduced transcriptional activity.

As for the more pronounced effect of AMD compared to Treacle depletion on EU incorporation, this can be explained by the fundamentally different mechanisms through which these conditions affect transcription. Treacle depletion reduces the local concentration of transcription factors at rDNA sites, thereby impairing transcription initiation and elongation to a certain extent. However, under Treacle depletion, RNA polymerase I still retains the ability to bind to the promoter and support a residual level of transcription. In contrast, AMD acts as a potent intercalator in GC-rich regions of rDNA, physically blocking the ability of RNA polymerase I to move along rDNA, resulting in near-complete cessation of rRNA synthesis.

Related to Figure 2.

The authors observe that AMD leads to coalescence of individual Treacle-2S+ bodies (e.g. Figure 2E) - does this suggest that ongoing rRNA transcription is required to prevent such events?

Thank you for your thoughtful question. Indeed, our observations strongly suggest that ongoing rRNA transcription is required to prevent the coalescence of Treacle-2S+ bodies, as observed upon AMD treatment. This interpretation aligns with the findings of Tetsuya Yamamoto et al., who demonstrated that nascent ribosomal RNA (pre-rRNA) acts as a surfactant to suppress the growth and fusion of fibrillar centers (FCs) in the nucleolus. Their work highlighted that nucleolar condensates formed via liquid-liquid phase separation (LLPS) tend to grow to minimize surface energy, provided sufficient components are available. However, the transcription of prerRNA stabilizes FCs by maintaining multiple microphases, preventing coalescence unless transcription is inhibited.

According to Yamamoto et al., nascent pre-rRNAs tethered to FC surfaces by RNA Polymerase I generate lateral pressure that counteracts interfacial tensions, effectively suppressing FC fusion. This activity is analogous to the surfactant properties of molecules in physical systems. When transcription is inhibited (e.g., by AMD), the loss of nascent rRNA allows condensates to coalesce, consistent with the behavior we observe.

We further propose that the AMD-induced coalescence of Treacle-2S+ bodies reflects the loss of this surfactant-like effect, as transcriptional activity ceases. This theory is also supported by the observation that Treacle condensates in the nucleoplasm, where rRNA transcription is absent, form larger structures. Collectively, these insights highlight the critical role of ongoing rRNA transcription in maintaining the structural integrity and dynamic organization of nucleolar substructures.

Related to Figure 3.

In the figure panels B-H the DAPI signal in gray obscures the Treacle localization, especially in Figure 3H. A non-merged image for each of these examples for the Treacle localization would be very helpful.

We thank the reviewer for this observation. To address this, we have included wide-field images without the DAPI overlay for the deletion mutant lacking the 1121-1488 region. These are now presented in Supplementary Figure S5G of the revised manuscript.

Related to Figure 5.

Only a single representative nucleus is shown in the PLA analysis presented in Figure 5B.

Quantification to assess the robustness of this response with the addition of VP16 is needed. The authors use ChIP and immunocytochemistry as orthogonal methods but it would be best to therefore show both for each manipulation that is performed - the immunostaining of TOPBP1 in the Treacle KD cells in S5A should be in the main Figure 5 to complement transformation of constructs as in Figure 5D.

We appreciate the reviewer’s comment. To address this, we performed a quantitative analysis of PLA fluorescence signals in control and etoposide-treated cells, and the results are now presented in Supplementary Figure S8C. Additionally, as recommended, we have transferred the results of the immunocytochemistry of TOPBP1 in Treacle KD and Treacle KN cells to the main figure, now included as Figures 7D-E in the revised manuscript.

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Author response:

The following is the authors’ response to the original reviews

Reviewer #3 (Recommendations for the authors):

Major concerns:

P.6, lines 223-224: The sentence sounds like the authors produced all the OVGP1s by themselves in their laboratories, which is not completely true. The recombinant human and mouse OVGP1s were purchased from OriGene. It is suggested that the authors should state and explain clearly here which OVGP1 is produced by their laboratories and that recombinant human and mouse OVGP1s were obtained and purchased from Origene.

It is already clearly included in the M&M.

P6, lines 227-229: The authors stated that "Western blots of the three OVGP1recombinants indicated expected sizes based on those of the proteins: 75 kDa for human and murine OVGP1 and around 60 kDa for bovine OVGP1 (Fig. 4B and S6)." I pointed out in my last review report that the size of the recombinant human OVGP1shown by the authors in their manuscript is not in agreement with what has been published previously in literature regarding the molecular weight of native human OVGP1 as well as that of recombinant human OVGP1. The authors did not address the above concern adequately. In fact, recombinant human OVGP1 has been produced a few years ago (Reproduction (2016) 152:561-573) and it has been previously demonstrated that a single protein band of approximately 110-130 kDa was detected for both native human OVGP1 (see Microscopy Research and Technique (1995) 32:57-69) and recombinant human OVGP1 (Reproduction (2016) 152:561-573; Carbohydrate Research (2012) 358:47-55) using antibodies specific for human OVGP1. Molecular weight of the protein core or polypeptide of human OVGP1 is approximately 75 kDa, but the glycosylated form of native human OVGP1 and recombinant human OVGP1 is approximately 110-130 kDa. Therefore, the authors might have been using the recombinant core protein of human OVGP1 instead of the fully glycosylated recombinant OVGP1 in their study. The same concern also applies to the commercially obtained mouse recombinant OVGP1 used by the authors in their study. I would also like to mention that the mature and fully glycosylated OVGP1s in mammals vary in molecular weight (90-95 kDa in domestic animals; 110-150 kDa in primates; 160-350 kDa in rodents). Again, the 75kDa of mouse OVGP1 detected by the authors could be the core protein or polypeptide of mouse OVGP1 instead of the fully glycosylated mouse OVGP1.

In our study, as previously mentioned, we included commercially available recombinant proteins from Origene for human and murine OVGP1, which are produced in mammalian cells, and we also produced and purified bovine OVGP1 in mammalian cells. Therefore, these proteins should be properly glycosylated. Moreover, we performed Western blot assays favouring the blotting of higher molecular weight proteins, ensuring the optimal conditions for the assay. Additionally, we tested the size of OVGP1 from murine and bovine oviductal fluids on the same blot. During oestrus, the size of OVGP1 from oviductal fluids matches that of the recombinant proteins, and this band is downregulated during anoestrus, confirming the proper size of recombinant protein.

P.7, lines 236 and 237: Please provide a figure or source to support the statement "...as confirmed by proteomics of the bands along with PEAKS Studio v11.5 search engine peptide identification software."

It is included in the text the amount of unique peptides obtained by Proteomics for OVGP1 identification over all protein groups identified.

P.7, lines 243 to 245: The statement "...using rabbit polyclonal antibody to human OVGP1 for bOVGP1 and endogenous OVGP1, and mouse monoclonal antibody against Flag (DDK)-tag for hOVGP1 and mOVGP1." is confusing and might be inaccurate. First of all, I wondered why the authors did not use an antibody against bovine OVGP1 for the recombinant bOVGP1 instead of using a rabbit polyclonal antibody to human OVGP1. Secondly, what does the "endogenous OVGP1" refer to in the statement? Thirdly, the authors in their study used the commercially available recombinant human OVGP1 and recombinant mouse OVGP1 purchased from Origene. Based on the data sheet provided by Origene, the tag used for both recombinant human OVGP1 and recombinant mouse is C-Myc/DDK-tag and not Flag-tag. Can the authors explain these discrepancies?

Firstly, for the recombinant protein of bOVGP1 we used the same antibody that we used in the Western blot for all the proteins and oviductal fluids because we do not have anti-His tag working for Immunofluorescence (the one we had only worked for Western blot) and neither we do not have any antibody against bovine OVGP1. In the case of human and murine since we had anti-Flag antibody that worked for Western blot and for immunofluorescence, we used this one. However, as has been shown in our figure and supplementary material, the antibody against human OVGP1 works properly for both techniques (Western blot and Immunofluorescence). Secondly, endogenous OVGP1 is referred to the OVGP1 present in the oviductal fluid. Thirdly, as you can see in the datasheet of the protein, the recombinant proteins purchased from Origene contains a c-myc tag (EQKLISEEDL) some amino acids and a ddk-tag (DYKDDDDK). The sequence of ddk is the same of Flag-tag (DYKDDDDK). Since the proteins have both tags we used the antibody against Flag (or ddk) epitope.

P12, lines 429-432: The newly added statement at the end of the Discussion saying "Additionally, future studies would be valuable to investigate whether incubating oocytes with oviductal fluid (or OVGP1) could reduce polyspermy in porcine IVF and whether ZPs could be leveraged to naturally enhance sperm selection in human ICSI" is very concerning and requires further attention. The statement reflects that the authors do not keep pace with and do not pay attention to what has been published in literature regarding porcine and human OVGP1s. In fact, porcine oviduct-specific glycoprotein (OVGP1) has already been reported to reduce the incidence of polyspermy in pig oocytes (Biology of Reproduction (2000) 63:242-250). Porcine oviductal fluid, used in porcine IVF, has also been found to exert a beneficial effect on oocytes by reducing the incidence of polyspermy without decreasing the penetration rate. (Theriogenology (2016) 86:495-502). Therefore, the studies deemed valuable by the authors to be investigated in the future have, in fact, already been carried out two decades ago by several other laboratories. I am surprised the authors were not aware of these published work in literature. All the above should have been incorporated in the Discussion.

This sentence is modified in the discussion and the references are included.

Furthermore, as mentioned earlier, recombinant human OVGP1 has also been produced (Reproduction (2016) 152:561-573), and recombinant human OVGP1 has been found to increase tyrosine phosphorylation of sperm proteins, a biochemical hallmark of sperm capacitation, and potentiate the subsequent acrosome reaction (Reproduction (2016) 152:561-573) as well as increase sperm-zona binding (Journal of Assisted Reproduction and Genetics (2019) 36:1363-1377). These earlier findings should be incorporated into the Discussion.

Thank you for your comment, but in this work we had not performed any experimental setting related to tyrosine phosphorylation and despite is a very interesting topic is not directly related to this work.

P.19, lines 678-683: Since the human and mouse recombinant oviductin proteins were purchased from Origene, the authors should be aware of the fact that these commercially available recombinant OVGP1s might not be fully glycosylated. While I appreciate the fact that the authors wanted to briefly describe how the human and mouse recombinant OVGP1s were prepared by the manufacturer, I strongly suggest that the authors should contact Origene, the manufacturer, for all information regarding the procedures for producing the human and mouse recombinant oviductin proteins. For example, the authors stated on lines 680-681 that "A sequence expressing FLAG-tagged epitope proteins (DYKDDDDK) was cloned into an expression vector." According to the data sheet provided by Origene, it appears that both human and recombinant oviductin proteins are C-Myc/DDK-tagged and not FLAG-tagged.

Thank you for your comment, as according to the sequence of Flag-tag it is matching with the sequence of the tag in the datasheet corresponding to DDK (this is in detail in previous comment). Besides, the protein is tagged also by C-Myc tag. Among both tags, the antibody selected to detect it was anti-Flag tag.

P.19, lines 692-697: The description of the primary and secondary antibodies used for detection of the various recombinant OVGP1s is also very confusing and not clearly presented. For example, it is mentioned here that "...membranes were...incubated with anti-OVGP1 rabbit monoclonal antibody for OVGP1,..". What specifically does "OVGP1" refer to here? The authors then stated that anti-Histamine Tag antibody was used to detect bOVGP1 and mOVGP1 and anti-Flag antibody was used to detect hOVGP1. As pointed out earlier, the human and mouse recombinant OVGP1s were produced using C-Myc/DDK tag and not His-tag or Flag-tag. Can the authors clarify these discrepancies?

We apologise for the complexity of the antibodies, we included in this paragraph the ones used to Western blot for both figures: anti- human OVGP1 was used for the principal figure that contains the three recombinant proteins and oviductal fluids; and the anti-Histidine and anti-Flag antibodies that are included in supplementary figure, specifically for recombinant bovine OVGP1 (Histidine tag) and for recombinant murine and human OVGP (DDK tag). A clarifying sentence has been included in the text.

P.31, lines 1143-1149: Figure 10 is not mentioned anywhere in the main text of the manuscript. Rewrite the second half of the sentence "...; being this specificity lost when OVGP1 is heterologous to the ZP (right diagram)." Which sounds awkward and grammatically not correct.

The figure is already mentioned in the text, thank you for your comment. The sentence is also corrected.

Other comments: P.1, the statement of "All authors contributed equally to this work" on line 14 can be deleted because detailed and specific contributions from each authors are listed in lines 1009-1017 on page 27.

Both authors contributed equally to this work, now is clear in authors contribution section.

P.2, lines 43 and 44: Do the authors mean "sperm-oocyte binding protein" instead of "sperm-oocyte fusion protein" in the sentence? "Fusion protein" is a protein composed of two or more domains encoded by different genes, or a hybrid molecule created by combining two different proteins for various purposes. I believe the term "fusion protein" is wrongly used in the sentence which should be rephrased with a proper term.

Done.

P2, line 73: Remove the comma after the word "Both".

Done.

P.5, line 179: "...mice ZP..." should be written as "...mouse ZP...".  

Done.

P.6, heading of 3rd paragraph on line 207: The term "binding" will be a better term than "fusion" used in the heading because the results do not actually show the fusion of the OVGP1 proteins with the ZP glycoprotein. Instead, binding of the OVGP1 proteins to the ZP occurred.

Done.

P.6, lines 215-217: Authors, please provide a reference or references to support the statement "Region A, corresponding to the amino acid end, shows high identity among monotremes, marsupials and placentals."

In the text was indicated a review (29) which includes the supporting idea of this statement for Figure 4. Moreover, we have included some if the references used for the description of the domains when performing the sequence alignment of Figure S5.

P.6, line 230 and line 233 on P.7: Authors, please be consistent in the use of either American English or British English. The word "oestrus" is British English whereas "estrus" is American English.

Done.

P.7, line 264: The word "sticking" used here means non-specific binding. I believe the author means specific binding here. If so, a more appropriate word should be used here instead of "sticking".

Done.

P.7, lines 267-269: This newly added sentence sounds very awkward and should be completely rewritten.

Done.

P.8, line 288: This reviewer finds it difficult to understand the meaning of the heading. The heading should be rephrased to bring out exactly what the authors want to say in well-written English.

Done.

P.8, line 290: The word "would" should be replaced by "could" in the sentence.

Done.

P.13, line 437: Authors, please provide the location of Sigma-Aldrich.

Done.

P.13, line 457: Here, the authors used "1800 rpm" to indicate the centrifugation speed but used the g-force elsewhere in the Materials and Methods. Please be consistent. The g-force is preferred.

Done.

P.14, lines 483-485: The procedure of sacrificing the cats should be provided in the Materials and Methods

Cats weren’t sacrificed they were vasectomized. It is now included in the text.

P.17, line 628: "...the ZPs were exposed or no exposed to..." should be written as "...the ZPs were either exposed or not exposed to...".

Done.

P.17, line 629: "...each groups were incubated with..." should be "...each group was incubated with...".

Done.

P.19, line 700: "As loading control, was used the primary antibody....." is not a complete sentence and it needs to be rewritten.

Done.

P.20, lines 744-754: For scanning electron microscopy and image processing, the procedures of prior treatment of the oocytes with and without oviductal fluid and OVGP1 should be included here.

Done.

P.21, line 756: It is stated here that "Two hundred isolated ZPs were treated with Clostridium perfringens neuraminidase....". However, it is not clear whether two hundred isolated ZPs of both porcine and murine ZPs were treated. Authors, please clarify.

We used 200 isolated ZPs of each specie, bovine and murine. It is classified in the text.

P.28, lines 1039 and 1040: The author only mentioned the use of bovine and murine sperm here. What about human sperm?

Done.

P.29, line 1076: "...in mammalian cells..." is very vague. Be specific what exactly the mammalian cells were.

Done.

P.29, line 1079: "Oviductal fluid from ovulated cows or anoestrus cows." is not a complete sentence and it needs to be rewritten.

Done.

according to https://stackoverflow.com/questions/9254189/whats-a-good-way-to-provide-versioned-rows-in-postgresql-how-to-query-them#comment131742383_37377481 and https://github.com/mlt/temporal_tables/releases/tag/v1.2.0

We should use https://github.com/xocolatl/periods instead

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

In this valuable study, García-Vázquez et al. provide solid evidence suggesting that G2 and S phases expressed protein 1 (GTSE1), is a previously unappreciated non-pocket substrate of cyclin D1-CDK4/6 kinases. To this end, this study holds a promise to significantly contribute to an improved understanding of the mechanisms underpinning cell cycle progression. Notwithstanding these clear strengths of the article, it was thought that the study may benefit from establishing the precise role of cyclin D1-CDK4/6 kinase-dependent GTSE1 phosphorylation in the context of cell cycle progression, …

We do not claim, as editors and reviewers appear to have interpreted, that GTSE1 is phosphorylated by cyclin D1-CDK4 in the G1 phase of the cell cycle under normal physiologic conditions.  Indeed, we agree with the existing literature indicating that in cells that do not express high levels of cyclin D1, GTSE1 is expressed predominantly during S and G2 phase (hence the name GTSE1, which stands for G-Two and S phases expressed protein 1) and is phosphorylated by mitotic cyclins in early mitosis.  Even during G1, when the levels of cyclin D1 peak, GTSE1 is not phosphorylated in normal cells.  This could be due to either a higher affinity between GTSE1 and mitotic cyclins as compared to D-type cyclins or to a higher concentration of mitotic cyclins compared to D-type cyclins.  In the current manuscript, we show that higher levels of cyclin D1 can drive the sustained phosphorylation of GTSE1 across all cell cycle points. To reach this conclusion, we do not rely only on the overexpression of exogenous cyclin D1. In fact, we observe similar effect when we deplete endogenous AMBRA1, resulting in the stabilization of endogenous cyclin D1 in all cell cycle phases (see Figure 2G and Figure supplement 3B).  As we had already mentioned in the Discussion section, we propose that GTSE1 is phosphorylated by CDK4 and CDK6 particularly in pathological states, such as cancers displaying overexpression of D-type cyclins (i.e., it is possible that the overexpression overcomes the lower affinity of the cyclin D-GTSE1 complex). In turn, phosphorylation of GTSE1 induces its stabilization, leading to increased levels that, as expected based on the existing literature, contribute to enhanced cell proliferation.  So, the role of the cyclin D1-CDK4/6 kinase-dependent GTSE1 phosphorylation is to stabilize GTSE1 independently of the cell cycle.  In sum, our study suggests that overexpression of cyclin D1, which is often observed in cancers cells beyond the G1 phase, induces phosphorylation of GTSE1 at all points in the cell cycle. 

… obtaining more direct evidence that cyclin D1-CDK4/6 kinase phosphorylate indicated sites on GTSE1 (e.g., S454) …

We show that treatment of cells with palbociclib completely abolished the effect of cyclin D1-CDK4 on the GTSE1 shift observed using Phos-tag gels (Figure 2H).  Moreover, mutagenesis analysis shows that S91, S262, and S724 are phosphorylated in a cyclin D1-CDK4-dependent manner (Figure 2F and Figure supplement 3A). Compared to wild-type GTSE1, a triple mutant (S91A/S262A/S724A) displayed loss of slower-migrating bands upon co-expression of cyclin D1-CDK4, suggesting diminished phosphorylation. Nevertheless, a residual slow-migrating band persisted, prompting further mutations of the triple GTSE1 mutant in S331 and S454 (individually), which do not have a CDK-phosphorylation consensus, but were identified in several published phospho-proteomics studies. From these two quadruple mutants, only the that containing the S454A mutation demonstrated a complete abrogation of any shift in phos-tagTM gels (Figure 2F). These studies suggest that four major sites (S91, S262, S454, and S724) are phosphorylated (either directly and/or indirectly) in a cyclin D1-CDK4-dependent manner.

… and mapping a degron in GTSE1 whose function may be blocked by cyclin D1-CDK4/6 kinase-dependent phosphorylation.

We show that stabilization or overexpression of cyclin D1, which is often observed in human cancers, promotes GTSE1 phosphorylation on S91, S262, S454, and S724, resulting in GTSE1 stabilization.  Similarly, a phospho-mimicking mutant with the 4 serine residues replaced with an aspartate at positions 91, 261, 454, and 724 display increased half-life. While we appreciate the editor’s suggestion and agree on these being interesting questions, we would like to respectfully point out that mapping the GTSE1 degron and understanding how it is affected by cyclin D1-CDK4/6-dependent phosphorylation is outside the scope of the current project and will require an extensive set of experiments and tools. Accordingly, the three reviewers did not ask to map the GTSE1 degron.  We plan on addressing these interesting questions as part of a follow-up study.

Reviewer #1 (public review):

Summary:

García-Vázquez et al. identify GTSE1 as a novel target of the cyclin D1-CDK4/6 kinases. The authors show that GTSE1 is phosphorylated at four distinct serine residues and that this phosphorylation stabilizes GTSE1 protein levels to promote proliferation.

Strengths:

The authors support their findings with several previously published results, including databases. In addition, the authors perform a wide range of experiments to support their findings.

Weaknesses:

I feel that important controls and considerations in the context of the cell cycle are missing. Cyclin D1 overexpression, Palbociclib treatment and apparently also AMBRA1 depletion can lead to major changes in cell cycle distribution, which could strongly influence many of the observed effects on the cell cycle protein GTSE1. It is therefore important that the authors assess such changes and normalize their results accordingly.

We have approached the question of GTSE1 phosphorylation to account for potential cell cycle effects from multiple angles: 

(i) We conducted in vitro experiments with purified, recombinant proteins and shown that GTSE1 is phosphorylated by cyclin D1-CDK4 in a cell-free system (Figure 2A-C). These experiments provide direct evidence of GTSE1 phosphorylation by cyclin D1-CDK4 without the influence of any other cell cycle effectors. 

(ii) We present data using synchronized AMBRA1 KO cells (new Figure 2G and Figure supplement 3B).  In agreement with what we had shown previously (Simoneschi et al., Nature 2021, PMC8875297), AMBRA1 KO cells progress faster in the cell cycle but they are still synchronized as shown, for example, by the mitotic phosphorylation of Histone H3, peaking at 32 hours after serum readdition like in parental cells. Under these conditions we observed that while phosphorylation of GTSE1 in parental cells is evident in the last two time points, AMBRA1 KO cells exhibited sustained phosphorylation of GTSE1 across all cell cycle phases.  This was evident enough when using Phos-tag gels as in the top panel of the old Figure 2G. We now re-run one the biological triplicates of the synchronized cells using higher concentration of Zn⁺²-Phos-tag reagent and lower voltage to allow better separation of the phosphorylated bands.  Under these conditions, GTSE1 phosphorylation is better appreciable (top panel of the new Figure 2G). This experiment provides evidence that high levels of cyclin D1 in AMBRA1 KO cells affect GTSE1 phosphorylation independently of the specific points in the cell cycle. 

(iii) The relative short half-life of GTSE1 (<4 hours) makes its levels sensitive to acute treatments such as Palbociclib or acute AMBRA1 depletion. The effects of these treatments on GTSE1 levels are measurable within a time frame too short to significantly affect cell cycle progression. For example, we used cells with fusion of endogenous AMBRA1 to a mini-Auxin Inducible Degron (mAID) at the N-terminus. This system allows for rapid and inducible degradation of AMBRA1 upon addition of auxin, thereby minimizing compensatory cellular rewiring. Again, we observed an increase in GTSE1 levels upon acute ablation of AMBRA1 (i.e., in 8 hours) (Figure 3B), when no significant effects on cell cycle distribution are observed (please see Simoneschi et al., Nature 2021, PMC8875297 and Rona et al., Mol. Cell 2024, PMC10997477).

Altogether, the above lines of evidence support our conclusion that GTSE1 is a target of cyclin D1-CDK4, independent of cell cycle effects.

In conclusion, we do not claim that GTSE1 is phosphorylated by cyclin D1-CDK4 in the G1 phase of the cell cycle under normal physiologic conditions.  Indeed, we agree with the existing literature indicating that in cells that do not express high levels of cyclin D1, GTSE1 is expressed predominantly during S and G2 phase (hence the name GTSE1, which stands for G-Two and S phases expressed protein 1) and is phosphorylated by mitotic cyclins in early mitosis.  Even during G1, when the levels of cyclin D1 peak, GTSE1 is not phosphorylated in normal cells. This could be due to either a higher affinity between GTSE1 and mitotic cyclins as compared to D-type cyclins or to a higher concentration of mitotic cyclins compared to D-type cyclins.  In the current manuscript, we show that higher levels of cyclin D1 can drive the sustained phosphorylation of GTSE1 across all cell cycle points. To reach this conclusion, we do not rely only on the overexpression of exogenous cyclin D1. In fact, we observe similar effect when we deplete endogenous AMBRA1, resulting in the stabilization of endogenous cyclin D1 in all cell cycle phases (see Figure 2G and Figure supplement 3B).  As we had already mentioned in the Discussion section of the original submission, we propose that GTSE1 is phosphorylated by CDK4 and CDK6 particularly in pathological states, such as cancers displaying overexpression of D-type cyclins (i.e., it is possible that the overexpression overcomes the lower affinity of the cyclin D1-GTSE1 complex). In turn, phosphorylation of GTSE1 induces its stabilization, leading to increased levels that, as expected based on the existing literature, contribute to enhanced cell proliferation.  In sum, our study suggests that overexpression of cyclin D1, which is often observed in cancers cells beyond the G1 phase, induces phosphorylation of GTSE1 at all points in the cell cycle.    

Reviewer #2 (public review):

Summary:

The manuscript by García-Vázquez et al identifies the G2 and S phases expressed protein 1(GTSE1) as a substrate of the CycD-CDK4/6 complex. CycD-CDK4/6 is a key regulator of the G1/S cell cycle restriction point, which commits cells to enter a new cell cycle. This kinase is also an important therapeutic cancer target by approved drugs including Palbocyclib. Identification of substrates of CycD-CDK4/6 can therefore provide insights into cell cycle regulation and the mechanism of action of cancer therapeutics. A previous study identified GTSE1 as a target of CycB-Cdk1 but this appears to be the first study to address the phosphorylation of the protein by Cdk4/6.

The authors identified GTSE1 by mining an existing proteomic dataset that is elevated in AMBRA1 knockout cells. The AMBRA1 complex normally targets D cyclins for degradation. From this list, they then identified proteins that contain a CDK4/6 consensus phosphorylation site and were responsive to treatment with Palbocyclib.

The authors show CycD-CDK4/6 overexpression induces a shift in GTSE1 on phostag gels that can be reversed by Palbocyclib. In vitro kinase assays also showed phosphorylation by CDK4. The phosphorylation sites were then identified by mutagenizing the predicted sites and phostag got to see which eliminated the shift.

The authors go on to show that phosphorylation of GTSE1 affects the steady state level of the protein. Moreover, they show that expression and phosphorylation of GTSE1 confer a growth advantage on tumor cells and correlate with poor prognosis in patients.

Strengths:

The biochemical and mutagenesis evidence presented convincingly show that the GTSE1 protein is indeed a target of the CycD-CDK4 kinase. The follow-up experiments begin to show that the phosphorylation state of the protein affects function and has an impact on patient outcomes.

Weaknesses:

It is not clear at which stage in the cell cycle GTSE1 is being phosphorylated and how this is affecting the cell cycle. Considering that the protein is also phosphorylated during mitosis by CycB-Cdk1, it is unclear which phosphorylation events may be regulating the protein.

Please see point (ii) and the last paragraph in the response to Reviewer #1.  Moreover, we show that, compared to the amino acids phosphorylated by cyclin D1-CDK4, cyclin B1-CDK1 phosphorylates GTSE1 on either additional residues or different sites (Figure 2H). We also show that expression of a phospho-mimicking GTSE1 mutant leads to accelerated growth and an increase in the cell proliferative index (Figure 4B,C and new Figure supplement 4D-E).  Finally, we have evaluated also the cell cycle distributions by flow cytometry (new Figure supplement 4F). These analyses show that the expression of a phospho-mimicking GTSE1 mutant induces a decrease in the percentage of cells in G1 and an increase in the percentage of cells in S, similarly to what observed in AMBRA1 KO cells.

Reviewer #3 (public review)

Summary:

This paper identifies GTSE1 as a potential substrate of cyclin D1-CDK4/6 and shows that GTSE1 correlates with cancer prognosis, probably through an effect on cell proliferation. The main problem is that the phosphorylation analysis relies on the over-expression of cyclin D1. It is unclear if the endogenous cyclin D1 is responsible for any phosphorylation of GTSE1 in vivo, and what, if anything, this moderate amount of GTSE1 phosphorylation does to drive proliferation.

Strengths:

There are few bonafide cyclin D1-Cdk4/6 substrates identified to be important in vivo so GTSE1 represents a potentially important finding for the field. Currently, the only cyclin D1 substrates involved in proliferation are the Rb family proteins.

Weaknesses:

The main weakness is that it is unclear if the endogenous cyclin D1 is responsible for phosphorylating GTSE1 in the G1 phase. For example, in Figure 2G there doesn't seem to be a higher band in the phos-tag gel in the early time points for the parental cells. This experiment could be redone with the addition of palbociclib to the parental to see if there is a reduction in GTSE1 phosphorylation and an increase in the amount in the G1 phase as predicted by the authors' model. The experiments involving palbociclib do not disentangle cell cycle effects. Adding Cdk4 inhibitors will progressively arrest more and more cells in the G1 phase and so there will be a reduction not just in Cdk4 activity but also in Cdk2 and Cdk1 activity. More experiments, like the serum starvation/release in Figure 2G, with synchronized populations of cells would be needed to disentangle the cell cycle effects of palbociclib treatment.   

Please see last paragraph in the response to Reviewer #1.  Concerning the experiments involving palbociclib, we limited confounding effects on the cell cycle by treating cells with palbociclib for only 4-6 hours. Under these conditions, there is simply not enough time for S and G2 cells to arrest in G1.

It is unclear if GTSE1 drives the G1/S transition. Presumably, this is part of the authors' model and should be tested.

We are not claiming that GTSE1 drives the G1/S transition (please see last paragraph in the response to Reviewer #1). GTSE1 is known to promote cell proliferation, but how it performs this task is not well understood.  Our experiments indicate that, when overexpressed, cyclin D1 promotes GTSE1 phosphorylation and its consequent stabilization.  In agreement with the literature, we show that higher levels of GTSE1 promote cell proliferation.  To measure cell cycle distribution upon expressing various forms of GTSE1, we have now performed FACS analyses (new Figure supplement 4F). These analyses show that the expression of a phospho-mimicking GTSE1 mutant induces a decrease in the percentage of cells in G1 and an increase in the percentage of cells in S, similarly to what observed in AMBRA1 KO cells shown in the same panel and in Simoneschi et al. (Nature 2021, PMC8875297).

The proliferation assays need to be more quantitative. Figure 4B should be plotted on a log scale so that the slope can be used to infer the proliferation rate of an exponentially increasing population of cells. Figure 4c should be done with more replicates and error analysis since the effects shown in the lower right-hand panel are modest.

In Figure 4B, we plotted data in a linear scale as done in the past (Donato et al. Nature Cell Biol. 2017, PMC5376241) to better underline the changes in total cell number overtime.  The experiments in Figure 4B were performed in triplicate, statistical significance was determined using unpaired T-tests with p-values<0.05, and error bars represent the mean +/- SEM.  In Figure 4C, error analysis was not included for simplicity, given the complexity of the data.  We have now included the other two sets of experiments (new Figure supplement 4D,E).  While the effects shown in the lower right-hand panel of Figure 4C are modest, they demonstrate the same trend as those observed in the AMBRA KO cells (Figure 4C and Simoneschi et al., Nature 2021, PMC8875297). It's important to note that this effect is achieved through the stable expression of a single phospho-mimicking protein, whereas AMBRA KO cells exhibit changes in numerous cell cycle regulators. Moreover, these effects are obtained by growing cells in culture for only 5 days. A similar impact on cell growth in vivo over an extended period could pose significant risks in the long term.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Figure 1E is referenced before 1D. The authors should consider switching D and E.

Done.

Figure 1D-E: The authors correctly note in the introduction that GTSE1 is encoded by a cell cycle-dependently expressed gene. Given that cell cycle genes are often associated with poor prognosis (e.g., see Whitfield et al., 2006 Nat. Rev. Cancer), this would be expected to correlate with poor prognosis. This should be mentioned in the results section.

We agree that the overexpression of certain (but not all) cell cycle-regulated genes are prognostically unfavorable across various cancer types, and we cited Whitfield et al., 2006 Nat. Rev. Cancer.  However, our data indicate that phosphorylation of GTSE1 induces its stabilization and, consequently, its levels do not oscillate during the cell cycle any longer (new Figure 2G and Figure supplement 3B).  Moreover, analyzing data from the Clinical Proteomic Tumor Analysis Consortium, we observed an enrichment of GTSE1 phospho-peptides (normalized to total protein) within a pan-cancer cohort as opposed to adjacent, corresponding normal tissues (Figure 2I).

Figure 2F: Contrast is too high. Blot images should not contain fully saturated black or white.

We corrected the contrast.

Figure 2G and Figure Supplement 3B: It looks like AMBRA1 KO cells do not synchronize properly in response to serum withdrawal. The cell cycle distribution should be checked by FACS. Otherwise, it is unclear whether changes in GTSE1 (phosphor) levels are only due to indirect changes in the cell cycle distribution.

Synchronization of both parental and AMBRA1 KO cells is demonstrated by the fact that the phosphorylation of Histone H3 peaks at 32 hours after serum readdition in both cases (Figure supplement 3B). 

Figure 2I: It is important that phosphor-GTSE1 levels are normalized to total GTSE1 levels to understand the distinct contribution of changes in GTSE1 levels and from CCND1-CDK4 driven phosphorylation.

Done.

Figure 3A-B: These experiments should also be controlled for cell cycle distribution. Is this effect specific to GTSE1 and other AMBRA1 targets or are other G2/M cell cycle proteins also affected?

The relative short half-life of GTSE1 (<4 hours) makes its levels sensitive to acute treatments such as Palbociclib or acute AMBRA1 depletion. The effects of these treatments on GTSE1 levels are measurable within a time frame too short to significantly affect cell cycle progression. For example, we used cells with fusion of endogenous AMBRA1 to a mini-Auxin Inducible Degron (mAID) at the N-terminus. This system allows for rapid and inducible degradation of AMBRA1 upon addition of auxin, thereby minimizing compensatory cellular rewiring. Again, we observed an increase in GTSE1 levels upon acute ablation of AMBRA1 (i.e., in 8 hours) (Figure 3B), when no significant effects on cell cycle distribution are observed (please see Simoneschi et al., Nature 2021, PMC8875297 and Rona et al., Mol. Cell 2024, PMC10997477).

Figure 4: It should be noted that the correlation with cell proliferation and cell cycle protein expression is expected for any cell cycle protein, including GTSE1.

Actually, the main point of Figure 4 is to show that expression of the phospho-mimicking mutant of GTSE1 promotes cell proliferation. Comparative analysis revealed that cells overexpressing either wild-type GTSE1 or its phospho-deficient form exhibited significantly reduced proliferation rates compared to those expressing the phospho-mimicking mutant (Figure 4B,C). 

The two-decades-old references 33 and 34 are not well suited to support the notion for Cyclin D1 that "the full spectrum of substrates and their impact on cellular function and oncogenesis remain poorly explored." More recent references should be used to show that this is still the case.

We added more recent references.

The authors conclude that their "data indicate that cyclin D1-CDK4 is responsible for the phosphorylation of GTSE1 on four residues (S91, S262, S454, and S724)." However, the authors' data do not exclude a role for their siblings cyclin D2, cyclin D3, and CDK6. Reflecting this, the conclusions should be toned down.

The analysis of the sites phosphorylated in GTSE1 was performed by experimentally co-expressing cyclin D1-CDK4 (Figure 2F, Figure 2H, and Figure supplement 3A), hence our statement.  Yet, we agree that in cells, cyclin D2, cyclin D3, and CDK6 can contribute to GTSE1 phosphorylation. 

The authors claim that they "observed that in human cells, when D-type cyclins are stabilized in the absence of AMBRA1, GTSE1 becomes phosphorylated also in G1." However, the G1-specific data presented by the authors are not controlled for, and it is unclear whether these phosphorylation events actually occur in G1 cells.

We now provide a WB in which GTSE1 phosphorylation is more evident (top panel of the new Figure 2G) (please see point (ii) in the response to the public review of Reviewer #1).  This experiment clearly shows that in AMBRA1 KO cells, GTSE1 is phosphorylated at all points in the cell cycle. Synchronization of both parental and AMBRA1 KO cells is demonstrated by the fact that phosphorylation of Histone H3 peaks at 32 hours after serum re-addition in both cases (Figure supplement 3B). 

Reviewer #2 (Recommendations for the authors):

(1) It is not clear from the presented data at which point in the cell cycle that phosphorylation of GTSE1 may be affecting the steady state level of the protein. The implication that GTSE1 is a target of CycD-CDK4 would suggest that the protein is stabilized at G1/S. Can this effect be observed?

Please see the last paragraph in the response to the public review of Reviewer #1.

(2) Considering the previous study showing that GTSE1 is also phosphorylated during mitosis by CycB-Cdk1, do levels of GTSE1 protein change during the cell cycle? Do changes in GTSE1 levels correlate with phosphorylation during the cell cycle? Cell synchronization experiments such as double thymidine and subsequent phostag analysis could shed some light on these questions.

Please see the last paragraph in the response to the public review of Reviewer #1.

(3) The authors show that the phosphomimetic mutants of GTSE1 confer a growth advantage on cells. The mechanism of this growth advantage is unclear. Is this effect due to a shorter cell cycle, enhanced survival, or another mechanism?

We did not observe increased cell survival when the phosphomimetic mutants of GTSE1 is expressed.  We show that phosphorylation of GTSE1 induces its stabilization, leading to increased levels that, as expected based on the existing literature, contribute to enhanced cell proliferation.  So, the role of the cyclin D1-CDK4/6 kinase-dependent phosphorylation of GTSE1 is to stabilize GTSE1. 

(4) Other minor points - all of the presented immunoblots do not show molecular weight markers. The IF images require scale bars.

To prevent overcrowding of the Figures, the sizes of blotted proteins are indicated in the uncropped scans of each blot. Uncropped scans have been deposited in Mendeley at:  https://data.mendeley.com/datasets/xzkw7hrwjr/1. Scale bars have been added to the IF images.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Gray and colleagues describe the identification of Integrator complex subunit 12 (INTS12) as a contributor to HIV latency in two different cell lines and in cells isolated from the blood of people living with HIV. The authors employed a high-throughput CRISPR screening strategy to knock down genes and assess their relevance in maintaining HIV latency. They had used a similar approach in two previous studies, finding genes required for latency reactivation or genes preventing it and whose knockdown could enhance the latency-reactivating effect of the NFκB activator AZD5582. This work builds on the latter approach by testing the ability of gene knockdowns to complement the latency-reactivating effects of AZD5582 in combination with the BET inhibitor I-BET151. This drug combination was selected because it has been previously shown to display synergistic effects on latency reactivation.

The finding that INTS12 may play a role in HIV latency is novel, and the effect of its knockdown in inducing HIV transcription in primary cells, albeit in only a subset of donors, is intriguing. However, there are some data and clarifications that would be important to include to complement the information provided in the current version of the manuscript.

We have now added the requested data and clarifications. In particular, we show that knockout of INTS12 has no effect on cell proliferation (new data added in Figure 2—figure supplement 3)), we clarify how the degree of knockout and the complementation were accomplished, we clarify the differences between the RNA-seq and the activation scores, and we have bolstered the claim that INTS12 affected transcription elongation by performing CUT&Tag on Ser2 phosphorylation of the C-terminal tail of RNAPII along the length of the provirus (new data added in Figure 5C) Please see detailed responses below.

Reviewer #2 (Public review):

Summary:

Identifying an important role for the Integrator complex in repressing HIV transcription and suggesting that by targeting subunits of this complex specifically, INTS12, reversal of latency with and without latency reversal agents can be enhanced.

Strengths:

The strengths of the paper include the general strategy for screening targets that may activate HIV latency and the rigor of exploring the mechanism of INTS12 repression of HIV transcriptional elongation. I found the mechanism of INTS12 interesting and maybe even the most impactful part of the findings.

Weaknesses:

I have two minor comments:

There was an opportunity to examine a larger panel of latency reversal agents that reactivate by different mechanisms to determine whether INTS12 and transcriptional elongation are limiting for a broad spectrum of latency reversal agents.

I felt the authors could have extended their discussion of how exquisitely sensitive HIV transcription is to pausing and transcriptional elongation and the insights this provides about general HIV transcriptional regulation.

We have now added data on latency reversal agents of different mechanisms of action. We show that INTS12 affects HIV latency reversal from agents that affect the non-canonical NF-kB pathway (AZD5582), the canonical NF-kB pathway (TNF-alpha), activation via the T-cell receptor (CD3/CD28 antibodies), through bromodomain inhibition (I-BET151), and through a histone deacetylase inhibitor (SAHA). This additional data has been added to the manuscript in Figure 7, panels B and C as well as adding text to the discussion.

We appreciate the suggestion to extend the discussion to emphasize how important pausing and elongation are to HIV transcription. Additionally, to further support our claim that INTS12KO with AZD5582 & I-BET151 leads to an increase in elongation, that we previously showed with CUT&Tag data showing an increase in total RNAPII seen in within HIV (Figure 5B), we measured RNAPII Ser2 phosphorylation (Figure 5C) and RNAPII Ser5 phosphorylation (Figure 5—figure supplement 2) and added these findings to the manuscript. Upon measuring Ser2 phosphorylation, a marker associated with elongation, we observed evidence of elongation-competent RNAPII in our AZD5582 & I-BET151 condition as well as our INTS12 KO with AZD5582 & I-BET151 condition, as we saw an increase of Ser2 phosphorylation within HIV. Despite seeing elongation-competent RNAPII in both conditions, we only saw a dramatic increase in total RNAPII for our INTS12 KO and AZD5582 & I-BET151 condition (Figure 5B), which supports that there are more elongation events and that an elongation block is overcome specifically with INTS12 KO paired with AZD5582 & I-BET151. This claim is further supported by our data showing an increase in virus in the supernatant only with the INTS12 KO with AZD5582 & I-BET151 condition in cells from PLWH (Figure 6C). We did not observe any statistically significant differences between RNAPII Ser5 phosphorylation, which might be expected as this mark is not associated with elongation (Figure 5—figure supplement 2).

Reviewer #3 (Public review):

Summary:

Transcriptionally silent HIV-1 genomes integrated into the host`s genome represent the main obstacle to an HIV-1 cure. Therefore, agents aimed at promoting HIV transcription, the so-called latency reactivating agents (LRAs) might represent useful tools to render these hidden proviruses visible to the immune system. The authors successfully identified, through multiple techniques, INTS12, a component of the Integrator complex involved in 3' processing of small nuclear RNAs U1 and U2, as a factor promoting HIV-1 latency and hindering elongation of the HIV RNA transcripts. This factor synergizes with a previously identified combination of LRAs, one of which, AZD5582, has been validated in the macaque model for HIV persistence during therapy (https://pubmed.ncbi.nlm.nih.gov/37783968/). The other compound, I-BET151, is known to synergize with AZD5582, and is a inhibitor of BET, factors counteracting the elongation of RNA transcripts.

Strengths:

The findings were confirmed through multiple screens and multiple techniques. The authors successfully mapped the identified HIV silencing factor at the HIV promoter.

Weaknesses:

(1) Initial bias:

In the choice of the genes comprised in the library, the authors readdress their previous paper (Hsieh et al.) where it is stated: "To specifically investigate host epigenetic regulators involved in the maintenance of HIV-1 latency, we generated a custom human epigenome specific sgRNA CRISPR library (HuEpi). This library contains sgRNAs targeting epigenome factors such as histones, histone binders (e.g., histone readers and chaperones), histone modifiers (e.g., histone writers and erasers), and general chromatin associated factors (e.g., RNA and DNA modifiers) (Fig 1B and 1C)".

From these figure panels, it clearly appears that the genes chosen are all belonging to the indicated pathways. While I have nothing to object to on the pertinence to HIV latency of the pathways selected, the authors should spend some words on the criteria followed to select these pathways. Other pathways involving epigenetic modifications and containing genes not represented in the indicated pathways may have been left apart.

(2) Dereplication:

From Figure 1 it appears that INTS12 alone reactivates HIV -1 from latency alone without any drug intervention as shown by the MACGeCk score of DMSO-alone controls. If INTS12 knockdown alone shows antilatency effects, why, then were they unable to identify it in their previous article (Hsieh et al., 2023)? The authors should include some words on the comparison of the results using DMSO alone with those of the previous screen that they conducted.

(3) Translational potential:

In order to propose a protein as a drug target, it is necessary to adhere to the "primum non nocere" principle in medicine. It is therefore fundamental to show the effects of INTS12 knockdown on cell viability/proliferation (and, advisably, T-cell activation). These data are not reported in the manuscript in its current form, and the authors are strongly encouraged to provide them.

Finally, as many readers may not be very familiar with the general principles behind CRISPR Cas9 screening techniques, I suggest addressing them in this excellent review: https://pmc.ncbi.nlm.nih.gov/articles/PMC7479249/.

(1) The CRISPR library used was more completely described in a previous publication (Hsieh et al, PLOS Pathogens, 2023). However, we now more explicitly refer the reader to information about the pathways targeted in the library. We also point out how initial hits in the library lead to finding genes outside of the starting library as in the follow-up screen in Figure 7 where each of the members of the INT complex are interrogated even though only INTS12 was the only member in the initial library.

(2) We understand the confusion between the hits in this paper and a previous publication. Indeed, INTS12 was observed in Hsieh et al., PLOS Pathogens, 2023 as a hit in the Venn diagram of Figure 3B of that paper, and in Figure 5A, right panel of that paper. However, it was not followed up on in the previous paper since that paper focused on a hit that was unique to increasing the potency of one particular LRA. We added text to the present manuscript to make it clear that the screens identified many of the same hits. We have also added additional data here on hit validation to underscore the reliability of the CRISPR screen. In one of the cell lines (5A8), EZH2 was a strong hit (Figure 1B). We have now added data that shows that an inhibitor to EZH2 augments the latency reversal of AZD5582/I-BET151 as predicted from the screen. This data has been added to Figure 1, figure supplement 1.

(3) We appreciate the concern that for INTS12 to be a drug target, it should not be essential to cell viability. We now show that knockout of INTS12 has no effect on cell proliferation (new data added in Figure 2—figure supplement 3). In addition, the discussion now adds additional literature references that describe how knockout of INTS12 has relatively minor effects on cell functions in comparison to knockout of other INT members which supports that the proposal that modulation of INTS12 may be more specific than targeting the catalytic modules of Integrator. Nonetheless, we completely agree with the reviewer that many other aspects of how INTS12 affects T cell functions have not been addressed as well as other potential detrimental effect of INTS12 as a drug target in vivo. We now more explicitly describe these caveats in the discussion but feel that the present manuscript is a first step with a long path ahead before the translational potential might be realized.

(4) We now cite the review of CRISPR screens suggested by the reviewer.

Responses to recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The authors report in the legend of Figure 2 (and similarly in other figures) that there was "a calculated INTS12 knockout score of 76% (for the one guide used) and 69% (for one of three guides used), respectively." However, it would be helpful to show representative data on the efficiency of INTS12 knockdown in cell lines and primary cells, as well as data on the efficiency of the complementation (Figure 2C).

The knockout scores cited are the genetic assays for the efficiency based on sequence files. As the knockouts are done with multiple guides the knockout for each guide is an underestimate of the total knockout. The complementation, however, was done by adding back INTS12 in a lentiviral vector that also contains a drug resistance marker (puromycin). Cells were then selected for puromycin resistance, and therefore, all of them contain the complemented gene. What one would ideally like is a Western blot to quantify the amount of INTS12 remaining in the knockout pools. Unfortunately, despite obtaining multiple different commercial sources of INTS12 antibodies, we were unable to identify one that was suitable for Western blotting (as opposed to two that did work for CUT&Tag). Nonetheless, the functional data in primary T cells from PLWH and in J-Lat cells lines does show the even if the knockout is suboptimal, we find activation after INTS12 knockout (e.g., Figure 6).

(2) Flow cytometry methods are not reported, but was a viability dye included when testing GFP reactivation (Figure S2)? More broadly, showing data on the viability of cells post-knockdown and drug treatments would help, as cell mortality is inherently associated with latency reactivation in J-Lat cells. For the same reason, reporting viability data would be important for primary cells, as the electroporation procedure can lead to significant mortality.

We did not include viability dyes in the data for GFP activation. However, as described in the public response, we have done growth curves in J-Lat 10.6 cells with and without INTS12 knockout and find no effects on cell proliferation (Figure 2—figure supplement 3). As the reviewer points out, it is not possible to do these experiments in primary cells since the electroporation itself causes a degree of cell death. Nonetheless, we do see effects on HIV activation in these primary cells (Figure 6).

(3) Figure S2 shows a relatively high baseline expression (approximately 15%) of HIV-GFP, which is not unusual for the J-Lat 10.6 clone. However, Figure 3 appears to show no HIV RNA reads in the control condition of this same cell clone. How do the authors reconcile this discrepancy?

We believe that the discrepancies in the flow cytometry versus RNA-seq assays are due to differences in the sensitivity of the assays, the linear range of the assays especially at the lower end, and the different half-lives of RNA versus protein. We now clarify that Figure 3 does not show “no” HIV RNA at baseline, but rather values of ~30 copies per million read counts. This increases to ~800 copies per million read counts when INTS12 knockout cells are treated with AZD5582/I-BET151. These values have the same fold change predicted in Figure 4, and more closely resemble the trend in Figure 2—figure supplement 1.

(4) The combination of AZD5582 and I-BET151 consistently reactivates HIV latency (including GFP protein expression), as previously reported and as shown here by the authors. However, in Figure 5B, RPB3/RNAPII occupancy in the DMSO control appears higher than in the AAVS1KO + AZD5582 and I-BET151 samples. This should be discussed, as it could raise concerns about the robustness of RPB3/RNAPII occupancy results as a proxy for provirus elongation.

As addressed in the public comments, in order to strengthen our claims about transcriptional elongation control, we measured RNAPII Ser2 and Ser5 phosphorylation levels. We see evidence of elongation with Ser2 in the condition of concern (AAVS1 KO + AZD5582 & I-BET151) as well as our main condition of interest (INTS12 KO + AZD5582 & I-BET151) and no change in Ser5 for any condition. With both the Ser2 phosphorylation and total RNAPII as well as our virus release and transcription data we believe that we are seeing evidence of increased elongation with INTS12 KO with AZD5582 & I-BET151. One potential nuance that may not be gathered from the CUT&Tag data is the turnover rate of the polymerase. Despite the levels of RNAPII appearing lower in the condition of concern (AAVS1 KO + AZD5582 & I-BET151) compared to DMSO it is possible that low levels of elongation are occurring but that in our INTS12 KO + AZD5582 & I-BET151 condition there is more rapid elongation and this is why we can observe more RNAPII within HIV. This new data is added in Figure 5C and Figure 5—supplement 2 and its implications are now described in more detail in the discussion.

(5) The authors write that "Degree of reactivation was correlated with reservoir size as donors PH504 (star symbol) and PH543 (upside down triangle) have the largest HIV reservoirs (supplemental Figure S2)." I could not find mention of the reservoir size of these donors in the figure provided.

This confusion was caused by mislabeling of the supplement number, which we fixed, and we added additional labeling to make finding the reservoir size even more clear as this is an important part of the manuscript. This is now found in Supplemental file S4.

Reviewer #3 (Recommendations for the authors):

(1) The MAGeCK gene score is a feature that is essential for the interpretation of the results in Figure 1. The authors do quote the Li et al. paper where this score was described for the first time (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0554-4), however, they may understand that not all readers may be familiar with this score. Therefore a didactic short description of this score should be done when introducing the results in Figure 1.

We have added a short description to the paper to address this.

(2) Figure 4. The authors write: "Among the host genes most prominently affected by INTS12 knockout with AZD5582 & I-BET151 are MAFA, MAFB, and ID2 (full list of genes in supplemental file S3)." I am a bit confused. In the linked Excel file there is only a list of a few genes. The differentially expressed genes appear to be many more from Figure 4. The full list should be uploaded.

We believe there was a mistake in our original uploading and naming of the supplements. We have now double-checked numbering on the supplements and added in text clarification of which excel tabs hold the desired information.

(3) Figure 6: The authors are right in highlighting that there is a high level of variability in viral RNA in supernatants in the early stages of viral reactivation. It is therefore advisable to repeat measurements at Day 7, at which variability decreases and data are more reliable (please, see: https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00443-7/fulltext).

While it would have been nice to prolong these measurements, our current assay conditions are not optimal for longer term growth of the cells. We note that the measurements were all done in biological triplicates (independent knockouts) and in different individuals. Because the number of activatable latent proviruses is variable and the number of cells tested is limiting, the variability in the assays is expected.

(4) Figure 7: The main genes outside the INTS family should be identified, also.

We include the full list in supplemental file S5 and sort by most enriched.

(5) Methods: A statistical paragraph should be added in the Methods section, detailing the data analysis procedures and the key parameters utilized (for example, which is the MAGeCK gene score threshold that they used to consider knockdown efficacy on HIV latency?).

There is no MAGeCK score threshold that we use to determine efficacy on HIV latency. In a previous publication using CRISPR screens for HIV Dependency Factors (Montoya et al, mBio 2023), we showed that there is a relationship between the MAGeCK and the effect of that gene knockout on HIV replication (Figure 5 that paper). However, it is a continuum rather than a strict threshold and we believe that the effects on HIV latency would respond similarly. In the current paper, we have focused on the top hits rather than a comprehensive analysis of all the entire list. In case the reviewer is referring to the average and standard deviation of the non-targeting controls, we have added this to the figure legend and methods.

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

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Reply to the reviewers

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

This study by Zimyanin et al. examines the role of the C. elegans chromokinesin KLP-19 in the formation and architecture of the anaphase central spindle in C. elegans zygotes. Through a combination of electron and light microscopy, along with RNAi-mediated perturbations, the authors propose that KLP-19 influences central spindle stiffness by regulating microtubule dynamics.

In Figure 5, the effect of KLP-19 depletion on central spindle microtubules appears unconvincing. The FRAP results show no significant difference with or without KLP-19, and overall microtubule density does not consistently respond to its depletion. Additionally, the double klp-19; gpr-1/2 (RNAi) condition does not exhibit a strong increase in microtubule density, though a statistical test is missing for this condition. Furthermore, the spd-1; gpr-1/2 double depletion produces a similar increase in microtubule density to most klp-19 depletion conditions, suggesting that the effect cannot be solely attributed to the absence of KLP-19.

Figure 5A shows that depletion of KLP-19 leads to an increase in tubulin signal in the spindle midzone. The reviewer is correct, that there are differences in the microtubule density between KLP-19 depletion alone and KLP-19 + GPR-1/2 depletion. While depletion of KLP-19 alone leads to a significant increase, co-depletion of GPR-1/2 and KLP-19 leads to a slight, but not significant increase. Along this line, we have added Supplementary Table 1 that contains all p-Values for the different conditions for Figure 5A. However, depletion of GPR-1/2 alone does not affect the microtubule density in the midzone, arguing that changes in pulling forces do not affect the microtubule density in the midzone. It is possible, that the double RNAi leads to a decrease in efficiency and thus a reduced effect on microtubule intensity. We will demonstrate the RNAi efficiency by western blot. Another possibility is that there are some feedback mechanisms that responds to presence/ absence of pulling forces and some of our data (not from this manuscript) hints in this direction, but we have not yet worked out the details of this. We are planning to publish this in a follow up publication.

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In response to the spd-1 + gpr-1/2 (RNAi), the reviewer is correct, that the microtubule density in the midzone is not significantly different from klp-19 (RNAi) conditions and we think it is interesting to note that spd-1 + gpr-1/2 (RNAi) leads to an increased microtubule density in the midzone. This could be, as above mentioned caused by some feedback mechanisms that responds to pulling forces, or also due to some functions of SPD-1 that affects microtubules in the midzone. Interestingly, our data also shows that metaphase spindles are significantly shorter in the absence of SPD-1 in comparison to spindles in control embryos, suggesting that SPD-1 plays a role in regulating microtubules or force transmission. We are currently working on understanding SPD-1's role in this process.

• *

We also agree that there is no significant effect on the microtubule turn-over as shown in Figure 5B and we have stated this in the text. Our data does show a trend to a decreased turn-over, but the difference is not significant. This could be due to the low sample number.

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Overall, we think our data, the light microscopy and even more so the EM data does show a clear effect on midzone microtubules.

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The use of hcp-6 depletion to argue that KLP-19 depletion affects central spindle elongation independently of stretched chromatin is problematic. hcp-6 encodes a component of the Condensin II complex in C. elegans, and its depletion leads to chromatin decompaction rather than the stretched, dense chromatin observed in the midzone during anaphase in klp-19 (RNAi) embryos. These conditions are not equivalent and do not effectively rule out the possibility that chromatin stretching contributes to the observed phenotype.

We agree with the reviewer that the HCP-6 experiments do not entirely rule out effects from lagging chromosomes. Proving that the reduced spindle and chromosome separation is not due to lagging chromosomes is challenging. Most of the depletions that lead to lagging chromosomes are based on defective kinetochore microtubule connections, such as depletion of KNL-1, NDC-80 or CLS-2 (CLASP). In C. elegans, this leads to the mass of Chromosomes staying behind in anaphase and increased spindle pole separation, which is not comparable to KLP-19 depletion. Perturbations that do not affect kinetochore microtubules but still lead to lagging chromosomes are often targeting cohesin or condensin. Ultimately none of these conditions are directly comparable.

A probably better way to test this would be to deplete KLP-19 only after metaphase to prevent its effect on chromosome alignment. However, this is currently not possible as the time window is about 1 minute or less. We currently do not have the tools to conduct this type of experiment. As other reviewers also criticized this experiment and its significance for the paper, we have removed this entirely and have added the following part to the discussion about the potential effect of lagging chromosomes:

" *We can not unambiguously rule out that failure to properly align chromosomes and the resulting lagging chromosomal material could also lead to some of the observed effects on spindle dynamics, such as slow chromosome segregation and pole separation rates as well as preventing spindle rupture in absence of SPD-1. However, several observations argue in favor of KLP-19 actively changing the midzone cytoskeleton network and thus affecting spindle dynamics. *

Most of the protein depletions in C. elegans that lead to lagging chromosomes are based on defective kinetochore microtubule connections, such as depletion of CeCENP-A, CeCENP-C, KNL-1 or NDC-80 (70-72). This mostly leads to the Chromosome mass staying behind in anaphase and increased spindle pole separation (70-72), which is not comparable to KLP-19 depletion. Perturbations that do not affect kinetochore microtubules but still lead to lagging chromosomes are often targeting cohesin or condensin, which depletion leads to chromatin decompaction (73-74) rather than the stretched, dense chromatin as observed in the midzone during anaphase in klp-19 (RNAi) embryos. Ultimately none of these conditions are directly comparable, making it difficult to completely rule out an effect of lagging chromosomes. A better way to test this would be to deplete KLP-19 only after metaphase to prevent its effect on chromosome alignment. However, this is currently not possible as the time window is about 1 minute or less and we do not have the tools to conduct this type of experiment.

*Based on our results we hypothesize that the observed spindle dynamics in absence of KLP-19 are not only caused by lagging chromosomes. Instead, KLP-19 RNAi results in a global rearrangement of the spindle and leads to a significant reduction of the spindle size, microtubule overlap, growth rate, and stability. Furthermore, the increase of microtubule interactions after klp-19 (RNAi) could also contribute to lagging of chromosomes and exacerbation of fragmented extrachromosomal material." *

Additionally, the authors report that KLP-19 influences astral microtubule dynamics (Figure 5E), yet in Figure 3E, they show that KLP-19 localizes exclusively to kinetochores and spindle microtubules, excluding astral microtubules and spindle poles. How do they reconcile this apparent contradiction?

We think that KLP-19 localizes also to astral Microtubules. Our KLP-19 GFP CRISPR line is very dim and this makes it hard to see. We are proposing to use a TIRF approach to image KLP-19 GFP on the C. elegans cortex, which we will include in the revised version. In addition, in support of our hypothesis of KLP-19 binding to astral Microtubules as well we would like to note that there is a PhD thesis available from Jack Martin in Josana Rodriguez Sanchez's Lab in Newcastle (LINK, will lead to a download of the thesis! ) that has reported KLP-19s localization to cortical Microtubules in C. elegans. In this thesis the author also reports an effect on astral microtubule growth.

Figure legends lack consistency and do not adhere to standard C. elegans nomenclature conventions (e.g., protein names should not be capitalized, and genetic perturbations should be italicized). Standardizing these elements would improve clarity and readability.

We have checked our figure legend and to our best knowledge the legends adhere to the C. elegans nomenclature. All RNAi conditions are lower case italicized and Protein names are capitalized as it is standard in other C. elegans publications. We have however noticed some variation in our Figures, i.e. EB-2 instead of EBP-2 and have corrected this in all figures.

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

Zimyanin et al, Chromokinesin Klp-19 regulates microtubule overlap and dynamics during anaphase in C. elegans.

The authors used a myriad of techniques, including confocal live-cell imaging, 2-photon microscopy, second harmonic generation imaging, FRAP, microfluidic-coupled TIRF, EM-tomography, to study spindle midzone assembly dynamics in C. elegans one-cell stage embryos. In particular, they illuminated the role of kinesin-4 KLP-19 in maintaining proper midzone length and organization. Inhibition of KLP-19 results in longer more stable midzones, implying KLP-19 functions in depolymerizing microtubules.

Indeed, much of the results in the current study are consistent with previously published results elsewhere. Nevertheless, the current work represents a tour-de-force showcase of diverse and state-of-the-art technology application to address spindle assembly dynamics. How KLP-19 functions to define microtubule length at the midzone is still not known. But the current work, with diverse and solid data, serves to highlight where future work should focus.

Minor comments:

Fig 3E / There is an unusual diagonal line bisecting the embryo. Visually this does not affect viewing of the His::GFP and KLP-19::GFP signals. However, when these signals are quantified and normalized (as in Fig 3F), the diagonal bisect displaying different background signal may impact the measurements.

We are very sorry about this line in the images. The line is due to a defect in the camera chip of the spinning disc. We will acquire new images for this Figure using our new spinning disc microscope.

Fig 4B / While the kymographs clearly show KLP-19::GFP motility on microtubules, they also show that the majority of KLP(-::GFP do not move. Perhaps some quantification and discussion of this result is appropriate?

The reviewer is correct that only a small fraction small fraction of molecules, maybe ~10%, moves. We will add this quantification to the paper and discussion. This could be due to several reasons: Many of the non-moving particles are not visibly colocalized with microtubules, which could mean they are sticking non-specifically to the surface (or sticking to small tubulin aggregates that aren't long enough to support movement). In addition, as this experiment is done in a lysate it is hard to interpret if the immobile KLP-19 is not moving because other proteins are bound along the microtubule blocking its way or if the KLP-19 requires some activation (i.e. phosphorylations) to become mobiles. We think this could be very interesting and will follow up on this in the future.

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Reviewer #2 (Significance (Required)):

Indeed, much of the results in the current study are consistent with previously published results elsewhere. Nevertheless, the current work represents a tour-de-force showcase of diverse and state-of-the-art technology application to address spindle assembly dynamics. How KLP-19 functions to define microtubule length at the midzone is still not known. But the current work, with diverse and solid data, serves to highlight where future work should focus.

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

Summary:

The anaphase spindle midzone is an essential structure for cell division. It consists of antiparallel overlapping microtubules organized by the antiparallel microtubule bundler PRC1, molecular motors and other regulatory proteins. This manuscript investigates the role of KLP-19 (C. elegans ortholog of human kinesin-4 KIF4A) and SPD-1 (C. elegans ortholog of PRC1) for spindle midzone organization in the C. elegans embryo and its relevance for proper spindle function. Advanced fluorescence microscopy, 3D electron tomography, and a fluorescence microscopy-based single molecule assay in embryo lysate are used in a unique combination. The authors confirm several aspects of PRC1 and KIF4A function in anaphase, as reported in previous work, mostly in human cells and Drosophila embryos and also in C. elegans embryos. Measurements are mostly very quantitative and to a high quality standard. The main difference to previous conclusions is that here, the authors propose that KLP-19 does not interact with SPD-1, in contrast to what has been established for other animal kinesin-4s and PRC1, and instead localizes to the spindle midzone independently of PRC1 by a mechanism that remains unknown. The authors provide evidence that KLP-19 nevertheless controls microtubule overlap length as in other species and that it produces outward forces sliding midzone microtubules apart a movement that SPD-1 counteracts (presumably by friction). The manuscript presents a rich resource of carefully measured quantitative structural and dynamic C. elegans anaphase spindle data.

Major comments:

Key conclusions convincing?

(1) The key conclusions that the length of the central anaphase spindle microtubule overlap remains constant as the C.elegans spindle elongates (Fig. 1), that PRC1 indeed localizes quite precisely to these overlaps as previously assumed based on its in vitro (purified protein) behavior (Fig. 3B) and that the kinesin-4 KLP-19 controls overlap length as in other species (Fig. 3B) are all convincingly shown. What's missing are quantitative KLP-19 together with microtubule polarity profiles in the presence and absence of SPD-1, leaving it unclear to which extent this kinesin localizes to microtubule overlaps in the two situations. Such data seem crucial, given the authors' claim that KLP-19 localizes to the midzone and that this localization of KLP-19 is mostly unaffected by the absence of SPD-1.

If we understand this correctly the reviewer is asking for second harmonic imaging (SHG) together with imaging of KLP-19 GFP. This is currently not possible due to the way this imaging must be done (2-photon of GFP-Tubulin followed by the SHG). The only thing we can do is provide KLP-19 GFP profiles for control and SPD-1 depleted embryos. We can also use the line co-expressing SPD-1 Halo-tag and KLP-19 GFP to plot their respective localizations in control conditions. We are happy to provide such plots. Generally, we see KLP-19 going to the midzone in absence of SPD-1 and the SHG data does show that the overlap is increased. If KLP-19 specifically localizes to microtubule overlap (rather to i.e. microtubule ends) can currently not be distinguished in the spindle midzone. In vitro data from other labs and our in vitro assay suggests that KLP-19 does not specifically bind to antiparallel overlaps but rather microtubules in general.

(2) 'Normalized KLP-19 intensities' are used to demonstrate that the total amount of this kinesin localizing to the spindle midzone does not depend on the presence of SPD-1 (Fig. 3F). Given that this claim represents a major novelty of the study, the efficiency of the SPD-1 knock-down should be documented, ideally by western blot and fluorescence microscopy.

We agree with the reviewer and will provide western blots.

(3) The authors show convincingly that the kinesin KLP-19 contributes to outward microtubule sliding (and can contribute to spindle rupture in the absence of SPD-1) (Fig. 2), which is interesting and in line with the author's main claim.

(4) The interaction between KIF4a and PRC1 is well established in other species and has been clearly demonstrated both in cells and in vitro (with purified proteins). The authors claim that this concept does not apply to the C. elegans orthologs. To show 'in vitro' (outside of the spindle) that the C. elegans homologs KLP-19 and SPD-1 do not interact, the authors use a novel microfluidic fluorescence-based single-molecule assay in lysate (Fig. 4). Although very original, these experiments do not reach the biochemical standard of previous experiments with purified proteins without appropriate controls. Given that the lysate setup is fairly novel, it's advisable to present at least one positive control demonstrating that interactions between soluble proteins can indeed be detected using this assay. It would also be useful to show the absence of interaction between KLP-19 and SPD-1 by a more conventional method like co-IP, again with a positive control, to support the authors' claim. Eventually, experiments with purified proteins will have to unequivocally demonstrate whether KLP-19 and SPD-1 indeed do not interact - something which appears, however, to be beyond the scope of this study. Without additional experimental proof, the authors may want to indicate that these results are of more preliminary nature.

*We agree with the reviewer, and we will conduct co-IPs of SPD-1 and KLP-19. We will also add CYK-4 as a positive control as previous publications have shown the interaction of CYK-4 with SPD-1. We are now generating lines co-expressing CYK-4 GFP and SPD-1 Halo-tag for the co-IP experiments. *

(5) Unfortunately, the authors do not propose an alternative mechanism for KLP-19 localization to the midzone in SPD-1 depleted embryos, limiting the conceptual advance. Does KLP-19 bind directly to antiparallel microtubules, for example in the assay presented in Fig. 4 (where signs of microtubule crosslinking are shown for SPD-1)? If not, how would it accumulate in the midzone (if it does) in the C. elegans embryo anaphase spindle? The authors do also not propose a mechanism explaining why central antiparallel microtubule overlap length does not change as the spindle elongates in anaphase. Moreover, there is no discussion regarding the potential mechanism leading to KLP-19 controlling microtubule dynamics globally instead of locally where the motor accumulates, indicating limitations in mechanistic insight.

*We agree with the reviewer and will add these points to the discussion. *

*To address some of the points: *

*How does KLP-19 end up in the midzone? : Our data shows that localization of KLP-19 does depend on AIR-2 and BUB-1 as previously reported. However, those proteins primarily affect the formation of the midzone. The in vitro assay does not suggest that KLP-19 has a preference for overlaps, unlike SPD-1, but rather binds microtubules in general. One possible mechanism of midzone localization could be microtubule end-tagging, as has been suggested for PRC1 (SPD-1 homolog). This could lead to an accumulation of KLP-19 in the midzone. *

Why does the central overlap stay constant? : This can be explained by constant microtubule growth at the plus-ends why maintaining the overlap length. Alternatively, this could be explained by some (sophisticated) rearrangements of microtubules that ensure the overlap length stays the same. Generally, this is a very interesting question, as each of this scenario still requires that the overlap length is tightly regulated. Our data suggests that this is correlated with microtubule length in the midzone, as KLP-19 depletion leads to longer microtubules and overlap. This suggests that the regulation of microtubule dynamics might be an important factor in this process. We will add this to the discussion.

• *

Potential mechanism leading to KLP-19 controlling microtubule dynamics globally: We think that KLP-19 localizes to spindle and astral microtubules and regulates the dynamics on all of those, leading to a global regulation. By increasing it's concentration locally, microtubule dynamics can be regulated in the midzone. We will add data showing the localization of KLP-19 to astral microtubules.

Claims justified/preliminary and clearly presented?

The observation that the spindle length remains constant throughout anaphase in C. elegans is based on elegant, but unconventional fluorescence microscopy data (Fig. 1A & B). It would be helpful to add images of SHG and two-photon microscopy to help the reader understand the graphs. Measurements are presented based on distances between the poles. It is unclear why the distances between 15-20 µm were chosen and how they translate to anaphase progression. Can measurements be carried out across the entire duration of cell division to demonstrate that the overlap's 'constant length' property is unique to anaphase? (This could demonstrate already in Fig. 1 that the method in principle is capable of measuring different overlap lengths.)

We agree with the reviewer and have moved the SHG images from supplementary Fig. 6 to the main Figure 1A for better visibility. In addition, we have added a plot as an inset in (now) Figure 1B and C explanation of how the used spindle pole distances related to the progression through anaphase. Unfortunately, we can only acquire a single timepoint and not a live movie during the SHG.

Even though the manuscript contains an impressive amount of data, it appears to be lengthy, the motivation for several experiments is not clearly described, and the order of data presentation can probably be improved. For example, it is unclear why SPD-1 profiles are presented late and why KLP-19 profiles are missing - one would expect to see them early on as an essential characterization of the system under study. The motivation of the paragraph investigating the relation of KLP-19 and SPD-1 to HCP-6 is especially unclear (more than 1 page of text describing supplementary material).

We will go through our text again and will revise the order of presented experiments. As stated above, we have removed the HCP-6 data.

The absence of interaction between KLP-19 and SPD-1 is not demonstrated to the same quality standard as the presence of interaction between the orthologs in the literature, which should at least be mentioned.

Additional experiments essential to support the claims of the paper?

KLP-19 profiles in the presence and absence of SPD-1 seem to be essential.

We agree with the reviewer and will add this.

A co-IP of KLP-19 and SPD-1 (including positive control) to prove that the proteins are not interacting would help to support the claim.

We agree with the reviewer and will add this

Data and methods presented so that they can be reproduced? Yes.

Experiments adequately replicated and statistical analysis adequate? Yes.

Minor comments:

Generating cellular electron tomography data is very laborious. It is a pity that no raw data is provided; for example, a slice of a reconstructed tomogram or a video of whole volumes without segmentation would be an informative addition and allow assessment of the data quality.

We agree with the reviewer and will add movies of the raw electron microscopy data.

The clear evidence for direct interaction between PRC1 and kinesin-4 in other species should be correctly acknowledged throughout the text.

We agree with the reviewer and have corrected this

The average (mean or median?) values and STDs reported in the text do not appear to match those in Fig. 1D.

*We thank the reviewer for pointing this out and have corrected the figure. The violin lot showed the mean and percentiles, we have now changed the plot to show mean and STD. *

• *

The kymograph of spd-1 RNAi in Fig. 2A seems stretched, and the size based on the scale bar does not fit the values stated in the text.

We thank the reviewer for pointing this out and have corrected the figure.

The figure numbering, as stated in the text, does not seem to agree with those in Supplementary Figure 8.

*We thank the reviewer for pointing this out and have corrected the text. *

Page numbers and/or line numbers and figure numbers on the figures would help the reader to navigate the manuscript more easily.

We agree with the reviewer and have added this.

Reviewer #3 (Significance (Required)):

The manuscript is a rich resource of quantitative measurements of C.elegans' structural and dynamic spindle properties, using advanced light microscopy and 3D electron microscopy imaging. In large parts, the work confirms previous conclusions of the function of PRC1 and kinesin-4 in the anaphase spindle, but also reports some interesting differences, namely that the C.elegans proteins differ from their orthologs in that they do not interact with each other, raising the question of how the kinesin-4 KLP-19 localizes to the central spindle in this organism. This work is of interest for researchers studying cell division, and specifically spindle architecture, dynamics, and function.

Prueba

Author response:

Reviewer #1:

The manuscript Xu et al. explores the regulation of the microtubule minus end protein CAMSAP2 localization to the Golgi by the Serine/threonine-protein kinase MARK2 (PAR1, PAR1B). The authors utilize immunofluorescence and biochemical approaches to demonstrate that MARK2 is localized at the Golgi apparatus via its spacer domain. They show that depletion of this protein alters Golgi morphology and diminishes CAMSAP2 localization to the Golgi apparatus. The authors combine mass spectroscopy and immunoprecipitation to show that CAMSAP2 is phosphorylated at S835 by MARK2, and that this phosphorylation regulates localization of CAMSAP2 at Golgi membranes. Further, the authors identify USO1 (p115) as the Golgi resident protein mediating CAMSAP2 recruitment to the Golgi apparatus following S835 phosphorylation. The authors would need to address the following queries to support their conclusions.

We sincerely thank the reviewer for their valuable time and effort in evaluating our manuscript. We deeply appreciate the constructive feedback and insightful suggestions, which have been instrumental in improving the quality and clarity of our study. We have carefully considered all the comments and have made the necessary revisions to address the concerns raised.

Major Comments 

(1) Dynamic localization of CAMSAP2 during Golgi reorientation

- The authors use fixed wound edges assays and co-localization analysis to describe changes in CAMSAP2 positioning during Golgi reorientation in response to polarizing cues (a free wound edge in this case). In Figure 1C, they present a graphical representation of quantified immunofluorescence images, using color coding to to describe the three states of Golgi reorientation in response to a wound (green, blue, red indicating non-polarised, partial and complete Golgi reorientation, respectively). They then use these 'colour coded' classifications to quantitate CAMSAP2/GM130 co-localization.It is unclear why the authors have not just used representative immunofluorescence images in the main figures. Transparent, color overlays could be placed over the cells in the representative images to indicate which of the three described states each cell is currently exhibiting. However, for clarity, I would recommend changing the color coded 'states' to a descriptor rather than a color. i.e. Figure 1D x axis labels should be 'complete' and 'partial', instead of 'red' and 'blue'. 

Thank you for this insightful suggestion. We have added representative immunofluorescence images with transparent color overlay to indicate the three Golgi orientation states. These images are included in Supplementary Figure 2B-C, providing a clear visual reference for the quantitative data. Additionally, we have revised the x-axis labels in Figure 1E from "Red" and "Blue" to "Complete" and "Partial" to ensure clarity and consistency with the descriptive terminology in the text. These changes are described in the Results section (page 7, lines 15-19) and the figure legend (page 29, lines 27-29).

We believe these updates improve the clarity and accessibility of our figures and hope they address the reviewer’s concerns.

- note- figure 2 F-G, is semi quantitative, why did the authors not just measure Golgi angle using the nucleus and Golgi distribution?

We appreciate the reviewer’s comment on this point. Following the recommendation, we have performed an additional analysis measuring Golgi orientation angles based on the nucleus-Golgi distribution. This quantitative approach complements our initial semi-quantitative analysis and provides a more precise assessment of Golgi orientation during cell migration.

The new data have been incorporated into Supplementary Figure 1F-H. These results clearly demonstrate the consistency between the quantitative and semi-quantitative methods, further validating our findings and highlighting the dynamic changes in Golgi orientation during cell migration. These changes are described in the Results section (page 6, lines 24-31).

- While it is established that the Golgi is dispersed during reorientation in wound edge migration, the Golgi apparatus also becomes dispersed/less condensed prior to cell division. As the authors have used fixed images - how are they sure that the Golgi morphology or CAMSAP2 localization in 'blue cells' are indicative of Golgi reorientation and not division? Live imaging of cells expressing CAMSAP2, and an additional Golgi marker could be used to demonstrate that the described changes in Golgi morphology and CAMSAP2 localization are occurring during the rear-to-front transition of the Golgi.

Thank you for raising this important question. To address this concern, we carefully examined the nuclear morphology of dispersed Golgi cells and found no evidence of mitotic features, indicating that these cells are not undergoing division (Figure 1A, Supplemental Figure 2A). Furthermore, during the scratch wound assay, we use 2% serum to culture the cells, which helps minimize the impact of cell division. This analysis has been added to the Results section (page7, lines 19-22 in the revised manuscript).

Additionally, we conducted live-cell imaging, as suggested, using cells expressing a Golgi marker. This approach confirmed that Golgi dispersion occurs transiently during reorientation in cell migration. The new live-cell imaging data have been incorporated into Supplementary Figure 2A, and the corresponding description has been updated in the Results section (page 7, lines 2-5).

Finally, considering that overexpression of CAMSAP2 can lead to artifactually condensed Golgi structures, we used endogenous staining to observe CAMSAP2 localization at different stages of migration. These observations provide a clearer understanding of CAMSAP2 dynamics during Golgi reorientation and are now presented in revised Figure 1A-B. This information has been described in the Results section (page 7, lines 5-10).

We hope these additions and clarifications address the reviewer’s concerns. Once again, we are deeply grateful for this constructive feedback, which has greatly improved the robustness of our study.

(2) MARK2 localization to the Golgi apparatus

- The authors investigated the positioning of endogenous MARK2 via immunofluorescence staining, and exogenous flag-tagged MARK2 in a KO background. The description of the protocol required to visualize Golgi localization of MARK2 is inconsistent between the results and methods text. The results text reads as through the 2% serum incubation occurs as a blocking step following fixation. Conversely, the methods section describes the 2% serum incubation as occurring just prior to fixation as a form of serum starvation. The authors need to clarify which of these protocols is correct. Further, whilst I can appreciate that the mechanistic understanding of why serum starvation is required for MARK2 Golgi localization is beyond the scope of the current work, the authors should at a minimum speculate in the discussion as to why they think it might occur.

We sincerely thank the reviewer for the constructive feedback on the localization of MARK2 at the Golgi. Due to the complexity and variability of this phenomenon, we decided to remove the related data from the current manuscript to maintain the rigor of our study. However, we have included a discussion of this phenomenon in the Discussion section (page 13, lines 31-39 and page 14, 1-6in the revised manuscript) and plan to further investigate it in future studies.

The localization of MARK2 at the Golgi was initially observed in experiments following serum starvation, where cells were fixed and stained (The data is not displayed). This observation was supported by the loss of Golgi localization in MARK2 knockdown cells, indicating the specificity of the antibody (The data is not displayed). However, this phenomenon was not consistently observed across all cells, likely due to its transient nature.We speculate that the localization of MARK2 to the Golgi depends on its activity and post-translational modifications. For example, phosphorylation at T595 has been reported to regulate the translocation of MARK2 from the plasma membrane to the cytoplasm (Hurov et al., 2004). Serum starvation might induce modifications or conformational changes in MARK2, leading to its temporary Golgi localization. Additionally, we hypothesize that this localization may coincide with specific Golgi dynamics, such as the transition from dispersed to ribbon-like structures during cell migration.

We also acknowledge the inconsistency in the Results and Methods sections regarding serum starvation. We confirm that serum starvation was performed prior to fixation as an experimental condition, rather than as a blocking step in immunostaining. This clarification has been incorporated into the revised Methods section (page 24, lines 11-12).

We hope this clarification, along with our planned future studies, adequately addresses the reviewer’s concerns. Once again, we deeply appreciate the reviewer’s valuable comments, which have provided important insights for our ongoing work. References：

Hurov, J.B., Watkins, J.L., and Piwnica-Worms, H. (2004). Atypical PKC phosphorylates PAR-1 kinases to regulate localization and activity. Curr Biol 14 (8): 736-741.

- The authors should strengthen their findings by using validated tools/methods consistent with previous publications. i.e. Waterman lab has published two MARK2 constructs- Apple and eGFP tagged versions (doi.org/10.1016/j.cub.2022.04.088), and the localization of MARK2 in U2Os cells (using the same antibody (Anti- MARK2 C-terminal, ABCAM Cat# ab136872). The authors should (1) image the cells live using eGFP-tagged MARK2 during serum starvation to show the dynamics of this localization, (2) image U2Os cells using the abcam ab136872 antibody +/- 2% serum starve. Two MARK2 antibodies are listed in Table 2. Does abcam (ab133724) show a similar localisation?

- The Golgi localization of MARK2 occurs in the absence of the T structural domain, but not when full length MARK2 is expressed. The authors conclude the T- domain is likely inhibitory. When combined with the requirement for serum starvation for this interaction to occur, the authors should clarify the physiological relevance of these observations.

We sincerely thank the reviewer for their valuable suggestions regarding the use of tools and methods and the physiological relevance of MARK2 localization to the Golgi. Regarding the question of how MARK2 itself localizes to the Golgi, we are currently unable to fully elucidate the underlying mechanism. Therefore, we have removed the discussion of MARK2’s Golgi localization from the manuscript to ensure scientific accuracy. However, Below, we provide our detailed response as soon as possible:

First, regarding the suggestion to use tools and methods developed by the Waterman lab to strengthen our findings, we have carefully evaluated their applicability. In our live-cell imaging experiments, we found that full-length MARK2 does not stably localize to the Golgi, even under serum starvation conditions. However, truncated MARK2 mutants lacking the Tail (T) domain exhibit robust Golgi localization. Furthermore, our immunofluorescence staining results indicate that the Spacer domain is the minimal region required for MARK2 localization at the Golgi. Based on these findings, we believe that live-cell imaging of EGFP-tagged full-length MARK2 may not effectively reveal the dynamics of its Golgi localization. However, we plan to focus on the truncated constructs in future studies to better explore the mechanisms underlying MARK2's dynamic behavior. 

Regarding the use of the ab136872 antibody to stain U2OS cells with and without serum starvation, we note that the protocol described by the Waterman lab involves pre-fixation and permeabilization steps, which are not compatible with live-cell imaging. Additionally, we observed that MARK2 Golgi localization appears to be condition-dependent and may coincide with specific Golgi dynamics, such as transitions from dispersed stacks to intact ribbon structures. These events are likely brief and challenging to capture consistently. Nevertheless, we recognize the value of this experimental design and plan to adapt the staining conditions in future work to validate our results further. As for the ab133724 antibody listed in Table 2, we clarify that it has only been validated for Western blotting in our study and does not yield reliable results in immunofluorescence experiments. For this reason, all immunofluorescence staining in this study relied exclusively on ab136872. This distinction has been clarified in the revised Table 2 .

Regarding the hypothesis that the Tail domain of MARK2 is inhibitory, our observations showed that truncated MARK2 mutants lacking the T domain stably localized to the Golgi, whereas fulllength MARK2 did not. Literature evidence supports this hypothesis, as studies on the yeast homolog Kin2 indicate that the C-terminal region (including the Tail domain) binds to the Nterminal catalytic domain to inhibit kinase activity (Elbert et al., 2005). We speculate that serum starvation disrupts this intramolecular interaction, relieving the inhibition by the T domain, activating MARK2, and promoting its localization to the Golgi. Moreover, we hypothesize that the transient nature of MARK2 localization to the Golgi may be related to specific Golgi remodeling processes, such as the transition from dispersed stacks to intact ribbon structures during cell migration or polarity establishment. 

References：

Elbert, M., Rossi, G., and Brennwald, P. (2005). The yeast par-1 homologs kin1 and kin2 show genetic and physical interactions with components of the exocytic machinery. Mol Biol Cell 16 (2): 532-549.

(3) Phosphorylation of CAMSAP2 by MARK2

- The authors examined the effects of MARK2 phosphorylation of CAMSAP2 on Golgi architecture through expression of WT-CAMSAP2 and two CAMSAP2 S835 mutants in CAMSAP2 KO cells. They find that CAMSAP2 S835A (non-phosphorylatable) was less capable of rescuing Golgi morphology than CAMSAP2 S835D (phosphomimetic). Golgi area has been measured to demonstrate this phenomenon. Representative immunofluorescence images in Fig. 4D appear to indicate that this is the case. However, quantification in Fig. 4E does not show significance between HA-CAMSAP2 and HA-CAMSAP2A that would support the initial claim. The authors could analyze other aspects of Golgi morphology (e.g. number of Golgi fragments, degree of dispersal around the nucleus) to capture the clear structural defects demonstrated in HACAMSAP2A cells.

We sincerely thank the reviewer for their valuable feedback and for pointing out potential areas of improvement in our analysis of Golgi morphology. We apologize for any misunderstanding caused by our description of the results in Figure 4E.

The quantification indeed shows a significant difference between HA-CAMSAP2 and HACAMSAP2A in terms of Golgi area, as indicated in the figure by the statistical annotations (pvalue provided in the legend). To ensure clarity, we have revised the figure legend (page 32, lines 19-23 in the revised manuscript) to explicitly describe the statistical significance, and the method used for quantification.

Because the quantification indeed shows a significant difference between HA-CAMSAP2 and HA-CAMSAP2A in terms of Golgi area, and to maintain consistency throughout the manuscript, we did not further analyze other aspects of Golgi morphology.

We hope this clarification, along with the additional analyses, will address the reviewer’s concerns. Once again, we are deeply grateful for these constructive comments, which have helped us improve the quality and robustness of our study.

- Wound edge assays are used to capture the difference in Golgi reorientation towards the leading edge between CAMSAP2 S835A and CAMSAP2 S835D. However, these studies lack comparison to WT-CAMSAP2 that would support the role of phosphorylated CAMSAP2 in reorienting the Golgi in this context.

We sincerely thank the reviewer for their insightful suggestion. In response, we have added a comparison between CAMSAP2 S835A/D and WT-CAMSAP2, in addition to HT1080 and MARK2 KO cells, to better evaluate the role of phosphorylated CAMSAP2 in Golgi reorientation.

The results, now shown in Figure 5A-C, indicate that in the absence of MARK2, there is no significant difference in Golgi reorientation between WT-CAMSAP2 and CAMSAP2 S835A. This observation supports the conclusion that MARK2-mediated phosphorylation of CAMSAP2 at S835 is essential for effective Golgi reorientation.

To enhance clarity, we have updated the corresponding Results section (page 9, lines 37-40 and page 10, line 1 in the revised manuscript) to describe this additional comparison. We believe this analysis strengthens our findings and provides a clearer understanding of the role of phosphorylated CAMSAP2 in Golgi dynamics.

We hope this additional data addresses the reviewer’s concerns. Once again, we are grateful for the constructive feedback, which has helped improve the clarity and robustness of our study.

(4) Identification of CAMSAP2 interaction partners

- Quantification of interaction ability between CAMSAP2 and CG-NAP, CLASP2, or USO1 in Fig. 5D, 5F and 5J respectively, lack WT-CAMSAP2 comparisons.

We sincerely thank the reviewer for their valuable suggestion. In response, we have included WT-CAMSAP2 data in the quantification of interaction ability between CAMSAP2 and CG-NAP, CLASP2, and USO1. These results, now shown in revised Figures 5 D-G and Figures 6 C-D, provide a direct comparison that further validates the differential interaction abilities of CAMSAP2 mutants.

The inclusion of WT-CAMSAP2 allows us to better contextualize the effects of specific mutations on CAMSAP2 interactions and strengthens our conclusions regarding the role of these interactions in Golgi dynamics.

We hope this addition addresses the reviewer’s concerns and enhances the clarity and robustness of our study. We deeply appreciate the constructive feedback, which has been instrumental in improving our manuscript.

- The CG-NAP immunoblot presented in Fig. 5C shows that the protein is 310 kDa, which is the incorrect molecular weight. CG-NAP (AKAP450) should appear at around 450 kDa. Further, no CG-NAP antibody is included in Table 2 - Information of Antibodies. The authors need to explain this discrepancy.

We sincerely apologize for the lack of clarity in our annotation and description, which may have caused confusion regarding the CG-NAP immunoblot presented in Figure 5C (Figure 5D in the revised manuscript). To clarify, CG-NAP (AKAP450) is indeed a 450 kDa protein, and the marker at 310 kDa represents the molecular weight marker’s upper limit, above which CG-NAP is observed. This has been clarified in the figure legend (page 33, lines 21-23 in the revised manuscript).

Regarding the CG-NAP antibody, it was custom-made and purified in our laboratory. Polyclonal antisera against CG-NAP, designated as αEE, were generated by immunizing rabbits with GSTfused fragments of CG-NAP (aa 423–542). This antibody has been validated extensively in our previous research, demonstrating its specificity and reliability (Wang et al., 2017). The details of the antibody preparation are included in the footnote of Table 2 for reference.

We hope this clarification, along with the additional context regarding the antibody validation, resolves the reviewer’s concerns. We are deeply grateful for the reviewer’s attention to detail, which has helped us improve the clarity and rigor of our manuscript.

References：

Wang, J., Xu, H., Jiang, Y., Takahashi, M., Takeichi, M., and Meng, W. (2017). CAMSAP3dependent microtubule dynamics regulates Golgi assembly in epithelial cells. Journal of genetics and genomics = Yi chuan xue bao 44 (1): 39-49.

Minor Comments

- Authors should change immunofluorescence images to colorblind friendly colors. The current presentation of merged overlays makes it really difficult to interpret- I would strongly encourage inverted or at a minimum greyscale individual images of key proteins of interest.

We sincerely thank the reviewer for their valuable suggestion regarding the presentation of immunofluorescence images. In response, we have converted the images in Figure 1C to greyscale individual images for each key protein of interest. This adjustment ensures that the figures are more accessible and interpretable, including for readers with color vision deficiencies.

We hope this modification addresses the reviewer’s concern and improves the clarity of our data presentation. We are grateful for the constructive feedback, which has helped us enhance the overall quality of our figures.

- On p. 8 text should be amended to 'Previous literature has documented MARK2's localization to the microtubules, microtubule-organizing center (MTOC), focal adhesions..'

We sincerely thank the reviewer for their comment regarding the text on page 8. Considering the reasoning provided in response to question 2, where we clarified that MARK2's Golgi localization is not fully understood, we have decided to remove this section from the manuscript to maintain the accuracy and rigor of our study.

We appreciate the reviewer’s attention to detail and constructive feedback, which has helped us improve the clarity and focus of our manuscript. 

- In Fig.1A scale bars are not shown on individual channel images of CAMSAP or GM130

We sincerely thank the reviewer for pointing out the omission of scale bars in the individual channel images of CAMSAP and GM130 in Figure 1A (Figure 1C in the revised manuscript). In response, we have added a scale bar (5 μm) to the CAMSAP2 channel, as shown in the revised Figure 1C. These updates have been described in the figure legend (page 29, line 21).

We hope this modification addresses the reviewer’s concern and improves the accuracy and clarity of our figure presentation. We greatly appreciate the reviewer’s constructive feedback, which has helped enhance the quality of our manuscript.

- In Fig. 1B the title should be amended to 'Colocalization of CAMSAP2/GM130'

We sincerely thank the reviewer for their suggestion to amend the title in Figure 1B (Figure 1D in the revised manuscript). In response, we have updated the title to "Colocalization of CAMSAP2/GM130," as shown in the revised Figure 1D.

We hope this modification addresses the reviewer’s concern and improves the clarity and accuracy of the figure. We greatly appreciate the reviewer’s valuable feedback, which has helped us refine the presentation of our results.

- In Fig. 2F, 5A, and Sup Fig 3C scale bars have been presented vertically

We sincerely thank the reviewer for pointing out the issue with the vertical orientation of scale bars in Figures 2F (Figure 2D in the revised manuscript), 5A, and Supplementary Figure 3C. In response, we have modified the scale bars in revised Figures 2D and 5A to a horizontal orientation for improved consistency and clarity. Additionally, Supplementary Figure 3C has been removed from the revised manuscript.

We hope these adjustments address the reviewer’s concerns and enhance the overall presentation quality of the figures. We greatly appreciate the reviewer’s constructive feedback, which has helped us refine our manuscript.

- Panels are not correctly aligned, and images are not evenly spaced or sized in multiple figures - Fig. 2F, 4D, Sup Fig. 1F, Sup Fig. 2C, Sup Fig. 3E, Sup Fig. 4C

We sincerely thank the reviewer for pointing out the misalignment and uneven spacing or sizing of panels in multiple figures, including Figures 2F, 4D, Supplementary Figures 1F, 2C, 3E, and 4C (Figure 2D, 4D, Supplementary Figures 1F, 2C, and 3H in the revised manuscript.

Supplementary Figure 3E was removed from our manuscript). In response, we have standardized the spacing and sizing of all panels throughout the manuscript to ensure consistency and improve visual clarity.

We hope this modification addresses the reviewer’s concerns and enhances the overall presentation quality of our figures. We greatly appreciate the reviewer’s constructive feedback, which has helped us improve the organization and professionalism of our manuscript.

- An uncolored additional data point is present in Fig. 3F

We sincerely thank the reviewer for pointing out the presence of an uncolored additional data point in Figure 3F. In response, we have removed this data point from the revised figure to ensure accuracy and clarity.

We hope this adjustment resolves the reviewer’s concern and improves the overall quality of the figure. We greatly appreciate the reviewer’s careful review and constructive feedback, which have helped us refine our manuscript.

- In Fig. 3A 'GAMSAP2/GM130' in the vertical axis label should be amended to 'CAMSAP2/GM130'

We sincerely thank the reviewer for pointing out the error in the vertical axis label of Figure 3A. In response, we have corrected "GAMSAP2/GM130" to "CAMSAP2/GM130," as shown in the revised Figure 3I.

We hope this correction resolves the reviewer’s concern and improves the accuracy of our figure. We greatly appreciate the reviewer’s careful review and constructive feedback, which have helped us refine our manuscript.

- In Fig 5A the green label should be amended to 'GFP-CAMSAP2' instead of 'GFP'

We sincerely apologize for the confusion caused by our labeling in Figure 5A. To clarify, the green label “GFP” refers to the antibody used, while “GFP-CAMSAP2” is indicated at the top of the figure to specify the construct being analyzed.

We hope this explanation resolves the misunderstanding and provides clarity regarding the labeling in Figure 5A. We greatly appreciate the reviewer’s feedback, which has allowed us to address this issue and improve the precision of our figure annotations.

- The repeated use of contractions throughout the manuscript was distracting, I would strongly encourage removing these.

We sincerely thank the reviewer for pointing out the distracting use of contractions in the manuscript. In response, we have removed and replaced all contractions with their full forms to improve the clarity and formal tone of the text.

We hope this modification addresses the reviewer’s concern and enhances the readability and professionalism of our manuscript. We greatly appreciate the reviewer’s constructive feedback, which has helped us refine the quality of our writing.

Reviewer #2: 

Summary  

This work by the Meng lab investigates the role of the proteins MARK2 and CAMSAP2 in the Golgi reorientation during cell polarisation and migration. They identified that both proteins interact together and that MARK2 phosphorylates CAMSAP2 on the residue S835. They show that the phosphorylation affects the localisation of CAMSAP2 at the Golgi apparatus and in turn influences the Golgi structure itself. Using the TurboID experimental approach, the author identified the USO1 protein as a protein that binds differentially to CAMSAP2 when it is itself phosphorylated at residue 835. Dissecting the molecular mechanisms controlling Golgi polarisation during cell migration is a highly complex but fundamental issue in cell biology and the author may have identified one important key step in this process. However, although the authors have made a genuine iconographic effort to help the reader understand their point of view, the data presented in this study appear sometimes fragile, lacking rigour in the analysis or over-interpreted. Additional analyses need to be conducted to strengthen this study and elevate it to the level it deserves.

We sincerely thank the reviewer for their thoughtful evaluation and recognition of our study's significance in understanding Golgi reorientation during cell migration. We appreciate the constructive feedback regarding data robustness, clarity, and interpretation. In response, we have conducted additional analyses, revised data presentation, and ensured cautious interpretation throughout the manuscript. These changes aim to address the reviewer’s concerns comprehensively and strengthen the scientific rigor of our study.

Major comments

In order to conclude as they do about the putative role of USO1, the authors need to perform a siRNA/CRISPR of USO1 to validate its role in anchoring CAMSAP2 to the Golgi apparatus in a MARK2 phosphorylation-dependent manner. In other words, does depletion of USO1 affect the recruitment of CAMSAP2 to the Golgi apparatus?

We sincerely thank the reviewer for their insightful suggestion regarding the role of USO1 in anchoring CAMSAP2 to the Golgi apparatus. In response, we performed USO1 knockdown using siRNA and quantified the Pearson correlation coefficient of CAMSAP2 and GM130 colocalization in control and USO1-knockdown cells.

The results show that CAMSAP2 localization to the Golgi is significantly reduced in USO1knockdown cells, confirming that USO1 plays a critical role in recruiting CAMSAP2 to the Golgi apparatus. These results are now presented in Figures 6 E–G, and corresponding updates have been incorporated into the Results section (page 10, lines 36-37 in the revised manuscript).

We hope this additional experiment addresses the reviewer’s concern and strengthens our conclusions regarding the role of USO1. We are grateful for the reviewer’s constructive feedback, which has greatly improved the robustness of our study.  

It is not clear from this study exactly when and where MARK2 phosphorylates CAMSAP2. What is the result of overexpression of the two proteins in their respective localisation to the Golgi apparatus? As binding between CAMSAP2 and MARK2 appears robust in the immunoprecipitation assay, this should be readily investigated. 

We sincerely thank the reviewer for their insightful comments and questions. To address the role of MARK2 in regulating CAMSAP2 localization to the Golgi apparatus, we overexpressed GFPMARK2 in cells and compared its effects on CAMSAP2 localization to the Golgi with control cells overexpressing GFP alone. Our results show that CAMSAP2 localization to the Golgi is significantly increased in GFP-MARK2-overexpressing cells, as shown in Supplementary Figures 3C and 3E. Corresponding updates have been incorporated into the Results section (page 8, lines 25-27 in the revised manuscript).

Regarding the question of how MARK2 itself localizes to the Golgi, we are currently unable to fully elucidate the underlying mechanism. Therefore, we have removed the discussion of MARK2’s Golgi localization from the manuscript to ensure scientific accuracy. Consequently, we have not conducted experiments to assess the effects of CAMSAP2 overexpression on MARK2’s localization to the Golgi.

We hope this explanation clarifies the reviewer’s concerns. We are grateful for the reviewer’s constructive feedback, which has guided us in improving the clarity and focus of our study.

To strengthen their results, can the author map the interaction domains between CAMSAP2 and MARK2? The authors have at their disposal all the constructs necessary for this dissection.

We sincerely thank the reviewer for their insightful suggestion to map the interaction domains between CAMSAP2 and MARK2. In response, we performed immunoprecipitation experiments using truncated constructs of CAMSAP2. Our results reveal that MARK2 interacts specifically with the C-terminus (1149F) of CAMSAP2, as shown in Supplementary Figures 3A and 3B. Corresponding updates have been incorporated into the Results section (page 7, lines 41-42 and page 8, line 1 in the revised manuscript).

We hope this additional analysis addresses the reviewer’s suggestion and further strengthens our conclusions. We greatly appreciate the reviewer’s constructive feedback, which has helped improve the depth of our study.

Minor comments

Sup-fig1  

H: It is not clear if the polarisation experiment has been repeated three times (as it should) and pooled or is just the result of one experiment?

We sincerely apologize for the lack of clarity regarding the experimental details for Supplementary Figure 1H. To clarify, the polarization experiment was repeated three times, and the results were pooled to generate the data presented. We have updated the figure legend for Supplementary Figure 1H to explicitly state this information (page 35, lines 27-29 in the revised manuscript).

We hope this clarification resolves the reviewer’s concern. We greatly appreciate the reviewer’s careful review and constructive feedback, which have helped us improve the accuracy and transparency of our manuscript.

Sup-fig2  

C: "Immunofluorescence staining plots" formula used in the legend is not clear. Which condition is presented in the panel, parental HT1080 or CAMSAP2 KO cells?  

We thank the reviewer for pointing out the lack of clarity regarding the conditions presented in Supplementary Figure 2C. To clarify, the immunofluorescence staining plots shown in this panel are from parental HT1080 cells. We have updated the figure legend to include this information (page 36, line 14 in the revised manuscript).

We hope this clarification resolves the reviewer’s concern and improves the transparency of our data presentation. We greatly appreciate the reviewer’s feedback, which has helped us refine the manuscript.

Figure 1  

D: In the plot, the colour of the points for the "red cells" are red but the one for the "blue cells" are green, this is confusing.

E: Once again, the colour choice is confusing as blue cells (t=0.5h) are quantified using red dots and red cells (t=2h) quantified using green dots. The t=0h condition should be quantified as well and added to the graph.  

F: Representative CAMSAP2 immunofluorescence pictures for the three time points should be provided in addition to the drawings.  

We thank the reviewer for their valuable comments regarding Figure 1D (revised Figure 1E), Figure 1E (revised Figure 1B), and Figure 1F (revised Supplementary Figure 2C).

- Figure 1D (revised Figure 1E): we have modified the x-axis labels and adjusted the color scheme of the data points to ensure consistency and avoid confusion.

- Figure 1E (revised Figure 1B): we have updated the x-axis and included the quantification of the t=0h condition, which has been added to the graph.

- Figure 1F (revised Supplementary Figure 2C): we have provided representative immunofluorescence images of CAMSAP2 for the three-time points to complement the schematic drawings.

We hope these revisions address the reviewer’s concerns and improve the clarity and completeness of our data presentation. We greatly appreciate the reviewer’s constructive feedback, which has significantly contributed to enhancing our manuscript.

Figure 2  

A: No methodology in the material and methods is provided for this analysis.  

B: Can the authors be more precise regarding the source of the CAMSAP2 interactants? Can the author provide the citation of the publication describing the CAMSAP2-MARK2 interaction?  

D: Genotyping for the MARK2 KO cell line should be provided the same way it was provided for the CAMSAP2 cell line in Sup-fig1. "MARK2 was enriched around the Golgi apparatus in a  significant proportion of HT1080 cells": which proportion of the cells?  

F: The time point of fixation is missing  

G: It is not clear if the polarisation experiment has been repeated three times (as it should) and pooled or is just the result of one experiment?  

We thank the reviewer for their detailed comments and suggestions regarding Figure 2. Below, we provide clarifications and outline the modifications made:

- Figure 2A: The methodology for this analysis has been added to section 5.14 (Data statistics). Specifically, we have stated: “GO analysis of proteins was plotted using https://www.bioinformatics.com.cn, an online platform for data analysis and visualization” (page 26 lines 5-6 in the revised manuscript).

- Figure 2B: The CAMSAP2 interactants were derived from the study by Wu et al., 2016, which provides the source of these interactants. The interaction between CAMSAP2 and MARK2 is referenced from Zhou et al., 2020. These citations have been added to the relevant sections of the manuscript (page 30, lines 10-11 and 13-14).

- Figure 2D (removed in the revised manuscript): Genotyping for the MARK2 KO cell line has been provided in the same format as for the CAMSAP2 KO cell line in Figure 2G. Additionally, as the MARK2 Golgi localization discussion cannot yet be fully elucidated, we have removed this portion from the manuscript.

- Figure 2F (revised Figure 2D): The time point of fixation, which occurred 2 hours after the scratch wound assay, has been added to the figure legend (page 30, lines 15-16).

- Figure 2G (revised Figure 2E-F): The polarization experiment was repeated three times, and the results were pooled. This information has been included in the figure legend (page 30, lines 26 and 29).

We hope these updates address the reviewer’s concerns and improve the clarity and completeness of the manuscript. We are grateful for the reviewer’s constructive feedback, which has greatly enhanced the rigor of our study. References：

Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

Sup-fig3  

E: Although colocalisation between CAMSAP2 and MARK2 is clear in your serum conditions in HT1080 and RPE1 cells, the deletion domain analysis appears weak and insufficient to implicate the role of the spacer domain. This part should be deleted or strengthened, but the data do not satisfactorily support your conclusion as it stands.  

We sincerely thank the reviewer for their critical comments regarding the deletion domain analysis of MARK2 and its role in colocalization with CAMSAP2. As the current data do not satisfactorily support our conclusions, we have removed all related content on MARK2 and the deletion domain analysis from the manuscript to maintain scientific rigor.

We appreciate the reviewer’s valuable feedback, which has helped us refine and improve the quality and focus of our study.

Figure 3  

A: Can the reduced CAMSAP2 Golgi localisation phenotype be rescued by the overexpression of MARK2 cDNA in the MARK2 KO cells?  

F: Presence of a white dot on the HT1080 plot  

G: The composition of the homogenization buffer is not indicated in the material and methods  

We thank the reviewer for their valuable comments and suggestions regarding Figure 3. Below, we detail the modifications made:

- Figure 3A: To address whether the reduced CAMSAP2 Golgi localization phenotype can be rescued, we overexpressed MARK2 cDNA in MARK2 KO cells. Our results show that overexpression of MARK2 successfully rescues the reduced CAMSAP2 localization to the Golgi, as demonstrated in Supplementary Figures 3C and 3E (page 8, lines 5-7).

- Figure 3F: We have removed the white dot on the HT1080 plot to ensure clarity and accuracy.

- Figure 3G: The composition of the homogenization buffer used in the experiment has been added to the Materials and Methods section for completeness (page 24, lines 34-41 and page 25, lines 1-10).

We hope these revisions address the reviewer’s concerns and enhance the clarity and rigor of our study. We are grateful for the reviewer’s constructive feedback, which has significantly improved the quality of our manuscript.

Figure 4  

B: Quantification of the effect of the S835A mutation should be provided  

D: Top left panel: Why Ha antibody stains Golgi structure in absence of Ha-CAMSAP2 transfection ? IF the Ha antibody has unspecific affinity towards the Golgi apparatus, may be it is not the good tag to use in this assay?  

E: The number of cells studied should be standardized. 119 cells were analyzed in the CAMSAP KO vs only 35 cells in the CAMSAP2 KO (HA-CAMSAP2-S835D) conditions. This could introduce strong bias to the analysis. Furthermore the CAMSAP2 S835A seems to provide a certain level of rescue. It would be interesting to see what is the result of the T test between the HT1080 and HA-CAMSAP S835A conditions.  

We thank the reviewer for their thoughtful comments and suggestions regarding Figure 4. Below, we detail the revisions and clarifications made:

- Figure 4B: The S835A mutation renders CAMSAP2 non-phosphorylatable by MARK2. This conclusion is based on our experimental observations and previously reported mechanisms.

- Figure 4D: The HA antibody does not exhibit non-specific affinity toward the Golgi apparatus. The observed labeling in the top left panel was due to an error in our annotation. We have corrected the label, replacing "HA" with "CAMSAP2" to accurately reflect the experimental conditions.

- Figure 4E: To standardize the number of cells analyzed across conditions, we reduced the number of CAMSAP2 KO cells analyzed to 50 and balanced the sample sizes for comparison. Additionally, we performed a t-test between the HT1080 and HACAMSAP2 S835A conditions. The results support that CAMSAP2 S835A provides partial rescue, as reflected in the updated analysis (page 32, lines 19-23).

We hope these revisions address the reviewer’s concerns and improve the accuracy and reliability of our results. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the quality of our study.

Figure 6  

6A: The wound position should be indicated on the picture.  

6B: Given that microtubule labelling is present on the vast majority of the cell surface, this type of quantification provides very little information using conventional light microscopy and should not be used to conclude any change in the microtubule network using Pearson's coefficient.  The text describing the figure 6A and 6B needs re written as I do not understand what the author want to say. "In cells located before the wound edge..." : I do not understand how a cell could be located before the wound edge. Which figure corresponds to the trailing edge of the wounding?

We thank the reviewer for their valuable comments on Figure 6A (revised Supplementary Figure 6E) and Figure 6B (revised Supplementary Figure 6F). Below, we detail the modifications made:

- Figure 6A (revised Supplementary Figure 6E), we have added arrows to indicate the wound position, providing clearer guidance for interpreting the image.

- Figure 6B (revised Supplementary Figure 6F), we revised our quantification method based on the approach used in literature (Wu et al., 2016). Specifically, we analyzed the relationship between microtubules and the Golgi apparatus in cells at the leading edge of the wound. The x-axis represents the distance from the Golgi center, while the y-axis shows the normalized radial fluorescence intensity of microtubules and the Golgi apparatus.

Additionally, we revised the accompanying text for clarity and accuracy. The original description:

“In cells located before the wound edge, the Golgi apparatus maintained a ribbon-like shape, with a higher density of microtubules. In contrast, at the trailing edge of the wounding, the Golgi apparatus appeared more as stacks around the nucleus, with fewer microtubules”  was replaced with:

“Finally, to comprehensively understand the dynamics between non-centrosomal microtubules and the Golgi apparatus during Golgi reorientation, we conducted cell wound-healing experiments (Supplementary Figure 6 E-F). Our observations revealed notable changes in the Golgi apparatus and microtubule network distribution in relation to the wounding. These findings corroborate our earlier results and suggest a highly dynamic interaction between the Golgi apparatus and microtubules during Golgi reorientation” (Revised manuscript page 11 lines 3-10).

We hope these changes address the reviewer’s concerns and improve the clarity and robustness of our study. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the presentation and interpretation of our data. References：

Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

Reviewer #3:  

Summary  

In this study, Xu et al. analyzed the wound healing process of HT1080 cells to elucidate the molecular mechanisms by which the Golgi apparatus exhibits transient dispersion before reorienting to the wound edge in the compact assembly structure. They focused on the role of the microtubule minus-end binding protein CAMSAP2, which mediates the linkage between microtubules and the Golgi membrane. At first, they noticed that CAMSAP2 transiently lost Golgi colocalization during the initial phase of the wound healing process. They further found that the cell polarity-regulating kinase MARK2 binds and phosphorylates S835 of CAMSAP2, thereby enhancing the interaction between CAMSAP2 and the Golgi protein Uso1. Together with the phenotypes of CAMSAP2, MARK2, and Uso1 KO cells, these authors argue that the MARK2dependent phosphorylation of CAMSAP2 plays an important role in the reassembly and reorientation of the Golgi apparatus after a transient dispersion observed during the wound healing process.

We sincerely thank the reviewer for their thoughtful summary of our study and constructive feedback. Your comments have been invaluable in refining our research and enhancing the clarity and impact of our manuscript.

Major comments

(1) The premise of this study was that during the wound healing process, the Golgi apparatus exhibits transient dispersion before reorientation to the front of the nucleus.  

In the first place, this claim has not been well established in previous studies or this paper. Therefore, the authors should present a proof of this claim in a clearer manner.  

To introduce this cellular event, the authors cite several papers in the introduction (page 4) and the results (page 6) sections. However, many papers cited are review articles, and some of them do not describe this change in the Golgi assembly structure before reorientation. Only two original articles discussed this phenomenon (Bisel et al. 2008 and Wu et al. 2016), and direct evidence was provided by only one paper (Wu et al. 2016) in which changes in the Golgi apparatus in wound-healing RPE1 cells were recorded by live imaging (Fig.7A in Wu et al. 2016).

Furthermore, it should be noted that this previous paper demonstrated that depletion of CAMSAP2 inhibits Golgi dispersion. Obviously, this conclusion is inconsistent with their statement to introduce this study (page4) that ‟This emphasizes CAMSAP2's role in sustaining Golgi integrity during critical cellular events like migration." In addition, it also contradicts the authors' model of the present paper (Fig. 6E), which argued that disruption of the Golgi association of CAMSAP2 facilitates the Golgi dispersion.  

We sincerely thank the reviewer for their detailed comments and for providing us with the opportunity to clarify the premise and conclusions of our study. Below, we address the main concerns raised:

First, to provide direct evidence of Golgi apparatus changes during the wound-healing process, we conducted live-cell imaging experiments. Our observations, presented in revised Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus exhibits a transient dispersion state before reorienting toward the leading edge of the nucleus during migration.

Regarding the interpretation of previous studies, we acknowledge the reviewer’s concerns about the citation of review articles. To address this, we have revisited the literature and clarified that the phenomenon of Golgi dispersion during reorientation has been directly demonstrated in Wu et al (Wu et al., 2016), where live imaging of wound-healing RPE1 cells showed this dynamic behavior. Furthermore, we note that in Wu et al paper explicitly demonstrates that CAMSAP2 depletion promotes Golgi dispersion, contrary to the reviewer’s interpretation that "depletion of CAMSAP2 inhibits Golgi dispersion."

Our model focuses on the role of CAMSAP2 in restoring the Golgi from a transiently dispersed structure back to an intact ribbon-like structure during reorientation. Specifically, we propose that during this process, the disruption of CAMSAP2’s association with the Golgi affects this restoration, rather than directly promoting Golgi dispersion as suggested by the reviewer. We believe this distinction aligns with our data and the existing literature.

To strengthen the background of our study, we have revised the introduction and results sections (page 6, lines 6-13 and page 7, lines 1-17) to minimize reliance on review articles and have provided more explicit citations to original research papers. We hope this addresses the reviewer’s concern about the sufficiency of the cited literature.

We trust these clarifications and revisions resolve the reviewer’s concerns and enhance the robustness of our study. Once again, we are grateful for the reviewer’s constructive feedback, which has greatly helped refine our manuscript. References：

Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

The authors did not provide experimental data for this temporal change in the Golgi assembly structures during the wound-healing process of HT1080 that they analyzed. They only provide an illustration of wound-healing cells (Fig.1F), in which cells are qualitatively discriminated and colored based on the Golgi states, without indicating the experimental basis of the discrimination.

According to their ambiguous descriptions in the text (page7), the reader can speculate that Fig. 1F is illustrated based on the images in Supplementary Fig. 2C. However, because of the low quality and presentation style of these data, it is impossible to recognize the assembly structures of the Golgi apparatus in wound-edge cells.  

If the authors hope to establish this premise claim for the present paper, they should provide their own data corresponding to the present Supplementary Fig. 2C in more clarity and present qualitative data verifying this claim, as Wu et al. did in Fig. 7A in their paper.

We sincerely thank the reviewer for their constructive feedback and the opportunity to address the concern regarding the lack of experimental data supporting the temporal changes in Golgi assembly during the wound-healing process.

To establish this premise, we conducted live-cell imaging experiments to observe the dynamic changes in the Golgi apparatus during directed cell migration. Our data, now presented in Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus undergoes a transient dispersed state before reorganizing into an intact structure. These findings provide direct experimental evidence supporting our claim.

In addition, we have revised the data originally presented in Supplementary Figure 2C and enhanced its quality and presentation style. This supplementary figure now includes clearer images and annotations to better illustrate the Golgi assembly structures in wound-edge cells. The improved data presentation aligns with the standards set by Wu et al reported (Wu et al., 2016) and provides qualitative support for our observations.

We hope these additions and revisions address the reviewer’s concerns and strengthen the scientific rigor and clarity of our manuscript. We are grateful for the reviewer’s valuable suggestions, which have significantly improved the quality of our study. References：

Wu, J., de Heus, C., Liu, Q., Bouchet, B.P., Noordstra, I., Jiang, K., Hua, S., Martin, M., Yang, C., Grigoriev, I., et al. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell 39 (1): 44-60.

(2) In Fig.1A-D, the authors claim that CAMSAP2 dissociates from the Golgi apparatus in cells "that have not yet completed Golgi reorientation and exhibit a transitional Golgi structure, characterized by relative dispersion and loss of polarity (page7)." However, I these analyses, they do not analyze the initial stage (0.5h after wound addition) of cells facing the wound edge, as they do in Supplementary Fig. 2C. Instead, they analyze cells separated from the wound edge at 2 h after wound addition when the wound-edge cells complete their polarization. These data are highly misleading because there is no evidence that the cells separated from the wound edge are really in the transitional state before polarization.  

In this regard, Fig. 1E shows the analysis of the wound-edge cells at 0.5 and 2 h after the addition of wound, which provides suitable data to verify the authors' claim. However, the corresponding legend indicates that these statistical data are based on the illustration in Fig. 1F, which is probably based on highly ambiguous data in Supplementary Fig. 2C (see above).  

Taken together, I strongly recommend the authors to remove Fig.1A-D. Instead, they should include the improved figure corresponding to the present Supplementary Fig.2C and present its statistical analysis similar to the present Fig.1E for this claim.

We sincerely thank the reviewer for their constructive feedback and recommendations. Below, we address the concerns raised regarding Figure 1A-D and Supplementary Figure 2C.

To provide stronger evidence for the transitional state of the Golgi apparatus during reorientation and the dynamic regulation of CAMSAP2 localization, we conducted live-cell imaging experiments. These results, now presented in Supplementary Figure 2A, clearly demonstrate that the Golgi apparatus undergoes a transitional state characterized by dispersion before reorienting toward the leading edge.

Additionally, we analyzed fixed wound-edge cells at different time points during directed migration to observe CAMSAP2’s colocalization with the Golgi apparatus. The results, shown in Figures 1A and 1B, reveal dynamic changes in CAMSAP2 localization, confirm its regulation during Golgi reorientation, and include a corresponding statistical analysis (page 7, lines 1-17).

These updates ensure that our claims are supported by robust and unambiguous data.

We hope these revisions address the reviewer’s concerns and provide clear and reliable evidence for the transitional state of the Golgi apparatus and CAMSAP2’s dynamic regulation. We are grateful for the reviewer’s constructive suggestions, which have greatly improved the quality and focus of our manuscript.

(3) In Supplementary Fig. 5 and Fig. 4, the authors claim that MARK2 phosphorylates S835 of CAMSAP2.  

There are many issues to be addressed. Otherwise, the above claim cannot be assumed to be reliable.  

First, the descriptions (in the text and method sections) and figures (Supplementary Fig.5) concerning the in vitro kinase assay and subsequent phosphoproteomic analysis are too immature and contain many errors.  

Legend to Supplementary Fig. 5 is too immature for comprehension. It should be completely rewritten in a more comprehensive manner. The figure in Supplementary Fig. 5C is also too immature for understanding. They simply paste raw mass spectrometric data without any modification for presentation.  

We sincerely apologize for the lack of clarity and inaccuracies in the original descriptions and figure legends for the in vitro kinase assay and phosphoproteomic analysis. We greatly appreciate the reviewer’s detailed comments, which have allowed us to address these issues comprehensively.

To improve clarity and accuracy, we have rewritten the figure legend for the original Supplementary Figure 5 (now Supplementary Figure 4) as follows:

(A): CBB staining of a gel with GFP-CAMSAP2, GST, and GST-MARK2. GFP-CAMSAP2 was expressed in Sf9 cells and purified. GST and GST-MARK2 were expressed in E. coli and purified.

(B): Western blot analysis of an in vitro kinase assay. GST or GST-MARK2 was incubated with GFP-CAMSAP2 in kinase buffer (50 mM Tris-HCl pH 7.5, 12.5 mM MgCl2, 1 mM DTT, 400 μM ATP) at 30°C for 30 minutes. Reactions were stopped by boiling in the loading buffer.

(C): Detection of phosphorylation at S835 in CAMSAP2 by mass spectrometry. The observed mass increases in b4, b5, b6, b7, b8, b10, b11, and b12 fragments indicate phosphorylation at Ser835.

(D): Kinase assay samples analyzed using Phos-tag SDS-PAGE. HEK293 cells were cotransfected with the indicated plasmids. Band shifts of CAMSAP2 mutants were examined via western blot. Phos-tag was used in SDS-PAGE, and arrowheads indicate the shifted bands caused by phosphorylation.

To address the reviewer’s concern about Supplementary Figure 5C, we have reformatted the mass spectrometry data to improve readability and presentation quality. The revised figure includes clearer annotations and graphical representations of the mass spectrometric evidence for phosphorylation at S835.

We believe these updates enhance the comprehensibility and reliability of our data, providing robust support for our claim that MARK2 phosphorylates CAMSAP2 at S835. We hope these

revisions address the reviewer’s concerns and demonstrate our commitment to improving the quality of our manuscript.

The readers cannot understand how the authors purified GFP-CAMSAP2 for the kinase assay.

The method section incorrectly states that the product was purified using Ni-resin.  

We thank the reviewer for their comment regarding the purification of GFP-CAMSAP2 for the kinase assay. We would like to clarify that GFP-CAMSAP2 carries a His-tag, which allows for purification using Ni-resin, as described in the Methods section (page 23, Lines 32-40). Therefore, the description in the Methods section is correct.

To avoid any potential misunderstanding, we have revised the Methods section to provide more detailed and precise descriptions of the purification process. Specifically, GFP-CAMSAP2 was cloned into the pOCC6_pOEM1-N-HIS6-EGFP vector, which includes a His-tag, and was expressed in Sf9 cells. The His-GFP-CAMSAP2 protein was purified using Ni-resin chromatography. Relevant details have been added to the Methods section (page 21, Lines 34-36:

“CAMSAP2 was cloned into the pOCC6_pOEM1-N-HIS6-EGFP vector expressed in Sf9, purified as His-GFP-CAMSAP2.”; page 23, Lines 32-33: “His-GFP-CAMSAP2 was cotransfected with bacmids into Sf9 cells to generate the passage 1 (P1) virus.”).

We hope these clarifications and revisions address the reviewer’s concern and improve the comprehensibility of our experimental details. We appreciate the reviewer’s feedback, which has helped us refine the manuscript.

In this relation, GST and GST-MARK2 are described as having been purified from Sf9 insect cells in the text section (page9) and legend to Supplementary Fig. 5, but from E. coli in the method section. Which is correct?  

We thank the reviewer for pointing out the inconsistencies in the descriptions regarding the source of GST and GST-MARK2. To clarify, both GST and GST-MARK2 were purified from E. coli, as stated in the Methods section (page 23, Lines 26-31). We have corrected the erroneous descriptions in the main text (page 8, Lines 35-36) and the legend to Supplementary Figure 4 to ensure consistency.

Additionally, we have updated the legend for Supplementary Figure 4A to state the sources of each protein explicitly:

“GFP-CAMSAP2 were expressed in Sf9 cells and purified. GST and GST-MARK2 were expressed in E. coli and purified.” (page 38, Lines 2-3)

These revisions ensure that the experimental details are accurate and consistent across the manuscript, eliminating any potential confusion. We appreciate the reviewer’s careful review and constructive feedback, which have helped us improve the clarity and reliability of our study.

Because the phosphoproteomic data (Supplementary Fig. 5C) are not provided clearly, the experimental data for Fig.4A, in which possible CAMSAP2 phosphorylation sites are illustrated, are completely unknown. For me, it is highly strange that only the serine residues are listed in Fig. 4A.

We sincerely thank the reviewer for raising this important point regarding Figure 4A and the phosphoproteomic data in Supplementary Figure 5C.

- Phosphorylation Sites in Figure 4A

The phosphorylation sites illustrated in Figure 4A are derived from our analysis of the original mass spectrometry data. These sites were included based on their high confidence scores and data reliability. Importantly, only serine residues met the stringent criteria for inclusion, as no threonine or tyrosine residues had sufficient evidence for phosphorylation. To clarify this, we have updated the figure legend for Figure 4A (page 32, Lines3-7).

- Improvements to Supplementary Figure 5C (Supplementary Figure 4D in the revised manuscript)

To enhance transparency and clarity, we have reformatted Supplementary Figure 4D to include clearer annotations. The revised figure highlights the phosphopeptides used to identify the phosphorylation sites and provides a more comprehensive presentation of the mass spectrometry data. To clarify this, we have updated the figure legend for Supplementary Figure 4D (page 38, Lines 11-13).

- Data Availability

We will follow the journal’s guidelines by uploading the raw mass spectrometry data to the required public database upon manuscript acceptance. This ensures that the data are accessible and reproducible in compliance with journal standards.

We hope these clarifications and updates address the reviewer’s concerns and improve the reliability and comprehensibility of our data presentation. We greatly appreciate the reviewer’s constructive feedback, which has helped us enhance the rigor and clarity of our manuscript.

Considering the crude nature of the GST-MARK2 sample used for the in vitro kinase assay (Supplementary Fig. 5A), it is unclear whether MARK2 is responsible for all phosphorylation sites on CAMSAP2 detected in the phosphoproteomic analysis. Furthermore, if GFP-CAMSAP2 was purified from Sf9 insect cells, these sites might have been phosphorylated before incubation for the in vitro kinase assay. The authors should address these issues by including a negative control using the kinase-dead mutant of MARK2 in their in vitro kinase assay.

We sincerely thank the reviewer for raising these important points regarding the potential prephosphorylation of GFP-CAMSAP2 and the role of MARK2 in the phosphorylation sites detected in our analysis.

To address the possibility that GFP-CAMSAP2 may have been pre-phosphorylated during its expression in Sf9 insect cells, we conducted an in vitro comparison. Specifically, we compared the band shifts observed in GST-MARK2 + GFP-CAMSAP2 versus GST + GFP-CAMSAP2 under identical conditions. As shown in Supplementary Figure 4B, the GST-MARK2 + GFP-CAMSAP2 group exhibited a clear upward band shift compared to the GST + GFP-CAMSAP2 group, indicating additional phosphorylation events induced by MARK2.

Regarding the inclusion of a kinase-dead MARK2 mutant as a negative control, we acknowledge this as a valuable suggestion for further confirming the specificity of MARK2 in phosphorylating CAMSAP2. While this experiment is not currently included, we plan to conduct it in our future studies to strengthen our findings.

We hope this clarification and the provided evidence address the reviewer’s concerns. We are grateful for this constructive feedback, which has helped us critically evaluate and refine our experimental approach.

(4) In Supplementary Fig.6A-C and Fig.5A-B, the authors claim that the phosphorylation of CAMSAP2 S835 is required for restoring the reduced reorientation of the Golgi in wound-healing cells and the delay in wound closure observed in MARK2 KO cells.  

If the aforementioned claim is adequately supported by experimental data, it indicates that the defects in Golgi repolarization and wound closure in MARK2 KO cells can be mainly attributed to the reduced phosphorylation of S835 of CAMSAP2 in HT1080. Considering the presence of many well-known substrates of MARK2 for regulating cell polarity, this claim is highly striking.  

However, to strongly support this conclusion, the authors should first perform a rescue experiment using MARK2 KO cells exogenously expressing MARK2. This step is essential for determining whether the defects observed in MARK2 KO cells are caused by the loss of MARK2 expression, but not by other artificial effects that were accidentally raised during the generation of the present MARK2 KO clone.  

We sincerely thank the reviewer for their insightful suggestion regarding the rescue experiment to confirm that the defects observed in MARK2 KO cells are specifically caused by the loss of MARK2 expression.

To address this, we performed a rescue experiment in MARK2 KO HT1080 cells by exogenously expressing GFP-MARK2. Our results, presented in Supplementary Figures 3C-E, demonstrate that GFP-MARK2 expression successfully restores the localization of CAMSAP2 on the Golgi apparatus in MARK2 KO cells.

These findings strongly support the conclusion that the defects in Golgi architecture and CAMSAP2 Golgi localization are directly attributable to the loss of MARK2 expression, rather than any artificial effects potentially introduced during the generation of the MARK2 KO clone.

We hope these additional experimental results address the reviewer’s concerns and provide robust evidence for the role of MARK2 in regulating Golgi reorientation and wound closure. We are grateful for the reviewer’s constructive feedback, which has significantly improved the rigor and clarity of our study.

In addition, to evaluate the impact of the rescue effect of CAMSAP2, the authors should include the data of wild-type HT1080 and MARK2 KO cells in Fig. 5B to reliably demonstrate the aforementioned claim.  

We thank the reviewer for their valuable suggestion to include data from wild-type HT1080 and MARK2 KO cells in Figure 5A-C to better evaluate the rescue effects of CAMSAP2.

In response, we have incorporated data from wild-type HT1080 and MARK2 KO cells into Figure 5A-C. These additions provide a comprehensive comparison and further demonstrate the impact of CAMSAP2-S835A and CAMSAP2-S835D on Golgi reorientation relative to the wild-type and MARK2 KO conditions.

These changes are reflected in Figures 5A-C.

We hope these updates address the reviewer’s concerns and strengthen the reliability of our conclusions. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the robustness of our study.

Principally, before checking the rescue effects in MARK2 KO cells, the authors should examine the rescue activity of the CAMSAP2 S835 mutants in restoring the reduced reorientation of the Golgi in wound-healing cells and the delay in wound closure observed in CAMSAP2 KO cells (Supplementary Fig.1F-H and Supplementary Fig.2A, B). These experiments are more essential experiments to substantiate the authors' claim.

We thank the reviewer for their insightful suggestion to examine the rescue activity of CAMSAP2 S835 mutants in CAMSAP2 KO cells to further substantiate our claims.

In Figure 4D-F, we observed significant differences between CAMSAP2 S835 mutants in their ability to restore Golgi structure and localization, indicating functional differences between these mutants. To better reflect the regulatory role of MARK2-mediated phosphorylation of CAMSAP2, we performed scratch wound-healing experiments in MARK2 KO cells by establishing stable cell lines expressing CAMSAP2 S835 mutants. These experiments allowed us to assess Golgi reorientation during wound healing and are presented in Figure 5A-C.

We also attempted to generate stable cell lines expressing GFP-CAMSAP2 and its mutants in CAMSAP2 KO cells. Unfortunately, these cells consistently failed to survive, preventing successful construction of the cell lines.

We hope these experiments and explanations address the reviewer’s concerns. We are grateful for the reviewer’s constructive feedback, which has helped us refine and improve our study.

(5) The data presented in Fig. 6A and B are not sufficient to support the authors' notion that "our observation revealed notable changes in the Golgi apparatus and microtubule network distribution in relation to the wounding. (page 11)"  

Fig. 6A, which includes only a single-cell image in each panel, does not demonstrate the general state of microtubules and the Golgi in the wound-edge cells. The reader cannot even know the migration direction of each cell.  

Fig.6 B are not suitable to quantitatively support the authors' claim. The authors should find a way to quantitatively estimate the microtubule density around the Golgi and the shape and compactness of the Golgi in each cell facing the wound, not estimating the colocalization of microtubules and the Golgi, as in the present Fig. 6B.  

We sincerely apologize for the confusion caused by our unclear descriptions and presentation.

Here, we clarify the purpose and improvements made to address the reviewer’s concerns. In this study, we primarily aimed to observe the relationship between microtubules and the Golgi apparatus in cells at the leading edge of the wound during directed migration. In Figure 6A (now Supplementary Figure 6E), the images represent cells located at the wound edge at different time points. To improve clarity, we have added arrows indicating the migration direction and updated the figure legend to describe these details (page 40 lines 13-14).

To better quantify the relationship between microtubules and the Golgi apparatus, we revised our analysis by referring to the quantitative method used in Figure 3F of the paper Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Specifically, we performed a radial analysis of fluorescence intensity in cells at the wound edge, measuring the distance from the Golgi center (x-axis) and the normalized radial fluorescence intensity of microtubules and the Golgi (y-axis). These results are now presented in Supplementary Figure 6E and 6F.

We hope these improvements address the reviewer’s concerns and provide stronger evidence for the changes in the Golgi apparatus and microtubule network distribution in relation to wound healing. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the clarity and rigor of our study.

The legends to Fig. 6A and B indicate that they compared immunofluorescent staining of cells at the edge of the wound after 0.5h and 2 h of migration. However, the authors state in the text that they compared "the cells located before the wound" and "the cells at the trailing edge of the wounding (page 11)."Although this description is highly ambiguous and misleading, if they compared the wound-edge cells and the cells separated from the wound edge at 2 h after cell migration here, they should improve the experimental design as I pointed out in the 2nd major comment.  

We thank the reviewer for their detailed feedback regarding the experimental design and the need to clarify our descriptions. We have addressed these concerns as follows:

- Clarification of descriptions:

We recognize that the previous description in the text regarding "the cells located before the wound" and "the cells at the trailing edge of the wounding" was ambiguous and potentially misleading. We have revised this text to accurately describe the experimental design. Specifically, we compared cells at the leading edge of the wound at different time points (0.5h and 2h post-migration). These corrections are reflected in figure legends (Supplementary Figure 6E and 6F ) and the Results section (page 11,lines 3-8).

- Improved experimental design:

To better support our conclusions, we performed live-cell imaging to observe the dynamic changes in the Golgi apparatus during directed migration. As shown in Supplementary Figure 2A, our results confirm that the Golgi apparatus undergoes a transient dispersed state before reorganizing into an intact structure.

Additionally, we performed fixed-cell staining at different time points to analyze the colocalization of CAMSAP2 with the Golgi apparatus in cells at the leading edge of the wound. The colocalization analysis, presented in Figures 1A-C, further demonstrates the dynamic regulation of CAMSAP2 during Golgi reorientation.

We hope these updates address the reviewer’s concerns and provide a clearer and more robust foundation for our conclusions. We are grateful for the reviewer’s constructive feedback, which has greatly enhanced the clarity and rigor of our study.

Minor comments  

(1) In Fig. 2 and Supplementary Fig. 3, the authors claim that MARK2 is enriched around the Golgi. However, this claim was based on immunofluorescent images of single cells and single-line scans.  

It is better to present the statistical data for Pearson's coefficient as shown in Figs. 1D and E. To demonstrateMARK2 enrichment around Golgi, but not localization in Golgi, the authors should find a way to quantify the specific enrichment of MARK2 signals in the Golgi region.  

We thank the reviewer for raising this important point regarding the enrichment of MARK2 around the Golgi apparatus. Upon further consideration, we acknowledge that our current data do not provide sufficient evidence to fully elucidate the mechanism of MARK2 localization to the Golgi.

To maintain the scientific rigor of our study, we have removed this claim and the corresponding content from the manuscript, including original Figures 2 and Supplementary Figure 3 that specifically discuss MARK2 enrichment. These changes do not affect the primary conclusions of the study, which focus on the role of MARK2-mediated phosphorylation of CAMSAP2.

We hope this clarification addresses the reviewer’s concerns. In the future, we plan to investigate the precise mechanism of MARK2 localization using additional experimental approaches. We are grateful for the reviewer’s constructive feedback, which has helped us refine the scope and focus of our manuscript.

(2) In Fig. 3 and Supplementary Fig. 4, the authors report that CAMSAP2 localization on the Golgi is reduced in cells lacking MARK2.  

Essentially, the present results support this claim. However, the authors should analyze the Golgi localization of CAMASP2 with the same quantification parameter because they used Pearson's coefficient in Fig. 1D, E and Supplementary Fig.4D but Mander's coefficient in Fig. 3C and Fig.4F.  

We thank the reviewer for their insightful comment regarding the consistency of quantification parameters used in our analysis of CAMSAP2 localization on the Golgi apparatus.

To address this concern, we have revised Figure 3C to use Pearson’s coefficient for consistency with Figure 1D, 1E (Figure 1B and 1E in the revised manuscript), and Supplementary Figure 4D (Supplementary Figure 3I in the revised manuscript). This ensures uniformity in the quantification parameters across these analyses.

For Figure 4F, we have retained Mander’s coefficient, as it accounts for variability in expression levels due to overexpression in individual cells. We believe this approach provides a more accurate reflection of CAMSAP2 localization under the experimental conditions shown in Figure 4F.

We hope these adjustments clarify our analysis and address the reviewer’s concerns. We greatly appreciate the reviewer’s constructive feedback, which has helped improve the consistency and accuracy of our study.

(3) In Fig.4D-F, the authors claim that S835 phosphorylation of CAMSAP2 is essential for its localization to the Golgi apparatus and for restoring the Golgi dispersion induced by CAMASAP2 depletion.  

Fig.4E indicates that the S835A mutant of CAMSAP2 significantly restores the compact assembly of the Golgi apparatus, and the differences in the rescue activities of the wild type, S835A, and S835D are rather small. These data contradict the authors' conclusions regarding the pivotal role of MARK2-mediated phosphorylation at the S835 site of CAMSAP2 in maintaining the Golgi architecture (page 9). The authors should remove the phrase "MARK2-mediated" from the sentence unless addressing the aforementioned issues (see 3rd major comment) and describe the role of S835 phosphorylation in more subdued tone.  

We thank the reviewer for their constructive feedback regarding the conclusions drawn about the role of MARK2-mediated phosphorylation of CAMSAP2 at S835.

In response, we have revised the relevant sentence to reflect a more nuanced interpretation of the data. Specifically, the original statement:

“These observations indicate that the phosphorylation of serine 835 in CAMSAP2 is essential for its proper localization to the Golgi apparatus.”

has been updated to:

“These observations indicate that MARK2 phosphorylation of serine at position 835 of CAMSAP2 affects the localization of CAMSAP2 on the Golgi and regulates Golgi structure” (page 9, Lines 27-29).

We hope this modification addresses the reviewer’s concerns. We are grateful for the feedback, which has helped us refine our conclusions and enhance the clarity of our manuscript.

(4) In Figs. 5I, J and Supplementary Fig.7A-E, the authors claim that the S835 phosphorylationdependent interaction of CAMSAP2 with Uso1 is essential for its localization to the Golgi apparatus.  

This claim was made based on immunofluorescent images of single cells and single-line scans, and was not sufficiently verified (Supplementary Fig.7B, C). Because this is a crucial claim for the present paper, the authors should present statistical data for Pearson's coefficient, as shown in Fig. 1D and E, to quantitatively estimate the Golgi localization of CAMSAP2.  

We thank the reviewer for their suggestion to present statistical data using Pearson's coefficient for a more robust quantification of the Golgi localization of CAMSAP2.

In response, we have revised the statistical analysis for Supplementary Figures 7B-C (Revised Figures 6F and 6G) to use Pearson's coefficient. This change ensures consistency with the quantification methods used in Figures 1D and 1E (Revised Figures 1B and 1E), allowing for a more standardized evaluation of CAMSAP2’s localization to the Golgi apparatus.

We hope this modification addresses the reviewer’s concerns and strengthens the quantitative support for our claims. We are grateful for the reviewer’s constructive feedback, which has helped improve the rigor of our study.

(5) The signal intensities of the immunofluorescent data in Fig. 4D, Fig. 5A, Sup-Fig. 3C and E, and Sup-Fig. 7S are very weak for readers to clearly estimate the authors' claims. They should be improved appropriately.  

We thank the reviewer for highlighting the need to improve the clarity of the immunofluorescent data presented in several figures.

In response, we have enhanced the signal intensities in Figures 4D, 5A, and Supplementary Figure 7D (Revised Supplementary Figure 6A) to make the signals clearer for readers, while ensuring that the adjustments do not alter the integrity of the original data. Supplementary Figures 3C and 3E was remove from our manuscript.

Additionally, to improve consistency and readability across the manuscript, we have standardized the quantification methods for similar analyses:

For CAMSAP2 localization to the Golgi, Pearson's coefficient has been used throughout the manuscript. Figure 3C has been updated to use Pearson's coefficient for consistency.

For Golgi state analysis in wound-edge cells, we have used the Golgi position relative to the nucleus as a uniform metric. This has been applied to Supplementary Figures 1F and 1G, Figures 2D and 2E, and Figures 5A and 5B.

We hope these adjustments address the reviewer’s concerns and improve the clarity and consistency of our study. We greatly appreciate the reviewer’s constructive feedback, which has significantly enhanced the quality of our manuscript.

(6) As indicated above, the authors frequently change the parameters or methods for quantifying the same phenomena (for example, the localization of CAMSAP on the Golgi and Golgi state in wound edge cells) in each figure. This is highly confusing. They should unify them.  

We thank the reviewer for their valuable feedback regarding the inconsistency in quantification methods across the manuscript.

To address this concern, we have carefully reviewed the entire manuscript and standardized the methods used for quantifying similar phenomena:

- CAMSAP2 localization on the Golgi: 

Pearson's coefficient is now consistently used throughout the manuscript. For example, Figure 3C has been updated to use Pearson's coefficient to align with other figures, such as Figures 1B and 1E.

- Golgi state in wound-edge cells: 

The Golgi state is now uniformly measured based on the position of the Golgi relative to the nucleus. This method has been applied to Supplementary Figures 1F and 1G, Figures 2D and 2E, and Figures 5A and 5B.

We believe these changes significantly improve the clarity and consistency of the manuscript, ensuring that readers can easily interpret the data. We are grateful for the reviewer’s constructive feedback, which has greatly helped us enhance the quality and rigor of our study.

(7) The legends frequently fail to clearly indicate the number of independent experiments on which each statistical analysis was based.  

We thank the reviewer for highlighting the need to clearly indicate the number of independent experiments for each statistical analysis.

In response, we have carefully reviewed the entire manuscript and updated the figure legends to include the number of independent experiments for every statistical analysis. This ensures transparency and allows readers to better evaluate the reliability of the data.

We hope these updates address the reviewer’s concerns and improve the clarity and rigor of the manuscript. We appreciate the reviewer’s constructive feedback, which has helped us enhance the quality of our work.

(8) Supplemental Figs. 4E and 4F are not cited in the text.  

We thank the reviewer for pointing out that Supplemental Figures 4E and 4F were not cited in the text.

To address this, we have updated the manuscript to cite these figures (Revised Figures 2H and 2I) in the appropriate section (page 8, lines 1-5).

“the absence of MARK2 can also influence the orientation of the Golgi apparatus during cell wound healing and cause a delay in wound closure (Figure 2 D-I and Figure 3 D).”

We hope this revision resolves the reviewer’s concern and improves the clarity and completeness of the manuscript. We appreciate the reviewer’s feedback, which has helped us refine our work.

(9) The data in Fig. 3 analyzed MARK2 knockout cells (not knockdown cells). The caption should be corrected.  

We thank the reviewer for pointing out the incorrect use of "knockdown" in the caption of Figure 3.

To address this, we have revised the title of Figure 3 from:

“MARK2 knockdown reduces CAMSAP2 localization on the Golgi apparatus.”

to:

“MARK2 affects CAMSAP2 localization on the Golgi apparatus.”

This updated caption reflects the inclusion of both MARK2 knockout and knockdown cell lines analyzed in Figure 3.

We hope this correction resolves the reviewer’s concern and ensures the accuracy of our manuscript. We greatly appreciate the reviewer’s attention to detail, which has helped us improve the clarity and consistency of our work.

(10) The present caption in Fig. 6 disagrees with the content of the figure.  

We thank the reviewer for pointing out the inconsistency between the caption and the content of Figure 6.

To address this issue, we have revised the content of Figure 6 to ensure it aligns accurately with the caption. The updated figure now reflects the description provided in the caption, eliminating any discrepancies and improving clarity for the readers.

We appreciate the reviewer’s constructive feedback, which has helped us enhance the accuracy and presentation of our manuscript.

(11) What do "CS" indicate in Fig. 4B and Supplementary Fig. 5D? The style used to indicate point mutants of CAMSAP2 should be unified. 835A or S835A?  

We thank the reviewer for pointing out the inconsistency in the naming of CAMSAP2 mutants.

To address this, we have revised all relevant figures and text to use the consistent format "S835A" and "S589A" for CAMSAP2 mutants. Specifically, in Figure 4B and Supplementary Figure 5D (now Supplementary Figure 4C), we have replaced the abbreviation "CS2" with "CAMSAP2" and updated the mutant names from "835A" and "589A" to "S835A" and "S589A," respectively. We hope these updates resolve the reviewer’s concerns and ensure clarity and consistency throughout the manuscript. We are grateful for the reviewer’s attention to detail, which has helped us improve the quality of our work.

(12) Uso1 is not a Golgi matrix protein.  

We thank the reviewer for pointing out the incorrect description of Uso1 as a Golgi matrix protein.

In response, we have revised the manuscript to replace all references to “USO1 as a Golgi matrix protein” with “USO1 as a Golgi-associated protein.” This correction ensures that the terminology used in the manuscript is accurate and consistent with current scientific understanding.

We appreciate the reviewer’s attention to detail, which has helped us improve the accuracy and quality of our manuscript.

There's a few things going on here. Generally at SGW a gray Olympia SM3 in excellent "looking" condition like this one will go for $120-150. This one is also hiding a script typeface which will usually add another $110-150 of value, which would put it at the $300 mark. I'm sort of surprised that the original winner didn't actually pay for it at this price as that's likely what someone would honestly pay for one like this. (It's also possible that they forgot they won or didn't know and didn't pay for it in time too.)

On today's listing, it's far, far more likely that someone wants it and either couldn't get it or pay for it now at the price that it was going to go for in a reasonable auction. They used a throw away accout to make an outrageous bid in hopes that in a week it'll be relisted and no one will notice the script typeface and it'll go for well under $200. (It won't.) This happens incredibly frequently for some of the less common typewriters. Usually it's machines with script or uncommon typefaces or uncommon character sets. Recent auctions for a gold plated Olympia SM3 and a Yellow Royal FP with a Gothic typeface come to mind. I've seen this also happen four or five times in a row before someone ultimately pays for a machine at some reasonable price.

Honestly, SGW should have a policy that the second and third runners up for auctions that don't get paid for by winners should have the right of last refusal on auctions like this to prevent this sort of "gaming" of the system. If you search back in this sub, you'll see this topic coming up every couple of weeks with the same discussions over and over. The common wisdom is that a SGW auction isn't gone until the machine doesn't pop up anymore and actually "sold". And even then, if you wait a week or two, you'll usually see the exact machine pop up less than a month later on eBay being listed by the winner for an exorbitant amount (almost always without having done any additional cleaning or restoration work on it aside from maybe dusting it out.)

Maybe we should add the tag #SGWgaming to all these conversations to make them easier to find?

reply to https://old.reddit.com/r/typewriters/comments/1jift5r/listing_on_the_right_was_a_month_ago_listing_on/

should be the tag line of our class haha

Leaving WVU didn't only affect the fans and the players it also affected the entire state of West Virginia's potential economy growth

Reviewer #1 (Public review):

Summary:

This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T) it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

3T layer-fMRI papers that are not cited:

Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.<br /> The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

(1) I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aims to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).<br /> It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins and the bipolar crushing is not expected to help with this.

(2) The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.<br /> VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

(3) The comparison with VASO is misleading.<br /> The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.<br /> Koiso et al. has performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation) and 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).<br /> Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that want to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?<br /> Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion it looks like random noise, with most of the activation outside the ROI (in white matter).

(4) The repeatability of the results is questionable.<br /> The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. Location of peaks turn into locations of dips and vice versa.<br /> The methods are not described in enough detail to reproduce these results.<br /> The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.<br /> No data are shared for reproduction of the analysis.

(5) The application of NODRIC is not validated.<br /> Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

Comments on revisions:

Among all the concerns mentioned above, I think there is only one of the specific issues that was sufficiently addressed.<br /> The authors implemented a combination of three consecutive-dimensional flow crushers. Other concerns were not sufficiently addressed to change my confidence level of the study.<br /> - While the abstract is still focusing on the utility of using 3T, they do not give credit to early 3T layer-fMRI papers leading the way to larger coverage and connectivity applications.<br /> - While the author's choice of using custom SMS 2D readout is justified for them. I do not think that this very method will utilize widespread 3T whole brain connectivity experiments across the global 3T community. This lowers the impact of the paper.<br /> - The images in Fig. 5 are still suspiciously similar. To the level that the noise pattern outside the brain is identical across large parts of the maps with and without PR.<br /> - Maybe it's my ignorance, but I still do not agree why flow crushing focuses the local BOLD responses to small vessels.<br /> - While my feel of a misleading representation of the literature had been accompanied by explicit references, the authors claim that they cannot find them?!? Or claim that they are about something else (which they are not, in my viewpoint).<br /> Data and software are still not shared (not even example data, or nii data).

Author response:

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

General responses:

The authors sincerely thank all the reviewers for their valuable and constructive comments. We also apologize for the long delay in providing this rebuttal due to logistical and funding challenges. In this revision, we modified the bipolar gradients from one single direction to all three directions. Additionally, in response to the concerns regarding data reliability, we conducted a thorough examination of each step in our data processing pipeline. In the original processing workflow, the projection-onto-convex-set (POCS) method was used for partial Fourier reconstruction. Upon examination, we found that applying the POCS method after parallel image reconstruction significantly altered the signal and resulted in considerable loss of functional feature. Futhermore, the original scan protocol employed a TE of 46 ms, which is notably longer than the typical TE of 33 ms. A prolonged TE can increase the ratio of extravascular to intravascular contributions. Importantly, the impact of TE on the efficacy of phase regression remains unclear, introducing potential confounding effects. To address these issues, we revised the protocol by shortening the TE from 46 ms to 39 ms. This adjustment was achieved by modifying the SMS factor to 3 and the in-plane acceleration rate to 3, thereby minimizing the confounding effects associated with an extended TE.

Following these changes, we recollected task-based fMRI data (N=4) and resting-state fMRI data (N=14) under the updated protocol. Using the revised dataset, we validated layer-specific functional connectivity (FC) through seed-based analyses. These analyses revealed distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with statistically significant inter-layer differences. Furthermore, additional analyses with a seed in the primary sensory cortex (S1) corroborated the robustness and reliability of the revised methodology. We also changed the ‘directed’ functional connectivity in the title to ‘layer-specific’ functional connectivity, as drawing conclusions about directionality requires auxiliary evidence beyond the scope of this study.

We provide detailed responses to the reviewers’ comments below.

Reviewer #1 (Public Review):

Summary:

(1)   This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

3T layer-fMRI papers that are not cited:

Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

We thank the reviewer for listing out 8 papers related to 3T layer-fMRI papers. The primary goal of our work is to develop a methodology for brain-wide, layer-dependent resting-state functional connectivity at 3T. Upon review of the cited papers, we found that:

(1) One study (Lifshits et al.) was not an fMRI study.

(2) One study (Olman et al.) was conducted at 7T, not 3T.

(3) Two studies (Taso et al. and Wu et al.) employed relatively large voxel sizes (1.6 × 2.3 × 5 mm³ and 1.5 mm isotropic, respectively), which limits layer specificity.

(4) Only one of the listed studies (Huber et al., Aperture Neuro 2023) provides coverage of more than half of the brain.

While each of these studies offers valuable insights, the VASO study by Huber et al. is the most relevant to our work, given its brain-wide coverage. However, the VASO method employs a relatively long TR (14.137 s), which may not be optimal for resting-state functional connectivity analyses.

To address these limitations, our proposed method achieves submillimeter resolution, layer specificity, brain-wide coverage, and a significantly shorter TR (<5 s) altogether. We believe this advancement provides a meaningful contribution to the field, enabling broader applicability of layer-fMRI at 3T.

(2) The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T), it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

We would like to thank the reviewer for their comments and the recognition of the technical efforts in implementing our sequence. We would like to address the points raised:

(1) We completely agree that in-house implementation of existing techniques does not constitute an advancement for the field. We did not claim otherwise in the manuscript. Our focus was on the development of a method for brain-wide, layer-dependent resting-state functional connectivity at 3T, as mentioned in the response above.

(2) The reviewer stated that "it is established to use 3D readouts over 2D (SMS) readouts". This is a strong claim, and we believe it requires robust evidence to support it. While it is true that 3D readouts can achieve higher tSNR in certain regions, such as the central brain, as shown in the study by Vizioli et al. (ISMRM 2020 abstract; https://cds.ismrm.org/protected/20MProceedings/PDFfiles/3825.html?utm_source=chatgpt.com ), higher tSNR does not necessarily equate to improved detection power in fMRI studies. For instance, Le Ster et al. (PLOS ONE, 2019; https://doi.org/10.1371/journal.pone.0225286 ). demonstrated that while 3D EPI had higher tSNR in the central brain, SMS EPI produced higher t-scores in activation maps.

(3) When choosing between SMS EPI and 3D EPI, multiple factors should be taken into account, not just tSNR. For example, SMS EPI and 3D EPI differ in their sensitivity to motion and the complexity of motion correction. The choice between them depends on the specific research goals and practical constraints.

(4) We are open to different readout strategies, provided they can be demonstrated suitable to the research goals. In this study, we opted for 2D SMS primarily due to logistical considerations. This choice does not preclude the potential use of 3D readouts in the future if they are deemed more appropriate for the project objectives.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

We will elaborate the mechanism and reasoning in the later responses.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

In this revision, we updated the scan protocol and recollected the imaging data. Detailed explanations and revised results are provided in the later responses.

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

We respect the reviewer’s personal opinion. However, we can only address scientific comments or critiques.

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.

The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

(1) I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aim to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The proposed VN fMRI method employs VN gradients to selectively suppress signals from fast-flowing blood in large vessels. Although this approach may initially appear to diverge from the principles of CBV-based techniques (Chai et al., 2020; Huber et al., 2017a; Pfaffenrot and Koopmans, 2022; Priovoulos et al., 2023), which enhance sensitivity to vascular changes in arterioles, capillaries, and venules while attenuating signals from static tissue and large veins, it aligns with the fundamental objective of all layer-specific fMRI methods. Specifically, these approaches aim to maximize spatial specificity by preserving signals proximal to neural activation sites and minimizing contributions from distal sources, irrespective of whether the signals are intra- or extra-vascular in origin. In the context of intravascular signals, CBV-based methods preferentially enhance sensitivity to functional changes in small vessels (proximal components) while demonstrating reduced sensitivity to functional changes in large vessels (distal components). For extravascular signals, functional changes are a mixture of proximal and distal influences. While tissue oxygenation near neural activation sites represents a proximal contribution, extravascular signal contamination from large pial veins reflects distal effects that are spatially remote from the site of neuronal activity. CBV-based techniques mitigate this challenge by unselectively suppressing signals from static tissues, thereby highlighting contributions from small vessels. In contrast, the VN fMRI method employs a targeted suppression strategy, selectively attenuating signals from large vessels (distal components) while preserving those from small vessels (proximal components). Furthermore, the use of a 3T scanner and the inclusion of phase regression in the VN approach mitigates contamination from large pial veins (distal components) while preserving signals reflecting local tissue oxygenation (proximal components). By integrating these mechanisms, VN fMRI improves spatial specificity, minimizing both intravascular and extravascular contributions that are distal to neuronal activation sites. We have incorporated the responses into Discussion section.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).

In the new results in Figure 4, the application of VN gradients attenuated the bias towards pial surface. Consistent with the results in Figure 4, Figure 5 also demonstrated the suppression of macrovascular signal by VN gradients.

It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

In this revision, the original Figure 5 has been removed. However, we would like to clarify that the two maps with TE = 43 ms in the original Figure 5 were not identical. This can be observed in the difference map provided in the right panel of the figure.

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins, and the bipolar crushing is not expected to help with this.

The reviewer’s statement that "most of the vein signal comes from extravascular dephasing around large unspecific veins" may hold true for 7T. However, at 3T, the susceptibility-induced Larmor frequency shift is reduced by 57%, and the extravascular contribution decreases by more than 35%, as shown by Uludağ et al. 2009 ( DOI: 10.1016/j.neuroimage.2009.05.051 ).

Additionally, according to the biophysical models (Ogawa et al., 1993; doi: 10.1016/S0006-3495(93)81441-3 ), the extravascular contamination from the pial surface is inversely proportional to the square of the distance from vessel. For a vessel diameter of 0.3 mm and an isotropic voxel size of 0.9 mm, the induced frequency shift is reduced by at least 36-fold at the next voxel. Notably, a vessel diameter of 0.3 mm is larger than most pial vessels. Theoretically, the extravascular effect contributes minimally to inter-layer dependency, particularly at 3T compared to 7T due to weaker susceptibility-related effects at lower field strengths. Empirically, as shown in Figure 7c, the results at M1 demonstrated that layer specificity can be achieved statistically with the application of VN gradients. We have incorporated this explanation into the Introduction and Discussion sections of the manuscript.

(2) The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.

VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

We understand the reviewer’s concern regarding the directional limitation of bipolar crushing. As noted in the responses above, we have updated the bipolar gradient to include three orthogonal directions instead of a single direction. Furthermore, flow-related signal suppression does not necessarily require a longer time period. Bipolar diffusion gradients have been effectively used to nullify signals from fast-flowing blood, as demonstrated by Boxerman et al. (1995; DOI: 10.1002/mrm.1910340103). Their study showed that vessels with flow velocities producing phase changes greater than p radians due to bipolar gradients experience significant signal attenuation. The critical velocity for such attenuation can be calculated using the formula: 1/(2gGDd) where g is the gyromagnetic ratio, G is the gradient strength, d is the gradient pulse width and D is the time between the two bipolar gradient pulses. In the framework of Boxerman et al. at 1.5T, the critical velocity for b value of 10 s/mm² is ~8 mm/s, resulting in a ~30% reduction in functional signal. In our 3T study, b values of 6, 7, and 8 s/mm² correspond to critical velocities of 16.8, 15.2, and 13.9 mm/s, respectively. The flow velocities in capillaries and most venules remain well below these thresholds. Notably, in our VN fMRI sequences, bipolar gradients were applied in all three orthogonal directions, whereas in Boxerman et al.'s study, the gradients were applied only in the z-direction. Given the voxel dimensions of 3 × 3 × 7 mm³ in the 1.5T study, vessels within a large voxel are likely oriented in multiple directions, meaning that only a subset of fast-flowing signals would be attenuated. Therefore, our approach is expected to induce greater signal reduction, even at the same b values as those used in Boxerman et al.'s study. We have incorporated this text into the Discussion section of the manuscript.

(3) The comparison with VASO is misleading.

The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.

Koiso et al. performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation), 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).

Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

We thank the reviewer for providing these references. While the protocol with a TR of 3.9 seconds in Koiso’s work demonstrated reasonable activation patterns, it was not tested for layer specificity. Given that higher acceleration factors (AF) can cause spatial blurring, a protocol should only be eligible for comparison if layer specificity is demonstrated.

Secondly, the TRs reported in Koiso’s study pertain only to either the VASO or BOLD acquisition, not the combined CBV-based contrast. To generate CBV-based images, both VASO and BOLD data are required, effectively doubling the TR. For instance, if the protocol with a TR of 3.9 seconds is used, the effective TR becomes approximately 8 seconds. The stable protocol used by Koiso et al. to acquire whole-brain data (94.08 mm along the z-axis) required 5.2 seconds for VASO and 5.1 seconds for BOLD, resulting in an effective TR of 10.3 seconds. The spatial resolution achieved was 0.84 mm isotropic.

Unfortunately, we could not find the Juelich paper mentioned by the reviewer.

To have a more comprehensive comparison, we collated relevant literature on brain-wide layer-specific fMRI. We defined brain-wide acquisition as imaging protocols that cover more than half of the human brain, specifically exceeding 55 mm along the superior-inferior axis. We identified five studies and summarized their scan parameters, including effective TR, coverage, and spatial resolution, in Table 1.

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that wants to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

In this revision, we decided to focus on cortico-cortical functional connectivity and have removed the LGN-related content. Consequently, the text mentioned by the reviewer was also removed. Nevertheless, we apologize if our original description gave the impression that functional mapping of deep brain regions using VASO is not feasible. The word of caution we used is based on the layer-fMRI blog ( https://layerfmri.com/2021/02/22/vaso_ve/ ) and reflects the challenges associated with this technique, as outlined by experts like Dr. Huber and Dr. Strinberg.

According to the information provided, including the video, functional mapping of the hippocampus and amygdala using VASO is indeed possible but remains technically challenging. The short arterial arrival times in these deep brain regions can complicate the acquisition, requiring RF inversion pulses to cover a wider area at the base of the brain. For example, as of 2023, four or more research groups were attempting to implement layer-fMRI VASO in the hippocampus. One such study at 3T required multiple inversion times to account for inflow effects, highlighting the technical complexity of these applications. This is the context in which we used the word of caution. We are not sure whether recent advancements like MAGEC VASO have improved its applicability. As of 2024, we have not identified any published VASO studies specifically targeting deep brain structures such as the hippocampus or amygdala. Therefore, it is difficult to conclude that “sub-millimeter VASO is routinely being performed by MRI physicists on deep brain structures such as the hippocampus.”

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?

We are enthusiastic about sharing our imaging sequence, provided its usefulness is conclusively established. However, it's important to note that without an online reconstruction capability, such as the ICE, the practical utility of the sequence may be limited. Unfortunately, we currently don’t have the manpower to implement the online reconstruction. Nevertheless, we are more than willing to share the offline reconstruction codes upon request.

Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion, it looks like random noise, with most of the activation outside the ROI (in white matter).

As we mentioned in the ‘general response’ in the beginning of the rebuttal, the POCS method for partial Fourier reconstruction caused the loss of functional feature, potentially accounting for the activation in white matter. In this revision, we have modified the pulse sequence, scan protocol and processing pipelines.

According to the results in Figure 4, stable activation in M1 was observed at the single-subject level across most scan protocols. Yet, the layer-dependent activation profiles in M1 were spatially unstable, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to various factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Furthermore, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons between protocols, leaving residual artifacts unaddressed. Inconsistency in performing the button-pressing task across sessions may also have contributed to the observed variability. These results suggest that submillimeter-resolution fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping, unless group-level statistics are incorporated to enhance robustness. We have incorporated this text into the Limitation section of the manuscript.

(4) The repeatability of the results is questionable.

The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact, the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. The location of peaks turns into locations of dips and vice versa.

The methods are not described in enough detail to reproduce these results.

The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

We thank the reviewer for the comments regarding reproducibility and data sharing. In response, we have revised the Methods section and elaborated on the technical details to improve clarity and reproducibility.

Regarding code sharing, we acknowledge that the current in-house MATLAB reconstruction code requires further refinement to improve its readability and usability. Due to limited manpower, we have not yet been able to complete this task. However, we are committed to making the code publicly available and will upload it to GitHub as soon as the necessary resources are available.

For data sharing, we face logistical challenges due to the large size of the dataset, which spans tens of terabytes. Platforms like OpenNeuro, for example, typically support datasets up to 10TB, making it difficult to share the data in its entirety. Despite this limitation, we are more than willing to share offline reconstruction codes and raw data upon request to facilitate reproducibility.

Regarding data robustness, we kindly refer the reviewer to our response to the previous comment, where we addressed these concerns in greater detail.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.

No data are shared for reproduction of the analysis.

Obtaining phase data is relatively straightforward when the images are retrieved directly from raw data. For coil combination, we employed the adaptive coil combination approach described by (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g ) The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab .

(5) The application of NODRIC is not validated.

Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

We appreciate the reviewer’s suggestion. To validate the application of NORDIC denoising in our study, we compared the BOLD activation maps before and after denoising in the visual and motor cortices, as well as the depth-dependent activation profiles in M1. These results are presented in Figure 3. The activation patterns in the denoised maps were consistent with those in the non-denoised maps but exhibited higher statistical significance. Notably, BOLD activation within M1 was only observed after NORDIC denoising, underscoring the necessity of this approach. Figure 3c shows the depth-dependent activation profiles in M1, highlighted by the green contours in Figure 3b. Both denoised and non-denoised profiles followed similar trends; however, as expected, the non-denoised profile exhibited larger confidence intervals compared to the NORDIC-denoised profile. These results confirm that NORDIC denoising enhances sensitivity without introducing distortions in the functional signal. The corresponding text has been incorporated into the Results section.

Regarding the implementation details of NORDIC denoising, the reconstructed images were denoised using a g-factor map (function name: NIFTI_NORDIC). The g-factor map was estimated from the image time series, and the input images were complex-valued. The width of the smoothing filter for the phase was set to 10, while all other hyperparameters were retained at their default values. This information has been integrated into the Methods section for clarity and reproducibility.

Reviewer #2 (Public Review):

This study developed a setup for laminar fMRI at 3T that aimed to get the best from all worlds in terms of brain coverage, temporal resolution, sensitivity to detect functional responses, and spatial specificity. They used a gradient-echo EPI readout to facilitate sensitivity, brain coverage and temporal resolution. The former was additionally boosted by NORDIC denoising and the latter two were further supported by parallel-imaging acceleration both in-plane and across slices. The authors evaluated whether the implementation of velocity-nulling (VN) gradients could mitigate macrovascular bias, known to hamper the laminar specificity of gradient-echo BOLD.

The setup allows for 0.9 mm isotropic acquisitions with large coverage at a reasonable TR (at least for block designs) and the fMRI results presented here were acquired within practical scan-times of 12-18 minutes. Also, in terms of the availability of the method, it is favorable that it benefits from lower field strength (additional time for VN-gradient implementation, afforded by longer gray matter T2*).

The well-known double peak feature in M1 during finger tapping was used as a test-bed to evaluate the spatial specificity. They were indeed able to demonstrate two distinct peaks in group-level laminar profiles extracted from M1 during finger tapping, which was largely free from superficial bias. This is rather intriguing as, even at 7T, clear peaks are usually only seen with spatially specific non-BOLD sequences. This is in line with their simple simulations, which nicely illustrated that, in theory, intravascular macrovascular signals should be suppressible with only minimal suppression of microvasculature when small b-values of the VN gradients are employed. However, the authors do not state how ROIs were defined making the validity of this finding unclear; were they defined from independent criteria or were they selected based on the region mostly expressing the double peak, which would clearly be circular? In any case, results are based on a very small sub-region of M1 in a single slice - it would be useful to see the generalizability of superficial-bias-free BOLD responses across a larger portion of M1.

We appreciate and understand the reviewer’s concerns. Given the small size of the hand knob region within M1 and its intersubject variability in location, defining this region automatically remains challenging. However, we applied specific criteria to minimize bias during the delineation of M1: 1) the hand knob region was required to be anatomically located in the precentral sulcus or gyrus; 2) it needed to exhibit consistent BOLD activation across the majority of testing conditions; and 3) the region was expected to show BOLD activation in the deep cortical layers under the condition of b = 0 and TE = 30 ms. Once the boundaries across cortical depth were defined, the gray matter boundaries of hand knob region were delineated based on the T1-weighted anatomical image and the cortical ribbon mask but excluded the BOLD activation map to minimize potential bias in manual delineation. Based on the new criteria, the resulting depth-dependent profiles, as shown in Figure 4, are no longer superficial-bias-free.

As repeatedly mentioned by the authors, a laminar fMRI setup must demonstrate adequate functional sensitivity to detect (in this case) BOLD responses. The sensitivity evaluation is unfortunately quite weak. It is mainly based on the argument that significant activation was found in a challenging sub-cortical region (LGN). However, it was a single participant, the activation map was not very convincing, and the demonstration of significant activation after considerable voxel-averaging is inadequate evidence to claim sufficient BOLD sensitivity. How well sensitivity is retained in the presence of VN gradients, high acceleration factors, etc., is therefore unclear. The ability of the setup to obtain meaningful functional connectivity results is reassuring, yet, more elaborate comparison with e.g., the conventional BOLD setup (no VN gradients) is warranted, for example by comparison of tSNR, quantification and comparison of CNR, illustration of unmasked-full-slice activation maps to compare noise-levels, comparison of the across-trial variance in each subject, etc. Furthermore, as NORDIC appears to be a cornerstone to enable submillimeter resolution in this setup at 3T, it is critical to evaluate its impact on the data through comparison with non-denoised data, which is currently lacking.

We appreciate the reviewer’s comments and acknowledge that the LGN results from a single participant were not sufficiently convincing. In this revision, we have removed the LGN-related results and focused on cortico-cortical FC. To evaluate data quality, we opted to present BOLD activation maps rather than tSNR, as high tSNR does not necessarily translate to high functional significance. In Figure 3, we illustrate the effect of NORDIC denoising, including activation maps and depth-dependent profiles. Figure 4 presents activation maps acquired under different TE and b values, demonstrating that VN gradients effectively reduce the bias toward the pial surface without altering the overall activation patterns. The results in Figure 4 and Figure 5 provide evidence that VN gradients retain sensitivity while reducing superficial bias. The ability of the setup to obtain meaningful FC results was validated through seed-based analyses, identifying distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with significant inter-layer differences (see Figure 7). Further analyses with a seed in the primary sensory cortex (S1) demonstrated the reliability of the method (see Figure 8). For further details on the results, including the impact of VN gradients and NORDIC denoising, please refer to Figures 3 to 8 in the Results section.

Additionally, we acknowledge the limitations of our current protocol for submillimeter-resolution fMRI at the individual level. We found that robust layer-dependent functional mapping often requires group-level statistics to enhance reliability. This issue has been discussed in detail in the Limitations section.

The proposed setup might potentially be valuable to the field, which is continuously searching for techniques to achieve laminar specificity in gradient echo EPI acquisitions. Nonetheless, the above considerations need to be tackled to make a convincing case.

Reviewer #3 (Public Review):

Summary:

The authors are looking for a spatially specific functional brain response to visualise non-invasively with 3T (clinical field strength) MRI. They propose a velocity-nulled weighting to remove the signal from draining veins in a submillimeter multiband acquisition.

Strengths:

- This manuscript addresses a real need in the cognitive neuroscience community interested in imaging responses in cortical layers in-vivo in humans.

- An additional benefit is the proposed implementation at 3T, a widely available field strength.

Weaknesses:

- Although the VASO acquisition is discussed in the introduction section, the VN-sequence seems closer to diffusion-weighted functional MRI. The authors should make it more clear to the reader what the differences are, and how results are expected to differ. Generally, it is not so clear why the introduction is so focused on the VASO acquisition (which, curiously, lacks a reference to Lu et al 2013). There are many more alternatives to BOLD-weighted imaging for fMRI. CBF-weighted ASL and GRASE have been around for a while, ABC and double-SE have been proposed more recently.

The major distinction between diffusion-weighted fMRI (DW-fMRI) and our methodology lies in the b-value employed. DW-fMRI typically measures cellular swelling using b-values greater than 1000 s/mm² (e.g., 1800 s/mm(sup>2</sup>). In contrast, our VN-fMRI approach measures hemodynamic responses by employing smaller b-values specifically designed to suppress signals from fast-flowing draining veins rather than detecting microstructural changes.

Regarding other functional contrasts, we agree that more layer-dependent fMRI approaches should be mentioned. In this revision, we have expanded the Introduction section to include discussions of the double spin-echo approach and CBV-based methods, such as MT-weighted fMRI, VAPER, ABC, and CBF-based method ASL. Additionally, the reference to Lu et al. (2013) has been cited in the revised manuscript. The corresponding text has been incorporated into the Introduction section to provide a more comprehensive overview of alternative functional imaging techniques.

- The comparison in Figure 2 for different b-values shows % signal changes. However, as the baseline signal changes dramatically with added diffusion weighting, this is rather uninformative. A plot of t-values against cortical depth would be much more insightful.

- Surprisingly, the %-signal change for a b-value of 0 is not significantly different from 0 in the gray matter. This raises some doubts about the task or ROI definition. A finger-tapping task should reliably engage the primary motor cortex, even at 3T, and even in a single participant.

- The BOLD weighted images in Figure 3 show a very clear double-peak pattern. This contradicts the results in Figure 2 and is unexpected given the existing literature on BOLD responses as a function of cortical depth.

- Given that data from Figures 2, 3, and 4 are derived from a single participant each, order and attention affects might have dramatically affected the observed patterns. Especially for Figure 4, neither BOLD nor VN profiles are really different from 0, and without statistical values or inter-subject averaging, these cannot be used to draw conclusions from.

We appreciate the reviewer’s suggestions. In this revision, we have made significant updates to the participant recruitment, scan protocol, data processing, and M1 delineation. Please refer to the "General Responses" at the beginning of the rebuttal and the first response to Reviewer #2 for more details.

Previously, the variation in depth-dependent profiles was calculated across upscaled voxels within a specific layer. However, due to the small size of the hand knob region, the number of within-layer voxels was limited, resulting in inaccurate estimations of signal variation. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section. Furthermore, while the initial submission used percentage signal change for the profiles of M1, the dramatic baseline fluctuations observed previously are no longer an issue after the modifications. For this reason, we retained the use of percentage signal change to present the depth-dependent profiles. After these adjustments, the profiles exhibited a bias toward the pial surface, particularly in the absence of VN gradients.

- In Figure 5, a phase regression is added to the data presented in Figure 4. However, for a phase regression to work, there has to be a (macrovascular) response to start with. As none of the responses in Figure 4 are significant for the single participant dataset, phase regression should probably not have been undertaken. In this case, the functional 'responses' appear to increase with phase regression, which is contra-intuitive and deserves an explanation.

We agreed with reviewer’s argument. In the revised results, the issues mentioned by the reviewer are largely diminished. The updated analyses demonstrate that phase regression effectively reduces superficial bias, as shown in Figures 4 and 5.

- Consistency of responses is indeed expected to increase by a removal of the more variable vascular component. However, the microvascular component is always expected to be smaller than the combination of microvascular + macrovascular responses. Note that the use of %signal changes may obscure this effect somewhat because of the modified baseline. Another expected feature of BOLD profiles containing both micro- and microvasculature is the draining towards the cortical surface. In the profiles shown in Figure 7, this is completely absent. In the group data, no significant responses to the task are shown anywhere in the cortical ribbon.

We agreed with reviewer’s comments. In the revised manuscript, the results have been substantially updated to addressing the concerns raised. The original Figure 7 is no longer relevant and has been removed.

- Although I'd like to applaud the authors for their ambition with the connectivity analysis, I feel that acquisitions that are so SNR starved as to fail to show a significant response to a motor task should not be used for brain wide directed connectivity analysis.

We appreciate the reviewer’s comments and share the concern about SNR limitations. In the updated results presented in Figure 5, the activation patterns in the visual cortex were consistent across TEs and b values. At the motor cortex, stable activation in M1 was observed at the single-subject level across most scan protocols. However, the layer-dependent activation profiles in M1 exhibited spatial instability, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Additionally, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons across protocols, leaving some residual artifacts unaddressed. Variability in task performance during button-pressing sessions may have further contributed to the observed inconsistencies.

Although these findings suggest that submillimeter-resolution fMRI may not yet be reliable for individual-level layer-dependent functional mapping, the group-level FC analyses can still yield robust results. In Figure 7, group-level statistics revealed distinct functional connectivity (FC) patterns associated with superficial and deep layers in M1. These FC maps exhibited significant differences between layers, demonstrating that VN fMRI enhances inter-layer independence. Additional FC analyses with a seed placed in S1 further validated these findings (see Figure 8).

The claim of specificity is supported by the observation of the double-peak pattern in the motor cortex, previously shown in multiple non-BOLD studies. However, this same pattern is shown in some of the BOLD weighted data, which seems to suggest that the double-peak pattern is not solely due to the added velocity nulling gradients. In addition, the well-known draining towards the cortical surface is not replicated for the BOLD-weighted data in Figures 3, 4, or 7. This puts some doubt about the data actually having the SNR to draw conclusions about the observed patterns.

We appreciate the reviewer’s comments. In the updated results, the efficacy of the VN gradients is evident near the pial surface, as shown in Figures 4 and 5. In Figure 4, comparing the second and third columns (b = 0 and b = 6 s/mm², respectively, at TE = 38 ms), the percentage signal change in the superficial layers is generally lower with b = 6 s/mm² than with b = 0. This indicates that VN gradient-induced signal suppression is more pronounced in the superficial layers. Additionally, in Figure 5, the VN gradients effectively suppressed macrovascular signals as highlighted by the blue circles. These observations support the role of VN gradients in enhancing specificity by reducing superficial bias and macrovascular contamination. Furthermore, bias towards cortical surface was observed in the updated results in Figure 4.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

(1) L141: "depth dependent" is slightly misleading here. It could be misunderstood to suggest that the authors are assessing how spatial specificity varies as a function of depth. Rather, they are assessing spatial specificity based on depth-dependent responses (double peak feature). Perhaps "layer-dependent spatial specificity" could be substituted with laminar specificity?

We thank the reviewer for the suggestion. The term “depth dependent” has been replaced by “layer dependent” in the revised manuscript.

(2) L146-149: these do not validate spatial specificity.

The original text is removed.

(3) L180: Maybe helpful to describe what the b-value is to assist unfamiliar readers.

We have clarified the b-value as “the strength of the bipolar diffusion gradients” where it is first mentioned in the manuscript.

(4) Figure 1B: I think it would be appropriate with a sentence of how the authors define micro/macrovasculature. Figure 1B seems to suggest that large ascending veins are considered microvascular which I believe is a bit unconventional. Nevertheless, as long as it is clearly stated, it should be fine.

In our context, macrovasculature refers to vessels that are distal to neural activation sites and contribute to extravascular contamination. These vessels are typically larger in size (e.g., > 0.1 mm in diameter) and exhibit faster flow rates (e.g., > 10 mm/s).

(5) I think the authors could be more upfront with the point about non-suppressed extravascular effects from macrovasculature, which was briefly mentioned in the discussion. It could already be highlighted in the introduction or theory section.

We thank the reviewer’s suggestions. We have expanded the discussion of extravascular effects from macrovasculature in both the Introduction (5th paragraph) and Discussion (3rd paragraph) sections.

(6) The phase regression figure feels a bit misplaced to me. If the authors agree: rather than showing the TE-dependency of the effect of phase regression, it may be more relevant for the present study to compare the conventional setup with phase regression, with the VN setup without phase regression. I.e., to show how the proposed setup compares to existing 3T laminar fMRI studies.

In this revision, both the TE-dependent and VN-dependent effects of phase regression were investigated. The results in Figure 4 and Figure 5 demonstrated that phase regression effectively suppresses macrovascular contributions primarily near the gray matter/CSF boundary, irrespective of TE or the presence of VN gradients.

(7) L520: It might be beneficial to also cite the large body of other laminar studies showing the double peak feature to underscore that it is highly robust, which increases its relevance as a test-bed to assess spatial specificity.

We agreed. More literatures have been cited (Chai et al., 2020; Huber et al., 2017a; Knudsen et al., 2023; Priovoulos et al., 2023).

(8) L557: The argument that only one participant was assessed to reduce inter-subject variability is hard to buy. If significant variability exists across subjects, this would be highly relevant to the authors and something they would want to capture.

We thank the reviewer for the suggestions. In this revision, we have increased the number of participants to 4 for protocol development and 14 for resting-state functional connectivity analysis, allowing us to better assess and account for inter-subject variability.

(9) L637: add download link and version number.

The download link has been added as requested. The version number is not applicable.

(10) L638: How was the phase data coil-combined?

The reconstructed multi-channel data, which were of complex values, were combined using the adaptive combination method (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g). The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab . The phase data were then extracted using the MATLAB function ‘angle’.

(11) L639: Why was the smoothing filter parameter changed (other parameters were default)?

The smoothing filter parameter was set based on the suggestion provided in the help comments of the NIFTI_NORDIC function:

function  NIFTI_NORDIC(fn_magn_in,fn_phase_in,fn_out,ARG)

% fMRI

%

%  ARG.phase_filter_width=10;

In other words, we simply followed the recommendation outlined in the NIFTI_NORDIC function’s documentation.

(12) I assume the phase data was motion corrected after transforming to real and imaginary components and using parameters estimated from magnitude data? Maybe add a few sentences about this.

Prior to phase regression, the time series of real and imaginary components were subjected to motion correction, followed by phase unwrapping. The phase regression was incorporated early in the data processing pipeline to minimize the discrepancy in data processing between magnitude and phase images (Stanley et al., 2021).

(13) Was phase regression applied with e.g., a deming model, which accounts for noise on both the x and y variable? In my experience, this makes a huge difference compared with regular OLS.

We appreciate the reviewer’s insightful comment. We are aware that the noise present in both magnitude and phase data therefore linear Deming regression would be a good fit to phase regression (Stanley et al., 2021). To perform Deming regression, however, the ratio of magnitude error variance to phase error variance must be predefined. In our initial tests, we found that the regression results were sensitive to this ratio. To avoid potential confounding, we opted to use OLS regression for the current analysis. However, we agreed Deming model could enhance the efficacy of phase regression if the ratio could be determined objectively and properly.

(14) Figure 2: What is error bar reflecting? I don't think the across-voxel error, as also used in Figure 4, is super meaningful as it assumes the same response of all voxels within a layer (might be alright for such a small ROI). Would it be better to e.g. estimate single-trial response magnitude (percent signal change) and assess variability across? Also, it is not obvious to me why b=30 was chosen. The authors argue that larger values may kill signal, but based on this Figure in isolation, b=48 did not have smaller response magnitudes (larger if anything).

We agreed with the reviewer’s opinion on the across-voxel error. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section.

Additionally, the bipolar diffusion gradients were modified from a single direction to three orthogonal directions. As a result, the questions and results related to b=30 or b=48 are no longer applicable.

(15) Figure 5: would be informative to quantify the effect of phase regression over a large ROI and evaluate reduction in macrovascular influence from superficial bias in laminar profiles.

We appreciate the reviewer’s suggestion. In the revised manuscript, the reduction in macrovascular influence from superficial bias across a large ROI is displayed in Figure 5. Additionally, the impact on laminar profiles is demonstrated in Figure 4.

(16) L406-408: What kind of robustness?

We acknowledge that describing the protocol as “robust” was an overstatement. The updated results indicate that the current protocol for submillimeter fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping. However, group-level functional connectivity (FC) analyses demonstrated clear layer-specific distinctions with VN fMRI, which were not evident in conventional fMRI. These findings highlight the enhanced layer specificity achievable with VN fMRI.

(17) Figure 8: I think C) needs pointers to superficial, middle, and deep layers? Why is it not in the same format as in Figure 9C? The discussion of the FC results could benefit from more references supporting that these observations are in line with the literature.

In the revised results, the layer pooling shown in Figure 9c has been removed, making the question regarding format alignment no longer applicable. Additionally, references supporting the FC results have been added to the revised Discussion section (7th paragraph).

(18) L456-457: But correlation coefficients may also be biased by different CNR across layers.

That is correct. In the updated FC results in Figure 7 to 9, we used group-level statistics rather than correlation coefficients.

Reviewer #3 (Recommendations For The Authors):

The results in Figure 2-6 should be repeated over, or averaged over, a (small) group of participants. N=6 is usual in this field. I would seriously reconsider the multiband acceleration - the acquisition seemingly cannot support the SNR hit.

A few more specific points are given below:

(1) Abstract: The sentence about LGN in the abstract came for me out of the blue - why would LGN be important here, it's not even a motor network node? Perhaps the aims of the study should be made more clear - if it's about networks as suggested earlier then a network analysis result would be expected too. Expanding the directed FC findings would improve the logical flow of the abstract. Given the many concerns, removing the connectivity analysis altogether would also be an option.

We thank the reviewer for the suggestions. The LGN-related results indeed diluted the focus of this study and have been completely removed in this revision.

(2) Line 105: in addition to the VASO method, ..

The corresponding text has been revised, and as a result, the reviewer’s suggestion is no longer applicable.

(3) If out of the set MB 4 / 5 / 6 MB4 was best, why did the authors not continue with a comparison including MB3 and MB2? It seems to me unlikely that the MB4 acquisition is actually optimal.

Results: We appreciate the reviewer’s suggestions. In this revision, we decreased the MB factor to 3, as it allowed us to increase the in-plane acceleration rate to 3, thereby shortening the TE. The resulting sensitivity for both individual and group-level results is detailed in earlier responses, such as the response to Q16 for Reviewer #2.

(4) The formatting of the references is occasionally flawed, including first names and/or initials. Please consider using a reliable reference manager.

We used Zotero as our reference manager in this revision to ensure consistency and accuracy. The references have been formatted according to the APA style.

(5) In the caption of Figure 5, corrected and uncorrected p values are identical. What multiple comparisons correction was made here? A multiple comparisions over voxels (as is standard) would usually lead to a cut-off ~z=3.2. That would remove most of the 'responses' shown in figure 5.

We appreciate the reviewer’s comment. The original results presented in Figure 5 have been removed in the revised manuscript, making this comment no longer applicable.

Reviewer #2 (Public review):

FOXP3 has been known to form diverse complexes with different transcription factors and enzymes responsible for epigenetic modifications, but how extracellular signals timely regulate FOXP3 complex dynamics remains to be fully understood. Histone H3K4 tri-methylation (H3K4me3) and CXXC finger protein 1 (CXXC1), which is required to regulate H3K4me3, also remain to be fully investigated in Treg cells. Here, Meng et al. performed a comprehensive analysis of H3K4me3 CUT&Tag assay on Treg cells and a comparison of the dataset with the FOXP3 ChIP-seq dataset revealed that FOXP3 could facilitate the regulation of target genes by promoting H3K4me3 deposition. Moreover, CXXC1-FOXP3 interaction is required for this regulation. They found that specific knockdown of Cxxc1 in Treg leads to spontaneous severe multi-organ inflammation in mice and that Cxxc1-deficient Treg exhibits enhanced activation and impaired suppression activity. In addition, they have also found that CXXC1 shares several binding sites with FOXP3 especially on Treg signature gene loci, which are necessary for maintaining homeostasis and identity of Treg cells.

Comments on revisions:

The authors have fully addressed the reviewers' comments and questions.

Here are the six Mazda CX-90 trim levels for 2025, including what you get with each one and the corresponding price tag

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Headings and paragraph content shouldn't be formatted as a list and colons can be removed from the headings

Move this section below the sentence ending with "and boasts a selection of both non-turbo as well as turbocharged gas engine options."

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Reply to the reviewers

Reviewer #1:

Major Comments:

1. The data in the paper strongly suggests that the new copper shuttles are selective for copper and have faster binding kinetics (Fig 1) than the previous one. However, the data regarding the copper shuttling from the copper(Aβ) peptides is not very convincing. It appears to be due to the Cu effect alone (Fig.3), as the reduction in viability with Cu(II)+ AscH- is almost the same as the Cu(II)(Aβ)+AscH-. To convincingly show that the peptide shuttle can strip copper from (Aβ) peptides, the authors need to show that the copper is bound to the (Aβ) peptide before it is used in the experiment. Rightfully so, the effect of the toxicity of Cu(II)+ AscH- is similar to that of Cu(II)(Aβ16)+AscH-. This is due to the fact that Aβ16 is not toxic to the cells, so therefore there is no compounded effect of Cu and Aβ16 as seen for Cu(II)(Aβ40). As for the toxicity of Cu(II)+ AscH-, it is be similar to Cu(II)(Aβ)+AscH- because Cu(II) will be bound to a weaker ligand in the medium and such loosely bound Cu is also able to produce ROS with AscH- with similar rates as Cu-Ab.

Data from our lab and others have shown that in HEPES solution at pH 7.4, Aβ forms a complex with Cu. The present work is also in line with Cu-binding to Ab, as in Figure 1C (GSH), the rate of Cu withdrawal by the shuttle can only be explained by Cu bound to Ab, as Cu in the buffer binds to the shuttle much faster. Also, the AscH- consumption rate measured in Fig S5D-E are congruent of Cu bound to Ab, unbound Cu has a much faster rate of AscH- consumption (Santoro et al. 2018, doi.org/10.1039/C8CC06040A).

The concentrations of Aβ and Cu used in our experimental condition were determined with a UV-Vis spectrophotometer.

Minor comments:

1. The paper does not cite Figure 1A and some supplementary figures, especially Supp. Fig. 1-2. All the figures and supplementary figures should be cited. This has been rectified for all the concerned figures.

The data presentation in Figures 3B and S8 is confusing."-" signs indicate no addition or the blank box means no addition. Also, the AKH-αR5W4 has no "-" sign in the first bar. For clarity, please indicate the -, +, or no sign means in the figure legends. Also, what does "Batch A" refer to in Figure 3B?

The figures have been modified as suggested by the reviewer.

Page 7, correct (Error! Referencesource not found.Figure 1C).

This has been rectified.

The Giantin staining in Figure 2B is making it hard to visualize ATP7A trafficking. If the Giantin image overlay is removed, it may be easier to see the movement of ATP7A from the perinuclear region to the vesicles.

The images have been modified to better appreciate the ATP7A change in distribution upon the increase in intracellular Cu level. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B

In the introduction, the authors mention, "These molecules have, however, a major pitfall as is seen for Elesclemol, a candidate for Menkes disease treatments 32. The authors cite reference " Tsvetkov, P. et al. Copper induces cell death by targeting lipoylated TCA cycle proteins." The paper showing elesclomol as a candidate for Menkes disease treatments is Guthrie L et al., Elesclomol alleviates Menkes pathology and mortality by escorting Cu to cuproenzymes in mice. Science. 2020.

We thank the reviewer for pointing this out, which was apparently not clearly explained. Our intention here was to show that a major pitfall of shuttles like Elesclomol, as seen in the study by Tsvetkov, P. et al. Science (2022), is cuprotoxicity. The sentence has been clarified and the work of Guthrie L et al is cited for Elesclomol as a candidate for Menkes disease.

Reviewer #2 :

Major issues:

1. This reviewer is not convinced that the authors' experimental system is well suited for studies of glia activation and protective effects. With the exception of a couple of panels it is very hard to see differences. The authors should significantly improve the quality of images in Figure 5 to make this set of data convincing. We thank the reviewer for his/her detailed evaluation and for bringing to light the quality of the image in Figure 5. We have therefore improved the quality of the images by improving the signal to noise ratio to better show the differences between conditions.

Similarly, the quality of giantin staining is low and needs to be improved and more experimental details are needed (see details below).

As stated in our answer to reviewer 1, the images have been modified to better appreciate ATP7A redistribution upon increase of intracellular Cu levels. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B.

Given that shuttles are found within vesicles, the authors should discuss the mechanism through which Cu is released into the cytosol to trigger ATP7B trafficking.

The mechanism of Cu escape from endosomes remains poorly understood. However, supported by our recent observations that Cu quickly (within 10 min) dissociates from the Cu-shuttle AKH-αR5W4NBD in endosomes (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963), we discuss the potential involvement CTR1/2 and DMT1 (page 16).

There are numerous small writing issues that make paper difficult to read. The authors are encouraged to carefully edit their manuscript.

We thank the reviewer for pointing this out and several errors have been corrected whereas various sentences have been clarified.

Minor issues

* „A solution of monomerized Aβ complex in 10% DMEM (diluted with DMEM salt solution) was prepared in microcentrifuge tubes" - here and further the description of media composition is confusing What is the rest 90%?

This has been rectified. The composition of the salt solution that makes up the 90% has been provided (page 4).

* „Afterwards, AscH- was added to the tubes and vortexed, the mixture was then added to PC12 cells" - concentration of ascorbate is mentioned only once (later in the figure legend) where it can be barely found, also without explaining the choice of concentration. Additionally, ascorbate's product code is not listed. Please, correct.

These points have been rectified.

* Description of the cell (PC12 line) handling conditions is absent (growth medium, passage number used etc) and should be included.

This information is now provided.

* ATP7A delocalization assay. Details for the secondary antibodies are absent (full name (e.g. AlexaFluor 488), manufacturer, code) and should be added.

Missing information has been added.

* page 6: „Next, we investigated the capacity of the shuttles to withdraw Cu(II) from cell culture media, DMEM 10% and DMEM/F12 1:1 (D/F)." Here and further explanation is needed why the mixture of DMEM/F12 is needed (F12 is also not listed in the materials list).

DMEM/F12 is a media that is commercially available used for some cell types, and it has been added to the materials list (page 4).

* Page 7. Legend to the figure 1B: „Conditions: Cu(II)=AKH-αR5W4NBD=DapHH-αR5W4NBD=HDapH-αR5W4NBD= 5 μM, DMEM 10%, D/F 100%, 25{degree sign}C, n=3." - „DMEM/F12" ratio equals to „100%" is confusing, please clarify

This has been clarified.

* Page 8-9. Legend to the Figure 2A. „Similar observations were obtained with 5 different cell cultures." Same remark goes to the legend to supplementary figure 7 ("Similar observations were obtained with at least 3 different cell cultures"). Do the authors mean independent experiments or different cell lines? Please clarify. If different cell lines, consider including these data into the supplement.

Indeed we meant independent experimentations. This has been clarified.

* Page 8-9, figure 2B. Giantin is a cis-golgi marker, which should localize perinuclearly. In the cells shown the signal is diffuse and appears non-specific. Please improve the quality.

We have reduced the number of conditions for which images are provides and are providing individual staining for clarity. Zoomed images are also provided allowing visualization of the typical cis-Golgi distribution of Giantin.

* Page 8-9, figure 2B. ATP7A is shown in green. The authors did not specify the secondary antibody has been used for it. If the secondary antibody used for labeling of ATP7A has green fluorescence then how does one distinguish between the transporter signal and signal of the green fluorescent shuttle? Please provide more details.

We thank the reviewer for pointing this point as we missed to mention this technical issue in the original manuscript. The Cu-shuttles labeled with NBD indeed emit in the green signal, but they are not fixable under our conditions and are washed out during ICC procedure. Accordingly, they do generate any background signal and do not interfere with the ICC as shown by the controls and test conditions (Figure S7B and Figure 2B). This is now mentioned (page 11).

* Page 9 and Figure 2B. Why did authors use Cu(II)EDTA for the experiment? What was the concentration? Please, add this information as well as Cu(II)GTSM treatment conditions to the experiment description in materials and methods.

EDTA is a strong chelator of Cu(II), however due to its negative charge it cannot penetrate the plasma membrane thus importing Cu. It is therefore used as a negative control, to eliminate the speculation of Cu non-specifically crossing the plasma membrane or through a channel.

* Figure 2 and supplementary figure 7. It would be beneficial to have higher magnification images. Please, add them, if possible.

These higher magnification images have been provided.

* Page 11. „In conclusion, the novel Cu(II)-selective peptide shuttles .... capable of instantly preventing ... toxicity on PC12 cells, whereas ... instantly rescue Cu(II)Aβ1-42 toxicity". Authors should be more careful with terminology. According to the materials and methods, the survival assay was carried out after 24h of cells' treatment with the reagents. Effect visible after 24h and „instant rescue" is not the same, Please clarify or modify the wording

In principle, the peptides cannot reverse the production of ROS, however they prevent ROS production. Therefore, for the peptides to have an effect, they have to instantly halt ROS production. This is justified by the novel shuttles being more effective than AKH-αR5W4NBD in preventing toxicity, given we modified just the Cu binding sequence. We have however restricted the use of the term instantly to ROS production.

* Page 13, figure 5, panels C and D. In both quantitations Cu(II) was used as one of the control conditions. Why in panel D the percentage of activated microglial cells (second graphs from right) is several fold higher (appr. 150% vs >500%)?

This variability was observed throughout our set of experiments and could be linked to the quality of the hippocampal slices used. Slight variations in the age of the animals or in the traces of metals in the mediums are likely explanations. However, the different groups that are compared represent experiments performed simultaneously.

* Supplementary Figure S3B. The lowest solid line does not correspond to any color in the legend (please, check and correct). However, by the method of exclusion, one may conclude that it refers to Cu(II)+HDapH-shuttle. What could be a potential explanation for stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper (also supported by the panel D)?

The stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper is not significant.

* In discussion the authors mention that the designed shuttles are prone to degradation in 48 hours. In the viability assays, they treat cells for 24 hours, in the fluorescent and confocal microscopy experiments for one hour or less. What is the lifetime of these shuttle peptides in the cells?

The lifetime of the shuttle peptide in the cells is currently unknown. However, after 24h incubation of PC12 cells with the AKH-αR5W4NBD, DapHH-αR5W4NBD and HDapH-αR5W4NBD, the Cu shuttles lose their punctate distribution and appear diffuse inside the cells. We have recently shown that AKH-αR5W4NBD cycles through different endosomal compartments and eventually reaches the lysosomes where it could be degraded (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963). Therefore, the diffuse distribution of the fluorescence signal could suggest degradation of the Cu-shuttles.

* From the microscopy observations, the mechanism of entry of apo-shuttles (with no Cu(II) in the complex) and in complex with Cu(II) looks quite different. Namely, in figure S7 the fluorescent signal is very strong in the plasma membrane with significantly less vesicular pattern when compared to figure 2A. It is especially apparent for DapHH shuttle at 15 minutes of incubation. Can authors hypothesize/discuss the reason for these differences?

The difference of the shuttle’s signal in the presence or absence of Cu binding, is due to fluorescence quenching by Cu bound and was at the heart of the design of these shuttles. Hence a strong signal at the plasma membrane is seen in the absence of Cu as these CPP-based shuttles interact strongly with the plasma membrane. However in presence of Cu, they become less visible due to quenching by Cu. Interestingly however, is that when Cu dissociates from the shuttle inside the cells (likely in acid endosomes), this quenching is suppressed and the fluorescence reappears. This is now better explained (page 10).

* Please, show the figures in the supplementary file in the same order as you refer to them.

This has been rectified.

* Introduction. Description of the shuttle peptides: „(3) a cell penetrating peptide (CPP), αR5W4, with sequence RRWWRRRWWR, for cell entry35" - one R is the middle is extra.

This has been rectified.

*Kd units are missing (pages 2, 3 and 15) and should be added.

This has been added.

* Figure 1A is either not referred at all or mislabeled.

* Page 7, Figure 1B: x axis on the second panel (+Mn+) misses a label.

* Page 8. „Upon addition of DapHH-αR5W4NBD or HDapH-αR5W4NBD, an immediate slow-down in ROS production was observed (Figure 1D and S1E), ..." - mislabeled supplementary figure, please, correct.

* Page 11. „...but not in the presence of AKH-αR5W4NBD which required pre-incubation to prevent toxicity (Figure 3AFigure)." Please, correct the reference to the figure.

* Page 11. „This is in line with the faster retrieval ... previously demonstrated in vitro (Figure 1)" - please, specify the panel.

* Supplementary materials and methods, subsection „Retrieval of Cu by peptide shuttles from Aβ", page 2: „The same was done for 10 μM Cu(II)...to give the estimated 100% saturated emission level." - check the spelling of the shuttle species.

* Supplementary Figure S4. By the behavior of AKH-shuttle in the presence of copper and other metals, it looks that panels are shuffled, i.e. panel C looks corresponding to the panel B with DMEM/F12 conditions, whish is also supported by the values in the Table S1. Please, check and correct, if needed.

* Supplementary figure S9, panel A. Apparently, mislabeled images with Abeta1-42 and Cu(II)Abeta1-42. Please, correct.

We apologize for the different issues in referencing figures. This has been rectified.

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

Minor Concerns

I think that authors can add some concepts of general interest on AD, as follows

evidence showed that AD top-line disease-modifying drugs employing monoclonal antibodies (donanemab, lecanemab, and aducanumab) that tag Aβ, based on the 'Amyloid cascade hypothesis', are able to rid the brain of Aβ plaques, but the drug benefits consist in a reduction of 35% of cognitive decline. The remaining disease burden (more than 65%) has no disease-modifying therapeutic options, at the moment. Furthermore, monoclonal antibodies against Aβ have strong side- events (ARIA). On this basis, it could be suggested that removing Aβ plaque might not be sufficient to slow the 100% percentage of clinical decline in AD. This is why the Cu(II) shuttle invention presented by the candidate may represent a valid and concrete means to fight AD, since also meta-analyses demonstrate that Cu and more specifically non-Cp Cu is increased in AD (PMID: 34219710). The authors can add some of these clinical considerations in the Discussion.

There is only a very brief description of the scenario of evidence of the involvement of copper in Alzheimer's, especially from a clinical point of view, I mean the scenario resulting from clinical studies carried out on AD patients. This would have highlighted the unmet medical need to which these new compounds (the Cu shuttles) can provide an answer. At least for a subpopulation of Alzheimer's patients, and we know that there are different subtypes of Alzheimer's disease (for example 10.1016/j.neurobiolaging.2004.04.001, but authors can find others), these Cu(II) selective shuttles could provide beneficial effects. Literature reports about a percentage of AD patients with increased levels of Cu (some papers on this topic e can be easily retrieved,), who may primarily benefit from these compounds. These can be easily identified as it is also characterized by a different biochemical, cognitive, and genetic profile. The current study is timely since AD patients with high Cu can be easily identified since they are characterized by a different biochemical, cognitive, and genetic profile as per recent findings (PMID: 37047347). This information can improve the quality of the manuscript by providing information about the unmet clinical need that this study can answer

We thank the reviewer for his very positive evaluation and for his suggestion that gives more perspective to our work. Accordingly, we have added these parts to the introduction and discussion sections.

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Referee #3

Evidence, reproducibility and clarity

Summary: The paper addresses the design and synthesis of novel copper (Cu)-selective peptide transporters to prevent Cu(Aβ)-induced toxicity and microglial activation in organotypic hippocampal slices.This is a very interesting study. I would define the study as pioneering and I hope that it is a seminal study, as it could be a study that opens the doors to future studies in the sector but above all applications in the clinical field. The methods are very complex and demonstrate an excellent knowledge of the biochemistry of beta-amyloid and copper. Methods are also clear and provide information for reproducibility

Minor Concerns

I think that authors can add some concepts of general interest on AD, as follows evidence showed that AD top-line disease-modifying drugs employing monoclonal antibodies (donanemab, lecanemab, and aducanumab) that tag Aβ, based on the 'Amyloid cascade hypothesis', are able to rid the brain of Aβ plaques, but the drug benefits consist in a reduction of 35% of cognitive decline. The remaining disease burden (more than 65%) has no disease-modifying therapeutic options, at the moment. Furthermore, monoclonal antibodies against Aβ have strong side- events (ARIA). On this basis, it could be suggested that removing Aβ plaque might not be sufficient to slow the 100% percentage of clinical decline in AD. This is why the Cu(II) shuttle invention presented by the candidate may represent a valid and concrete means to fight AD, since also meta-analyses demonstrate that Cu and more specifically non-Cp Cu is increased in AD (PMID: 34219710). The authors can add some of these clinical considerations in the Discussion

there is only a very brief description of the scenario of evidence of the involvement of copper in Alzheimer's, especially from a clinical point of view, I mean the scenario resulting from clinical studies carried out on AD patients. This would have highlighted the unmet medical need to which these new compounds (the Cu shuttles) can provide an answer. At least for a subpopulation of Alzheimer's patients, and we know that there are different subtypes of Alzheimer's disease (for example 10.1016/j.neurobiolaging.2004.04.001, but authors can find others), these Cu(II) selective shuttles could provide beneficial effects. Literature reports about a percentage of AD patients with increased levels of Cu (some papers on this topic e can be easily retrieved,), who may primarily benefit from these compounds. These can be easily identified as it is also characterized by a different biochemical, cognitive, and genetic profile. The current study is timely since AD patients with high Cu can be easily identified since they are characterized by a different biochemical, cognitive, and genetic profile as per recent findings (PMID: 37047347). This information can improve the quality of the manuscript by providing information about the unmet clinical need that this study can answer

Significance

The significance of the study relies on that the Cu(II) selective shuttles can import Cu into cells and also release Cu once inside the cells, which was shown to be bioavailable, as revealed by the delocalization of ATP7A from the TGN to the sub-plasmalemma zone in PC12 cells. The novelty is well expressed by the implementation of Cu(II) selective shuttles that can release Cu inside the cells. Thus, they can restore Cu physiological levels in conditions of brain Cu deficiency that typify the neuronal cells in AD. Furthermore, this Cu trafficking can be finely tuned, thus preventing potential adverse drug reactions when transferred into human clinical phase I and II studies. This application may represent a step forward concerning previous copper attenuating compounds/Cu(II) ionophores such as Clioquinol and GTSM which mediated non-physiological Cu import into the cells that have likely contributed to neurotoxicity processes in previous unsuccessful phase II clinical trials.

Furthermore, the originality of the current study relies on the fact that these shuttles can be tracked in real-time, once in the cell, since they employ a fluorophore moiety sensitive to Cu binding. Furthermore, DapHH-αR5W4NBD and HDapH-αR5W4NBD, can import bioavailable Cu(II) and can prevent ROS production by Cu(II)Aβ instantly, due to the much faster Cu-binding. Importantly, DapHH-αR5W4NBD and HDapH-αR5W4NBD shuttles have been also capable of preventing OHSC slices from Cu-induced neurotoxicity, microglial proliferation, and activation. Another important feature of the Cu shuttles is that they can be designed to control their site of cell delivery. In fact, previous ionophores had the tendency to accumulate in the mitochondria, and, in doing so, excess Cu in the mitochondria might have competed with other transitional metals (mainly Fe) and triggered mitochondrial dysfunction as well as cuproptosis. These are the main strengths of the study.

The audience of this article is currently that of expert biochemists, but by adding aspects regarding the unmet clinical need relating to the large population of AD patients with high copper in the introduction and discussion, the article can capture the attention of clinicians.

I am a neuroscientist working on biochemical pathways and metals in Alzheimer's disease.

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Reply to the reviewers

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

• The authors investigate in this study the function of LIN-42 for the process of precise molting timing in C. elegans. To achieve this, they compare LIN-42 with its mammalian ortholog, Period. They found that similar to Period, LIN-42 interacted with the kinase KIN-20, a mammalian Casein kinase 1 (CK1) ortholog. Hence, two different proteins involved in rhythmic processes, LIN-42 and Period function in a conserved manner. *
• First, they used mutants with specific deletions to untangle various phenotypes during C. elegans development. From this analysis they identify a specific region, corresponding to a CK1-binding region in mammals, to be mainly involved in the rhythmic molting phenotype. Next, they identify KIN-20, the CK1 ortholog as interaction partner of LIN-42. They even were able to demonstrate an interaction of CK1 with the region of LIN-42. Using CK1, they identified potential phosphorylation sites within LIN-42 and compared those with immunoprecipitated protein in vivo. There was a substantial overlap. While the C-terminal tail of LIN-42 was heavily phosphorylated, deletion of the C-terminal part resulted only in a minor phenotype for rhythmic molting. Last but not least, they demonstrated that point mutations that inactivate the catalytic function of KIN-20 produced a rhythmic molting phenotype. The interaction of LIN-42 with KIN-20 affected the localization of the kinase, similar to what was found to Period and CK1. *

• Overall, the experiments are well done, well controlled and well described even for non-specialists. I guess it was not easy to kind of sort out the many overlapping phenotypes. It was certainly helpful just to focus on the clear rhythmic molting phenotype. *

• I have no major or minor comments. *

• Reviewer #1 (Significance (Required)): *

• The manuscript is well written and can be followed by non-specialists of the field. The experiments are well performed. Even if some experiments did not yield the expected phenotype, e.g. deletion of the C-terminal tail of LIN-42 had only a minor phenotype inspire of heavy phosphorylation, these experiments are anyhow included and explained. *

• Overall, the study is interesting for people in the C. elegans field and by similarity mammalian chronobiology. I would expect that most of the progress based on this study will be on the further elucidation of the molting phenotype and how the other phenotypes related to this. Then this could emerge as a blueprint for molting phenomena in other species as well. *
• I am a mammalian chronobiologist working on Period proteins. *

We thank the reviewer for their positive evaluation of our work.

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

• This study represents pioneering work on LIN-42, the C. elegans ortholog of PER, uncovering its role in molting rhythms and heterochronic timing. A key strength of this work lies in its integrative approach, combining genetic and developmental analyses in C. elegans with biochemical characterization of LIN-42 protein. *

• At the organismal level, the authors take advantage of the power of C. elegans as a model system, employing precise genetic manipulations and high-resolution developmental assays to dissect the contributions of LIN-42 and its interaction partner KIN-20, the C. elegans ortholog of CK1, to molting rhythms. Their findings provide in vivo evidence that binding of LIN-42 with KIN-20 promotes the nuclear accumulation of KIN-20 and is crucial for molting rhythms, while its PAS domain appears dispensable for this function. This detailed phenotypic analysis of multiple LIN-42 and KIN-20 mutants represents a significant contribution to our understanding of the developmental clock. *

• At the biochemical level, the study provides a detailed analysis of the mechanism underlying LIN-42's interaction with CK1, demonstrating that LIN-42 contains a functionally conserved CK1-binding domain (CK1BD). Through their in vitro kinase assays and structural insights, the authors identified distinct roles for CK1BD-A and CK1BD-B: the former in kinase inhibition and the latter in stable CK1 binding and phosphorylation. Importantly, their data align well with previous findings on PER-CK1 regulation in mammalian and Drosophila systems, reinforcing the evolutionary conservation of key clock components. *

• Overall, this work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology. *

We thank the reviewer for the strong endorsement for publication of our work

*Major comment 1: * * In Figure 2D, I could not find a crucial control if the authors claim that KIN-20 binds to LIN-42. For example, a single mutant of LIN-42-3xFLAG could be used as a control for the double mutant. *

We will do an appropriate control experiment.

*Major comment 2: * * The sizes of the KIN20 bands were very diverged (~40 kDa and ~60 kDa), but the authors provide no explanation for this. *

The worm produces several KIN-20 isoforms. We will state this in the revised manuscript.

*Major comment 3: * * Regarding the MS study, the raw data are available, but the detailed supplemental Excel files would be more informative for readers. For example, are other interactors such as REV-ERB/NHR-85 detected in Figure 2A? Regarding Figure 4F, the list of phosphorylation sites and MS scores is also informative. *

We apologize for our omission in stating clearly in the figure legend that the significantly enriched proteins were labeled with a red dot. These were only LIN-42 itself and KIN-20. NHR-85 was not enriched. We will state this explicitly in a revised version and provide all relevant information.

*Major comment 4: * * It is an important finding that the PAS domain of LIN-42 is not essential for the molting rhythms. Is the PAS domain also dispensable for binding with KIN-20? *

Although we have currently no reason to assume that the PAS domain would be required for KIN-20 binding, we will perform an in vitro experiment to test for binding.

*Major comment 5 (Optional): * * In this study, the authors carefully performed in vitro kinase assays, and I strongly suggest that they investigate whether the CKI-mediated phosphorylation of LIN-42 is temperature-compensated and whether the CKI-BD-AB regions affect it. *

Although this is an interesting question, addressing it appears outside the scope of the manuscript and a revision; please see section 4 below.

*Major comment 6 (Optional): * * In Figure 6, the authors argue that the CKI-BD of LIN-42 is important for CK1 nuclear translocation. It would be better to show the effect of the nuclear accumulation of CKI on nuclear proteins, like the mammalian CKI-PER2-CLOCK story. Does CKI localization affect phosphorylation status of other clock-related proteins including REV-ERB/NHR-85? * * Phospho-proteome analysis would identify nuclear substrates of CK1. In addition, is phosphorylation of LIN-42 dispensable for the CK1 nuclear translocation? *

This is another interesting question yet currently nothing is known about other CK1/KIN-20 targets, and we have no evidence for NHR-85 being one. Please see our detailed comments in the section 4 below.

To address whether LIN-42 phosphorylation affects CK1/KIN-20 nuclear accumulation, we will seek to examine KIN-20 localization in LIN-42∆Tail animals.

*Major comment 7 (Optional): * * LIN-42 rhythmic expression could drive rhythmic nuclear accumulation of KIN-20. It would be better to examine this possibility using kin-20::GFP in lin-42 mutants. *

We agree that the mutant analysis is important for this and Fig. 6C shows reduced KIN-20 nuclear accumulation in LIN-42∆CK1BD.

Minor 1: * * I could not find the full gel images of the Western blot analyses as supplemental materials.

This data will be added.

Minor 2: * * The authors discussed a conserved module in two different clocks. A statement regarding a recently published paper (Hiroki and Yoshitane, Commun Biol, 2024) would be informative for readers.

We will add such a statement.

***Referee cross-commenting** *

• I basically agree with reviewer 1 and hope that this paper will be published soon as it is very valuable for our field. I have constructively pointed out some parts that could be improved, but depending on the editor's judgement, I believe that even if not all of these are revised, it will be sufficient for publication. *

• Reviewer #2 (Significance (Required)): *

• This work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology. *

• I strongly suggest editors to accept this study with minor modifications according to the following comments.*

We thank the reviewer for their strong support and the clear indication of required vs. optional revisions.

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

• In their manuscript "A conserved chronobiological complex times C. elegans development", Spangler, Braun, Ashley et al. investigate the mechanisms through which the PERIOD orthologue, lin-42, regulates rhythmic molting in C. elegans. Through precise genetic manipulations, the authors identify a particular region of lin-42, the 'CK1BD', which regulates molting timing, with less effect on other lin-42 phenotypes (e.g. heterochrony). They show that LIN-42 and the casein kinase 1 (CK1) homologue KIN-20 interact in vivo, and identify phosphorylation sites of LIN-42. Using biochemical assays, they find that the CK1BD of LIN-42 is sufficient for interaction with the human homologue of KIN-20, CK1, in vitro. The LIN-42 CK1BD is also required for the proper nuclear accumulation of KIN-20 in vivo. Furthermore, a point mutation that should disrupt the catalytic activity of KIN-20 also shows an irregular molting phenotype, similar to the lin-42 CK1BD mutant. The manuscript is very well-written and the data and methods are well-presented and detailed. Overall this work makes a convincing case that the C. elegans lin-42:Kin-20 and mammalian period:Ck1 interactions have functionally conserved roles in the oscillatory developmental programs of each organism (molting timing and circadian rhythms, respectively), with a few caveats below that can be addressed.*

We thank the reviewer for their positive evaluation of our work.

*Major comments: *

• 1. The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants.*

We agree, and acknowledged in the discussion, that phoshorylation of LIN-42 by KIN-20 in vivo has not been demonstrated by us. However, as discussed in the section 4 below, we find that this costly, challenging and time-consuming experiment is not warranted by the expected gain.

For technical reasons, the in vitro biochemistry was done using human CK1 protein. There are a few places (e.g. results, line 248 and discussion line 437), where the language, in my opinion, is extrapolating the CK1 results too strongly to KIN-20. The authors mention that feedback inhibition is a known property of human CK1. It is indeed quite striking that the LIN-42 CK1BD region interacts with and is phosphorylated by the human counterpart of KIN-20, and that feedback inhibition is also seen! However, the language about KIN-20 itself should be softened, since there does not appear to be clear evidence that KIN-20 exhibits the same properties as human CK1 (unless perhaps human CK1 can functionally replace KIN-20 in worms, or the proteins were extremely similar?)

We will follow the reviewer’s advice and carefully examine the text for instances where we extrapolated too much and tone these down. (We note that this does not apply to the example of line 248 where we wrote “Collectively, our data establish that the LIN-42

CK1BD is functionally conserved and mediates stable binding to the CK1 kinase domain.”, i.e., there was no mentioning of KIN-20.)

The role of the three LIN-42 isoforms should be further clarified. Minimally, it should be explained why the alleles where both b and c isoforms should be flag-tagged seem to only produce detectable b isoform (e.g. Fig. 2C).

We will clarify that the individual roles of the isoforms are largely unknown and that we can only speculate that the c-isoform may exhibit either generally low expression or expression in only few cells or tissues.

4. Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD.

We will attempt to generate a FLAG-tagged LIN-42∆CK1BD to perform IP and check for binding of KIN-20.

As detailed in section 4, we cannot tag LIN-42a individually due to the structure of the genomic locus, and its level appear very low to begin with.

In the molting timing assay, there is an unexpected result where the delta-C-terminal-tail lin-42 allele resembles the n1089 (N-terminal deletion) (line 315). Could the authors more clearly explain this finding?

As we point out in the manuscript, n1089 is a partial deletion with a breakpoint in a noncoding (intronic) region of lin-42. Accordingly, it is currently unknown, what mature transcripts and proteins are made in the mutant animals. This prevents us from making educated guesses as to why there is a phenotypic resemblance between these and lin-42∆tail mutant animals. We will clarify in the manuscript that this is an interesting, but currently unexplained observation.

*Minor comments: *

• 1. The correspondence between the LIN-42 "SYQ" and "LT" motifs and the motifs referred to as "A" and "B" should be clarified, and consistent names/labels used. Are these interchangeable names? If it is necessary to use both names, the differences between SYQ/LT and A/B should be made more clear.*

We agree that the situation is not completely satisfactory but feel that we need to use both names since they have both been used in the literature. We will work to revise the text to reflect more clearly the correspondence.

For data presented as "% of animals", please indicate the number of animals scored (e.g. egl, alae assays - ~ how many animals per replicate (dot)?).

We will provide these numbers.

Line 145-148 - Mentioning the relevant phenotype(s) of the lin-42 null allele from the cited paper would provide a good point of comparison here.

We will mention the previously described phenotypes.

Line 201 - the phrase "This is also true for the proteins:" is unclear, as the previous sentence states that both lin-42 and kin-20 mRNAs oscillate, while the next sentence says that only LIN-42 protein oscillates.

We apologize for the confusion and will correct the text.

Line 231 - please explain the significance of the 'lower response signal' in the BLI assay for the CKIBD(no tail).

We will clarify that the lower response signal observed for the CK1BD compared to the CK1BD+Tail (residues 402-589; same construct used in Fig. 3B) reflects its smaller molecular weight, which reduces the overall mass contribution to the BLI sensor.

Fig. 2 - C/D - the genotype lane labels should I think indicate an N-terminal rather

We will fix this mistake.

7. Fig. 6, line 367 - lin-42 is variably described as promoting increased KIN-20 'nuclear accumulation' or 'localization'. I think that 'accumulation' is more accurate, as it doesn't imply a specific mechanism for the difference (transport vs stabilization, etc.)

We will revise the manuscript accordingly.

*8. Fig 6B - an overlay of the panels or another way of quantifying the colocalization would make this result more clear. *

We will supply the requested overlay.

*Reviewer #3 (Significance (Required)): *

• This work presents a major mechanistic and conceptual advance in our understanding of the role of lin-42/Period, a conserved key regulator of C. elegans development. Previously, it was not clear if the heterochronic and circadian functions of lin-42 were genetically separable, nor was it known how LIN-42 physically interacted with the CK1 homologue. This work addresses these questions using precise genome engineering and detailed phenotypic and biochemical approaches. The work also reveals the conservation of bi-directional/reciprocal regulation between lin-42 and kin-20. The main limitations of the study, which can potentially be addressed as outlined in the 'major points' above, are that evidence should be provided that lin-42 phosphorylation depends on kin-20 in vivo, and that the CK1BD mediates the interaction in vivo (since the in vitro work is with human CK1). As the authors indicate, this is the first 'conserved clock module' of this type, and this work will therefore be of significant interest to both the C. elegans developmental biology and the more general biological timing fields. *

• Field of expertise of the reviewer- C. elegans genetics and development.*

Description of the studies that the authors prefer not to carry out

*Major comment 5 (Optional): * * In this study, the authors carefully performed in vitro kinase assays, and I strongly suggest that they investigate whether the CKI-mediated phosphorylation of LIN-42 is temperature-compensated and whether the CKI-BD-AB regions affect it. *

Temperature compensation is of course one of the most striking features of circadian clocks, and CK1-mediated phosphorylation of PER appears a critical component. We agree that it would be interesting to examine whether or not this feature exists in an animal whose development is not or only partially temperature-compensated. However, these studies are not straightforward – we would first have to set up an assay and demonstrate temperature compensation for the mammalian PER – CK1 pair as a positive control. We were not able to purify KIN-20 so could only test whether the LIN-42 substrate promoted temperature compensation. Moreover, either result for LIN-42 – CK1 would immediately raise new questions that would deserve extensive follow-up: if there is temperature compensation, why is worm development not compensated? If there is none, where/how do the interactions between CK1 and LIN-42 differ from those between CK1 and PER? Hence, we propose that these studies are outside the scope of the current study.

*Major comment 6 (Optional): * * In Figure 6, the authors argue that the CKI-BD of LIN-42 is important for CK1 nuclear translocation. It would be better to show the effect of the nuclear accumulation of CKI on nuclear proteins, like the mammalian CKI-PER2-CLOCK story. Does CKI localization affect phosphorylation status of other clock-related proteins including REV-ERB/NHR-85? * * Phospho-proteome analysis would identify nuclear substrates of CK1. In addition, is phosphorylation of LIN-42 dispensable for the CK1 nuclear translocation? *

We agree that it will be important to identify relevant targets of KIN-20 in future work. Unfortunately, at this point, none are known, and we especially do not have any knowledge of the phosphorylation status of NHR-85. Indeed, in unrelated (and unpublished) work we have done a phosphoproteomics time course of wild-type animals. We have not detected any NHR-85-derived phosphopeptides in our analysis. Thus, this would establish a completely new line of research, incompatible with the timelines of a revision.

@Ref. 3:

1. *The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants. * We agree, and acknowledged in the discussion, that phoshorylation of LIN-42 by KIN-20 in vivo has not been demonstrated by us. However, since our data from the LIN-42∆Tail mutant also suggest that LIN-42 phosphorylation be functionally largely dispensable for KIN-20’s function in rhythmic molting, we consider further elucidation of this point a lower priority, especially considering the challenges involved. As we have seen for our unpublished work on wild-type animals, a phosphoproteomics experiments would be costly and time-consuming, with a non-trivial analysis (due to the underlying dynamics of protein level changes). A phos-tag gel would be subject to multiple confounders given the abundance of the phosphosites that we detected on immunoprecipitated LIN-42 – unlikely to stem only from KIN-20 activity – and an increase in total LIN-42 levels that we observe upon KIN-20 depletion, and thus appears unsuited to providing a meaningful answer.

*Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD. *

As detailed in section 2, we will address the point concerning LIN-42∆CK1BD. However, due to the overlapping exons, we are unable to tag the a-isoform independently of the b-isoform. Moreover, in a western blot of a line where both a- and b-isoforms are tagged, we have observed only little or no LIN-42a signal, suggesting that, like the c-isoform, its expression may be more limited, making biochemical characterization difficult. Hence, these experiments are not feasible.

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Referee #3

Evidence, reproducibility and clarity

In their manuscript "A conserved chronobiological complex times C. elegans development", Spangler, Braun, Ashley et al. investigate the mechanisms through which the PERIOD orthologue, lin-42, regulates rhythmic molting in C. elegans. Through precise genetic manipulations, the authors identify a particular region of lin-42, the 'CK1BD', which regulates molting timing, with less effect on other lin-42 phenotypes (e.g. heterochrony). They show that LIN-42 and the casein kinase 1 (CK1) homologue KIN-20 interact in vivo, and identify phosphorylation sites of LIN-42. Using biochemical assays, they find that the CK1BD of LIN-42 is sufficient for interaction with the human homologue of KIN-20, CK1, in vitro. The LIN-42 CK1BD is also required for the proper nuclear accumulation of KIN-20 in vivo. Furthermore, a point mutation that should disrupt the catalytic activity of KIN-20 also shows an irregular molting phenotype, similar to the lin-42 CK1BD mutant. The manuscript is very well-written and the data and methods are well-presented and detailed. Overall this work makes a convincing case that the C. elegans lin-42:Kin-20 and mammalian period:Ck1 interactions have functionally conserved roles in the oscillatory developmental programs of each organism (molting timing and circadian rhythms, respectively), with a few caveats below that can be addressed.

Major comments:

1. The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants.
2. For technical reasons, the in vitro biochemistry was done using human CK1 protein. There are a few places (e.g. results, line 248 and discussion line 437), where the language, in my opinion, is extrapolating the CK1 results too strongly to KIN-20. The authors mention that feedback inhibition is a known property of human CK1. It is indeed quite striking that the LIN-42 CK1BD region interacts with and is phosphorylated by the human counterpart of KIN-20, and that feedback inhibition is also seen! However, the language about KIN-20 itself should be softened, since there does not appear to be clear evidence that KIN-20 exhibits the same properties as human CK1 (unless perhaps human CK1 can functionally replace KIN-20 in worms, or the proteins were extremely similar?)
3. The role of the three LIN-42 isoforms should be further clarified. Minimally, it should be explained why the alleles where both b and c isoforms should be flag-tagged seem to only produce detectable b isoform (e.g. Fig. 2C).
4. Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD.
5. In the molting timing assay, there is an unexpected result where the delta-C-terminal-tail lin-42 allele resembles the n1089 (N-terminal deletion) (line 315). Could the authors more clearly explain this finding?

Minor comments:

1. The correspondence between the LIN-42 "SYQ" and "LT" motifs and the motifs referred to as "A" and "B" should be clarified, and consistent names/labels used. Are these interchangeable names? If it is necessary to use both names, the differences between SYQ/LT and A/B should be made more clear.
2. For data presented as "% of animals", please indicate the number of animals scored (e.g. egl, alae assays - ~ how many animals per replicate (dot)?).
3. Line 145-148 - Mentioning the relevant phenotype(s) of the lin-42 null allele from the cited paper would provide a good point of comparison here.
4. Line 201 - the phrase "This is also true for the proteins:" is unclear, as the previous sentence states that both lin-42 and kin-20 mRNAs oscillate, while the next sentence says that only LIN-42 protein oscillates.
5. Line 231 - please explain the significance of the 'lower response signal' in the BLI assay for the CKIBD(no tail).
6. Fig. 2 - C/D - the genotype lane labels should I think indicate an N-terminal rather than C-terminal LIN-42 tag.
7. Fig. 6, line 367 - lin-42 is variably described as promoting increased KIN-20 'nuclear accumulation' or 'localization'. I think that 'accumulation' is more accurate, as it doesn't imply a specific mechanism for the difference (transport vs stabilization, etc.)
8. Fig 6B - an overlay of the panels or another way of quantifying the colocalization would make this result more clear.

Significance

This work presents a major mechanistic and conceptual advance in our understanding of the role of lin-42/Period, a conserved key regulator of C. elegans development. Previously, it was not clear if the heterochronic and circadian functions of lin-42 were genetically separable, nor was it known how LIN-42 physically interacted with the CK1 homologue. This work addresses these questions using precise genome engineering and detailed phenotypic and biochemical approaches. The work also reveals the conservation of bi-directional/reciprocal regulation between lin-42 and kin-20. The main limitations of the study, which can potentially be addressed as outlined in the 'major points' above, are that evidence should be provided that lin-42 phosphorylation depends on kin-20 in vivo, and that the CK1BD mediates the interaction in vivo (since the in vitro work is with human CK1). As the authors indicate, this is the first 'conserved clock module' of this type, and this work will therefore be of significant interest to both the C. elegans developmental biology and the more general biological timing fields.

Field of expertise of the reviewer- C. elegans genetics and development.

This feels a lot like the tags in an annotation. We've thought about structured tags, and possibly the ability for different users or groups to publish different schemas for those structures. A tag or possibly a group layer could inherit one or more schemas by referencing them.

docker e2b sandbox

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews:

Reviewer #1 (Public review):

Comment of Review of Revised Version:

Although the authors have partly corrected the manuscript by removing the mislabeling in their Co-IP experiments, my primary concern on the actual functional connotations and direct interaction between PA28y and C1QBP still remains unaddressed. As already mentioned in my previous review, since the core idea of the work is PA28y's direct interaction with C1QBP, stabilizing it, the same should be demonstrated in a more convincing manner.

My other observation on the detection of C1QBP as a doublet has been addressed by usage of anti-C1QBP Monoclonal antibody against the polyclonal one used before. C1QBP doublets have not been observed in the present case.

The authors have also worked on the presentation of the background by suitably modifying the statements and incorporating appropriate citations.

However, the authors are requested to follow the recommendations provided to them by the reviewers to address the major concerns.

Thank you very much for your comments. We appreciate your concerns regarding the need for more direct evidence to support the stabilizing interaction between PA28γ and C1QBP. In response to your feedback, we have taken additional steps to provide more convincing evidence of this interaction.

To complement our existing pull-down and Co-IP experiments, we utilized AlphaFold 3 to predict the three-dimensional structure of the PA28γ-C1QBP complex. The predicted model reveals specific residues and interfaces that are likely involved in the direct interaction between PA28γ and C1QBP. Our analysis indicates that this interaction may depend on amino acids 1-167 and 1-213 of C1QBP (Revised Appendix Figure 1E-H). Furthermore, aspartate (ASP), as the 177th amino acids of PA28γ, was predicted to interact with the 76th amino acid threonine (THR) and the 78th amino acid glycine (GLY) of C1QBP (Revised Appendix Figure 1I). This structural insight was further validated by our immunoprecipitation experiments (Revised Figure 1J). These findings provide a molecular basis for the observed stabilizing effect and suggest potential mechanisms by which PA28γ influences C1QBP stability. Specifically, the identified interaction sites offer clues into how PA28γ may stabilize C1QBP at the molecular level.

Furthermore, we performed proximity ligation assays (PLA) to detect in situ interactions between PA28γ and C1QBP at the single-cell level. PLA results clearly demonstrate the presence of PA28γ-C1QBP complexes within cells, providing direct evidence of their physical interaction (Revised Figure 1D). This approach overcomes some of the limitations associated with traditional IP experiments and confirms the direct nature of the interaction.

In summary, the integration of AlphaFold 3 predictions, PLA data, and our previous Pull-down and Co-IP experiments provides robust and direct evidence for a stable interaction between PA28γ and C1QBP. We believe that these additional findings significantly reinforce our conclusions and effectively address the concerns raised by the reviewers. Once again, thank you for your valuable feedback, which has been instrumental in refining and enhancing our study.

Reviewer #2 (Public review):

Comment of Review of Revised Version:

Weaknesses:

Many data sets are shown in figures that cannot be understood without more descriptions either in the text or the legend, e.g., Fig. 1A. Similarly, many abbreviations are not defined.

The revision addressed these issues.

Some of the pull-down and coimmunoprecipitation data do not support the conclusion about the PA28g-C1QBP interaction. For example, in Appendix Fig. 1B the Flag-C1QBP was detected in the Myc beads pull-down when the protein was expressed in the 293T cells without the Myc-PA28g, suggesting that the pull-down was not due to the interaction of the C1QBP and PA28g proteins. In Appendix Fig. 1C, assume the SFB stands for a biotin tag, then the SFB-PA28g should be detected in the cells expressing this protein after pull-down by streptavidin; however, it was not. The Western blot data in Fig. 1E and many other figures must be quantified before any conclusions about the levels of proteins can be drawn.

The revision addressed these problems.

The immunoprecipitation method is flawed as it is described. The antigen (PA28g or C1QBP) should bind to the respective antibody that in turn should binds to Protein G beads. The resulting immunocomplex should end up in the pellet fraction after centrifugation, and analyzed further by Western blot for coprecipitates. However, the method in the Appendix states that the supernatant was used for the Western blot.

The revision corrected this method.

To conclude that PA28g stabilizes C1QBP through their physical interaction in the cells, one must show whether a protease inhibitor can substitute PA28q and prevent C1QBP degradation, and also show whether a mutation that disrupt the PA28g-C1QBP interaction can reduce the stability of C1QBP. In Fig. 1F, all cells expressed Myc-PA28g. Therefore, the conclusion that PA28g prevented C1QBP degradation cannot be reached. Instead, since more Myc-PA28g was detected in the cells expressing Flag-C1QBP compared to the cells not expressing this protein, a conclusion would be that the C1QBP stabilized the PA28g. Fig. 1G is a quantification of a Western blot data that should be shown.

The binding site for PA28g in C1QBP was mapped to the N-terminal 167 residues using truncated proteins. One caveat would be that some truncated proteins did not fold correctly in the absence of the sequence that was removed. Thus, the C-terminal region of the C1QBP with residues 168-283 may still bind to the PA29g in the context of full-length protein. In Fig. 1I, more Flag-C1QBP 1-167 was pull-down by Myc-PA28g than the full-length protein or the Flag-C1QBP 1-213. Why?

The interaction site in PA28g for C1QBP was not mapped, which prevents further analysis of the interaction. Also, if the interaction domain can be determined, structural modeling of the complex would be feasible using AlphaFold2 or other programs. Then, it is possible to test point mutations that may disrupt the interaction and if so, the functional effect.

The revision added AlphaFold models for the protein interaction. However, the models were not analyzed and potential mutations that would disrupt the interact were not predicted, made and tested. The revision did not addressed the request for the protease inhibitor.

Thank you for your insightful comments regarding the binding site of PA28γ in C1QBP. We appreciate your concern about the potential misfolding of truncated proteins and the possible interaction between the C-terminal region (residues 168-283) of C1QBP and PA28γ in the context of full-length protein.

To address these concerns, we have conducted additional analyses and experiments to provide a more comprehensive understanding of the interaction between PA28γ and C1QBP. Using AlphaFold 3, we predicted the three-dimensional structure of the PA28γ-C1QBP complex. The model reveals specific residues and interfaces that are likely involved in the direct interaction between PA28γ and C1QBP. Notably, our structural analysis indicates that the interaction may primarily depend on amino acids 1-167 and 1-213 of C1QBP (Revised Appendix Figure 1E-H). Furthermore, aspartate (ASP), as the 177th amino acids of PA28γ, was predicted to interact with the 76th amino acid threonine (THR) and the 78th amino acid glycine (GLY) of C1QBP (Revised Appendix Figure 1I). This prediction supports the idea that the N-terminal region of C1QBP is crucial for its interaction with PA28γ. Regarding the observation in old Figure 1I (Revised Figure 1J), where more Flag-C1QBP 1-167 was pulled down by Myc-PA28γ compared to the full-length protein or Flag-C1QBP 1-213, we believe this can be explained by several factors:

A. The truncation of C1QBP to residues 1-167 may expose key interaction sites that are partially obscured in the full-length protein. This enhanced accessibility could lead to stronger binding affinity and higher pull-down efficiency.

B. While it is possible that some truncated proteins do not fold correctly, our data suggest that the N-terminal fragment (1-167) retains sufficient structural integrity to interact effectively with PA28γ. The increased pull-down of this fragment suggests that it captures the essential elements required for binding.

C. The C-terminal region (168-283) might exert steric hindrance or allosteric effects on the N-terminal binding site in the context of the full-length protein. This interference could reduce the overall binding efficiency, leading to less pull-down of full-length C1QBP compared to the truncated version.

Compared with the control group, the presence of Myc-PA28γ significantly increased the expression level of Flag-C1QBP (r Revised Figure 1G). Gray value analysis showed that in cells transfected with Myc-PA28γ, the decay rate of Flag-C1QBP was significantly slower than that of the control group (Revised Figure 1H), suggesting that PA28γ can delay the protein degradation of C1QBP and stabilize its protein level. This indicates that an increase in the level of PA28γ protein can significantly enhance the expression level of C1QBP protein, while PA28γ can slow down the degradation rate of C1QBP and improve its stability. In addition, our western blot analysis also proved that PA28γ could still prevent the degradation of C1QBP under the action of proteasome inhibitor MG-132 (Revised Appendix Figure 1D). Moreover, PA28γ could not stabilize the mutation of C-terminus of C1QBP (amino acids 94-282), which was not the interaction domain of PA28γ-C1QBP (Revised Figure 1K).

Author response:

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

Reviewer #1 (Public review):

Summary:

This manuscript presents evidence of ’vocal style’ in sperm whale vocal clans. Vocal style was defined as specific patterns in the way that rhythmic codas were produced, providing a fine-scale means of comparing coda variations. Vocal style effectively distinguished clans similar to the way in which vocal repertoires are typically employed. For non-identity codas, vocal style was found to be more similar among clans with more geographic overlap. This suggests the presence of social transmission across sympatric clans while maintaining clan vocal identity.

Strengths:

This is a well-executed study that contributes exciting new insights into cultural vocal learning in sperm whales. The methodology is sound and appropriate for the research question, building on previous work and ground-truthing much of their theories. The use of the Dominica dataset to validate their method lends strength to the concept of vocal style and its application more broadly to the Pacific dataset. The results are framed well in the context of previous works and clearly explain what novel insights the results provide to the current understanding of sperm whale vocal clans. The discussion does an overall great job of outlining why horizontal social learning is the best explanation for the results found.

Weaknesses:

The primary issues with the manuscript are in the technical nature of the writing and a lack of clarity at times with certain terminology. For example, several tree figures are presented and ’distance’ between trees is key to the results, yet ’distance’ is not clearly defined in a way for someone unfamiliar with Markov chains to understand. However, these are issues that can easily be dealt with through minor revisions with a view towards making the manuscript more accessible to a general audience.

I also feel that the discussion could focus a bit more on the broader implications - specifically what the developed methods and results might imply about cultural transmission in other species. This is specifically mentioned in the abstract but not really delved into in detail during the discussion.

We are grateful for the Reviewer’s recognition of the study’s contributions to understanding cultural vocal learning in sperm whales. In response to the concerns regarding clarity and accessibility, we have revised the manuscript to improve the definition of key concepts, such as the notion of “distance” between subcoda trees. This adjustment ensures clarity for readers unfamiliar with the technical details of Markov chains. Additionally, we have expanded the discussion to highlight broader implications of our findings, particularly their relevance to understanding cultural transmission in other species, as suggested.

Reviewer #2 (Public review):

Summary:

The current article presents a new type of analytical approach to the sequential organisation of whale coda units.

Strengths:

The detailed description of the internal temporal structure of whale codas is something that has been thus far lacking.

Weaknesses:

It is unclear how the insight gained from these analyses differs or adds to the voluminous available literature on how codas varies between whale groups and populations. It provides new details, but what new aspects have been learned, or what features of variation seem to be only revealed by this new approach? The theoretical basis and concepts of the paper are problematical and indeed, hamper potentially the insights into whale communication that the methods could offer. Some aspects of the results are also overstated.

We appreciate the Reviewer’s acknowledgment of the novelty in describing the internal temporal structure of whale codas. Regarding the concern about the unique contributions of this approach, we have further emphasized in the revised manuscript how our methodology reveals previously uncharacterized dimensions of coda structure. Specifically, our work highlights how non-identity codas, which have received limited attention, play a significant role in inter-clan acoustic interactions. By leveraging Variable Length Markov Chains, we provide a nuanced understanding of coda subunits that complements existing studies and demonstrates the value of this analytical approach.

Reviewer #3 (Public review):

Summary:

The study presented by Leitao et al., represents an important advancement in comprehending the social learning processes of sperm whales across various communicative and socio-cultural contexts. The authors introduce the concept of ”vocal style” as an addition to the previously established notion of ”vocal repertoire,” thereby enhancing our understanding of sperm whale vocal identity.

Strengths:

A key finding of this research is the correlation between the similarity of clan vocal styles for non-ID codas and spatial overlap (while no change occurs for ID codas), suggesting that social learning plays a crucial role in shaping symbolic cultural boundaries among sperm whale populations. This work holds great appeal for researchers interested in animal cultures and communication. It is poised to attract a broad audience, including scholars studying animal communication and social learning processes across diverse species, particularly cetaceans.

Weaknesses:

In terms of terminology, while the authors use the term ”saying” to describe whale vocalizations, it may be more conservative to employ terms like ”vocalize” or ”whale speech” throughout the manuscript. This approach aligns with the distinction between human speech and other forms of animal communication, as outlined in prior research (Hockett, 1960; Cheney & Seyfarth, 1998; Hauser et al., 2002; Pinker & Jackendoff, 2005; Tomasello, 2010).

We thank the Reviewer for recognizing the importance of our findings and their appeal to broader audiences interested in animal cultures and communication. In response to the suggestion regarding terminology, we have adopted a more conservative language to align with distinctions between human and non-human communication systems. For example, terms like “vocalize” and “vocal repertoire” are used in place of anthropomorphic terms such as “saying”. This ensures consistency with established conventions while maintaining clarity for a broad readership.

Reviewer #1 (Recommendations):

Comment 1

Lines 11-13: As mentioned above, the implications for comparing communication systems and cultural transmission in other species isn’t really discussed much and I think it’s a really interesting component of the study’s broader implications.

Thank you for the comment.

Action - We added a few more sentences to the discussion regarding this.

Comment 2

Figure 1: More information on the figure of these trees would help. What do the connecting lines represent? What do the plain black dots and the black dot with the white dot represent? Especially since the ”distance between trees” is a key result, it’s important that someone unfamiliar with Markov chains can understand the basics of how this is calculated and what it represents. It is explained in the methods, but a brief explanation here would make the results and the figure a lot clearer since the methods are the last section of the manuscript.

These were omitted as we believed that attempting to introduce the mathematical structure and the methodology to compare two instances, in a figure caption, would have caused more ambiguity than necessary.

Action - Added an informal introduction to these concepts on the figure caption. Also added a pointer to the Supplementary Materials.

Comment 3

Table 1: A definition of dICIs should be included here.

Added the definition of discrete ICI to the table.

Comment 4

Figure 2: The placement of the figures is a bit confusing because they are quite far from the text that references them.

We thank the reviewer for pointing this out, we tried to edit the manuscript to improve this issue, but this part of the editing is more within the journal’s powers than our own.

Action - Moved images closes to the corresponding text in manuscript.

Comment 5

Line 117: Probabilistic distance needs to be briefly explained earlier when you first mention distance (see Lines 11-13 comments).

Action - Clarifications added in the caption of figure 1. as per comment on Lines 11-13

Comment 6

Figure 4: Is order considered in these pairwise comparisons? It looks like there are two dots for each pairwise comparison. Additionally, why is the overlap different in these two comparisons? For example, short:four-plus has an overlap of 0.6, while four-plus:short has an overlap of 0.95.

The x-axis of the plots in Figure 4 is geographical clan overlap. This is calculated as per (Hersh et al., 2022) and is described in our Methods (see “Measuring clan overlap” section). Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger ( 0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

Action - We have now added a reference to that section of the Methods in our Figure 4 caption and include the clan spatial overlap matrix as a supplemental table (Table S5).

Comment 7

Figure 4: I think the reference should be Hersh et al. [11].

Thank you for catching this.

Action - Reference corrected

Comment 8

Line 227: What aspect of your analysis looked at how often codas were produced? You mention coda frequency, but it is unclear how this was incorporated into your analysis. If this is included in the methods, the language is a bit too technical to easily parse it out.

Indeed here we are referencing the results of the paper mentioned in the previous line. We do not look at coda production frequency.

Action - Added citation to paper that actually performs this analysis.

Comment 9

Lines 253-255: I think you could dig into this a little more, as ”there is currently no evidence” is not the most convincing argument that something is not a driver. Perhaps expanding on the latter sentence that clans are recognizable across oceans basins would be helpful. Does this suggest that clans with similar geographic overlap experience diverse environmental conditions across ocean basins? If so, this might better strengthen your argument against environmental drivers.

Thank you for pointing this out. We feel that the next sentence highlights that clans are recognizable across environmental variation from one side to the other of the ocean basin, which supports the inductive reasoning that codas do not vary systematically with environment. However, we have edited these sentences for clarity.

Comment 10

Lines 311-314: It would also be interesting to look at vocal style across non-ID coda types. Are some more similar to each other across clans than others? Perhaps vocal style can further distinguish types of non-ID codas.

In supplementary Materials 3.4.2 and 3.5 we highlight our results when the codas are separated by coda type summarized in Table S4. We do compare the vocal style across non-ID coda types across clans and within the same clan. The results however are aggregated to highlight the differences in style between the clans and a a coda type-only comparison is not shown.

Comment 11

Lines 390-392: I’m assuming this is why pairwise comparisons were directional (i.e., there was both an A:B and a B:A comparison)? Can you speak to why A:B and B:A comparisons can have such different overlap values?

Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger (0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

Action - We now include the clan spatial overlap matrix as a supplemental table (Table S5).

Comment 13

Line 56: Can you briefly explain what memory means in the context of Markov chains?

We provide an explanation of the meaning of memory in the Methods section on ”Variable length Markov Chains”. Briefly, the memory in this case means how many states in the past of the Markov chain’s current state are required to predict the next transition of the chain itself. Standard Markov chains “look” back only one time step, while k-th order Markov chains look back k steps. In our case, there was no reason to assume that the memory required to predict different sequences of states (interclick intervals) should be the same across all sequences, and thus we adopted the formalism of variable length Markov chains, that allow for different levels of memory across the system.

Comment 14

Supplementary Figure S3: Like in the main manuscript, briefly explain or remind us what the blank nodes and the yellow nodes are.

Action - Clarified that the orange node represents the root of the tree in the figures.

Comment 15

Supplementary Figure S7: Put the letters before the dataset name.

Action - Done.

Comment 16

Supplementary Figure S10: Unclear what ’inner vs outer’ means.

One specifies comparisons across clans (outer) and the other within the same clan (inner)

Action - Added clarification on the caption of Figure S10

Comment 17

Supplementary Figure S14: Include a-c labels in the figure itself.

Action - Labels added to figure

Comment 18

Supplementary Figure S14: The information about the nodes is what needs to be included earlier and in the main body when discussing the trees.

Action - Added the explanation earlier in the text and in the main body

Reviewer #2 (Recommendations):

Comment 19

Line 22: ”Symbolic” and ”Arbitrary” are not synonyms. Please see the comment above.

We agree. Here, we make the point that the evolution of symbolic markers of group identity can be explained from what are initially arbitrary, and meaningless, signals (see [L1, L2]). Our point being that any vocalization, any coda, could have become selected for as an identity coda, and to become symbolic, and evolve to play a key role in cultural group formation and in-group favoritism because they enable a community of individuals to solve the problem of with whom to collaborate. The specific coda itself does not affect collaborative pay offs, but group specific differences in behavior can, as such the coda is arguably symbolic; as it is observable and recognizable, and can serve as a means for social assortment even when the behavioural differences are not. This can explain the means by which the social segregation which is observed among behaviorally distinct clans of sperm whales. However, in this manuscript, we do not extend this discussion of existing literature and have attempted to concisely describe this in a couple of lines, which clearly do a disservice to the large body of literature on the evolution of symbolic markers and human ethnic groups. We have added some citations to this section so that the reader may follow up should they disagree with out brief introductory statements.

Action - Added citations and pointers to the literature.

Comment 20

Line 24: The authors’ terminology around ”markers”, ”arbitrary”, ”symbolic” is unnecessarily confusing and mystifying, giving the impression these terms are interchangeable. They are not. These terms are an integral and long-established part of key definitions in signal theory. Term use should be followed accordingly. The observation that whale vocal signals vary per population does not necessarily mean that they function as a social tag. The word ”dog” varies per population but its use relates to an animal, not the population that utters the word. ”Dog” is not ”symbolic” of England, English-speaking populations or the English language. Furthermore, the function of whale vocal signals is extremely challenging to determine. In the best conditions, researchers can pin the signal’s context, this is distinct from signal’s function and further even for the signal’s meaning. How exactly the authors determine that whale vocal signals are arbitrary is, thus, perplexing given that this would require a detailed description and understanding of who is producing the song, when, towards whom, and how the receivers react, none of which the authors have and without which no claim on the signals’ function can be made. This terminological laxness and the sensu latu in extremis to various terms in an unjustified, unnecessary and unhelpful.

We use these terms as established in Hersh et al 2022 and the works leading up to it over the last 20 years in the study of sperm whales. These are often derived from definitions by Boyd and Richerson’s work on culture in humans and animals along with evolution of symbolic markers both in theory and in humans. We agree with the reviewer that these are difficult to establish in non-humans, whales or otherwise, but feel strongly that the accumulating evidence provides strong support for the function of these signals as symbolic markers of cultural groups, and that they likely evolved from initially arbitrary calls which were a part of the vocal repertoire (similar to the process and selective environment in Efferson et al. [L1] and McElreath et al. [L2]). We feel that we do not use these terms interchangeably here, and have inherited their use from definitions from anthropology. The work presented here uses terminology built across two decades of work in cetacean, and sperm whale, culture. And do not feel that these terms should be omitted here.

Comment 21

Lines 21-27: Overly broad and hazy paragraph.

We hope the replies above and our changes satisfy this comment and clarify the text.

Comment 22

Figure 1 legend: What are ”memory structures”? Unjustified descriptor.

The phrase was chosen to make draw some intuition on the variation of context length in variable length markov models.

Action - Re-worded from memory structures to statistical properties

Comment 23

Line 30: Omit ”finite”.

Action - Omitted.

Comment 24

Line 31: Please define and distinguish ”rhythm” and ”tempo”. Also see comment above, rhythm and tempo definitions require the use of IOIs.

We disagree with the reviewer’s claims here. In our research specifically, and for sperm whale research generally, coda inter-click intervals (ICIs) are calculated as the time between the start of the first click and the start of the subsequent click. This makes ICIs identical to inter-onset intervals (IOIs) under all definitions we are aware of. For example, Burchardt and Knornschild [L3] define IOIs as such: “In a sequence of acoustic signals, the time span between the start of an element and the next element, comprising the element duration and the following gap duration”. We now include a sentence making this point.

Regardless, we disagree on a more fundamental level with the statement that unless researchers quantify inter-onset intervals (IOIs), they cannot make any claims about rhythm. There are many studies that investigate rhythmic aspects of human and animal vocalizations without using IOIs [L4–L7]. If the duration of sound elements of interest is relatively constant (as is the case for sperm whale clicks), then rhythm analyses can still be meaningfully conducted on inter-call intervals (the silent intervals between calls).

For sperm whales, coda rhythm is defined by the relative ICIs standardized by their total duration. These can be clustered into discrete, defined rhythm types based on characteristic ICI patterns. Coda tempo is relative to the total duration of the coda itself. This can also be clustered into discrete tempo types across all coda durations as well (see [L8]).

Action - We added a sentence specifying that in this case we can use both ICIs and IOIs because of the standardized length of a single click.

Comment 25

Line 36: Are there non-vocalized codas to require the disambiguation here?

No, we have omitted for clarity.

Comment 26

Line 44: ”Higher” than which other social group class?

Sperm whales live in a multi-level social organization. Clans are a “higher” level of social organization than the social “units” which we define in line 40. Clans are made up of all units which share similar production repertoire of codas.

Action - We have added ’above social units’ on line 44 to make this clear.

Comment 27

Line 47: The use of “symbolic” continues to be enigmatic, even if authors are taking in this classification from other researchers. In signal theory (semiotics), not all biomarkers are necessarily symbols. I advise the authors to avoid the use of the term colloquially and instead adopt the definition used in the research field within which the study falls in.

There is ample examples of the use of ”symbolic” when referring to markers of in-group membership both in human and non-human cultures.Our choice to use the term “symbolic” is based on a previous study [L9] that found quantitative evidence that sperm whale identity codas function as symbolic markers of cultural identity, at least for Pacific Ocean clans. The full reasoning behind why the authors used the term “symbolic markers” is given in that paper, but briefly, they found evidence that identity coda usage becomes more distinct as clan overlap increases, while non-identity coda usage does not change. This matches theoretical and empirical work on human symbolic markers[L1, L2, L10, L11].

Action - We retain the use of the term here, as defined in the works cited, and based on its prior usage in the study of both human and non-human cultures.

Comment 28

Line 50: This statement is not technically accurate. The use of a signal as a marker by individuals can only be determined by how individuals ”interpret” and react to that signal - e.g., via playback experiments - it cannot be determined by how different populations use and produce the signals.

We respectfully disagree. While we agree that the optimal situation would be that of playback, the contextual use can provide insight into the functional use of signals; as can expected patterns of use and variation, as was tested in the papers we cite. However, this argument is not the scope nor the synthesis of this paper. These statements are supported by existing published works, as cited, and we encourage the reviewer to take exception with those papers.

Comment 29

Line 69: ”Meaningful speech characteristics”??? These terms do not logically or technically follow the previous statement. Why not stay faithful to the results and state that the method used seems to be valid and reliable because it confirms former studies and methods?

Action - Reworded to better underline the method’s results with previous studies

Comment 30

Lines 72-74: This statement doesn’t seem to accurately capture/explain/resume the difference between ID and non-ID codas.

We are not sure what the reviewer is referring to in this case. The sentence in this case was meant to explain the different relations that ID/non-ID codas have with clan sympatry.

Comment 31

Line 75: The information provided in the few previous sentences does not allow the reader to understand why these results support the notion that cultural transmission and social learning occurs between clans.

We conclude out introduction with a brief summary of our overall findings, which we then use the rest of the manuscript to support these statements.

Comment 32

Table 1: So far, the authors refer to their analyses as capturing the ”rhythm” of whale clicks. Consequently, it is not readily clear at this point why the authors rely on ”ICIs” (inter click intervals) instead of the ”universal” measure used across taxa to capture the rhythm of signal sequences - IOIs (inter onset intervals). If ICIs are the same measure as IOIs, why not use the common term, instead of creating a new term name? Alternatively, if ICIs are not equivalent to IOIs, then arguably the analyses do not capture the ”rhythm” of whale clicks, as claimed by the authors. Any rhythmic claim will need to be based on IOI measures. In animal behaviour, stereotyped is primarily used to describe pathological, dysfunctional behaviour. I suggest the use of other adjective, such as ”regular”, ”repetitive”, ”recurring”, ”predictable”. Another deviation from typical terminology: ”usage frequency” -¿ ”production rate”. Why is a clan a ”higher-order” level of social organization? This requires explanation, at least a mention, of what are the ”lower-order” levels. To the non-expert reader, there is a logical circularity/gap here: Clans are said to produce clan-specific codas, and then, it is said that codas are used to delineate clans. Either one deduces, or one infers, but not both. This raises the question, are clans confirmed by any other means than codas?

We are not creating a “new term name”: inter-click interval (ICI) is the standard terminology used in odontocete (toothed whale) research. We take the reviewer’s point that some readers will not be coming to our paper with that background, however, and now explicitly point out that ICI is synonymous with IOI for sperm whales. Please see our response to your earlier comment for more on this point.

Comment 33

Line 92: Unclear term, ”sub-sequence”. Fig. 1B doesn’t seem to readily help disambiguate the meaning of the term.

In fact reference to Fig. 1B is misplaced as it does not refer to the text. A sub-sequence is simply a contiguous subset of a coda, a subset of it.

Action - Removed ambiguous reference to Fig. 1B

Comment 34

Line 94: How does the use of ”sequence” compare here with ”sub-sequence” above?

In fact its the same situation although the previous comment highlighted a source of ambiguity.

Action - Reworded the sentence to be less confusing.

Comment 35

Line 95: Signal sequences don’t ”contain” memory, they require memory for processing.

Action - Rephrased from “sequences contain memory” to “states depend on previous sequences of varying length”.

Comment 36

Lines 95-97: The analogy with human language seems forced, combinatorics in any given species are expected to entail different transitions between unit/unit-sequences.

Thank you for the comment. Indeed, the purpose of the analogy is to illustrate how variable length Markov Chains work (which have been shown to be good at discerning even accents of the same language). We used human language as an analogy to provide the readers’ with a more intuitive understanding of the results.

Action - Revised paragraph to read: “Despite we do not have direct evidence of unitary blocks in sperm whale communication, on can imagine this effect similarly to what happens with words (e.g., a word beginning with “re” can continue in more ways than one starting with “zy”).”

Comment 37

Line 97: Unclear which possibility is this.

Action - Made the wording clearer.

Comment 38

Line 99: Invocation of memory, although common in the use of Markov chains, in inadequate here given that the research did not study how individuals perceived or processed click sequences, only how individual produced click sequences. If the authors are referring to the cognitive load imposed by producing clicks sequences, terms such as ”sequence planning” will be more accurate.

Here, we use the term “fixed-memory” in relation to the definition of a variable length Markov model. We feel that, in this section of the manuscript, the context is clear that it is a mathematical definition and in no way invokes the biological idea of memory or cognition. It is rather standard to use memory to describe the order of Markov chains. Swapping words in the definition of mathematical objects when the context is clear seems to cause unnecessary ambiguity.

Action - We clarified this in the manuscript (see comments above).

Reviewer #3 (Recommendations):

Comment 39

Line 16: Add ”broadly defined” as there are many other more restricted definitions (see for example Tomasello 1999; 2009). Tomasello M (1999) The cultural origins of human cognition. Harvard University Press, Cambridge Tomasello M (2009) The question of chimpanzee culture, plus postscript (chimpanzee culture 2009). In: Laland KN, Galef BG (eds) The question of animal culture. Harvard University Press, Cambridge, pp 198-221.

Thanks for the clarification.

Action - We added the term “broadly” and added the last reference.

Comment 40

Line 22: Is all stable social learned behavior that becomes idiosyncratic and ”distinguishable” considered symbolic markers? If not, consider adding ”potentially.”

No, but the evolution of cultural groups with differing behavior can reorganize the selective environment in such a way that it can favour an in-group bias that was not initially advantageous to individuals and lead to a preference towards others who share an overt symbolic marker that initially had no meaning and a random frequency in both populations. That is to say, even randomly assigned trivial groups can evolve arbitrary symbolic markers through in-group favouritism once behavioural differences exist even in the absence of any history of rivalry, conflict, or competition between groups. See for example [L1, L2].

Comment 41

Table 1: Identity codas are defined as a ”Subset of coda types most frequently used by a sperm whale clan; canonically used to define vocal clans.” Therefore, I infer that an identity coda is not exclusively used by a specific clan and may be utilized by other clans, albeit less frequently. If this is the case, what criteria determine the frequency of usage for a coda to be categorized as an identity or non-identity coda? Does the criteria used to differentiate between ID and non-ID codas reflect the observed differences in micro changes between the two and within clans?

The methods for this categorization are defined, discussed, and justified in previous work in [L9, L12]. We feel its outside the scope of this paper to review these details here in this manuscript. However, the differences between vocal styles discussed here and the frequency production repertoires which allow for the definition of identity codas are on different scales. The differences between identity and non-identity codas are not the observed differences in vocal style reported here.

Comment 42

Table 1: The definition of vocal style states that it ”Encodes the rhythmic variations within codas.” However, if rhythm changes, does the type of coda change as well? Typically, in musical terms, the component that maintains the structure of a rhythm is ”tempo,” not ”rhythm.” How much microvariation is acceptable to maintain the same rhythm, and when do these variations constitute a new rhythm?

Thank you for raising this important point about the relationship between rhythmic variations and coda categorization. In our definition, ”vocal style” refers to subtle, micro-level variations in the rhythmic structure of codas that do not alter their overarching categorical identity. These microvariations are akin to ”tempo” changes in musical terms, which can modify the expression of a rhythm without fundamentally altering its structure.

The threshold at which microvariations constitute a new rhythm, and thus a new coda type, remains an open question and is a limitation of current analytical approaches. In our study, we used established classification methods to group codas into types, treating variations within these groups as part of the same rhythm. Future work could refine these thresholds to better distinguish between meaningful rhythmic variation and the emergence of new coda types.

Comment 43

Table 1: Change ”say” to ”vocalize” (similarly as used in line 273 for humpback whales ”vocalizations”).

Thanks.

Action - Done.

Comment 44

Lines 33-35 and Figure 1-C: Can a lay listener discern the microvariations within each coda type by ear? Consider including sound samples of individual rhythmic microvariations for the same coda type pattern (e.g., Four plus, Palindrome, Plus One, Regular) to provide readers/listeners with an impression of their detectability. If authors considered too much or redundant Supplemental material at least give a sound sample for each the 4 subcodas modeled structures examples of 4R2 coda variations depicted in Figure 1-C so the reader can have an acoustic impression of them.

We do not think that human listeners would be able to all of the variation detected here. However, this does not mean that it is not important variation for the whales. Human observers being able to classify call variation aurally shouldn’t be seen as a bar representing important biological variation for non-human species, given that their hearing and vocal production systems have evolved independently. Importantly, ’Four Plus’,’Palindrome’, etc are names of Clans; sympatric, but socially segregated, communities of whale families, which share a distinct vocal dialect of coda types. These clans each have have distinguishable coda dialects made up of dozens of coda types (and delineated based on identity codas), these are not names/categorical coda types themselves.

Action - We now provide audio samples of all coda types listed in Figure 1B in the paper’s Github repository.

Comment 45

Line 69: As stated above, it may be confusing to refer to it as ”speech.” I suggest adding something like: ”Our method does capture one essential characteristic of human speech: phonology.” Reply 45.—Thank you for drawing our attention to this.

Action - We removed the word “speech” from the manuscript, using “communication” and/or “vocalization” depending on the context.

Comment 46

Line 111-112: Consider adding a sound sample of the variation of the 4R2 coda type that can be vocalized as BCC but also as CBB as supplementary data.

What the reviewer has correctly observed is that the traditional categorical coda type ’names’ do not capture the variation within a type by rhythm nor by tempo.

Action - We have added samples of all coda types listed in Figure 1B in the paper’s Github repo.

Comment 47

Figure 3: Include a sound sample for each of the 7 coda types in Figure 1B (”specific vocal repertoires”) to illustrate the set of coda types used and their associated usage frequencies, or at least for each of the 7 coda types in Figure 3 and tables S1 and S2.

Sperm whales in the Eastern Caribbean produce dozens of rhythm types across at least five categorical tempo types [L8, L13]. The coda types represented in Figure 1B do not demonstrate all the variability inherent in the sperm whales’ vocal dialect. Importantly, Figure 3, as well as table S1 and S2, refer to clan-level dialects not specific individual coda types.

Action - We added sound samples for each coda rhythm type listed in Figure 1B to the Github repository.

Comment 48

Lines 184-190: It is unclear what human analogy term is used for ID codas. This needs clarification.

We are not making an analogy in humans for the role of ID vs non-ID codas, but only providing the example of accents as changes in vocalization (style) without a change in the actual words used (repertoire).

Action - We tried to make it clearer in the manuscript.

Comment 49

Line 190: Change ”whale speech” to ”whale vocalizations.”

Thanks.

Action - Done.

Comment 50

Figure 4: Correct citation number Hersh ”10” to Hersh ”11.”

Thanks.

Action - Fixed the reference.

Comment 51

Lines 224-232: Clarify whether the reference to how spatial overlap affects the frequency of ID codas refers to shared ID codas between clans or the production frequency of each coda within the total repertoire of codas.

The similarity between ID coda repertoires we are referring to there is based on the ID codas of both clans.

More details on the comparison can be found in [L9].

Action - We added a sentence explaining the comparison is made using the joint set of ID codas.

Comment 52

Lines 240-241: What are non-ID codas vocal cues for?

Non-ID codas likely serve as flexible, context-dependent signals that facilitate group coordination, convey environmental or social context, and promote social learning, especially in mixed-clan or overlapping habitats. Their variability suggests multifunctional roles shaped by ecological and social pressures.

Comment 53

Lines 267-268: It’s unclear whether non-ID coda vocal styles are genetically inherited or not, as argued in lines 257-258.

We did not intend to argue that non-ID coda vocal styles are genetically inherited. Instead, we aimed to present a hypothetical consideration: if non-ID coda vocal styles were genetically inherited, one would expect a direct correlation between vocal style similarity and genetic relatedness. This hypothetical framework was introduced to strengthen our argument that the observed patterns are unlikely to be explained by genetic inheritance, as such correlations have not been observed. While we acknowledge that we lack definitive proof to rule out genetic influences entirely, the evidence available strongly suggests that social learning, rather than genetic transmission, is the more plausible mechanism.

Action - Clarified in manuscript.

Comment 54

Line 277: Can males mate with females from different clans?

Yes, genetic evidence shows that males may even switch ocean basins.

Action - We have clarified that we mean the female members of units from different clans have only rarely been observed to interact at sea between clans.

Comment 55

Lines 287-292: Consider discussing the difference between controlled/voluntary and automatic/involuntary imitation and their implications for cultural selection and social learning (see Heyes 2011; 2012). Heyes, C. (2011). Automatic imitation. Psychological bulletin, 137(3), 463. Heyes, C. (2012). What’s social about social learning?. Journal of comparative psychology, 126(2), 193.

Thank you for your insightful comment regarding this. The distinction between controlled/voluntary and automatic/involuntary imitation, as highlighted by Heyes [L14, L15], provides a potentially valuable framework for interpreting social learning mechanisms in sperm whales. Automatic imitation refers to reflexive, often unconscious mimicry driven by perceptual or motor coupling, while controlled imitation involves deliberate and goal-directed efforts to replicate behaviors. Both forms likely play complementary roles in the cultural transmission observed in sperm whales.

This dual-process perspective highlights the potential for cultural selection to act at different levels. Automatic imitation may drive convergence in shared environments, promoting acoustic homogeneity and facilitating inter-clan communication. In contrast, controlled imitation ensures the preservation of clan-specific vocal traditions, maintaining cultural diversity. This interplay between automatic and controlled processes could reflect a balancing act between cultural assimilation and differentiation, underscoring the adaptive value of these mechanisms in dynamic social and ecological contexts.

Action - We have incorporated a short discussion of this distinction and its implications for our findings in the Discussion. Additionally, we have cited [L14, L15] to provide theoretical grounding for this interpretation.

Comment 56

Methods: Consider integrating the paragraph from lines 319-321 into lines 28-35 and eliminate redundant information.

Thanks.

Action - We implemented the suggestion, removing the first paragraph of the Dataset description and integrating the information when we introduce the concepts of codas and clicks.

[L1] C. Efferson, R. Lalive, and E. Fehr, Science 321, 1844 (2008).

[L2] R. McElreath, R. Boyd, and P. Richerson, Curr. Anthropol. 44, 122 (2003).

[L3] L. S. Burchardt and M. Knornschild, PLoS Computational Biology 16, e1007755 (2020).

[L4] A. Ravignani and K. de Reus, Evolutionary Bioinformatics 15, 1176934318823558 (2019).

[L5] C. T. Kello, S. D. Bella, B. Med´ e, and R. Balasubramaniam, Journal of the Royal Society Interface 14, 20170231 (2017).

[L6] D. Gerhard, Canadian Acoustics 31, 22 (2003).

[L7] N. Mathevon, C. Casey, C. Reichmuth, and I. Charrier, Current Biology 27, 2352 (2017).

[L8] P. Sharma, S. Gero, R. Payne, D. F. Gruber, D. Rus, A. Torralba, and J. Andreas, Nature Communications 15, 3617 (2024).

[L9] T. A. Hersh, S. Gero, L. Rendell, M. Cantor, L. Weilgart, M. Amano, S. M. Dawson, E. Slooten, C. M. Johnson, I. Kerr, et al., Proc. Natl. Acad. Sci. 119, e2201692119 (2022).

[L10] R. Boyd and P. J. Richerson, Cult Anthropol 2, 65 (1987). [L11] E. Cohen, Curr. Anthropol. 53, 588 (2012).

[L12] T. A. Hersh, S. Gero, L. Rendell, and H. Whitehead, Methods Ecol. Evol. 12, 1668 (2021), ISSN 2041-210X, 2041-210X.

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[L14] C. Heyes, Psychological Bulletin 137, 463 (2011).

[L15] C. Heyes, Journal of Comparative Psychology 126, 193 (2012).

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Reply to the reviewers

Reviewer #1 Evidence, reproducibility and clarity Summary: Bhatt et al. seek to define factors that influence H3.3 incorporation in the embryo. They test various hypotheses, pinpointing the nuclear/cytoplasmic ratio and Chk1, which affects cell cycle state, as influencers. The authors use a variety of clever Drosophila genetic manipulations in this comprehensive study. The data are presented well and conclusions reasonably drawn and not overblown. I have only minor comments to improve readability and clarity. I suggest two OPTIONAL experiments below. We thank the reviewer for their positive and helpful comments. Major comments: We found this manuscript well written and experimentally thorough, and the data are meticulously presented. We have one modification that we feel is essential to reader understanding and one experimental concern: The authors provide the photobleaching details in the methodology, but given how integral this measurement is to the conclusions of the paper, we feel that this should be addressed in clear prose in the body of the text. The authors explain briefly how nuclear export is assayed, but not import (line 99). Would help tremendously to clarify the methods here. This is especially important as import is again measured in Fig 4. This should also be clarified (also in the main body and not solely in the methods). We have added the following sentences to the main body of the text to clarify how photobleaching and import were assayed. “We note that these differences are not due to photobleaching as our measurements on imaged and unimaged embryos indicate that photobleaching is negligible under our experimental conditions (see methods, Figure S1G-H)” lines 98-101 and “Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate.” lines 111-113 Revision Plan In addition we have clarified how import was calculated to figure legends in Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” We also added the following explanation of how nuclear import rates were calculated to the methods section: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods If the embryos appeared "reasonably healthy" (line 113) after slbp RNAi, how do the authors know that the RNAi was effective, especially in THESE embryos, given siblings had clear and drastic phenotype? This is especially critical given that the authors find no effect on H3.3 incorporation after slbp RNAi (and presumably H3 reduction), but this result would also be observed if the slbp RNAi was just not effective in these embryos. We apologize for the confusion caused by our word choice. The “healthy” slbp-RNAi embryos had measurable phenotypes consistent with histone depletion that we have reported previously (Chari et al, 2019) including cell cycle elongation and early cell cycle arrest (Figure S4D). However, they did not have the catastrophic mitosis observed in more severely affected embryos. We agree with the reviewer that a concern of this experiment is that the less severely affected embryos likely have more remaining RD histones including H3. To address this we also tested H3.3 incorporation in the embryos that fail to progress to later cell cycles in the cycles that we could measure. Even in these more severely affected embryos we were not able to detect a change in H3.3 incorporation relative to controls (lines 240-243 and Fig S4B). Unfortunately, it is impossible to conduct the ideal experiment, which would be a complete removal of H3 since this is incompatible with oogenesis and embryo survival. To address this confusion we have added supplemental videos of control, moderately affected and severely affected SLBP-RNAi embryos as movies 3-5 and modified the text to read: “All embryos that survive through at least NC12, had elongated cell cycles in NC12 and 60% arrested in NC13 as reported previously indicating the effectiveness of the knockdown (Figure S4C, Movie 3-5)39. In these embryos, H3.3 incorporation is largely unaffected by the reduction in RD H3 (Figure 6B).” lines 236-240 Finally, to characterize the range of SLBP knockdown in the RNAi embryos we propose to do single embryo RT-qPCRs for SLBP mRNA for multiple individual embryos. This will provide a measure of the range knockdown that we observed in our H3.3 movies. Minor comments: Introduction: Revision Plan Consider using "replication dependent" (RD) rather than "replication coupled." Both are used in the field, but RD parallels RI ("replication independent"). We thank the reviewer for this suggestion. We have made the text edits to change "replication coupled" (RC) to "replication dependent" (RD) throughout the manuscript. Would help for clarity if the authors noted that H3 is equivalent to H3.2 in Drosophila. Also it is relevant that there are two H3.3 loci as the authors knock mutations into the H3.3A locus, but leave the H3.3B locus intact. The authors should clarify that there are two H3.3 genes in the Drosophila genome. We have changed the text as follows to increase clarity as suggested: “Similarly, we have previously shown that RD H3.2 (hereafter referred to as H3) is replaced by RI H3.3 during these same cycles, though the cause remains unclear29” lines 52-54 “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact.” lines 127-131 Please add information and citation (line 58): H3.3 is required to complete development when H3.2 copy number is reduced (PMID: 37279945, McPherson et al. 2023) We have added the suggested information. The text now reads “Nonetheless, H3.3 is required to complete development when H3.2 copy number is reduced54.” lines 61-62 Results: Embryo genotype is unclear (line 147): Hira[ssm] haploid embryos inherit the Hira mutation maternally? Are Hira homozygous mothers crossed to homozygous fathers to generate these embryos, or are mothers heterozygous? This detail should be in the main text for clarity. The Hira mutants are maternal effect. We crossed homozygous Hirassm females to their hemizygous Hirassm or FM7C brothers. However, the genotype of the male is irrelevant since the Hira phenotype prevents sperm pronuclear fusion and therefore there is no paternal contribution to the embryonic genotype. We have clarified this point in the text: “We generated embryos lacking functional maternal Hira using Hirassm-185b (hereafter Hirassm) homozygous mothers which have a point mutation in the Hira locus57.” lines 160-162 Revision Plan Line 161: Shkl affects nuclear density, but it also appears from Fig 3 to affect nuclear size? The authors do not address this, but it should at least be mentioned. We thank the reviewer for the astute observation. More dense regions of the Shkl embryos do in fact have smaller nuclei. We believe that this is a direct result of the increased N/C ratio since nuclear size also falls during normal development as the N/C ratio increases. We have added a new figure 1 in which we more carefully describe the events of early embryogenesis in flies including a quantification of nuclear size and number in the pre-ZGA cell cycles (Figure 1C). We also note the correlation of nuclear size with nuclear density in the text: “During the pre-ZGA cycles (NC10-13), the maximum volume that each nucleus attains decreases in response to the doubling number of nuclei with each division (Figure 1C).” lines 86-87 “To test this, we employed mutants in the gene Shackleton (shkl) whose embryos have non-uniform nuclear densities and therefore a gradient of nuclear sizes across the anterior/posterior axis (Figure 3A-B, Movie 1-2)58.” lines 180-183 The authors often describe nuclear H3/H3.3 as chromatin incorporated, but these image-based methods do not distinguish between chromatin-incorporated and nuclear protein. To distinguish between chromatin incorporated and nuclear free histone we have exploited the fact that histones that are not incorporated into DNA freely diffuse away from the chromatin mass during mitosis while those that are bound into nucleosomes remain on chromatin during this time. In our previous study we showed that H3-Dendra2 that is photoconverted during mitosis remains stably associated with the mitotic chromatin through multiple cell cycles (Shindo and Amodeo, 2019) strengthening our use of this metric. To help clarify this point as well as other methodological details we have added a new Figure 1B which documents the time points at which we make various measurements within the lifecycle of the nucleus. We also edited the text to read: “We have previously shown that with each NC, the pool of free H3 in the nucleus is depleted and its levels on chromatin during mitosis decrease (Figure 1D, S1C-D)29. In contrast, H3.3 mitotic chromatin levels increase during the same cycles (Figure 1D, S1C-D)29.” lines 89-92 I very much appreciate how the authors laid out their model in Fig 3 and then used the same figure to explain which part of the model they are testing in Figs 4 and 5. This is not a critique- we can complement too! Thank you! Revision Plan OPTIONAL experimental suggestion: The experiments in Figure 4 and 5 are clever. One would expect that H3 levels might exhaust faster in embryos lacking all H3.2 histone genes (Gunesdogan, 2010, PMID: 20814422), allowing a comparison testing the H3 availability > H3.3 incorporation portion of the hypothesis without manipulating the N/C ratio. This might also result in a more consistent system than slbp RNAi (below). We thank the reviewer for the experimental suggestion. We also considered this experimental manipulation to decrease RD histone H3.2. We chose not to do this experiment because in the Gunesdogan paper they show that the zygotic HisC nulls have normal development until after NC14 (unlike the maternal SLBP-RNAi that we used) suggesting that maternal H3.2 supplies do not become limiting until after the stages under consideration in our paper. Maternal HisC-nulls are, of course, impossible to generate since histones are essential. O'Haren 2024 (PMID: 39661467) did not find increased Pol II at the HLB after zelda RNAi (line 227). Might also want to mention here that zelda RNAi does not result in changes to H3 at the mRNA level (O'Haren 2024), as that would confound the model. We thank the reviewer for the suggestion. We have removed the discussion of Pol II localization and replaced it with the information about histone mRNA : “zelda controls the transcription of the majority of Pol II genes during ZGA but disruption of zelda does not change RD histone mRNA levels67–70”. lines 249-251 Discussion: Should discuss results in context of McPherson et al. 2023 (PMID: 37279945), who showed that decreasing H3.2 gene numbers does not increase H3.3 production at the mRNA or protein levels. We expanded our discussion to include the following: “Given the fact that H3.3 pool size does not respond to H3 copy number in other Drosophila tissues,54 our results suggest that H3.3 incorporation dynamics are likely independent of H3 availability.” lines 278-280 The Shackleton mutation is a clever way to alter N/C ratio, but the authors should point out that it is difficult (impossible?) to directly and cleanly manipulate the N/C ratio. For example, Shkl mutants seem to also have various nuclear sizes. As discussed above, we think that nuclear size is a direct response to the N/C ratio. We have added the following sentence to the discussion as well as a citation to a paper which discusses how the N/C ratio might contribute to nuclear import in early embryos to the discussion: “This may be due to N/C ratio-dependent changes in nuclear import dynamics which may also contribute to the observed changes in nuclear size across the shkl embryo75.” lines 307-309 Revision Plan How is H3.3 expression controlled? Is it possible that H3.3 biosynthesis is affected in Chk1 mutants? To address this question we propose to perform RT-qPCR for H3.3A and H3.3B as well as Hira in the Chk1 mutant. Unfortunately, we do not have antibodies that reliably distinguish between H3 and H3.3 in our hands (despite literature reports), but we will also perform a pan-H3 immunostaining in the Chk1 embryos to measure how the total H3-type histone pool changes as a result of the loss of Chk1. Figures: While I appreciate the statistical summaries in tables, it is still helpful to display standard significance on the figures themselves. We have added statistical comparisons in Figure 3 (formerly Figure 2). We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Fig 1: A: Is it possible to label panels with the nuclear cycle? We have done this. B: Statistics required - caption suggests statistics are in Table S2, but why not put on graph? Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. C/D: Would be helpful if authors could plot H3/H3.3 on same graph because what we really need to compare is NC13 between H3/H3.3 (and statistics between these curves) Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. E: The comparison in the text is between H3.3 and H3, but only H3.3 data is shown. I realize that it is published prior, but the comparison in figure would be helpful. We have added the previously published values to the text. Revision Plan “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Fig 2: A: A very helpful figure. Slightly unclear that the H3 that is not Dendra tagged is at the H3.3 locus. Also unclear that the H3.3A-Dendra2 line exists and used as control, as is not shown in figure. Should show H3 and H3.3 controls (Figure S2) We have edited the figure to add Dendra2 to all of the constructs and made clear the location of each construct including adding the landing site for H3-Dendra2. We have also cited Figure S1 in the legend which contains a more detailed diagram of the integration strategy. F/H- As the comparison is between H3 and ASVM, it would help to combine these data onto the same graph. As the color is currently used unnecessarily to represent nuclear cycle, the authors could use their purple/pink color coding to represent H3/ASVM. We have combined these data onto a single graph as requested and changed the colors appropriately. We have not added statistical comparisons to this graph as we again believe that they would be inappropriate. In the legend of Fig 2 the authors write "in the absence of Hira." Technically, there is only a point mutation in Hira. It is not absent. Good catch! We have changed this to “in Hirassm mutants”. Fig 3: G: Please show WT for comparison. Can use data in Fig 3A. We have added the color-coded number of neighbor embryo representations for WT and Shkl embryos underneath the example embryo images in 4A-B (formerly 3A-B,G). Model in H is very helpful (complement)! Thank you. Fig 4: B/C/F/G: The authors use a point size scale to represent the number of nuclei, but the graphs are so overlaid that it is not particularly useful. Is there a better way to display this dimension? We chose to represent the data in this way so that the visual impact of each line is representative of the amount of data (number of nuclei in each bin) that underlies it. This helps to prevent sparsely populated outlier bins at the edges of the distribution from dominating the interpretation of the data. If the reviewer has a suggestion for a better way to visualize this information we would welcome their suggestion, but we cannot think of a better way at this time. D/E/H/I: What does "min volume" mean on the X axis? Since the uneven N/C ratio in the shkl embryos results in a wavy cell cycle pattern there is no single time point where we can calculate the number of neighbors for the whole embryo (since Revision Plan not all nuclei are in the same cell cycle at a given point). Therefore, we had to choose a criterion for when we would calculate the number of neighbors for each nucleus. We chose nuclear size as a proxy for nuclear age since nuclear size increases throughout interphase (see new figure 1B). So, the minimum volume is the newly formed nucleus in a given cell cycle. We also tested other timepoints for the number of neighbors (maximum nuclear volume, just before nuclear envelope breakdown and midway between these two points) and found similar results. We chose to use minimum volume in this paper because this is the time point when the nucleus is growing most quickly and nuclear import is at its highest. We have added the following explanation to the methods: “For shkl embryos, as the nuclear cycles are asynchronous, nuclear divisions start at different timepoints within the same cell cycle and the nuclear density changes as the neighboring nuclei divide. Therefore, the total intensity traces were aligned to match their minimum volumes (as shown in Figure 1B) to T0.” lines 485-488, methods And the following detail to the figure legend: “...plotted by the number of nuclear neighbors at their minimum nuclear volume…” Figure 5 legend We also added a depiction of the lifecycle of the nucleus in which we marked the minimum volume as the new Figure 1B. Fig 5: F: OPTIONAL Experimental request: Here I would like to see H3 as a control. This is a very good suggestion, and we are currently imaging H3-Dendra2 in the Chk1 background. However, our preliminary results suggest that there may be some synthetic early lethality between the tagged H3-Dendra2 and Chk1 since these embryos are much less healthy than H3.3-Dendra2 Chk1 embryos or Chk1 with other reporters. In addition, we have observed a much higher level of background fluorescence in this cross than in the H3-Dendra2 control. We are uncertain if we will be able to obtain usable data from this experiment, but will continue to try to find conditions that allow us to analyze this data. As an orthogonal approach to answer the question, we will perform immunostaining with a pan-H3 antibody in Chk1 mutant embryos to measure total H3 levels under these conditions. Since the majority of H3-type histone is H3.2 and we know how H3.3 changes, this staining will give us insight into the dynamics of H3 in Chk1 mutant embryos. Significance General assessment: Many long-standing mysteries surround zygotic genome activation, and here the authors tackle one: what are the signals to remodel the zygotic chromatin around ZGA? This is a tricky question to answer, as basically all manipulations done to the embryo Revision Plan have widespread effects on gene expression in general, confounding any conclusions. The authors use clever novel techniques to address the question. Using photoconvertible H3 and H3.3, they can compare the nuclear dynamics of these proteins after embryo manipulation. Their model is thorough and they address most aspects of it. The hurdle this study struggles to overcome is the same that all ZGA studies have, which is that manipulation of the embryo causes cascading disasters (for example, one cannot manipulate the nuclear:cytoplasmic ratio without also altering cell cycle timing), so it's challenging to attribute molecular phenotypes to a single cause. This doesn't diminish the utility of the study. Advance: The conceptual advance of this study is that it implicates the nuclear:cytoplasmic ratio and Chk1 in H3.3 incorporation. The authors suggest these factors influence cell cycle closing, which then affects H3.3 incorporation, although directly testing the granularity of this model is beyond the scope of the study. The authors also provide technical advancement in their use of measuring histone dynamics and using changes in the dynamics upon treatment as a useful readout. I envision this strategy (and the dendra transgenes) to be broadly useful in the cell cycle and developmental fields. Audience: The basic research presented in this study will likely attract colleagues from the cell cycle and embryogenesis fields. It has broader implications beyond Drosophila and even zygotic genome activation. This reviewer's expertise: Chromatin, Drosophila, Gene Regulation Reviewer #2 (Evidence, reproducibility and clarity (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. We thank the reviewers for their careful reading of this manuscript. We have sought to clarify the concerns regarding clarity through numerous text edits detailed below. We did include ANOVA analysis for all of the relevant statistical comparisons in the supplemental table. However, to increase clarity we have also added some statistical comparisons in the main figures. We note that we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of the new Figure 1 Revision Plan explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Major Concerns The manuscript would benefit from a clearer introduction that explicitly distinguishes between previously known mechanisms of histone regulation during ZGA and the novel contributions of this study. Currently, the introduction lacks sufficient background on early embryonic chromatin regulation, making it difficult for readers unfamiliar with the field to grasp the significance of the findings. The authors should also be more precise when discussing the timing of ZGA. While they state that ZGA occurs after 13 nuclear divisions, it is well established that a minor wave of ZGA begins at nuclear cycle 7-8, whereas the major wave occurs after cycle 13. Clarifying this distinction will improve the manuscript's accessibility to a broader audience. We have added a new figure 1 to make the timing and nuclear behaviors of the embryo during ZGA in Drosophila more clear. We have also added information about how the chromatin changes during Drosophila ZGA in the following sentence: “ In Drosophila, these changes include refinement of nucleosomal positioning, partitioning of euchromatin and heterochromatin and formation of topologically associated domains20–22,24.” lines 39-41 We have clarified the major and minor waves of ZGA in the introduction and results by adding the following sentences to the introduction and results respectively: “In most organisms ZGA happens in multiple waves but the chromatin undergoes extensive remodeling to facilitate bulk transcription during the major wave of ZGA (hereafter referred to as ZGA)18–20,22–25..” lines 36-39 “In Drosophila, ZGA occurs in 2 waves. The minor wave starts as early as the 7th cycle, while major ZGA occurs after 13 rapid syncytial nuclear cycles (NCs) and is accompanied by cell cycle slowing and cellularization (Figure 1A-B).” lines 83-85 We hope that these changes help to reduce confusion and make the paper more accessible. However, we are happy to add additional information if the reviewer can provide specific points which require further attention. One of the primary weaknesses of this study is the lack of adequate control experiments. In Figure 1, the authors suggest that the levels of H3 and H3.3 are influenced by the N/C ratio, but Revision Plan it is unclear whether transcription itself plays a role in these dynamics. To properly test this, RNA-seq or Western blot analyses should be performed at nuclear cycles 10 and 13-14 to compare the levels of newly transcribed H3 or H3.3 against maternally supplied histones. Without such data, the authors cannot rule out transcriptional regulation as a contributing factor. In the pre-ZGA cell cycles the vast majority of protein including histones is maternally loaded. Gunesdogan et al. (2010) showed that the zygotic RD histone cluster nulls survive past NC14 (well past ZGA) with no discernible defects indicating that maternal RD histone supplies are sufficient for normal development during the cell cycles under consideration. Therefore, new transcription of replication coupled histones is not needed for apparently normal development during this period. Moreover, we have done the western blot analysis using a Pan-H3 antibody as suggested by the reviewer in our previously published paper (Shindo and Amodeo, 2019 supplemental figure S3A-B) and found that total H3-type histone proteins only increase moderately during this period of development, nowhere near the rate of the nuclear doublings. We have added the following sentence to clarify this point. “These divisions are driven by maternally provided components and the total amount of H3 type histones do not keep up with the pace of new DNA produced29.” lines 88-89 We have also previously done RNA-seq on wild-type embryos (and those with altered maternal histone levels) (Chari et al 2019). In this RNA-seq (like most RNA-seq in flies) we used poly-A selection and therefore cannot detect the RD histone mRNAs (which have a stem-loop instead of a poly-A tail). We have plotted the mRNA concentrations for both H3.3 variants from that dataset below for the reviewers reference (we have not included this in the revised manuscript). The total H3.3 mRNA levels are nearly constant from egg laying (NC0- these are from unfertilized embryos) until after ZGA (NC14). These data combined with the westerns discussed above give us confidence that what we are observing is the partitioning of large pools of maternally provided histones with only a relatively small contribution of new histone synthesis. Revision Plan In Figure 2, the manuscript introduces chimeric embryos expressing modified histone variants, but their developmental viability is not addressed. It is essential to determine whether these embryos survive and whether they exhibit any phenotypic consequences such as altered hatching rates, defects in nuclear division, or developmental arrest. Tagging histones is often deleterious to organismal health. In Drosophila there are two H3.3 loci (H3.3A and H3.3B). In all of our chimera experiments we have left the H3.3B and one copy of the H3.3A locus unperturbed to provide a supply of untagged H3.3. This allows us to study H3.3 and chimera dynamics without compromising organism health. All of our chimeras are viable and fertile with no obvious morphological defects. We have added the following sentences to the text to clarify this point: “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact….These chimeras were all viable and fertile. ” lines 127-131, 136 In addition we propose performing hatch rate assays for embryos from the chimeric embryos of S31A, SVM and ASVM to assess if there is any decrease in fecundity due to the presence of the chimeras. Moreover, given that H3.3 is associated with actively transcribed genes, an RNA-seq analysis of chimeric embryos should be included to assess transcriptional changes linked to H3.3 incorporation. This is an excellent suggestion and will definitely be a future project for the lab. However, to do this experiment correctly we will need to generate untagged chimeric lines that will (hopefully) allow for the full replacement of H3.3 with the chimeric histones instead of a single copy among 4. This is beyond the scope of this paper. Figures 3 and 4 raise additional concerns about whether histone cluster transcription is altered in shkl mutant embryos. The authors propose that the shkl mutation affects the N/C ratio, yet it remains unclear whether this leads to changes in the transcription of histone clusters. Furthermore, since HIRA is a key chaperone for H3.3, it would be important to assess whether its levels or function are compromised in shkl mutants. To address these gaps, RT-qPCR or RNA-seq should be performed to quantify histone cluster transcription, and Western blot analysis should be used to determine if HIRA protein levels are affected. The changes in the N/C ratio that are observed in the shkl mutant are within SINGLE embryo (differences in nuclear spacing). In these experiments we are comparing nuclei within a common cytoplasm that have different local nuclear densities (N/C ratios). Therefore, if Shkl Revision Plan were somehow affecting the transcription of histones or their chaperones we would expect all of the nuclei within the same mutant embryo to be equally affected since they are genetically identical and share a common cytoplasm. We do not directly compare the behavior of shkl embryos to wildtype except to demonstrate that there is no positional effect on the import of H3 and H3.3 across the length of the embryo in wildtype. To clarify our experimental system for these experiments we have added additional panels to Figure 4A and B that depict the number of neighbors for both control and Shkl embryos. Nonetheless, to address the reviewer’s concern that shkl may change the amount of H3 present in the embryo, we propose to conduct a western blot comparison of wildtype and shkl embryos using a pan-H3 antibody. There are no tools (antibodies or fluorescently tagged proteins) to assess HIRA protein levels in Drosophila. We therefore propose to perform RT-qPCR for HIRA in wildtype and shkl embryos. A similar issue arises in Figure 5, where the authors claim that H3.3 incorporation is dependent on cell cycle state but do not sufficiently test whether this is linked to changes in HIRA levels. Given the importance of HIRA in H3.3 deposition, its levels should be examined in Slbp, Zelda, and Chk1 RNAi embryos to verify whether changes in H3.3 incorporation correlate with HIRA function. Without this, it is difficult to conclude that the observed effects are strictly due to cell cycle regulation rather than histone chaperone dynamics. Since H3.3 incorporation is unaffected in the Slbp and Zelda-RNAi lines there is no reason to suspect a change in HIRA function. There are no available tools (antibodies or fluorescently tagged proteins) to directly measure HIRA protein in Drosophila. To test if changes in HIRA loading might contribute to the decreased H3.3 incorporation in the Chk1 mutant we propose to perform RT-qPCR for HIRA in wildtype and Chk1 embryos. Several figures require additional statistical analyses to support the claims made. In Figure 1B, statistical testing should be included to validate the reported differences. Figure 1C-D states that "H3.3 accumulation reduces more slowly than H3," yet there is no quantitative comparison to substantiate this claim. Similarly, Figure 1E presents the conclusion that "These changes in nuclear import and incorporation result in a less dramatic loss of the free nuclear H3.3 pool than previously seen for H3," despite the fact that H3 data are not included in this figure. The conclusions drawn from these data need to be supported with appropriate statistical comparisons and more precise descriptions of what is being measured. For Figure 1B (now 2B) we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings and therefore we do not feel that direct statistical tests are appropriate. Rather, we plot the two histones on the same graph normalized to their own NC10 values so that the trend in their decrease over time may be compared. The statistical tests for H3.3 compared to the chimeras which were originally in the supplemental table have been added to Figure 3 (formerly figure 2). Revision Plan It is important to note that in this directly comparable situation the ASVM mutant (whose trends closely mirror H3) is highly statistically distinct from H3.3. We have added a note to the legend of the new Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” For Figure 1C-D (now 2C-D) we have removed this claim from the text. We were referring to the plateau in nuclear import for H3 that is less dramatic in H3.3, but this is more carefully discussed in the next paragraph and its addition at that point generated confusion. The text now reads: “To further assess how nuclear uptake dynamics changed during these cycles, we tracked total nuclear H3 and H3.3 in each cycle (Figure 2C-D). Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate. Though the change in initial import rates between NC10 and NC13 are similar between the two histones (Figure S1F), we observed a notable difference in their behavior in NC13. H3 nuclear accumulation plateaus ~5 minutes into NC13, whereas H3.3 nuclear accumulation merely slows (Figure 2C-D).” lines 109-116 For Figure 1E (now 2E), to address the difference between H3 and H3.3 free pools we have added the previously published values to the text and changed the phrasing from “less dramatic” to “less complete”. The sentence now reads: “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Figure 2 presents additional concerns regarding data interpretation. The comparisons between H3.3 and H3.3S31A to H3 and H3.3SVM/ASVM lack statistical analysis, making it difficult to determine the significance of the observed differences. As discussed above, it is not appropriate to directly compare H3 to H3.3 and the chimeras at the H3.3A locus since they are expressed from different promoters and imaged with different settings. The ANOVA comparisons between all of the constructs in the H3.3A locus can be found in the supplemental table. We have also added the statistical significance between each chimera and H3.3 within a cell cycle to the figure. Including the full set of comparisons for all genotypes and timepoints makes the figure nearly impossible to interpret, but they remain available in the supplemental table. Revision Plan The disappearance of H3.3 from mitotic chromosomes in Figure 2E is also not explained. If this phenomenon is functionally relevant, the authors should provide a mechanistic interpretation, or at the very least, discuss potential explanations in the text. In Figures 2F-H, the reasoning behind comparing the nuclear intensity of H3.3 to H3 in Hira mutants is unclear. To properly assess the role of HIRA in H3.3 chromatin accumulation, a more appropriate comparison would be between wild-type H3.3 and H3.3 levels in Hira knockdown embryos. As explained in the text and depicted in Figure 3D (formerly 2D), the HIRAssm mutant is a point mutation that prevents observable H3.3 chromatin incorporation, but not nuclear import. This is what is depicted in Figure 3E (formerly 2E). The loss of H3.3 from mitotic chromatin is due to the inability to incorporate H3.3 into chromatin as expected for a HIRA mutant. We have edited the figure 3 legend to make this more clear. It now reads: “Hirassm mutation nearly abolishes the observable H3.3 on mitotic chromatin (E).” In Figure 3F (formerly 2F-H) we ask what happens to H3 chromatin incorporation when there is almost no incorporation of H3.3 due to the HIRA mutation. In this mutant there is so little H3.3 incorporation that we cannot quantify H3.3 levels on mitotic chromatin (see the new Figure 1B for the stage where chromatin levels are quantified). This experiment was done to test if H3.3ASVM (expressed at the H3.3A locus) is incorporated into chromatin in embryos lacking the function of H3.3’s canonical chaperone. We have edited the text to make this more clear: “Since the chromatin incorporation of the H3/H3.3 chimeras appears to depend on their chaperone binding sites, we asked if impairing the canonical H3.3 chaperone, Hira, would affect the incorporation of H3.3ASVMexpressed from the H3.3A locus.”lines 158-160 A broader concern is that the authors only test HIRA as a histone chaperone but do not consider alternative chaperones that could influence H3.3 deposition. Since multiple chaperone systems regulate histone incorporation, it would strengthen the conclusions if additional chaperones were tested. Since HIRAssm reduced H3.3-Dendra2 incorporation to nearly undetectable levels (Figure 3E) we believe that it is the primary H3.3 incorporation pathway during this period of development. Therefore, we believe that removing HIRA function is a sufficient test of the dependance of H3.3ASVM on the major H3.3 chaperone at this time. Although it would be interesting to fully map how all H3 and H3.3 chimera constructs respond to all histone chaperone pathways, we believe that this is beyond the scope of this manuscript. Additionally, the manuscript does not include any validation of the RNAi knockdown efficiencies used throughout the study. This raises concerns about whether the observed phenotypes are truly due to target gene depletion or off-target effects. RT-qPCR or Western blot analyses should be performed to confirm knockdown efficiency. Revision Plan Both the Zelda and slbp-RNAi lines used for knockdowns have been used and validated in the early fly embryo in previously published works ((Yamada et al., 2019), (Duan et al., 2021), (O’Haren et al., 2025), (Chari et al, 2019)) and the phenotypes that we observe in our embryos are consistent with the published data including altered cell cycle durations (Figure S4C) and lack of cellularization/gastrulation. We note that the zelda RNAi phenotypes are also highly consistent with the effects of Zelda germline clones. To validate that slbp-RNAi knocks down histones we included a western blot for Pan-H3 in slbp-RNAi embryos that demonstrates a large effect on total H3 levels (Figure S4A). To further demonstrate the phenotypic effects of the slbp-RNAi we have added supplemental movies (Videos 4 and 5). To fully characterize the RNAi efficiency under our conditions we propose to perform RT-qPCR for slbp in slbp-RNAi and Zelda in Zelda-RNAi compared to control (w) RNAi embryos. Finally, the section discussing "H3.3 incorporation depends on cell cycle state, but not cell cycle duration" is unclear. The term "cell cycle state" is vague and should be explicitly defined. Does this refer to a specific phase of the cell cycle, changes in chromatin accessibility, or another regulatory mechanism? The term cell cycle state is deliberately vague. We know that Chk1 regulates many aspects of cell cycle progression and cannot determine from our data which aspect(s) of cell cycle regulation by Chk1 are important for H3.3 incorporation. Our data indicate that it is not simply interphase duration as we originally hypothesized. We have expanded our discussion section to underscore some aspects of Chk1 regulation that we speculate may be responsible for the change in H3.3 behavior. “Chk1 mutants decrease H3.3 incorporation even before the cell cycle is significantly slowed. Cell cycle slowing has been previously reported to regulate the incorporation of other histone variants in Drosophila15. However, our results indicate that cell cycle state and not duration per se, regulates H3.3 incorporation. In most cell types, the primary role of Chk1 is to stall the cell cycle to protect chromatin in response to DNA damage. Therefore, Chk1 activity directly or indirectly affects the chromatin state in a variety of ways. We speculate that Chk1’s role in regulating origin firing may be particularly important in this context73,74. Late replicating regions and heterochromatin first emerge during ZGA, and Chk1 mutants proceed into mitosis before the chromatin is fully replicated22,23,25,71. Since H3.3 is often associated with heterochromatin, the decreased H3.3 incorporation in Chk1 mutants may be an indirect result of increased origin firing and decreased heterochromatin formation73,74.” lines 287-298 Reviewer #2 (Significance (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome Revision Plan activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary: Based on previous findings of the changing ratios of histone H3 to its variant H3.3, the authors test how H3.3 incorporation into chromatin is regulated for ZGA. They demonstrate here that H3 nuclear availability drops and replacement by H3.3 relies on chaperone binding, though not on its typical chaperone Hira. Furthermore, they show that nuclear-cytoplasmic (N/C) ratios can influence this histone exchange likely by influencing cell cycle state. We thank the reviewer for their thoughtful comments. We note that our data ARE consistent with H3.3 incorporation depending on Hira through its chaperone binding site. Major comments: 1. The claims are largely supported by the data but I think a couple more experiments could help bolster the claims about cell cycle and chk1 regulation. a. Creating a phosphomimetic of the chk1 phosphorylation site on H3.3 to see if it can overcome the defects seen in chk1 mutants b. Assessing heterochromatin of embryos without chk1 (or ASVM mutants) for example, by looking at H3K9me3 levels The first experiments could take several months if the flies haven't already been generated by the authors but the second should be quicker. a. This is an excellent experimental suggestion which is bolstered by the fact that in frogs H3.3 S31A cannot rescue H3.3 morpholino during gastrulation, but H3.3S31D can (Sitbon et al, 2020). However, to correctly conduct this experiment would require generating and validating multiple additional endogenous H3.3 replacement lines, likely without a fluorescent tag as they can interfere with histone rescue constructs in most species. As the reviewer notes, this would take several months of work (we have not generated the critical flies yet) and may not yield a satisfying answer since there are reports that H3.3 may be dispensable in flies aside from as a source of H3-type histone outside of S-phase (Hödl and Bassler, 2012). While we hope to continue experiments along these lines in the future we feel that this is beyond the scope of the current manuscript. Revision Plan b. To address this we propose to stain for H3K9me3 in wildtype and Chk1-/- embryos. Since the ASVM line is not a full replacement of all H3.3 we think that staining for H3K9me3 in this line is unlikely to yield a detectable difference. 2. It would also be interesting to see what the health of the flies with some mutations in this paper are beyond the embryo stage if they are viable (e.g., development to adulthood, fertility etc.) a. the SVM, ASVM mutations b. the hira + ASVM mutations The authors might already have this data but if not they have the flies and it shouldn't take long to get these data. a. To address this concern we propose to conduct hatch rate assays for embryos from the Dendra tagged H3.3, S31A, SVM, ASVM flies. However, we do note that in our experiments only one copy of the H3.3A locus was mutated and tagged with Dendra2 leaving one copy of H3.3A and both copies of H3.3B untouched to ensure normal development as tagging all copies of histone genes can lead to lethality. b. All Hira mutants develop as haploids due to the inability to decondense the sperm chromatin (which is dependent on Hira). This leads to one extra division to restore the N/C ratio prior to cell cycle slowing and ZGA. These embryos go on to gastralate and die late in development after cuticle formation (presumably due to their decreased ploidy) (Loppin et al., 2000). The addition of ASVM into the Hira background does not appear to rescue the ploidy defect as these embryos also undergo the extra division (Figure 3H). We are therefore confident that these embryos will not hatch. We have added the information about the development of Hira mutant to the text as follow: “These embryos develop as haploids and undergo one additional syncytial division before ZGA (NC14). Hirassmembryos develop otherwise phenotypically normally through organogenesis and cuticle formation, but die before hatching57.” lines 164-167 3. In the discussion section, can the authors speculate on how they think H3.3 ASVM is getting incorporated if not through Hira. Are there other known H3 variant chaperones, or can the core histone chaperone substitute? We have expanded our discussion to include the the following: “In the case of the chimeric histone proteins the incorporation behavior was dependent on the chaperone binding site. For example, H3.3ASVM import and incorporation was similar to H3 in control embryos and H3.3ASVM was still incorporated in Hirassm mutants. This is consistent with the chaperone binding site determining the chromatin incorporation pathway and suggests that H3.3ASVM likely interacts with H3 chaperones such as Caf1.” lines 280-285 Revision Plan Minor comments: While the paper is well written, I found the figures very confusing and difficult to interpret. Comments here are meant to make it easier to interpret. 1. Fig 1 and most of the paper would benefit from a schematic of early embryo transitions labelled with time and stages of cell cycle to make interpreting data easier This is an excellent suggestion! We have added a new figure (Figure 1) to explain both the biological system and the way that we measured many properties in this paper. 2. Fig 1- same green color is used for nuclear cycle 12 and for H3.3 making it confusing when reading graphs. Please check other figures where there is a similar use of color for two different things We have changed the colors so that they are more distinct. 3. Fig 1C,D might benefit more from being split up into 3 graphs by cell cycle with H3 and H3.3 plotted on the same graphs rather than the way it is now We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. 4. Line 130-133: can they also comment on the different between SVM and ASVM. It seems like SVM might be even worse than ASVM (Fig 2C). Is this related to chk1 phosphorylation? We think that this is a property of the mixed chimeras since S31A is also imported less efficiently than H3.3 (though we cannot be sure without further experiments). We have added this explanation to the text: “We speculate that chimeric histone proteins (H3.3S31A and H3.3SVM) are not as efficiently handled by the chaperone machinery as species that are normally found in the organism including H3.3ASVM which is protein-identical to H3.” lines 150-152 5. Fig 2F-G: It is very difficult to compare between histones when they are on different graphs, please consider putting H3, H3.3 and H3.3ASVM in a hirassm background on the same graph. We have done this in the new Figure 3F. Revision Plan 6. Fig 3- move G to become A and then have A and B. We have restructured this figure to include the nuclear density map of control in response to a comment from Reviewer 1. Although not exactly what the reviewer has envisioned, we hope that this adds clarity to the figure. 7. The initial slope graphs in 4D, E, H and I are not easy to understand and would benefit from an explanation in the legend. We have edited the legend of Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” In addition we have updated the methods to include: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods Reviewer #3 (Significance (Required)): This paper addresses an important and understudied question- how do histones and their variants mediate chromatin regulation in the early embryo before zygotic genome activation? The authors follow up on some previous findings and provide new insights using clever genetics and cell biology in Drosophila melanogaster. However, the authors do not directly look at chromatin structural changes using existing genomic tools. This may be beyond the scope of this work but would make for a nice addition to strengthen their claims if they can implement these chromatin accessibility techniques in the early embryo. Histones affect a majority of biological processes and understanding their role in the early embryo is key to understanding development. I believe this study applies to a broad audience interested in basic science. However, I do think the authors might benefit from a more broad discussion of their results to attract a broad readership.

JUnit 4 到 5 的主要变化

A file has a name for easy identification and can be accessed or moved while keeping the same name. Copies of a file are separate unless they are synchronized. File attributes include its name, a unique system identifier, its type, size, and access permissions. It also has timestamps that record when it was created, modified, or used.

File attributes vary across operating systems, but they generally include name, identifier, type, location, size, protection, timestamps, and access permissions. These attributes help manage and secure files effectively. However, the concept of extended attributes, such as file checksums and character encoding, raises questions. How do extended attributes improve security, and how are they implemented differently across file systems? Understanding these attributes is crucial for file management, particularly in environments that require strict access control and data integrity measures. I would like to explore how modern file systems, such as NTFS and ext4, utilize extended attributes to enhance security and organization.

Imagine 3d Reading.... something well beyond writing suggestions.

1.) Source material. eg. NY Times. 2.) Social layer e.g. I tag you on a sentence talking about their Grammarly/Coda article and rumors about Notion 3.) Engaging AI to test facts, offer alternative povs, summaries, etc.

Now Grammarly has read, write and collab + AI. * Suggestions * Collaboration & Mentions * AI in the browser for all

Users can also tag topics and soon @mention their peers as read.

Thesis the united states has lost a lot of money due to racism and discrimination.

What is the process for determining who will review (if only 1 reviewer is needed)?

Author response:

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

Reviewer #1 (Public review):

Fuchs describes a novel method of enzymatic protein-protein conjugation using the enzyme Connectase. The author is able to make this process irreversible by screening different Connectase recognition sites to find an alternative sequence that is also accepted by the enzyme. They are then able to selectively render the byproduct of the reaction inactive, preventing the reverse reaction, and add the desired conjugate with the alternative recognition sequence to achieve near-complete conversion. I agree with the authors that this novel enzymatic protein fusion method has several applications in the field of bioconjugation, ranging from biophysical assay conduction to therapeutic development. Previously the author has published on the discovery of the Connectase enzymes and has shown its utility in tagging proteins and detecting them by in-gel fluorescence. They now extend their work to include the application of Connectase in creating protein-protein fusions, antibody-protein conjugates, and cyclic/polymerized proteins. As mentioned by the author, enzymatic protein conjugation methods can provide several benefits over other non-specific and click chemistry labeling methods. Connectase specifically can provide some benefits over the more widely used Sortase, depending on the nature of the species that is desired to be conjugated. However, due to a similar lengthy sequence between conjugation partners, the method described in this paper does not provide clear benefits over the existing SpyTag-SpyCatcher conjugation system.  Additionally, specific disadvantages of the method described are not thoroughly investigated, such as difficulty in purifying and separating the desired product from the multiple proteins used. Overall, this method provides a novel, reproducible way to enzymatically create protein-protein conjugates.

The manuscript is well-written and will be of interest to those who are specifically working on chemical protein modifications and bioconjugation.

I'd like to comment on two points.

(1) The benefits over the SpyTag-SpyCatcher system. Here, the conjugation partners are fused via the 12.3 kDa SpyCatcher protein, which is considerably larger than the Connectase fusion sequence (19 aa). This is mentioned in the introduction (p. 1 ln 24-26). Furthermore, SpyTag-SpyCatcher fusions are truly irreversible, while Connectase/BcPAP fusions may be reversed (p. 8, ln 265-273). For example, target proteins (e.g., AGAFDADPLVVEI-Protein) may be covalently fused to functionalized magnetic beads (e.g., Bead-ELASKDPGAFDADPLVVEI) in order to perform a pulldown assay. After the assay, the target protein and any bound interactors could be released from the beads by the addition of a Connectase / peptide (AGAFDAPLVVEI) mixture.

In a related technology, the SpyTag-SpyCatcher system was split into three components, SpyLigase, SpyTag and KTag  (Fierer et al., PNAS 2014). The resulting method introduces a sequence between the fusion partners (SpyTag (13aa) + KTag (10aa)), which is similar in length to the Connectase fusion sequence (p. 8, ln 297 - 298). Compared to the original method, however, this approach seems to require longer incubation times, while yielding less fusion product (Fierer et al., Figure 2).

(2) Purification of the fusion product. The method is actually advantageous in this respect, as described in the discussion (p. 8, ln 258-264). Examples are now provided in Figure 6.

Reviewer #2 (Public review):

Summary:

Unlike previous traditional protein fusion protocols, the author claims their proposed new method is fast, simple, specific, reversible, and results in a complete 1:1 fusion. A multi-disciplinary approach from cloning and purification, biochemical analyses, and proteomic mass spec confirmation revealed fusion products were achieved.

Strengths:

The author provides convincing evidence that an alternative to traditional protein fusion synthesis is more efficient with 100% yields using connectase. The author optimized the protocol's efficiency with assays replacing a single amino acid and identification of a proline aminopeptidase, Bacilius coagulans (BcPAP), as a usable enzyme to use in the fusion reaction. Multiple examples including Ubiquitin, GST, and antibody fusion/conjugations reveal how this method can be applied to a diverse range of biological processes.

Weaknesses:

Though the ~100% ligation efficiency is an advancement, the long recognition linker may be the biggest drawback. For large native proteins that are challenging/cannot be synthesized and require multiple connectase ligation reactions to yield a complete continuous product, the multiple interruptions with long linkers will likely interfere with protein folding, resulting in non-native protein structures. This method will be a good alternative to traditional approaches as the author mentioned but limited to generating epitope/peptide/protein tagged proteins, and not for synthetic protein biology aimed at examining native/endogenous protein function in vitro.

The assessment is fair, and I have no further comments to add.

Reviewer #1 (Recommendations for the authors):

Major/Experimental Suggestions:

(1) Throughout the paper only one reaction shown via gels had 100% conversion to desired product (Figure 3C). It is misleading to title a paper with absolutes such as "100% product yield", when the majority of reactions show >95% product yield, without any purification. Please change the title of the manuscript to something along the lines of "Novel Irreversible Enzymatic Protein Fusions with Near-Complete Product Yield".

The conjugation reaction is thermodynamically favored. It is driven by the hydrolysis of a peptide bond (P|GADFDADPLVVEI), which typically releases 8 - 16 kJ/mol energy. This should result in a >99.99% complete reaction (DG° = -RT ln (Product/Educt)). In line with this, 99% - 100% of the less abundant educts (LysS, Figure 3A; MBP, Figure 3B; Ub-Strep, Figure 3C) are converted in the time courses (Figure 3D-F show different reaction conditions, which slow down conjugate formation). 100% conversion are also shown in Figure 5, Figure 6, and Figure S4. Likewise, 99.6% relative fusion product signal intensity in an LCMS analysis (Figure S2) after 4h reaction time (0.13% and 0.25% educts). In this experiment, the proline had been removed from 99.8% of the peptide byproducts (P|GADFDADPLVVEI). It is clear that this reaction is still ongoing and that >99.99% of the prolines will be removed from the peptides in time. These findings suggest that the conjugation reaction gradually slows down the less educt is available, but eventually reaches completion.

For some experiments, lower product yields (e.g. 97% in Figure 3B) are reported in the paper. These were calculated with Yield = 100% x Product / (Educt1 + Educt 2 + Product). With this formula, 100% conjugation can only be achieved with exactly equimolar educt quantities, because both educt 1 and educt 2 need to be converted entirely. If one educt 1 is available in excess, for example because of protein concentration measurement inaccuracies or pipetting errors, some of it will be left without fusion partner. In case of Figure 3B, 3% more GST seemed to have been in the mixture. These are methodological inaccuracies.

(2) Please provide at least one example of a purified desired product, and mention the difficulties involved as a disadvantage to this particular method. Separating BcPAP, Connectase, and the desired protein-protein conjugate may prove to be quite difficult, especially when Connectase cleaves off affinity tags.

Examples are now provided in Figure 6. As described in the discussion (p. 8, ln 258-264), the simple product purification is one of the advantages of the method.

(3) For the antibody conjugate, please provide an example of conjugating an edduct that would prove to be more useful in the context of antibodies. For example, as you mention in the introduction, conjugation of fluorophores, immobilization tags such as biotin, and small molecule linker/drugs are useful bioconjugates to antibodies.

Antibody-biotinylation is now shown in Figure S6; Antibody-fluorophore conjugates are part of Figures S5 and S7.

(4) Please assess the stability of these protein-protein conjugates under various conditions (temperature, pH, time) to ensure that the ligation via Connectase is stable over a broad array of conditions. In particular, a relevant antibody-conjugate stability assay should be done over the period of 1-week in both buffer and plasma to show applicability for potential therapeutics.

The stability of an antibody-biotin conjugate in blood plasma over 7 days at different temperatures is now shown in Figure S7.

Generally, Connectase introduces a regular peptide bond (Asp-Ala) with a high chemical and physical stability (e.g. 10 min incubation at 95°C in SDS-PAGE loading buffer; H2O-formic acid / acetonitrile gradients for LC-MS). The sequence may be susceptible to proteases, although this is not the case in HEK293 cells (antibody expression), E. coli, or blood plasma (Figure S7).

(5) Please conduct functional assays with the antibody-protein/peptide conjugates to show that the antibody retains binding capabilities to the HER-2 antigen and the modification was site-selective, not interfering with the binding paratope or binding ability of the antibody in any way. This can be done through bio-layer interferometry, surface plasmon resonance, ELISA, etc.

We plan the immobilization of the HER2 antibody on microplates and its use in an ELISA. However, this experiment requires significant testing and optimizations. It will be part of a future paper on the use of Connectase for protein immobilization.

For now, the mass spectrometry data provide clear evidence of a single site-selective conjugation, as the C-terminal ELASKDPGAFDADPLVVEI-Strep sequence is replaced by ELASKDAGAFDADPLVVEI(-Ub). Given that the conjugation sites at the C-termini are far from the antigen binding sites, and have already been used in a number of other approaches (e.g., SpyTag, SnapTag, Sortase), it appears unlikely that these conjugations interfere with antigen binding.

(6) Please include gels of all proteins used in ligation reactions after purification steps in the SI to show that each species was pure.

The pure proteins are now shown in Figure S9.

(7) Please provide the figures (not just tables) of LC/MS deconvoluted mass spectra graphs for all conjugates, either in the main text or the SI.

Please specify which spectra you are missing. I believe all relevant spectra are shown in Figures 4, 5, and S3. The primary data can be found in Dataset S2.

(8) Please provide more information in the methods section on exactly how the densitometry quantification of gel bands was performed with ImageJ.

Details on the quantification with Image Studio Lite 5.2 were added in the method section (p. 17, ln 461-463).

Minor Suggestions:

(1) Page 1, line 19: can include one sentence on what assays these particular bioconjugations are usefule for (e.g. internalization cell studies, binding assays, etc.)

I prefer not to provide additional details here to keep the text concise and focused.

(2) Page 1, line 22: "three to ten equivalents" instead of 3x-10x.

Done.

(3) Page 1, line 23: While NHS labeling is widely considered non-specific, maleimide conjugation to free cysteines is generally considered specific for engineered free cysteine residues, since native proteins often do not have free cysteine residues available for conjugation. If you are referring to the potential of maleimides to label lysines as well, that should be specifically stated.

I modified the sentence, now stating that these methods are "can be" unspecific.

As pointed out, it is possible to achieve specificity by eliminating all other free cysteines and/or engineering a cysteine in an appropriate position. In many other cases, however (e.g., natural antibodies), several cysteines are available, or the sample contains other proteins/peptides. I did not want to go into more detail here and refer to the cited review.

(4) Page 1, line 31: "and an oligoglycine G(1-5)-B"

Done.

(5) Page 1, line 34: It is not clear where in the source these specific Km values are coming from, considering these are variable based on specific conditions/substrates and tend to be reaction-specific.

I cited another review, which lists the same values, along with a few other measurements (Jacobitz et al., Adv Protein Chem Struct Biol 2017, Table 2). It is clear that each of these measurements differs somewhat, but they are generally comparable (K_M(LPETG) = 5500 - 8760 µM; K_M(GGGGG) = 140 - 196 µM). I chose the cited study (Frankel et al., Biochemistry 2005), because it also investigated hydrolysis rates. In this study, the measurements are derived from the plots in Figure 2.

(6) Page 1, line 47: the comparison to western blots feels a little like apples to oranges, even though this comparison was made in previous literature. Engineering an expressed protein to have this tag and then using the tag to detect and quantify it, feels more akin to a tagging/pull down assay than a western blot in which unmodified proteins are easily detected.

It is akin to a frequently used type of western blots with tag-specific antiboies, e.g. Anti-His₆, -Streptavidin, -His₆, -HA ,-cMyc, -Flag. I modified the sentence to clarify this.

(7) Page 2, line 51: "Connectase cleaves between the first D and P amino acids in the recognition sequence, resulting in an N-terminal A-ELASKD-Connectase intermediate and a C-terminal PGAFDADPLVVEI peptide."

I prefer the current sentence, because we assume that a bond between the aspartate and Connectase is formed before PGAFDADPLVVEI is cleaved off.

(8) Page 3, line 94: "Exact determination is not possible due to reversibility of the reaction", the way it is stated now sounds like it is a flaw in the methods. Also, update Figure 2 to read "Estimated relative ligation rate".

Done.

(9) Page 3, lines 101-107: This is worded in a confusing way. It can either be X₁ or X₂ that is inactivated depending on if the altered amino acid is on the original protein sequence or on the desired edduct to conjugate. You first give examples of how to render other amino acids inactive, but then ultimately state that proline made inactive, so separate the two distinct possibilities a bit more clearly.

The reaction requires the inactivation of X₁, without affecting X₂ (ln 100 - 102). This is true, no matter whether it is X₁ = A, C, S, or P that is inactivated. I added a sentence to clarify this (ln 102 – 103).

(10) Page 4, line 118: Give a one-sentence justification for why these proteins were chosen to work with (easy to express, stable, etc).

Done.

(11) Page 5, line 167: "payload molecules".

Done.

(12) Page 5, lines 170-173: Word this more clearly- "full conversion with many of these methods is difficult on antibodies due to each heavy and light chain being modified separately, resulting in only a total yield of 66% DAR4 even when 90% of each chain is conjugated."

I rephrased the section.

(13) Page 8, line 290: Discuss other disadvantages of this method including difficulties purifying and in incorporating such a long sequence into proteins of interest.

Product purification is shown in the new Figure 6. As stated above, I consider the simple purification process an advantage of the method.  The genetic incorporation of the sequence into proteins is a routine process and should not make any difficulties. The disadvantages of long linker sequences between fusion partners are now discussed (p.8 – 9, ln 300-302).

(14) Page 10, line 341: 'The experiment is described and discussed in detail in a previously published paper.31"

Done.

Reviewer #2 (Recommendations for the authors):

Minor Points:

(1) It's unclear how the author derived 100% ligation rate with X = Proline in Figure 2 when there is still residual unligated UB-Strep at 96h. Please provide an expanded explanation for those not familiar with the protocol. Is the assumption made that there will be no UB-Strep if the assay was carried out beyond 96h?

I clarified the figure legend. The assay shows the formation of an equilibrium between educts and products. Therefore, only ~50% Ub-Strep is used with X = Proline (see p. 2, ln 79 - 81). The "relative ligation rate" refers to the relative speed with which this equilibrium is established. The highest rate is seen with X = Proline, and it is set to 100%. The other rates are given relative to the product formation with X = Proline.

(2) Though the qualitative depiction of the data in Figure 3 is appreciated, an accompanying graphical representation of the data in the same figure will greatly enhance reception and better comprehension of several of the author's conclusions.

Graphs are now shown in Figure S1.

(3) Figure 3 panel E is misaligned. Please align it with panel B above it.

Done, thank you.

(4) The author refers to 'The resulting circular assemblies (37% UB2...)' in the text but identifies it as UB-C2 in Figure 5B. Is this a mistake or does UB2 refer to another assembly not mentioned in the Figures? Please check for inconsistencies.

All circular assemblies are now labeled Ub-C _1-6.

(5) Finishing with a graphical schematic that depicts the entire protocol in a simple image would be much appreciated and well-received by readers. Including the scheme with A and B proteins, the recognition linkers, the addition of connectase and BcPAP, etc. to the final resulting protein with connected linker.

A graphical summary of the reaction is now included in Figure 6.

Author response:

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

eLife Assessment

(1) This is a valuable manuscript that successfully integrates several data sets to determine genomic interactions with nuclear bodies.

In this paper we both challenge and/or revise multiple long-standing “textbook” models of nuclear genome organization while also revealing new features of nuclear genome organization. Therefore, we argue that the contributions of this paper extend well beyond “valuable”. Specifically, these contributions include:

a. We challenge a several decades focus on the correlation of gene positioning relative to the nuclear lamina. Instead, through comparison of cell lines, we show a strong correlation of di4erences in gene activity with di4erences in relative distance to nuclear speckles in contrast to a very weak correlation with di4erences in relative distance to the nuclear lamina. This inference of little correlation of gene expression with nuclear lamina association was supported by direct experimental manipulation of genome positioning relative to the nuclear lamina. Despite pronounced changes in relative distances to the nuclear lamina there was little change relative to nuclear speckles and little change in gene expression.

b. We similarly challenge the long-standing proposed functional correlation between the radial positioning of genes and gene expression. Here, and in a now published companion paper (doi.org/10.1038/s42003-024-06838-7), we demonstrate how nuclear speckle positioning relative to nucleoli and the nuclear lamina varies among cell types, as does the inverse relationship between genome positioning relative to nuclear speckles and the nuclear lamina. Again, this is consistent with the primary correlation of gene activity being the positioning of genes relative to nuclear speckles and also explains previous observations showing a strong relationship between radial position and gene expression only in some cell types.

c. We identified a new partially repressed, middle to late DNA replicating type of chromosome domain- “p-w-v fILADs”- by their weak interaction with the nuclear lamina, which, based on our LMNA/LBR KO experimental results, compete with LADs for nuclear lamina association. Moreover, we show that when fLADs convert to iLADs, most conversions are to this p-w-v fiLAD state, although ~ one third are to a normal, active, early replicating iLAD state. Thus, fLADs can convert between repressed, partially repressed, and active states, challenging the prevailing assumption of the division of the genome into two states – active, early replicating A compartment/iLAD regions versus inactive, late replicating, B compartment/LAD regions.

d. We identified nuclear speckle associated domains as DNA replication initiation zones, with the domains showing strongest nuclear speckle attachment initiating DNA replication earliest in S-phase.

e. We describe for the first time an overall polarization of nuclear genome organization in adherent cells with the most active, earliest replicating genomic regions located towards the equatorial plane and less expressed genomic regions towards the nuclear top or bottom surfaces. This includes polarization of some LAD regions to the nuclear lamina at the equatorial plane and other LAD regions to the top or bottom nuclear surfaces.

We have now rewritten the text to make the significance of these new findings clearer.

(2) Strength of evidence: The evidence supporting the central claims is varied in its strength ranging from solid to incomplete. Orthogonal evidence validating the novel methodologies with alternative approaches would better support the central claims.

We argue that our work exploited methods, data, and analyses equal to or more rigorous than the current state-of-the-art. This indeed includes orthogonal evidence using alternative methods which both supported our novel methodologies as well as demonstrating their robustness relative to more conventional approaches. This explains how we were able to challenge/revise long-standing models and discover new features of nuclear genome organization. More specifically:

a. Unlike most previous analyses, we have integrated both genomic and imaging approaches to examine the nuclear genome organization relative to not one, but several di4erent nuclear locales and we have done this across several cell types. To our knowledge, this is the first such integrated approach and has been key to our success in appreciating new features of nuclear genome organization.

b. The 16-fraction DNA replication Repli-seq data we developed and applied to this project represents the highest temporal mapping of DNA replication timing to date.

c. The TSA-seq approach that we used remains the most accurate sequence-based method for estimating microscopic distance of chromosome regions to di4erent nuclear locales. As implemented, this method is unusually robust and direct as it exploits the exponential micron-scale gradient established by the di4usion of the free-radicals generated by peroxidase labeling to measure relative distances of chromosome regions to labeled nuclear locales. We had previously demonstrated that TSA-seq was able to estimate the average distances of genomic regions to nuclear speckles with an accuracy of ~50 nm, as validated by light microscopy. The TSA-seq 2.0 protocol we developed and applied to this project maintained the original resolution of TSA-seq to estimate to an accuracy of ~50 nm the average distances of genomic regions from nuclear speckles, as validated by light microscopy, while achieving more than a 10-fold reduction in the required number of cells.

We have rewritten the text to address the reviewer concerns that led them to their initial characterization of the TSA-seq as novel and not yet validated.

First, we have added a discussion of how the use of nuclear speckle TSA-seq as a “cytological ruler” was based on an extensive initial characterization of TSA-seq as described in previous published literature. In that previous literature we showed how the conventional molecular proximity method, ChIP-seq, instead showed local accumulation of the same marker proteins over short DNA regions unrelated to speckle distances. Second, we reference our companion paper, now published, and describe how the extension of TSA-seq to measure relative distances to nucleoli was further validated and shown to be robust by comparison to NAD-seq and extensive multiplexed immuno-FISH data. We further discuss how in the same companion paper we show how nucleolar DamID instead was inconsistent with both the NAD-seq and multiplexed immuno-FISH data as well as the nucleolar TSA-seq.

Third, we have added scatterplots showing exactly how highly the estimated microscopic distances to all three nuclear locales, measured in IMR90 fibroblasts, correlate with the TSA-seq measurements in HFF fibroblasts. This addresses the concern that we were not using the exact same fibroblast cell line for the TSA-seq versus microscopic measurements. The strong correlation already observed would only be expected to become even stronger with use of the exact same fibroblast cell lines for both measurements.

Fourth, we have addressed the reviewer concern that the nuclear lamin TSA-seq was not properly validated because it did not match nuclear lamin Dam-ID. We have now added to the text a more complete explanation of how microscopic proximity assays such as TSA-seq measure something di4erent from molecular proximity assays such as DamID or NAD-seq. We have added further explanation of how TSA-seq complements molecular proximity assays such as DamID and NAD-seq, allowing us to extract further information than either measurement alone. We also briefly discuss why TSA-seq succeeds for certain nuclear locales using multiple independent markers whereas molecular proximity assays may fail against the same nuclear locales using the same markers. This includes brief discussion from our own experience attempting unsuccessfully to use DamID against nucleoli and nuclear speckles.

Reviewer #1 (Public Review):

(1) The weakness of this study lies in the fact that many of the genomic datasets originated from novel methods that were not validated with orthogonal approaches, such as DNAFISH. Therefore, the detailed correlations described in this work are based on methodologies whose efficacy is not clearly established. Specifically, the authors utilized two modified protocols of TSA-seq for the detection of NADs (MKI67IP TSA-seq) and LADs (LMNB1-TSA-seq).

We disagree with the statement that the TSA-seq approach and data has not been validated by orthogonal approaches. We have now addressed this point in the revised manuscript text:

a) We added text to describe how previously FISH was used to validate speckle TSA-seq by demonstrating a residual of ~50 nm between the TSA-seq predicted distance to speckles and the distance measured by light microscopy using FISH:

"In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diffusion radius of tyramide-biotin free-radicals (Chen et al., 2018). Exploiting the measured exponential decay of the tyramide-biotin free-radical concentration, we showed how the mean distance of chromosomes to nuclear speckles could be estimated from the TSA-seq data to an accuracy of ~50 nm, as validated by FISH (Chen et al., 2018)."

b) We note that we also previously have validated lamina (Chen et al, JCB 2018) and nucleolar (Kumar et al, 2024) TSA-seq and further validated speckle TSA-seq (Zhang et al, Genome Research 2021) by traditional immuno-FISH and/or immunostaining. The overall high correlation between lamina TSA-seq and the orthogonal lamina DamID method was also extensively discussed in the first TSA-seq paper (Chen et al, JCB 2018). Included in this discussion was description of how the di4erences between lamina TSA-seq and DamID were expected, given that DamID produces a signal more proportional to contact frequency, and independent of distance from the nuclear lamina, whereas TSA-seq produces a signal that is a function of microscopic distance from the lamina, as validated by traditional FISH.

c) We added text to describe how the nucleolar TSA-seq previously was validated by two orthogonal methods- NAD-seq and multiplexed DNA immuno-FISH:

"We successfully developed nucleolar TSA-seq, which we extensively validated using comparisons with two different orthogonal genome-wide approaches (Kumar et al., 2024)- NAD-seq, based on the biochemical isolation of nucleoli, and previously published direct microscopic measurements using highly multiplexed immuno-FISH (Su et al., 2020)."

d) We have now added panels A&B to Fig. 7 and a new Supplementary Fig. 7 demonstrating further validation of TSA-seq based on showing the high correlation between the microscopically measured distances of many hundreds of genomic sites across the genome from di4erent nuclear locales and TSA-seq scores. As discussed in response #2 below, we have used comparison of distances measured in IMR90 fibroblasts with TSA-seq scores measured in HFF fibroblasts. We would argue therefore that these correlations are a lower estimate and therefore the correlation between microscopic distances and TSAseq scores would likely have been still higher if we had performed both assays in the exact same cell line.

(2) Although these methods have been described in a bioRxiv manuscript by Kumar et al., they have not yet been published. Moreover, and surprisingly, Kumar et al., work is not cited in the current manuscript, despite its use of all TSA-seq data for NADs and LADs across the four cell lines.

The Kumar et al, Communications Biology, 2024 paper is now published and is cited properly in our revision. We apologize for this oversight and confusion our initial omission of this citation may have created. We had been writing this manuscript and the Kumar et al manuscript in parallel and had intended to co-submit. We planned to cross-reference the two at the time we co-submitted, adding the Kumar et al reference to the first version of this manuscript once we obtained a doi from bioRxiv. But we then submitted the Kumar et al manuscript several months earlier, but meanwhile forgot that we had not added the reference to our first manuscript version.

(3) Moreover, Kumar et al. did not provide any DNA-FISH validation for their methods.

As we described in our response to Reviewer 1's comment #1, we had previously provided traditional FISH validation of lamina TSA-seq in our first TSA- seq paper as well as validation by comparison with lamina DamID (Chen et al, 2018).

We also described how the nucleolar TSA-seq was extensively cross-validated in the Kumar et al, 2024 paper by both NAD-seq and the highly multiplexed immuno-FISH data from Su et al, 2020).

We note additionally that in the Kumar et al, 2024 paper the nucleolar TSA-seq was additionally validated by correlating the predicted variations in centromeric association with nucleoli across the four cell lines predicted by nucleolar TSA-seq with the variations observed by traditional immunofluorescence microscopy.

(4) Therefore, the interesting correlations described in this work are not based on robust technologies.

This comment was made in reference to the Kumar et al paper not having been published, and, as noted in responses to points #2 and #3, the paper is now published.

But we wanted to specifically note, however, that our experience is that TSA-seq has proven remarkably robust in comparison to molecular proximity assays. We've described in our responses to the previous points how TSA-seq has been cross-validated by both microscopy and by comparison with lamina DamID and nucleolar NAD-seq. We note also that in every application of TSA-seq to date, all antibodies that produced good immunostaining showed good TSA-seq results. Moreover, we obtained nearly identical results in every case in which we performed TSA-seq with different antibodies against the same target. Thus anti-SON and antiSC35 staining produced very similar speckle TSA-seq data (Chen et al, 2018), anti-lamin A and anti-lamin B staining produced very similar lamina TSA-seq data (Chen et al, 2018), antinucleolin and anti-POL1RE staining produced very similar DFC/FC nucleolar TSA-seq data (Kumar et al, 2024), and anti-MKI67IP and anti-DDX18 staining produced very similar GC nucleolar TSA-seq data (Kumar et al, 2024).

This independence of results with TSA-seq to the particular antibody chosen to label a target differs from experience with methods such as ChIP, DamID, and Cut and Run/Tag in which results can differ or be skewed based on variable distance and therefore reactivity of target proteins from the DNA or due to other factors such as non-specific binding during pulldown (ChIP) or differential extraction by salt washes (Cut and Tag).

Our experience in every case to date is that antibodies that produce similar immunofluorescence staining produce similar TSA-seq results. We attribute this robustness to the fact that TSA-seq is based only on the original immunostaining specificity provided by the primary and secondary antibodies plus the diffusion properties of the tyramide-free radical.

We've now added the following text to our revised manuscript:

"As previously demonstrated for both SON and lamin TSA-seq (Chen et al., 2018), nucleolar TSA-seq was also robust in the sense that multiple target proteins showing similar nucleolar staining showed similar TSA-seq results (Kumar et al., 2024); this robustness is intrinsic to TSA-seq being a microscopic rather than molecular proximity assay, and therefore not sensitive to the exact molecular binding partners and molecular distance of the target proteins to the DNA."

(5) An attempt to validate the data was made for SON-TSA-seq of human foreskin fibroblasts (HFF) using multiplexed FISH data from IMR90 fibroblasts (from the lung) by the Zhuang lab (Su et al., 2020). However, the comparability of these datasets is questionable. It might have been more reasonable for the authors to conduct their analyses in IMR90 cells, thereby allowing them to utilize MERFISH data for validating the TSA-seq method and also for mapping NADs and LADs.

We disagree with the reviewer's overall assessment that that the use of the IMR90 data to further validate the TSA-seq is questionable because the TSA-seq data from HFF fibroblasts is not necessarily comparable with multiplexed immuno-FISH microscopic distances measured in IMR90 fibroblasts.

In response we have now added panels to Fig. 7 and Supplementary Fig. 7, showing:

a) There is very little di4erence in correlation between speckle TSA-seq and measured distances from speckles in IMR90 cells whether we use IMR90 or HFF cells SON TSA-seq data (R² = 0.81 versus 0.76) (new Fig. 7A).

b) There is also a high correlation between lamina (R² = 0.62) and nucleolar (R² = 0.73) HFF TSA-seq and measured distances in IMR90 cells. Thus, we conclude that this high correlation shows that the multiplexed data from ~1000 genomic locations does validate the TSA-seq. These correlations should be considered lower bounds on what we would have measured using IMR90 TSA-seq data. Thus, the true correlation between distances of loci from nuclear locales and TSA-seq would be expected to be either comparable or even stronger than what we are seeing with the IMR90 versus HFF fibroblast comparisons.

c) This correlation is cell-type specific (Fig. 7B, new SFig. 7). Thus, even for speckle TSAseq, highly conserved between cell types, the highest correlation of IMR90 distances with speckle TSA-seq is with IMR90 and HFF fibroblast data. For lamina and nucleolar TSA-seq, which show much lower conservation between cell types, the correlation of IMR90 distances is high for HFF data but much lower for data from the other cell types. This further justifies the use of IMR90 fibroblast distance measurements as a proxy for HFF fibroblast measurements.

Thus, we have added the following text to the revised manuscript:

"We reasoned that the nuclear genome organization in the two human fibroblast cell lines would be sufficiently similar to justify using IMR90 multiplexed FISH data [43] as a proxy for our analysis of HFF TSA-seq data. Indeed, the high inverse correlation (R= -0.86) of distances to speckles measured by MERFISH in IMR90 cells with HFF SON TSA-seq scores is nearly identical to the inverse correlation (R= -0.89) measured instead using IMR90 SON TSA-seq scores (Fig. 7A). Similarly, distances to the nuclear lamina and nucleoli show high inverse correlations with lamina and nucleolar TSA-seq, respectively (Fig. 7A). These correlations were cell type specific, particularly for the lamina and nucleolar distance correlations, as these correlations were reduced if we used TSA-seq data from other cell types (SFig. 7A). Therefore, the high correlation between IMR90 microscopic distances and HFF TSA-seq scores can be considered a lower bound on the likely true correlation, justifying the use of IMR90 as a proxy for HFF for testing our predictions."

Reviewer #2 (Public Review):

Weaknesses:

(1) The experiments are largely descriptive, and it is difficult to draw many cause-andeffect relationships...The study would benefit from a clear and specific hypothesis.

This study was hypothesis-generating rather than hypothesis-testing in its goal. Our research was funded through the NIH 4D-Nucleome Consortium, which had as its initial goal the development, benchmarking, and validation of new genomic technologies. Our Center focused on the mapping of the genome relative to different nuclear locales and the correlation of this intranuclear positioning of the genome with functions- specifically gene expression and DNA replication timing. By its very nature, this project took a discovery-driven versus hypothesis-driven scientific approach. Our question fundamentally was whether we could gain new insights into nuclear genome organization through the integration of genomic and microscopic measurements of chromosome positioning relative to multiple different nuclear compartments/bodies and their correlation with functional assays such as RNA-seq and Repliseq.

Indeed, this study resulted in multiple new insights into nuclear genome organization as summarized in our last main figure. We believe our work and conclusions will be of general interest to scientists working in the fields of 3D genome organization and nuclear cell biology. We anticipate that each of these new insights will prompt future hypothesis-driven science focused on specific questions and the testing of cause-and-effect relationships.

However, we do want to point out that our comparison of wild-type K562 cells with the LMNA/LBR double knockout was designed to test the long-standing model that nuclear lamina association of genomic loci contributes to gene silencing. This experiment was motivated by our surprising result that gene expression differences between cell lines correlated strongly with differences in positioning relative to nuclear speckles rather than the nuclear lamina. Despite documenting in these double knockout cells a decreased nuclear lamina association of most LADs, and an increased nuclear lamina association of the “p-w-v” fiLADs identified in this manuscript, we saw no significant change in gene expression in any of these regions as compared to wild-type K562 cells. Meanwhile, distances to nuclear speckles as measured by TSA-seq remained nearly constant.

We would argue that this represents a specific example in which new insights generated by our genomics comparison of cell lines led to a clear and specific hypothesis and the experimental testing of this hypothesis.

(2) Similarly, the paper would be very much strengthened if the authors provided additional summary statements and interpretation of their results (especially for those not as familiar with 3D genome organization).

We appreciate this feedback and agree with the reviewer that this would be useful, especially for those not familiar with previous work in the field of 3D genome organization. In an earlier draft, we had included additional summary and interpretation statements in both the Introduction and Results sections. At the start of each Results section, we had also previously included brief discussion of what was known before and the context for the subsequent analysis contained in that section. However, we had thought we might be submitting to a journal with specific word limits and had significantly cut out that text.

We have now restored this text and, in certain cases, added additional explanations and context.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Figures 1C and D. Please add the units at the values of each y-axis.

We have done that.

The representation of Figure 2C lacks clarity and is diJicult to understand. The x-axis labeling regarding the gene fraction number needs clarification.

We've modified the text to the Fig. 2C legend: "Fraction of genes showing significant di=erence in relative positioning to nuclear speckles (gene fraction, x-axis) versus log2 (HFF FKPM / H1 FKPM) (y-axis);"

"We next used live-cell imaging to corroborate that chromosome regions close to nuclear speckles, primarily Type I peaks, would show the earliest DNA replication timing." This sentence requires modification as Supplementary Figure 3F does not demonstrate that Type I peaks exhibit the earliest DNA replication timing; it only indicates that the first PCNA foci in S-phase are in proximity to nuclear speckles.

We've modified the text to: "We next used live-cell imaging to show that chromosome regions close to nuclear speckles show the earliest DNA replication timing; this is consistent with the earliest firing DNA replication IZs, as determined by Repli-seq, aligning with Type 1 peaks that are closely associated with nuclear speckles."

In Figure 5, the authors employed LaminB1-DamID to quantify LADs in LBR-KO and LMNA/LBR-DKO K562 cells. These are interesting results. However, for these experiments, it is crucial to assess LMNB1 signal at the nuclear periphery via immunofluorescence (IF) to confirm the absence of changes, ensuring that the DamID signal solely reflects contacts with the nuclear lamina. Furthermore, in this instance, their findings should be validated through DNA-FISH.

Immunostaining of LMNB1 was performed and showed a normal staining pattern as a ring adjacent to the nuclear periphery. Images of this staining were included in the metadata tied to the sequencing data sets deposited on the 4D Nucleome Data portal. We thank the reviewer for bringing up this point, and have added a sentence mentioning this result in the Results Section:

"Immunostaining against LMNB1 revealed the normal ring of staining around the nuclear periphery seen in wt cells (images deposited as metadata in the deposited sequencing data sets)."

Because both TSA-seq and DamID have been extensively validated by FISH, as detailed in our previous responses to the public reviewer comments, we feel it is unnecessary to validate these findings by FISH.

p-w-v-fiLADs should be labelled in Figure 5B.

We've added labeling as suggested.

"The consistent trend of slightly later DNA replication timing for regions (primarily p-w-v fiLADs) moving closer to the lamina" is not visible in the representation of the data of Figure 5G.

We did not make a change as we believed this trend was apparent in the Figure.

To reduce the descriptive nature of the data, it would be pertinent to conduct H3K9me3 and H3K27me3 ChIP-seq analyses in both the parental and DKO mutant cells. This would elucidate whether p-w-v-fiLADs and NADs anchoring to the nuclear lamina undergo changes in their histone modification profile.

We believe further analysis of the reasons underlying these shifts in positioning, including such ChIP-seq or equivalent analysis, is of interest but beyond the scope of this publication. We see such measurements as the beginning of a new story but insuJicient alone to determine mechanism. Therefore we believe such experiments should be part of that future study.

The description of Figure 7 lacks clarity. Additionally, it appears that TSA-seq for NADs and LADs may not be universally applicable across all cell types, particularly in flat cells, whereas DamID scores demonstrate less variation across cell lines, as also stated by the authors.

TSA-seq is a complement to rather than a replacement for either DamID or NAD-seq. TSAseq reports on microscopic distances whereas both DamID and NAD-seq instead are more proportional to contact frequency with the nuclear lamina or nucleoli, respectively, and insensitive to distances of loci away from the lamina or nucleoli. Thus, TSA-seq provides additional information based on the intrinsic diJerences in what TSA-seq measures relative to molecular proximity methods such as DamID or NAD-seq. The entire point is that the convolution of the exponential point-spread-function of the TSA-seq with the shape of the nuclear periphery allows us to distinguish genomic regions in the equatorial plane versus the top and bottom of the nuclei. The TSA-seq is therefore highly "applicable" when properly interpreted in discerning new features of genome organization. As we stated in the revised manuscript, the lamina DamID and TSA-seq are complementary and provide more information together then either method along. The same is true for the NAD-seq and nucleolar TSA-seq comparison, as described in more detail in the Kumar, et al, 2024 paper.

Introduction:

The list of methodologies for mapping genomic contacts with nucleoli (NADs) should also include recent technologies, such as Nucleolar-DamID (Bersaglieri et al., PMID: 35304483), which has been validated through DNA-FISH.

We did not include nucleolar DamID in the mention in the Introduction of methods for identifying diJerential lamina versus nucleolar interactions of heterochromatin- either from our own collaborative group or from the cited reference- because we did not have confidence in the accuracy of this method in identifying NADs. In the case of the published nucleolar DamID from our collaborative group, published in Wang et al, 2021, we later discovered that despite apparent agreement of the nucleolar DamID with a small number of published FISH localization the overall correlation of the nucleolar DamID with nucleolar localization was poor. As described in detail in the Kumar et al, 2024 publication, this poor correlation of the nucleolar DamID was established using three orthogonal methods- nucleolar TSA-seq, NAD-seq, and multiplexed immuno-FISH measurements from ~1000 genomic locations. Instead, we found that this nucleolar DamID showed high correlation with lamina DamID. We note that many strong NADs are also LADs, which we think is why validation with only several FISH probes is inadequate to demonstrate overall validation of the approach.

We could not compare our nucleolar-DamID data in human cells with the alternative nucleolar-DamID results cited by the reviewer which were performed in mouse cells. We note that in this paper the nucleolar DamID FISH validation only included several putative NAD chromosome regions and, I believe, one LAD region. However, our initial comparison of the nucleolar DamID cited by the reviewer with unpublished TSA-seq data from mouse ESCs produced by the Belmont laboratory and with NAD-seq data from the Kaufman laboratory shows a similar lack of correlation of the nucleolar DamID signal with nucleolar TSA-seq and NAD-seq, as well as multiplexed immuno-FISH data from the Long Cai laboratory, as we saw in our analysis of own nucleolar DamID data in human cells.

We have added explanation concerning the lack of correlation of our nucleolar DamID with orthogonal measurements of nucleolar proximity in the added text (below) to our revised manuscript:

"Nucleolar DamID instead showed broad positive peaks over large chromatin domains, largely overlapping with LADs mapped by LMNB1 DamID (Wang et al., 2021). However, this nucleolar DamID signal, while strongly correlated with lamin DamID, showed poor correlation with either NAD-seq or nucleolar distances mapped by multiplexed immunoFISH (Kumar et al., 2024). We suspect the problem is that with molecular proximity assays the output signals are disproportionally dominated by the small fraction of target proteins juxtaposed in su=icient proximity to the DNA to produce a signal rather than the amount of protein concentrated in the target nuclear body. "

Our mention of nucleolar TSA-seq was in the context of why we focused on nucleolar TSAseq and excluded our own nucleolar DamID. We chose not to discuss the second nucleolar DamID method cited above 1) because it was not appropriate to our discussion of our own experimental approach and 2) also because we cannot yet make a definitive statement of its accuracy for nucleolar mapping.

Reviewer #2 (Recommendations For The Authors):

(1) The authors start the manuscript by describing the 'radial genome organization' model and contrast it with the 'binary model' of genome organization. It would be helpful for the authors to contextualize their results a bit more with regard to these two diJerent models in the discussion.

We have added several sentences in the first paragraph of the Discussion to accomplish this contextualization. The new paragraph reads:

"Here we integrated imaging with both spatial (DamID, TSA-seq) and functional (Repli-seq, RNA-seq) genomic readouts across four human cell lines. Overall, our results significantly extend previous nuclear genome organization models, while also demonstrating a cell-type dependent complexity of nuclear genome organization. Briefly, in contrast to the previous radial model of genome organization, we reveal a primary correlation of gene expression with relative distances to nuclear speckles rather than radial position. Additionally, beyond a correlation of nuclear genome organization with radial position, in cells with flat nuclei we show a pronounced correlation of nuclear genome organization with distance from the equatorial plane. In contrast to previous binary models of genome organization, we describe how both iLAD / A compartment and LAD / B compartment contain within them smaller chromosome regions with distinct biochemical and/or functional properties that segregate di=erentially with respect to relative distances to nuclear locales and geometry."

(2) Data should be provided demonstrating KO of LBR and LMNA - immunoblotting for both proteins would be one approach. In addition, it would be helpful to provide additional nuclear morphology measurements of the DKO cells (volume, surface area, volume of speckles/nucleoli, number of speckles/nucleoli).

We've added additional description describing the generation and validation of the KO lines:

"To create LMNA and LBR knockout (KO) lines and the LMNA/LBR double knockout (DKO) line, we started with a parental "wt" K562 cell line, clone #17, expressing an inducible form of Cas9 (Brinkman et al., 2018). The single KO and DKO were generated by CRISPR-mediated frameshift mutation according to the procedure described previously (Schep et al., 2021). The "wt" K562 clone #17 was used for comparison with the KO clones.

The LBR KO clone, K562 LBR-KO #19, was generated, using the LBR2 oligonucleotide GCCGATGGTGAAGTGGTAAG to produce the gRNA, and validated previously, using TIDE (Brinkman et al., 2014) to check for frameshifts in all alleles as described elsewhere (Schep et al., 2021). The LMNA/LBR DKO, K562 LBR-LMNA DKO #14, was made similarly, starting with the LBR KO line and using the combination of two oligonucleotides to produce gRNAs:

LMNA-KO1: ACTGAGAGCAGTGCTCAGTG, LMNA-KO2: TCTCAGTGAGAAGCGCACGC.

Additionally, the LMNA KO line, K562 LMNA-KO #14, was made the same way but starting with the "wt" K562 cell line. Validation was as described above; additionally, for the new LMNA KO and LMNA/LBR DKO lines, immunostaining showed the absence of anti-LMNA antibody signal under confocal imaging conditions used to visualize the wt LMNA staining while the RNA-seq from these clones revealed an ~20-fold reduction in LMNA RNA reads relative to the wt K562 clone."

As suggested, we also added morphological data for the DKO line in a modified SFig.5.

(3) The rationale for using LMNB1 TSA-seq and LMNB1 DAMID is not immediately clear. The LMNB1 TSA-seq is more variable across cell types and replicates than the DAMID. Could the authors please compare the datasets a bit more to understand the diJerences? For example, the authors demonstrate that "40-70% of the genome shows statistically significant diJerences in Lamina TSA-seq over regions 100 kb or larger, with most of these regions showing little or no diJerences in speckle TSA-seq scores." If the LMNB1 DAMID data is used for this analysis or Figure 2D, is the same conclusion reached? Also, in Figure 6, the authors conclude that C1 and C3 LAD regions are enriched for constitutive LADs, while C2 and C4 LAD regions are fLADs. This is a bit surprising because the authors and others have previously shown that constitutive LADs have higher LMNB1 contact frequency than facultative LADs (Kind, et al Cell 2015, Figure 3C).

Indeed, in the first TSA-seq paper (Chen et al, 2018) we did observe that cLADs had the highest LMNB TSA-seq scores; this was for K562 cells with round nuclei in which there is therefore no diJerence in lamina TSA-seq scores produced by nuclear shape over the entire nucleus.

However, there are diJerences between TSA-seq and DamID in terms of what they measure and we refer the reviewer to the first TSA-seq paper (Chen et al, 2018) that explains in greater depth these diJerences. This first paper explains how DamID is indeed related to contact frequency but how the TSA-seq instead estimates mean distances from the target, in this case the nuclear lamina. This is because the diJusion of tyramide free radicals from the site of their constant HRP production produces an exponential decay gradient of tyramide free radical concentration at steady state.

We have summarized these diJerences in in text we have added to introduce both DamID and TSA-seq in the second Results section:

"DamID is a well-established molecular proximity assay; DamID applied to the nuclear lamina divides the genome into lamina-associated domains (LADs) versus nonassociated “inter-LADs” or “iLADs” (Guelen et al., 2008; van Steensel and Belmont, 2017). In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diJusion radius of tyramide-biotin free-radicals (Chen et al., 2018)... While LMNB1 DamID segments LADs most accurately, lamin TSA-seq provides distance information not provided by DamID- for example, variations in relative distances to the nuclear lamina of diJerent iLADs and iLAD regions. These diJerences between the LMNB1 DamID and LMNB TSA-seq signals are also crucial to a computational approach, SPIN, that segments the genome into multiple states based on their varying nuclear localization, including biochemically and functionally distinct lamina-associated versus near-lamina states (Consortium et al., 2024; Wang et al., 2021).

Thus, lamin DamID and TSA-seq complement each other, providing more information together than either one separately."

We note that these diJerences in lamina DamID and TSA-seq are crucial to being able to gain additional information by comparing variations in the lamina TSA-seq for LADs in Figs. 6&7. See our response to point (4) below, for further explanation.

(4) In 7B/C, the authors show that the highest LMNB1 regions in HFF are equator of IMR90s. However, in Figure 7G, their cLAD score indicates that constitutive LADs are not at the equator. This is a bit surprising given the point above and raises the possibility that SON signals (as opposed to LMNB1 signals) might be more responsible for correlation to localization relative to the equator. Hence, it might be helpful if the authors repeat the analyses in Figures 7B/C in regions with diJering LMNB1 signals but similar SON signals (and vice versa).

Again, this is based on the apparent assumption by the reviewer that DamID and TSA-seq work the same way and measure the same thing. But as explained above in the previous point, this is not true.

In our first TSA-seq paper (Chen et al, 2018) we showed how we could use the exponential decay point-spread-function produced by TSA, measured directly by light microscopy, to convert sequencing reads from the TSA-seq into a predicted mean distance from nuclear speckles, approximated as point sources. These mean distances predicted from the SON TSA-seq data agreed with measured FISH distances to nuclear speckles to within ~50 nm for a set of DNA probes from diJerent chromosome regions. Moreover, varying TSA staining conditions changed the decay constants of this exponential decay, thus producing diJerences in the SON TSA-seq signals. By using these diJerent exponential decay functions to convert the TSA-seq scores from these independent data sets to estimated distances from nuclear speckles, we again observed a distance residual of ~50 nm; in this case though this distance residual of ~50 nm represented the mean residual observed genome-wide. This gives us great confidence that the TSA-seq is working as we have modeled it.

As we mentioned in our response to point 3 above, we did see the highest LMNB TSA-seq signal for cLADs in K562 cells with round nuclei (Chen et al, 2018).

But as we now show in our simulation performed in this paper for Fig. 7, the observed tyramide free radical exponential decay gradient convolved with the flat nuclear lamina shape produces a higher equatorial LMNB1 TSA-seq signal for LADs at the equatorial plane. We confirmed that LADs with this higher TSA-seq signal were enriched at the equatorial plane by mining the multiplexed IMR90 imaging data. Similar mining of the multiplexed FISH IMR90 data showed localization of cLADs away from the equatorial plane.

We are not clear about the rationale for what the reviewer is suggesting about SON signals "being more responsible for correlation to localization to the equator". We have provided an explanation for the higher lamina TSA-seq scores for LADs near the equator based on the measured spreading of the tyramide free radicals convolved with the eJect of the nuclear shape. This makes a prediction that the observed variation in lamina TSA-seq scores for LADs with similar DamID scores is related to their positioning relative to the equatorial plane as we then validated through our mining of the IMR90 multiplexed FISH data.

(5) FISH of individual LADs, v-fiLADs, and p-w-v-fiLADs relative to the lamina and speckle would be helpful to understand their relative positioning in control and LBR/LMNA double KO cells. This would significantly bolster the claim that "histone mark enrichments..more precisely revealed the diJerential spatial distribution of LAD regions...".

Adequately testing these predictions made from the lamina/SON TSA-seq scatterplots by direct FISH measurements would require measurements from large numbers of diJerent chromosome regions through a highly multiplexed immuno-FISH approach. We are not equipped currently in any of our laboratories to do such measurements and we leave this therefore for future studies.

Rather our statement is based on our use of TSA-seq analyzed through these 2D scatterplots and should be valid to the degree that our TSA-seq measurements do indeed correlate with microscopy derived distances.

However, we do now include demonstration of a high correlation of speckle, lamina, and nucleolar TSA-seq with highly multiplexed immuno-FISH measurement of distances to these locales in a revised Fig. 7. The high correlation shown between the TSA-seq scores and measured distances does therefore add additional support to our claim that the reviewer is discussing, even without our own multiplexed FISH validation.

(6) "In contrast, genes within genomic regions which in pair-wise comparisons of cell lines show a statistically significant diJerence in lamina TSA-seq show no obvious trend in their expression diJerences (Figure 2C).". This appears to be an overstatement based on the left panel of 2D.

We do not follow the reviewer's point. In Fig. 2C we show little bias in the diJerences in gene expression between the two cell types for regions that showed diJerences in lamina TSA-seq. The reviewer is suggesting something otherwise based on their impression, not explicitly stated, of the left panel of Fig. 2D. But we see similar shades of blue extending vertically at low SON values and similar shades of red extending vertically at high SON values, suggesting a correlation of gene expression only with the SON TSA-seq score but not with the LMNB1 TSA-seq score displayed on the y-axis. This is also consistent with the very small and/or insignificant correlation coeJicients measured in our linear model relating diJerences in LMNB1 TSA-seq to diJerences in expression but the large correlation coeJicient observed for SON TSA-seq (Fig. 2E). Thus, we see Fig. 2C-E as self-consistent.

(7) In the section on "Polarity of Nuclear Genome Organization" - "....Using the IMR90 multiplexed FISH data set [43]...." - The references are not numbered.

We thank the reviewer for this correction.

(8) I believe there is an error in the Figure 7B legend. The descriptions of Cluster 1 and 2 do not match those indicated in the figure.

We again thank the reviewer for this correction.

Just a general comment about navigation in the training hub: I appreciate that the layout and navigation may not be a priority at this stage, as content is still under development. However, there are some issues with the UI/UX throughout - I will add a tag "#navigation" so that all my comments on navigation throughout the courses can be collated seperately to my content comments.

Navigation on this particular page is good though - highly intuitive and clearly labelled.

reply to u/Sept-27 at https://old.reddit.com/r/Zettelkasten/comments/1itbduv/how_to_begin_storytelling_with_smart_notes/

Ahrens' book will likely leave you with more questions than answers and really has nothing directly to say on the practice of fictional storytelling. Doto's book does a better job of filling in these pieces but approaches writing in general rather than specifically fiction.

For fiction writers, I often recommend they don't practice putting Luhmann-artig numbers on their cards, but organize them in a more impromptu manner and allow them to shift more as you write. This allows things to shift more easily during the process and provides for a bit more creativity.

Some resources and examples for fiction with a ZK:<br /> - Vladimir Nabokov - David Lynch - take particular note of his method taught by Frank Daniel at AFI - Dustin Lance Black - Card index for fiction writing - The Zettelkasten Method for Fiction Writing

Take the inspiration these suggest, but don't go down the rabbit hole too deeply. You're going to want to evolve something that works best for you and your modes of writing, so trying to imitate someone else's system too closely will be the kiss of death.

Otro interactivo que muestra sesgos de género, como he referido en otras anotaciones

Reviewer #1 (Public review):

Summary:

This manuscript describes the role of PRDM16 in modulating BMP response during choroid plexus (ChP) development. The authors combine PRDM16 knockout mice and cultured PRDM16 KO primary neural stem cells (NSCs) to determine the interactions between BMP signaling and PRDM16 in ChP differentiation.

They show PRDM16 KO affects ChP development in vivo and BMP4 response in vitro. They determine genes regulated by BMP and PRDM16 by ChIP-seq or CUT&TAG for PRDM16, pSMAD1/5/8, and SMAD4. They then measure gene activity in primary NSCs through H3K4me3 and find more genes are co-repressed than co-activated by BMP signaling and PRDM16. They focus on the 31 genes found to be co-repressed by BMP and PRDM16. Wnt7b is in this set and the authors then provide evidence that PRDM16 and BMP signaling together repress Wnt activity in the developing choroid plexus.

Strengths:

Understanding context-dependent responses to cell signals during development is an important problem. The authors use a powerful combination of in vivo and in vitro systems to dissect how PRDM16 may modulate BMP response in early brain development.

Main weaknesses of the experimental setup:

(1) Because the authors state that primary NSCs cultured in vitro lose endogenous Prdm16 expression, they drive expression by a constitutive promoter. However, this means the expression levels are very different from endogenous levels (as explicitly shown in Supplementary Figure 2B) and the effect of many transcription factors is strongly dose-dependent, likely creating differences between the PRDM16-dependent transcriptional response in the in vitro system and in vivo.

(2) It seems that the authors compare Prdm16_KO cells to Prdm16 WT cells overexpressing flag_Prdm16. Aside from the possible expression of endogenous Prdm16, other cell differences may have arisen between these cell lines. A properly controlled experiment would compare Prdm16_KO ctrl (possibly infected with a control vector without Prdm16) to Prdm16_KO_E (i.e. the Prdm16_KO cells with and without Prdm16 overexpression.)

Other experimental weaknesses that make the evidence less convincing:

(1) The authors show in Figure 2E that Ttr is not upregulated by BMP4 in PRDM16_KO NSCs. Does this appear inconsistent with the presence of Ttr expression in the PRDM16_KO brain in Figure1C?

(2) Figure 3: The authors use H3K4me3 to measure gene activity. This is however, very indirect, with bulk RNA-seq providing the most direct readout and polymerase binding (ChIP-seq) another more direct readout. Transcription can be regulated without expected changes in histone methylation, see e.g. papers from Josh Brickman. They verify their H3K4me3 predictions with qPCR for a select number of genes, all related to the kinetochore, but it is not clear why these genes were picked, and one could worry whether these are representative.

(3) Line 256: The overlap of 31 genes between 184 BMP-repressed genes and 240 PRDM16-repressed genes seems quite small.

(4) The Wnt7b H3K4me3 track in Fig. 3G is not discussed in the text but it shows H3K4me3 high in _KO and low in _E regardless of BMP4. This seems to contradict the heatmap of H3K4me3 in Figure 3E which shows H3K4me3 high in _E no BMP4 and low in _E BMP4 while omitting _KO no BMP4. Meanwhile CDKN1A, the other gene shown in 3G, is missing from 3E.

(5) The authors use PRDM16 CUT&TAG on dissected dorsal midline tissues to determine if their 31 identified PRDM16-BMP4 co-repressed genes are regulated directly by PRDM16 in vivo. By manual inspection, they find that "most" of these show a PRDM16 peak. How many is most? If using the same parameters for determining peaks, how many genes in an appropriately chosen negative control set of genes would show peaks? Can the authors rigorously establish the statistical significance of this observation? And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.

(6) In comparing RNA in situ between WT and PRDM16 KO in Figure 7, the authors state they use the Wnt2b signal to identify the border between CH and neocortex. However, the Wnt2b signal is shown in grey and it is impossible for this reviewer to see clear Wnt2b expression or where the boundaries are in Figure 7A. The authors also do not show where they placed the boundaries in their analysis. Furthermore, Figure 7B only shows insets for one of the regions being compared making it difficult to see differences from the other region. Finally, the authors do not show an example of their spot segmentation to judge whether their spot counting is reliable. Overall, this makes it difficult to judge whether the quantification in Figure 7C can be trusted.

(7) The correlation between mKi67 and Axin2 in Figure 7 is interesting but does not convincingly show that Wnt downstream of PRDM16 and BMP is responsible for the increased proliferation in PRDM16 mutants.

Weaknesses of the presentation:

Overall, the manuscript is not easy to read. This can cause confusion.

Reviewer #2 (Public review):

Summary:

This article investigates the role of PRDM16 in regulating cell proliferation and differentiation during choroid plexus (ChP) development in mice. The study finds that PRDM16 acts as a corepressor in the BMP signaling pathway, which is crucial for ChP formation.

The key findings of the study are:<br /> (1) PRDM16 promotes cell cycle exit in neural epithelial cells at the ChP primordium.<br /> (2) PRDM16 and BMP signaling work together to induce neural stem cell (NSC) quiescence in vitro.<br /> (3) BMP signaling and PRDM16 cooperatively repress proliferation genes.<br /> (4) PRDM16 assists genomic binding of SMAD4 and pSMAD1/5/8.<br /> (5) Genes co-regulated by SMADs and PRDM16 in NSCs are repressed in the developing ChP.<br /> (6) PRDM16 represses Wnt7b and Wnt activity in the developing ChP.<br /> (7) Levels of Wnt activity correlate with cell proliferation in the developing ChP and CH.

In summary, this study identifies PRDM16 as a key regulator of the balance between BMP and Wnt signaling during ChP development. PRDM16 facilitates the repressive function of BMP signaling on cell proliferation while simultaneously suppressing Wnt signaling. This interplay between signaling pathways and PRDM16 is essential for the proper specification and differentiation of ChP epithelial cells. This study provides new insights into the molecular mechanisms governing ChP development and may have implications for understanding the pathogenesis of ChP tumors and other related diseases.

Strengths:

(1) Combining in vitro and in vivo experiments to provide a comprehensive understanding of PRDM16 function in ChP development.

(2) Uses of a variety of techniques, including immunostaining, RNA in situ hybridization, RT-qPCR, CUT&Tag, ChIP-seq, and SCRINSHOT.

(3) Identifying a novel role for PRDM16 in regulating the balance between BMP and Wnt signaling.

(4) Providing a mechanistic explanation for how PRDM16 enhances the repressive function of BMP signaling. The identification of SMAD palindromic motifs as preferred binding sites for the SMAD/PRDM16 complex suggests a specific mechanism for PRDM16-mediated gene repression.

(5) Highlighting the potential clinical relevance of PRDM16 in the context of ChP tumors and other related diseases. By demonstrating the crucial role of PRDM16 in controlling ChP development, the study suggests that dysregulation of PRDM16 may contribute to the pathogenesis of these conditions.

Weaknesses:

(1) Limited investigation of the mechanism controlling PRDM16 protein stability and nuclear localization in vivo. The study observed that PRDM16 protein became nearly undetectable in NSCs cultured in vitro, despite high mRNA levels. While the authors speculate that post-translational modifications might regulate PRDM16 in NSCs similar to brown adipocytes, further investigation is needed to confirm this and understand the precise mechanism controlling PRDM16 protein levels in vivo.

(2) Reliance on overexpression of PRDM16 in NSC cultures. To study PRDM16 function in vitro, the authors used a lentiviral construct to constitutively express PRDM16 in NSCs. While this approach allowed them to overcome the issue of low PRDM16 protein levels in vitro, it is important to consider that overexpressing PRDM16 may not fully recapitulate its physiological role in regulating gene expression and cell behavior.

(3) Lack of direct evidence for AP1 as the co-factor responsible for SMAD relocation in the absence of PRDM16. While the study identified the AP1 motif as enriched in SMAD binding sites in Prdm16 knockout cells, they only provided ChIP-qPCR validation for c-FOS binding at two specific loci (Wnt7b and Id3). Further investigation is needed to confirm the direct interaction between AP1 and SMAD proteins in the absence of PRDM16 and to rule out other potential co-factors.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript describes the role of PRDM16 in modulating BMP response during choroid plexus (ChP) development. The authors combine PRDM16 knockout mice and cultured PRDM16 KO primary neural stem cells (NSCs) to determine the interactions between BMP signaling and PRDM16 in ChP differentiation.

They show PRDM16 KO affects ChP development in vivo and BMP4 response in vitro. They determine genes regulated by BMP and PRDM16 by ChIP-seq or CUT&TAG for PRDM16, pSMAD1/5/8, and SMAD4. They then measure gene activity in primary NSCs through H3K4me3 and find more genes are co-repressed than co-activated by BMP signaling and PRDM16. They focus on the 31 genes found to be co-repressed by BMP and PRDM16. Wnt7b is in this set and the authors then provide evidence that PRDM16 and BMP signaling together repress Wnt activity in the developing choroid plexus.

Strengths:

Understanding context-dependent responses to cell signals during development is an important problem. The authors use a powerful combination of in vivo and in vitro systems to dissect how PRDM16 may modulate BMP response in early brain development.

Main weaknesses of the experimental setup:

(1) Because the authors state that primary NSCs cultured in vitro lose endogenous Prdm16 expression, they drive expression by a constitutive promoter. However, this means the expression levels are very different from endogenous levels (as explicitly shown in Supplementary Figure 2B) and the effect of many transcription factors is strongly dose-dependent, likely creating differences between the PRDM16-dependent transcriptional response in the in vitro system and in vivo.<br />

We acknowledge that our in vitro experiments may not ideally replicate the in vivo situation, a common limitation of such experiments, our primary aim was to explore the molecular relationship between PRDM16 and BMP signaling in gene regulation. Such molecular investigations are challenging to conduct using in vivo tissues. In vitro NSCs treated with BMP4 has been used a model to investigate NSC proliferation and quiescence, drawing on previous studies (e.g., Helena Mira, 2010; Marlen Knobloch, 2017). Crucially, to ensure the relevance of our in vitro findings to the in vivo context, we confirmed that cultured cells could indeed be induced into quiescence by BMP4, and this induction necessitated the presence of PRDM16. Furthermore, upon identifying target genes co-regulated by PRDM16 and SMADs, we validated PRDM16's regulatory role on a subset of these genes in the developing Choroid Plexus (ChP) (Fig. 7 and Suppl.Fig7-8). Only by combining evidence from both in vitro and in vivo experiments could we confidently conclude that PRDM16 serves as an essential co-factor for BMP signaling in restricting NSC proliferation.

(2) It seems that the authors compare Prdm16_KO cells to Prdm16 WT cells overexpressing flag_Prdm16. Aside from the possible expression of endogenous Prdm16, other cell differences may have arisen between these cell lines. A properly controlled experiment would compare Prdm16_KO ctrl (possibly infected with a control vector without Prdm16) to Prdm16_KO_E (i.e. the Prdm16_KO cells with and without Prdm16 overexpression.)

We agree that Prdm16 KO cells carrying the Prdm16-expressing vector would be a good comparison with those with KO_vector. However, despite more than 10 attempts with various optimization conditions, we were unable to establish a viable cell line after infecting Prdm16 KO cells with the Prdm16-expressing vector. The overall survival rate for primary NSCs after viral infection is low, and we observed that KO cells were particularly sensitive to infection treatment when the viral vector was large (the Prdm16 ORF is more than 3kb).

As an alternative oo assess vector effects, we instead included two other control cell lines, wt and KO cells infected with the 3xNLS_Flag-tag viral vector, and presented the results in supplementary Fig 2.  When we compared the responses of the four lines — wt, KO, wt infected with the Flag vector, KO infected with the Flag vector — to the addition and removal of BMP4, we confirmed that the viral infection itself has no significant impacts on the responses of these cells to these treatments regarding changes in cell proliferation and Ttr induction.

Given that wt cells and the KO cells, with or without viral backbone infection behave quite similarly in terms of cell proliferation, we speculate that even if we were successful in obtaining a cell line with Prdm16-expressing vector in the KO cells, it may not exhibit substantial differences compared to wt cells infected with Prdm16-expressing vector.

Other experimental weaknesses that make the evidence less convincing:

(1) The authors show in Figure 2E that Ttr is not upregulated by BMP4 in PRDM16_KO NSCs. Does this appear inconsistent with the presence of Ttr expression in the PRDM16_KO brain in Figure1C?<br />

The reviwer’s point is that there was no significant increase in Ttr expression in Prdm16_KO cells after BMP4 treatment (Fig. 2E), but there remained residule Ttr mRNA signals in the Prdm16 mutant ChP (Fig. 1C). We think the difference lies in the measuable level of Ttr expression between that induced by BMP4 in NSC culture and that in the ChP. This is based on our immunostaining expreriment in which we tried to detect Ttr using a Ttr antibody. This antibody could not detect the Ttr protein in BMP4-treated Prdm16_expressing NSCs but clearly showed Ttr signal in the wt ChP. This means that although Ttr expression can be significantly increased by BMP4 in vitro to a level measurable by RT-qPCR, its absolute quantity even in the Prdm16_expressing condition is much lower compared to that in vivo. Our results in Fig 1C and Fig 2E, as well as Fig 7B, all consistently showed that Prdm16 depletion significantly reduced Ttr expression in in vitro and in vivo.

(2) Figure 3: The authors use H3K4me3 to measure gene activity. This is however, very indirect, with bulk RNA-seq providing the most direct readout and polymerase binding (ChIP-seq) another more direct readout. Transcription can be regulated without expected changes in histone methylation, see e.g. papers from Josh Brickman. They verify their H3K4me3 predictions with qPCR for a select number of genes, all related to the kinetochore, but it is not clear why these genes were picked, and one could worry whether these are representative.

H3K4me3 has widely been used as an indicator of active transcription and is a mark for cell identity genes. And it has been demonstrated that H3K4me3 has a direct function in regulating transciption at the step of RNApolII pausing release. As stated in the text, there are advantages and disadvantages of using H3K4me3 compared to using RNA-seq. RNA-seq profiles all gene products, which are affected by transcription and RNA stability and turnover. In contrast, H3K4me3 levels at gene promoter reflects transcriptional activity. In our case, we aimed to identify differential gene expression between proliferation and quiescence states. The transition between these two states is fast and dynamic. RNA-seq may not be able to identify functionally relevant genes but more likely produces false positive and negative results. Therefore, we chose H3K4me3 profiling.

We agree that transcription may change without histone methylation changes. This may cause an under-estimation of the number of changed genes between the conditions. 

We validated 7 out of 31 genes (Wnt7b, Id3, Mybl2, Spc24, Spc25, Ndc80 and Nuf2). We chose these genes based on two critira: 1) their function is implicated in cell proliferation and cell-cycle regulation based on gene ontology analysis; 2) their gene products are detectable in the developing ChP based on the scRNA-seq data. Three of these genes (Wnt7b, Id3, Mybl2) are not related to the kinetochore. We now clarify this description in the revised text.

(3) Line 256: The overlap of 31 genes between 184 BMP-repressed genes and 240 PRDM16-repressed genes seems quite small.

This indicates that in addition to co-repressing cell-cycle genes, BMP and PRDM16 have independent fucntions. For example, it was reported that BMP regulates neuronal and astrocyte differentiation (Katada, S. 2021), while our previous work demonstrated that Prdm16 controls temporal identity of NSCs (He, L. 2021).

(4) The Wnt7b H3K4me3 track in Fig. 3G is not discussed in the text but it shows H3K4me3 high in _KO and low in _E regardless of BMP4. This seems to contradict the heatmap of H3K4me3 in Figure 3E which shows H3K4me3 high in _E no BMP4 and low in _E BMP4 while omitting _KO no BMP4. Meanwhile CDKN1A, the other gene shown in 3G, is missing from 3E.

The track in Fig 3G shows the absolute signal of H3K4me3 after mapping the sequencing reads to the genome and normaliz them to library size. Compare the signal in Prdm16_E with BMP4 and that in Prdm16_E without BMP4, the one with BMP4 has a lower peak. The same trend can be seen for the pair of Prdm16_KO cells with or without BMP4.  The heatmap in Fig. 3E shows the relative level of H3K4me3 in three conditions. The Prdm16_E cells with BMP4 has the lowest level, while the other two conditions (Prdm16_KO with BMP4 and Prdm16_E without BMP4) display a higher level. These two graphs show a consistent trend of H3K4me3 changes at the Wnt7b promoter across these conditions.

(5) The authors use PRDM16 CUT&TAG on dissected dorsal midline tissues to determine if their 31 identified PRDM16-BMP4 co-repressed genes are regulated directly by PRDM16 in vivo. By manual inspection, they find that "most" of these show a PRDM16 peak. How many is most? If using the same parameters for determining peaks, how many genes in an appropriately chosen negative control set of genes would show peaks? Can the authors rigorously establish the statistical significance of this observation? And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.

In our text, we indicated the genes containing PRDM16 binding peaks in the figures and described them as “Text in black in Fig. 6A and Supplementary Fig. 5A”. We will add the precise number “25 of these genes” in the main text to clarify it. To define a negative control set of genes, we will use BMP-only repressed 184-31 =153 genes (excluding PRDM16-BMP4 co-repressed), and of these 153 genes, we will determine how many have PRDM16 peaks in the E12.5 ChP data, say X. Then we will use binomial test to calculate p-value binom_test(25, 31, X/153, alternative=“greater).

We are confused with the second part of the comment “And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.” If the reviewer meant why we didn’t sequence the material from sequential-ChIP or validate more taget genes, the reason is the limitation of the material. Sequential ChIP requires a large quantity of the antibodies, and yields little material barely sufficient for a few qPCR after the second round of IP. This yielded amount was far below the minimum required for library construction. The PRDM16 antibody was a gift, and the quantity we have was very limited. We made a lot of efforts to optimize all available commercial antibodies in ChIP and Cut&Tag, but none of them worked.

(6) In comparing RNA in situ between WT and PRDM16 KO in Figure 7, the authors state they use the Wnt2b signal to identify the border between CH and neocortex. However, the Wnt2b signal is shown in grey and it is impossible for this reviewer to see clear Wnt2b expression or where the boundaries are in Figure 7A. The authors also do not show where they placed the boundaries in their analysis. Furthermore, Figure 7B only shows insets for one of the regions being compared making it difficult to see differences from the other region. Finally, the authors do not show an example of their spot segmentation to judge whether their spot counting is reliable. Overall, this makes it difficult to judge whether the quantification in Figure 7C can be trusted.

To address these questions, in the revised manuscript we will include an individal channel of Wnt2b and mark the boundaries. We will also provide full-view images and examples of spot segmentation in supplementary figures as space limitation in the main figures.

(7) The correlation between mKi67 and Axin2 in Figure 7 is interesting but does not convincingly show that Wnt downstream of PRDM16 and BMP is responsible for the increased proliferation in PRDM16 mutants.

We agree that this result (the correlation between mKi67 and Axin2) alone only suggests that Wnt signaling is related to the proliferation defect in the Prdm16 mutant, and does not necessarily mean that Wnt is downstream of PRDM16 and BMP. Our concolusion is backed up by two additional lines of evidences:  the Cut&Tag data in which PRDM16 binds to regulatory regions of Wnt7b and Wnt3a; BMP and PRDM16 co-repress Wnt7b in vitro.

An ideal result is that down-regulating Wnt signaling in Prdm16 mutant can rescue Prdm16 mutant phenotype. Such an experiment is technically challenging. Wnt plays diverse and essential roles in NSC regulation, and one would need to use a celltype-and stage-specific tool to down-regulate Wnt in the background of Prdm16 mutation. Moreover, Wnt genes are not the only targets regulated by PRDM16 in these cells, and downregulating Wnt may not be sufficient to rescue the phenotype. 

Weaknesses of the presentation:

Overall, the manuscript is not easy to read. This can cause confusion.

We will revise the text to improve the clarity.

Reviewer #2 (Public review):

Summary:

This article investigates the role of PRDM16 in regulating cell proliferation and differentiation during choroid plexus (ChP) development in mice. The study finds that PRDM16 acts as a corepressor in the BMP signaling pathway, which is crucial for ChP formation.

The key findings of the study are:

(1) PRDM16 promotes cell cycle exit in neural epithelial cells at the ChP primordium.

(2) PRDM16 and BMP signaling work together to induce neural stem cell (NSC) quiescence in vitro.

(3) BMP signaling and PRDM16 cooperatively repress proliferation genes.

(4) PRDM16 assists genomic binding of SMAD4 and pSMAD1/5/8.

(5) Genes co-regulated by SMADs and PRDM16 in NSCs are repressed in the developing ChP.

(6) PRDM16 represses Wnt7b and Wnt activity in the developing ChP.

(7) Levels of Wnt activity correlate with cell proliferation in the developing ChP and CH.

In summary, this study identifies PRDM16 as a key regulator of the balance between BMP and Wnt signaling during ChP development. PRDM16 facilitates the repressive function of BMP signaling on cell proliferation while simultaneously suppressing Wnt signaling. This interplay between signaling pathways and PRDM16 is essential for the proper specification and differentiation of ChP epithelial cells. This study provides new insights into the molecular mechanisms governing ChP development and may have implications for understanding the pathogenesis of ChP tumors and other related diseases.

Strengths:

(1) Combining in vitro and in vivo experiments to provide a comprehensive understanding of PRDM16 function in ChP development.

(2) Uses of a variety of techniques, including immunostaining, RNA in situ hybridization, RT-qPCR, CUT&Tag, ChIP-seq, and SCRINSHOT.

(3) Identifying a novel role for PRDM16 in regulating the balance between BMP and Wnt signaling.

(4) Providing a mechanistic explanation for how PRDM16 enhances the repressive function of BMP signaling. The identification of SMAD palindromic motifs as preferred binding sites for the SMAD/PRDM16 complex suggests a specific mechanism for PRDM16-mediated gene repression.

(5) Highlighting the potential clinical relevance of PRDM16 in the context of ChP tumors and other related diseases. By demonstrating the crucial role of PRDM16 in controlling ChP development, the study suggests that dysregulation of PRDM16 may contribute to the pathogenesis of these conditions.

Weaknesses:

(1) Limited investigation of the mechanism controlling PRDM16 protein stability and nuclear localization in vivo. The study observed that PRDM16 protein became nearly undetectable in NSCs cultured in vitro, despite high mRNA levels. While the authors speculate that post-translational modifications might regulate PRDM16 in NSCs similar to brown adipocytes, further investigation is needed to confirm this and understand the precise mechanism controlling PRDM16 protein levels in vivo.

While mechansims controlling PRDM16 protein stability and nuclear localization in the developing brain are interesting, the scope of this paper is revealing the function of PRDM16 in the choroid plexus and its interaction with BMP signaling. We will be happy to pursuit this direction in our next study.

(2) Reliance on overexpression of PRDM16 in NSC cultures. To study PRDM16 function in vitro, the authors used a lentiviral construct to constitutively express PRDM16 in NSCs. While this approach allowed them to overcome the issue of low PRDM16 protein levels in vitro, it is important to consider that overexpressing PRDM16 may not fully recapitulate its physiological role in regulating gene expression and cell behavior.

As stated above, we acknowledge that findings from cultured NSCs may not directly apply to ChP cells in vivo. We are cautious with our statements. The cell culture work was aimed to identify potential mechanisms by which PRDM16 and SMADs interact to regulate gene expression and target genes co-regulated by these factors. We expect that not all targets from cell culture are regulated by PRDM16 and SMADs in the ChP, so we validated expression changes of several target genes in the developing ChP and now included the new data in Fig. 7 and Supplementary Fig. 7. Out of the 31 genes identified from cultured cells, four cell cycle regulators including Wnt7b, Id3, Spc24/25/nuf2 and Mybl2, showed de-repression in Prdm16 mutant ChP. These genes can be relevant downstream genes in the ChP, and other target genes may be cortical NSC-specific or less dependent on Prdm16 in vivo.

(3) Lack of direct evidence for AP1 as the co-factor responsible for SMAD relocation in the absence of PRDM16. While the study identified the AP1 motif as enriched in SMAD binding sites in Prdm16 knockout cells, they only provided ChIP-qPCR validation for c-FOS binding at two specific loci (Wnt7b and Id3). Further investigation is needed to confirm the direct interaction between AP1 and SMAD proteins in the absence of PRDM16 and to rule out other potential co-factors.

We agree that the finding of the AP1 motif enriched at the PRDM16 and SMAD co-binding regions in Prdm16 KO cells can only indirectly suggest AP1 as a co-factor for SMAD relocation. That’s why we used ChIP-qPCR to examine the presence of C-fos at these sites. Although we only validated two targets, the result confirms that C-fos binds to the sites only in the Prdm16 KO cells but not Prdm16_expressing cells, suggesting AP1 is a co-factor.  We results cannot rule out the presence of other co-factors.

Reviewer #3 (Public review):

Summary:

Bone morphogenetic protein (BMP) signaling instructs multiple processes during development including cell proliferation and differentiation. The authors set out to understand the role of PRDM16 in these various functions of BMP signaling. They find that PRDM16 and BMP co-operate to repress stem cell proliferation by regulating the genomic distribution of BMP pathway transcription factors. They additionally show that PRDM16 impacts choroid plexus epithelial cell specification. The authors provide evidence for a regulatory circuit (constituting of BMP, PRDM16, and Wnt) that influences stem cell proliferation/differentiation.

Strengths:

I find the topics studied by the authors in this study of general interest to the field, the experiments well-controlled and the analysis in the paper sound.

Weaknesses:

I have no major scientific concerns. I have some minor recommendations that will help improve the paper (regarding the discussion).

We will revise the discussion according the suggestions.

"Wer sind meine Freunde?"<br /> ist halt doch eine illegale Frage...

https://github.com/milahu/alchi

Von den 5 Zeitungsartikeln<br /> zu diesem lächerlichen "Anti-Terror Einsatz"<br /> ist der hier der Beste.

"ein Gewaltpotenzial sei nie von ihm ausgegangen"<br /> richtig.<br /> aber meine schwarze jacke ist jetzt eine waffe?

"dingfest gemacht" klingt so schön.<br /> das letzte mal<br /> wo sich irgendwer von mir "bedroht gefühlt" hat,<br /> bin ich für 6 monate im psychiatrie-knast wasserburg verschwunden (sechs monate!!),<br /> dafür zahlt die krankenversicherung 500 euro pro tag,<br /> und davon soll ich dann auch noch 10% bezahlen.<br /> meinen laptop haben die ärzte mir weggenommen,<br /> weil das ist "schlecht für meine Gesundheit"<br /> genauso wie meine Omega 3 Kapseln,<br /> Multivitamin Tabletten, CBD Öl, ...<br /> nicht dass die arme Pharmaindustrie pleite geht.

wenn ich in 6 monaten wieder frei bin,<br /> dann werde ich genau da weiter machen,<br /> wo ich aufgehört habe:<br /> 300 bücher pro monat drucken,<br /> und überall verschenken,<br /> bis ich endlich weiß,<br /> ob meine hypothese wahr oder falsch ist.

Viel Spaß beim zensieren von meinem Kommentar,<br /> ich hab genug andere Publisher:<br /> darktea.onion<br /> righttoprivacy.onion<br /> hypothes.is<br /> ...<br /> aber es liest eeh keiner,<br /> die leute ersticken im spam.

Author response:

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

We thank Reviewers for highlighting the strengths of our work along with suggestions for future directions.

We agree with the Reviewers that RPS26 depletion may impact not only RAN translation initiation and codon selection (as showed in the experiments in Figure 4G), but also other mechanisms, such as speed of PIC scanning, as we stated in the discussion. Although, we did provide the data showing that mRNA of exogenous FMR1-GFP does not change upon RPS26 depletion (Figure 3B&C), hence observed effect most likely stems from translation regulation. In addition, an experiment with ASO-ACG treatment (Figure 4G) suggests that near cognate start codon selection or speed of PIC scanning may be a part of the regulation of RAN translation sensitive to RPS26 depletion. In addition, our latest unpublished results (Niewiadomska D. et al., in revision), indicate that FMRpolyG in fusion with GFP is fairly stable, in particular, while derived from long repeats (>90xCGG), suggesting that the protein stability is not at play in RPS26-dependent regulation.

We would like to stress that in order to avoid bias in result interpretation and to mimic the natural situation, the majority of experiments concerning levels of FMRpolyG were performed in cell models with stable expression of ACG-initiated FMRpolyG. Currently, we do not possess a cell model with stable expression of AUG-initiated FMRpolyG, and the experiments based on transient transfection system would not necessarily be comparable to the results obtained in stable expression system. However, we believe that the experiment presented in Figure 2B serves as a good control for overall translation level upon RPS26 depletion indicating that RPS26 insufficiency does not affect global translation and the observed regulation is specific to some mRNAs including the one encoding FMRpolyG frame. We also show that the level of ca. 80% of identified canonical proteins, including FMRP, did not change upon RPS26 silencing (SILAC-MS, Figure 4A). Indeed, we did not explore the ribosome composition upon RPS26 and TSR2 depletion, although, most likely the pool of functional ribosomes in the cell is sufficient enough to support the basal translation level (SUnSET assays, Figure 2B & 5C). However, we cannot exclude possibility that for some mRNAs, including one encoding for FMRpolyG, the observed effect can be partially caused by lowering the number of fully active ribosomes, especially in experiments with transient transfection experiments where transgene expression is hundreds times higher than for average native mRNA.

Finally, we agree with the Reviewer that in vitro translation assay would provide the evidence of direct effect of RPS26 on FMRpolyG level, however, we did not manage to overcome technical difficulties in obtaining cellular lysate devoid of RPS26 from vendor companies.

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

General Comments

We thank Reviewers for the critical comments and experimental suggestions. We considered most of the advices in the revised version of the manuscript, which allowed for a more balanced interpretation of the results presented, and further supported major statement of the manuscript that insufficiency of the RPS26 and RPS25 plays a role in modulating the efficiency of noncanonical RAN translation from FMR1 mRNA, which results in the production of toxic polyglycine protein (FMRpolyG). Firstly, performing new experiments, we showed that silencing of the RPS26 and its chaperone protein TSR2, which regulates loading/exchange of RPS26 in maturing small ribosome subunit, did not elicit global translation inhibition. Secondly, we demonstrated that in contrary to RPS26 and RPS25 depletion, silencing the RPS6 protein, a core component of 40S subunit, did not affect FMRpolyG production, further supporting the specific effect of RPS26 and RPS25 on RAN translation regulation of mutant FMR1 mRNA. We also observed that depletion of RPS26, RPS25 and RPS6 had significant negative effect on cells proliferation which is in line with previously published results indicating that insufficiencies of ribosomal proteins negatively affect cell growth. Moreover, we showed that FMRpolyG production is significantly affected by RPS26 depletion while initiated at ACG, but not other near cognate start codons. Importantly, translation of FMRP initiated at canonical AUG codon of the same mRNA upstream the CGGexp was not affected by RPS26 silencing, similarly to vast majority of the human proteome. This implies that RAN translation of FMR1 mRNA mediated by RPS26 insufficiency is likely to be dependent on start codon selection/fidelity. In essence, we provide a series of evidences indicating that cellular amount of 40S ribosomal proteins RPS26 and RPS25 is important factor of CGGrelated RAN translation regulation. Finally, we also decided to tone down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion, affects RAN translation, rather than composition of 40S ribosomal subunit per se influences RAN translation. We have addressed all specific concerns below and made changes to the new version of manuscript.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Tutak et al use a combination of pulldowns, analyzed by mass spectrometry, reporter assays, and fluorescence experiments to decipher the mechanism of protein translation in fragile X-related diseases. The topic is interesting and important.

Although a role for Rps26-deficient ribosomes in toxic protein translation is plausible based on already available data, the authors' data are not carefully controlled and thus do not support the conclusions of the paper.

We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process. We believe your guidance has been instrumental in significantly enhancing the quality of our research. Below, we have addressed your comments pointby-point.

Strengths:

The topic is interesting and important.

Weaknesses:

In particular, there is very little data to support the notion that Rps26-deficient ribosomes are even produced under the circumstances. And no data that indicate that they are involved in the RAN translation. Essential controls (for ribosome numbers) are lacking, no information is presented on the viability of the cells (Rps26 is an essential protein), and the differences in protein levels could well arise from block in protein synthesis, and cell division coupled to differential stability of the proteins.

We agree that data presented in the first version of the manuscript did not directly address the following processes: ribosome content, global translation rate and cell viability upon RPS26 depletion. Therefore we addressed some of the issues in the revised version of the manuscript. In particular, we showed that RPS26 and TSR2 knock down did not inhibit global translation (new Figure 2B & 4C), hence we concluded that the changes of FMRpolyG level did not arise from general translational shut down. On the other hand, RPS26, RPS25 and RPS6 depletion negatively affected cells proliferation (new Figure 2A,5D,6C), which is in line with a number of previously published researches (e.g. Cheng et al, 2019; Havkin-Solomon et al, 2023). However, the rate of proliferation abnormalities is limited. We agree that observed effects on RAN translation from mutant FMR1 mRNA may stem from the combination of altered protein synthesis, conditions of the cells but also cis-acting factors of mRNA sequence/structure. In new experiments we showed that single nucleotide substitution of ACG by other near cognate start codons change sensitivity of RAN translation to insufficiency of RPS26 (new Figure 4F). Also the inhibitory effect of antisense oligonucleotide binding to the region of 5’UTR containing ACG initiation codon (ASO_ACG) is different in cells differing in amount of RPS26 (new Figure 4G).

We also agree that our data only partially supports the role of RPS26-defficient ribosomes in RAN translation. Therefore, we have toned down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion affects RAN translation. We also changed the title of the manuscript to: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25, negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions” (Previously it was: “Ribosomal composition affects the noncanonical translation and toxicity of polyglycine-containing proteins in fragile X-associated conditions”.

Specific points:

(1) Analysis of the mass spec data in Supplemental Table S3 indicates that for many of the proteins that are differentially enriched in one sample, a single peptide is identified. So the difference is between 1 peptide and 0. I don't understand how one can do a statistical analysis on that, or how it would give out anything of significance. I certainly do not think it is significant. This is exacerbated by the fact that the contaminants in the assay (keratins) are many, many-fold more abundant, and so are proteins that are known to be mitochondrial or nuclear, and therefore likely not actual targets (e.g. MCCC1, PC, NPM1; this includes many proteins "of significance" in Table S1, including Rrp1B, NAF1, Top1, TCEPB, DHX16, etc...).

The data in Table S6/Figure 3A suffer from the same problem.

I am not convinced that the mass spec data is reliable.

We thank Reviewer for the comment concerning MS data; however, we believe that it may stem from misunderstanding of the data presented in Table S3 and S6. Both tables represent the output from MaxQuant analysis (so-called ProteinGroup) of MS .raw files, without any filtering. As stated in the Material&Methods, we applied default parameters suggested by MaxQuant developers to analyze MS data, these include identification of proteins based on at least 1 unique peptide, and thus some of the proteins with only 1 unique peptide are shown in Tables S1 and S3. Reviewer is also right that in this output table common contaminants, such as keratins are included. However, these identifications are denoted as “CON_”, and are further filtered out during statistical analysis in Perseus software. During the statistical analysis we first filtered out irrelevant protein groups identifications, such as contaminants, or only identified by site modifications.

We have changed the names of Supplementary Table files, giving more detailed description. We hope this will help to avoid misunderstanding for broader public. Secondly, when comparing the data presented in Table S3 and volcano plot presented in Figure 1B, one can notice that indeed the majority of identified proteins are not statistically significant (grey points), thus not selected for further stratification. Lack of significance of these proteins may be partially due to poor MS identification, however, they are not included in the following parts of the manuscript. Further, we selected only eight proteins (out of over 150) for stratification by orthogonal techniques, thus we argue that this step validates the biological relevance of chosen candidate RAN-translation modifiers. One should also keep in mind that pull down samples analyzed by MS often yield lower intensity and identification rates, when comparing to whole cell analysis, as a result of lower protein input or stringent washes used during sample preparation.

Regarding the data presented in Table S6 (SILAC data), we argue that these data are of very good quality. More than 2,000 proteins were identified in a 125min gradient, with over 80% of proteins that were identified with at least 2 unique peptides. Each of three biological replicates was analyzed three times (technical replicates), giving total of 9 high resolution MS runs. Together, we strongly believe that this data is of high confidence.

(2) The mass-spec data however claims to identify Rps26 as a factor binding the toxic RNA specifically. The rest of the paper seeks to develop a story of how Rps26-deficient ribosomes play a role in the translation of this RNA. I do not consider that this makes sense.

Indeed, we identified RPS26 as a protein that co-precipitated with FMR1 containing expanded CGG repeats (Supplementary Figure 1G) and found that depletion of RPS26 hindered RAN translation of FMRpolyG, suggesting that RPS26 positively affects RAN translation. However, we did not state that RPS26 directly interacts with toxic RNA. In order to confirm the specificity of RAN translation regulation by RPS26 insufficiency, we tested whether depletion of other 40S ribosomal protein, RPS6, affects FMRpolyG synthesis. Our experiments showed that there was no any significant effect on RAN translation efficiency post RPS6 silencing (new Figure 5C). Importantly, we showed that RPS26 depletion did not inhibit global translation (new Figure 2B). In addition, mutagenesis of near-cognate start codon (new Figure 4F) and ASO_ACG treatment (new Figure 4G) provided the evidences that modulation of FMRpolyG biosynthesis by RPS26 level may depend on start codon selection. In essence, our data suggest that RPS26 depletion specifically affects synthesis of FMRpolyG, but not FMRP derived from the same FMR1 mRNA with CGGexp. However, we do not claim that the observed effect is the consequence of a direct interaction between RPS26 and 5’UTR of FMR1 mRNA. Downregulation of FMRpolyG biosynthesis could be an outcome of the alteration of ribosomal assembly, decrease of efficiency and fidelity of PIC scanning/initiation or impeded elongation or a combination of all these processes. In the manuscript we presented the results of experiments which tested many of these possibilities.

(3) Rps26 is an essential gene, I am sure the same is true for DHX15. What happens to cell viability? Protein synthesis? The yeast experiments were carefully carried out under experiments where Rps26 was reduced, not fully depleted to give small growth defects.

We agree with the Reviewer that RPS26 and DHX15 are essential proteins, similarly to all RNA binding proteins, and caution should be taken during experimental design. To address this, we titrated different concentrations of siRPS26, and found that administration of 5 nM siRPS26, which just partially silenced RPS26, decreased FMRpolyG by around 50% (new Figure 1D). This impact was even greater with 15 nM siRPS26, as we observed around 80% decrease of FMRpolyG.

Havkin-Solomon et al. (2023), showed that proliferation rate is decreased in cells with mutated C-terminus of RPS26, which is required for contacting mRNA. In accordance with this study, we showed that cells with knocked down RPS26 proliferate less efficiently (new Figure 2A), but depletion of RPS26 did not impact the global translation (new Figure 2B). In addition, our SILAC-MS data indicates that ~80% of proteins with determined expression level were not affected by RPS26 insufficiency, and ~20% of the proteins turned out to be sensitive to RPS26 decrease. Although, these data do not take into account the protein stability.

(4) Knockdown efficiency for all tested genes must be shown to evaluate knockdown efficiency.

The current version of the manuscript contains representative western blots with validation of knock-down efficiency (for example in Figure 3B, C, E, Figure 6A) and we included knock-down validations where applicable (Figures 1D, 2B, 4G and 5C).

(5) The data in Figure 1E have just one mock control, but two cell types (control si and Rps26 depletion).

Mock control corresponds to the cells treated with lipofectamine reagent and was included in the study to determine the “background” signal from cells treated with delivery agent and reagents used to measure the apoptosis process. These cells were neither expressing FMRpolyG, nor siRNAs. Luminescence signals were normalized to the values obtained from mock control. We added more details describing this assay in the Figure 1 legend.

(6) The authors' data indicate that the effects are not specific to Rps26 but indeed also observed upon Rps25 knockdown. This suggests strongly that the effects are from reduced ribosome content or blocked protein synthesis. Additional controls should deplete a core RP to ascertain this conclusion.

We agree that observed effects may stem from reduced ribosome content, however, we argue that this is the only possibility and explanation. Previously, it was shown that RPS25 regulates G4C2-related RAN translation, but knock out of RPS25 does not affect global translation (Yamada S, 2019, Nat. Neuroscience). Similarly, we showed that KD of RPS26 or TSR2 did not reduce significantly global translation rate (SUnSET assay; new Figure 2B and 5C, respectively).

Moreover, in a new version of manuscript we included a control experiment, where we silenced core ribosomal protein (RPS6) and found that RPS6 depletion did not affect RAN translation from mutant FMR1 mRNA (new Figure 5C), thus strengthening our conclusion about specific RAN translation regulation by the level of RPS26 and RPS25.

Finally, our observation aligns well with current knowledge about how deficiency of different ribosomal proteins alters translation of some classes of mRNAs (Luan Y, 2022, Nucleic Acids Res; Cheng Z, 2019, Mol Cell). It was shown that depletion of RPS26 affects translation rate of different mRNAs compared to depletion of other proteins of small ribosomal subunit.

(7) Supplemental Figure S3 demonstrates that the depletion of S26 does not affect the selection of the start codon context. Any other claim must be deleted. All the 5'-UTR logos are essentially identical, indicating that "picking" happens by abundance (background).

Supplementary Figure 3D represents results indicating that the mutation in -4 position (from G to A) did not affect the RAN translation regardless of RPS26 presence or depletion. However, this result does not imply that RPS26 does not affect the selection of start codon of sequence- or RNA structure-context. We verified this particular -4 position, as it was suggested previously as important RPS26-sensitive site in yeasts (Ferretti M, 2017, Nat Struct Mol Biol). We agree with Reviewer that all 5’UTR logos presented in our paper did not show statistical significance for neither tested position for human mRNAs. On the contrary, we observed that regulation sensitive to RPS26 level depends on the selection of start codon of RAN translation, in particular ACG initiation (new Figure 4F&G). RPS26 depletion affected ACG-initiated but not GTG- or CTG-initiated RAN translation.

In the previous version of the manuscript, we wrote that we did not identify any specific motifs or enrichment within analyzed transcripts in comparison to the background. On the other hand, we found that the GC-content among analyzed transcripts is higher within 5’UTRs and in close proximity to ATG in coding sequences (Figure 4D), what suggests the importance of RNA stable structures in this region. In addition, we showed that mRNAs encoding proteins responding to RPS26 depletion have shorter than average 5’UTRs (new Figure 4E).

(8) Mechanism is lacking entirely. There are many ways in which ribosomes could have mRNA-specific effects. The authors tried to find an effect from the Kozak sequence, unsuccessfully (however, they also did not do the experiment correctly, as they failed to recognize that the Kozak sequence differs between yeast, where it is A-rich, and mammalian cells, where it is GGCGCC). Collisions could be another mechanism.

Indeed, collisions as well as other mechanisms such as skewed start codon fidelity may have an effect on efficiency of FMRpolyG biosynthesis. In the current version of the manuscript, we show that RPS26 amount-sensitive regulation seems to be start codonselection dependent (new Figure 4F&G).

Reviewer #2 (Public Review):

Summary:

Translation of CGG repeats leads to the accumulation of poly G, which is associated with neurological disorders. This is a valuable paper in which the authors sought out proteins that modulate RAN translation. They determined which proteins in Hela cells bound to CGG repeats and affected levels of polyG encoded in the 5'UTR of the FMR1 mRNA. They then showed that siRNA depletion of ribosomal protein RPS26 results in less production of FMR1polyG than in control. There are data supporting the claim that RPS26 depletion modulates RAN translation in this RNA, although for some results, the Western results are not strong. The data to support increased aggregation by polyG expression upon S26 KD are incomplete.

We thank the Reviewer for critical comments and suggestions. We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process.

Below each specific point, we addressed the mentioned issues.

Strengths:

The authors have proteomics data that show the enrichment of a set of proteins on FMR1 RNA but not a related RNA.

We thank Reviewer for appreciation of provided MS-screening results, which identified proteins enriched on FMR1 RNA with expanded CGG repeats.

Weaknesses:

- It is insinuated that RPS26 binds the RNA to enhance CGG-containing protein expression. However, RPS26 reduction was also shown previously to affect ribosome levels, and reduced ribosome levels can result in ribosomes translating very different RNA pools.

In previous version of the manuscript we did not state that RPS26 binds directly to RNA with expanded CGG repeats and we did not show the experiment indicating direct interaction between studied RNA and RPS26. What we showed is that RPS26 was enriched on FMR1 RNA MS samples, however, we did not verify whether it is direct or indirect interaction. We also tried to test hypothesis that lack of RPS26 in PIC complex may affect efficiency of RAN translation initiation via specific, previously described in yeast Kozak context (Ferretti M, 2017, Nat Struct Mol Biol). As we described this hypothesis was negatively validated. However, we showed that other features of 5’UTR sequences (e.g. higher GC-content or shorter leader sequence) are potentially important for translation efficiency in cells with depleted RPS26.

Indeed, RPS26 is involved in 40S maturation steps (Plassart L, 2021, eLife) and its insufficiency or mutations or blocking its inclusion to 40S ribosome may result in incomplete 40S maturation, which subsequently might negatively affect translation per se. However, we did not observe global translation inhibition after RPS26 depletion or depletion of TSR2, the chaperon involved in incorporation/exchange RPS26 to small ribosomal subunit (new Figure 2B and 5C). In addition, our SILAC-MS data indicates that majority of studied proteins (including FMRP, the main product of FMR1 gene) were not affected by RPS26 depletion which can be carefully extrapolated to global translation. In revised manuscript we also showed that relatively low silencing of RPS26 also decreased FMRpolyG production in model cells (new Figure 1D).

We agree that reduced ribosome levels can result in different efficiency of translation of different RNA pools. We enhance this statement in revised manuscript. However, we also showed that the same mRNA containing different near cognate start codons (single/two nucleotide substitution) specific to RAN translation, or targeting this codon with antisense oligonucleotides resulted in altered sensitivity of FMR1 mRNA translation to RPS26 depletion (new Figure 4F).

- A significant claim is that RPS26 KD alleviates the effects of FMRpolyG expression, but those data aren't presented well.

We thank the Reviewer for this comment. In the new version of the manuscript, we have added new microscopic images and improved the explanation of Figure 1E. We have also completed the interpretation of Figure 1F in the main text, figure image as well as figure legend, and we hope that these changes will ameliorate understanding of our data.

Recommendations For The Authors:

- A significant claim is that RPS26 KD alleviates the effects of FMR polyG expression, but those data aren't presented well:

Figure 1D (supporting data in S2) and 2D - the authors need to show representative images of a control that has aggregation and indicate aggregates being counted on an image. The legend states that there are no aggregates, but the quantification of aggregates/nucleus is ~1, suggesting there are at least 1 per cell. It is preferred to show at least a representative of what is quantified in the main figure instead of a bar graph.

The representative images of control and siRPS26-treated cells are now shown in revised version of Figure 1E. Additionally, we completed the Figure legend concerning this part, as well as extended description of the experiment in Materials&Methods section.

Figure 1E - it is unclear what luminescence signal is being measured. Is this a dye for an apoptotic marker? More information is needed in the legend.

This information was added to the legend of modified Figure 1F (previously 1E) as suggested.

- Some of the Western blots are not very convincing. Better evidence for the changes in bar graphs would improve how convincing the data are:

Fig 2B. The western for FMR95G in the first model is not very convincing. The difference by eye for the second siRNA seems to give a larger effect than the first for 95G construct but they appear almost the same on the graph. More supporting information for the quantification is needed.

We provided better explanation for WB quantification in M&M section in the manuscript. Alos, we provided additional blot demonstrating independent biological replicate of the mentioned experiment in supplementary materials (Supplementary Figure S2E).

Figure 4A, the blots for RPS26 and FMR95G are not convincing. They are quite smeary compared to all of the others shown for these proteins in other figures. Could a different replicate be shown?

We provided additional blot demonstrating the effect on transiently expressed FMRpolyG affected by depletion of TSR2 in COS7 cell line (Supplementary Figure S4A).

Figure 5A and 5B blots are not ideal. Could a different replicate be shown? Or show multiple replicates in the supplemental figure?

We provided additional blots from the same experiment, although data is not statistically significant, most likely due to low quality of normalization factor, which is Vinculin (Supplementary Figure S5A). Nevertheless, the level of FMRpolyG is decreased by ~70% after RPS25 silencing in SH-SY5Y cells.

Figure 2C. Please use the same y axes for all four Westerns in B and C. One would like to compare 95 and 15 repeats, but it is difficult when the y axes are different.

Thank you for this comment. The y axis was adjusted as suggested by the Reviewer.

Figure 3D-The text suggests a significant difference between positive and negative responders that is not clear in the figure.

In the main body of the manuscript we state that: “We did not observe any significant differences in the frequency of individual nucleotide positions in the 20-nucleotide vicinity of the start codon relative to the expected distribution in the BG”, which is in line with the graph showed in Figure 4D (previously 3D).

Reviewer #3 (Public Review):

Tutak et al provide interesting data showing that RPS26 and relevant proteins such as TSR2 and RPS25 affect RAN translation from CGG repeat RNA in fragile X-associated conditions. They identified RPS26 as a potential regulator of RAN translation by RNAtagging system and mass spectrometry-based screening for proteins binding to CGG repeat RNA and confirmed its regulatory effects on RAN translation by siRNA-based knockdown experiments in multiple cellular disease models and patient-derived fibroblasts. Quantitative mass spectrometry analysis found that the expressions of some ribosomal proteins are sensitive to RPS26 depletion while approximately 80% of proteins including FMRP were not influenced. Since the roles of ribosomal proteins in RAN translation regulation have not been fully examined, this study provides novel insights into this research field. However, some data presented in this manuscript are limited and preliminary, and their conclusions are not fully supported.

(1) While the authors emphasized the importance of ribosomal composition for RAN translation regulation in the title and the article body, the association between RAN translation and ribosomal composition is apparently not evaluated in this work. They found that specific ribosomal proteins (RPS26 and RPS25) can have regulatory effects on RAN translation (Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B), and that the expression levels of some ribosomal proteins can be changed by RPS26 knockdown (Figure 3B, however, the change of the ribosome compositions involved in the actual translation has not been elucidated). Therefore, their conclusive statement, that is, "ribosome composition affects RAN translation" is not fully supported by the presented data and is misleading.

We thank the Reviewer for critical comments and suggestions. We agree that the initial title and some statements in the text were misleading and the presented data did not fully support the aforementioned statement regarding ribosomal composition affecting FMRpolyG synthesis. Therefore, in the revised version of the manuscript we included a control experiment indicating that depletion of another core 40S ribosomal protein (RPS6) did not impact the FMRpolyG synthesis (new Figure 5C), which supports our hypothesis that RPS26 and RPS25 are specific CGG-related RAN translation modifiers. To precisely deliver a main message of our work, we changed the title that will indicate the specific effect of RPS26 and RPS25 insufficiency on RAN translation of FMRpolyG. Proposed title: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25 negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions”. We also changed all statements regarding “ribosomal composition” in main text of the new version of manuscript.

(2) The study provides insufficient data on the mechanisms of how RPS26 regulates RAN translation. Although authors speculate that RPS26 may affect initiation codon fidelity and regulate RAN translation in a CGG repeat sequence-independent manner (Page 9 and Page 11), what they really have shown is just identification of this protein by the screening for proteins binding to CGG repeat RNA (Figure 1A, 1B), and effects of this protein on CGG repeat-RAN translation. It is essential to clarify whether the regulatory effect of RPS26 on RAN translation is dependent on CGG repeat sequence or near-cognate initiation codons like ACG and GUG in the 5' upstream sequence of the repeat. It would be better to validate the effects of RPS26 on translation from control constructs, such as one composed of the 5' upstream sequence of FMR1 with no CGG repeat, and one with an ATG substitution in the 5' upstream sequence of FMR1 instead of near-cognate initiation codons.

We agree that the data presented in the manuscript implies that insufficiency of RPS26 plays a pivotal role in the regulation of CGG-related RAN translation and in the revised version of the manuscript we included a series of experiments indicating that ACG codon selection seems to be an important part of RPS26 level-dependent regulation of polyglycine production (new Figure 4F&G; see point 3 below for more details). Importantly, in the luciferase assay showed on Figure 4F we used the AUG-initiated firefly luciferase reporter as normalization control.

Moreover, to verify if FMRpolyG response to RPS26 deficiency depends on the type of reporter used, we repeated many experiments using FMRpolyG fused with different tags. The luciferase-based assays were in line with experiments conducted on constructs with GFP tag (new Figure 1D), thus strengthening our previous data. Moreover, in the series of experiments, we show that FMRP synthesis which is initiated from ATG codon located in FMR1 exon 1, was not affected by RPS26 depletion (Figure 3E & 4C), even though its translation occurs on the same mRNA as FMRpolyG. This indicates a specific RPS26 regulation of polyglycine frame initiated from ACG near cognate codon.

(3) The regulatory effects of RPS26 and other molecules on RAN translation have all been investigated as effects on the expression levels of FMRpolyG-GFP proteins in cellular models expressing CGG repeat sequences Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B). In these cellular experiments, there are multiple confounding factors affecting the expression levels of FMRpolyG-GFP proteins other than RAN translation, including template RNA expression, template RNA distribution, and FMRpolyG-GFP protein degradation. Although authors evaluated the effect on the expression levels of template CGG repeat RNA, it would be better to confirm the effect of these regulators on RAN translation by other experiments such as in vitro translation assay that can directly evaluate RAN translation.

We agree that there are multiple factors affecting final levels of FMRpolyG-GFP proteins including aforementioned processes. We evaluated the level of FMR1 mRNA, which turned out not to be decreased upon RPS26 depletion (Figure 3B&C), therefore, we assumed that what we observed, was the regulation on translation level, especially that RPS26 is a ribosomal protein contacting mRNA in E-site. We believe that direct assays such as in vitro translation may be beneficial, however, depletion of RPS26 from cellular lysate provided by the vendor seems technically challenging, if not completely impossible. Instead, we focused on sequence/structure specific regulation of RAN translation with the emphasis on start-codon initiation selection. It resulted in generating the valuable results pointing out the RPS26 role in start codon fidelity (Figure 4F&G). These new results showed that translation from mRNAs differing just in single or two nucleotide substitution in near cognate start codon (ACG to GUG or ACG to CUG), although results in exactly the same protein, is differently sensitive to RPS26 silencing (new Figure 4F). Similar differences were observed for translation efficiency from the same mRNA targeted or not with antisense oligonucleotide complementary to the region of RAN translation initiation codon (new Figure 4G). These results also suggest that stability of FMRpolyG is not affected in cells with decreased level of RPS26.

(4) While the authors state that RPS26 modulated the FMRpolyG-mediated toxicity, they presented limited data on apoptotic markers, not cellular viability (Figure 1E), not fully supporting this conclusion. Since previous work showed that FMRpolyG protein reduces cellular viability (Hoem G, 2019,Front Genet), additional evaluations for cellular viability would strengthen this conclusion.

We thank the Reviewer for this suggestion. We addressed the apoptotic process in order to determine the effect of RPS26 depletion on RAN translation related toxicity (Figure 1F). In revised version of the manuscript, we also added the evaluation on how cells proliferation was affected by RPS26, RPS25, RPS6 and TSR2 depletion. Our data indicate that TSR2 silencing slightly impacted the cellular fitness (new Figure 5D), whereas insufficiencies of RPS26, RPS25 and RPS6 had a much stronger negative effect on proliferation (new Figure 2A, 5D, 6C), which is in line with previous data (Cheng Z 2019, Mol Cell; Luan Y, 2022, Nucleic Acids Res). The difference in proliferation rate after treatment with siRPS26 makes proper interpretation of cellular viability assessment very difficult.

Recommendations For The Authors:

(1) It would be nice to validate the effects of overexpression of RPS26 and other regulators on RAN translation, not limited to knockdown experiments, to support the conclusion.

We did not performed such experiments because we believed that RPS26 overexpression may have no or marginal effect on translation or RAN translation. It is likely impossible to efficiently incorporate overexpressed RPS26 into 40S subunits, because the concentration of all ribosomal proteins in the cells is very high.

(2) It would be better to explain how authors selected 8 proteins for siRNA-based validation (Figure 1C, 1D, S1D) from 32 proteins enriched in CGG repeat RNA in the first screening.

We selected those candidates based on their functions connected to translation, structured RNA unwinding or mRNA processing. For example, we tested few RNA helicases because of their known function in RAN translation regulation described by other researchers. This explanation was added to the revised version of the manuscript.

(3) Original image data showing nuclear FMRpolyG-GFP aggregates should be presented in Figure 1D.

The representative images of control and siRPS26-treated cells are now shown in modified version of Figure 1E and described with more details in the legend.

(4) Image data in Figure 2A and 2D have poor signal/noise ratio and the resolution should be improved. In addition, aggregates should be clearly indicated in Figure 2D in an appropriate manner.

The stable S-FMR95xG cellular model is characterized by very low expression of RANtranslated FMR95xG, therefore, it is challenging to obtain microscopic images of better quality with higher GFP signal. In the L-99xCGG model expression of transgene is higher. Therefore, we provided new image in the new version of Figure 3D (former 2D). Moreover, we showed aggregates on the image obtained using confocal microscopy (new Supplementary Figure 2D).

(5) The detailed information on patient-derived fibroblast (age and sex of the patient, the number of CGG repeats, etc.) in Figure 2F needed to be presented.

This information was added to the figure legend (Figure 3F; previously 2F) and in the Material and Methods section as suggested.

(6) It would be better to normalize RNA expression levels of FMR1 and FMR1-GFP by the housekeeping gene in Figure S2C, like other RT-qPCR experimental data such as Figure 2B.

Normalization of FMR1-GFP to GAPDH is now shown in modified version of Figure S2C (right graph) as requested by the Reviewer.

(7) It would be better to add information on molecular weight on all Western blotting data.

(8) Marks corresponding to molecular weight ladder were added to all images.

Full blots, including protein ladders were deposited in Zenodo repository, under doi: 10.5281/zenodo.13860370

References

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Havkin-Solomon T, Fraticelli D, Bahat A, Hayat D, Reuven N, Shaul Y & Dikstein R (2023) Translation regulation of specific mRNAs by RPS26 C-terminal RNA-binding tail integrates energy metabolism and AMPK-mTOR signaling. Nucleic Acids Res 51: 4415–4428

Hoem,G., Larsen,K.B., Øvervatn,A., Brech,A., Lamark,T., Sjøttem,E. and Johansen,T. (2019) The FMRpolyGlycine protein mediates aggregate formation and toxicity independent of the CGG mRNA hairpin in a cellular model for FXTAS. Front. Genet., 10, 1–18.

Luan Y, Tang N, Yang J, Liu S, Cheng C, Wang Y, Chen C, Guo YN, Wang H, Zhao W, et al (2022) Deficiency of ribosomal proteins reshapes the transcriptional and translational landscape in human cells. Nucleic Acids Res 50: 6601–6617

Plassart L, Shayan R, Montellese C, Rinaldi D, Larburu N, Pichereaux C, Froment C, Lebaron S, O’donohue MF, Kutay U, et al (2021) The final step of 40s ribosomal subunit maturation is controlled by a dual key lock. Elife 10

Author response:

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

Reviewer #1 (Public Review):

Summary:

his study shows a new mechanism of GS regulation in the archaean Methanosarcina mazei and clarifies the direct activation of GS activity by 2-oxoglutarate, thus featuring another way in which 2-oxoglutarate acts as a central status reporter of C/N sensing.

Mass photometry and single particle cryoEM structure analysis convincingly show the direct regulation of GS activity by 2-OG promoted formation of the dodecameric structure of GS. The previously recognized small proteins GlnK1 and Sp26 seem to play a subordinate role in GS regulation, which is in good agreement with previous data. Although these data are quite clear now, there remains one major open question: how does 2-OG further increase GS activity once the full dodecameric state is achieved (at 5 mM)? This point needs to be reconsidered.

Weaknesses:

It is not entirely clear, how very high 2-OG concentrations activate GS beyond dodecamer formation.

The data presented in this work are in stark contrast to the previously reported structure of M. mazei GS by the Schumacher lab. This is very confusing for the scientific community and requires clarification. The discussion should consider possible reasons for the contradictory results.

Importantly, it is puzzling how Schumacher could achieve an apo-structire of dodecameric GS? If 2-OG is necessary for dodecameric formation, this should be discussed. If GlnK1 doesn't form a complex with the dodecameric GS, how could such a complex be resolved there?

In addition, the text is in principle clear but could be improved by professional editing. Most obviously there is insufficient comma placement.

We thank Reviewer #1 for the professional evaluation and raising important points. We will address those comments in the updated manuscript and especially improve the discussion in respect to the two points of concern.

(1) How can GlnA1 activity further be stimulated with further increasing 2-OG after the dodecamer is already fully assembled at 5 mM 2-OG.

We assume a two-step requirement for 2-OG, the dodecameric assembly and the priming of the active sites. The assembly step is based on cooperative effects of 2-OG and does not require the presence of 2-OG in all 2-OG-binding pockets: 2-OG-binding to one binding pocket also causes a domino effect of conformational changes in the adjacent 2-OG-unbound subunit, as also described for Methanothermococcus thermolithotrophicus GS in Müller et al. 2023. Due to the introduction of these conformational changes, the dodecameric form becomes more favourable even without all 2-OG binding sites being occupied. With higher 2-OG concentrations present (> 5mM), the activity increased further until finally all 2-OG-binding pockets were occupied, resulting in the priming of all active sites (all subunits) and thereby reaching the maximal activity.

(2) The contradictory results with previously published data on the structure of M. mazei by Schumacher et al. 2023.

We certainly agree that it is confusing that Schumacher et al. 2023 obtained a dodecameric structure without the addition of 2-OG, which we claim to be essential for the dodecameric form. 2-OG is a cellular metabolite that is naturally present in E. coli, the heterologous expression host both groups used. Since our main question focused on analysing the 2-OG effect on GS, we have performed thorough dialysis of the purified protein to remove all 2-OG before performing MP experiments. In the absence of 2-OG we never observed significant enzyme activity and always detected a fast disassembly after incubation on ice. We thus assume that a dodecamer without 2-OG in Schumacher et al. 2023 is an inactive oligomer of a once 2-OG-bound form, stabilized e.g. by the presence of 5 mM MgCl2.

The GlnA1-GlnK1-structure (crystallography) by Schumacher et al. 2023 is in stark contrast to our findings that GlnK1 and GlnA1 do not interact as shown by mass photometry with purified proteins. A possible reason for this discrepancy might be that at the high protein concentrations used in the crystallization assay, complexes are formed based on hydrophobic or ionic protein interactions, which would not form under physiological concentrations.

Reviewer #2 (Public Review):

Summary:

Herdering et al. introduced research on an archaeal glutamine synthetase (GS) from Methanosarcina mazei, which exhibits sensitivity to the environmental presence of 2-oxoglutarate (2-OG). While previous studies have indicated 2-OG's ability to enhance GS activity, the precise underlying mechanism remains unclear. Initially, the authors utilized biophysical characterization, primarily employing a nanomolar-scale detection method called mass photometry, to explore the molecular assembly of Methanosarcina mazei GS (M. mazei GS) in the absence or presence of 2-OG. Similar to other GS enzymes, the target M. mazei GS forms a stable dodecamer, with two hexameric rings stacked in tail-to-tail interactions. Despite approximately 40% of M. mazei GS existing as monomeric or dimeric entities in the detectable solution, the majority spontaneously assemble into a dodecameric state. Upon mixing 2-OG with M. mazei GS, the population of the dodecameric form increases proportionally with the concentration of 2-OG, indicating that 2-OG either promotes or stabilizes the assembly process. The cryo-electron microscopy (cryo-EM) structure reveals that 2-OG is positioned near the interface of two hexameric rings. At a resolution of 2.39 Å, the cryo-EM map vividly illustrates 2-OG forming hydrogen bonds with two individual GS subunits as well as with solvent water molecules. Moreover, local side-chain reorientation and conformational changes of loops in response to 2-OG further delineate the 2-OG-stabilized assembly of M. mazei GS.

Strengths & Weaknesses:

The investigation studies the impact of 2-oxoglutarate (2-OG) on the assembly of Methanosarcina mazei glutamine synthetase (M mazei GS). Utilizing cutting-edge mass photometry, the authors scrutinized the population dynamics of GS assembly in response to varying concentrations of 2-OG. Notably, the findings demonstrate a promising and straightforward correlation, revealing that dodecamer formation can be stimulated by 2-OG concentrations of up to 10 mM, although GS assembly never reaches 100% dodecamerization in this study. Furthermore, catalytic activities showed a remarkable enhancement, escalating from 0.0 U/mg to 7.8 U/mg with increasing concentrations of 2-OG, peaking at 12.5 mM. However, an intriguing gap arises between the incomplete dodecameric formation observed at 10 mM 2-OG, as revealed by mass photometry, and the continued increase in activity from 5 mM to 10 mM 2-OG for M mazei GS. This prompts questions regarding the inability of M mazei GS to achieve complete dodecamer formation and the underlying factors that further enhance GS activity within this concentration range of 2-OG.

Moreover, the cryo-electron microscopy (cryo-EM) analysis provides additional support for the biophysical and biochemical characterization, elucidating the precise localization of 2-OG at the interface of two GS subunits within two hexameric rings. The observed correlation between GS assembly facilitated by 2-OG and its catalytic activity is substantiated by structural reorientations at the GS-GS interface, confirming the previously reported phenomenon of "funnel activation" in GS. However, the authors did not present the cryo-EM structure of M. mazei GS in complex with ATP and glutamate in the presence of 2-OG, which could have shed light on the differences in glutamine biosynthesis between previously reported GS enzymes and the 2-OG-bound M. mazei GS.

Furthermore, besides revealing the cryo-EM structure of 2-OG-bound GS, the study also observed the filamentous form of GS, suggesting that filament formation may be a universal stacking mechanism across archaeal and bacterial species. However, efforts to enhance resolution to investigate whether the stacked polymer is induced by 2-OG or other factors such as ions or metabolites were not undertaken by the authors, leaving room for further exploration into the mechanisms underlying filament formation in GS.

We thank Reviewer #2 for the detailed assessment and valuable input. We will address those comments in the updated manuscript and clarify the message.

(1) The discrepancy of the dodecamer formation (max. at 5 mM 2-OG) and the enzyme activity (max. at 12.5 mM 2-OG). We assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site. See also Reviewer #1 R.1). We assume this is the reason why the activity of dodecameric GlnA1 can be further enhanced by increased 2-OG concentration until all catalytic sites are primed.

(2) The lack of the structure of a 2-OG and ATP-bound GlnA1. Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

(3) The observed GlnA1-filaments are an interesting finding. We certainly agree with the referee on that point, that the stacked polymers are potentially induced by 2-OG or ions. However, it is out of the main focus of this manuscript to further explore those filaments. Nevertheless, this observation could serve as an interesting starting point for future experiments.

Reviewer #3 (Public Review):

Summary:

The current manuscript investigates the effect of 2-oxoglutarate and the Glk1 protein as modulators of the enzymatic reactivity of glutamine synthetase. To do this, the authors rely on mass photometry, specific activity measurements, and single-particle cryo-EM data.

From the results obtained, the authors convey that glutamine synthetase from Methanosarcina mazei exists in a non-active monomeric/dimeric form under low concentrations of 2-oxoglutarate, and its oligomerization into a dodecameric complex is triggered by higher concentration of 2-oxoglutarate, also resulting in the enhancement of the enzyme activity.

Strengths:

Glutamine synthetase is a crucial enzyme in all domains of life. The dodecameric fold of GS is recurrent amongst prokaryotic and archaea organisms, while the enzyme activity can be regulated in distinct ways. This is a very interesting work combining protein biochemistry with structural biology.

The role of 2-OG is here highlighted as a crucial effector for enzyme oligomerization and full reactivity.

Weaknesses:

Various opportunities to enhance the current state-of-the-art were missed. In particular, omissions of the ligand-bound state of GnK1 leave unexplained the lack of its interaction with GS (in contradiction with previous results from the authors). A finer dissection of the effect and role of 2-oxoglurate are missing and important questions remain unanswered (e.g. are dimers relevant during early stages of the interaction or why previous GS dodecameric structures do not show 2-oxoglutarate).

We thank Reviewer #3 for the expert evaluation and inspiring criticism.

(1) Encouragement to examine ligand-bound states of GlnK1. We agree and plan to perform the suggested experiments exploring the conditions under which GlnA1 and GlnK1 might interact. We will perform the MP experiments in the presence of ATP. In GlnA1 activity test assays when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

(2) The exact role of 2-OG could have been dissected much better. We agree on that point and will improve the clarity of the manuscript. See also Reviewer #1 R.1.

(3) The lack of studies on dimers. This is actually an interesting point, which we did not consider during writing the manuscript. Now, re-analysing all our MP data in this respect, GlnA1 is likely a dimer as smallest species. Consequently, we will add more supplementary data which supports this observation and change the text accordingly.

(4) Previous studies and structures did not show the 2-OG. We assume that for other structures, no additional 2-OG was added, and the groups did not specifically analyse for this metabolite either. All methanoarchaea perform methanogenesis and contain the oxidative part of the TCA cycle exclusively for the generation of glutamate (anabolism) but not a closed TCA cycle enabling them to use internal 2-OG concentration as internal signal for nitrogen availability. In the case of bacterial GS from organisms with a closed TCA cycle used for energy metabolism (oxidation of acetyl CoA) like e.g. E. coli, the formation of an active dodecameric GS form underlies another mechanism independent of 2-OG. In case of the recent M. mazei GS structures published by Schumacher et al. 2023, the dodecameric structure is probably a result from the heterologous expression and purification from E. coli. (See also Reviewer #1 R.2). One example of methanoarchaeal glutamine synthetases that do in fact contain the 2-OG in the structure, is Müller et al. 2023.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Specific issues:

L 141: 2-OG levels increase due to slowing GOGAT reaction (due to Gln limitation as a consequence of N-starvation).... (2-OG also increases in bacteria that lack GDH...)

As the GS-GOGAT cycle is the major route of ammonium assimilation, consumption of 2-OG by GDH is probably only relevant under high ammonium concentrations.

In Methanoarchaea, GS is strictly regulated and expression strongly repressed under nitrogen sufficiency - thus glutamate for anabolism is mainly generated by GDH under N sufficiency consuming 2-OG delivered by the oxidative part of the TCA cycle (Methanogenesis is the energy metabolism in methanoarchaea, a closed TCA cycle is not present) thus 2-OG is increasing under nitrogen limitation, when no NH3 is available for GDH.

L148: it is not clear what is meant by: "and due to the indirect GS activity assay"

We apologize for not being clear here. The GS activity assay used is the classical assay by Sahpiro & Stadtman 1970 and is a coupled optical test assay (coupling the ATP consumption of the GS activity to the oxidation of NADH by lactate dehydrogenase). Based on the coupled test assay the measurements of low activities show a high deviation. We now added this information in the revised MS respectively.

L: 177: arguing about 2-OG affinities: more precisely, the 0.75 mM 2-OG is the EC50 concentration of 2-OG for triggering dodecameric formation; it might not directly reflect the total 2-OG affinity, since the affinity may be modulated by (anti)cooperative effects, or by additional sites... as there may be different 2-OG binding sites involved... (same in line 201)

Thank you for the valuable input. We changed KD to EC50 within the entire manuscript. Concerning possible additional 2-OG binding sites: we did not see any other 2-OG in the cryo-EM structure aside from the described one and we therefore assume that the one described in the manuscript is the main and only one. Considering the high amounts of 2-OG (12.5 mM) used in the structure, it is quite unlikely that additional 2-OG sites exist since they would have unphysiologically low affinities.

In this respect, instead of the rather poor assay shown in Figure 1D, a more detailed determination of catalytic activation by different 2-OG concentrations should be done (similar to 1A)... This would allow a direct comparison between dodecamerization and enzymatic activation.

We agree and performed the respective experiments, which are now presented in revised Fig. 1D

Discussion: the role of 2-OG as a direct activator, comparison with other prokaryotic GS: in other cases, 2-OG affects GS indirectly by being sensed by PII proteins or other 2-OG sensing mechanisms (like 2OG-NtcA-mediated repression of IF factors in cyanobacteria)

We agree and have added that information in the discussion as suggested.

290. Unclear: As a second step of activation, the allosteric binding of 2-OG causes a series of conformational.... where is this site located? According to the catalytic effects (compare 1A and 1D) this site should have a lower affinity …

Thank you very much for pointing this out. Binding of 2-OG only occurs in one specific allosteric binding-site. Binding however, has two effects on the GlnA1: dodecamer assembly and priming of the active site (with two specific EC50, which are now shown in Fig. 1A and D).

See also public comment #1 (1).

Reviewer #2 (Recommendations For The Authors):

The primary concern for me is that mass photometry might lead to incorrect conclusions. The differences in the forms of GS seen in SEC and MP suggest that GS can indeed form a stable dodecamer when the concentration of GS is high enough, as shown in Figure S1B. I strongly suggest using an additional biophysical method to explore the connection between GS and 2-OG in terms of both assembly and activity, to truly understand 2-OG's role in the process of assembly and catalysis.

We apologize if we did not present this clear enough, however the MP analysis of GlnA1 in the absence of 2-OG showed always (monomers/) dimers, dodecamers were only present in the presence of 2-OG. The SEC analysis in Fig. S1B has been performed in the presence of 12.5 mM 2-OG, we realized this information is missing in the figure legend - we now added this in the revised version. The 2-OG is in addition visible in the Cryo EM structure. Thus, we do not agree to perform additional biophysical methods.

As for the other experimental findings, they appear satisfactory to me, and I have no reservations regarding the cryoEM data.

(1) Mass photometry is a fancy technique that uses only a tiny amount of protein to study how they come together. However, the concentration of the protein used in the experiment might be lower than what's needed for them to stick together properly. So, the authors saw a lot of single proteins or pairs instead of bigger groups. They showed in Figure S1B that the M. mazei GS came out earlier than a 440-kDa reference protein, indicating it's actually a dodecamer. But when they looked at the dodecamer fraction using mass photometry, they found smaller bits, suggesting the GS was breaking apart because the concentration used was too low. To fix this, they could try using a technique called analytic ultracentrifuge (AUC) with different amounts of 2-OG to see if they can spot single proteins or pairs when they use a bit more GS. They could also try another technique called SEC-MALS to do similar tests. If they do this, they could replace Figure 1A with new data showing fully formed GS dodecamers when they use the right amount of 2-OG.

Thank you for this input. In MP we looked at dodecamer formation after removing the 2-OG entirely and re-adding it in the respective concentration. We think that GlnA1 is much more unstable in its monomeric/dimeric fraction and that the complete and harsh removal of 2-OG results in some dysfunctional protein which does not recover the dodecameric conformation after dialysis and re-addition of 2-OG. Looking at the dodecamer-peak right after SEC however, we exclusively see dodecamers, which is now included as an additional supplementary figure (suppl. Fig. 1C). Consequently, we did not perform additional experiments.

(2) Building on the last point, the estimated binding strength (Kd) between 2-OG and GS might be lower than it really is, because the GS often breaks apart from its dodecameric form in this experiment, even though 2-OG helps keep the pairs together, as seen with cryoEM. What if they used 5-10 times more GS in the mass photometry experiment? Would the estimated bond strength stay the same? Could they use AUC or other techniques like ITC to find out the real, not just estimated, strength of the bond?

We agree that the term KD is not suitable. We have changed the term KD to EC50 as suggested by reviewer #1, which describes the effective concentration required for 50 % dodecamer assembly. Furthermore, we disagree that the dodecamer breaks apart when the concentrations are as low as in MP experiments. The actual reason for the breaking is rather the harsh dialysis to remove all 2-OG before MP experiments. Right after SEC, the we exclusively see dodecamer in MP (suppl. Fig. S1C). See also #2 (1).

(3) The fact that the GS hardly works without 2-OG is interesting. I tried to understand the experiment setup, but it wasn't clear as the protocol mentioned in the author's 2021 FEBS paper referred to an old paper from 1970. The "coupled optical test assay" they talked about wasn't explained well. I found other papers that used phosphometry assays to see how much ATP was used up. I suggest the authors give a better, more detailed explanation of their experiments in the methods section. Also, it's unclear why the GS activity keeps going up from 5 to 12.5 mM 2-OG, even though they said it's saturated. They suggested there might be another change happening from 5 to 12.5 mM 2-OG. If that's the case, they should try to get a cryo-EM picture of the GS with lots of 2-OG, both with and without ATP/glutamate (or the Met-Sox-P-ADP inhibitor), to see what's happening at a structural level during this change caused by 2-OG.

We agree with the reviewer that the GS assay was not explained in detail (since published and known for several years). However, we now added the more detailed description of the assay in the revised MS, which also measures the ATP used up by GS, but couples the generation of ADP to an optical test assay producing pyruvate from PEP with the generated ADP catalysed by pyruvate kinase present in the assay. This generated pyruvate is finally reduced to lactate by the present lactate dehydrogenase consuming NADH, the reduction of which is monitored at 340 nm.

The still increasing activity of GS after dodecamer formation (max. at 5 mM 2-OG) and the continuously increasing enzyme activity (max. at 12.5 mM 2-OG): See also public reviews, we assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site.

The suggested additional experiments with and without ATP/Glutamate: Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

(4) Please remake Figure S2, the panels are too small to read the words. At least I have difficulty doing so.

We assume the reviewer is pointing to Suppl. Fig S3, we now changed this figure accordingly.

Line 153, the reference Schumacher et al. 23, should be 2023?

Yes, thank you. We corrected that.

Line 497. I believe it's UCSF ChimeraX, not Chimera.

We apologize and corrected accordingly.

Reviewer #3 (Recommendations For The Authors):

Recent studies on the Methanothermococcus thermolithotrophicus glutamine synthetase, published by Müller et al., 2024, have identified the binding site for 2-oxoglutarate as well as the conformational changes that were induced in the protein by its presence. In the present study, the authors confirm these observations and additionally establish a link between the presence of 2-oxoglutarate and the dodecameric fold and full activation of GS.

Curiously, here, the authors could not confirm their own findings that the dodecameric GS can directly interact with the PII-like GlnK1 protein and the small peptide sP26. However, the lack of mention of the GlnK-bound state in these studies is very alarming since it certainly is highly relevant here.

We agree with the reviewer that we have not observed the interaction with GlnK1 and sP26 in the recent study. Consequently, we speculate that yet unknown cellular factor(s) might be required for an interaction of GlnA1 with GlnK1 and sP26, which were not present in the in vitro experiments using purified proteins, however they were present in the previous pull-down approaches (Ehlers et al. 2005, Gutt et al. 2021). Another reason might be that post-translational modifications occur in M. mazei, which might be important for the interaction, which are also not present in purified proteins expressed in E. coli.

The manuscript interest could have been substantially increased if the authors had done finer biochemical and enzymatic analyses on the oligomerization process of GS, used GlnK1 bound to known effectors in their assays and would have done some more efforts to extrapolate their findings (even if a small niche) of related glutamine synthetases.

We thank the reviewer for their valuable encouragement to explore ligand-bound-states of GlnK1. However, in this manuscript we mainly focused on 2-OG as activator of GlnA1 and decided to dedicate future experiments to the exploration of conditions that possibly favor GlnK1-binding.

In principle, we have explored the ATP bound GlnK1 effects on GlnA1 activity in the activity assays (Fig. 2E) since ATP (3.6 mM) is present. GlnK1 however showed no effects on GlnA1 activity.

In general, the manuscript is poorly written, with grammatically incorrect sentences that at times, which stands in the way of passing on the message of the manuscript.

Particular points:

(1) It is mentioned that 2-OG induces the active oligomeric (dodecamer, 12-mer) state of GlnA1 without detectable intermediates. However, only 62 % of the starting inactive enzyme yields active 12-mers. Note that this is contradicted in line 212.

Thanks for pointing out this discrepancy. After removing all 2-OG as we did before MP-experiments, GlnA1 doesn’t reach full dodecamers anymore when 2-OG is re-added. This is not because the 2-OG amount is not enough to trigger full assembly, but because the protein is much more unstable in the absence of 2-OG, so we predict that some GlnA1 breaks during dialysis. See also answer reviewer #2 (1) and supplementary figure S1C.

Is there any protein precipitation upon the addition of 2-OG? Is all protein being detected in the assay, meaning, is monomer/dimer + dodecamer yields close to 100% of the total enzyme in the assay?

There is no protein precipitation upon the addition of 2-OG, indeed, GlnA1 is much more stable in the presence of 2-OG. In the mass photometry experiments, all particles are measured, precipitated protein would be visible as big entities in the MP.

Please add to Figure 1 the amount of monomer/dimer during titration. Some debate why there is no full conversion should be tentatively provided.

We agree with the reviewer and included the amount of monomer/dimer in the figure, as well as some discussion on why it is not fully converted again. GlnA1 is unstable without 2-OG and it was dialysed against buffer without 2-OG before MP measurements. This sample mistreatment resulted in no full re-assembly after re-adding 2-OG (although full dodecamers before dialysis (suppl. Fig. S1C).

(2) Figure 1B reflects an exemplary result. Here, the addition of 0.1 mM 2-OG seems to promote monomer to dimer transition. Why was this not studied in further detail? It seems highly relevant to know from which species the dodecamer is assembled.

We thank the reviewer for their comment. However, we would like to point out that, although not shown in the figure, GlnA1 is always mainly present as dimers as the smallest entity. As suggested earlier, we have added the amount of monomers/dimers to Figure 1A, which shows low monomer-counts at all 2-OG concentrations (Fig.1A). Although not depicted in the graph starting at 0.01 mM OG, we also see mainly dimers at 0 mM 2-OG.

How does the y-axis compare to the number and percentage of counts assigned to the peaks? In line 713, it is written that the percentage of dodecamer considers the total number of counts, and this was plotted against the 2-OG concentration.

We thank the reviewer for addressing this unclarity. Line 713 corresponds to Figure 1A, where we indeed plotted the percentage of dodecamer against the 2-OG-concentration. Thereby, the percentage of dodecamer corresponds to the percentage calculated from the Gaussian Fit of the MP-dodecamer-peak. In Figure 1 B, however, the y-axis displays the relative amount of counts per mass, multiple similar masses then add up to the percentage of the respective peak (Gaussian Fit above similar masses).

(3) Lines 714 and 721 (and elsewhere): Why only partial data is used for statistical purposes?

We in general only show one exemplary biological replicate, since the quality of the respective GlnA1 purification sometimes varied (maximum activity ranging from 5 - 10 U/mg). Therefore, we only compared activities within the same protein purification. For the EC50 calculations of all measurements, we refer to the supplement.

(4) Lines 192-193: It is claimed that GlnK1 was previously shown to both regulate the activity of GlnA1 and form a complex with GlnA1. Please mention the ratio between GlnK1 and GlnA1 in this complex.

We now included the requested information (GlnA1:GlnK1 1:1, (Ehlers et al. 2005); His6-GlnA1 (0.95 μM), His6-GlnK1 (0.65 μM); 2:1,4, Gutt et al. 2021).

It is also known that PII proteins such as GlnK1 can bind ADP, ATP, and 2-OG. Interestingly, however, for various described PII proteins, 2-OG can only bind after the binding of ATP.

So, the crucial question here is what is the binding state of GlnK1? 

Were these assays performed in the absence of ATP? This is key to fully understand and connect the results to the previous observations. For example, if the GlnK1 used was bound to ADP but not to ATP, then the added 2-OG might indeed only be able to affect GlnA1 (leading to its activation/oligomerization). If this were true and according to the data reported, ADP would prevent GlnK1 from interacting with any oligomeric form of GlnA1. However, if GlnK1 bound to ATP is the form that interacts with GlnA1 (potentially validating previous results?) then, 2-OG would first bind to GlnK1 (assuming a higher affinity of 2-OG to GlnK1), eventually causing its release from GlnA1 followed by binding and activation of GlnA1.

These experiments need to be done as they are essential to further understand the process. Given the ability of the authors to produce the protein and run such assays, it is unclear why they were not done here. As written in line 203, in this case, "under the conditions tested" is not a good enough statement, considering what is known in the field and how many more conclusions could easily be taken from such a setup.

Thanks for the encouragement to investigate the ligand-bound states of GlnK1. We agree and plan to perform the suggested mass photometry experiments exploring the conditions under which GlnA1 and GlnK1 might interact in future work. In GlnA1 activity test assays, when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

(5) Figure 2D legend claims that the graphic shows the percentage of dodecameric GlnA1 as a function of the concentration of 2-OG. This is not what the figure shows; Figure 2D shows the dodecamer/dimer (although legend claims monomer was used, in line 732) ratio as a function of 2-OG (stated in line 736!). If this is true, a ratio of 1 means 50 % of dodecamers and dimers co-exist. This appears to be the case when GlnK1 was added, while in the absence of GlnK1 higher ratios are shown for higher 2-OG concentration implying that about 3 times more dodecamers were formed than dimers. However, wouldn´t a 50 % ratio be physiologically significant?

We apologize for the partially incorrect and also misleading figure legend and corrected it. Indeed, the ratio of dodecamers and dimers is shown. Furthermore, we did not use monomeric GlnA1 (the smallest entity is mainly a dimer, see Fig 1A), however, the molarity was calculated based on the monomer-mass. Concerning the significance of the difference between the maximum ratio of GlnA1 and GlnK1: The ratio does appear higher, but this is mostly because adding large quantities of GlnK1 broadens all peaks at low molecular weight. This happens because the GlnK1 signal starts overlapping with the signal from GlnA1, leading to inflated GlnA1 dimer counts. We therefore do not think that this is biologically significant, especially as the activities do not differ under these conditions.

(6) Is it possible that the uncleaved GlnA1 tag is preventing interaction with GlnK1? This should be discussed.

This is of course a very important point. We however realized that Schumacher et al. also used an N-terminal His-tag, so we assume that the N-terminal tag is not hampering the interaction.

(7) Line 228: Please detail the reported discrepancies in rmsd between the current protein and the gram-negative enzymes.

The differences in rmsd between our M.mazei GlnA1 structure and the structure of gram-negative enzymes is caused by a) sequence similarity: E.g. M.mazei GlnA1 compared to B.subtilis GlnA have a sequence percent identity of 58.47; b) ligands in the structure: The B.Subtilis structure contains L-Methionine-S-sulfoximine phosphate, a transition state inhibitor, while the M. mazei  structure contains 2OG; c) Methodology: The structural determination methods also contribute to these differences. B. subtilis GlnA was determined using X-ray crystallography, while the M. mazei GlnA1 structure was resolved using Cryo-EM, where the protein behaves differently in ice compared to a crystal.

(8) Line 747: The figure title claims "dimeric interface" although the manuscript body only refers to "hexameric interface" or "inter-hexamer interface" (line 224). Moreover, the figure 4 legend uses terms such as vertical and horizontal dimers and this too should be uniformized within the manuscript.

Thank you for your valuable feedback. We have updated both the figure title and the figure legend as well in the main text to ensure consistency in the description.

(9) Line 752: The description of the color scheme used here is somehow unclear.

Thanks for pointing this out. We changed the description to make it more comprehensive.

(10) Please label H14/15 and H14´/H15´in Fig 4C zoom.

We agree that this has not been very clear. We added helix labels.

(11) In Figure 4D legend, make sure to note that the binding sites for the substrate are based on homologies with another enzyme poised with these molecules.

The same should be clear in the text: sites are not known, they are assumed to be, based on homologies (paragraph starting at line 239).

Concerning this comment we want to point out that we studied the exact same enzyme as the Schumacher group, except that we used 2-OG in our experiments, which they did not.

(12) Figure 3 appears redundant in light of Figure 4. 

(13) Line 235: When mentioning F24, please refer to Figure 5.

Thank you, we changed that accordingly.

(14) Please provide the distances for the bonds depicted in Figure 4B.

Thanks for pointing this out, we added distance labels to Figure 4B. For reasons of clarity only to three H-bonds.

(15) Line 241: D57 is likely serving to abstract a proton from ammonium, what is residue Glu307 potentially doing? The information seems missing in light of how the sentence is built.

Thanks for pointing this out. According to previous studies both residues are likely involved in proton abstraction - first from ammonium, and then from the formed gamma-ammonium group. Additionally, they contribute in shielding the active site from bulk solvent to prevent hydrolysis of the formed phospho-glutamate.

(16) Why do the authors assume that increased concentrations of 2-OG are a signal for N starvation only in M. mazei and not in all prokaryotic equivalent systems (line 288)?

In line 288, we did not claim that this is a unique signal for M. mazei. It is also the central N-starvation signal in Cyanobacteria but not directly perceived by the cyanobacterial GS through binding directly to GS.

The authors should look into the residues that bind 2-OG and check if they are conserved in other GS. The results of this sequence analysis should be discussed in line with the variable prokaryotic glutamine synthetase types of activity modulation that were exposed in the introduction and Figure 7.

Please refer to supplementary figure S5, where we already aligned the mentioned glutamine synthetase sequences. Since this was also already discussed in Müller et al. 2024, we did not want to repeat their observations and refer to our supplementary figure in too much detail.

(17) Figure 5 title: Replace TS by transition state structures of homology enzymes, or alike.

Thank you for this suggestion. We did not change the title however, since it is not a homologue but the exact same glutamine synthetase from Methanosarcina mazei.

(18) Line 249: D170 is not shown in Figure 5A or elsewhere in Figure 5.

Thank you for pointing this out. We added D170 to figure 5A.

(19) Representative density for the residues binding 2-OG should be provided, maybe in a supplemental figure.

Thank you for the suggestion. We added the densities of 2-OG-binding residues to figure 4B

(20) Line 260: Please add a reference when describing the phosphoryl transfer.

We thank the reviewer for this important point and added that accordingly.

(21) Line 296: The binding of 2-OG indeed appears to be cooperative, such that at concentrations above its binding affinity to the protein, only dodecamers are seen (under experimental conditions). However, claiming that the oligomerization is fast is not correct when the experimental setup includes 10 minutes of incubation before measurements are done. Please correct this within the entire manuscript.

A (fast) continuous kinetic assay could have confirmed this point and revealed the oligomerization steps and the intermediaries in the process (maybe monomer/dimers, then dimers/hexamers, and then hexamers/dodecamers). Such assays would have been highly valuable to this study.

We thank the reviewer for this suggestion, but disagree. It is indeed a rather fast regulation (as activity assays without pre-incubation only takes 1 min longer to reach full activity, see the newly included suppl. Fig S6). Considering other regulation mechanisms like e.g. transcription or translation regulation, an activation that takes only 60 s is actually quite quick.

(22) Line 305 (and elsewhere in the manuscript): the authors state that 2-OG primes the active site for a transition state. This appears incorrect. The transition state is the highest energy state in an enzymatic reaction progressing from substrate to product. Meaning, the transition state is a state that has a more or less modified form of the original substrate bound to the active site. This is not the case.

In line 366 an "active open state" appears much more adequate to use. 

We agree and changed accordingly throughout the manuscript.

(23) Line 330: Please delete "found". Eventually replace it with "confirmed": As the authors write, others have described this residue as a ligand to glutamine.

Thanks, we changed that accordingly, although previous descriptions were just based on homologies without the experimental validation.

(24) The discussion in at various points summarizing again the results. It should be trimmed and improved.

(25) Line 381: replace "two fast" with "fast"?

We thank the reviewer for this suggestion, but disagree on this point. We especially wanted to highlight that there are two central nitrogen-metabolites involved in the direct regulation of GlnA1, that means TWO fast direct processes mediated by 2-OG and glutamine.

Reviewer #2 (Public review):

In this study, Kavaklıoğlu et al. investigated and presented evidence for a role for domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation dependent manner, due to DNMT1 deletion in HAP1 cell line. The authors then identified L1TD1 associated RNAs using RIP-Seq, which display a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found L1TD1 protein associated with L1-RNPs and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expression, and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish feasibility of this relationship existing in vivo in either development or disease, or both.

Comments on revised version:

Thank you for this revised manuscript and for addressing our concerns and suggestions. These improvements have significantly enhanced the quality and reliability of the results presented and have addressed all our questions.

Reviewer #3 (Public review):

Summary:

In the manuscript by Lapao et al., the authors uncover a role for the RAB27A effector protein SYTL5 in regulating mitochondrial function and turnover. The authors find that SYTL5 localizes to mitochondria in a RAB27A-dependent way and that loss of SYTL5 (or RAB27A) impairs lysosomal turnover of an inner mitochondrial membrane mitophagy reporter but not a matrix-based one. As the authors see no co-localization of GFP/mScarlet tagged versions of SYTL5 or RAB27A with LC3 or p62, they propose that lysosomal turnover is independent of the conventional autophagy machinery. Finally, the authors go on to show that loss of SYTL5 impacts mitochondrial respiration and ECAR and as such may influence the Warburg effect and tumorigenesis. Of relevance here, the authors go on to show that SYTL5 expression is reduced in adrenocortical carcinomas and this correlates with reduced survival rates.

Strengths:

There are clearly interesting and new findings here that will be relevant to those following mitochondrial function, the endocytic pathway, and cancer metabolism.

Weaknesses:

The data feel somewhat preliminary in that the conclusions rely on exogenously expressed proteins and reporters, which do not always align.

As the authors note there are no commercially available antibodies that recognize endogenous SYTL5, hence they have had to stably express GFP-tagged versions. However, it appears that the level of expression dictates co-localization from the examples the authors give (though it is hard to tell as there is a lack of any kind of quantitation for all the fluorescent figures). Therefore, the authors may wish to generate an antibody themselves or tag the endogenous protein using CRISPR.

In relation to quantitation, the authors found that SYTL5 localizes to multiple compartments or potentially a few compartments that are positive for multiple markers. Some quantitation here would be very useful as it might inform on function.

The authors find that upon hypoxia/hypoxia-like conditions that punctate structures of SYTL5 and RAB27A form that are positive for Mitotracker, and that a very specific mitophagy assay based on pSu9-Halo system is impaired by siRNA of SYTL5/RAB27A, but another, distinct mitophagy assay (Matrix EGFP-mCherry) shows no change. I think this work would strongly benefit from some measurements with endogenous mitochondrial proteins, both via immunofluorescence and western blot-based flux assays.

A really interesting aspect is the apparent independence of this mitophagy pathway on the conventional autophagy machinery. However, this is only based on a lack of co-localization between p62 or LC3 with LAMP1 and GFP/mScarlet tagged SYTL5/RAB27A. However, I would not expect them to greatly colocalize in lysosomes as both the p62 and LC3 will become rapidly degraded, while the eGFP and mScarlet tags are relatively resistant to lysosomal hydrolysis. -/+ a lysosome inhibitor might help here and ideally, the functional mitophagy assays should be repeated in autophagy KOs.

The link to tumorigenesis and cancer survival is very interesting but it is not clear if this is due to the mitochondrially-related aspects of SYTL5 and RAB27A. For example, increased ECAR is seen in the SYTL5 KO cells but not in the RAB27A KO cells (Fig.5D), implying that mitochondrial localization of SYTL5 is not required for the ECAR effect. More work to strengthen the link between the two sections in the paper would help with future directions and impact with respect to future cancer treatment avenues to explore.

Escher girls of the turn of the century.

https://en.wikipedia.org/wiki/Knocking_on_wood

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Reply to the reviewers

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

Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

In the study, performed experiments are well described.

My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

Major comments:

1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms. Response: Thank you very much for your insightful comments. 1) To address "what happens to endogenous wild-type full-length P53 in the context of mutant/truncated isoforms," we employed a human A549 cell line expressing endogenous wild-type p53 under DNA damage conditions such as an etoposide treatment1. We choose the A549 cell line since similar to H1299, it is a lung cancer cell line (www.atcc.org). For comparison, we also transfected the cells with 2 μg of V5-tagged plasmids encoding FLp53 and its isoforms Δ133p53 and Δ160p53. As shown in Figure R1A, lanes 1 and 2, endogenous p53 expression, remained undetectable in A549 cells despite etoposide treatment, which limits our ability to assess the effects of the isoforms on the endogenous wild-type FLp53. We could, however, detect the V5-tagged FLp53 expressed from the plasmid using anti-V5 (rabbit) as well as with anti-DO-1 (mouse) antibody (Figure R1). The latter detects both endogenous wild-type p53 and the V5-tagged FLp53 since the antibody epitope is within the N-terminus (aa 20-25). This result supports the reviewer's comment regarding the low level of expression of endogenous p53 that is insufficient for detection in our experiments. (Figure R1 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

In summary, in line with the reviewer's comment that 'under normal physiological conditions p53 expression is usually low,' we could not detect p53 with an anti-DO-1 antibody. Thus, we proceeded with V5/FLAG-tagged p53 for detection of the effects of the isoforms on p53 stability and function. We also found that protein expression in H1299 cells was more easily detectable than in A549 cells (Compare Figures R1A and B). Thus, we decided to continue with the H1299 cells (p53-null), which would serve as a more suitable model system for this study.

2) We agree with the reviewer that 'It is hard to differentiate if aggregation of full-length p53 happens only in overexpression scenario'. However, it is not impossible to imagine that such aggregation of FLp53 happens under conditions when p53 and its isoforms are over-expressed in the cell. Although the exact physiological context is not known and beyond the scope of the current work, our results indicate that at higher expression, p53 isoforms drive aggregation of FLp53. Given the challenges of detecting endogenous FLp53, we had to rely on the results obtained with plasmid mediated expression of p53 and its isoforms in p53-null cells.

Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.

Response: Thank you very much for your valuable comments and suggestions. To investigate the potential functional impairment of endogenous wild-type p53 by p53 isoforms, we initially utilized A549 cells (p53 wild-type), aiming to monitor endogenous wild-type p53 expression following DNA damage. However, as mentioned and demonstrated in Figure R1, endogenous p53 expression was too low to be detected under these conditions, making the ChIP assay for analyzing endogenous p53 activity unfeasible. Thus, we decided to utilize plasmid-based expression of FLp53 and focus on the potential functional impairment induced by the isoforms.

3. On similar lines, authors described:

"To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)."

Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.

Response: Thank you for raising this point regarding the physiological relevance of the ratios used in our study. 1) In the revised manuscript (lines 193-195), we added in this direction that "The elevated Δ133p53 protein modulates p53 target genes such as miR‑34a and p21, facilitating cancer development2, 3. To mimic conditions where isoforms are upregulated relative to FLp53, we increased the ratios to 1:5 and 1:10." This approach aims to simulate scenarios where isoforms accumulate at higher levels than FLp53, which may be relevant in specific contexts, as also elaborated above.

2) Regarding the issue of protein expression, where one allele is wild-type and the other is isoform, this assumption is not valid in most contexts. First, human cells have two copies of TPp53 gene (one from each parent). Second, the TP53 gene has two distinct promoters: the proximal promoter (P1) primarily regulates FLp53 and ∆40p53, whereas the second promoter (P2) regulates ∆133p53 and ∆160p534, 5. Additionally, ∆133TP53 is a p53 target gene6, 7 and the expression of Δ133p53 and FLp53 is dynamic in response to various stimuli. Third, the expression of p53 isoforms is regulated at multiple levels, including transcriptional, post-transcriptional, translational, and post-translational processing8. Moreover, different degradation mechanisms modify the protein level of p53 isoforms and FLp538. These differential regulation mechanisms are regulated by various stimuli, and therefore, the 1:1 ratio of FLp53 to ∆133p53 or ∆160p53 may be valid only under certain physiological conditions. In line with this, varied expression levels of FLp53 and its isoforms, including ∆133p53 and ∆160p53, have been reported in several studies3, 4, 9, 10.

3) In our study, using the pcDNA 3.1 vector under the human cytomegalovirus (CMV) promoter, we observed moderately higher expression levels of ∆133p53 and ∆160p53 relative to FLp53 (Figure R1B). This overexpression scenario provides a model for studying conditions where isoform accumulation might surpass physiological levels, impacting FLp53 function. By employing elevated ratios of these isoforms to FLp53, we aim to investigate the potential effects of isoform accumulation on FLp53.

4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

Response: Thank you for your insightful comments. In the experiment with A549 cells (p53 wild-type), endogenous p53 levels were too low to be detected, even after DNA damage induction. The evaluation of the function of endogenous p53 in the presence of isoforms is hindered, as mentioned above. In the revised manuscript, we utilized H1299 cells with overexpressed proteins for apoptosis studies using the Caspase-Glo® 3/7 assay (Figure 7). This has been shown in the Results section (lines 254-269). "The Δ133p53 and Δ160p53 proteins block pro-apoptotic function of FLp53.

One of the physiological read-outs of FLp53 is its ability to induce apoptotic cell death11. To investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on FLp53-induced apoptosis, we measured caspase-3 and -7 activities in H1299 cells expressing different p53 isoforms (Figure 7). Caspase activation is a key biochemical event in apoptosis, with the activation of effector caspases (caspase-3 and -7) ultimately leading to apoptosis12. The caspase-3 and -7 activities induced by FLp53 expression was approximately 2.5 times higher than that of the control vector (Figure 7). Co-expression of FLp53 and the isoforms Δ133p53 or Δ160p53 at a ratio of 1: 5 significantly diminished the apoptotic activity of FLp53 (Figure 7). This result aligns well with our reporter gene assay, which demonstrated that elevated expression of Δ133p53 and Δ160p53 impaired the expression of apoptosis-inducing genes BAX and PUMA (Figure 4G and H). Moreover, a reduction in the apoptotic activity of FLp53 was observed irrespective of whether Δ133p53 or Δ160p53 protein was expressed with or without a FLAG tag (Figure 7). This result, therefore, also suggests that the FLAG tag does not affect the apoptotic activity or other physiological functions of FLp53 and its isoforms. Overall, the overexpression of p53 isoforms Δ133p53 and Δ160p53 significantly attenuates FLp53-induced apoptosis, independent of the protein tagging with the FLAG antibody epitope."

**Referees cross-commenting**

I think the comments from the other reviewers are very much reasonable and logical.

Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

Response: Thank you for these comments. The endogenous p53 protein was undetectable in A549 cells induced by etoposide (Figure R1A). Therefore, we conducted experiments using FLAG/V5-tagged FLp53. To avoid any potential side effects of the FLAG tag on p53 aggregation, we introduced untagged p53 isoforms in the H1299 cells and performed subcellular fractionation. Our revised results, consistent with previous FLAG-tagged p53 isoforms findings, demonstrate that co-expression of untagged isoforms with FLAG-tagged FLp53 significantly induced the aggregation of FLAG-FLp53, while no aggregation was observed when FLAG-tagged FLp53 was expressed alone (Supplementary Figure 6). These results clearly indicate that the FLAG tag itself does not contribute to protein aggregation.

Additionally, we utilized the A11 antibody to detect protein aggregation, providing additional validation (Figure R3). Given that the fluorescent proteins (~30 kDa) are substantially bigger than the tags used here (~1 kDa) and may influence oligomerization (especially GFP), stability, localization, and function of p53 and its isoforms, we avoided conducting these vital experiments with such artificial large fusions.

Reviewer #1 (Significance (Required)):

The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

Response: Thank you for your insightful comments. We appreciate your recognition of the significance of our work in providing mechanistic insights into how wild-type FLp53 can be inactivated by truncated isoforms. We agree that these findings have potential for exploring new strategies to restore p53 function as a therapeutic approach against cancer.

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

The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:

1. • Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.* Response: Thank you very much for the valuable comments. We have added the following paragraph in the revised manuscript (lines 74-82): "Protein aggregation has become a central focus of modern biology research and has documented implications in various diseases, including cancer13, 14, 15. Protein aggregates can be of different types ranging from amorphous aggregates to highly structured amyloid or fibrillar aggregates, each with different physiological implications. In the case of p53, whether protein aggregation, and in particular, co-aggregation with large N-terminal deletion isoforms, plays a mechanistic role in its inactivation is yet underexplored. Interestingly, the Δ133p53β isoform has been shown to aggregate in several human cancer cell lines16. Additionally, the Δ40p53α isoform exhibits a high aggregation tendency in endometrial cancer cells17. Although no direct evidence exists for Δ160p53 yet, these findings imply that p53 isoform aggregation may play a major role in their mechanisms of actions."

2. Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.

Response: Thank you for your insightful suggestion. We addressed protein aggregation using the A11 antibody which specifically recognizes amyloid-like protein aggregates. We analyzed insoluble nuclear pellet samples prepared under identical conditions as described in Figure 6B. To confirm the presence of p53 proteins, we employed the anti-p53 M19 antibody (Santa Cruz, Cat No. sc-1312) to detect bands corresponding to FLp53 and its isoforms Δ133p53 and Δ160p53. The monomer FLp53 was not detected (Figure R3, lower panel), which may be attributed to the lower binding affinity of the anti-p53 M19 antibody to it. These samples were also immunoprecipitated using the A11 antibody (Thermo Fischer Scientific, Cat No. AHB0052) to detect aggregated proteins. Interestingly, FLp53 and its isoforms, Δ133p53 and Δ160p53, were clearly visible with Anti-A11 antibody when co-expressed at a 1:5 ratio suggesting that they underwent co-aggregation__.__ However, no FLp53 aggregates were observed when it was expressed alone (Figure R2). These results support the conclusion in our manuscript that Δ133p53 and Δ160p53 drive FLp53 aggregation.

(Figure R2 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

3. Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).

Response: We appreciate the suggestion to use live-cell fluorescence microscopy with EGFP and mCherry tags for the visualization FLp53 and its isoforms. While we understand the advantages of live-cell imaging with EGFP / mCherry tags, we restrained us from doing such fusions as the GFP or corresponding protein tags are very big (~30 kDa) with respect to the p53 isoform variants (~30 kDa). Other studies have shown that EGFP and mCherry fusions can alter protein oligomerization, solubility and aggregation18, 19. Moreover, most fluorescence proteins are prone to dimerization (i.e. EGFP) or form obligate tetramers (DsRed)20, 21, 22, potentially interfering with the oligomerization and aggregation properties of p53 isoforms, particularly Δ133p53 and Δ160p53.

Instead, we utilized FLAG- or V5-tag-based immunofluorescence microscopy, a well-established and widely accepted method for visualizing p53 proteins. This method provided precise localization and reliable quantitative data, which we believe meet the needs of the current study. We believe our chosen method is both appropriate and sufficient for addressing the research question.

Reviewer #2 (Significance (Required)):

The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

Response: We sincerely thank the reviewer for the thoughtful and positive comments on our manuscript and for highlighting the significance of our findings on the p53 isoforms, Δ133p53 and Δ160p53.

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

In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.

First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

Response: Thank you for your suggestion. We understand the importance of clearly specifying the isoforms under study. Following your suggestion, we have added α in the title, abstract, and introduction and added the following statement in the Introduction (lines 57-59): "For convenience and simplicity, we have written Δ133p53 and Δ160p53 to represent the α isoforms (Δ133p53α and Δ160p53α) throughout this manuscript."

One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples:

• Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599.
• Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244.
• Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10.
• Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369.
• Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281.
• Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028.
• Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90. Response: Thank you very much for your comment and for highlighting these important studies.

We agree that Δ133p53 isoforms exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. However, our mission here was primarily to reveal the molecular mechanism for the dominant-negative effects exerted by the Δ133p53α and Δ160p53α isoforms on FLp53 for which the Δ133p53α and Δ160p53α isoforms are suitable model systems. Exploring the oncogenic potential of the isoforms is beyond the scope of the current study and we have not claimed anywhere that we are reporting that. We have carefully revised the manuscript and replaced the respective terms e.g. 'pro-oncogenic activity' with 'dominant-negative effect' in relevant places (e.g. line 90). We have now also added a paragraph with suitable references that introduces the oncogenic and non-oncogenic roles of the p53 isoforms.

After reviewing the papers you cited, we are not sure that they reflect on oncogenic /non-oncogenic role of the Δ133p53α isoform in different cancer cases. Although our study is not about the oncogenic potential of the isoforms, we have summarized the key findings below:

• Hofstetter et al., 2011: Demonstrated that Δ133p53α expression improved recurrence-free and overall survival (in a p53 mutant induced advanced serous ovarian cancer, suggesting a potential protective role in this context.
• Bischof et al., 2019: Found that Δ133p53 mRNA can improve overall survival in high-grade serous ovarian cancers. However, out of 31 patients, only 5 belong to the TP53 wild-type group, while the others carry TP53 mutations.
• Knezović et al., 2019: Reported downregulation of Δ133p53 in renal cell carcinoma tissues with wild-type p53 compared to normal adjacent tissue, indicating a potential non-oncogenic role, but not conclusively demonstrating it.
• Gong et al., 2015: Showed that Δ133p53 antagonizes p53-mediated apoptosis and promotes DNA double-strand break repair by upregulating RAD51, LIG4, and RAD52 independently of FLp53.
• Gong et al., 2016: Demonstrated that overexpression of Δ133p53 promotes efficiency of cell reprogramming by its anti-apoptotic function and promoting DNA DSB repair. The authors hypotheses that this mechanism is involved in increasing RAD51 foci formation and decrease γH2AX foci formation and chromosome aberrations in induced pluripotent stem (iPS) cells, independent of FL p53.
• Horikawa et al., 2017: Indicated that induced pluripotent stem cells derived from fibroblasts that overexpress Δ133p53 formed non-cancerous tumors in mice compared to induced pluripotent stem cells derived from fibroblasts with complete p53 inhibition. Thus, Δ133p53 overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but it still compromises certain p53-mediated tumor-suppressing pathways. "Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative inhibition of a subset of p53 target genes."
• Gong, 2016: Suggested that Δ133p53 promotes cell survival under low-level oxidative stress, but its role under different stress conditions remains uncertain. We have revised the Introduction to provide a more balanced discussion of Δ133p53's dule role (lines 62-73):

"The Δ133p53 isoform exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. Recent studies demonstrate the non-oncogenic yet context-dependent role of the Δ133p53 isoform in cancer development. Δ133p53 expression has been reported to correlate with improved survival in patients with TP53 mutations23, 24, where it promotes cell survival in a non-oncogenic manner25, 26, especially under low oxidative stress27. Alternatively, other recent evidences emphasize the notable oncogenic functions of Δ133p53 as it can inhibit p53-dependent apoptosis by directly interacting with the FLp53 4, 6. The oncogenic function of the newly identified Δ160p53 isoform is less known, although it is associated with p53 mutation-driven tumorigenesis28 and in melanoma cells' aggressiveness10. Whether or not the Δ160p53 isoform also impedes FLp53 function in a similar way as Δ133p53 is an open question. However, these p53 isoforms can certainly compromise p53-mediated tumor suppression by interfering with FLp53 binding to target genes such as p21 and miR-34a2, 29 by dominant-negative effect, the exact mechanism is not known."

On the figures presented in this manuscript, I have three major concerns:

*1- Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rules out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added. *

Response: Thank you for raising this important concern. We have carefully considered your comments and have made several revisions to clarify and strengthen our conclusions.

First, to address the potential influence of the FLAG and V5 tags on p53 isoform aggregation, we have revised Figure 2 and removed the previous Supplementary Figure 3, where non-specific antibody bindings and higher molecular weight aggregates were not clearly interpretable. In the revised Figure 2, we have removed these potential aggregates, improving the clarity and accuracy of the data.

To further rule out any tag-related artifacts, we conducted a co-immunoprecipitation assay with FLAG-tagged FLp53 and untagged Δ133p53 and Δ160p53 isoforms. The results (now shown in the new Supplementary Figure 3) completely agree with our previous result with FLAG-tagged and V5-tagged Δ133p53 and Δ160p53 isoforms and show interaction between the partners. This indicates that the FLAG / V5-tags do not influence / interfere with the interaction between FLp53 and the isoforms. We have still used FLAG-tagged FLp53 as the endogenous p53 was undetectable and the FLAG-tagged FLp53 did not aggregate alone.

In the revised paper, we added the following sentences (Lines 146-152): "To rule out the possibility that the observed interactions between FLp53 and its isoforms Δ133p53 and Δ160p53 were artifacts caused by the FLAG and V5 antibody epitope tags, we co-expressed FLAG-tagged FLp53 with untagged Δ133p53 and Δ160p53. Immunoprecipitation assays demonstrated that FLAG-tagged FLp53 could indeed interact with the untagged Δ133p53 and Δ160p53 isoforms (Supplementary Figure 3, lanes 3 and 4), confirming formation of hetero-oligomers between FLp53 and its isoforms. These findings demonstrate that Δ133p53 and Δ160p53 can oligomerize with FLp53 and with each other."

Additionally, we performed subcellular fractionation experiments to compare the aggregation and localization of FLAG-tagged FLp53 when co-expressed either with V5-tagged or untagged Δ133p53/Δ160p53. In these experiments, the untagged isoforms also induced FLp53 aggregation, mirroring our previous results with the tagged isoforms (Supplementary Figure 5). We've added this result in the revised manuscript (lines 236-245): "To exclude the possibility that FLAG or V5 tags contribute to protein aggregation, we also conducted subcellular fractionation of H1299 cells expressing FLAG-tagged FLp53 along with untagged Δ133p53 or Δ160p53 at a 1:5 ratio. The results showed (Supplementary Figure 6) a similar distribution of FLp53 across cytoplasmic, nuclear, and insoluble nuclear fractions as in the case of tagged Δ133p53 or Δ160p53 (Figure 6A to D). Notably, the aggregation of untagged Δ133p53 or Δ160p53 markedly promoted the aggregation of FLAG-tagged FLp53 (Supplementary Figure 6B and D), demonstrating that the antibody epitope tags themselves do not contribute to protein aggregation."

We've also discussed this in the Discussion section (lines 349-356): "In our study, we primarily utilized an overexpression strategy involving FLAG/V5-tagged proteins to investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on the function of FLp53. To address concerns regarding potential overexpression artifacts, we performed the co-immunoprecipitation (Supplementary Figure 6) and caspase-3 and -7 activity (Figure 7) experiments with untagged Δ133p53 and Δ160p53. In both experimental systems, the untagged proteins behaved very similarly to the FLAG/V5 antibody epitope-containing proteins (Figures 6 and 7 and Supplementary Figure 6). Hence, the C-terminal tagging of FLp53 or its isoforms does not alter the biochemical and physiological functions of these proteins."

In summary, the revised data set and newly added experiments provide strong evidence that neither the FLAG nor the V5 tag contributes to the observed p53 isoform aggregation.

2- The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.

Response: Thank you for your insightful comment. However, evidence suggests that the expression levels of these isoforms such as Δ133p53, can be significantly elevated relative to FLp53 in certain physiological conditions3, 4, 9. For example, in some breast tumors, with Δ133p53 mRNA is expressed at a much levels than FLp53, suggesting a distinct expression profile of p53 isoforms compared to normal breast tissue4. Similarly, in non-small cell lung cancer and the A549 lung cancer cell line, the expression level of Δ133p53 transcript is significantly elevated compared to non-cancerous cells3. Moreover, in specific cholangiocarcinoma cell lines, the Δ133p53 /TAp53 expression ratio has been reported to increase to as high as 3:19. These observations indicate that the dominant-negative effect of isoform Δ133p53 on FLp53 can occur under certain pathological conditions where the relative amounts of the FLp53 and the isoforms would largely vary. Since data on the Δ160p53 isoform are scarce, we infer that the long N-terminal truncated isoforms may share a similar mechanism.

Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation.

3-The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

Response: Thank you for your thoughtful comments. We have thoroughly reviewed all the papers you recommended (Bourdon JC et al., 2005; Mondal A et al., 2018; Horikawa I et al., 2017; Joruiz S. et al., 2024)4, 29, 30, 31. Among these, only the study by Bourdon JC et al. (2005) provided data regarding the localization of Δ133p534. Interestingly, their findings align with our observations, indicating that the protein does not exhibit predominantly nuclear localization in the Figure below. The discrepancy may be caused by a potentially confusing statement in that paper4

(The Figure from Bourdon JC et al. (2005) is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

The localization of p53 is governed by multiple factors, including its nuclear import and export32. The isoforms Δ133p53 and Δ160p53 contain three nuclear localization sequences (NLS)4 . However, the isoforms Δ133p53 and Δ160p53 were potentially trapped in the cytoplasm by aggregation and masking the NLS. This mechanism would prevent nuclear import.

Further, we acknowledge that Δ133p53 co-aggregates with autophagy substrate p62/SQSTM1 and autophagosome component LC3B in cytoplasm by autophagic degradation during replicative senescence33. We agree that high overexpression of these aggregation-prone proteins may induce endoplasmic reticulum (ER) stress and activates autophagy34. This could explain the cytoplasmic localization in our experiments. However, it is also critical to consider that we observed aggregates in both the cytoplasm and the nucleus (Figures 6B and E and Supplementary Figure 6B). While cytoplasmic localization may involve autophagy-related mechanisms, the nuclear aggregates likely arise from intrinsic isoform properties, such as altered protein folding, independent of autophagy. These dual localizations reflect the complex behavior of Δ133p53 and Δ160p53 isoforms under our experimental conditions.

In the revised manuscript, we discussed this in Discussion (lines 328-335): "Moreover, the observed cytoplasmic isoform aggregates may reflect autophagy-related degradation, as suggested by the co-localization of Δ133p53 with autophagy substrate p62/SQSTM1 and autophagosome component LC3B33. High overexpression of these aggregation-prone proteins could induce endoplasmic reticulum stress and activate autophagy34. Interestingly, we also observed nuclear aggregation of these isoforms (Figure 6B and E and Supplementary Figure 6B), suggesting that distinct mechanisms, such as intrinsic properties of the isoforms, may govern their localization and behavior within the nucleus. This dual localization underscores the complexity of Δ133p53 and Δ160p53 behavior in cellular systems."

Minor concerns:

- Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"

Response: Thank you! The revised Figure 1A has been created in the revised paper.

• Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands

Response: Thank you for this suggestion. We've changed the image and the new Figure 2 has been shown in the revised paper.

• Figure 3C: what ratio of FLp53/Delta isoform was used?

Response: We have added the ratio in the figure legend of Figure 3C (lines 845-846) "Relative DNA-binding of the FLp53-FLAG protein to the p53-target gene promoters in the presence of the V5-tagged protein Δ133p53 or Δ160p53 at a 1: 1 ratio."

• Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.

Response: Thank you for your thoughtful comments and suggestions. In Figure 3C, the presence of Δ133p53 or Δ160p53 only significantly reduced the binding of FLp53 to the p21 promoter. However, isoforms Δ133p53 and Δ160p53 demonstrated a significant loss of DNA-binding activity at all four promoters: p21, MDM2, and apoptosis target genes BAX and PUMA (Figure 3B). This result suggests that Δ133p53 and Δ160p53 have the potential to influence FLp53 function due to their ability to form hetero-oligomers with FLp53 or their intrinsic tendency to aggregate. To further investigate this, we increased the isoform to FLp53 ratio in Figure 4, which demonstrate that the isoforms Δ133p53 and Δ160p53 exert dominant-negative effects on the function of FLp53.

These results demonstrate that the isoforms can compromise p53-mediated pathways, consistent with Horikawa et al. (2017), which showed that Δ133p53α overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but still affects specific tumor-suppressing pathways. Furthermore, as noted by Gong et al. (2016), Δ133p53's anti-apoptotic function under certain conditions is independent of FLp53 and unrelated to its dominant-negative effects.

We appreciate your suggestion to investigate DNA repair genes such as RAD51, RAD52, or Lig4, especially under stress conditions. While these targets are intriguing and relevant, we believe that our current investigation of p53 targets in this manuscript sufficiently supports our conclusions regarding the dominant-negative effect. Further exploration of additional p53 target genes, including those involved in DNA repair, will be an important focus of our future studies.

• Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

Response: Thank you very much for this valuable suggestion. In the revised paper, Figure 5B has been recreated. Changes have been made in lines 214-215: "The cytoplasm-to-nucleus ratio of Δ133p53 and Δ160p53 was approximately 1.5-fold higher than that of FLp53 (Figure 5B)."

**Referees cross-commenting**

I agree that the system needs to be improved to be more physiological.

Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

Using overexpression always raises concerns, but in this case, I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

Response: Thank you for these comments. We've addressed the motivation of overexpression in the above responses. We needed to use the plasmid constructs in the p53-null cells to detect the proteins but the expression level was certainly not 'overwhelmingly high'.

First, we tried the A549 cells (p53 wild-type) under DNA damage conditions, but the endogenous p53 protein was undetectable. Second, several studies reported increased Δ133p53 level compared to wild-type p53 and that it has implications in tumor development2, 3, 4, 9. Third, the apoptosis activity of H1299 cells overexpressing p53 proteins was analyzed in the revised manuscript (Figure 7). The apoptotic activity induced by FLp53 expression was approximately 2.5 times higher than that of the control vector under identical plasmid DNA transfection conditions (Figure 7). These results rule out the possibility that the plasmid-based expression of p53 and its isoforms introduced artifacts in the results. We've discussed this in the Results section (lines 254-269).

Reviewer #3 (Significance (Required)):

Overall, the paper is interesting particularly considering the range of techniques used which is the main strength.

The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

Response: Thank you very much for your positive and critical comments. We've included a fair discussion on the oncogenic and non-oncogenic function of Δ133p53 in the Introduction following your suggestion (lines 62-73).

References

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Fujita K, et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nature cell biology 11, 1135-1142 (2009).

Fragou A, et al. Increased Δ133p53 mRNA in lung carcinoma corresponds with reduction of p21 expression. Molecular medicine reports 15, 1455-1460 (2017).

Bourdon JC, et al. p53 isoforms can regulate p53 transcriptional activity. Genes & development 19, 2122-2137 (2005).

Ghosh A, Stewart D, Matlashewski G. Regulation of human p53 activity and cell localization by alternative splicing. Molecular and cellular biology 24, 7987-7997 (2004).

Aoubala M, et al. p53 directly transactivates Δ133p53α, regulating cell fate outcome in response to DNA damage. Cell death and differentiation 18, 248-258 (2011).

Marcel V, et al. p53 regulates the transcription of its Delta133p53 isoform through specific response elements contained within the TP53 P2 internal promoter. Oncogene 29, 2691-2700 (2010).

Zhao L, Sanyal S. p53 Isoforms as Cancer Biomarkers and Therapeutic Targets. Cancers 14, (2022).

Nutthasirikul N, Limpaiboon T, Leelayuwat C, Patrakitkomjorn S, Jearanaikoon P. Ratio disruption of the ∆133p53 and TAp53 isoform equilibrium correlates with poor clinical outcome in intrahepatic cholangiocarcinoma. International journal of oncology 42, 1181-1188 (2013).

Tadijan A, et al. Altered Expression of Shorter p53 Family Isoforms Can Impact Melanoma Aggressiveness. Cancers 13, (2021).

Aubrey BJ, Kelly GL, Janic A, Herold MJ, Strasser A. How does p53 induce apoptosis and how does this relate to p53-mediated tumour suppression? Cell death and differentiation 25, 104-113 (2018).

Ghorbani N, Yaghubi R, Davoodi J, Pahlavan S. How does caspases regulation play role in cell decisions? apoptosis and beyond. Molecular and cellular biochemistry 479, 1599-1613 (2024).

Petronilho EC, et al. Oncogenic p53 triggers amyloid aggregation of p63 and p73 liquid droplets. Communications chemistry 7, 207 (2024).

Forget KJ, Tremblay G, Roucou X. p53 Aggregates penetrate cells and induce the co-aggregation of intracellular p53. PloS one 8, e69242 (2013).

Farmer KM, Ghag G, Puangmalai N, Montalbano M, Bhatt N, Kayed R. P53 aggregation, interactions with tau, and impaired DNA damage response in Alzheimer's disease. Acta neuropathologica communications 8, 132 (2020).

Arsic N, et al. Δ133p53β isoform pro-invasive activity is regulated through an aggregation-dependent mechanism in cancer cells. Nature communications 12, 5463 (2021).

Melo Dos Santos N, et al. Loss of the p53 transactivation domain results in high amyloid aggregation of the Δ40p53 isoform in endometrial carcinoma cells. The Journal of biological chemistry 294, 9430-9439 (2019).

Mestrom L, et al. Artificial Fusion of mCherry Enhances Trehalose Transferase Solubility and Stability. Applied and environmental microbiology 85, (2019).

Kaba SA, Nene V, Musoke AJ, Vlak JM, van Oers MM. Fusion to green fluorescent protein improves expression levels of Theileria parva sporozoite surface antigen p67 in insect cells. Parasitology 125, 497-505 (2002).

Snapp EL, et al. Formation of stacked ER cisternae by low affinity protein interactions. The Journal of cell biology 163, 257-269 (2003).

Jain RK, Joyce PB, Molinete M, Halban PA, Gorr SU. Oligomerization of green fluorescent protein in the secretory pathway of endocrine cells. The Biochemical journal 360, 645-649 (2001).

Campbell RE, et al. A monomeric red fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America 99, 7877-7882 (2002).

Hofstetter G, et al. Δ133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. British journal of cancer 105, 1593-1599 (2011).

Bischof K, et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Scientific reports 9, 5244 (2019).

Gong L, et al. p53 isoform Δ113p53/Δ133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell research 25, 351-369 (2015).

Gong L, et al. p53 isoform Δ133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Scientific reports 6, 37281 (2016).

Gong L, Pan X, Yuan ZM, Peng J, Chen J. p53 coordinates with Δ133p53 isoform to promote cell survival under low-level oxidative stress. Journal of molecular cell biology 8, 88-90 (2016).

Candeias MM, Hagiwara M, Matsuda M. Cancer-specific mutations in p53 induce the translation of Δ160p53 promoting tumorigenesis. EMBO reports 17, 1542-1551 (2016).

Horikawa I, et al. Δ133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell death and differentiation 24, 1017-1028 (2017).

Mondal AM, et al. Δ133p53α, a natural p53 isoform, contributes to conditional reprogramming and long-term proliferation of primary epithelial cells. Cell death & disease 9, 750 (2018).

Joruiz SM, Von Muhlinen N, Horikawa I, Gilbert MR, Harris CC. Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence. Cell death & disease 15, 454 (2024).

O'Brate A, Giannakakou P. The importance of p53 location: nuclear or cytoplasmic zip code? Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy 6, 313-322 (2003).

Horikawa I, et al. Autophagic degradation of the inhibitory p53 isoform Δ133p53α as a regulatory mechanism for p53-mediated senescence. Nature communications 5, 4706 (2014).

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Referee #3

Evidence, reproducibility and clarity

In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.

First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples: Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599. Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244. Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10. Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369. Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281. Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028. Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90.

On the figures presented in this manuscript, I have three major concerns:

1. Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rule[s] out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added.
2. The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.
3. Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation. The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

Minor concerns:

• Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"
• Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands
• Figure 3C: what ratio of FLp53/Delta isoform was used?
• Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.
• Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

Referees cross-commenting

I agree that the system needs to be improved to be more physiological.

Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

Using overexpression always raises concerns, but in this case I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

Significance

Overall, the paper is interesting particularly considering the range of techniques used which is the main strength. The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

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Referee #1

Evidence, reproducibility and clarity

Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

In the study, performed experiments are well described.

My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

Major comments:

1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms.
2. Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.
3. On similar lines, authors described: "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)." Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.
4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

Referees cross-commenting

I think the comments from the other reviewers are very much reasonable and logical. Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

Significance

The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

Reviewer #1 (Public review):

Summary:

Wang et al. generate XAP5 and XAP5L knockout mice and find that they are male infertile due to spermatogonial/meiotic arrest and reduced sperm motility, respectively. CUT & Tag data were added in this revision in order to support that XAP5 and XAP5L are antagonistic transcription factors of cilliogenesis.

Strengths:

Knockout mouse models provided strong evidence to indicate that XAP5 and XAP5L are critical for spermatogenesis. RNA-seq and CUT & Tag are valuable sources to further explore their molecular mechanisms.

Weaknesses:

The authors claim that XAP5 and XAP5L transcriptionally regulate sperm flagella development; however, expression, physiological role, and molecular evidence do not well support this concept. This reviewer still thinks the physiological roles of XAP5 and XAP5l are different. (i) XAP5 is expressed at spermatogonia within testes while XAP5l is localized at round/elongating spermatids (their expressions are different). (ii) Spermatogenesis was arrested at spermatogonia/early spermatocyte stage in Xap5-KO mice while sperm abnormalities were observed in Xap5l-KO mice (their roles are different). This reviewer still can't get the authors' viewpoint that XAP5 and XAP5l are 'antagonistic relationship' to regulate sperm flagella development. RNA-seq and CUT & Tag data are valuable source; however, this reviewer suggests exploring how XAP5 regulates spermatogonia differentiation and how XAP5l regulates sperm flagella motility.

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Referee #2

Evidence, reproducibility and clarity

Summary

In the first part of the manuscript the authors present a thorough description of the background and theoretical basis to the identification of a fluorescent pair that permits both FCS and FCCS measurements at the single cell level to enable the determination of Kd values between labelled protein pairs (Figures 1 and 2). The generation of the reagents and subsequent experimental details are thorough and would permit the experiments to be repeated. The first two sections are well argued and appropriately controlled.

They then tag the endogenous S. pombe cdk1 and cdc13 genes at their 3' ends with sequences that encode miRFP670 (a near infrared fluorescent protein) and mNG (mNeonGreen) respectively and from measurements collected on 13 cells derive a mean Kd value calculated for each of the 13 cells of 0.31{plus minus}0.22 μM. They note that this value agrees with that reported by the Pines lab following labelling of cyclin B1 and CDK1 with genome editing in RPE-1/hTERT cells.

The final part of the manuscript then extends the technique to a pair-wise analysis of 9 cyclins and 4 CDKs in a human cell line.

Major Comments

(i) Materials and Methods: Page 10 "The fitting process was constrained by initial estimates and bounded by physically reasonable limits." Please define physically reasonable limits"

(ii) For the characterisation of the cell cycle dependent expression of Cdc13 and its association with Cdc2, the level of Cdc13 expression is used to identify cell cycle stage. It would be appropriate to have an independent measure of cell cycle stage (?cell length). In using Cdc13 to identify cell cycle stage, please define the criteria used ie what level of Cdc13-mNG fluorescence intensity was used to define G1 vs S vs G2?

(iii) Include a control experiment to compare the level of Cdc13 expression in untagged wild-type cells vs the Cdc13-mNG, CDK1- miRFP670 expressing cells to confirm that tagging does not affect Cdc13 expression, cell cycle duration or Cdc13 function.

(iv). Could the authors consider exploiting the tractability of yeast cells to block and release and/or genetic means to establish synchronous populations to improve data acquisition? This approach could also be employed to assess whether CDK1-cyclin B1 affinity changes with cell cycle stage (as was shown by Pines et al in RPE-1 cells) and would demonstrate that their approach is as equally suitable to sensitively distinguish CDK-cyclin pairs in yeast cells.

Minor points

(i) Figure 1. Panels C, F, G and H. Please improve color palette to distinguish the overlapping traces. It might be helpful to remove the edge grey and broaden the color spectrum for visual inclusion (eg straw/blue vs green/red). Could the statement "As expected, mNG exhibited tolerance to the photobleaching when excited at low laser power (< 5%) (Fig. 1C)." be supported by additional labelling on the figure panel.

The manuscript then goes on to describe the measurement of Kds for 36 CDK-cyclin pairs in HeLa cells by overexpression of labelled CDKs and cyclins following transient overexpression by plasmid co-transfection. This last section of the manuscript requires significant revision.

Major points

(i) In analysing the data, the model assumes that the monomeric CDK and cyclin subunits are either bound to form a binary complex or not. Can the authors discuss whether this can be presumed to be the case when they present the results. Either the labelled proteins are overexpressed to such a level that it can be presumed in the data handling that they are behaving as monomeric proteins and the resulting derived Kds reflect binary CDK-cyclin interactions. However, within the cell, the situation is more complex, and both CDKs and cyclins will mostly likely (and dependent on identity) be variably associated with multiple alternative protein partners. Can such effects be discounted in the analysis presented here and what would be the experimental grounds to do so. The authors make note of this fact in the discussion when they note that the results presented in this manuscript differ by circa an order of magnitude for the CDK1-cyclin B1 pairing reported by Pines et al using endogenously labelled proteins. They suggest that the discrepancy might result in part from competition from endogenously unlabelled proteins. This discrepancy has to be addressed.

(ii) Please provide the confidence interval for the data fit for each CDK-cyclin pair. In panel Figure 4I, the results are represented as a heat map to define the Kd for each CDK-cyclin pair. This panel suggests that the technique can sensitively distinguish alternative CDK-cyclin complexes where their Kd values differ in 1 uM increments. The heat map is presented with block colours, but the key to the color coding is a graded color scheme and it is not possible to move between the two. This disconnect has to be addressed. The accompanying text on pages 18 and 19 is a qualitative description of the results, a comparative and quantitative analysis of the data (Kd values with accompanying confidence intervals) has to be included to justify the apparent strength of the technique to discriminate different CDK-cyclin pairs that Figure 4 implies.

(iii) For "low affinity" interactions that are determined to be >10 uM. Please define how this value was calculated. Would it be more appropriate to say a value could not be determined as the data could not be fitted?

(iv) Previous work from the Pines lab using FCS and FCCS to measure the binding of CDK1 to cyclin B1 in RPE-1 cells reported not only a higher affinity for the pair but also that their apparent affinity was dependent on cell cycle stage suggesting that their assembly might be multi-stepped. Both affinity and cell cycle dependency of CDK-cyclin pairings are of great interest to scientists working in the cell cycle field. It could be argued that measurements of the affinities of multiple CDK-cyclin pairs each "averaged out" over the cell cycle will have less impact on the field than a few well-chosen CDK-cyclin pairs characterised in greater depth.

Minor Points

(i) For both Figures 3 and 4 address red/green color pair choice.

Referee cross-commenting

I would like to thank the other reviewer for their comments about requirements and possible control experiments for the use of the fluorescent probes.

We agree that the use of tagged proteins overexpressed in cells to measure Kd values has significant limitations:

(i) Competition between tagged and endogenous proteins

(ii) Limiting factors that affect CDK-cyclin complex stability (PTMs and contributions from binding and assembly factors mentioned).

(iii) Cell cycle dependent protein expression

Points (ii) and (iii) are not applicable to all protein-protein pairs but are significant when trying to determine CDK-cyclin affinities.

Ideally it would be demonstrated that this approach can return the established values for a limited subset of CDK-cyclin pairs in mammalian cells and so extrapolate the results from yeast cells where endogenous labelling was carried out.

We also have shared concerns about the data presentation in Figure 4.

Significance

Technology: The paper describes a technical advance in identifying a fluorescent probe pair suitable for FCCS in living cells.

Cell cycle: The ability of CDKs and cyclins to discriminate each other and pair to form complexes that characterise different cell cycle stages and drive progression has long been appreciated. The formation of non-cognate pairings when the cell cycle is perturbed has also been noted and a greater understanding of the in-cell affinities of all possible CDK-cyclin complexes would be a significant advance in our understanding. However, this manuscript currently does not (i) provide statistically validated measures of apparent differences in affinity between different CDK-cyclin pairs and (ii) address whether the measurements are cell cycle dependent. (iii) Interpretation of the results has to take into consideration that both the CDK and cyclin components are transiently over expressed in cells and therefore the values that are measured are difficult to interpret in terms of CDK and cyclin function. These considerations would dampen interest in the findings by cell cycle biologists.

Expertise: CDKs, cyclin, cell cycle biology.

Non-expert in technical aspects of fluorescence microscopy

Missing tag on the first story.

DOI: 10.1016/j.xcrm.2025.101943

Resource: (Cell Signaling Technology Cat# 4691, RRID:AB_915783)

Curator: @scibot

SciCrunch record: RRID:AB_915783

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