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Referee #3

Evidence, reproducibility and clarity

Michael R. van Gerven and his co-workers studied the role of ATRX in neuroblastoma. Specifically, they focused on multi-exon deletions, the most frequent aberrations found in neuroblastoma. The multi-exon deletions often generate in-frame fusion protein with the potential gain-off function generation. To understand the importance of ATRX multi-exon deletions and mutations, the authors generated several isogenic cell lines using the CRISPR/Cas9 system and performed RNA-seq analysis. The gene set enrichment analysis showed increased gene expression in ribosome biosynthesis and metabolic processes in ATRX KO and ATRXΔ2-13 and deceased expression in ATRXΔ2-10 model. They validated the expression of ribosome biosynthesis genes with qPCR. They concluded that this study suggests the need for different therapeutic options for neuroblastoma patients.

Major Comments:

1. In Figures 3 and 4, the authors showed two distinct gene set enrichment profiles in the ATRX deletion constructs ATRXΔ2-13 and ATRXΔ2-10. They used GI-ME-N WT clones C1 and C2 for Figure 4D, whereas in Figure 4E, they utilized WT clones C3 and C4. It is not clear from the above two Figures how WT C1, C2 are different with WT GI-ME-N C3 and C4 and share distinct gene signatures. The authors should put the supplementary Figure 15A into the main Figure 4 and use the same WT GI-ME-N clones while comparing the gene expression with ATRX KO or ATRXΔ2-13, or ATRXΔ2-10. Is the difference in gene signature between ATRXΔ2-13 and ATRXΔ2-10 due to the heterogeneity present in the WT GI-ME-N cells?
2. In Figure 3, the authors compare the differential gene expression and gene ontology analysis with ATRX deletion conditions. The authors should do the gene set enrichment analysis/Gene ontology term with WT vs ATRX KO or ATRXΔ2-10 or ATRXΔ2-13 and see whether the ribosome biogenesis pathway shows up. It is unclear from Figure 4B-E why authors have used two different cell lines for GO term comparison as their genetic background is different.
3. The ribosome biogenesis pathway is up-regulated in the ATRXΔ2-13 model. It is better to test their hypothesis in mice with a xenograft model with WT and ATRXΔ2-13 cell lines in combination with Pol I inhibitor or other well-known drugs which will inhibit the ribosome biogenesis and determine the effects on the growth of the tumor.
4. In Figure 2D-E, cell cycle analysis was performed with ATRX WT and multi-exon ATRX deletions and there is an increased percentage of cells visible in the S phase compared to WT cells. Still, it is not clear from the Figure whether the result is statistically significant. The experiment should be repeated one more time and a statistical test should be done.
5. As the ribosome biosynthesis was increased in ATRX KO/ ATRXΔ2-13 compared to ATRXΔ2-10. ATRXΔ2-13 deletion was generated only in GI-ME-N cell line model. To bypass the cell line-specific effect, it is necessary to prepare ATRXΔ2-13 deletion in other cell lines and validate whether the ribosome biogenesis pathway is still induced in another cell line.
6. Statistical analyses is missing from almost every Figure.

Minor Comments:

1. The immunofluorescence labeling text in the supplementary Figures is not visible. The imaging should be done with confocal microscopy to avoid the background signal and to get a better resolution.
2. Please put the appropriate color symbol in supplementary Figure 12A. Currently, the color symbol in the Figure panel does not match the Figure.
3. The WT GI-ME-N clone should be consistent in all supplementary western blots.

Significance

The ATRX multi-exon deletions people have studied before in the context of neuroblastoma. But, in this manuscript, the authors showed for the first time the in-frame multi-exon deletions and their involvement in ribosome biogenesis using isogenic cell lines.

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Referee #2

Evidence, reproducibility and clarity

This is an important study investigating the roles of ATRX mutations in neuroblastoma using isogenic CRISPR models. The authors introduced ATRX multi-exon deletions into neuroblastoma cell lines and characterized those cell lines and tumoroids using RNA-Seq, ALT assays, Western blot, and rRNA assays. The study found two different patterns of gene expression and a potential role for ATRX in ribosome biogenesis. The authors state that these findings are potentially very important for the clinic, as patients with the different types of ATRX mutations should be treated differently.

I found the study well-written and well-thought-out. I recommend the manuscript for publication.

Significance

This is a very important study for the field of neuroblastoma, but also for the pediatric field more broadly, as many tumors harbor mutations in ATRX.

Minor comments:

Can the authors generalize these observations to other cancers with ATRX mutations? RNA-Seq data for many cancers are now available, and so the authors could perform RNA-Seq analysis across ATRX mutant tumors and correlate with the type of ATRX mutation to see if the dichotomy they observed is present in patient data. This could be done for neuroblastoma and other tumors. The authors state that other tumors do not typically contain multi-exon deletions, but the effect of point mutations on the ATRX protein could similarly be non-uniform.

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Referee #1

Evidence, reproducibility and clarity

Summary: In this report, van Gerven et al have characterized several available neuroblastoma cell lines with or without ATRX multi exon deletions (MEDs) that produce in-frame fusions, as well as comparable CRISPR-Cas9 generated ATRX MEDs in diverse cell lines. They examined the length and cellular localization of ATRX proteins produced by Western blot, the ALT status by immunofluorescence staining and southern blot, proliferation with the violet trace approach and co-localization with HP1alpha (heterochromatin) by immunofluorescence staining. Finally, they compared transcriptional profiles of the different original and modified neuroblastoma cell lines. They observed that several MED products appeared to be largely cytoplasmic by Western blot. They observed no consistent changes in proliferation or S and G2/M phase length. Transcriptional profiling demonstrated that while MED exon2-10 resulted in a prolife most similar to ATRX-null cells, MED exons2-13 had a very different profile. Importantly, the effect on genes involved in ribosomal and metabolic processes were opposite between these two types of deletions.

Major comments:

1.Unfortunately, not all the claims made are adequately supported by the data presented. In many experiments, the number of biological replicates is insufficient (sometimes n=1). This would have to be remedied prior to publication, to ensure the data can be properly interpreted. Each data point should be indicated in bar graphs (for example in Figure 5, especially given the variability observed). 2.The Western blot data is often very difficult to interpret, given that many bands are present in addition to the specific ones for the WT and FTT bands. Even for some controls presented as WT, the full length protein is very faint while other bands predominate. This should be explained in the text. If no explanation is available, I would recommend confirming the results with other ATRX antibodies.<br /> 3.While the Western blot data suggests that ATRX protein products from MEDs are largely retained in the cytoplasm, this is not observed in the immunofluorescence pictures shown in supplementary figures. The authors should make a decision whether to provide more convincing and clear data, or to remove the immunofluorescence data.<br /> 4. The immunofluorescence data shown in supplementary figures are not of adequate quality. It is impossible to see much of what the authors are claiming. The Telomere and PML images are especially problematic. 5. More generally, the data is presented in a disorganized way, making it difficult to follow. Some are in main figures, some in supplementary, some experiments are done on only a subset of clones (i.e. cytoplasmic vs nuclear distribution). The authors should try to show all relevant results (for example western, facs data) for all their lines in the main figure, so that they can be compared, with adequate number of replicates and statistical analysis.

Minor comments:

Some grammatical errors should be corrected throughout.

Supplementary Table 1 was mislabelled as "Supplementary Figure 1"

Significance

A major finding from this study is that there is an opposite effect of MED exon 2-10 vs MED exon 2-13 on expression of genes involved in ribogenesis and metabolic processes. While a role of ATRX in ribogenesis is not new, as pointed out by the authors, it indicates that tumor states could be quite different depending on the type of ATRX MED fusion products and could potentially require very different therapeutic approaches. The authors should comment on the differences between the protein products (MED exon 2-10 vs MED exon 2-13) that could cause opposite transcriptional effects. What are the protein motifs that will be affected in one but not the other, and could this explain different effects on transcription, especially considering their claim that the majority of these protein products remain in the cytoplasm. It will be interesting to start exploring the location of these ATRX mutants on chromatin, chromatin structure changes and histone modifications, histone variants by ATAC-seq and ChIP-seq to better under understand the underlying mechanisms.

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Reply to the reviewers

The authors do not wish to provide a response at this time

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Referee #3

Evidence, reproducibility and clarity

This study by Ghosh et al. proposes a role for phosphatidylinositol 5-phosphate 4-kinase (PIP4K) in regulating PI3P levels in vivo. They use loss-of-function Drosophila model of the only PIP4K gene (dPIP4K29) to investigate the PI3P and PI(3,5)P2 metabolizing enzymes. First, they showed that loss of function of PIP4K leads to reduced cell size in larval salivary glands and this was attributed to the elevated level of PI3P. Then, they modulated enzymes involved in PI3P metabolism (kinases and phosphatases) and propose the implication of the PI3P phosphatase myotubularin (Mtm) and the Pi3k Class III (PI3K59F) in PIP4K-dependent cell seize control. Finally, as PI3P has an established role in autophagy, they modulate the autophagy related gene (atg1) and connect the observed increase of PI3P level to the upregulation of autophagy in dPIP4K29 model. The authors used genetic manipulations of dPIP4K29 models as well as specialized lipidomic expertise (phosphoinositide measurement using mass spectrometry and PI-kinase/phosphatase assays) to address their conclusions. The experimental strategies were well designed and major conclusions were in line with the obtained results.

Major comments:

• Are the key conclusions convincing?

Almost yes, however there is two major concerns for me: Concern 1 is about the level of PIP2/PI4,5P2, the product of PIP4K, in the dPIP4K29 model. This was not measured in the study. The authors claim page 5 that: "This observation suggests that the ability of dPIP4K to regulate cell size does not depend on the pool of PI(4,5)P2 that it generates... based on the fact that re-expression a mutation that hPIP4Kβ[A381E] in the salivary glands of dPIP4K29 (AB1> hPIP4Kβ[A381E]; dPIP4K29) (Figure S1A) did not rescue the reduced cell size. This mutation hPIP4Kβ[A381E] was generated in a study by Kunz et al. (2002) where they demonstrated by in vitro kinase assay that it cannot utilize PI5P as a substrate but can produce PI(4,5)P2 using PI4P as a substrate. In the same study, using MG-63 cells, Kunz et al. propose that the A381E mutation did not metabolize PI5P as it lost its plasma membrane localization. In my opinion the author should strength their claim about the role of dPIP4K independently of PI(4,5)P2 by addressing the level of PI(4,5)P2 in their model biochemically by mass spectrometry as they have this powerful tool and support this by using PH-PLCd probe to detect PI(4,5)P2. Also, as they use completely different model as Kunz et al. they should verify, if possible, the localization of hPIP4Kβ[A381E] vs WT PIP4Kβ in salivary glands.

Concern 2: Page 7: The author used Mtm tagged constructs (mCherry and GFP) and measure its phosphatase activity toward PI(3,5)P2 and they did not show any obvious activity. I would like to suggest the use of untagged (or small tag construct, Flag or HA) for the expression experiment in S2R+ cell as it is known that active myotubularins in other cell model as well as in vitro have a strong 3-phosphatase activity toward PI(3,5)P2. By looking at the graph FigS2 Bii, we could clearly see a big disparity within mCherry-Mtm data points. This experiment should be more strengthen by additional experimental points but also by using a positive CTRL where PI(3,5)P2 level drops (inhibition of PIKfyve by Apilimod).

Concern 3: Page 10: "we tagged dPIP4K with the tandem FYVE domain at the C-terminus end of the protein (dPIP4K2XFYVE) to target it to the PI3P enriched endosomal compartment and reconstituted this in the background of dPIP4K29. We did not observe a significant change in the cell size of dPIP4K29" I really don't understand the relevance of this experiment. FYVE tandem will bind to PI3P whenever it was in the cell (Lysosomes, autophagosome). Why the authors claim that the expression of restricted dPIP4K2XFYVE will be restricted to the endosomes. I think that this experiment is confusing and should be removed. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

See concern 1 to 3. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Yes, the proposed experiments in concern 1-3 are not difficult to address as the authors have all the appropriate tools to manage this. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

Yes. It is not time consuming and not costly according to their expertise, available tools and materials that they used through the study. - Are the data and the methods presented in such a way that they can be reproduced?

Yes - Are the experiments adequately replicated and statistical analysis adequate?

Yes

Minor comments:

• Specific experimental issues that are easily addressable.

  1. Address the level of PI(4,5)P2 in dPIP4K29 model by mass spectrometry.
  2. Address the localization of hPIP4Kβ[A381E] vs WT PIP4Kβ in salivary glands.
  3. Test the Mtm phosphatase activity toward PI(3,5)P2 using untagged or small tagged (HA or Flag) Mtm and repeat/homogenize the PI(3,5)P2-phosphatase assay (FigS2ii).
  4. Are prior studies referenced appropriately?

Yes - Are the text and figures clear and accurate?

The figures needsmore organization. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

NO

Referees cross-commenting

Overall, Reviewer #1 and #2 found the study by Ghosh et al interesting well designed and written providing insights into the role of PIP4K in regulating cell seize. However, they comment few points that would be very helpful to improve the study. I am agreeing with both reviewers for the raised comments.

Significance

The author addressed how elevated PI3P in dPIP4K29 model impacted cell seize. Indeed, they connected this cell phenotype to the autophagy where PI3P plays a crucial role. However, I am still questioning how deletion of PIP4K enhances PI3P level.

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

The role of PIP4K in cellular homeostasis and organismal physiology is still unclear. This study brings additional insights into how PIP4K could be involved in important cellular process such as autophagy by regulating additional phsophoinositides.<br /> - State what audience might be interested in and influenced by the reported findings.

Phosphoinositide metabolism<br /> - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Phosphoinositides, Myotubularin, endolysosomal trafficking, skeletal muscle.

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Referee #2

Evidence, reproducibility and clarity

The authors utilise a drosophila model to investigate the molecular mechanisms underlying the role of dPIP4K in regulating cell size. They suggest that PIP4K directly regulates PI3P levels in cells, which through upregulating autophagy, can reduce cell size. Overall, this is an interesting and well-designed study. The main downfall of this study is whether the regulation of PI3P levels by dPIP4K is occurs via direct or indirect mechanisms, which is unclear in the data provided in this study.

More specific comments are as follows:

Major Comments:

It's not clear why there are no differences in PI3P/PIP2 levels in Figure 4B, but this is overcome by normalising to organic phosphate levels (4C)? Can differences in PI3P/PIP2 levels be seen in Figure 4B without normalisation if additional controls such as PI3K59F/VPS34 KD were used (as done in figure 5B)? A discussion of this could be useful.

Figure 4D: Does the A381E mutant of PIP4K affect PI3P levels in cells as it cannot reverse the cell size phenotype in Figure S1B?

Figure 4G: The conclusion on line 255 that all phosphatase transcripts are unchanged in this figure when two of them appear to have significant reduction appears inaccurate. In addition, changes of transcript levels of these enzymes may not necessarily reflect their overall activity in cells. A localised reduction in MTM levels or activity may well play a role in dPIP4K29 cells even though an overall phosphatase activity is seen increased in the in vitro assay in Figure 4F. Similarly, not clear that the authors can completely rule out a potential activation of PIP3K59/vps34 and subsequent increase in PI3P levels in cells by simply looking at RNA levels. Is there a reason why the authors could not measure the enzyme levels in cells as mentioned in the text? VPS34 activity can be measured in mammalian systems. This is important as PI3PK59 KD does seem to reverse change in cell size (Figure 5A).

Another method to test the involvement of PI3K59/Vps34 is to target its adaptor proteins. Can the authors distinguish the endosomal and autophagosomal PIP3K59/vps34 complex and PI3P production by looking at drosophila homologues of Atg14 and UVRAG? The majority of PI3P in mammalian cells is found in the endosomal compartment rather than autophagosomal vesicles. If the authors predict that only autophagosomal PI3P levels are changed, then an overall change in enzymatic activity required for PI3P accumulation may not be easy to detect in total cell extracts.

Figure 5C&D: how specific is the FYVE domain fused probes to endosomal PI3P? Such probes are used in mammalian cells to measure overall PI3P, whether endosomal or autophagosomal. In addition, such probes when expressed in live cells can alter PI3P generation. In line with this comment, FYVE-domain probes can be used to quantify PI3P levels in fixed cells, this method could be used to verify changes in PI3P levels seen in PIP4K mutant flies.

Minor Comments:

Fig 1A: this is a slightly confusing diagram and could perhaps be made a little clearer. For an example, the arrows are not clearly differentiating phosphorylation from dephosphorylation events. Also, the choice of colour for the phosphatase arrows (brown-red) and kinases (also appearing brown-red) makes it harder to follow this figure.

Similar comment applies to S4B: PI could be depicted as an unphosphorylated version of PI3P/PI5P and drown in the centre.

Line 301: "lipidated Atg8a fuses with the formed omegasome" Atg8a fusion with omegasome is not an accurate description of the early autophagosome biogenesis events.

A new image (similar to Fig 1A) depicting how PIP4K affect PI3P levels to summarise the findings of this manuscript would be helpful.

The material and methods is an important section in this paper: a more thorough description of the methods, especially those referred to previous publications would be very helpful. The authors can at least add a brief outline of the methods they followed and include contents of buffers used.

Significance

Overall, this is a well designed and written study providing insights into the role of PIP4K in regulating PI3P levels and cell size in Drosophila. The authors develop interesting methods to measure endogenous levels of PI species, which can be useful for the wide research community. As I am not an expert in these Mass Spec analyses, it would be important for these assays to be thoroughly reviewed by a specialist to ensure that the methods used to quantify these phospholipids have been carefully controlled.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Here Ghosh et al describe a potentially novel role for PIP4K in controlling PI3P in Drosophila tissues. Using PIP4K loss of function mutants and innovative lipid measurement techniques the authors try to address how cell size and autophagy are affected and report it may be through a PIP4K-regulated PI3P pool in flies.

Comments:

Overall, the paper is interesting, pretty well written and it has a lot of details in it that have addressed most of the questions, however they do refrain from stating that actually the PIP4Ks phosphorylate PI3P. Is it possible to measure the product PI3,4P2 if this were true? The authors claim that the regulation is direct but never show if by expressing dPIP4K it can phosphorylate PI3P to PI34P2. Using their optimized label-free LC-MS/MS methods this should not be trivial or by performing an invitro kinase assay.

Further, the authors claim there is an increase in autophagy in the dPIP4K animals however they only measured autophagosome numbers. Autophagy flux and lysosome functional assays need to be performed to accurately show this, as it has been demonstrated that the inhibition of PIP4kinases in mammalian cells does indeed cause an increase in the autophagosome pools but because of an autophagosome-lysosome fusion defect which ultimately impairs autophagy not increasing autophagy. This needs to be addressed in the fly system.

Also, localization studies with PIP4K in Drosophila should be performed to explain the role in autophagy or see if they localize at the same compartments as the enzymes that have been shown to regulate PI3P levels in flies.

Also of note, PI3P in mammalian epithelial cells has been shown to control cell size through regulation of autophagy (https://pubmed.ncbi.nlm.nih.gov/31941925/), but I guess it's novel in flies.

Significance

Overall, as it has already been shown that the PIP4K can regulate PI3P levels in vitro as well as PI3P has been shown to control cell size in mammalian cells so the novelty is diminished as well as how their results really impact autophagy are not complete as the authors only quantified Atg8a puncta. If the authors can show the activity in flies is real by measuring the product PI34P2 this would be compelling evidence. Also, they need to complete localization, autophagy flux assays, westerns of LC3 or p62, etc to accurately state that autophagy is enhanced.

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Reply to the reviewers

1. General Statements

We want to thank the three reviewers for their thorough revisions provided, which have helped us improve the manuscript. Following their comments, we have pursued two new main lines of analysis, the results of which are included in the new version of the manuscript.

• We have now validated the signal of platinum CN footprints observed across tumor types in the Hartwig Medical Foundation cohort in an independent cohort of metastatic tumors (POG570). Despite differences between these two cohorts, the same signal of increased chromosomal fragments of size below 10 Mb is observed in two tumor types.
• We have extended the analysis of CN signatures to those recently identified by Steele et al., 2022 (https://www.nature.com/articles/s41586-022-04738-6) across primary tumors. This new analysis revealed that no CN signature shows significant different activity between tumors exposed or unexposed to most frequent anticancer therapies. A simple measurement –i.e., the number of CN fragments of a range of lengths– proves in this case more effective than more complex CN signatures that capture combinations of CN features to identify the signal of exposure to platinum.

Other minor points raised by the three reviewers have also been addressed in the new version of the manuscript (see below).

Reviewer #1 (Evidence, reproducibility and clarity):

In this study, the author examine WGS data from two cohorts of cancer samples from previous studies: PCAWG, mostly representing primary tumors, and HMF, representing metastatic tumors. The HMF dataset represented 4902 metastatic tumors (including 2709 whole-genome doubling, i.e., WGD, metastatic tumors) from patients who had been exposed to 85 anticancer therapies. The study identified a pattern of large LOH events associated with the exposure of tumors of some cancer types to platinum-based therapies. This pattern is characterized by a significant increase in the number of chromosomal fragments in exposed tumors with respect to their unexposed counterparts. The findings could support the hypothesis that WGD may provide tumors with an advantage to withstand the effects of structural variation.

We thank the reviewer for their accurate summarization of our manuscript.

Specific comments:

1. There are a number of statements that would suggest that there is some uncertainty regarding the robustness of results, and that the analysis of additional cohorts may be needed to substantiate the overall findings. For example, page 4: "It is also plausible that more numerous cohorts of exposed tumors are required to understand whether the observed differences are indeed robust." Page 5: "Further analysis with larger cohorts are required to clarify this point, which appears especially to clarify whether a significant imbalance in favor of deleted chromosomal fragments does occur across platinum-exposed lung tumors." However, the abstract does not seem to reflect this level of uncertainty in reporting the main findings.

We thank the reviewer for pointing this out. It is important to highlight that the first statement cited by the reviewer refers to the potential taxanes-related CN pattern, which is not mentioned in the abstract. The second statement refers to the fact that while we observe no significant differences of ploidy between platinum exposed and not exposed WGD tumors, we caution that this may change in larger cohorts. Following the reviewer’s observation, we have now deleted this sentence from the abstract. Therefore, in the current version of the manuscript, all statements presented in the abstract are robustly observed across tumors.

Moreover, we have now replicated the finding of the platinum CN footprint across the POG570 cohort (https://www.nature.com/articles/s43018-020-0050-6), the second largest cohort of whole-genome sequenced metastatic tumors available. (See answers below for details.)

1. Many of the findings made appear to apply not to all tumors but are found within tumors of specific cancer types. However, the abstract does not appear to note this.

As stated in the previous point, all statements contained in the abstract in the current version of the manuscript are replicated across platinum-exposed tumors of different cancer types across the Hartwig Medical Foundation metastatic cohort. Results on different tumor types with WGD samples exposed to platinum treatment are presented in Figures 2d and 3c and Supplementary Figure 5. A summary of the platinum CN footprint resulting from the aggregation of platinum CN patterns observed across all WGD exposed tumors is presented in Figure 3a.

We consider precisely this robustness of the CN pattern observed across platinum-exposed tumors of different cancer types as evidence supporting the proposed universal platinum CN footprint. Conversely, we do not propose a taxane-related CN footprint, precisely due to the lack of such robustness, as explained in the manuscript. We haver rewritten this part of the manuscript to make this point clearer.

Finally, as mentioned above, we have now replicated the platinum CN footprint across platinum-exposed tumors in the POG570 cohort (https://www.nature.com/articles/s43018-020-0050-6). Despite the limitations in sample size and the overall shorter time elapsed between the end of the treatment and the biopsy of the metastasis compared to the HMF cohort, as well as a different method for CN calling 2 out of 3 tumor types (breast and lung) with at least 10 platinum exposed and unexposed samples show exactly the same footprint while comparing platinum treated vs untreated. (See details below.)

1. With regards to additional cohorts, there is a POG570 cohort of WGS data on 570 recurrent or metastatic tumors (Nature Cancer 2020, PMID: 35121966), some 82% of which were from patients receiving systemic therapy before biopsy. Is it possible that some of the patterns identified using the HMF datasets could be validated in the POG570 datasets? If not, what numbers of tumors would be needed for the patterns of interest to be reliably identified?

We thank the reviewer for pointing us in the direction of this very interesting dataset. We have now analyzed the POG570 cohort (https://www.nature.com/articles/s43018-020-0050-6) using the clinical and treatment data and chromosomal fragment calls provided by the authors. Briefly, for each tumor, we computed the length of each chromosomal fragment identified and then counted the number of fragments with copy number 1-4 and length below 10 Mb, which constitutes our measurement of the intensity of the platinum CN footprint. Then, we compared the number of these fragments across platinum exposed and unexposed colon, breast and lung tumors. We selected these tumor types because only for them are there at least 10 exposed and unexposed tumors in the POG570 cohort.

The results of this comparison are shown in the new Figure S7a of the manuscript, which we reproduce here.

(See PDF version included as Supplemental Material)

We found that platinum-exposed breast and lung tumors have a significantly higher number of chromosomal fragments of CN 1-4 and length below 10Mb than their unexposed counterparts, thus replicating our observation across HMF tumors. In the case of colon tumors the difference is not significant. Several differences in the composition of the cohorts and the analysis of the data must be taken into account in the analysis of these results. On the side of the data, the calls of chromosomal fragments have been done using different algorithms. On the side of cohorts composition, one important difference is the number of days elapsed between the end of the exposure of the patients to platinum and the moment of the biopsy of the metastatic or recurrent tumor. The differences between the three tumor types analyzed across both cohorts are now represented in Figure S7b of the manuscript, reproduced here.

(See PDF version included as Supplemental Material)

Across the HMF cohort, colorectal tumor patients exposed to platinum have a median of 300 days between end of treatment and biopsy of the metastasis, but only 178 across POG570 colon tumors (difference close to significance). The same gap is appreciable across breast tumors (289 days vs 41 days; significantly higher across HMF patients) and lung tumors (242 days vs 187.5 days; borderline significantly higher across HMF patients).

In a previous work (https://www.nature.com/articles/s41467-021-24858-3) we demonstrated that longer time elapsed between the end of the treatment and the biopsy accounted for a higher likelihood of a full clonal expansion upon treatment and, as a consequence a higher probability to detect the SBS platinum footprint through bulk sequencing. Since the same must apply to the CN footprint, the significantly shorter time between the end of treatment and biopsy across POG570 tumors would make its detection more difficult. Nevertheless, it is still nicely reproduced at least across breast and lung tumors.

In summary, despite the differences between the HMF and POG570 cohorts, the platinum CN footprint is replicated across tumors of the latter, thus providing independent support to its presence in platinum-exposed tumors.

1. The PCAWG cohort is described as comprising all primary tumors, but in fact there are some metastatic tumors in PCAWG cohort. In particular, most of the TCGA melanoma (SKCM) samples are metastatic (PMID: 30401717). This may have bearing on using comparisons between PCAWG and HMF as a surrogate for primary versus metastases.

The reviewer is correct that there is a very minor representation of metastatic tumors in the PCAWG cohort, specifically, across melanomas. It is nevertheless a good choice for a whole-genome sequenced cohort of tumors, which has been used repeatedly in primary-to-metastatic comparisons (see, for example, PMID: 31645765; https://www.biorxiv.org/content/10.1101/2022.06.17.496528v1). Since the vast majority of tumors in the PCAWG cohort are primary, and the comparisons presented in Figure 1 encompass the entire cohort (with the exception of Fig. 1b, where melanomas are not included), the influence of a few tumors are not expected to confound the results. In any case, differences between primary and metastatic tumors are still very apparent in Figure 1c; exclusion of PCAWG melanomas, if anything, would make them still more apparent.

1. For each boxplot, the number of tumors represented in each group should be indicated somewhere (e.g., along the bottom).

We thank the reviewer for bringing this oversight to our attention. We have now added the number of tumors in each group in all relevant figures. Specifically, the number of tumors in each group appear now in Figures 1c and d, 2d and e, 3 c and d, and 4 a and b, as well as in relevant Supplementary Figures.

1. For Figure 1a, is a color legend needed here?

Thanks for bringing this to our attention. We have now added a color legend to represent WGD and near diploid (non-WGD) tumors in this panel.

1. For analyses comparing HMF to PCAWG (e.g., Figure 1c), the p-values ought to corrected for cancer type (e.g., using a linear regression model with cancer type as a factor).

We thank the reviewer for the suggestion to look at the influence of tumor types in the observed differences in the fraction of LoH tumors in primary and metastatic cohorts. Following their suggestion, we have now carried out separate comparisons of the overall ploidy and fraction of genome LoH of tumors per cancer type (Fig. S1b-i in the reviewed version of the manuscript). This analysis is, of course, limited to tumor types represented in both PCAWG and HMF cohorts. As in the pan-cancer analysis, no significant differences in ploidy are observed in any cancer type. Conversely, significant differences in LoH fraction appear in colorectal, prostate and kidney WGD primary and metastatic tumors. Regardless of statistical significance, in the majority of cancer types, the fraction of LoH genome across tumors appears greater in metastasis than in primaries. Therefore, our starting observation that more LoH is observed across metastatic than primary WGD tumors and that this could be related with the former’s exposure to anticancer therapies holds in this per-tumor-type analysis.

1. For Figure 1d, are the numbers of tumors in each category indicated in parentheses?

The reviewer is correct. We have clarified this in the caption of Figure 1d.

1. For figures 2d and 2e legend, the numbers of tumors in exposed vs unexposed groups for each category should be indicated. Similar for Figures 3a, 3c, 3d.

We thank the reviewer for pointing out this oversight. We have now added the numbers of exposed and unexposed tumors in the relevant plots in Figures 2 and 3. Moreover, we have added a new Supplementary Table (Table S2) with the numbers of tumors of each cancer type exposed and unexposed to each treatment across the HMF cohort.

1. For figure 2c, what is the statistical test used and multiple testing correction applied? Could this be noted in the figure legend?

Following the reviewer’s observation, we have included the name of the test (Wilcoxon signed-rank test) in the caption of Figure 2c. The p-values shown are corrected for False Discovery Rate: this is now indicated in the Figure caption.

Reviewer #1 (Significance):

The study makes effective use of public genomic resources to make new observations regarding platinum-based anticancer therapies. The observations identify patterns within specific cancer types. The analysis is exploratory in nature and would benefit from independent observation in an independent cohort, though it is not clear whether such cohorts may exist in sufficient numbers.

As explained above in detail, motivated by this comment by the reviewer, we have now validated the platinum CN footprint across an independent cohort of metastatic tumors (POG570).

Reviewer #3 (Evidence, reproducibility and clarity):

Analyzing the genome wide copy number patterns across publicly available ~2700 primary and ~5000 metastatic tumors treated by a number of different classes of chemotherapy agents, the authors find a distinct signature of CNVs in tumors treated with platinum-based agents. These platinum-exposed tumors are characterized by a significant increase in the number of chromosomal fragments of lengths between 10 Kb-10 Mb, and this tendency correlates with dosage (approximated by previously published platinum induced mutational signatures). Also, it is interesting that comparison of WGD with non-WGD treated-vs-untreated samples shows that WGD samples tolerate larger CNVs, suggesting relaxed selection against large CNVs in WGD (or WGD as a mechanism to accumulate large CNVs).

Previous works have focused on mutational signatures of various environmental exposures and drugs. This paper attempts to extend the previous research by looking at patterns of copy number variations. The work is somewhat motivated (see comment below) and the experimental design and execution are reasonable.

The manuscript is well written. The method section could be elaborated more for reproducibility.

We thank the reviewer for their appreciative comments on our manuscript. Following their observation, we have carefully reviewed the methods section thinking on the reproducibility of our results.

Major comments:

1. Overall, the results are modest. Although statistically significant, the increase in specific classes of CNVs in treated v untreated WGD mets is modest (Fig 2d), casting a doubt on clinical significance.

While the statistical significance of the association of independent CN features to the exposure to platinum-based drugs is not as high as that observed for platinum-related single nucleotide variants (https://www.nature.com/articles/s41588-019-0525-5), this does not mean that the signal is modest. When the number of CN fragments of length below 10Mb with CN 1-4 of tumors exposed and unexposed to platinum (across three cancer types) are compared, the signal is very clear (Figure 4a). Across cancer types, these particular chromosomal fragments are more abundant (significantly in most cases) in platinum-exposed tumors than in their unexposed counterparts. The increase across exposed tumors is between 13% and 387%.

The number of CN fragments in this range of sizes that may be identified due to the exposure to platinum is much more stringently limited by the size of the genome than the corresponding number of SNVs. This is why while we observe thousands of platinum-related SNVs in exposed tumors(https://www.nature.com/articles/s41588-019-0525-5), the numbers of observed CN fragments are smaller, making the signal less strong. However, while each single nucleotide variant affects a single nucleotide, a chromosomal fragment in the middle of the range observed would affect thousands of base pairs. This makes the cumulative effect of the platinum CN footprint on exposed tumors and normal cells is much larger than that of single nucleotide variants. In other words, the effect of the exposure to platinum on the landscape of CN fragments, far from modest is more consequential than that of SNVs. Thus, while a clinical application of the identified platinum CN footprint is out of the scope of this work, we do believe that, like its mutational footprint counterpart (described in the abovementioned papers) it does have implications for chemotherapy survivors.

To clarify further the effect size of the exposure to platinum, following the reviewer’s comment, we have now added a fold-change to the comparisons between exposed and unexposed tumors presented in figure 4a. Furthermore, we have added the following consideration to the last paragraph of the Discussion section:

Moreover, these SVs –as described by the platinum CN footprint– are bound to affect much larger genomic portions than platinum-contributed point mutations (6, 11). Therefore, their impact on exposed healthy cells and on the development of late effects of the chemotherapy could potentially be greater than those caused by previously recognized platinum-related SBS footprints.

1. Three of the four drugs that yielded significant patterns seems to have largest sample sizes (Fig 2a). Is there a link between sample size and detection power? In general, robustness of the signals is not analyzed, relative to subsampling of tumors or genomic regions etc. Indeed, the authors have noted the potential lack of robustness somewhere in the manuscript.

The reviewer is right that the sample size is an important limiting factor for the detection of CN patterns related to anticancer therapies. This is much more acute than in the case of footprints of single base substitutions, thousands of which are contributed by platinum (for example) to the genome of exposed cells. In contrast, limited by their sheer size, only a few dozen extra chromosomal fragments are contributed by the same treatment to metastatic tumors. This is the reason why, rather than carrying out a subsampling exercise, we have resorted to identifying CN patterns in the tumors of different tumor types separately. As suggested by the reviewer, we have only considered robust the CN pattern that is detected across all cancer types with exposed samples, that is, that of platinum-based therapies.

Adding robustness to the detected platinum CN footprint, we have now replicated its finding in a totally independent cohort of tumors, the POG570 cohort. See above answer to point 3 raised by reviewer 1.

Moreover, we include a paragraph in the Discussion section dedicated to comment on the question of power for the detection of the CN footprints associated to other therapies.

1. Given inter-individual heterogeneity, analyzing longitudinal data of pre-treated primary and post-treated mets rom same individuals would really help strengthen the findings.

We agree with the reviewer that such a comparison would be very interesting. Unfortunately, pretreatment samples of the primary tumors of patients in the Hartwig Medical Foundation cohort are not available.

1. The authors show that several CN features show and increase upon platinum treatment. Are these independent observations? A global correlation among the 48 features in various samples classes (WGD/non-WGD, Primary/Met, treated/untreated) should be done and equivalence class of features defined. Otherwise, the biological significance of these observations could be overstated.

The reviewer is right that there is correlation between the CN features used to identify the CN footprint. Nevertheless, these features, which have been defined elsewhere (https://www.nature.com/articles/s41586-022-04738-6) for the identification of CN signatures are only used in the context of our analysis to determine that some of them (despite their potential correlation) are different between platinum exposed and unexposed tumors. Precisely, taking into account the correlations between CN features, our conceptualization of the platinum CN footprint is the number of chromosomal fragments with copy number between 1 and 4 with length below 10 Mb. Moreover, using this definition and not the original CN features, we are able to replicate the observation of the platinum CN footprint across an independent cohort, which provides further robustness to its identification.

In our work, in summary, the CN features are only a means to the end of identifying a quantitative difference in the structure of chromosomal fragments between tumors exposed or unexposed to a certain anticancer therapy.

1. The only mechanistic link between platinum treatment and the observed CNV patterns is speculated to be via platinum-induced DNA breaks and errors during correction. This seems like a very general mechanisms applicable to any exposures (environmental or drug) that induces breaks. This lack of specificity makes it hard for me to understand the rationale to study CNV patterns - why, after all, should one expect to see a CNV signature?

The question posed by the reviewer –are there any treatment related CN footprints?– is precisely the starting point of our study. We thus carried out an unbiased discovery of CN patterns related with the exposure to different treatments. Upon identification of the association between the exposure to platinum and the increase of LoH chromosomal fragments of 10kb-10Mb signatures with different copy number, we hypothesize that the platinum-induced increase of double strand breaks and their faulty repair may be the underlying mechanism. We absolutely agree with the reviewer that other therapies inducing double strand breaks could lead to a similar –or other– CN footprint. Nevertheless, we have not been able to detect other consistent CN footprint associated with any anticancer therapy across tumors in the Hartwig Medical Foundation cohort. Whether this is due to the lack of statistical power or some underlying mechanistic difference between platinum-based and other drugs (see for example the causes underlying differences in the detectability of platinum and 5FU-related footprints; https://www.nature.com/articles/s41467-021-24858-3) we are not currently able to answer.

1. Authors should contrast their findings with those in https://www.nature.com/articles/s41586-022-04738-6.pdf

We thank the reviewer for this suggestion. Actually, taking advantage of the fact that the tool employed to extract CN signatures de novo (the original SigProfiler aimed at mutational signatures extended to CN features) was available prior to the publication of this article, we had already carried out a CN signatures extraction de novo from the HMF cohort. We then asked if any of these CN signatures (their activity across tumors) is significantly associated with the treatments in the cohort, and found none (current Fig. S3a). In the manuscript we hypothesize that this is due to the intrinsic difficulties in defining CN signatures, as opposed to SBS and DBS signatures. This is why we decided in our study to focus on a collection of individual CN features that show differences between platinum-exposed and unexposed tumors to define the platinum CN footprint.

Following the suggestion by this (and other) reviewer, we have now carried out the same analysis (identification of CN signatures potentially related to exposure to anticancer therapies) using the set of CN signatures originally defined by Steele et al in their paper (reference 21 in our manuscript). This analysis yields negative results too (current Fig. S3c). We also now include the equivalence (established through linear reconstruction) between the CN signatures extracted de novo from the HMF cohort and the CN signatures originally defined by Steele et al. across primary tumors. (This equivalence is provided by the SigProfiler upon extraction.) In general, the signatures extracted across HMF tumors bear little resemblance to those extracted from primary tumors (highest cosine similarity of a linearly reconstructed signature, 0.775). This is presented in current Figure S3b.

Taken together, these results further strengthen our point that a CN footprint defined from differences in individual CN features are probably more appropriate than CN signatures in their current format to identify the effects of anticancer therapies on the CN landscape of exposed cells.

1. For pyrimidine treated samples... "significance is lost across non-overlapping tumors". Authors should ascertain that this is not simply a matter of power. Also, would the significance not be lost for non-overlapping platinum-treated samples?

We thank the reviewer for pointing out the lack of clarity in our statement. To solve it, we have included a new Supplementary Table (Table S4) containing the number of WGD tumors exposed to different pairs of anticancer therapies across cancer types in the HMF cohort.

Let’s look at three cancer types showing the platinum CN footprint with different degrees of overlap of platinum and pyrimidine analogs exposed tumors. In the case of colorectal tumors, 194 out of 220 pyrimidine analogs exposed WGD tumors are also exposed to platinum. No signal is observed when the 26 tumors solely exposed to pyrimidine analogs are compared to tumors that are unexposed to pyrimidine or platinum (as shown in the Figure below, the p-values of which correspond to a two-tailed Wilcoxon-Mann-Whitney test).

(See PDF version included as Supplemental Material)

The reverse analysis is impossible, as only 4 WGD tumors are exposed to platinum but not to pyrimidine analogs. In the case of lung tumors, the 22 tumors exposed to platinum and pyrimidine analogs constitute the entire pyrimidine analogs exposed set. When only the 84 tumors exposed solely to platinum are compared to tumors unexposed to platinum or pyrimidine analogs, CN features associated to platinum exposure still appear different. Finally, in the case of ovarian tumors, no exposure to pyrimidine analogs is recorded, as it is not employed in the treatment of this malignancy.

In summary, the significance of platinum-related CN features is present when only platinum-exposed tumors are included in the comparison. The signal observed is thus attributable to the exposure to platinum-based drugs. The number of exclusively pyrimidine-exposed tumors are few across tumor types, and thus at this stage we are not able to rule out the existence of a pyrimidine associated footprint.

1. Interpretation of Fig 3b. "Had this increase in the number of 10Kb-10Mb chromosomal fragments across exposed tumors arisen through positive selection, we would expect to observe a concentration at specific genomic regions containing resistance genes." This needs to be tested specifically. A "concentration" perhaps would not jump out in a visual inspection of the global pattern, which does seem to show variability.

Following the reviewer’s suggestion, we have now compared the number of chromosomal fragments of CN 1-4 and size smaller than 10 Mb observed in each chromosome across platinum exposed or unexposed lung and colorectal tumors. The results of these comparisons are presented in Figure S4a,b. This figure shows that more fragments of this size range are observed for all chromosomes across exposed tumors than across their unexposed counterparts. In most cases the differences are significant. This means that the increase of chromosomal fragments of size below 10Mb is not restricted to one or few chromosomes. It is rather a general effect distributed along the entire genome.

Minor comments:

1. "the ploidy of tumors with WGD varies in a range between 2.9 and 3.6 (quartiles 1 and 3, Fig. 1a)". I am not sure, there seem to several orange (WGD) points with ploidy below 2.9.

The reviewer is correct that several WGD tumors possess a ploidy below 2.9. This is because the cited values 2.9 and 3.6 correspond, respectively to the lower and upper limit of the first and third quartiles. In other words, 25% of all WGD tumors possess ploidy below 2.9. Following the reviewer’s comment, we have clarified this statement.

Reviewer #3 (Significance):

The novelty is to look at CNV signatures upon drug treatment (beyind mutational signatures). However, as mentioned above, it is not clear how different exposures that ultimately cause DNA break would have distinct CNV pattern. Overall, the results seem modest to me. Although statistically significant, the increase in specific classes of CNVs in treated v untreated WGD mets is modest (Fig 2d), casting a doubt on clinical significance. This work could still be of interest to some researchers, in particular, those interested in mutational signatures of environmental exposure. This work should be interpreted in the context of pan-cancer signatures of CNVs very recently published https://www.nature.com/articles/s41586-022-04738-6.pdf.

The increase of chromosomal fragments below 10 Mb across platinum-exposed tumors is between 13% and 387% with respect to unexposed tumors (Fig. 4a). The statistical significance of the signal of platinum exposure on the number of CN fragments is smaller than that observed for single nucleotide variants produced by the exposure to platinum (https://www.nature.com/articles/s41588-019-0525-5). However, while each single nucleotide variant affects a single nucleotide, a chromosomal fragment in the middle of the range observed would affect thousands of base pairs. In other words the cumulative effect of the platinum CN footprint on exposed tumors and normal cells is much larger than that of single nucleotide variants. (See also response to point 1.)

With respect to CN signatures, motivated by the reviewer’s comments we now demonstrate, using the activity of CN signatures extracted from the HMF cohort (using the methodology presented in https://www.nature.com/articles/s41586-022-04738-6) that none of them is significantly different between tumors exposed or unexposed to major anticancer therapies. Note are there any significant differences in the activities of the original CN signatures extracted in the aforementioned paper across primary tumors between exposed and unexposed tumors in the HMF cohort.

My background is in computational biology, working on transcriptional regulation for decades and more recently in cancer systems biology. I am comfortable with the techniques employed in this work but not so much with the mechanisms linking a specific drug to specific copy number signatures and also with the clinical significance of this problem. Keywords: "Computational biology", "Bioinformatics", "Transcriptional regulation", "NGS", "Omics", "Cancer systems biology"

Reviewer #4 (Evidence, reproducibility and clarity):

Summary:

This paper by Gonzalez et al attempts to identify copy number footprints of anti-cancer therapies. It follows previous work by the group looking at single base mutational footprints of anti-cancer therapies, which provides clear evidence of the effect of these drugs on the genome. This study into copy number footprints is less convincing, mainly due to the challenges in identifying these low frequency copy number signatures. The authors present weak evidence using CN signatures for an increase in the number of chromosomal fragments less than 10 Mb in size. However, this is not consistently significant between different cancer types treated with the same drugs. The interesting finding of an effect of platinum treatment intensity on copy number is seen quite nicely in Fig 4b when pooled into one simple effect. Signature analysis in this case seems unnecessary as the main finding is that platinum treatment results in increased 10 kb- 10 Mb fragments but only when pooled in this way. The paper is otherwise nicely written, although some clarity adjustments are required in the Figures and Figure legends.

We thank the reviewer for their appreciative summarization of our work.

Major comments:

Whilst a commendable effort has been made to identify copy number footprints, the evidence presented here for the identification of CN signatures is not so convincing. The main focus of the paper is the effect of Platinum based therapies and yet the two featured cancer types lung non-small cell and colorectal do not have consistent significant effects in the signature analysis.

The reviewer is correct that we don’t identify a CN signature (in the sense understood in recently published manuscript by Steele et al. https://www.nature.com/articles/s41586-022-04738-6) associated with platinum treatment. A clearer statement to this effect has now been added to the manuscript as a result of novel analyses of these CN signatures in the cohort studied in our work.

What we identify (as the reviewer states in their summary of our work) is a general increase of chromosomal fragments below 10Mb among platinum-exposed tumors. This is consistent across tumor types as shown in Figure 4a, and it is what we describe in the manuscript as the platinum CN footprint. We precisely avoid the term signature in an effort to prevent confusion with the canonical usage of this term.

The referenced BioRXiv paper by Steele et al. is now published https://www.nature.com/articles/s41586-022-04738-6 and one wonders whether additional methods and analyses performed during their peer review may be useful in this paper as well. Can reanalyse using the predefined 21 CN signatures from Steele et al?

We thank the reviewer for this suggestion. Actually, taking advantage of the fact that the tool employed to extract CN signatures de novo (the original SigProfiler aimed at mutational signatures extended to CN features) was available prior to the publication of this article, we had already carried out a CN signatures extraction de novo from the HMF cohort. We then asked if any of these CN signatures (their activity across tumors) is significantly associated with the treatments in the cohort, and found none (current Fig. S3a). In the manuscript we hypothesize that this is due to the intrinsic difficulties in defining CN signatures, as opposed to SBS and DBS signatures. This is why we decided in our study to focus on a collection of individual CN features that show differences between platinum-exposed and unexposed tumors to define the platinum CN footprint.

Following the suggestion by this (and other) reviewer, we have now carried out the same analysis (identification of CN signatures potentially related to exposure to anticancer therapies) using the set of CN signatures originally defined by Steele et al in their paper (reference 21 in our manuscript). This analysis yields negative results too (current Fig. S3c). We also now include the equivalence (established through linear reconstruction) between the CN signatures extracted de novo from the HMF cohort and the CN signatures originally defined by Steele et al. across primary tumors. (This equivalence is provided by the SigProfiler upon extraction.) In general, the signatures extracted across HMF tumors in general bear little resemblance to those extracted from primary tumors (highest cosine similarity of a linearly reconstructed signature, 0.775). This is presented in current Figure S3b.

Taken together, these results further strengthen our point that a CN footprint defined from differences in individual CN features are probably more appropriate than CN signatures in their current format to identify the effects of anticancer therapies on the CN landscape of exposed cells.

From Fig 2a there are 5 cancer types in HMF with Platinum treatment: Lung non-small cell, colorectal, esophagus, urothelial and ovary and it is not clear if all these cancer types are combined or separated in the final analysis. All but urothelial are featured at some point though, but e.g. ovary has no significant differences between treated and untreated. Are samples from all 5 cancer types combined in the "treated versus untreated" analyses?

We thank the reviewer for pointing this out. The analysis is carried out separately by cancer type. We have now included a statement in the Profiles of chromosomal fragments associated with anticancer therapies section clarifying this.

The strongest evidence for a real effect on copy number for platinum treatment comes in Figure 4, where there is a significant increase in LoH segments CN 1-4 with samples showing high SBS 35 mutations (a clever idea!). Attempting to separate out the samples into a "Copy number signature" in Figures 2 and 3 seem a bit like fillers to get to this actual potentially interesting finding. What is the benefit of separating out the copy number and zygosity when the real effect is much clearer when you pool everything and simplify it?

As the reviewer points out, and we highlight above, it is this type of chromosomal fragments that we conceptualize as the platinum CN footprint. This, however, is a discovery that stems from the unbiased analysis carried out across tumor types and treatments, the results of which are presented in Figure 2 and which is further characterized in Figure 3. It would have been impossible to identify this footprint from the outset. We reasoned that the CN features defined by Steele et al. were a good starting point to capture differences in the overall landscape of chromosomal fragments of tumors exposed or unexposed to DNA damaging drugs.

Can you investigate other drug treatments using this bulk approach using the proxy of the SBS drug mutations to indicate the "strength" of the mutational process of the drug.

In theory this is possible for treatments that leave both discernible mutational and CN footprints. So far, only platinum-based drugs fulfill this criteria. In the case of 5-FU a salient mutational footprint is associated with the exposure to the drug, but we were unable to identify any discernible CN footprint.

Another point of interest is the overlap between treatments. A judgement call is made as to which is the overriding drug corresponding to the effect. Is it possible to separate these effects with NMF as per SBS? Or could a combination effect be detected? Likely the numbers would be too low for this separated analysis. But just looking at LoH 10kb to 10 Mb might show something?

This is a very interesting suggestion. Several lines of evidence support the idea that the observed CN footprint is associated with exposure to platinum and not 5-FU and that it is not a combination of both drugs (see above response to point 7 raised by reviewer 3). The most important is that the CN footprint is also observed across tumors that are not exposed to 5-FU. For example, in the case of the ovarian tumor cohort, which are not exposed to 5-FU the CN footprint is recovered (Fig. 3c; 4a), although the individual CN features are not significant, due to the low numbers.

With respect to the overlap specifically between platinum-based therapies and pyrimidine analogs, we have included in the manuscript a new Supplementary Table (Table S4) presenting the numbers of WGD tumors exposed to different pairs of drugs across cancer types. We have also extended the statements about the overlaps in the manuscript to further clarify the decisions made. (See above our reply to point 7 by reviewer 3.)

A table summarizing the included samples, treatments, overlap, etc per cancer type is missing.

Following the reviewer’s suggestion, we have prepared and included in the manuscript two new Supplementary Tables. Table S2 details the number of WGD and non-WGD metastatic tumors of each tumor type across the HMF cohort exposed to different anti-cancer therapies. Table S4 presents the number of tumors exposed to different pairs of treatments across tumor types in the cohort.

Your study may also benefit from a comparison to the latest Hartwig cohort paper https://www.biorxiv.org/content/10.1101/2022.06.17.496528v1.full particularly focusing on the suggestion of Treatment enriched drivers (TED) some of which are infact copy number driven.

We thank the reviewer for this interesting suggestion. The only TED identified by the authors, which is related to platinum-based drugs (with the criteria described in the Supplementary Table 8 of their manuscript) concerns point mutations of TP53 across metastatic stomach adenocarcinomas (where we are unable to identify the CN footprint due to a low sample size). Although some driver amplification or deletion events do appear significantly enriched across platinum exposed tumors of different cancer types, they are discarded by the authors due to lack of orthogonal evidence of being associated with the specific mechanism of action of the drug.

We now include this observation in the revised manuscript:

As anticipated, we observed that chromosomal fragments smaller than 10 Mb (representative of the platinum CN signature) are evenly distributed along the genomes of WGD colorectal and lung tumors (Fig. 3b; Fig. S4a,b). Had this increase in the number of platinum-related chromosomal fragments across exposed tumors been constrained to one or few genomic regions, it would point to positive selection of one or more resistance-associated genes. A recent systematic analysis of the HMF cohort revealed that only mutations in TP53 across stomach adenocarcinomas appear as a potential bona fide driver event associated with the exposure to platinum in the HMF cohort (Martínez-Jiménez et al, 2022).

Minor comments:

The Figure legends and Figures themselves need to be altered for clarity. Axis should be labelled more specifically, e.g Fig 1d axis currently reads "percentage". Fig 3b says left and right and there is no such thing. What do the sizes of the circles in 2c represent? Can you indicate cancer type as well in this plot (shading or line type) or are all treated samples pooled- this is not clear?

We thank the reviewer for this suggestion. We have checked all figures and figure legends to enhance their clarity.

It is not clear why Fig 2a only includes HMF samples and not PCAWG- PCAWG could be in supplement?

Figure 2a describes the types of anticancer treatments received by patients bearing different types of malignancies. PCAWG tumors are primary and treatment-naive; this is why all the study to identify treatment-related CN features focuses on the HMF cohort.

Be consistent with labelling as well, in the text everything is referred to as 10 kb -10 Mb and some Figures labeled as such but others with 10^4- 10^7. How is the size 10 kb established? All the plots show 0-100 kb, where did the 10 kb limit come from?

We thank the reviewer for this recommendation. It actually led us to review our definition of the platinum CN footprint and to realize that, indeed, fragments smaller than 10 kb are part of this footprint. All analyses (and relevant figures and supplementary tables) have been updated accordingly. The rationale to define the CN footprint is now more thoroughly explained (Fig. 3a).

Why is ovary not in 4b?

There are very few platinum-exposed ovarian tumors with WGD and activity of SBS35. Therefore, the groups of tumors with high and low activity of SBS35 are too small to carry out a meaningful comparison of the platinum CN footprint in Fig. 4b.

Methods needs clarification. Are the visualized samples the average of the cancer types in each of the two groups (untreated vs treated) how many samples in each group? The table suggested above would help a lot with understanding what is actually being compared. How reliable is the calculation of WGD status? Some explanation into the values used in the calculation "WGD: 2.9 -1.7*LoH <= Ploidy" is warranted.

Following the reviewer’s suggestion, we have added two new Supplementary Tables (Tables S2 and S4) presenting a more thorough description of the subset of HMF tumors employed in the detection of treatment-related CN features (Table S2) and the overlap between treatments in terms of numbers co-treated tumors (Table S4). We have also expanded the rationale behind the inequality used to separate WGD and non-WGD tumors (see new version of Methods).

CROSS-CONSULTATION COMMENTS

Everyone seems to be in relative agreement that the results are modest, should be compared to https://www.nature.com/articles/s41586-022-04738-6.pdf and require some clarity throughout the manuscript.

Additional analyses, such as comparing to other datasets such as POG570 would benefit the paper.

Reviewer #4 (Significance):

Whilst previous studies have looked at the effect of anti-cancer drugs on single base mutations, owing to the challenges also seen here, no thorough investigation of the effect of these drugs on copy number has been performed. Therefore, this is an advance, albeit minor as the "copy number signature" of the exposed cancers was not particularly clear.

The use of WGD samples was a clever step forward for the analysis of copy number, as the effects of selection are weakened with an extra copy of the genome.

The finding that increased treatment with platinum results in increased copy number changes of size 10 kb to 10 Mb is an interesting finding, and something that could be considered when looking at treatment options in the future, particularly if it is shown to also affect normal cells in this way.

The cancer genomics field in which I am a part, would be interested in this finding.

We thank the reviewer for their appreciative comment of our work.

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

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Referee #4

Evidence, reproducibility and clarity

Summary:

This paper by Gonzalez et al attempts to identify copy number footprints of anti-cancer therapies. It follows previous work by the group looking at single base mutational footprints of anti-cancer therapies, which provides clear evidence of the effect of these drugs on the genome. This study into copy number footprints is less convincing, mainly due to the challenges in identifying these low frequency copy number signatures. The authors present weak evidence using CN signatures for an increase in the number of chromosomal fragments less than 10 Mb in size. However, this is not consistently significant between different cancer types treated with the same drugs. The interesting finding of an effect of platinum treatment intensity on copy number is seen quite nicely in Fig 4b when pooled into one simple effect. Signature analysis in this case seems unnecessary as the main finding is that platinum treatment results in increased 10 kb- 10 Mb fragments but only when pooled in this way. The paper is otherwise nicely written, although some clarity adjustments are required in the Figures and Figure legends.

Major comments:

Whilst a commendable effort has been made to identify copy number footprints, the evidence presented here for the identification of CN signatures is not so convincing. The main focus of the paper is the effect of Platinum based therapies and yet the two featured cancer types lung non-small cell and colorectal do not have consistent significant effects in the signature analysis.<br /> The referenced BioRXiv paper by Steele et al. is now published https://www.nature.com/articles/s41586-022-04738-6 and one wonders whether additional methods and analyses performed during their peer review may be useful in this paper as well. Can reanalyse using the predefined 21 CN signatures from Steele et al?

From Fig 2a there are 5 cancer types in HMF with Platinum treatment Lung non-small cell, colorectal, esophagus, urothelial and ovary and it is not clear if all these cancer types are combined or separated in the final analysis. All but urothelial are featured at some point though, but e.g. ovary has no significant differences between treated and untreated. Are samples from all 5 cancer types combined in the "treated versus untreated" analyses?

The strongest evidence for a real effect on copy number for platinum treatment comes in Figure 4, where there is a significant increase in LoH segments CN 1-4 with samples showing high SBS 35 mutations (a clever idea!). Attempting to separate out the samples into a "Copy number signature" in Figures 2 and 3 seem a bit like fillers to get to this actual potentially interesting finding. What is the benefit of separating out the copy number and zygosity when the real effect is much clearer when you pool everything and simplify it?

Can you investigate other drug treatments using this bulk approach using the proxy of the SBS drug mutations to indicate the "strength" of the mutational process of the drug.

Another point of interest is the overlap between treatments. A judgement call is made as to which is the overriding drug corresponding to the effect. Is it possible to separate these effects with NMF as per SBS? Or could a combination effect be detected? Likely the numbers would be too low for this separated analysis. But just looking at LoH 10kb to 10 Mb might show something?

A table summarizing the included samples, treatments, overlap, etc per cancer type is missing.

Your study may also benefit from a comparison to the latest Hartwig cohort paper https://www.biorxiv.org/content/10.1101/2022.06.17.496528v1.full particularly focusing on the suggestion of Treatment enriched drivers (TED) some of which are infact copy number driven.

Minor comments:

The Figure legends and Figures themselves need to be altered for clarity. Axis should be labelled more specifically, e.g Fig 1d axis currently reads "percentage". Fig 3b says left and right and there is no such thing. What do the sizes of the circles in 2c represent? Can you indicate cancer type as well in this plot (shading or line type) or are all treated samples pooled- this is not clear?<br /> It is not clear why Fig 2a only includes HMF samples and not PCAWG- PCAWG could be in supplement?<br /> Be consistent with labelling as well, in the text everything is referred to as 10 kb -10 Mb and some Figures labeled as such but others with 10^4- 10^7. How is the size 10 kb established? All the plots show 0-100 kb, where did the 10 kb limit come from?<br /> Why is ovary not in 4b?<br /> Methods needs clarification. Are the visualized samples the average of the cancer types in each of the two groups (untreated vs treated) how many samples in each group? The table suggested above would help a lot with understanding what is actually being compared. How reliable is the calculation of WGD status? Some explanation into the values used in the calculation "WGD: 2.9 -1.7*LoH <= Ploidy" is warranted.

Referees cross-commenting

Everyone seems to be in relative agreement that the results are modest, should be compared to https://www.nature.com/articles/s41586-022-04738-6.pdf and require some clarity throughout the manuscript.<br /> Additional analyses, such as comparing to other datasets such as POG570 would benefit the paper.

Significance

Whilst previous studies have looked at the effect of anti-cancer drugs on single base mutations, owing to the challenges also seen here, no thorough investigation of the effect of these drugs on copy number has been performed. Therefore, this is an advance, albeit minor as the "copy number signature" of the exposed cancers was not particularly clear.

The use of WGD samples was a clever step forward for the analysis of copy number, as the effects of selection are weakened with an extra copy of the genome.

The finding that increased treatment with platinum results in increased copy number changes of size 10 kb to 10 Mb is an interesting finding, and something that could be considered when looking at treatment options in the future, particularly if it is shown to also affect normal cells in this way.<br /> The cancer genomics field in which I am a part, would be interested in this finding.

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Referee #3

Evidence, reproducibility and clarity

Analyzing the genome wide copy number patterns across publicly available ~2700 primary and ~5000 metastatic tumors treated by a number of different classes of chemotherapy agents, the authors find a distinct signature of CNVs in tumors treated with platinum-based agents. These platinum-exposed tumors are characterized by a significant increase in the number of chromosomal fragments of lengths between 10 Kb-10 Mb, and this tendency correlates with dosage (approximated by previously published platinum induced mutational signatures). Also, it is interesting that comparison of WGD with non-WGD treated-vs-untreated samples shows that WGD samples tolerate larger CNVs, suggesting relaxed selection against large CNVs in WGD (or WGD as a mechanism to accumulate large CNVs).

Previous works have focused on mutational signatures of various environmental exposures and drugs. This paper attempts to extend the previous research by looking at patterns of copy number variations. The work is somewhat motivated (see comment below) and the experimental design and execution are reasonable.

The manuscript is well written. The method section could be elaborated more for reproducibility.

Major comments:

1. Overall, the results are modest. Although statistically significant, the increase in specific classes of CNVs in treated v untreated WGD mets is modest (Fig 2d), casting a doubt on clinical significance.
2. Three of the four drugs that yielded significant patterns seems to have largest sample sizes (Fig 2a). Is there a link between sample size and detection power? In general, robustness of the signals is not analyzed, relative to subsampling of tumors or genomic regions etc. Indeed, the authors have noted the potential lack of robustness somewhere in the manuscript.
3. Given inter-individual heterogeneity, analyzing longitudinal data of pre-treated primary and post-treated mets rom same individuals would really help strengthen the findings.
4. The authors show that several CN features show and increase upon platinum treatment. Are these independent observations? A global correlation among the 48 features in various samples classes (WGD/non-WGD, Primary/Met, treated/untreated) should be done and equivalence class of features defined. Otherwise, the biological significance of these observations could be overstated.
5. The only mechanistic link between platinum treatment and the observed CNV patterns is speculated to be via platinum-induced DNA breaks and errors during correction. This seems like a very general mechanisms applicable to any exposures (environmental or drug) that induces breaks. This lack of specificity makes it hard for me to understand the rationale to study CNV patterns - why, after all, should one expect to see a CNV signature?
6. Authors should contrast their findings with those in https://www.nature.com/articles/s41586-022-04738-6.pdf
7. For pyrimidine treated samples... "significance is lost across non-overlapping tumors". Authors should ascertain that this is not simply a matter of power. Also, would the significance not be lost for non-overlapping platinum-treated samples?
8. Interpretation of Fig 3b. "Had this increase in the number of 10Kb-10Mb chromosomal fragments across exposed tumors arisen through positive selection, we would expect to observe a concentration at specific genomic regions containing resistance genes." This needs to be tested specifically. A "concentration" perhaps would not jump out in a visual inspection of the global pattern, which does seem to show variability.

Minor comments:

1. "the ploidy of tumors with WGD varies in a range between 2.9 and 3.6 (quartiles 1 and 3, Fig. 1a)". I am not sure, there seem to several orange (WGD) points with ploidy below 2.9.

Significance

The novelty is to look at CNV signatures upon drug treatment (beyind mutational signatures). However, as mentioned above, it is not clear how different exposures that ultimately cause DNA break would have distinct CNV pattern. Overall, the results seem modest to me. Although statistically significant, the increase in specific classes of CNVs in treated v untreated WGD mets is modest (Fig 2d), casting a doubt on clinical significance. This work could still be of interest to some researchers, in particular, those interested in mutational signatures of environmental exposure. This work should be interpreted in the context of pan-cancer signatures of CNVs very recently published https://www.nature.com/articles/s41586-022-04738-6.pdf.

My background is in computational biology, working on transcriptional regulation for decades and more recently in cancer systems biology. I am comfortable with the techniques employed in this work but not so much with the mechanisms linking a specific drug to specific copy number signatures and also with the clinical significance of this problem. Keywords: "Computational biology", "Bioinformatics", "Transcriptional regulation", "NGS", "Omics", "Cancer systems biology"

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Referee #1

Evidence, reproducibility and clarity

In this study, the author examine WGS data from two cohorts of cancer samples from previous studies: PCAWG, mostly representing primary tumors, and HMF, representing metastatic tumors. The HMF dataset represented 4902 metastatic tumors (including 2709 whole-genome doubling, i.e., WGD, metastatic tumors) from patients who had been exposed to 85 anticancer therapies. The study identified a pattern of large LOH events associated with the exposure of tumors of some cancer types to platinum-based therapies. This pattern is characterized by a significant increase in the number of chromosomal fragments in exposed tumors with respect to their unexposed counterparts. The findings could support the hypothesis that WGD may provide tumors with an advantage to withstand the effects of structural variation.

Specific comments:

1. There are a number of statements that would suggest that there is some uncertainty regarding the robustness of results, and that the analysis of additional cohorts may be needed to substantiate the overall findings. For example, page 4: "It is also plausible that more numerous cohorts of exposed tumors are required to understand whether the observed differences are indeed robust." Page 5: "Further analysis with larger cohorts are required to clarify this point, which appears especially to clarify whether a significant imbalance in favor of deleted chromosomal fragments does occur across platinum-exposed lung tumors." However, the abstract does not seem to reflect this level of uncertainty in reporting the main findings.
2. Many of the findings made appear to apply not to all tumors but are found within tumors of specific cancer types. However, the abstract does not appear to note this.
3. With regards to additional cohorts, there is a POG570 cohort of WGS data on 570 recurrent or metastatic tumors (Nature Cancer 2020, PMID: 35121966), some 82% of which were from patients receiving systemic therapy before biopsy. Is it possible that some of the patterns identified using the HMF datasets could be validated in the POG570 datasets? If not, what numbers of tumors would be needed for the patterns of interest to be reliably identified?
4. The PCAWG cohort is described as comprising all primary tumors, but in fact there are some metastatic tumors in PCAWG cohort. In particular, most of the TCGA melanoma (SKCM) samples are metastatic (PMID: 30401717). This may have bearing on using comparisons between PCAWG and HMF as a surrogate for primary versus metastases.
5. For each boxplot, the number of tumors represented in each group should be indicated somewhere (e.g., along the bottom).
6. For Figure 1a, is a color legend needed here?
7. For analyses comparing HMF to PCAWG (e.g., Figure 1c), the p-values ought to corrected for cancer type (e.g., using a linear regression model with cancer type as a factor).
8. For Figure 1d, are the numbers of tumors in each category indicated in parentheses?
9. For figures 2d and 2e legend, the numbers of tumors in exposed vs unexposed groups for each category should be indicated. Similar for Figures 3a, 3c, 3d.
10. For figure 2c, what is the statistical test used and multiple testing correction applied? Could this be noted in the figure legend?

Significance

The study makes effective use of public genomic resources to make new observations regarding platinum-based anticancer therapies. The observations identify patterns within specific cancer types. The analysis is exploratory in nature and would benefit from independent observation in an independent cohort, though it is not clear whether such cohorts may exist in sufficient numbers.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

The manuscript reports effects on brood size, lifespan and healthspan upon manipulation of C. elegans genes encoding RagA, TOR and Pol III orthologs, as well as other well-characterized lifespan-affecting genes. The results point to complex relationships among TOR and Pol III that are not fully resolved, suggest a role for rpc-1 Pol III that is additive with well-characterized lifespan pathways, indicate a late-life requirement for rpc-1 Pol III to limit lifespan, and, contrary to a previous publication, suggest a muscle requirement for rpc-1 Pol III for lifespan limitation.

Major comments regarding key conclusions:

The work demonstrates that brood size is reduced upon rpc-1 Pol III RNAi feeding from the L4 stage. However, no further analysis is provided to show how later aspects of reproduction impair brood. Minimally, ruling out effects on spermatogenesis would be important since sperm number limits self-fertile brood size. It is also unclear from the methods whether the brood size results include embryonic lethality (post-reproduction). Internal hatching, if it occurred, could also affect interpretation of the results. A change in the reproductive period should be noted if it occurred.

The reviewer is correct that it is important to address the role of Pol III more thoroughly in relation to reproduction.

• The brood size experiments we present simply record the number of hatched progeny. To develop this analysis further we will present the age-specific fecundity data that we generated whilst doing these assays to demonstrate the impact of Pol III on the reproductive period. In addition, we will quantify and present data on the total brood size (dead eggs and hatched progeny) to address whether Pol III also impact embryonic development.
• At 25oC (the temperature that we did these experiments) very few animals suffered internal hatching and those that did were taken out of the analysis – therefore this is unlikely to skew the results.
• The question as to whether Pol III limits egg or sperm function (or later developmental roles) is also interesting and is not yet addressed. To examine this we will: Quantify brood size (dead eggs and hatched progeny) in elegans +/- Pol III RNAi that have been exposed to males during the reproductive period compared to those that reproduce solely as hermaphrodites.

The authors claim that, similar to the relationship previously concluded from aging studies, rpc-1 acts downstream of TORC1. However, this claim is not well supported. In an effort to circumvent early lethality caused by loss of let-363 ("CeTOR"), they use a mutation in raga-1 RagA and demonstrate a further reduction in brood with rpc-1 RNAi. If raga-1(ok386) were a null this result would demonstrate a relationship that is at least partially parallel, not linear. By contrast, double RNAi with let-363 was "non-additive", suggesting a more linear relationship. However, interpretation of these experiments requires (1) that the raga-1 mutation is null and affects only TORC1 signaling, (2) evidence that the double RNAi worked well (e.g., qPCR; see Ahringer et al. 2006 review regarding issues with multi-RNAi), and (3) failure to consider alternative effects of loss of let-363 (e.g., TORC2). Negative results with RNAi are particularly problematic in the absence of convincing evidence that the RNAi worked well. Moreover, results in Figure 1G are difficult to interpret since the initial values are low. Here and elsewhere the genetics descriptions are unconventional, hampering interpretation. For example, what is meant by a mutation being "incomplete"? That it acts as a hypomorph?

We understand the concerns of the reviewer:

• The raga mutant strain that we use is raga-1 (ok386.) This allele harbours a 1242 bp deletion at the raga-1 locus that removes almost the entire coding region of the gene. Details on this allele can be found on Wormbase, and we will reference this in the methods. https://wormbase.org/species/c_elegans/variation/WBVar00091681#02-456-10

For reference, this has been used in several other studies, e.g. doi.org/10.7554/eLife.49158

• We agree that double RNAi can be challenging. Appropriate controls were used here e.g. each RNAi diluted 50:50 with control RNAi in the single treatments and phenotypes were observed in each case (either brood size or lifespan). However, to address the precise knockdown of rpc-1 and let-363 obtained with RNAi we will perform qPCR in response to single and double RNAi treatment (both in WT and raga-1 mutant elegans).
• In addition, we will attempt to measure S6Kinase phosphorylation, a downstream readout of TORC1 signalling in response to raga-1 mutation or let-363 RNAi treatment with and without rpc-1 A phosphor S6 Kinase antibody is commercially available and has been used successfully in C. elegans - doi.org/10.7554/eLife.31268
• Our apologies that the nomenclature was confusing. The CeTOR RNAi nomenclature was ’borrowed’ from other papers describing this tool e.g. org/10.7554/eLife.31268 and doi: 10.1371/journal.pgen.1000972. Here, to make our work clearer, we will change ceTOR to let-363 TOR RNAi and raga-1 to raga-1 RagA in the manuscript – as suggested by the reviewer (see below). The description of ‘incomplete’ mutations will also be amended, and informed by our proposed qPCR analysis.

Another claim is that rpc-1 Pol III limits adult lifespan downstream of TOR. These results are not convincing. The two treatments (raga-1 mutation as "embryonic" and L4 stage "CeTOR" let-363 RNAi as late) are not directly comparable for reasons noted above, and the double RNAi problem hampers interpretation.

Our lifespan data points out that the longevity increase upon Pol III knockdown is additive with TOR/let-363, suggesting a mechanism independent of TOR. Indeed, due to lack of ideal reagents, we were forced to try the double RNAi knockdown approach for TOR/let-363 and Pol III/ rpc-1. To make the data interpretation easier, and rule out the possibility of confounding background RNAi to the maximum possible extent, we have included appropriate RNAi controls. Wherever double RNAi has been used, the effect on the phenotype by 50% dilution of target RNAi with empty-vector control, has also been shown independently and used for the statistical comparison with combinatorial RNAi. Our results have shown that diluting let-363 RNAi and rpc-1 RNAi both to 50%, is enough to impart lifespan increase when initiated from L4 stage.

The nomenclature might be easier to follow if the authors state the actual C. elegans genes manipulated (e.g., let-363 TOR versus raga-1 RagA) rather than using "CeTOR" as a catch-all since these genes are not identical in action.

Thank you for this suggestion. We will implement this in the manuscript where appropriate.

Based on genetic interactions (rsks-1, ife-2, ppp-1, daf-2 and germline loss) they show that rpc-1 RNAi further extends the long lifespan conferred by each of the mutant alleles tested, as well as germline loss induced by two different mutant conditions. These results, though negative, are important. The statement that rpc-1 does not affect global protein synthesis is somewhat overstated without additional experimental support.

We thank the reviewer for supporting our inclusion of ‘negative data’. We agree that our statement relating to protein synthesis is overstated given the data presented. We will soften this to: “rpc-1 does not seem to affect the lifespan incurred by reducing global protein synthesis, although this does not rule out the possibility that Pol III affect protein synthesis by other mechanisms”.

Extending and challenging their own previous work showing an intestinal focus of activity for rpc-1 in limiting longevity (Filer et al., 2017), and noting that RPC-1::GFP detection can be knocked down by RNAi in several tissues, they use a tissue restricted rde-1 expression approach (or sid-1 for neurons) to test the contribution of intestine, hypodermis, neurons, muscle and germline. This new analysis points to a role for the muscle. This result is intriguing and warrants further experiments. To shore up tissue-specific claims the authors could consider (1) additional drivers for intestine and muscle rde-1 in the RNAi experiments, or, ideally, a different approach such as tissue-specific protein degradation (again with multiple drivers), (2) a sufficiency experiment for muscle (wild-type muscle expression in the mutant to demonstrate reversal of the phenotype, or rescue of RNAi defects with an RNAi-insensitive reagent expressed in muscle).

Thank for you appreciating the work we have done here and suggesting further experiments. To take your points one at a time: (1) We have already used the most robust tissue-specific alleles generated and reported in the C. elegans literature so far. It would be a significant amount of work to generate new rde-1 driven tissue specific alleles to double check the Pol III levels/ rpc-1 knockdown response in certain tissues, and we feel this is beyond the scope of this project. Suggestion (2) is interesting and would require us to generate a muscle specific rpc-1 strain. However, there are issues with this approach. Firstly, it would require that we have a rpc-1 mutant to rescue – which we don’t as it is embryonically lethal and secondly it would not be possible to do this experiment using RNAi as the RNAi would then knock down the muscle construct.

The possible explanation for the differences in rde-1 results from the previous work should not be buried in the legends of Figure 3 and Figure S3. Perhaps this leaky background hypothesis should be directly tested (e.g., using the RPC-1::GFP to examine whether residual expression exists in ne219 but not in ne300)? In any case, legend to Figure S3 needs editing: The ne219 background is not itself "intestine-specific", as implied, and the last sentence of Figure S3 legend should be "Thus, the rde-1(ne219)...", right?

The differences between the different tissue-specific strains is interesting. On reflection we agree with the reviewer that it should be included in the main text. We will describe the differences between the two rde-1 alleles ne219 and ne300 in the appropriate section in the manuscript and state our results.

Finally, they show that late-adult rpc-1 RNAi extends lifespan over control RNAi and that, by several movement assays, healthspan is improved upon L4 rpc-1 RNAi, even when RNAi is active in muscle (based on WM118).<br /> The most significant new results are that rpc-1(RNAi) affects brood size, can extend lifespan (though modestly) after day 5 of adulthood, and that muscle may be involved rather than intestine.

Additional comments:

Text throughout should clarify TOR vs presumed TORC1. Methods are insufficient. Important aspects of the lifespan methods and raw data are missing - e.g. exact numbers of worms censored. Exact information regarding statistical analysis is lacking (e.g., which tests, corrections for multiple testing). References should be given for all strains. For the rde-1 strains, it would be helpful to include, in addition to the transgene alleles, the actual promoters used to claim tissue specificity. Note, worms do not have "skeletal" muscle, as implied in the discussion. Figure 5 was not helpful for this reviewer. Figure legend to S3A is confusing: the intestinal signal appears stronger or at least equal, not weaker, in the rpc-1 RNAi background. Were these images collected using the exact same exposure settings?

To address this we will:

• Standardise genetic notation throughout the manuscript (see specific comments above)
• Provide more detail on the transgenic alleles used e.g. promoters driving rde-1.
• The majority of strains were obtained from the CGC but wherever appropriate we will also supply a reference.
• Expand and revise Material and Methods section to appropriately describe all the statistical analyses performed.
• Revise lifespan methods to include censoring detail and lifespan Tables to include information on censored animals.
• Remove the reference to ‘skeletal muscle’ and replace with ‘body wall muscle’.
• Once we have generated new data on the specific knockdowns and downstream targets achieved with let-363 TOR RNAi and raga-1 RagA mutation, as well as on the brood size/dead eggs effects, we will incorporate this information into Fig. 5A for better clarity and readability.
• We can see on reflection that Figure S3A is confusing, mainly due to the gut autofluorescence in both the control and rpc-1 RNAi conditions. We will amend this figure to make this clear and include a selection of close up images of each tissue to make it easier to see the tissue specific knockdown by RNAi.

Reviewer #1 (Significance):

See above. Study will be of interest to aging community.

Reviewer #2 (Evidence, reproducibility and clarity):

The study by Malik and Silva et al describes results of the study investigating the role of RNA Polymerase III in regulating fecundity and lifespan in C. elegans. The authors show that knockdown of Pol III, similar to mTOR suppression, is detrimental for reproduction. Likewise, suppression of either Pol III or mTOR in adult animals extends lifespan via apparently the same pathway. In contrast, Pol III knockdown has an additive effect on lifespan in combination with other established genetic lifespan-extending approaches suggesting that they are working via different mechanisms. Furthermore, using the tissue-specific knockdown of Pol III the authors found that suppression Pol III expression is the muscle, but not other major worm tissues, is sufficient for its lifespan extending effect. Finally, the lifespan extension is also observed when Pol III knockdown is initiated late in adulthood. The overall conclusion is that suppression of Pol III expression late in animal life, particularly in the muscle, is a potential strategy to extend life- and health-span. Overall, the study is well-designed, the tools and results are robust and analysed appropriately. The data presentation is excellent, and the manuscript is clearly written. Addressing the points below will help to improve the clarity further.

We thank the reviewer for their very positive response to our study and are pleased that they found the data convincing. We are extremely pleased that the reviewer agrees with the design and tools used in this study. We can address all of the review’s comments – as discussed below.

Major:

Significant amount of GFP signal is still present in RNAi treated animals, what is the tissue that maintains particularly high levels of expression (Fig. 3A) and how does it affect the conclusions? What is the level of Pol III reduction in different tissues? Could different efficiency of knockdown explain the tissue-specific effect of Pol III downregulation on lifespan? It would be important to show (and, if possible, to quantify) the knockdown efficiency in different tissues using the available reporter

• This experiment had originally been done to test the efficiency of the RNAi, particularly in tissues where rpc-1 RNAi did not impact lifespan. The reviewer is right though, and this information could be analysed further to enhance our study. Figure 3A shows C. elegans expressing the rpc-1::3xflag::gfp reporter. This was used to a) determine the expression pattern of RPC-1 and b) determine the effect of rpc-1 RNAi on this. We noted that RPC-1::GFP is expressed a wide number of tissues and when the reporter strain is treated with rpc-1 RNAi, it is decreased in all tissues. The ‘green’ observed in the RNAi treatment is unfortunately attributable to autofluorescence generated by lysozymes in the C. elegans intestine and masks some of the effects we saw by eye.
• To establish the tissue-specific efficiency of Pol III knockdown and also address the confounding issue of the autofluorescence we will now use a combination of quantitative and qualitative fluorescent microscopy to measure the percentage RPC-1::GFP knockdown in each tissue relevant to this study.

Minor:<br /> Fig. S3B is not cited in the text and the legend for the figure is somewhat confusing, potentially containing errors, this needs to be clarified.

We thank the reviewers for pointing this out. The legend for this figure will be re-written as a result of the analysis described above and we will cite it in the main text.

Reviewer #2 (Significance):

This is the first thorough study of Pol III knockdown as a lifespan extending strategy in C. elegans. In addition to the different laboratory model (previous study of Pol III in ageing primarily focused on Drosophila), this manuscript also offers several novel insights into consequences of Pol III perturbation at phenotypic, as well as mechanistic level in terms of interaction with other longevity pathways. The study will be of interest to those interested in processes underlying longevity and ageing. Considering that this topic is currently in fashion the publication will probably attract attention of not only specialist but also general public.

We are extremely pleased that the reviewer shares our enthusiasm for this study and that they find the experimental evidence compelling.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary: The paper by Yasir Malik et al investigates the genetic interrelationship between TOR signalling and Pol III expression regarding fecundity and longevity in C. elegans. Based on a previous study that defined a role of Pol III downstream of TOR in longevity across various species, this study looks particularly at the relative timing and tissue requirements for TOR and Pol III inhibition in longevity. Data indicate that Pol III acts downstream of TOR in regulating fecundity while there are additive effects regarding survival. The Pol III effect on longevity is based on its role in the muscle. Finally, health-span parameters mirror the survival data.

Major comments: This is a nice study the relies on genetic interaction to ask how TOR and Pol III interact. I find the observation that Pol III inhibition extends survival when initiated at day 5 of adulthood very exciting. In general, the study would benefit from additional data that back up the genetic observations._We thank the reviewer for appreciating the study and the novel insights it provides about the TOR-Pol III inter-relationship. We can address reviewer’s comments with the a few, limited experiments. Discussed below.

In Fig. 1, experiments are done to inhibit TOR to varying degrees in order to perform epistasis experiment. Of course these are difficult to interpret without the use of full KOs/loss of function. So while this is a good solution, it would be important to quantify the level to which TOR signalling is inhibited, optimally with biochemical experiments. We fully appreciate the reviewer’s point. A similar concern was raised by reviewer 1. We propose to address this in two ways: 1) by quantifying mRNA levels by qPCR of let-363 in response to either let-363 TOR RNAi; and 2) by determining the extend of TORC1 activity by using a biochemical readout of the pathway’s activity – S6 Kinase phosphorylation using Western blotting as described here: doi.org/10.7554/eLife.31268 2. General brood size is very low in the WT worms. Normally, one would expect 250-300 offspring per adult worm. It would be helpful if the authors could address this.

Indeed, as pointed out by the reviewer, the WT worms have a brood size of 250-300 eggs when kept at 20oC. but C. elegans exhibit different brood sizes dependent on temperature and these decline in size with increasing temperature. The experiments shown here were carried out at 25oC, where C. elegans produce less offspring. Our observation is in agreement with other studies of similar nature e.g. doi:10.1371/journal.pone.0112377 and doi.org/10.1371/journal.pone.0145925

1. Why were lifespan assays performed at 25C? The standard temperature for the worm is 20C and here I think this is very relevant as the TOR pathway is responsive to suboptimal conditions. I wonder if the results are also true for lower temperatures.

The reviewer raises an interesting point. This study follows from the previous study of Filer et al., Nature 2017 which demonstrated the role of Pol III in ageing. During this study we found and reported that there was a high proportion of intestinal bursting when lifespans were carried out at 20oC, which was ameliorated by carrying out the experiments at 25oC. This was quantified in the original manuscript. To maintain consistency, we continued carrying out Pol III lifespans at this slightly higher temperature. Due to this limitation it is not possible to test the impact of TOR signalling on Pol III at lower temperatures.

Minor comments: 1. It would help to better delineate the rationale for the experiments in Fig. S1. Experiments here are aimed to find mediators of TOR effects distinct from Pol III. Such distinct mediators would be additive to Pol III (as is the case in the figure) and downstream of TOR.

Interpreting epistasis analysis is challenging. We were looking for interactors of Pol III using this targeted genetic approach and working on the premise that if two genes interacted then their effects would be non-additive. However, the reviewer is correct that if two genes are doing the same thing independently then their effects may be additive. Although our data does not suggest these mediators interacting with Pol III in the same pathway, it does not rule out the other possibility. When we re-work the manuscript we will explain our rational more clearly and outline the two scenarios.

Reviewer #3 (Significance):

Strengths: The study advances our knowledge regarding the timing of the Pol III targeting intervention for survival effects.<br /> Limitations: The study relies only on genetic data and not all of it is conclusive.

This study will be interesting for the geroscience community with an eye on TOR inhibition and is relevant to worm biology. I work with C. elegans as a genetic model and I am interested in protein homeostasis, metabolism, health, and longevity.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The paper by Yasir Malik et al investigates the genetic interrelationship between TOR signalling and Pol III expression regarding fecundity and longevity in C. elegans. Based on a previous study that defined a role of Pol III downstream of TOR in longevity across various species, this study looks particularly at the relative timing and tissue requirements for TOR and Pol III inhibition in longevity. Data indicate that Pol III acts downstream of TOR in regulating fecundity while there are additive effects regarding survival. The Pol III effect on longevity is based on its role in the muscle. Finally, health-span parameters mirror the survival data.

Major comments:

This is a nice study the relies on genetic interaction to ask how TOR and Pol III interact. I find the observation that Pol III inhibition extends survival when initiated at day 5 of adulthood very exciting. In general, the study would benefit from additional data that back up the genetic observations.

1. In Fig. 1, experiments are done to inhibit TOR to varying degrees in order to perform epistasis experiment. Of course these are difficult to interpret without the use of full KOs/loss of function. So while this is a good solution, it would be important to quantify the level to which TOR signalling is inhibited, optimally with biochemical experiments.
2. General brood size is very low in the WT worms. Normally, one would expect 250-300 offspring per adult worm. It would be helpful if the authors could address this.
3. Why were lifespan assays performed at 25C? The standard temperature for the worm is 20C and here I think this is very relevant as the TOR pathway is responsive to suboptimal conditions. I wonder if the results are also true for lower temperatures.

Minor comments:

It would help to better delineate the rationale for the experiments in Fig. S1. Experiments here are aimed to find mediators of TOR effects distinct from Pol III. Such distinct mediators would be additive to Pol III (as is the case in the figure) and downstream of TOR.

Significance

Strengths: The study advances our knowledge regarding the timing of the Pol III targeting intervention for survival effects.

Limitations: The study relies only on genetic data and not all of it is conclusive.

This study will be interesting for the geroscience community with an eye on TOR inhibition and is relevant to worm biology.

I work with C. elegans as a genetic model and I am interested in protein homeostasis, metabolism, health, and longevity.

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Referee #2

Evidence, reproducibility and clarity

The study by Malik and Silva et al describes results of the study investigating the role of RNA Polymerase III in regulating fecundity and lifespan in C. elegans. The authors show that knockdown of Pol III, similar to mTOR suppression, is detrimental for reproduction. Likewise, suppression of either Pol III or mTOR in adult animals extends lifespan via apparently the same pathway. In contrast, Pol III knockdown has an additive effect on lifespan in combination with other established genetic lifespan-extending approaches suggesting that they are working via different mechanisms. Furthermore, using the tissue-specific knockdown of Pol III the authors found that suppression Pol III expression is the muscle, but not other major worm tissues, is sufficient for its lifespan extending effect. Finally, the lifespan extension is also observed when Pol III knockdown is initiated late in adulthood. The overall conclusion is that suppression of Pol III expression late in animal life, particularly in the muscle, is a potential strategy to extend life- and health-span. Overall, the study is well-designed, the tools and results are robust and analysed appropriately. The data presentation is excellent, and the manuscript is clearly written. Addressing the points below will help to improve the clarity further.

Major:

Significant amount of GFP signal is still present in RNAi treated animals, what is the tissue that maintains particularly high levels of expression (Fig. 3A) and how does it affect the conclusions?

What is the level of Pol III reduction in different tissues? Could different efficiency of knockdown explain the tissue-specific effect of Pol III downregulation on lifespan? It would be important to show (and, if possible, to quantify) the knockdown efficiency in different tissues using the available reporter.

Minor:

Fig. S3B is not cited in the text and the legend for the figure is somewhat confusing, potentially containing errors, this needs to be clarified.

Significance

This is the first thorough study of Pol III knockdown as a lifespan extending strategy in C. elegans. In addition to the different laboratory model (previous study of Pol III in ageing primarily focused on Drosophila), this manuscript also offers several novel insights into consequences of Pol III perturbation at phenotypic, as well as mechanistic level in terms of interaction with other longevity pathways. The study will be of interest to those interested in processes underlying longevity and ageing. Considering that this topic is currently in fashion the publication will probably attract attention of not only specialist but also general public.

My expertise is in cellular proteostasis and its perturbation in age-related diseases.

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Referee #1

Evidence, reproducibility and clarity

The manuscript reports effects on brood size, lifespan and healthspan upon manipulation of C. elegans genes encoding RagA, TOR and Pol III orthologs, as well as other well-characterized lifespan-affecting genes. The results point to complex relationships among TOR and Pol III that are not fully resolved, suggest a role for rpc-1 Pol III that is additive with well-characterized lifespan pathways, indicate a late-life requirement for rpc-1 Pol III to limit lifespan, and, contrary to a previous publication, suggest a muscle requirement for rpc-1 Pol III for lifespan limitation.

Major comments regarding key conclusions:

1. The work demonstrates that brood size is reduced upon rpc-1 Pol III RNAi feeding from the L4 stage. However, no further analysis is provided to show how later aspects of reproduction impair brood. Minimally, ruling out effects on spermatogenesis would be important since sperm number limits self-fertile brood size. It is also unclear from the methods whether the brood size results include embryonic lethality (post-reproduction). Internal hatching, if it occurred, could also affect interpretation of the results. A change in the reproductive period should be noted if it occurred.
2. The authors claim that, similar to the relationship previously concluded from aging studies, rpc-1 acts downstream of TORC1. However, this claim is not well supported. In an effort to circumvent early lethality caused by loss of let-363 ("CeTOR"), they use a mutation in raga-1 RagA and demonstrate a further reduction in brood with rpc-1 RNAi. If raga-1(ok386) were a null this result would demonstrate a relationship that is at least partially parallel, not linear. By contrast, double RNAi with let-363 was "non-additive", suggesting a more linear relationship. However, interpretation of these experiments requires (1) that the raga-1 mutation is null and affects only TORC1 signaling, (2) evidnce that the double RNAi worked well (e.g., qPCR; see Ahringer et al. 2006 review regarding issues with multi-RNAi), and (3) failure to consider alternative effects of loss of let-363 (e.g., TORC2). Negative results with RNAi are particularly problematic in the absence of convincing evidence that the RNAi worked well. Moreover, results in Figure 1G are difficult to interpret since the initial values are low. Here and elsewhere the genetics descriptions are unconventional, hampering interpretation. For example, what is meant by a mutation being "incomplete"? That it acts as a hypomorph?
3. Another claim is that rpc-1 Pol III limits adult lifespan downstream of TOR. These results are not convincing. The two treatments (raga-1 mutation as "embryonic" and L4 stage "CeTOR" let-363 RNAi as late) are not directly comparable for reasons noted above, and the double RNAi problem hampers interpretation. The nomenclature might be easier to follow if the authors state the actual C. elegans genes manipulated (e.g., let-363 TOR versus raga-1 RagA) rather than using "CeTOR" as a catch-all since these genes are not identical in action.
4. Based on genetic interactions (rsks-1, ife-2, ppp-1, daf-2 and germline loss) they show that rpc-1 RNAi further extends the long lifespan conferred by each of the mutant alleles tested, as well as germline loss induced by two different mutant conditions. These results, though negative, are important. The statement that rpc-1 does not affect global protein synthesis is somewhat overstated without additional experimental support.
5. Extending and challenging their own previous work showing an intestinal focus of activity for rpc-1 in limiting longevity (Filer et al., 2017), and noting that RPC-1::GFP detection can be knocked down by RNAi in several tissues, they use a tissue restricted rde-1 expression approach (or sid-1 for neurons) to test the contribution of intestine, hypodermis, neurons, muscle and germline. This new analysis points to a role for the muscle. This result is intriguing and warrants further experiments. To shore up tissue-specific claims the authors could consider (1) additional drivers for intestine and muscle rde-1 in the RNAi experiments, or, ideally, a different approach such as tissue-specific protein degradation (again with multiple drivers), (2) a sufficiency experiment for muscle (wild-type muscle expression in the mutant to demonstrate reversal of the phenotype, or rescue of RNAi defects with an RNAi-insensitive reagent expressed in muscle). The possible explanation for the differences in rde-1 results from the previous work should not be buried in the legends of Figure 3 and Figure S3. Perhaps this leaky background hypothesis should be directly tested (e.g., using the RPC-1::GFP to examine whether residual expression exists in ne219 but not in ne300)? In any case, legend to Figure S3 needs editing: The ne219 background is not itself "intestine-specific", as implied, and the last sentence of Figure S3 legend should be "Thus, the rde-1(ne219)...", right?
6. Finally, they show that late-adult rpc-1 RNAi extends lifespan over control RNAi and that, by several movement assays, healthspan is improved upon L4 rpc-1 RNAi, even when RNAi is active in muscle (based on WM118).
7. The most significant new results are that rpc-1(RNAi) affects brood size, can extend lifespan (though modestly) after day 5 of adulthood, and that muscle may be involved rather than intestine.

Additional comments

Text throughout should clarify TOR vs presumed TORC1. Methods are insufficient. Important aspects of the lifespan methods and raw data are missing - e.g. exact numbers of worms censored. Exact information regarding statistical analysis is lacking (e.g., which tests, corrections for multiple testing). References should be given for all strains. For the rde-1 strains, it would be helpful to include, in addition to the transgene alleles, the actual promoters used to claim tissue specificity. Note, worms do not have "skeletal" muscle, as implied in the discussion. Figure 5 was not helpful for this reviewer. Figure legend to S3A is confusing: the intestinal signal appears stronger or at least equal, not weaker, in the rpc-1 RNAi background. Were these images collected using the exact same exposure settings?

Significance

See above. Study will be of interest to aging community.

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Reply to the reviewers

Manuscript number: RC-2022-01480

Corresponding author(s): Ananda, Sarkar

1. General Statements

We are thankful to Review commons platform that helped our manuscript critically reviewed with very constructive and valuable feedback. This gave us the opportunity to do the experiments accordingly and significantly improve the manuscript. We are hopeful that this platform will help our manuscript get published in a journal of repute.

2. Point-by-point description of the revisions

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

The manuscript entitled "LDL1 and LDL2 histone demethylases interact with FVE to regulate flowering in Arabidopsis" characterized that LDL1 regulates flowering by binding on the chromatin of MAF4 and MAF5 to repress their expression. Further the authors proposed LDL1/LDL2-FVE model. Here are some comments for this manuscript.

Major problems: 1. This experiment is still not testing or showing/concluding that the whole complex forms on the MAF4 and MAF5

Response: We understand reviewer’s concern regarding the complex. Previously FVE was shown to be a part of co-repressor complex including HDA6, HDA5 and FLD to regulate the expression of FLC and its clade members during floral transition1-3 We showed that LDL1 binds directly to the chromatin of MAF4 and MAF5 to suppress their expression (Figure 1 and 2). Furthermore, we discovered that LDL1 and LDL2 interact with FVE to influence floral transition (Figure 8 and 9). Hung et al., 2018 reported the interaction of LDL1 and LDL2 with HDA6 to regulate circadian rhythm4 and we found that the expression of MAF4 and MAF5 was upregulated in ldl1ldl2hda6 than ldl1ldl2 (Figure 5C and 5D). Therefore, our experimental data, together with previously reported data makes it evident that LDL1 and LDL2 are a part of co-repressor complex through their interaction with FVE and HDA6, which we concluded here. We agree with the reviewer that an additional experiment, such as complex pull-down, will be helpful, but in our opinion, it will only provide additional confirmatory evidence.

2.It is not shown LDL1/LDL2 repress MAF4 and MAF5 by removing H3K4me2 activity. It would be useful to test whether the methylation level of MAF4 and MAF5 has been altered in ldl1/ldl2 mutant

Response: We found altered methylation level in MAF4 and MAF5 chromatin during floral transition in ldl1 and ldl1ldl2 mutants (Figure 6 and 7). We observed that the absence of LDL1, or both LDL1 and LDL2 disturbs the shift in H3K4 methylation status on MAF4 and MAF5 during floral transition and ends up in a more active (enriched in H3K4me3 marks) chromatin state at 19 days. This result, taken together with the increased MAF4 and MAF5 expression in ldl1 and ldl1ldl2 double mutants (Figure 5C and 5D) indicates that LDL1/LDL2 repress MAF4 and MAF5 by altering H3K4 methylation.

3.I suggest that further research is required to provide conclusive evidence concerning the physiology function of LDL1/LDL2-FVE. Such as the expression pattern of LDL1/LDL2, the methylation level of MAF4 and MAF5 before or after floral transition

Response: Taking this suggestion into account, we performed quantification of rosette leaves and flowering time of fvec, ldlfvec and ldl2fvec along with WT, ldl1 and ldl2 (Figure 9). We also observed decreased expression of floral activator genes, FT and SOC1 (targets of MAF4 and MAF5) in fvec, ldlfvec and ldl2fvec in comparison to the WT (Supplementary Figure 10C), which corresponds to their late flowering phenotype.

To understand the role of LDL1and LDL2 during floral transition, we first analyzed the expression of LDL1 and LDL2 during floral transition (Supplementary Figure 8). We observed that the expression of LDL1 and LDL2 expression peaks at 16 days and gets stabilized till 19 days. Then we checked the enrichment of H3K4me1, H3K4me2 and H3K4me3 on MAF4 and MAF5 chromatin in ldl1 and ldl1ldl2 plants with respect to the WT at 16 days (before floral transition) and 19 days (after floral transition). We found an increase in the conversion of H3K4me1 to H3K4me3, when LDL1 and LDL2 were not present (Figure 6 and 7).

Reviewer #1 (Significance (Required)):

The manuscript provide some evidences how LDL1 involve in flowering through epigenetic regulation.

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

Mahima and colleagues investigated LDL1/LDL2-MAF4/MAF5 in Arabidopsis flowering time control. The manuscript contains some interesting observations. To my point of view, however, the data need to be consolidated to support conclusions drawn in the manuscript.

• Title: it does not correctly reflect the manuscript content. Data in relation with FVE were limited to Fig 6, where the data themselves appear preliminary.

Response: We agree with the reviewers that our title didn’t reflect the manuscript content precisely and are happy to take this criticism into consideration. We have revised the title to, “LDL1 and LDL2 affect the dynamics of H3K4 methylation on the chromatin of MAF4 and MAF5 to allow floral transition in Arabidopsis”. Additionally, have provided the quantification data for fvec, ldlfvec and ldl2fvec with respect to WT, ldl1 and ldl2 plants (Figure 9)

• Abstract: most conclusions are over-stated. The current data shown in the manuscript cannot support such strong conclusions.

Response: We have rigorously revised the abstract and toned down the overstated conclusions

• Introduction: It is necessary to make clear that the role of the LDL1 and LDL2 genes in flowering time control had been well established in previous studies, including their repression of transcription of FLC, MAF4 and MAF5 (Berr et al., 2015, Plant J 81:316).

Response: We have revised the introduction to include the previously known roles of LDL1 and LDL2 in regulating flowering time.

• Results:

Regarding LDL1-overexpression lines, 'Relative expression' in Supplementary Fig 2B referred to normalization to WT? The phenotype of plants needs to be shown.

Response: Yes, the level of upregulation of LDL1 expression in different T1 plants (after selection from Hygromycin) was calculated with respect to the WT.

Regarding flowering time, have the observation and measures been performed in the same experiments for the ldl1, ldl1 flc, ldl1 maf4 and ldl1 maf5 mutants (Fig 3 and Supplementary Fig 1)? The late-flowering phenotype of ldl1 shown in Fig 3D-F is much severe than the same mutant shown in the other Figs, any explanation? What's the interpretation that ldl1 is epistatic to flc, maf4 and maf5?

Response: We agree with the reviewer’s observation which is correct. The following quantifications were taken at various points during the study:

flc, ldl1, and ldlflc (Supplementary Figure 1)

WT, ldl1, and ldl1maf4 (Figure 3A, 3B and 3C)

WT, ldl1, and ldl1maf5 (Figure 3D, 3E and 3F)

The rosette leaf numbers and flowering time of the plants in Figure 3D-3F are more severe than the others because seeds were directly sprinkled onto the soil in this phenotyping, whereas in previous phenotypings, plants were grown on 1/2MS plates before being transferred to soil. However, all the components of a single experiment were grown in the same condition. We appreciate your observation, the present data does suggest ldl1 being epistatic to flc, maf4 and maf5.

The in vitro test of LDL1 for its enzyme activity (Fig 4) appears preliminary and fragmented. The quantification data in Fig 4C-D need repeats. Have other histone methylation types (e.g. H3K4me3, H3K27me3, H3K36me3) been tested? The only two types (H3K4me2 and H3K9me2) shown are both down-regulated by LDL1-GST. Can H3K9 demethylation also play a role in flowering time control? In any case, the current in vitro data only are not sufficient to draw the strong conclusions as those appeared in the manuscripts.

Response: Before concluding that LDL1 has H3Kme2 and H3K9me2 demethylase activity, we confirmed it several times__. __Please refer to the PDF file for “response to reviewers” for supporting data.

We analyzed the western band intensity by calculating the area under the curve with imageJ software, which varies between experiments depending on the band intensities, therefore, rather than plotting absolute values of band intensity, we plotted the ratio of LDL1-GST/GST from three independent experiments in Figure 4B. We did perform a preliminary experiment to see if LDL1 has demethylation activity against different methylation marks, such as H3k4me1, me3, H3K9me1, and me3 (1=GST, 2=LDL1-GST), but there was no significant change in the methylation marks in the presence of LDL1. Please refer to the PDF file for “response to reviewers” for supporting data.

H3K9 is a repressive chromatin mark, and its removal would suggest gene activation. Upregulation of FLC, MAF4, and MAF5 in ldl1 and ldl2 mutant suggests LDL1 and LDL2 removes H3k4me2 methylation marks during flowering. However, JMJ28, Jumonji C (JmjC) domain-containing histone demethylase have been shown to positively regulate flowering by removing repressive H3K9me2 marks from the chromatin marks from the chromatin of CONSTANS (CO)5.

In the manuscript, it is saying that LDL1 binds on the chromatin of MAF4 and MAF5. However, I cannot find any data shown to support this conclusion.

Response: We would like to refer to Figure 2A and B where we have provided this information.

Protein-protein interactions, e.g. LDL1/LDL2-FVE in Fig 6A and LDL1-LDL2 and LDL1-HDA5 in Supplementary Fig 5, are examined in yeast two-hybrid assay. Other independent assays would be required.

Response: We have confirmed the interaction of LDL1 and LDL2 with FVE using co-immunoprecipitation assay (Figure 8B). Since Co-IP is a confirmatory experiment, we have done it for positive interactions found through Y2H only. Moreover, in the current manuscript our focus has not been on HDA5, so we didn’t proceed with further experiments.

The study of genetic interaction between fve and ldl1/ldl2 (Fig 6B-D) looks very preliminary. It is unclear how ldl1 fve and ldl2 fve were obtained: by crosses or by CRISPR-Cas9 using ldl1 and ldl2? The phenotypes need more investigations and some molecular data regarding flowering regulatory genes (e.g. MAF4/5) are necessary. In any case, the current title and the related conclusions drawn in the manuscript are over-stated.

Response: We performed the quantification of the genetic interaction between fve and ldl1/ldl2. The binary vector pHSE401-FVE was transformed in ldl1 and ldl2 to produce ldl1fvec and ldl2fvec, respectively. We previously mentioned it in the material methods, but we have now updated it in the results section to avoid confusion.

Following the suggestions, we have scored the phenotype (Figure 9) and checked the expression of flowering regulatory genes (Supplementary Figure 10C).

Fig 7 showed data about MAF5-FLC, MAF5-SVP and MAF5-MAF5 interactions in yeast two-hybrid and about transcriptional repressor activity assay in tobacco leaves using the LUC-reporter. Again, the data need to be confirmed and reproducibility of experiments need to be shown. In addition to proFT:LUC, it is also necessary to have an internal normalization reference construct. Anyway, currently it is far away to allow a strong conclusion such as drawn in the manuscript that MAF5 interacts with FLC and SVP and repress FT to delay floral transition. Response: We have confirmed the interaction of MAF5-FLC, MAF5-SVP and MAF5-MAF5 using co-immunoprecipitation (Figure 10B). We quantified the firefly luciferase activity under proFT using renilla luciferase under pro35s as an internal control and the ratio of LUC/REN represented the promoter activity of FT promoter (Figure 10C).

Reviewer #2 (Significance (Required)):

Topic is interesting, but data are poor to support the conculsions drawn.

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

LDL1 and LDL2 histone demethylases interact with FVE to regulate flowering in Arabidopsis Summary This work study the role on flowering time of LDL1 and LDL2, two Arabidopsis homologs of the histone demethylase LSD1. Although this phenotype was previously described, the authors explore if LDL1 and LDL2 regulate other genes in addition to the floral repressor FLC. In fact, mRNA expression experiments and genetic analyse suggest that LDL1 modules flowering regulating the expression of MAF4 and MAF5, two FLC-like genes that has been less characterized. The also provide some in vitro biochemical evidence of the demethylase activity of LDL1 protein and yeast-two-hybrid data showing the interaction with FVE, another chromatin regulator involved in flowering time.

Major comments 1. Lines 116-117. Please rephrase these lines and remove panels C, D and E from figure 1 (these could be supplementary material). The flowering time phenotype of MAF4 and MAF5 in Col background is very well documented and was described before, see Gu et al Nat. Comm., 2013 (10.1038/ncomms2947) and Kim et al. Plant Cell, 2013 (10.1105/tpc.112.104760)

Response: As per the suggestion, we have modified the discussion and moved the panels 1C, 1D and 1E to the supplementary.

Lines 128-130 and Fig Sup3. The proLDL1:LDL1-GUS cannot be described as fully functional because its flowering time and LDL1 mRNA expression levels has not been compared to the wild-type plant. The line flowers earlier that the ldl1 mutant but it may only partially complement the flowering phenotype.

Response: We have provided additional experiment that the transgene is functional in proLDL1:LDL1-GUS (ldl1) with respect to the WT plants (Supplementary Figure 5A).

Line 135 and Figure 2. How the Chip data was normalized? What are you comparting in your statistical significance tests? Only two regions of each gene were analysed; to assess the binding of LDL1 to MAF4 and MAF5 loci more regions must be analysed.

Response: Normalization of the ChIP data and significance of enrichment of LDL1 was calculated with respect to the fold enrichment in the empty vector control (EV (ldl1)) plants. We only examined the promoter and exon1 of MAF4 and MAF5 for LDL1 enrichment because Hung et al,2019's6 study demonstrated that LDL1 is enriched on the promoter and exon1 of the downstream protein coding genes. However, to check for methylation marks during flowering, we have employed different primer sets on various positions between the promoter and exon1 on MAF4 and MAF5 chromatin.

Figures 6C and 6D. The genetic analysis of ldl mutant with fve-c line is prelaminar and incomplete. The epistasis cannot be evaluated as no quantitative flowering time data is provided. A questionable picture of one lonely plant cannot sustain the conclusions of lines 207-208.

Response: We have modified the picture and quantified the flowering time data to show genetic interaction of ldl1 and ldl2 with fvec mutant plants (Figure 9).

METODS. Please clarify the used mutant alleles for LDL1 LDL2, MAF4, MAF5 and FLC; if they has been previously described; if they are full knock-outs; and, consequently, use the appropriated allele name across the manuscript.

Response: As per the suggestion, we have clarified the different mutant alleles used in the study.

Minor points: 6. I think the title does not describe the work - the interaction with FVE is very relevant but it is not the central theme of the article.

Response: We have changed the title of the study to “LDL1 and LDL2 affect the dynamics of H3K4 methylation on the chromatin of MAF4 and MAF5 to allow floral transition in Arabidopsis”.

It would be very informative to have short-day flowering tome data of the genetic combinations of ldl mutants with flc, maf4 and maf5 mutations.

Response: We absolutely agree that elaborate SD experiment may open interesting avenue for LDL1 mediated regulation of flowering, which might be good for future studies. However, ldl1ldl2 shows late flowering, while maf4 and maf5 exhibit the early flowering phenotype irrespective of the day length7,8.

I found the Discussion section rather too long.

Response: We have shortened the discussion to make it more focused.

Reviewer #3 (Significance (Required)):

Although it is clear that LDL proteins regulate MAF4 and MAF 5. I found that the manuscript lacks of a general overview of flowering time regulation. At the end, it is not clear how LDL proteins regulate flowering time because they regulate FLC, FWA, MAF4 and MAF5: What is more important? Which is the main role of each protein? Are they reductant or do they have specialized functions? In a nut shell, this study is an interesting piece of work for the flowering time field: However, in my opinion, some of the presented data are redundant with previous works and the manuscript may not be relevant for a general audience.

1. Yu, C.-W. et al. HISTONE DEACETYLASE6 Interacts with FLOWERING LOCUS D and Regulates Flowering in Arabidopsis. Plant Physiology 156, 173-184 (2011).
2. Luo, M. et al. Regulation of flowering time by the histone deacetylase HDA 5 in A rabidopsis. The Plant Journal 82, 925-936 (2015).
3. Yu, C.-W., Chang, K.-Y. & Wu, K. Genome-wide analysis of gene regulatory networks of the FVE-HDA6-FLD complex in Arabidopsis. Frontiers in plant science 7, 555 (2016).
4. Hung, F.-Y. et al. The Arabidopsis LDL1/2-HDA6 histone modification complex is functionally associated with CCA1/LHY in regulation of circadian clock genes. Nucleic acids research 46, 10669-10681 (2018).
5. Hung, F.-Y. et al. The Arabidopsis histone demethylase JMJ28 regulates CONSTANS by interacting with FBH transcription factors. The Plant Cell 33, 1196-1211 (2021).
6. Hung, F.-Y. et al. The expression of long non-coding RNAs is associated with H3Ac and H3K4me2 changes regulated by the HDA6-LDL1/2 histone modification complex in Arabidopsis. NAR Genomics and Bioinformatics 2 (2020). 7 Berr, A. et al. The trx G family histone methyltransferase SET DOMAIN GROUP 26 promotes flowering via a distinctive genetic pathway. The Plant Journal 81, 316-328 (2015).

8 Kim, D.-H. and Sibum, S. Coordination of the vernalization response through a VIN3 and

        FLC gene family regulatory network in Arabidopsis. *The Plant Cell *__25, __454-469 (2013)

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Referee #3

Evidence, reproducibility and clarity

LDL1 and LDL2 histone demethylases interact with FVE to regulate flowering in Arabidopsis

Summary

This work study the role on flowering time of LDL1 and LDL2, two Arabidopsis homologs of the histone demethylase LSD1. Although this phenotype was previously described, the authors explore if LDL1 and LDL2 regulate other genes in addition to the floral repressor FLC. In fact, mRNA expression experiments and genetic analyse suggest that LDL1 modules flowering regulating the expression of MAF4 and MAF5, two FLC-like genes that has been less characterized. The also provide some in vitro biochemical evidence of the demethylase activity of LDL1 protein and yeast-two-hybrid data showing the interaction with FVE, another chromatin regulator involved in flowering time.

Major comments

1. Lines 116-117. Please rephrase these lines and remove panels C, D and E from figure 1 (these could be supplementary material). The flowering time phenotype of MAF4 and MAF5 in Col background is very well documented and was described before, see Gu et al Nat. Comm., 2013 (10.1038/ncomms2947) and Kim et al. Plant Cell, 2013 (10.1105/tpc.112.104760)
2. Lines 128-130 and Fig Sup3. The proLDL1:LDL1-GUS cannot be described as fully functional because its flowering time and LDL1 mRNA expression levels has not been compared to the wild-type plant. The line flowers earlier that the ldl1 mutant but it may only partially complement the flowering phenotype.
3. Line 135 and Figure 2. How the Chip data was normalized? What are you comparting in your statistical significance tests? Only two regions of each gene were analysed; to assess the binding of LDL1 to MAF4 and MAF5 loci more regions must be analysed.
4. Figures 6C and 6D. The genetic analysis of ldl mutant with fve-c line is prelaminar and incomplete. The epistasis cannot be evaluated as no quantitative flowering time data is provided. A questionable picture of one lonely plant cannot sustain the conclusions of lines 207-208.
5. METODS. Please clarify the used mutant alleles for LDL1 LDL2, MAF4, MAF5 and FLC; if they has been previously described; if they are full knock-outs; and, consequently, use the appropriated allele name across the manuscript.

Minor points:

1. I think the tittle does not describe the work - the interaction with FVE is very relevant but it is not the central theme of the article.
2. It would be very informative to have short-day flowering tome data of the genetic combinations of ldl mutants with flc, maf4 and maf5 mutations.
3. I found the Discussion section rather too long.

Significance

Although it is clear that LDL proteins regulate MAF4 and MAF 5. I found that the manuscript lacks of a general overview of flowering time regulation. At the end, it is not clear how LDL proteins regulate flowering time because they regulate FLC, FWA, MAF4 and MAF5: What is more important? Which is the main role of each protein? Are they reductant or do they have specialized functions?

In a nut shell, this study is an interesting piece of work for the flowering time field: However, in my opinion, some of the presented data are redundant with previous works and the manuscript may not be relevant for a general audience.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Mahima and colleagues investigated LDL1/LDL2-MAF4/MAF5 in Arabidopsis flowering time control. The manuscript contains some interesting observations. To my point of view, however, the data need to be consolidated to support conclusions drawn in the manuscript.

• Title: it does not correctly reflect the manuscript content. Data in relation with FVE were limited to Fig 6, where the data themselves appear preliminary.
• Abstract: most conclusions are over-stated. The current data shown in the manuscript cannot support such strong conclusions.
• Introduction: It is necessary to make clear that the role of the LDL1 and LDL2 genes in flowering time control had been well established in previous studies, including their repression of transcription of FLC, MAF4 and MAF5 (Berr et al., 2015, Plant J 81:316).
• Results:

Regarding LDL1-overexpression lines, 'Relative expression' in Supplementary Fig 2B referred to normalization to WT? The phenotype of plants needs to be shown.

Regarding flowering time, have the observation and measures been performed in the same experiments for the ldl1, ldl1 flc, ldl1 maf4 and ldl1 maf5 mutants (Fig 3 and Supplementary Fig 1)? The late-flowering phenotype of ldl1 shown in Fig 3D-F is much severe than the same mutant shown in the other Figs, any explanation? What's the interpretation that ldl1 is epistatic to flc, maf4 and maf5?

The in vitro test of LDL1 for its enzyme activity (Fig 4) appears preliminary and fragmented. The quantification data in Fig 4C-D need repeats. Have other histone methylation types (e.g. H3K4me3, H3K27me3, H3K36me3) been tested? The only two types (H3K4me2 and H3K9me2) shown are both down-regulated by LDL1-GST. Can H3K9 demethylation also play a role in flowering time control? In any case, the current in vitro data only are not sufficient to draw the strong conclusions as those appeared in the manuscripts.

In the manuscript, it is saying that LDL1 binds on the chromatin of MAF4 and MAF5. However, I cannot find any data shown to support this conclusion.

Protein-protein interactions, e.g. LDL1/LDL2-FVE in Fig 6A and LDL1-LDL2 and LDL1-HDA5 in Supplementary Fig 5, are examined in yeast two-hybrid assay. Other independent assays would be required.

The study of genetic interaction between fve and ldl1/ldl2 (Fig 6B-D) looks very preliminary. It is unclear how ldl1 fve and ldl2 fve were obtained: by crosses or by CRISPR-Cas9 using ldl1 and ldl2? The phenotypes need more investigations and some molecular data regarding flowering regulatory genes (e.g. MAF4/5) are necessary. In any case, the current title and the related conclusions drawn in the manuscript are over-stated.

Fig 7 showed data about MAF5-FLC, MAF5-SVP and MAF5-MAF5 interactions in yeast two-hybrid and about transcriptional repressor activity assay in tobacco leaves using the LUC-reporter. Again, the data need to be confirmed and reproducibility of experiments need to be shown. In addition to proFT:LUC, it is also necessary to have an internal normalization reference construct. Anyway, currently it is far away to allow a strong conclusion such as drawn in the manuscript that MAF5 interacts with FLC and SVP and repress FT to delay floral transition.

Significance

Topic is interesting, but data are poor to support the conculsions drawn.

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Referee #1

Evidence, reproducibility and clarity

The manuscript entitled "LDL1 and LDL2 histone demethylases interact with FVE to regulate flowering in Arabidopsis" characterized that LDL1 regulates flowering by binding on the chromatin of MAF4 and MAF5 to repress their expression. Further the authors proposed LDL1/LDL2-FVE model. Here are some comments for this manuscript.

Major problems:

1. This experiment is still not testing or showing/concluding that the whole complex forms on the MAF4 and MAF5
2. It is not shown LDL1/LDL2 repress MAF4 and MAF5 by removing H3K4me2 activity. It would be useful to test whether the methylation level of MAF4 and MAF5 has been altered in ldl1/ldl2 mutant
3. I suggest that further research is required to provide conclusive evidence concerning the physiology function of LDL1/LDL2-FVE. Such as the expression pattern of LDL1/LDL2, the methylation level of MAF4 and MAF5 before or after floral transition

Significance

The manuscript provide some evidences how LDL1 involve in flowering through epigenetic regulation.

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Reply to the reviewers

We are grateful to the reviewers for their efforts in critically reading our work. Their meaningful input led us to make the revisions detailed below in our “point-by-point” answers to the reviewer’s comments. The insightful comments have helped us significantly improve the manuscript, allowing us to more accurately quantify and convey our data – we are thankful for that.

Reviewer #1

1. The Figure 1 legend indicates that the BirA tagged strains are mated with ~6000 AviTag yeast strains but results in Figure 2 pie chart account for 4812 total readouts. Presumably 1000 or more strains could not mate or did not produce viable diploids with the BirA tagged strains? It would be helpful to explain this differential. We thank Reviewer #1 for pointing out this gap which occurred exactly as they have interpreted. We have now corrected the figure legend to say exactly how many strains were in the library (5330) and have clearly stated the attrition of strains.

If possible, suggest including more of the raw data (in supplementary) that supports the pie chart in Figure 2. Table S1 shows the 111 proteins that display preference for Ssh1 (out of 586 total interactors?) and the fold change (in rank order) for interaction preference. At a minimum, similar data on Sec61 preference and the list of positive interactors should be included. There may also be useful information in the relative biotinylation signal for each BirA and AviTag combination when significantly above background. This is presumably a readout of AviTag protein abundance, dwell time and orientation to BirA activity. The data could be useful to other investigators.

This is a very good suggestion. We have now added a supplementary table (Supplementary Table S2) with the interaction results for proteins that preferred Sec61 and proteins that did not show any preference.

The authors might want to be more cautious in interpreting impact of the UPR on ssh1 phenotypes in the results and discussion. The Wilkinson et al 2002 paper referenced used very different conditions to detect UPR in ssh1 deletions strains. Jonikas et al 2009 does not detect a chronic UPR in ssh1 deletion cells and the conditions used in the current study seem more similar to the 2009 report. It seems more likely that deficits in translocating/localizing specific proteins causes the observed phenotypes instead of chronic UPR due to reduced ER levels of PDI.

*We agree that as result of the different conditions it is difficult to compare our data to the Wilkinson et al 2002 paper. We have therefore adjusted the text to remove this interpretation. *

Reviewer #2

1. Why was BirA used to study transient interactions? Biotinylation through BirA is slow (that is why it takes several hours to label proximity proteins) and thus it may not be suitable for capturing transient interactions. Instead, TurboID would be more suitable as the biotinylation reaction is faster than BirA. A reasonable explanation using BirA is required. We thank the reviewer for this comment which indeed also reflects our “process” of thinking. Originally, we did try to use TurboID to identify potential cargo proteins. We now have a very robust methodology to look at protein substrates by TurboID (see: https://www.biorxiv.org/content/10.1101/2022.04.27.489741v1) and so this would have obviously been the easier and faster method. However using this approach we mainly observed ribosome subunits and cytosolic proteins for Sec61 and very few, mostly cytosolic, proteins for Ssh1. Our interpretation of this is that since all biotinylation of TurboID strains occurs in parallel there is “competition” from the highly abundant and strong interactors and this does not leave a possibility to detect the low-abundance and even more transient interactions that we would like to measure. It seems that although birA/AviTag are much slower, the specificity and singular ligation site that should be exposed also in co-translational-translocation events, are more suitable for this specific experimental setup. We have now explained this also in the text.

One key question is whether biotinylated proteins identified by this method are substrates or proteins just proximal to Sec61 or Ssh1 due to close cellular localization (e.g. ER membrane) or same protein complex members. An experiment or analysis would be required to confirm that the proteins they identified are indeed potential substrates.

*This is indeed an extremely important point and we have now carefully addressed it in the text. We are certain that the reviewer is right and that many of the biotinylated proteins are same complex members and cytosolic components that happen to be in proximity (maybe regulators?) just as the reviewer suggested. We now clearly write this in the results section. This is why we focused on signal peptide containing proteins. These proteins CAN NOT be complex members nor biotinylated simply due to proximal location on the ER membrane. This is since they reside inside the lumen of the ER if they are soluble or are inserted (if they contain also a transmembrane domain) with their tagged N’ facing the lumen of the ER (So called Type I proteins). The only way such proteins could be biotinylated by the slow BirA on the cytosolic surface is if they passed through the pore of the translocon. *

Along the same line, if proteins identified by this approach are bona fide substrates of Sec61 and Ssh1, proteins having signal peptides should be enriched in the candidate list of substrates. However, it does not look like that according to Figure 2A where the secretome proteins/total proteins ratio appears to be similar among the 4 categories (e.g., Ssh1 preferring, No preference, and Not interacting or excluded). The authors should comment on this.

*We thank Reviewer #2 for highlighting this point that was not clear from our text and figures. There is definitely an enrichment of Signal Peptide (SP) containing proteins amongst the proteins that we think are bona fide substrates however this was not visualized clearly. To highlight this point we have modified Figure 2 and added a bar graph showing the distribution of SP and TMD proteins within the potential secretome. This graph now highlights the enrichment of SP containing proteins in the groups of proteins that preferred Sec61 or Ssh1 in comparison to the group that did not show a preference. *

*We also now add a citation from a new manuscript from the Hegde lab that suggests that indeed soluble SP containing proteins are the key clients for the translocon pore (https://pubmed.ncbi.nlm.nih.gov/36261528/). We have also added a section to the discussion as to why we do not see differential enrichment of SRP or its receptor on either pore although in the past this was suggested to be the key difference between the two translocons. *

Figures 1-2: They should comment on the reproducibility of the method. How many independent experiments were performed? If performed, how was reproducibility of results?

Thank you for highlighting that this was not clarified enough – we have now extended the materials and methods section to make all of the above issues clear and apparent. In short, we performed 3 biological repetitions for each experiment and for each biological repeat we performed 3 technical repeats making our results altogether rely on 9 repeats. We then excluded proteins in two cases

1. If strains were missing in either of the collections (so there was no complete set to compare them) – this caused us to drop 661 strains.
2. In cases where the proteins were expressed at very low levels of extracted poorly in our assay – we defined this as the signal being ten standard deviations (or more) lower than the rest of the signals on the same membrane – this caused us to lose an additional 93 strains. Importantly, the SD between all 9 repeats never rose above 3 (see graph below showing al strains arranged by order in library and the SD between all 9 repeats) and also now stated clearly in the text) hence we think that our method is highly reproducible

Figure 3: It is important to know the overlap of proteins commonly identified in both the interaction screening and protein localization assay. A Venn diagram that compares results between the two high-throughput assays would be useful.

*We have indeed considered making this Venn diagram (See below). However, since the connection between the screens is not direct due to the fact that the protein localization is downstream to translocation events or maybe completely independent of it, we found that the number of specific proteins that are in both screens is low. However, there is a much larger overlap in joining processes and functions, therefore we decide to make the grouping showed in Figure 4B. We would prefer not to show this figure in the manuscript however we leave this to editorial decision. *

Figure 4A (GO term): The authors mentioned that " the most consistent and repeating GO term group was those related to budding and polarity process. These include: "Establishment or maintenance of cell polarity"; "Development process involved in reproduction"; "Bipolar cellular bud site selection"; "Cell budding" and "Structural constituent of cell wall". Are protein sets in these functional categories similar or different? I am asking because GO enrichment analysis often provides apparently different functional categories but similar protein sets are included.

Indeed, this reviewer is totally correct and this is also the case here to some extent. There is some level of overlap between the GO terms. However, in our case this overlap is quite small: Out of the 77 genes that have one of those GO terms assigned only 2 have all 4, 9 have 3 and 4 have 2 of the GO terms therefore we believe that in this case this issue with GO terms hierarchy and assignment is not redundant. We are happy to highlight this in the figure or text if this is deemed to be important.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Cohen et al. presented a high-throughput approach to analyze protein-(putative) substrate interactions in yeast using BirA biotin ligase and its acceptor peptide AVI tag. Using this system, the authors identified the common and unique substrates of translocation pores, Sec61 and Ssh1. Interestingly, the differential substrates between Sec61 and Ssh1 seem to be explained by the degree of hydrophobicity in signal peptide sequences, which was also nicely demonstrated by an experiment showing that swapping the first three amino acids of substrate proteins is sufficient to convert the substrate specificity. While I appreciate that the approach is high-throughput and simple (does not require mass spectrometers), there are some technical comments to be addressed.

1. Why was BirA used to study transient interactions? Biotinylation through BirA is slow (that is why it takes several hours to label proximity proteins) and thus it may not be suitable for capturing transient interactions. Instead, TurboID would be more suitable as the biotinylation reaction is faster than BirA. A reasonable explanation using BirA is required.
2. One key question is whether biotinylated proteins identified by this method are substrates or proteins just proximal to Sec61 or Ssh1 due to close cellular localization (e.g. ER membrane) or same protein complex members. An experiment or analysis would be required to confirm that the proteins they identified are indeed potential substrates.
3. Along the same line, if proteins identified by this approach are bona fide substrates of Sec61 and Ssh1, proteins having signal peptides should be enriched in the candidate list of substrates. However, it does not look like that according to Figure 2A where the secretome proteins/total proteins ratio appears to be similar among the 4 categories (e.g., Ssh1 preferring, No preference, and Not interacting or excluded). The authors should comment on this.
4. Figures 1-2: They should comment on the reproducibility of the method. How many independent experiments were performed? If performed, how was reproducibility of results?
5. Figure 3: It is important to know the overlap of proteins commonly identified in both the interaction screening and protein localization assay. A Venn diagram that compares results between the two high-throughput assays would be useful.
6. Figure 4A (GO term): The authors mentioned that " the most consistent and repeating GO term group was those related to budding and polarity process. These include: "Establishment or maintenance of cell polarity"; "Development process involved in reproduction"; "Bipolar cellular bud site selection"; "Cell budding" and "Structural constituent of cell wall". Are protein sets in these functional categories similar or different? I am asking because GO enrichment analysis often provides apparently different functional categories but similar protein sets are included. 

Referees cross-commenting

The comments from reviewer #1 are reasonable and would further strengthen the quality of the paper.

Significance

The approach is high-throughput and simple (does not require mass spectrometers).

The differential substrates between Sec61 and Ssh1 seem to be explained by the degree of hydrophobicity in signal peptide sequences, which was also nicely demonstrated by an experiment showing that swapping the first three amino acids of substrate proteins is sufficient to convert the substrate specificity.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript the authors develop an unbiased method to measure protein-protein interactions in cells through application of BirA biotin ligase technology. The novel feature of the approach is to append BirA to homologous ER translocon proteins (Sec61 and Ssh1) and then measure biotinylation of the AviTag when fused to the N-terminus of each protein in the yeast proteome. This is accomplished by mating the specific BirA tagged strain with a collection of 6000 yeast strains each with a distinct AviTagged protein. The level of AviTag biotinylation in diploid strains is measured by probing cell lysates applied to filters with fluorescent streptavidin. 2070 proteins displayed interactions with BirA-Sec61 and/or BirA-Ssh1 with a subset of secretory proteins showing preference for one of the translocons. The influence of ssh1 deletion on localization of the GFP-tagged protein library (4127 strains) was also determined and compared with protein interaction data. Moreover, analyses of signal peptides revealed that hydrophobicity of the N-terminal three residues can be sufficient to impart specificity for Sec61 or Ssh1 interaction. The reported findings support their primary conclusions. I have only a few suggestions to strengthen this study.

1. The Figure 1 legend indicates that the BirA tagged strains are mated with ~6000 AviTag yeast strains but results in Figure 2 pie chart account for 4812 total readouts. Presumably 1000 or more strains could not mate or did not produce viable diploids with the BirA tagged strains? It would be helpful to explain this differential.
2. If possible, suggest including more of the raw data (in supplementary) that supports the pie chart in Figure 2. Table S1 shows the 111 proteins that display preference for Ssh1 (out of 586 total interactors?) and the fold change (in rank order) for interaction preference. At a minimum, similar data on Sec61 preference and the list of positive interactors should be included. There may also be useful information in the relative biotinylation signal for each BirA and AviTag combination when significantly above background. This is presumably a readout of AviTag protein abundance, dwell time and orientation to BirA activity. The data could be useful to other investigators.
3. The authors might want to be more cautious in interpreting impact of the UPR on ssh1 phenotypes in the results and discussion. The Wilkinson et al 2002 paper referenced used very different conditions to detect UPR in ssh1 deletions strains. Jonikas et al 2009 does not detect a chronic UPR in ssh1 deletion cells and the conditions used in the current study seem more similar to the 2009 report. It seems more likely that deficits in translocating/localizing specific proteins causes the observed phenotypes instead of chronic UPR due to reduced ER levels of PDI.

Significance

The reported findings support their primary conclusions. The technology development and results are significant, highly relevant and will be of interest to a broad readership in cell and membrane biology.

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Reply to the reviewers

1. General Statements

        We greatly appreciate the valuable comments from the referees, which have generally been very positive and constructive. The three referees have emphasized the significance of our study that opens a new direction of research regarding the role of RNA modification in viral defense. In addition, the reviewers confirm our view that the audience of our work would be broad.

        The major concerns of the reviewers are limited to four main points:

1. i) to be clearer in our description on the effect of the m6A-YTHDF axis on the viral infectivity and avoid making assumptions on effects on replication (ref. #1 and #3);

2. ii) reviewer 1 finds that the title and conclusion of this manuscript defining YTHDF proteins (ECTs) as "direct effectors of antiviral immunity" is misleading. Nonetheless, as detailed below, Reviewer 1 confuses mere knowledge of effects of m6A with those conferred by YTHDF proteins binding to m6A, and indeed overlooks nearly all evidence presented in the paper for how m6A in AMV confers antiviral resistance (i.e. mechanistic insight); iii) the discussion on the relative importance of antiviral RNA silencing and m6A-YTHDF against AMV;

3. iv) to establish more clearly whether the phase separating capability of IDRs in the reading proteins correlates with the antiviral activity (reviewer 2). We have already completed substantial experimental work to address several of these points. Nonetheless, we find it prudent to ask for an extension of the revision time beyond four weeks to allow for repeats of a few of the infection experiments in question. In the following section, we specify a plan of action for the revisions.

2. Description of the planned revisions

• *Regarding the four major concerns raised by the reviewers, we will experimentally address the last two, whereas we think the first two do not need any further experimental work, as explained in section 4. Thus, the working plan for points #3 and #4 will be as follows:

iii) the discussion on the relative importance of antiviral RNA silencing and m6A-YTHDF against AMV and related viruses

As we mention in the manuscript (discussion, first chapter), AMV *“is one of only very few studied plant RNA viruses for which no anti-RNAi effector has been identified. In addition, prunus necrotic ringspot virus (PNRSV), a virus genetically and functionally closely related to AMV (Pallas et al, 2013), does not induce easily detectable siRNAs, unlike nearly all other studied plant RNA viruses (Herranz et al, 2015)”. *

Thus, we do not come up with a strong judgment on whether RNAi is more or less important than m6A-YTHDFs for AMV resistance.

In any case, although these indirect observations seem to be quite solid, we agree with the reviewer that conclusive evidence to discard RNAi as a defense layer against AMV, at least at the time where ECTs are acting, is lacking. Thus, we plan to evaluate how the absence of the main components of the RNAi machinery affects AMV infection and if this ‘universal’ defense layer interferes/overlaps with the ECTs antiviral defense observed here. Realistically, this will take us 8-10 weeks. The experiments within this topic are based on established and published methods and thus, on solid experience. We do not expect any fallback solution and the results will be conclusive in this sense. We also note that the very time-consuming part of constructing mutants defective in both RNAi and m6A-ECT components (in this case, ect2/ect3/rdr6), as well as a first round of infection assays has already been completed at this point

iv) To establish more clearly whether the phase separating capability of IDRs in the reading proteins correlates with the antiviral activity (Reviewer 2).

We agree with Reviewer 2 that this is an interesting and important question. Hence, we have teamed up with the group of Prof. Kresten Lindorff-Larsen, expert in molecular simulations of protein folding and interaction. The Lindorff-Larsen group has recently published a powerful computational approach to simulate phase separation behavior of intrinsically disordered proteins (IDPs) or regions of proteins (IDRs) (Tesei et al., 2021, Accurate model of liquid-liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties, PNAS 118, (44) e2111696118). Applying this simulation method to the Arabidopsis ECT proteins establishes two facts that we will incorporate into a revised version:

• The IDR of ECT2 shows marked phase separation propensity, in agreement with the experimental evidence published in Arribas-Hernández et al., 2018, Plant Cell.
• The deletion mutant of ECT2 (ΔN5) with defective antiviral activity, yet unaffected ability to accelerate growth of leaf primordia shows markedly reduced phase separation propensity driven, in the main, by the many tyrosine residues in the region deleted in the mutant. These results suggest that phase separation capability indeed correlates with antiviral activity.

Since not only ECT2, but also ECT3, ECT5 and, to some extent, ECT4, participate in AMV resistance, we plan further simulation work on these proteins during the first two weeks of January 2023 before submission of a revised version of the manuscript.

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

• • All the minor concerns raised by the three reviewers have been addressed and we have incorporated all of their suggestions in this intermediate version.

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

• *As previously mentioned, we believe that points 1 and 2 do not require an experimental approach to be addressed for the following reasons:

i) to be clearer in our description on the effect of the m6A-YTHDF axis on the viral infectivity and avoid making assumptions on effects on replication (ref. #1 and #3)

        We agree with the reviewer that the term 'inhibition of viral replication' was not very appropriate because the idea that was intended to be conveyed was that of viral accumulation.        Hence, we will change this use of language, and we thank the reviewer for pointing out this inaccurate description.

When it comes to differences between effects on infection in inoculated and non-inoculated leaves, there may be a slight misunderstanding, perhaps because we were not clear enough in our originally submitted version. In reality, there are some differences even in inoculated leaves between wild type and ect mutants, especially in the triple mutant, but the slightly higher accumulation in ect mutants is not clearly observed in every experiment and hence, does not always rise to the level of significance. Although it is possible that, at local level, ALKBH9B-mediated m6A would have other ECTs-independent effects, similar to what has been described for some animal viruses (Baquero-Pérez et al., 2021. Viruses), we think that the most likely explanation for this phenomenon is a combination of infection titers and ECT redundancy.

The suggestion to use protoplasts is very accurate, but it would not resolve any doubt in this scenario, because ECTs are mainly expressed in mitotically active cells (Arribas-Hernández et al, 2020, 2018) and, since mature tissues make up the better part of the leaves used to isolate protoplasts, only few of the isolated cells would be useful. In addition, we previously showed that AMV accumulation is reduced in alkbh9b protoplasts compared to WT (Martínez-Pérez et al., 2021. Front. Microbiol.), which suggests that m6A levels of vRNAs are critical for the first stages of the infection, but in that case no problems with the expression pattern of the demethylase were expected.

ii) The title and conclusion of this manuscript defined YTHDF proteins (ECTs) as "direct effectors of antiviral immunity", which is misleading. Effector molecules of an antiviral immunity cannot be identified when the effector mechanism is unknown;

In this regard, we have a very different vision from the one the reviewer proposes. We believe that it is not correct to say that the effector molecules of an antiviral immunity cannot be identified until its mechanism is demonstrated. In fact, RNA silencing effectors were discovered long before their mechanism was elucidated in detail. One molecular interpretation of the Flor’s seminal gene-for-gene model, in terms of receptor/effector recognition, is that specific interaction between the receptor and its recognized (cognate) effector protein triggers resistance.

Furthermore, we strongly believe that we provide enough arguments to propose a model, although, as we comment in the end of the discussion, “we view this model as a conceptual framework of value in the design of future experiments to test its validity”. The reasoning that we show here is the following:

1. The m6A binding proteins are necessary for the antiviral response.
2. At least ECT2 recognizes AMV RNAs in vivo and that its m6A-binding capacity is necessary to play a role in AMV infection.
3. Simply losing methylase activity – with the same developmental defects as ect2/3/ – does not lead to the same degree of loss of resistance, and you can affect AMV resistance without affecting developmental functions of ECT2. Altogether, these observations justify the proposal that m6A exerts antiviral effects by acting as binding sites of ECT proteins in viral RNA, which we consider a clear mechanistic advance.

Bearing in mind that m6A-modified vRNAs might concentrate in replication complexes and that MeRIP-seq methodology to map m6A revealed site multiplicity in the genome of some RNA viruses (Gokhale et al., 2016. Cell Host&Microb; Martínez-Pérez et al., 2017; Lichinchi et al., 2016. Nat Microbiol; Lichinchi et al., 2016. Cell Host&Microb; Marquez-Molins et al, 2022), our results recalled the previously proposed model in which m6A sites multiplicity causes the phase separation of these RNAs through the interaction of the IDRs of the YTH proteins (Ries et al, 2019; Fu & Zhuang, 2020; Gao et al, 2019). Now, with the new simulations of phase separation behavior, although still a model that requires further experimental tests, we have better evidence to support the model that it is related to LLPS of ECT-bound viral RNA. Therefore, we firmly believe that our title conceptually reflects the basic concepts of resistance induction in virus-plant interactions.

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Referee #3

Evidence, reproducibility and clarity

Previously, the authors showed evidence that m6A modifications of AMV RNAs erased by the host ALKBH9b enhances AMV spread in Arabidopsis. In this paper, the authors show by transcriptome analysis and RT-qPCR that the accumulation of m6A "reader" proteins, ECT2, 3 and 5 are increased during AMV infection in Arabidopsis. Combined mutations of ect2,3 and 5 led to increased AMV accumulation, suggesting that ECT2,3,5 are critical in inhibition of AMV accumulation in systemic leaves of Arabidopsis. Mutagenesis of ECT2 putative m6A-binding pocket did not restore AMV resistance in double-mutant de23 plant, arguing that m6A reader function of this protein is needed to provide resistance against AMV. Then, proximity-labeling was used to show that ECT2 binds to AMV RNA2 and likely RNA1 in planta. Finally, debilitating both the m6A eraser (ALKBH9b) and the reader (ect2,3,5) restored susceptibility to AMV infection in Arabidopsis, thus providing evidence that the m6A reader proteins are critical for resistance against AMV and that AMV exploits ALKBH9b to fight against ECT2,3,5 in plants. Altogether, these are novel and important findings. The paper is well-written.

I do not have main concerns.

Minor points:

• Abstract: "AMV replication" should be replaced by "AMV infection" or "AMV spread", because the locally infected leaves show similar AMV replication/accumulation in de23 and wt Arabidopsis.

• Alkbh9b mutation causes inhibition of local and systemic movement of AMV, whereas de23 mutant increases AMV accumulation only in systemic leaves (Fig. 1). What is the explanation that de23 does not affect local movement of AMV?

• The last chapter (p19-20) is too speculative and too long, it does not make the paper more interesting: I recommend shortening it and to minimize speculation.

Significance

This paper shows evidence for a new antiviral strategy present in plants. Overall, this is a significant new finding.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this manuscript, the authors asked the important question of how RNA modification is associated with viral defense in plants. Based on the previous findings that the infectivity of AMV relies on demethylation of viral RNA by recruitment of the cellular m6A demethylase ALKBH9B, in this study, they showed that inactivation of the m6A reader proteins, ECT2, ECT3, and ECT5, is sufficient to restore AMV infectivity in partially resistant alkbh9b mutants. Considering the potential roles of m6A modification in viral defense but the limited knowledge on this topic, the current study opens a new direction of research regarding the role of RNA modification in viral defense.

Major comments

• It is interesting to see that the IDR of ECT2 harbors two separable activities employed to achieve different goals: one that stimulates cellular proliferation by binding to endogenous m6A-containing mRNA, and one that effects basal antiviral resistance when ECT2 binds to hypermethylated viral RNA. Considering that the IDR of a protein contributes the chaperone activity of the protein and then can increase the binding capacity of the protein to different substrates, it would be more informative if the authors discuss whether the RNA chaperone activity of IDR of ECT2 is possibly involved in the different processes.

• It is interesting to propose a working model that m6A site multiplicity in AMV RNA may be a key factor distinguishing it from endogenous mRNA. In that sense, it would be clearer if the authors describe how many m6A sites are present in AMV RNA. Are these m6A sites clustered in certain regions of the viral RNA important for viral replication?

• It is not clear what the correlation between the phase separation capability of ECT proteins and viral infection is.

Minor comments

• In page 6, what is the alfalfa mosaic virus (AMV) RNA 3? Are there AMV RNA 1 and 2? What are their differences? Is the m6A-YTH module specific to AMV RNA 3?

• Name of the mutants; it would be better if same name was used for the mutant throughout the manuscript and in figures; for instance, ect2-1/ect5-2 and de25, as well as ect2-1/ect3-1/ect5-4 and te235 were used, which is not easy to follow.

• Figure 4G legend is missing.

Significance

Considering the potential roles of m6A modification in viral defense but the limited knowledge on this topic, the current study opens a new direction of research regarding the role of RNA modification in viral defense.

The audience will be broad, including any persons who are working on epitranscriptomics, plant sciences, viral infection, and clinical application. I am working on epitranscriptomic RNA modification in plant development and abiotic stress responses.

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Referee #1

Evidence, reproducibility and clarity

The authors have shown previously that local and systemic infection of alfalfa mosaic virus (AMV) is inhibited and the relative abundance of m6A in the viral RNAs is increased in mutant Arabidopsis plants defective in the m6A demethylase gene ALKBH9B. Here the authors show that genetic inactivation of 2 or 3 m6A-binding proteins (ECTs or m6A readers) enhances the systemic, but not the local, infection of AMV. Notably, the systemic virus resistance of the demethylase mutant plants is largely eliminated by inactivating the same set of ECTs. Moreover, the authors detected in vivo association of ECT2 with AMV RNAs and identified a N-terminal motif of ECT2 that is necessary for AMV resistance, but dispensable for its role in endogenous developmental functions. These findings together provide further evidence to support their earlier conclusion for an antiviral role of m6A methylation of viral RNAs. Unfortunately, several key questions remain unknown and should be addressed to justify publication in EMBO J.

Major comments

1. The authors reported in 2017 that both the local and systemic infection of AMV is inhibited in the m6A demethylase mutant plants (PNAS 114:10755-60). In this work, they show that inactivation of ECTs enhances only the systemic AMV infection, but has no effect on the local infection (Fig. 2). Studies presented in neither Fig. 5 nor Fig. 6 examined possible effects on the local virus infection. Moreover, line 1 of page 13 mentioned a model that ECT binding "causes inhibition of viral replication". To resolve these contradictory descriptions on the step of the virus infection cycle targeted by m6A RNA methylation, it is essential to perform protoplast replication assays to determine whether the mutation of either the demethylase gene or ECTs affects viral RNA replication at single-cell level and to examine local infection for the studies presented in Figs 5 & 6.

2. The title and conclusion of this manuscript defined YTHDF proteins (ECTs) as "direct effectors of antiviral immunity", which is misleading. It remains completely unknown why m6A methylation of viral RNAs is inhibitory to virus infection. The available data suggest that it may act by blocking virus replication and/or movement or by enhancing any of the several known antiviral responses. Effector molecules of an antiviral immunity cannot be identified when the effector mechanism is unknown.

3. At the end of page 13 and elsewhere in this manuscript, the authors conclude that "the m6A-ECT axis constitutes a first, basal layer of antiviral defense", a conclusion that is not supported by the evidence presented. This conclusion will be incorrect if m6A methylation of viral RNAs does not inhibit virus accumulation levels in the protoplast virus replication assays as requested above.

4. In the first paragraph of page 17 and elsewhere in this manuscript, the authors question the relative importance of antiviral RNA silencing against AMV and related viruses as compared to m6A RNA methylation. It is important to determine if AMV becomes more virulent in RNAi-defective mutant plants such as dcl2/4 double mutant and if m6A RNA methylation also confers AMV resistance in RNAi-defective mutant plants.

Minor comments:

1. State the time post-inoculation when the samples were taken for the RNA-seq analysis in Fig.1.

2. State whether all of the northern blotting experiments used RNA extracted from single plant or pooled plants and had been repeated.

3. Verify that the statistical analysis method used in virus titer quantitative analysis is student t-test or one-way ANOVA.

4. The legend to Fig. 4F should be Fig. 4F and 4G.

5. Explain what ∆2 means in Fig. 6B.

6. If both the left and right bars correspond to 1 cm, 9-DAG-old plants would be much bigger than 16-DAG-old plants, which cannot be true.

Significance

Advance:

The authors have shown previously that local and systemic infection of alfalfa mosaic virus (AMV) is inhibited and the relative abundance of m6A in the viral RNAs is increased in mutant Arabidopsis plants defective in the m6A demethylase gene ALKBH9B. Here the authors show that genetic inactivation of 2 or 3 m6A-binding proteins (ECTs or m6A readers) enhances the systemic, but not the local, infection of AMV. Notably, the systemic virus resistance of the demethylase mutant plants is largely eliminated by inactivating the same set of ECTs. Moreover, the authors detected in vivo association of ECT2 with AMV RNAs and identified a N-terminal motif of ECT2 that is necessary for AMV resistance, but dispensable for its role in endogenous developmental functions. These findings together provide further evidence to support their earlier conclusion for an antiviral role of m6A methylation of viral RNAs.

Audience:

Broad, including those interested in RNA modifications, antiviral immunity, and plant biology.

Your expertise:

Antiviral immunity, plant biology

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Reply to the reviewers

Reviewer 1

Although this is an interesting, and generally well-performed study, it is primarily observational and there are few mechanistic insights provided into how MUC13 modulates barrier function. The authors propose a presumably direct interaction between MUC13 and PKC, which apparently sequesters PKC, preventing this kinase from triggering PKC-dependent increases in TJ barrier function; however, there is no evidence that a MUC13-PKC interaction occurs, that MUC13 is phosphorylated by PKC, or that phosphorylation of MUC13 has any impact on its function or overall barrier function. Thus, the hypothesis is not directly tested and all observations in this manuscript are generally correlative in nature.

While the MUC13 cytoplasmic tail contains a putative PKC-binding motif, we indeed do not show a direct interaction between MUC13 and a member of the PKC family in this manuscript. Unfortunately, we have so far not been able to successfully perform (co-)immunoprecipitation of MUC13 with our current anti-MUC13 antibodies.

To provide more insights into the possible MUC13-PKC interaction, we plan to perform several experiments.

• First, we will determine the expression levels of the different PKC isotypes (PKC alpha, beta, gamma, delta, epsilon, and zeta) in the HRT18 cell lines by western blot.
• Next, we will determine the localization of the relevant PKC isoforms and MUC13 by immunofluorescence microscopy. We are curious to see if we can find a colocalization between MUC13 and a PKC member on the lateral or apical membrane. If we can demonstrate a colocalization, we could follow up with a proximity ligation assay, but this would require the MUC13 antibody directed against the cytoplasmic tail (which only detects the lateral population) and might therefore be challenging.
• Furthermore, since PKC delta protein levels were upregulated in the total lysate of ∆MUC13 cells, we will test a PKC delta-specific inhibitor in the TEER assay.

  Consider quantifying all blots (Fig. 5C, Fig. 6B).

As suggested, we will quantify both blots.

Consider using dot-plots for all quantified data.

The graphs will be altered to include individual measurement points.

Reviewer 2

Fig2E showed two bands with different size in the two MUC13 WT control cell lines. They hypothesized that this could be the consequences of glycosylation different patterns. A sample with untransfected HRT18 might be included in the western blot panel. Additionally, what is the 100kDa band?

Mucin blots are notoriously difficult and these MUC13 blots are the result of a lot of trial and error. We repeated the Western Blot with original HRT18 cells, HRT18 original cell line, as well as the two CRISPR control cells used in the study (WT 1 and WT 2) and one of the full-length MUC13 knockout cells. The higher band was absent from the MUC13 knockout cells, but a small shift in the MUC13 band size can be noted in the WT 1 cells compared to the original and the WT 2 cell lines, possibly indicating a change in the glycosylation pattern. The 100 kDa band remains detectable in all cell lines including the ∆MUC13 cell line, therefore we consider this to be an aspecific background band of the MUC13 antibody. We will add a more extensive Western Blot analysis to the manuscript.

Did the transfection of the inducible GFP-MUC13 plasmid induce any decrease of Claudin1/3/4 in HRT18 or Caco2 cells? Same question regarding PKCdelta.

These are indeed interesting questions. We will perform these experiments with our MUC13-overexpression HRT18 cells.

Reviewer 3

Moreover, the authors should determine if MUC13∆CT localize to TJs, as suggested by the working model in Figure 7C. The subcellular localization of MUC3∆CT could give critical clues for its function, but Figure 2G fails to provide any information and the authors do not present any additional data concerning the localization of MUC13∆CT. Detection of MUC13 in membrane fractions of WT, MUC13∆CT and cells lacking the mucin domain could be a feasible strategy forward.

We will perform additional immunofluorescence experiments to determine the subcellular localization of MUC13-∆CT more accurately. However, detection of the extracellular domain by western blot, as suggested, is not possible due to the incompatibility of the extracellular MUC13-directed hybridoma antibody with the western blot technique. We currently do not have a suitable antibody that recognizes the ED and can be used for western blot.

The authors introduce an inducible MUC13-GFP fusion protein into WT and ∆MUC13 cells and show that it reverses the enhanced TEER upon MUC13 deletion. Unfortunately, the "Materials and Methods" section lacks adequate information on how this fusion protein was designed. Critical questions are the position of the GFP tag within MUC13, whether the fusion protein is correctly processed in HRT18 cells, and if it localizes to the apical or apico-lateral membrane domains? Figure 2H is of low magnification and fails to provide information on the subcellular localization of the MUC13-GFP fusion protein.

The materials and methods section will be adjusted to describe all the design details of the fusion protein. The GFP tag was added to the MUC13 C-terminus with a GGGS linker sequence in between. Processing of the fusion protein seems correct as we observed MUC13-GFP localization to both lateral and apical membranes and no access intracellular build up. As suggested by the reviewer, we will add more detailed immunofluorescence pictures to the manuscript.

Figures 6B-C suggest that PKCdelta levels increase in ∆MUC13 cells, which correlates with higher enrichment of Claudins in membrane fractions. The authors then inhibited PKCdelta and observed reduced recruitment of Claudins to membrane fractions. Since the family of Claudins are differentially regulated by phosphorylation (PMID: 29186552), the authors should investigate the TEER phenotype of WT, ∆MUC13 and MUC13∆CT upon PKC inhibition.

We must clarify that figures 6C-D are done using the PKC inhibitor targeting all conventional PKCs (alpha, beta, gamma) as well as delta (https://www.tocris.com/products/gf-109203x_0741). We recently obtained a PKCdelta-specific inhibitor which we will test in the TEER build-up experiments.

Moreover, the authors predict phosphorylation sites in MUC13CT and suggest a link between PKC and MUC13 (Figure. 6A), however no evidence is presented to support this hypothesis. The authors should either determine if PKC phosphorylates MUC13 and if this modification has implication on MUC13 localization and TJ function, or remove statements regarding MUC13 phosphorylation. The data provided suggest that PKC regulates TJ proteins independent of MUC13.

We will adjust the manuscript to put less emphasis on the putative PKC motifs in the MUC13 cytoplasmic tail. For further details on how we will proceed regarding the possible MUC13-PKC interaction see question 1 from reviewer #1.

Figure 5C. Quantification of at least 3 independent experiments is required.

These data will be added to the manuscript.

Figure 6B. Quantification of at least 3 independent experiments is required.

These data will be added to the manuscript.

Reviewer 4

OPTIONAL: MUC13 is expressed both, in the basolateral membranes and in the apical membrane of intestinal epithelial cells (IECs). Does the authors check the relevance of MUC13 in the formation of microvilli in IECs? Are microvilli different (microvilli staining, number of positive cells to microvilli, length, width or distribution of microvilli) in ΔMUC13 and in MUC13-ΔCT? How the glycocalyx looks like in these cells genetically modified for MUC13?

HRT18 cells do not seem to develop microvilli. However, we plan to stain these cells with a microvilli-specific antibody (ACTUB). The HRT18 cells express mostly MUC13 and relatively low levels of the larger TM mucin MUC1. To study changes in the glycocalyx, we will stain using a MAL-II antibody which targets α-2,3 sialic acids, which are abundantly present in mucins. In this way, we will determine any big changes in the total glycocalyx that may occur in response to the removal of MUC13.

In the figure 1D would be nice to represent the co-localization of MUC13 together with occluding in a graph in each Z-stack so you can visualize in which part of the cell is maximum colocalization of these both components.

These data will be provided.

In the figure 1E, would be great to compare between the two different MUC13 antibodies the apical fraction stained in HRT18 and Caco-2. Specially in the HRT18 cell line since the first antibody did not label apical MUC13 expression meanwhile the second antibody detects the apical expression in these cells. How much lateral lateral stain the C terminal antibody compare with the extracellular antibody for MUC13 and how much stain apically the C terminal antibody compare with the extracellular antibody? Would be nice to see some comparative results using the intensity by Z-stack and plotting in a graph.

This is a good suggestion as it is quite intriguing that both MUC13 antibodies seem to target (partially) different MUC13 populations. We will perform co-staining with both MUC13 antibodies to provide information on which MUC13 populations are detected by each antibody (apical vs lateral membrane).

Manuscript would be improved if in the figure 2H to compare within the same cell line the number of MUC13 positive cells in the WT, number of MUC13 positive cells in WT+pMUC13 and the number of MUC13 positive cells in the ΔMUC13+pMUC13

We will quantify the percentage of MUC13-GFP positive cells in both the WT and ΔMUC13 backgrounds by either microscopy or flow cytometry.

In figure 5C would be helpful to plot in a graph the normalized expression of each TJ protein and compare between the different cells used (WT, ΔMUC13 and MUC13-ΔCT) as you did in figure 5A

We will provide the quantification data of three independent experiments.

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

Reviewer 1

In addition, this model does not explain why all kinase inhibitors tested reverse the increase in TER observed in deltaMUC13 cell lines. Does this reflect the lack of inhibitor specificity or the likelihood that many kinases are involved?

As stated in the manuscript, we think that MLCK, ROCK, and PKC are all essential for TER buildup in the ∆MUC13 cells. Because the roles of MLCK and ROCK are well established, we choose to follow up on the PKC results. We adjusted the text to clarify this point.

The authors do observe that there is an increase in expression of several tight junction-associated proteins, including the claudins, in deltaMUC13 cells. Affected CLDNs include 1, 2, 3, 4, 7, 12. (1) While it appears the authors are arguing that this increased claudin expression results in increased barrier function, they do not sufficiently highlight the well-known role that CLDN2 has in cation transport, and both CLDN-4 and -7 have also been implicated in paracellular ion flux (although this is apparently cell-type specific). These observations would seem to argue against a simple correlation between claudin expression and tight junction barrier function.

The reviewer is right about the different functions of claudins. Claudin-2, -4 and -7 have (potentially) pore-forming properties, while the other claudins restrict paracellular passage. It has been previously demonstrated that the magnitude of paracellular ion and water flux is reflected by the specific repertoire of claudin family members (Shashikanth et al., 2022). In this paper, overexpression of claudin-4 was shown to mobilize and affect polymeric strands of claudin-2, thus blocking its channel activity. Our mass spectrometry data demonstrated a striking increase in claudin-1, -2, -3, -4, -7, and -12 in the MUC13 knockout membranes compared to WT. We hypothesize that the claudin repertoire in the MUC13 knockout cells leads to a more restricted paracellular route (as observed in the TEER and tracer experiments). The pore-forming claudins may be subject to “interclaudin interference” therefore leading to restriction of the total paracellular ion and water flux. We have adjusted the text of the manuscript to clarify this point.

We attempted to investigate claudin-2 expression levels in isolated membranes by Western Blot but were unsuccessful as the antibody did not detect any protein while claudins-1 and -4 could be detected with the same method.

Furthermore, the authors should note the disconnect between paracellular ion flux mediated by claudins and the flux of markers such as dextrans and lucifer yellow, which can be dissociated from claudin function.

We acknowledge that the flux of larger particles (the leak pathway) is not regulated by claudins (which regulates the pore pathway). We aimed to assess both the pore and the leak paracellular pathways, by using different techniques including TEER, small solutes (Lucifer Yellow CH), and larger molecules (4 and 70 kDa FITC-Dextrans). HRT18 wild type cells are already very restrictive to the pass of larger molecules (FITC-Dextrans) but are more permeable to smaller solutes such as Lucifer Yellow (400 Da). We observed that removal of the MUC13 cytoplasmic tail did not affect the TEER, but reduced the paracellular passage of Lucifer Yellow, demonstrating that manipulation of MUC13 can affect both the pore and leak pathways. We adjusted to text to include this point.

The increased expression of claudins in the nominally tail-minus MUC13 without a corresponding change in TER would again seem to argue against a simple correlation;

MUC13-dCT cells showed consistently increased levels of claudins-1 and -2, but not the other claudins. This claudin repertoire (with high claudins-1 and -2, but lower claudin-3, -4, -7, and -12) is apparently not enough to increase TEER. We think that this again reflects the importance of the total claudin composition for the control of the paracellular pathway.

Watch the use of decimal points instead of commas (lines 253 and 256).

Corrected.

Line 543: MilliQ is not a washing agent (or is it?). (Line 535) We use MilliQ as a final step before mounting the glass slides to remove any possible salt deposition that would affect the visualization by microscopy.

We have specified this in the text.

Line 553: TER is the product of total resistance times the area. The units are ohms times area.

Indeed, we have changed this mistake (line 545).

Line 630: Please provide the transfer conditions (voltage, amp, watts?) and transfer buffer when describing the Western blot protocol.

For immunoblotting of MUC13, protein lysates were transferred to 0.2 µm PVDF membranes using the Trans-Blot Turbo Transfer system (Biorad). The transfer was run using the protocol (High MW) which consisted in running for 10 min at 25 volts (V) and 1,3 amperes (A). These experimental data were added to the manuscript.

Reviewer 2

My main concern about this manuscript is that the authors analyzed MUC13 role in intestinal homeostasis and function using colorectal cancer cells. As helpful as cancer cells are, we should always be cautious about extrapolating roles in normal intestinal epithelium or IBD pathology. Obviously, these finding are also interesting in a cancer context. Using GEPIA (http://gepia.cancer-pku.cn/), I observed that MUC13 is overexpressed in colorectal cancer COAD-TCGA dataset (compared to normal colon from GTEX). Similar results were obtained previously by Gupta et al. (ref #10). I am aware that this would be difficult to confirm the main findings in a non-cancerous intestinal cell line but this limit (normal intestine using cancer cells) should be at least discussed in the manuscript.

We appreciate the reviewers’ comments and are aware of the downsides of using cancer-derived cell lines. We have performed the GEPIA analysis ourselves and have an ongoing project about the possible role of MUC13 in colorectal cancer progression. In a separate project, we are collaborating with the Gaultier Laboratory at the University of Virginia which has generated a MUC13 knockout mouse. This model will allow us to study the role of MUC13 in non-cancerous tissue. We recently received intestinal biopsies from these mice which will be stained with MUC13 and claudin antibodies to determine localization in healthy tissue. These experiments will reveal if MUC13 colocalizes with claudin on the lateral membrane in the healthy mouse intestinal tract. In future experiments, we will also address MUC13 localization and function in human intestinal organoids. We have adjusted the discussion to refer to the limitations of using cancer cell lines.

Massey et al (Micro 2021, PMC7014956) previously showed that MUC13 overexpression increased rigidity in PDAC cells and discussed involvement MUC13 link with EMT. MUC13-Her2 interaction was also associated with decrease of E-cadherin suggesting an EMT phenotype. This should be included in the discussion section.

The discussion has been adjusted to include the link with EMT.

The authors performed mass spectrometry analysis. Results are deposited on ProteomeXchange but are not yet publicly released. Among the 1189 membrane protein identified. Did the authors observed alteration of EMT proteins? (decrease of vimentin for example). In the discussion section (lane 347), the authors mentioned the relationship between other membrane bound mucins such as MUC1, MUC4, MUC16 or MUC17 and AJ/TJproteins. Did the authors observed any alteration of these mucin in the mass spectrometry data?

The mass spec analysis was performed on membrane fractions, therefore our dataset will not contain true cytosolic proteins. One of the key EMT proteins, Vimentin, is a cytosolic protein, and indeed it was not found in our dataset. Other EMT-related proteins are shown in the following table. TGF beta 1 was slightly decreased, while E-cadherin and Integrin beta 6 were slightly increased in the ∆MUC13 cells compared to WT cells.

Gene Name

Mean WT

Mean ∆MUC13

Mean MUC13-∆CT

TGFBI (TGB beta 1)

20,54

16,48

18,83

CDH1 (E-cadherin)

22,69

24,57

24,24

ITGB6 (Integrin beta 6)

18,86

21,74

19,19

Vimentin - Cytosolic

-

-

-

CDH2 (Cadherin-2, N-cadherin)

-

-

-

Mucins are large proteins comprised of densely O-glycosylated mucin domains, which makes them extremely challenging to study by mass spectrometry (MS) (Rangel-Angarita et al., 2021). We did not specifically employ mucin-directed technologies in this dataset, thus making the detection of mucins hard. No mucins other than MUC13 were detected. For MUC13, two peptides corresponding to the EGF-like domains in the extracellular domain, a region that is less densely glycosylated. We added a sentence to the description of the mass spec results to include the EMT proteins and other mucins.

Minor points:

Lane 126: HRT18 and Caco2 colon cancer cells instead of intestinal epithelial cells

Corrected.

Lane 181 and lane 514: add "full length" MUC13 DNA sequence

Corrected.

Lane 234: TEER was measured every 12h. How the authors did observed the largest increase at 42h? Was it 48h? Please clarify.

We aimed at measuring every 12 h, however the exact measurements were done at 18h, 24h, and 42 h post-infection. We have corrected this in the manuscript.

Reviewer 3

Line 43 and 46. "Enterocytes" should be replaced with "intestinal epithelial cells", since enterocytes are themselves a distinct subpopulation of IECs.

We have changed it in the manuscript.

Lines 58-60. References in support of the statements should be added.

We added a reference to this sentence.

Lines 188-190. Authors comment on "roundness" of different cell lines. If the parameter is critical for the manuscript, the authors should quantify this phenotype.

The parameter is not critical for the manuscript. We removed the sentence.

Figure 3A. Staining of cell lines should include panels showing localization of MUC13.

Co-staining of MUC13 with occludin in HRT18 cell lines can be found in figure 1D, and MUC13 with E-cadherin in supplementary figure 1.

Lines 323-327 and 390-392. Sentences on these lines contradict each other. The sentences should describe/discuss quantified data presented in Figure 6D.

The reviewer is right that we should be discussing the quantified data in 6D. We adjusted the sentence in line 323-327.

Proteomic data sets should be made publicly available on data depositories.

All proteomics raw data were deposited to the ProteomeXchange Consortium with the dataset identifier PXD029606.

Reviewer 4

OPTIONAL: In the figure 2E, is the extracellular antibody still detecting the MUC13-ΔCT?

No, unfortunately the antibody directed against the MUC13 ED is not compatible with western blot.

In the figure 2G, would be nice to comment possible reasons why the deletion in the first cell line of the MUC13-CT you can still detect with the extracellular antibody some lateral expression of MUC13 meanwhile in the second cell line, the same deletion (MUC13-CT) you cannot see any lateral MUC13 staining with the extracellular antibody.

Yes, this is indeed a puzzling finding, especially because the CRISPR deletion is the same in both cell lines. We will add a sentence about possible reduced stability of the MUC13 without CT domain that leads to a different outcome in both cell lines.

It would be nice that the results from Figure 3H are better explained since it is difficult to follow.

We adjusted the text to explain the experiment in more detail.

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

Reviewer 1

The authors may be overly reliant on TER measurements. Epithelial cells have two parallel resistive pathways: transcellular and paracellular. TER measure the contribution of both. Thus, an increase in TER could result from a decrease in transcellular ion transport. The authors need to measure transcellular ion flow or selectively measure the junctional resistance in a select set of experiments to rule this possibility out.

The reviewer is right that TEER is a sum of the resistance of the transcellular and paracellular pathways. However, due to the high resistance of cell membranes, the current predominantly travels via the paracellular route (Elbrecht et al., 2016). For this reason, TEER measurements are widely accepted techniques for the assessment of ions passage through the paracellular pathway (Shen et al., 2011).

Reviewer 3

Figure 1C. Caco2 and HRT18 cells exhibit distinct MUC13 expression patterns when probed with an antibody against the MUC13 CT; MUC13 localizes almost exclusively to lateral cell junction in HRT18 cells, while a higher portion of MUC13 is present on the apical surface of Caco2 cells. This observation has two possible explanations: 1) the two cell lines express distinct forms of MUC13, or 2) the two cell lines carry distinct machineries for anchoring MUC13 to apical versus apico-lateral membranes. Thus, The authors should take the opportunity to determine the impact of MUC13 deletion on TEER and TJ function in Caco2 cells. Proteomic analysis and functional assays in Caco2 cells may provide more a general mechanism for how MUC13 regulates TJ proteins.

Yes, this would be a great line of investigation. However, we aimed to knockout MUC13 in Caco-2 cell lines (with the same CRISPR/Cas9 protocol as the HRT18 cells) but were unable to obtain Caco-2 knockout clones. We think this might be a consequence of the poor capability of Caco-2 cells to grow as single colonies (a required step in the protocol). Another option is Caco-2 MUC13 knockout cells have reduced viability.

The authors generate cell lines that either lack MUC13 or express MUC13 lacking the cytoplasmic domain. Loss of MUC13 cells resulted in enhanced TEER and increased recruitment of TJ proteins to membrane fractions. MUC13∆CT cells show moderate recruitment of TJ proteins to membranes and no increase in TEER but inhibit paracellular diffusion of Luciferase Yellow across monolayers. Figure 3A suggests that Occludin redistributes to tricellular junctions in ∆MUC13 cells, whereas it is found more laterally in WT and MUC13∆CT cells. These finding suggest that full-length MUC13 interferes with TJ protein complexes. However the impact of the extracellular and intracellular (CT) domains is not fully elucidated. Does the O-glycosylated mucin domain interfere with the extracellular domains Occludin and Claudins? The authors should clarify the contribution of the mucin domain to the observed phenotype, for example by performing the described experiments in a cell line expressing MUC13 lacking the mucin domain.

Mucins are type I membrane proteins with the N-terminal part of the protein on the extracellular site. Therefore, a CRISPR method to specifically remove the glycosylated domain but leave the remainder of the protein in frame is challenging. An additional difficulty is that the ED contains a lot of repeats, complicating the design of specific guide RNAs. To specifically address the contribution of the glycosylated domain, we could complement the MUC13 knockout cell with a construct lacking the ED. However, this would not be comparable to the endogenous MUC13∆CT cell line presented in this manuscript. In future studies, we will strive to address the functions of the different MUC13 domains in more detail.

Figure 5A. Turnover of TJ proteins in membrane fractions occurs faster than over a period of 1-3 days (PMID: 18474622). The authors should determine TJ protein turnover over a period of minutes and hours.

We acknowledge the findings in this interesting paper concerning the continuous remodeling of tight junctions. However, the readout of our biotinylation assay is degradation and the timeframe of degradation turns out to be days and not hours. Within this timeframe remodeling is taking place but it cannot be captured in the total lysate.

Reviewer 4

OPTIONAL: The authors show that the probiotic Lactobacillus plantarum increase epithelial barrier independently of MUC13. Have the authors considered to use other probiotics as Lactobacillus paracasei (10.3389/fcimb.2015.00026), Akkermansia muciniphila (10.1038/emm.2017.282) or some metabolic products from intestinal microbiota as short-chain fatty acids (SCFAs) (10.3389/fphys.2021.650313) to check what is the role of MUC13 and if it is related with other microbe or microbiota metabolite?

Thank you for the suggestion. We have an ongoing project in which we investigate the impact of different probiotic bacteria and plan to investigate whether they have an impact on the epithelial barrier function in a MUC13-dependent manner. This study will lead to a separate publication.

OPTIONAL: The authors successfully delete MUC13 in IECs, both, full length and the cytosolic tail. Have the authors considered targeting the deletion of the PTS domain in MUC13? Could affect that something different from paracellular trafficking as the extracellular detection of microbes and microbial products?

Removal of a domain in the extracellular domain of MUC13 with CRISPR is challenging because mucins are type I membrane proteins, the repeats and possible frameshift, as described above.

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Referee #4

Evidence, reproducibility and clarity

This work highlight the importance of the transmembrane mucin MUC13 in the control of the intestinal epithelial integrity by the negative regulation of tight junction (TJ) proteins mediated by Protein Kinase C (PKC). To achieve this conclusion, the authors designed CRISPR/Cas9 strategie to generate two types of HRT18 and Caco-2 MUC13 knockout cell lines: one to delete the full MUC13 length (ΔMUC13) and other to target the deletion of the MUC13 cytoplasmic tail (MUC13-ΔCT). More, they design MUC13-GFP plasmid to overexpress MUC13 in WT cells and to rescue the ΔMUC13 cells.

The key conclusions of this project are:

• Transepithelial electrical resistance (TEER) was upregulated in ΔMUC13 with no changes in MUC13-ΔCT compared with the control. The rescue of ΔMUC13 by MUC13-GFP plasmid reduce the TEER to values similar to the WT cells. More, ΔMUC13 and MUC13-ΔCT were more restrictive in the paracellular translocation of small tracers than WT cells suggesting the negative correlation of MUC13 and paracellular conductance of small ions together with higher TEER.
• Absence of MUC13 leads to an upregulation of TJ proteins, which explain the decrease of the paracelullar ion traffic
• Upregulation of TEER in cells lacking MUC13 is dependent on MLCK, ROCK and PKC kinases meanwhile the upregulation of TJ proteins is mediated by PKC proteins. Finally, the authors intend to address in the future the molecular link between MUC13 and PKC during TJ regulation.

The conclusions and the claims from the authors of this work are supported by the data where they extensively test the close relation between the transmembrane mucin MUC13 and the intestinal epithelial integrity.

Major comments:

• OPTIONAL: The authors show that the probiotic Lactobacillus plantarum increase epithelial barrier independently of MUC13. Have the authors considered to use other probiotics as Lactobacillus paracasei (10.3389/fcimb.2015.00026), Akkermansia muciniphila (10.1038/emm.2017.282) or some metabolic products from intestinal microbiota as short-chain fatty acids (SCFAs) (10.3389/fphys.2021.650313) to check what is the role of MUC13 and if it is related with other microbe or microbiota metabolite?
• OPTIONAL: MUC13 is expressed both, in the basolateral membranes and in the apical membrane of intestinal epithelial cells (IECs). Does the authors check the relevance of MUC13 in the formation of microvilli in IECs? Are microvilli different (microvilli staining, number of positive cells to microvilli, length, width or distribution of microvilli) in ΔMUC13 and in MUC13-ΔCT? How the glycocalyx looks like in these cells genetically modified for MUC13?
• OPTIONAL: The authors successfully delete MUC13 in IECs, both, full length and the cytosolic tail. Have the authors considered targeting the deletion of the PTS domain in MUC13? Could affect that something different from paracellular trafficking as the extracellular detection of microbes and microbial products?
• OPTIONAL: In the figure 2E, is the extracellular antibody still detecting the MUC13- ΔCT?

Minor comments:

• In the figure 1D would be nice to represent the co-localization of MUC13 together with occluding in a graph in each Z-stack so you can visualize in which part of the cell is maximum colocalization of these both components.
• In the figure 1E, would be great to compare between the two different MUC13 antibodies the apical fraction stained in HRT18 and Caco-2. Specially in the HRT18 cell line since the first antibody did not label apical MUC13 expression meanwhile the second antibody detects the apical expression in these cells. How much lateral lateral stain the C terminal antibody compare with the extracellular antibody for MUC13 and how much stain apically the C terminal antibody compare with the extracellular antibody? Would be nice to see some comparative results using the intensity by Z-stack and plotting in a graph.
• In the figure 2G, would be nice to comment possible reasons why the deletion in the first cell line of the MUC13-CT you can still detect with the extracellular antibody some lateral expression of MUC13 meanwhile in the second cell line, the same deletion (MUC13-CT) you cannot see any lateral MUC13 staining with the extracellular antibody.
• Manuscript would be improved if in the figure 2H to compare within the same cell line the number of MUC13 positive cells in the WT, number of MUC13 positive cells in WT+pMUC13 and the number of MUC13 positive cells in the ΔMUC13+pMUC13
• It would be nice that the results from Figure 3H are better explained since it is difficult to follow.
• In figure 5C would be helpful to plot in a graph the normalized expression of each TJ protein and compare between the different cells used (WT, ΔMUC13 and MUC13-ΔCT) as you did in figure 5A

Significance

This is a novel study where the authors directly correlate the lack of MUC13 expression with paracellular transport and tight junction proteins. This study describe the high correlation between the transmembrane mucin MUC13 and the integrity of the intestinal epithelium. Therefore, this project is highly valuable not only for the scientific research nowadays, but for future investigations of the intestinal epithelial physiology and biochemistry.

The strengths parts of this study are the different cell constructs including the full deletion of MUC13 and the targeting deletion of MUC13 cytosolic tail. Then, they have been able to directly correlate lack of MUC13 with paracellular traffic where PKC intracellular signal is involved. A limitation of the study is the lack of a cell line lacking the extracellular domain of MUC13, which could give some clues about the direct relation of this membrane mucin with the outer world of the cell (i.e. bacteria).

My field of expertise is intestinal epithelial defense including, but not limited, to mucins.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The authors describe a novel function for transmembrane mucin MUC13 in regulation of tight junctions (TJs) that create an impermeable cell monolayer that allows. paracellular diffusion of very small molecules. The authors use cultured intestinal epithelial cell monolayers. to demonstrate that MUC13 localizes to the apical aspects of IECs as well laterally to tight junctions. CRSIPR/Cas-mediated deletion of MUC13 increased transepithelial resistance (TEER) and reduced the extent of paracellular diffusion of <0.5 kDa molecules across the monolayer. Proteomic analysis revealed that specific TJ proteins are enriched in cell membrane fractions upon deletion of MUC13, while pharmacological inhibition of PKC involved in actomyosin contractility, resulted in loss of TJ proteins from cell membranes and TEER reduction. See major comments for a detailed discussion concerning the findings.

Major comments:

1. Figure 1C. Caco2 and HRT18 cells exhibit distinct MUC13 expression patterns when probed with an antibody against the MUC13 CT; MUC13 localizes almost exclusively to lateral cell junction in HRT18 cells, while a higher portion of MUC13 is present on the apical surface of Caco2 cells. This observation has two possible explanations: 1) the two cell lines express distinct forms of MUC13, or 2) the two cell lines carry distinct machineries for anchoring MUC13 to apical versus apico-lateral membranes. Thus, The authors should take the opportunity to determine the impact of MUC13 deletion on TEER and TJ function in Caco2 cells. Proteomic analysis and functional assays in Caco2 cells may provide more a general mechanism for how MUC13 regulates TJ proteins.
2. The authors generate cell lines that either lack MUC13 or express MUC13 lacking the cytoplasmic domain. Loss of MUC13 cells resulted in enhanced TEER and increased recruitment of TJ proteins to membrane fractions. MUC13∆CT cells show moderate recruitment of TJ proteins to membranes and no increase in TEER but inhibit paracellular diffusion of Luciferase Yellow across monolayers. Figure 3A suggests that Occludin redistributes to tricellular junctions in ∆MUC13 cells, whereas it is found more laterally in WT and MUC13∆CT cells. These finding suggest that full-length MUC13 interferes with TJ protein complexes. However the impact of the extracellular and intracellular (CT) domains is not fully elucidated. Does the O-glycosylated mucin domain interfere with the extracellular domains Occludin and Claudins? The authors should clarify the contribution of the mucin domain to the observed phenotype, for example by performing the described experiments in a cell line expressing MUC13 lacking the mucin domain. Moreover, the authors should determine if MUC13∆CT localize to TJs, as suggested by the working model in Figure 7C. The subcellular localization of MUC3∆CT could give critical clues for its function, but Figure 2G fails to provide any information and the authors do not present any additional data concerning the localization of MUC13∆CT. Detection of MUC13 in membrane fractions of WT, MUC13∆CT and cells lacking the mucin domain could be a feasible strategy forward.
3. The authors introduce an inducible MUC13-GFP fusion protein into WT and ∆MUC13 cells and show that it reverses the enhanced TEER upon MUC13 deletion. Unfortunately, the "Materials and Methods" section lacks adequate information on how this fusion protein was designed. Critical questions are the position of the GFP tag within MUC13, whether the fusion protein is correctly processed in HRT18 cells, and if it localizes to the apical or apico-lateral membrane domains? Figure 2H is of low magnification and fails to provide information on the subcellular localization of the MUC13-GFP fusion protein.
4. Figures 6B-C suggest that PKCdelta levels increase in ∆MUC13 cells, which correlates with higher enrichment of Claudins in membrane fractions. The authors then inhibited PKCdelta and observed reduced recruitment of Claudins to membrane fractions. Since the family of Claudins are differentially regulated by phosphorylation (PMID: 29186552), the authors should investigate the TEER phenotype of WT, ∆MUC13 and MUC13∆CT upon PKC inhibition. Moreover, the authors predict phosphorylation sites in MUC13CT and suggest a link between PKC and MUC13 (Figure. 6A), however no evidence is presented to support this hypothesis. The authors should either determine if PKC phosphorylates MUC13 and if this modification has implication on MUC13 localization and TJ function, or remove statements regarding MUC13 phosphorylation. The data provided suggest that PKC regulates TJ proteins independent of MUC13.

Minor comments:

1. Line 43 and 46. "Enterocytes" should be replaced with "intestinal epithelial cells", since enterocytes are themselves a distinct subpopulation of IECs.
2. Line 59. The authors should note that MUC13 does not have a canonical SEA domain that generates a cleaved heterodimer (PMID: 16369486).
3. Lines 58-60. References in support of the statements should be added.
4. Lines 188-190. Authors comment on "roundness" of different cell lines. If the parameter is critical for the manuscript, the authors should quantify this phenotype.
5. Figure 3A. Staining of cell lines should include panels showing localization of MUC13.
6. Figure 5A. Turnover of TJ proteins in membrane fractions occurs faster than over a period of 1-3 days (PMID: 18474622). The authors should determine TJ protein turnover over a period of minutes and hours.
7. Figure 5C. Quantification of at least 3 independent experiments is required.
8. Figure 6B. Quantification of at least 3 independent experiments is required.
9. Lines 323-327 and 390-392. Sentences on these lines contradict each other. The sentences should describe/discuss quantified data presented in Figure 6D.
10. Proteomic data sets should be made publicly available on data depositories.

Significance

Mucins participate in critical functions in the human intestine. Gel-forming mucins form the mucus layers that separate the gut microbiota from the underlying intestinal epithelial cells (IECs) (PMID: 18806221). Transmembrane mucins are instead anchored to the plasma membrane of various populations of IECs (PMID: 32169835; PMID: 28052300). Despite its discovery over 20 years ago, the functional role of MUC13 in the intestinal epithelium is still debated. MUC13 is expressed in human small intestine and colon under baseline conditions and is dysregulated during inflammation and tumorigenesis, as described by the authors. Thus, understanding how MUC13 expression and localization impact cell function is of great importance for elucidating its function in health and disease. Studies so far have identified transmembrane mucins as biophysical barriers against bacteria (PMID: 33596425) or facilitators of bacterial invasion (PMID: 33824202). The current manuscript can potentially offer novel conceptual insights into how transmembrane mucins govern the integrity of the epithelial monolayer that serves as a firewall between the multitude of microbes in the gut lumen and the immune system. Such insights have implication for both basic and clinical research on inflammatory bowel disease (IBD) and colorectal cancer (CRC). However, while the authors present convincing data that deletion of MUC13 enhances TEER and recruitment of TJ proteins, the study in its current form fail to provide mechanistic proof of how MUC13 impacts individual TJ proteins. Moreover, it is not clear if findings in a specific cultured cell line (HRT18) can be extrapolated to other frequently used intestinal cell lines (e.g. Caco2) and IECs in an in vivo setting. The latter is particularly important since the authors argue that their findings have important implication in intestinal inflammation and cancer.

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Referee #2

Evidence, reproducibility and clarity

Summar: In this manuscript, Segui-Perez and colleagues investigated the role of MUC13 in tight junctions and trans-epithelial barrier function in colon cancer cells. The authors showed that MUC13 is highly expressed throughout the intestine at the apical and lateral membrane. They established Crispr/Cas9 HRT18 cells in which MUC13 (deltaMUC13) or the cytoplasmic tail of MUC13 were deleted. They also performed rescue experiments using GFP-MUC13 constructs. The authors observed that deletion of MUC13 promoted TEER for bigger particles and strengthen tight junctions. Analysis of membrane composition by mass spectrometry showed an upregulation of TJ proteins (Claudins) that is dependent of PKCdelta.

Major points:

1. My main concern about this manuscript is that the authors analyzed MUC13 role in intestinal homeostasis and function using colorectal cancer cells. As helpful as cancer cells are, we should always be cautious about extrapolating roles in normal intestinal epithelium or IBD pathology. Obviously, these finding are also interesting in a cancer context. Using GEPIA (http://gepia.cancer-pku.cn/), I observed that MUC13 is overexpressed in colorectal cancer COAD-TCGA dataset (compared to normal colon from GTEX). Similar results were obtained previously by Gupta et al. (ref #10). I am aware that this would be difficult to confirm the main findings in a non-cancerous intestinal cell line but this limit (normal intestine using cancer cells) should be at least discussed in the manuscript.
2. Massey et al (Micro 2021, PMC7014956) previously showed that MUC13 overexpression increased rigidity in PDAC cells and discussed involvement MUC13 link with EMT. MUC13-Her2 interaction was also associated with decrease of E-cadherin suggesting an EMT phenotype. This should be included in the discussion section.
3. Fig2E showed two bands with different size in the two MUC13 WT control cell lines. They hypothesized that this could be the consequences of glycosylation different patterns. A sample with untransfected HRT18 might be included in the western blot panel. Additionally, what is the 100kDa band?
4. The authors performed mass spectrometry analysis. Results are deposited on ProteomeXchange but are not yet publicly released. Among the 1189 membrane protein identified. Did the authors observed alteration of EMT proteins? (decrease of vimentin for example). In the discussion section (lane 347), the authors mentioned the relationship between other membrane bound mucins such as MUC1, MUC4, MUC16 or MUC17 and AJ/TJproteins. Did the authors observed any alteration of these mucin in the mass spectrometry data?
5. Did the transfection of the inducible GFP-MUC13 plasmid induce any decrease of Claudin1/3/4 in HRT18 or Caco2 cells? Same question regarding PKCdelta.

Minor points:

1. Lane 126: HRT18 and Caco2 colon cancer cells instead of intestinal epithelial cells
2. Lane 181 and lane 514: add "full length" MUC13 DNA sequence
3. Lane 234: TEER was measured every 12h. How the authors did observed the largest increase at 42h? Was it 48h? Please clarify.

Significance

This manuscript is relevant as basic research for both the mucin field and for the intestinal epithelium field. It brings conceptual hypothesis about the role of MUC13 that is less characterized than MUC1 or MUC4.

I have been working on mucins for over 20 years. I found this work well done and very interesting.

I feel that the conclusions are mostly supported by the results. The one semantic limit is that this work is based on cancer cell lines and it is a little bit speculative to extrapolate the finding on normal intestinal epithelium.

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Sequi-Perez explores a somewhat novel role for the transmembrane mucin MUC13 in regulating the tight junction barrier. They report that MUC13 is localized, in part, to the apical junctional complex; that depletion of MUC13 increases TER and expression of claudins and OCLN in a membrane fraction; and that partial removal of MUC13 cytoplasmic domain also results in increased expression of OCLN and claudins, but without a corresponding change in TER. Finally, the authors hypothesize that PKCdelta may act in conjunction with MUC13 to regulate paracellular flux in intestinal epithelial cell lines.

Major points:

1. Although this is an interesting, and generally well-performed study, it is primarily observational and there are few mechanistic insights provided into how MUC13 modulates barrier function. The authors propose a presumably direct interaction between MUC13 and PKC, which apparently sequesters PKC, preventing this kinase from triggering PKC-dependent increases in TJ barrier function; however, there is no evidence that a MUC13-PKC interaction occurs, that MUC13 is phosphorylated by PKC, or that phosphorylation of MUC13 has any impact on its function or overall barrier function. Thus, the hypothesis is not directly tested and all observations in this manuscript are generally correlative in nature. In addition, this model does not explain why all kinase inhibitors tested reverse the increase in TER observed in deltaMUC13 cell lines. Does this reflect the lack of inhibitor specificity or the likelihood that many kinases are involved?
2. The authors do observe that there is an increase in expression of several tight junction-associated proteins, including the claudins, in deltaMUC13 cells. Affected CLDNs include 1, 2, 3, 4, 7, 12. (1) While it appears the authors are arguing that this increased claudin expression results in increased barrier function, they do not sufficiently highlight the well-known role that CLDN2 has in cation transport, and both CLDN-4 and -7 have also been implicated in paracellular ion flux (although this is apparently cell-type specific). These observations would seem to argue against a simple correlation between claudin expression and tight junction barrier function. (2) The increased expression of claudins in the nominally tail-minus MUC13 without a corresponding change in TER would again seem to argue against a simple correlation; (3) Furthermore, the authors should note the disconnect between paracellular ion flux mediated by claudins and the flux of markers such as dextrans and lucifer yellow, which can be dissociated from claudin function.
3. The authors may be overly reliant on TER measurements. Epithelial cells have two parallel resistive pathways: transcellular and paracellular. TER measure the contribution of both. Thus, an increase in TER could result from a decrease in transcellular ion transport. The authors need to measure transcellular ion flow or selectively measure the junctional resistance in a select set of experiments to rule this possibility out.

Minor points:

1. Watch the use of decimal points instead of commas (lines 253 and 256).
2. Consider quantifying all blots (Fig. 5C, Fig. 6B).
3. Line 543: MilliQ is not a washing agent (or is it?).
4. Line 553: TER is the product of total resistance times the area. The units are ohms times area.
5. Line 630: Please provide the transfer conditions (voltage, amp, watts?) and transfer buffer when describing the Western blot protocol.
6. Consider using dot-plots for all quantified data.

Significance

This study advances the fields of mucin biology and tight junction barrier function in an incremental manner. The study is well done, but there are few mechanistic insights into how MUC13 modulates paracellular flux in cultured gut epithelial cells.

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Reply to the reviewers

1. General Statements

We thank all the reviewers for their positive and constructive comments on the manuscript. Changes to the main text have been marked in red text in the uploaded file. We address each of the reviewers’ comments point-by-point below. The major revisions include:

Improved statistical details and attention to subjective language throughout. New TEM data included in the new Figure 1—figure supplement 1 to illustrate the drastic ultrastructural differences between MCs and neighboring epidermal cells. Inclusion of an estimate of the “recombination efficiency” of our keratinocyte lineage trace in Figure 4. Additional quantification of MC density in the different body regions (Figure 6) and prior to squamation (Figure 7F). Imaging of the zebrafish oral mucosa (new Response Figure 1). More nuanced interpretations of the eda and fgf8a mutant phenotypes.

2. Point-by-point description of the revisions

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

The authors describe and characterize the touch system in zebrafish as a new model to study MC development and maintenance. The manuscript is very well written, and the experiments are carefully executed and beautifully illustrated. This study addresses the origin of zebrafish MCs, shows that they are innervated by somatosensory neurons and that they share molecular properties with mammalian MCs. In addition, the authors developed transgenic lines that allow them to study MCs in vivo.

Genetic lineage tracing shows that zf MCs are derived from the epidermis as in mouse and not from neural crest cells, as described for avian MCs. In addition, longevity and turnover of murine MCs was controversial. Here, the authors show that zf MCs constantly turnover and that the distribution and turnover rate in the trunk depends on underlying scales. They show that the loss of scales in eda mutants leads to a decrease in MC production and an increase in MC death showing that scales are required for MC production and maintenance. Using a specific fgf8 mutant allele that causes an increase in Fgf signaling and an increase in scale size they demonstrate that scales are sufficient to induce MCs.

In summary, this manuscript is a rigorous and beautiful characterization of MCs development and maintenance. The authors demonstrate that zebrafish MCs share many characteristics with mammalian MCs. The generation of MCs specific transgenic lines, coupled with existing transgenic lines that label somatosensory cells and cells in the scales sets the stage for detailed further analyses. For example, using these tools one can now study how the size of the MC progenitor domain is controlled, if progenitors migrate and what the identities of the molecular signals from the nerves and scales to progenitors and differentiated MCs are.

Minor comments:

Line 71: Why is the heterogeneity a limitation? Couldn't it also exist in zebrafish?

Thank you for raising this question. The limitations are meant to refer to current limitations of the rodent system and demonstrate an opportunity for a new model system to complement the rodent system. We have rephrased this section to better articulate this point.

Introduction:

“While this system has been useful for understanding many aspects of MC development and function, the rodent system also has several significant limitations that warrant additional models to improve the understanding of MCs.”

We also added the following to the discussion: *“As the majority of the analyses completed here focus on MCs found in the trunk epidermis, it will be intriguing to determine whether all MCs in different skin compartments in the juvenile and adult zebrafish share similar molecular, cellular, and functional properties.” *

Line 295: The authors write: 'Thus, our observations indicate that the decrease in MC cell density in eda mutants is likely due to both reduced MC production and increased MC turnover'.

It should say: '.. increased MC loss'. In mutants the MCs show poor turnover. I believe the term 'turnover' implies that the cells are being replaced, which is only partially happening here.

Thank you for the clarification. We agree with the reviewer and have changed the wording from “turnover” to “loss” in lines 295 and 301.

Line 301: 'The authors state: 'these data suggest that Eda signaling is required for MC development, maintenance, and distribution along the trunk.'

The authors do not show any data that Eda signaling is involved in MC development but only that scales are needed. The MC inducing signals from scales to the epidermis could be independent from Eda signaling. Please rephrase.

Please discuss that not all MC specification/development depends on scales. Even in the scale-less eda mutants some MCs form (as in the inter scale regions in wt?) and even turnover. Do scales secrete a signal that increases proliferation of existing MC progenitors but scales do not affect specification?

We respectfully disagree with the reviewer on the interpretation of these results. Our experimental manipulation (examination of eda-/- vs. sibling controls) only allows us to conclude that Eda signaling - either directly or indirectly - is required for these processes along the trunk. To determine whether signaling from scales is required would require identification of the signal(s) and/or loss/ablation of scales independent of Eda. We have rephrased the results to more clearly state our interpretation. The corresponding portion of the discussion now reads “Further investigations are required to determine whether Eda signaling directly regulates the differentiation of MC progenitors. Alternatively, since eda mutants lack scales (Harris et al., 2008) and have decreased epidermal innervation (Rasmussen et al., 2018), MC development may require scale-derived and/or somatosensory neuron-derived signals.”

Line 320: The authors describe that the fgf8 allele leads to a redistribution of MCs. Is it really a redistribution, or is it ectopic induction or expansion of existing progenitors? Redistribution implies that the expansion is due to a loss of MCs in another region, which I do not see in the data.

Thank you for raising this point about the potentially poor wording choice relating to “redistribution”. We do not yet know whether the distribution of MCs in fgf8a mutants reflects a redistribution, ectopic induction, or expansion of existing progenitors (these are excellent ideas for future studies). Thus, in response to the reviewers comment, we have changed the heading for this results section to “The MC pattern is not predetermined along the trunk” and concluded the section as follows: “... the distribution of MCs tracked with the altered scale size and shape in the mutants, suggesting the MC pattern is not predetermined within the trunk skin compartment (Figure 9E-H).”

Figures:

• Figure 1, panels B-C': EM images are very dark and difficult to see. Letter 'a' is on top of the axon, maybe move to the side and pseudo-color different structures.

In response to these suggestions, we have adjusted the brightness and contrast to lighten the TEM images in Figure 1B-C’ as much as possible. We also moved the ‘a’ off to the side in Figure 1B’ to make the axon more visible. In response to Reviewer #3’s comments (see below), we also added an additional TEM image in the new Figure 1—figure supplement 1 that has presumptive keratinocytes and a MC differentially pseudo-colored. We hesitate to pseudo-color the cells/structures in Figure 1B-C’ for fear of obscuring the underlying TEM images.

• Figure 1, panel D: very difficult to see the magenta axons in the cartoon. Please enlarge and make brighter.

We agree that this needed improvement. In the revised Figure 1D, we made the axons clearer and illustrated the different types of MC-axon associations we observe in Figure 2. We also refer the reader back to this figure in the corresponding axon innervation results section.

• Figure 2, panels A and D: keeping the same antibody stainings in the same color would help with visualization. Matching the bar plots in panel C would be even nicer.

Thank you for the suggestion. The revised Figure 2 now has a consistent color scheme.

• Figure 2, panel C: please identify in the legend if the error bars are SD, SEM or other.

These error bars represent 95% confidence intervals. This information has been added to the figure legend.

• Figure 2, panels G and H: MCs are in cyan in the image, but green in the legend.

This has been corrected.

• Figure 3: include percentages and total number in the image instead of the legend.

The numbers and percentages have now been added to the Figure 3 panels. We have left them in the figure legend for clarity on what was scored.

• Figure 6, panel B: which part of the eye is being depicted?

Thank you for the question. We imaged the corneal epithelium above the lens. This has been clarified in the appropriate parts of Figures 6 and 8 and the corresponding figure legends.

• Figure 6, panel F: please provide error bars and statistics to show that the operculum has a higher density of MCs.

Thank you for the suggestion. In response to the comment, we revised Figure 6F by: 1) increasing the sample size; 2) replotting the data as boxplots rather than bar graphs; and 3) including the results of a one-way ANOVA.

• Figure 7, panels F-H: for simple linear regression, please also provide F and p values.

Thank you for the suggestion. This information has been added to the figure legend.

• Figure 8, panel D: colors for SL do not follow a scale, very hard to understand which is which.

In response to the reviewer’s suggestion, we tried numerous different color palettes. However, we were unable to find a color palette that allowed us to distinguish individual points as well as the rainbow palette used in Figure 8D. Thus, after careful consideration, we have elected to keep the original palette here. For consistency, we have used the same palette in the revised Figure 8–figure supplement 1D and Figure 9–figure supplement 1D.

Methods:

• Line 472: the word "sex" should be used instead of "gender".

Thank you for the correction. This is fixed in the revision.

• Image analysis, line 593. Please provide a more detailed explanation or describe the ImageJ macro used for the analysis.

Our ImageJ macro has been fully annotated and is provided as Figure 2—source code 1 in the revision. The corresponding methods section has also been updated to clarify the methodology.

Reviewer #1 (Significance (Required)):

Soft touch is perceived by Merkel cells (MCs). How MCs develop and are maintained is not well understood because MC development is difficult to study in mammals due to their in utero development. The authors describe and characterize the touch system in zebrafish as a new model to study MC development and maintenance. The study demonstrates that the zebrafish touch system shares many characteristics with its mammalian counterpart, namely its developmental origin, innervation and molecular characteristics. In contrast to mammals, zebrafish transgenic lines that the authors generated, allow the in vivo analysis of Merkel Cell specification, development and maintenance. Therefore this study is the foundation for future detailed cellular and molecular analyses of the touch sensory system and will be of interest to developmental biologists studying stem cells, regeneration and aging, as well as neuroscientists.

We thank the reviewer for their positive assessment of the manuscript.

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

This is a very nice and straightforward paper characterizing mechanosensory Merkel cells in the zebrafish skin. The paper uses a number of criteria, based on our knowledge of Merkel cells in mammals, to identify a population atoh1a expressing cells, with neurosecretory granules and actin rich microvilli as Merkel cells in the zebrafish skin. The authors have used existing transgenic lines and and developed some of their own, described in this paper, to follow the development of Merkel cell in zebrafish. They show that Merkel cells are derived from basal keratinocytes not neural crest cells. They have region specific densities that influenced by underlying structures like scales and fin rays. They go to show that Ectodysplasin signaling promotes Merkel cell development in the trunk skin but not above the eye or operculum. Reduction of Merkel cells in eda mutants suggest that Eda signaling is required for their development and maintenance. Finally they show that alteration of zebfrafish scale pattern using a mutant with exaggerated fgf8a expression also alters merkel cell distribution.

The data presented is clear and the conclusions are supported by their observations.

I have no significant issue with the paper as is.

Reviewer #2 (Significance (Required)):

This study will serve as an excellent basis for future work looking at studies of Merkel cell development and function in fish. Though Merkel cells have been studied in mammals, establishing a zebrafish model for their study will help overcome many barriers that make their analysis difficult in mammals.

We thank the reviewer for their positive assessment of the manuscript.

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

In this manuscript, Brown et al. (2022) seek to characterize and address fundamental questions regarding the development and dynamics of Merkel cells (MCs) in zebrafish (Danio rerio). The authors utilize a diverse and complementary suite of methods to characterize presumptive MCs in the epidermis of adult zebrafish, including electron microscopy, novel transgenic lines, confocal imaging, and various immuno- and non-immunohistological staining techniques. These studies demonstrate that zebrafish MCs share many features with vertebrate (including mammalian) MCs, particularly regarding morphology/structure, putative functions, genetic markers, and bodily distribution.

After establishing the identity of zebrafish MCs, the authors employ lineage tracing and cell tracking analyses to determine that trunk MCs derive from basal keratinocytes and exhibit regular cell turnover. Finally, the authors examine how trunk scales may affect MC development by using established scale mutants. These results show that the presence/absence of scales influences trunk MC development, while scale characteristics (e.g. shape, size) change the distribution of MCs.

MAJOR COMMENTS:

The key conclusions of the manuscript are convincing, however, several points should be addressed by the authors.

Throughout the manuscript, the authors make general claims about zebrafish MCs (zMCs) based on the evidence collected. Yet, most of this evidence (particularly claims about MC turnover, development, structure) comes from examination and experimentation of a specific MC population: trunk MCs located in the scale epidermis. The authors remark upon mammalian MC diversity (lines 73-74) and go on to highlight the diversity of MCs throughout the adult zebrafish (Figure 6), which have differing densities and distribution patterns. Any statements that suggest all zebrafish MCs share certain qualities/features should be carefully considered given the evidence presented.

Thank you for raising this important point. We have added wording in the results and discussion to clearly articulate that the majority of our analyses and conclusions are based on trunk MCs:

Results:

“Anticipating the conclusion of our analysis below, we shall hereafter refer to the epidermal atoh1a+ cells as MCs, with the majority of the analyses completed on trunk MCs unless stated otherwise.”

Discussion:

“As the majority of the analyses completed here focus on MCs found in the trunk epidermis, it will be intriguing to determine whether all MCs in different skin compartments in the juvenile and adult zebrafish share similar molecular, cellular, and functional properties.”

In the manuscript, the authors validate several markers for the identification of zMCs based upon known mammalian markers (e.g. atoh1a, sox2, piezo2, SV2, 5-HT, and AM1-43; Figures 1-3). Yet, another well-known marker for MCs (CK8) is not addressed (Moll, 1995; Moll, 2005). One zebrafish ortholog for CK8 is krt4, a transgene successfully employed in this study to label keratinocytes. Do zMCs express krt4 or other mammalian MC keratins? Answering this question or addressing this discrepancy would further strengthen the authors claims that these cells are bona fide zMCs.

We agree with the reviewer that 1) identification of a keratin(s) that distinguishes MCs from other epidermal cell types in zebrafish would be an excellent reagent; and 2) readers familiar with the mammalian MC literature may similarly wonder why this was not addressed in the manuscript. Indeed, we had considered whether we could identify homologs of CK8, CK20 or other mammalian MC keratins that would label zebrafish MCs. However, despite the confusing nomenclature that would indicate otherwise, the zebrafish keratins share more homology with each other than the corresponding mammalian proteins (Ho et al., 2022; PMID: 34991727). Our revised results section now includes the following to clarify this point: *“For example, keratins, most notably keratin 8 and keratin 20, have been used extensively as markers of mammalian MCs (Moll et al., 1995, 1984). However, zebrafish keratins have undergone extensive gene loss and duplication and are not orthologous to mammalian keratin genes (Ho et al., 2022). Thus, we considered alternative molecular markers to label zebrafish MCs.” *

The authors utilize a previously validated eda mutant line to see if ectodysplasin signaling affects zMC development. While the results of these experiments are convincing, the authors need to make clear whether they are claiming that scales, scale-derived Eda signaling, or Eda signaling alone dictate trunk MC development. It appears that there is some conflation of these ideas, particularly with line 306 ("blocking dermal appendage formation inhibited MC development" is a different claim from 'blocking Eda signaling inhibited MC development'). One way to make this differentiation would be to perform a similar experiment as detailed in Xiao et al. 2016: using a Shh agonist in eda mutants. If scale-specific signals are required in addition to Eda, we would expect to see similar MC densities and patterns in both Shh agonist-treated and non-treated eda mutants.

We agree that our interpretation of these results could have been more clearly articulated in our initial submission. As discussed above in response to Reviewer #1, we do not yet know whether Eda signaling directly or indirectly influences MC development. We have revised the results section to clarify our interpretation of the results as follows: “Together, these data suggest that either Eda signaling, or a scale-derived signal, is required for MC development, maintenance, and distribution along the trunk. Further studies are required to determine the specific scale-derived signal that regulates MC development in the trunk."

The suggestion of using a Shh pathway agonist in eda mutants to attempt to rescue MC differentiation similar to Xiao et al. 2016 is an interesting one. To our knowledge, experiments validating the Smo agonist used by Xiao and colleagues (Hh-Ag1.5) in zebrafish have not been published. We also note that activation of Shh signaling by heat-shock induction of shha expression during squamation led to kyphosis and epidermal migration off of the trunk (Aman et al., 2018; PMID: 30014845). Thus, we respectfully suggest that distinguishing between the various possibilities downstream of Eda is beyond the scope of the current manuscript. We have added a discussion point along these lines: “Further investigations are required to determine whether Eda signaling directly regulates the differentiation of MC progenitors. Alternatively, since eda mutants lack scales (Harris et al., 2008) and have decreased epidermal innervation (Rasmussen et al., 2018), MC development may require scale- and/or somatosensory neuron-derived signals. Finally, we note that trunk MCs are not completely absent in eda mutants, suggesting that a subset of MCs develop independent of Eda signaling.”

Throughout the manuscript, the authors use subjective language (e.g. line 106). While this reviewer does not wish to suppress or alter the authors' voices, careful consideration should be used when employing these types of descriptors. Furthermore, the authors use suggestively quantitative language inappropriately or unjustifiably. For example, in line 221, the authors use "extensive" when describing the co-labeling between atoh1a+ MCs and lineage-traced basal keratinocytes; the percentage of co-labeled cells ranged from 29-32%. Other quantitative descriptors such as "frequently" (line 171) or "uniform" (line 249) describe various features or phenomena without quantification in figures or supplements.

Thank you for this comment. We have paid careful attention to our subjective/statistical language in the revision. Regarding the usage of “uniform” - we have added the wording “relatively uniformly” to descriptions and a statement that our term “uniform” was not specifically quantified. Although the uniform appearance was not specifically quantified, we believe this provides an accurate description of the MC localization pattern in certain skin compartments.

Example word change in Results:

“For example, MCs were distributed relatively uniformly across the eye, although this spatial pattern was not specifically quantified”

In the lineage tracing experiments (Figure 4), the authors note that "recombination is not complete" (lines 1016-1017) to explain why not all zMCs express the basal keratinocyte lineage marker. While this idea could be supported by Figure 4-figure supplement 1, one could postulate that zMCs are derived from multiple progenitor lineages. Using the basal keratinocyte lineage tracing validation, the authors could in theory calculate a "recombination efficiency" of this transgenic line and determine approximately the percent of zMCs they 'lose' as a result. Otherwise, the authors could perform other experiments to support the claim that zMCs derive from basal keratinocytes. For example, could the authors photoconvert basal keratinocytes at 1 dpf and see how many derived MCs are still photoconverted later? Could they do this photoconversion experiment with neural crest cells? Could they ablate neural crest cells and determine if MC number is affected? These additional experiments are not necessarily required for publication, but some explanation of the unexpectedly low percentage of basal keratinocyte lineage marker-labeled MCs would suffice.

We thank the reviewer for raising this important point and the suggestion of calculating a “recombination efficiency”. We note that Cre responsive transgenes are far from a perfect technology in zebrafish as recently characterized by Lalonde et al. (2022; PMID:35582941). In response to the reviewer’s comment, we added an estimate of the recombination efficiency to Figure 4 (panels E, G, H). Importantly, a comparison between the recombination efficiency and percentage of MCs labeled by the basal keratinocyte Cre tracing was not significantly different. Our revised results section reads as follows: “After raising 4-OHT-treated animals to adulthood, we observed variable (2-81%) co-labeling between the basal keratinocyte lineage trace and a MC reporter (Figure 4D’,F). We note that our lineage tracing strategy did not label all basal keratinocytes (Figure 4D; Figure 4—figure supplement 1), suggestive of incomplete Cre-ERT2 induction and/or transgene recombination. Consistent with the latter possibility, a recent analysis demonstrated Tg(actb2:LOXP-BFP-LOXP-DsRed) has a low recombination efficiency compared to other Cre reporter transgenes (Lalonde et al., 2022). To estimate the local recombination efficiency in imaged regions, we thresholded the DsRed channel and calculated the fraction of skin cells labeled (Figure 4E). Importantly, the proportion of MCs labeled by the basal keratinocyte lineage trace was not significantly different from the local recombination efficiency (Figure 4G-H). These observations support a basal keratinocyte origin of most or all zebrafish MCs.”

The authors use appropriate statistics and have sufficient replicates when this information is presented. Yet, the presence or absence of these data is not consistent within figure captions. The authors must ensure that they provide the N of adults and scales (when appropriate), the SL range of adults, and transgenic lines used. Statistics are missing in some figures (for example: Figures 4E, 5D, 5E, 6F, 8S-1E, 9E-H) where it would be appropriate to include them. In some figures, the N changes over time (example: 5D, 5E); an explanation in the 'Methods' section would suffice.

Thank you for noting the need for additional statistics. We have added statistics to the above figures. For Figure 9E-H, we have not added additional statistics. Figure 9E-H serve to graphically visualize differences. We show statistical differences in Figure 9—figure supplement 1 for scale area, aspect ratio, and Feret’s diameter. We have added an explanation related to Figure 5D,E in the methods section: “Animals that died over the course of the experiment were excluded from further analysis.”

MINOR COMMNETS

While the authors present an extensive argument for their claims, addressing these additional comments would further strengthen their story.

Are zMC nuclei lobulated? This ultrastructure characteristic seems to be common in MCs (Chew & Leung, 1994; Tachibana & Nawa, 2002; Moll, 2005; Boulais, 2009).

We have not observed any lobulation of the MC nuclei by TEM, nor was this commented on in the TEM studies of Whitear and colleagues in other teleosts (Lane and Whitear, 1977; PMID: 198137; Whitear, 1989; PMID: 2510796). Nevertheless, we cannot rule out the possibility that serial sectioning or other high resolution analysis of the nuclear shape may reveal such features. In response to the reviewer’s comment, we have added the following paragraph to the discussion: “While our characterization revealed substantial similarities between mammalian and zebrafish MCs, we did observe anatomical differences in line with previous ultrastructural characterizations of teleost MCs (Lane and Whitear, 1977; Whitear, 1989). For example, the nuclei of mammalian MC are commonly lobulated (Boulais et al., 2009; Cheng Chew and Leung, 1994; Moll et al., 2005; Tachibana and Nawa, 2002). While we did not observe lobulation of zebrafish MC nuclei by TEM, we cannot rule out that serial sectioning or high-resolution reconstruction of nuclear shape would reveal lobulation. Mammalian MCs typically localize adjacent to basal keratinocytes (Boot et al., 1992; Cheng Chew and Leung, 1994; Fradette et al., 1995; Mihara et al., 1979; Moll et al., 1996; Smith, Jr, 1977), whereas zebrafish MCs appear in upper strata, typically beneath the periderm (Figure 1D,G’’). As the majority of the analyses completed here focus on MCs found in the trunk epidermis, it will be intriguing to determine whether all MCs in different skin compartments in the juvenile and adult zebrafish share similar properties.”

In Figure 3C and 3", the authors show that AM1-43 labels zMCs. Yet, this technique should also stain sensory axons that associate with MCs (Meyers, 2003). Are axons also stained? Other positive controls for the stains could be useful as a supplement.

The reviewer is correct that Meyers et al., (2003; PMID: 12764092) report AM1-43 staining of neurites that innervate MCs in the whisker follicle. However, they did not report similar staining of neurites innervating touch dome MCs. In murine hairy skin, the related styryl dye FM1-43 appears to most prominently stain MCs and hair follicle-associated lanceolate endings (Banks et al., 2013 PMID: 23440964; Villarino et al., 2022 preprint DOI: 10.1101/2022.05.26.493600). Our revised legend for Figure 3 now includes the following: “AM1-43 has been reported to stain neurites innervating MCs in murine whisker vibrissae (Meyers et al., 2003). However, our AM1-43 staining regiment did not strongly label cutaneous axons, although we cannot exclude low levels of staining.”

All of the stains used in our original Figure 3 have been previously validated in zebrafish, which we have more clearly stated and cited in the corresponding results section of the revision. Because these reagents have all been previously validated and our staining patterns are consistent with the literature, we respectfully suggest that positive controls would add little value to the current manuscript. Nevertheless, in response to the reviewer’s comment, we confirmed our piezo2 FISH staining using an independent method (a piezo2 HCR probe). We have included these HCR results as the updated Figure 3D and moved the original Figure 3D to Figure 3—figure supplement 1.

In Figure 7, the authors argue that as scales develop, MC density increases with scale area. Did the authors compare MC densities of differently-sized scales at the same age? Is fish SL/age a potential confound in the interpretation of these data?

Thank you for the suggestion. In response to the reviewer’s comment, we have replotted the data in Figure 7G,H for animals in the range 8-10 mm SL in Figure 7—figure supplement 1. We have revised the corresponding results section as follows: “The density and number of MCs positively correlated with scale area (Figure 7G,H), although this trend was less pronounced at stages less than 10 mm (Figure 7—figure supplement 1)”. As discussed above in response to reviewer #1’s suggestion, we also now report F-statistics and P-values for the linear regressions in the figure legends.

The authors claim that squamation begins at ~9 mm SL (line 268), prior to which MCs were "rare" in the epidermis (supported by data in Figure 7F). However, Figures 8A and 8G suggest that MCs are not rare prior to squamation/9 mm SL. Are these data in conflict?

Thank you for raising this observation. We do not believe these data are in conflict. Figure 8A and B show images of fish 8.8-8.9 mm SL, immediately prior to squamation. MCs appear about the same time as scales develop but the exact timing varies between animals. To further strengthen this section of the manuscript, we now include quantification of the density of trunk MCs at various stages prior to 9 mm SL (new data added to the developmental timeline in Figure 7F). These data are consistent with our initial interpretation. In the revised results section we clarify this as follows: “Using reporters that label MCs and scale-forming osteoblasts, we rarely observed MCs in the epidermis prior to 8 mm SL (Figure 7B, F). Between 8-10 mm SL, MCs appeared at a low density along the trunk (Figure 7F). MC density rapidly increased from 10-15 mm SL, a period of active scale growth (Figure 7C-F).”

In Figure 6B-E, the panels are incorrectly labeled as "atoh1a:nls-Eos" (figure caption and fluorescence localization show they are atoh1a:Lifeact-EGFP).

The low magnification panels were correctly labeled as atoh1a:nls-Eos. The insets showed atoh1a:Lifeact-EGFP as described in the figure legend. We apologize for the confusion and poor data presentation. We have revised Figure 6 to eliminate the problematic labeling/display.

Figure 9 panels E-H are not referenced in the main body of the text.

Thank you for pointing this out. Fixed in the revised manuscript.

In Figure 6, the authors examine MC densities in the tail, but do not quantify changes here with eda mutants as they did for other regions (eye, operculum) in Figure 8. Why was this region not examined?

We have clarified this point in the revised results section as follows: “eda mutants lack fins at the stages analyzed (Harris et al., 2008) precluding analysis of these regions in the homozygous mutants.”

The authors do a good job in detailing the current literature regarding MCs, however, two missing areas are noticeable: 1) there is no mention of mammalian MCs that reside in the oral mucosa (Hashimoto, 1972) or whether they exist in zebrafish, and 2) no mention of Merkel-like cells (Halata, 2003) and why the cells in this paper are or are likely not Merkel-like cells.

Thank you for the suggestions. Regarding the first point, we revised the introduction to reference (Hashimoto, 1972) as follows: “...vertebrates have diverse types of skin and MCs are found in both hairy and glabrous (non-hairy) skin, as well as mucocutaneous regions such as the gingiva and palate (Hashimoto, 1972; Lacour et al., 1991; Moayedi et al., 2021).” We also imaged the mucosal tissue along the roof palate of the adult mouth and identified atoh1a+ cells (see Response Figure 1 below). Close examination of the atoh1a:Lifeact-EGFP signal revealed these cells have a spherical morphology and extend short processes similar to the MCs described across the body regions examined in Figure 6. However, as the microvillar morphology of the palatal atoh1a+ cells is not identical to those identified in other skin regions, we hesitate to call these MCs without performing additional in-depth analyses. We feel that inclusion of these data in the manuscript could distract the reader from the main focus of our study, therefore we have included them here:

__Response Figure 1. atoh1a+ cells in the adult oral epithelium. (A,B) __Low- (A) and high-magnification (B) confocal micrographs of oral roof palate epithelium in an adult expressing reporters for keratinocytes (Tg(krt4:DsRed)) and atoh1a-expressing cells (Tg(atoh1a:Lifeact-EGFP)). (B’) Reconstructed cross section along the yellow line in B showing two atoh1a+ cells in the upper strata of the oral epithelium. Scale bars: 50 µm (A) and 10 µm (B,B’).

Regarding the second point, we have added the following sentence to the first paragraph of the discussion: “Second, zebrafish MCs extend numerous short, actin-rich microvilli and complex with somatosensory axons, classic morphological hallmarks of MCs (Mihara et al., 1979; Smith, Jr, 1977; Toyoshima et al., 1998). Our morphological observations support the interpretation that these cells are MCs rather than Merkel-like cells, which lack axon association and microvillar processes (reviewed by Halata et al., 2003).”

It may help readers understand MC morphology in context if the authors include a larger picture of the TEM data that highlights the drastic difference in ultrastructure between MCs and neighboring keratinocytes.

Thank you for the suggestion. We added a new figure (Figure 1—figure supplement 1) to the revised manuscript that contains an additional TEM image that we believe illustrates the different morphologies of keratinocytes and MCs. We hope this will help the reader contextualize the morphology and position of MCs within the zebrafish epidermis. This is now referenced in the first results section as follows: “The cells appeared relatively small and spherical with a low cytoplasmic-to-nuclear ratio compared to neighboring keratinocytes (Figure 1B,C; Figure 1—figure supplement 1) …”

Reviewer #3 (Significance (Required)):

The current manuscript provides significant advancements in various biological fields and research communities. For researchers that utilize zebrafish as a model organism, these findings present a new cell type along with novel and essential genetic tools for study. These developments open the possibilities to further understand MCs, their roles in somatosensory function, mechanisms of cell type diversification, and to engage in translational research. For those already researching MCs, this manuscript shows that fundamental questions regarding MC functioning can be rigorously addressed with a new model that can fill the methodological limitations imposed by mammalian biology. Indeed, the authors do a thorough job of introducing and contextualizing our knowledge of MCs and any outstanding gaps. The authors then sit their findings comfortably alongside previous works, largely supporting those findings, and take the extra step to address MC controversies/matters of debate. This technique of supporting the current literature and then uplifting it with new findings makes this work even more impressive. Various audiences will find value in this manuscript, including but not limited to those that study epidermal cell types, the development and influence of skin appendages, somatosensation and sensory disorders, developmental biology, and Merkel cell carcinoma.

We thank the reviewer for their positive assessment of the manuscript.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Brown et al. (2022) seek to characterize and address fundamental questions regarding the development and dynamics of Merkel cells (MCs) in zebrafish (Danio rerio). The authors utilize a diverse and complementary suite of methods to characterize presumptive MCs in the epidermis of adult zebrafish, including electron microscopy, novel transgenic lines, confocal imaging, and various immuno- and non-immunohistological staining techniques. These studies demonstrate that zebrafish MCs share many features with vertebrate (including mammalian) MCs, particularly regarding morphology/structure, putative functions, genetic markers, and bodily distribution.

After establishing the identity of zebrafish MCs, the authors employ lineage tracing and cell tracking analyses to determine that trunk MCs derive from basal keratinocytes and exhibit regular cell turnover. Finally, the authors examine how trunk scales may affect MC development by using established scale mutants. These results show that the presence/absence of scales influences trunk MC development, while scale characteristics (e.g. shape, size) change the distribution of MCs.

MAJOR COMMENTS:

The key conclusions of the manuscript are convincing, however, several points should be addressed by the authors.

• Throughout the manuscript, the authors make general claims about zebrafish MCs (zMCs) based on the evidence collected. Yet, most of this evidence (particularly claims about MC turnover, development, structure) comes from examination and experimentation of a specific MC population: trunk MCs located in the scale epidermis. The authors remark upon mammalian MC diversity (lines 73-74) and go on to highlight the diversity of MCs throughout the adult zebrafish (Figure 6), which have differing densities and distribution patterns. Any statements that suggest all zebrafish MCs share certain qualities/features should be carefully considered given the evidence presented.

• In the manuscript, the authors validate several markers for the identification of zMCs based upon known mammalian markers (e.g. atoh1a, sox2, piezo2, SV2, 5-HT, and AM1-43; Figures 1-3). Yet, another well-known marker for MCs (CK8) is not addressed (Moll, 1995; Moll, 2005). One zebrafish ortholog for CK8 is krt4, a transgene successfully employed in this study to label keratinocytes. Do zMCs express krt4 or other mammalian MC keratins? Answering this question or addressing this discrepancy would further strengthen the authors claims that these cells are bona fide zMCs.

• The authors utilize a previously validated eda mutant line to see if ectodysplasin signaling affects zMC development. While the results of these experiments are convincing, the authors need to make clear whether they are claiming that scales, scale-derived Eda signaling, or Eda signaling alone dictate trunk MC development. It appears that there is some conflation of these ideas, particularly with line 306 ("blocking dermal appendage formation inhibited MC development" is a different claim from 'blocking Eda signaling inhibited MC development'). One way to make this differentiation would be to perform a similar experiment as detailed in Xiao et al. 2016: using a Shh agonist in eda mutants. If scale-specific signals are required in addition to Eda, we would expect to see similar MC densities and patterns in both Shh agonist-treated and non-treated eda mutants.

• Throughout the manuscript, the authors use subjective language (e.g. line 106). While this reviewer does not wish to suppress or alter the authors' voices, careful consideration should be used when employing these types of descriptors. Furthermore, the authors use suggestively quantitative language inappropriately or unjustifiably. For example, in line 221, the authors use "extensive" when describing the co-labeling between atoh1a+ MCs and lineage-traced basal keratinocytes; the percentage of co-labeled cells ranged from 29-32%. Other quantitative descriptors such as "frequently" (line 171) or "uniform" (line 249) describe various features or phenomena without quantification in figures or supplements.

• In the lineage tracing experiments (Figure 4), the authors note that "recombination is not complete" (lines 1016-1017) to explain why not all zMCs express the basal keratinocyte lineage marker. While this idea could be supported by Figure 4-figure supplement 1, one could postulate that zMCs are derived from multiple progenitor lineages. Using the basal keratinocyte lineage tracing validation, the authors could in theory calculate a "recombination efficiency" of this transgenic line and determine approximately the percent of zMCs they 'lose' as a result. Otherwise, the authors could perform other experiments to support the claim that zMCs derive from basal keratinocytes. For example, could the authors photoconvert basal keratinocytes at 1 dpf and see how many derived MCs are still photoconverted later? Could they do this photoconversion experiment with neural crest cells? Could they ablate neural crest cells and determine if MC number is affected? These additional experiments are not necessarily required for publication, but some explanation of the unexpectedly low percentage of basal keratinocyte lineage marker-labeled MCs would suffice.

• The authors use appropriate statistics and have sufficient replicates when this information is presented. Yet, the presence or absence of these data is not consistent within figure captions. The authors must ensure that they provide the N of adults and scales (when appropriate), the SL range of adults, and transgenic lines used. Statistics are missing in some figures (for example: Figures 4E, 5D, 5E, 6F, 8S-1E, 9E-H) where it would be appropriate to include them. In some figures, the N changes over time (example: 5D, 5E); an explanation in the 'Methods' section would suffice.

MINOR COMMENTS:

While the authors present an extensive argument for their claims, addressing these additional comments would further strengthen their story.

• Are zMC nuclei lobulated? This ultrastructure characteristic seems to be common in MCs (Chew & Leung, 1994; Tachibana & Nawa, 2002; Moll, 2005; Boulais, 2009).

• In Figure 3C and 3", the authors show that AM1-43 labels zMCs. Yet, this technique should also stain sensory axons that associate with MCs (Meyers, 2003). Are axons also stained? Other positive controls for the stains could be useful as a supplement.

• In Figure 7, the authors argue that as scales develop, MC density increases with scale area. Did the authors compare MC densities of differently-sized scales at the same age? Is fish SL/age a potential confound in the interpretation of these data?

• The authors claim that squamation begins at ~9 mm SL (line 268), prior to which MCs were "rare" in the epidermis (supported by data in Figure 7F). However, Figures 8A and 8G suggest that MCs are not rare prior to squamation/9 mm SL. Are these data in conflict?

• In Figure 6B-E, the panels are incorrectly labeled as "atoh1a:nls-Eos" (figure caption and fluorescence localization show they are atoh1a:Lifeact-EGFP).

• Figure 9 panels E-H are not referenced in the main body of the text.

• In Figure 6, the authors examine MC densities in the tail, but do not quantify changes here with eda mutants as they did for other regions (eye, operculum) in Figure 8. Why was this region not examined?

• The authors do a good job in detailing the current literature regarding MCs, however, two missing areas are noticeable:

1) there is no mention of mammalian MCs that reside in the oral mucosa (Hashimoto, 1972) or whether they exist in zebrafish, and

2) no mention of Merkel-like cells (Halata, 2003) and why the cells in this paper are or are likely not Merkel-like cells.

• It may help readers understand MC morphology in context if the authors include a larger picture of the TEM data that highlights the drastic difference in ultrastructure between MCs and neighboring keratinocytes.

Significance

The current manuscript provides significant advancements in various biological fields and research communities. For researchers that utilize zebrafish as a model organism, these findings present a new cell type along with novel and essential genetic tools for study. These developments open the possibilities to further understand MCs, their roles in somatosensory function, mechanisms of cell type diversification, and to engage in translational research. For those already researching MCs, this manuscript shows that fundamental questions regarding MC functioning can be rigorously addressed with a new model that can fill the methodological limitations imposed by mammalian biology. Indeed, the authors do a thorough job of introducing and contextualizing our knowledge of MCs and any outstanding gaps. The authors then sit their findings comfortably alongside previous works, largely supporting those findings, and take the extra step to address MC controversies/matters of debate. This technique of supporting the current literature and then uplifting it with new findings makes this work even more impressive. Various audiences will find value in this manuscript, including but not limited to those that study epidermal cell types, the development and influence of skin appendages, somatosensation and sensory disorders, developmental biology, and Merkel cell carcinoma.

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Referee #2

Evidence, reproducibility and clarity

This is a very nice and straightforward paper characterizing mechanosensory Merkel cells in the zebrafish skin. The paper uses a number of criteria, based on our knowledge of Merkel cells in mammals, to identify a population atoh1a expressing cells, with neurosecretory granules and actin rich microvilli as Merkel cells in the zebrafish skin. The authors have used existing transgenic lines and and developed some of their own, described in this paper, to follow the development of Merkel cell in zebrafish. They show that Merkel cells are derived from basal keratinocytes not neural crest cells. They have region specific densities that influenced by underlying structures like scales and fin rays. They go to show that Ectodysplasin signaling promotes Merkel cell development in the trunk skin but not above the eye or operculum. Reduction of Merkel cells in eda mutants suggest that Eda signaling is required for their development and maintenance. Finally they show that alteration of zebfrafish scale pattern using a mutant with exaggerated fgf8a expression also alters merkel cell distribution.

The data presented is clear and the conclusions are supported by their observations.

I have no significant issue with the paper as is.

Significance

This study will serve as an excellent basis for future work looking at studies of Merkel cell development and function in fish. Though Merkel cells have been studied in mammals, establishing a zebrafish model for their study will help overcome many barriers that make their analysis difficult in mammals.

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Referee #1

Evidence, reproducibility and clarity

The authors describe and characterize the touch system in zebrafish as a new model to study MC development and maintenance. The manuscript is very well written, and the experiments are carefully executed and beautifully illustrated. This study addresses the origin of zebrafish MCs, shows that they are innervated by somatosensory neurons and that they share molecular properties with mammalian MCs. In addition, the authors developed transgenic lines that allow them to study MCs in vivo.

Genetic lineage tracing shows that zf MCs are derived from the epidermis as in mouse and not from neural crest cells, as described for avian MCs. In addition, longevity and turnover of murine MCs was controversial. Here, the authors show that zf MCs constantly turnover and that the distribution and turnover rate in the trunk depends on underlying scales. They show that the loss of scales in eda mutants leads to a decrease in MC production and an increase in MC death showing that scales are required for MC production and maintenance. Using a specific fgf8 mutant allele that causes an increase in Fgf signaling and an increase in scale size they demonstrate that scales are sufficient to induce MCs.

In summary, this manuscript is a rigorous and beautiful characterization of MCs development and maintenance. The authors demonstrate that zebrafish MCs share many characteristics with mammalian MCs. The generation of MCs specific transgenic lines, coupled with existing transgenic lines that label somatosensory cells and cells in the scales sets the stage for detailed further analyses. For example, using these tools one can now study how the size of the MC progenitor domain is controlled, if progenitors migrate and what the identities of the molecular signals from the nerves and scales to progenitors and differentiated MCs are.

Minor comments:

Line 71: Why is the heterogeneity a limitation? Couldn't it also exist in zebrafish? Line 295: The authors write: 'Thus, our observations indicate that the decrease in MC cell density in eda mutants is likely due to both reduced MC production and increased MC turnover'. It should say: '.. increased MC loss'. In mutants the MCs show poor turnover. I believe the term 'turnover' implies that the cells are being replaced, which is only partially happening here. Line 301: 'The authors state: 'these data suggest that Eda signaling is required for MC development, maintenance, and distribution along the trunk.' The authors do not show any data that Eda signaling is involved in MC development but only that scales are needed. The MC inducing signals from scales to the epidermis could be independent from Eda signaling. Please rephrase. Please discuss that not all MC specification/development depends on scales. Even in the scale-less eda mutants some MCs form (as in the inter scale regions in wt?) and even turnover. Do scales secrete a signal that increases proliferation of existing MC progenitors but scales do not affect specification? Line 320: The authors describe that the fgf8 allele leads to a redistribution of MCs. Is it really a redistribution, or is it ectopic induction or expansion of existing progenitors? Redistribution implies that the expansion is due to a loss of MCs in another region, which I do not see in the data.

Figures:

• Figure 1, panels B-C': EM images are very dark and difficult to see. Letter 'a' is on top of the axon, maybe move to the side and pseudo-color different structures.

• Figure 1, panel D: very difficult to see the magenta axons in the cartoon. Please enlarge and make brighter.

• Figure 2, panels A and D: keeping the same antibody stainings in the same color would help with visualization. Matching the bar plots in panel C would be even nicer.

• Figure 2, panel C: please identify in the legend if the error bars are SD, SEM or other.

• Figure 2, panels G and H: MCs are in cyan in the image, but green in the legend.

• Figure 3: include percentages and total number in the image instead of the legend.

• Figure 6, panel B: which part of the eye is being depicted?

• Figure 6, panel F: please provide error bars and statistics to show that the operculum has a higher density of MCs.

• Figure 7, panels F-H: for simple linear regression, please also provide F and p values.

• Figure 8, panel D: colors for SL do not follow a scale, very hard to understand which is which.

Methods:

• Line 472: the word "sex" should be used instead of "gender".

• Image analysis, line 593. Please provide a more detailed explanation or describe the ImageJ macro used for the analysis.

Significance

Soft touch is perceived by Merkel cells (MCs). How MCs develop and are maintained is not well understood because MC development is difficult to study in mammals due to their in utero development. The authors describe and characterize the touch system in zebrafish as a new model to study MC development and maintenance. The study demonstrates that the zebrafish touch system shares many characteristics with its mammalian counterpart, namely its developmental origin, innervation and molecular characteristics. In contrast to mammals, zebrafish transgenic lines that the authors generated, allow the in vivo analysis of Merkel Cell specification, development and maintenance. Therefore this study is the foundation for future detailed cellular and molecular analyses of the touch sensory system and will be of interest to developmental biologists studying stem cells, regeneration and aging, as well as neuroscientists.

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Reply to the reviewers

Official Revision Plan Document:

Manuscript number: #RC-2022-01681

Corresponding author(s): Nicholas, Leigh

1. General Statements

We sincerely appreciate these positive and helpful reviews. We are grateful for the constructive comments and we outline our responses below. Addressing these comments will further broaden the impact of the work and increase the power, reliability, and application of single cell approaches while decreasing the cost and labor intensive collection steps.

As single cell sequencing approaches have entered the mainstream, we are still finding flaws and artifacts from these methods. A major limitation of widely used collection approaches is a difficulty in obtaining biological replicates, which are required to generate robust sequencing datasets. In general, a lack of biological replicates has been a major oversight in the vast majority of single cell studies, and any technique that can facilitate biological replicate collection should be widely applied. The elegance of SNP-based demultiplexing lies in the fact that it can be applied regardless of any external label, applied to previously collected data, and the data are already collected for every sample sequenced. We were pleased to have the reviewers agree and identify the many conceptual advances in this manuscript, with one major critique being noted by one reviewer as a lack of novelty.

Regarding the lack of novelty, we appreciate that SNP-based demultiplexing was not developed as a method within this manuscript, but disagree that a broad benchmarking and validation study that opens the doors to the use of SNP-based demuxing in any species with sufficient between animal genetic heterogeneity lacks novelty. To address this concern, we will now further emphasize the drawbacks and artifacts that can arise in the currently common practice of pooling samples and choosing not to demultiplex, while improving our explanation of our discoveries in this manuscript. The lack of biological replicates in single cell sequencing studies is rampant and needs to be addressed with approaches such as those demonstrated here. We also want to emphasize the importance of validating and benchmarking bioinformatic approaches with orthogonal, priorly established approaches (eg. wet-lab based methods), which had previously not been conducted for SNP-based demultiplexing, outside of human samples. The inbred nature of common lab animals and broad range in quality and availability of genomic resources make this a major step forward in bringing SNP-demultiplexing to all labs. We believe that our paper broadly extends, benchmarks and most importantly validates the advantages and limitations of SNP-based demuxing across various species.

2. Description of the planned revisions

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

“Cardiello et al tested if souporcell (https://pubmed.ncbi.nlm.nih.gov/32366989/) can be used to demultiplex samples for some model organisms, based on identified SNPs. For this, they used synthetic multiplexed data, publicly available datasets and some new datasets, spanning samples from five model organisms. Their analysis indicates that souporcell could be used to

demultiplex scRNA-seq experiments for multiple species, which offers a cost-beneficent approach.

The manuscript reads well and shows this approach can work for different model organisms. However, unfortunately, I am confused about the amount of novelty in this manuscript. The method, souporcell, is already published. The authors indicate souporcell is not validated in non-human samples, but the original paper states that their method works with malaria parasite data (Fig 3b, FigS4). Adapting and using an available tool for different model organisms is good and groups working on different model organisms may find this manuscript useful, but the same could be said for the original article. Due to these reasons, I am not sure whether this manuscript has novelty sufficient for publication.”

__Our response: __We appreciate this constructive criticism that helped us realize that our novelty was not clearly stated in the first version of the manuscript. We need to improve our Introduction and our verbiage as to what has been previously performed and how this current manuscript provides novel insight into multiple previously unanswered questions which broadly extend the utility of SNP-based demultiplexing. To address this comment, we will revamp our Introduction, Results, and Discussion to more clearly highlight the novelty of this work.

Planned revisions:

Defining “validation”. We define validation as establishing the accuracy or validity of a method. Therefore, validation of SNP-based demultiplexing for use in non-human species requires comparison to an already proven, orthogonal method, such as a wet-lab based demultiplexing approach. The souporcell paper does not validate (i.e., confirm with an orthogonal wet-lab method) the results from souporcell in any species but humans. This lack of validation for SNP-based demultiplexing in samples from non-human species made it unclear how and if these approaches would work in other species. Human samples are expected to perform exceptionally well in this approach due to their extremely high genetic diversity and wealth of available genomic resources. Thus, while it was exciting that the original souporcell authors chose to try applying their algorithm to a non-human (e.g., malarial parasite) dataset, the paper left many unanswered questions about potential uses and accuracy. In addition to validating the accuracy of souporcell results in many species, we demonstrated that souporcell shows a relatively poor ability to call doublets in many non-human vertebrates. In addition to highlighting a novel drawback of the method, this demonstrates the need to validate the accuracy of different aspects of tools like souporcell when applied to new systems rather than use souporcell or other SNP-demuxers prior to validation. Highlighting other novel findings in this work: For instance, our assessment of which genomic resources are required for using SNP-based demultiplexing in different species, whether this could be applied to lab animals likely to be inbred to various degrees (and to address other reviewers comments, the inbred level permitted), assessment of the accuracy of SNP-demultiplexing in species with alignment references of varying qualities (i.e., only de novo transcriptome) and genomes of varying sizes (up to 30Gb, 10 times larger that of human, which can be extremely computational intensive), and the exploration of pooling and demultiplexing of multiple species in a single library. Making clear how we made the necessary adjustments to the original souporcell pipeline to successfully apply it to datasets with various resources available in these species.

(Reviewer #1): I also wrote down two minor points below:

“1- Doublets assigned by souporcell compared to the fluor-based assignment look random. In Fig 2 doublet recovery rate looks smaller, and in fig 3 doublet rate prediction looks more random. This is a bit confusing. Is there any explanation for this?”

__Our response: __We agree and thus noted in the manuscript that the detection of doublets in these datasets by Souporcell are not very reliable.

Planned revisions:

We will expand our Discussion to include brief hypotheses for factors that likely contributed to poor doublet detection by souporcell in these analyses. In the Discussion we will clearly suggest complementary approaches for improving the detection/removal of doublets in pooled scRNA-seq experiments through applying external gene expression-based doublet detection programs. We will also attempt to use these programs on at least one of our datasets to see how well independant doublet detection methods complement souporcell on pooled datasets. A full benchmarking of these doublet detection methods already exists and will be referenced in our Discussion.

Reviewer #1: “2- The authors discussed the immune system cells might show some variability in their discussion (referring to fig 3), but this is not clearly shown in the figures as data. Having a percentage bar graph could make it clearer for the readers.”

__Our response: __This is a valid point that we plan to address with the addition of a new figure as well as some clarifications in the text.

Planned revisions:

We will make a supplemental figure for Figure 3 in which we clearly demonstrate animal to animal variability. (bar plot of absolute cell numbers present from each individual animal present in each cell cluster as requested). In the new supplemental figure we will also include a new UMAP plot of fluorescently assigned cell identities belonging only to one of the three animals, which makes it easier to visualize the difference in numbers of cells from each animal present in each individual cell cluster. We will also cite papers that have already demonstrated the phenomena of animal to animal variability in scRNA-seq datasets. We will further emphasize that even in the absence of animal-to-animal variability in co-clustering, that demultiplexing pooled datasets is important because differential expression analysis is greatly enhanced with biological replicates.

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

Major comments:

“1. SNP-based demultiplexing performed well on some species, such as zebrafish and Africa green monkey, from which over 90% of the cells analyzed were correctly identified. However, this accuracy decreases in Pleurodeles samples when a common SNPs VCF file is absent (Fig.3). It showed that cell identity can be more precisely defined with the increase of average read depth (Fig 3B). So, I am wondering whether the mis-defined cells shown in Fig. 3E, actually are cells with lower reads. It is better if the authors can test such a correlation between the cell identity and the depth of reads using the data from Fig. 3E.”

__Our response: __We are thankful to reviewer #2 for raising such a great point. We do see the accuracy of the benchmarking results for this experiment increase with increasing sequence depth/cell quality. However, the reasons for this are potentially more complex than just higher accuracy of souporcell in higher quality cells: The fluorescent-based demultiplexing that is being used for “ground truth” in benchmarking souporcell for this figure is more accurate in cells with higher read depth because more fluorescent gene reads are likely to be captured. Therefore analyzing the accuracy of souporcell relative to fluorescent-based demultiplexing over varying read depths can be confusing because it is possible that both methods improve in accuracy with higher read depth. Figure 3B attempts to illustrate this concept, and to demonstrate why we chose to benchmark only the cells with sufficient read depth (read depth between 5K, and 40K, and >1 fluorescent gene read per cell). We plan to complement our manuscript with additional figures and text that will make this clearer.

Planned revisions:

We will produce a plot similar to Figure 3B, but with a Y axis that is the percent agreement between the two methods. For Figure 4 we will also make a plot showing percent agreement between demux methods versus read depth. This plot will be a useful comparison to investigate whether scRNA read depth is directly affecting the quality of souporcell’s SNP-based demux results. Plotting this comparison for a dataset in which Cellplex/Cell hashing is the benchmarking demux method is a more fair test of the effect of sequencing depth on the souporcell demux results because cellplex results rely on reads from the cellplex library, which are an independent sequencing library from the scRNA reads. We will investigate whether the use of a common VCF file or lack thereof affects souporcell accuracy. To test this, we will try repeating souporcell demux of one dataset with and without a common VCF file input to see if the VCF file inclusion affects the accuracy of souporcell results.

Reviewer #2:

“2. Please discuss limitations of this approach in the manuscript. (1) To which extent, when SNPs are roughly present in the individuals of same species, SNP-based demultiplexing can be applied, e.g., individuals from an inbred strain (c57bl6 mice) would not work.(2) The authors experimentally tested two newt species using SNP-based demultiplexing. When multiple species are experimentally applied, may the cell/nuclei size variation cause problem?”

__Our response: __We agree with Reviewer 2 that this paper brings up many technical questions about the limits to which SNP-based demultiplexing will succeed. These limitations should be addressed more thoroughly in our Discussion section.

Planned revisions:

We will expand our Discussion to more fully discuss the predicted limits for SNP-based demuxing for separating pooled cells from genetically similar individuals. We referenced the single paper previously published which reported that Freemuxlet, a similar approach to souporcell, did not succeed when applied to cells pooled from multiple animals within an inbred mouse strain, but did succeed across mouse strains (though without any validation of results). We will expand this Discussion to address the expected effects of genetic diversity on the success of SNP-based demultiplexing methods. We will also note in this expanded Discussion that SNP-based demuxing worked in this paper on siblings (some of the xenopus, some of the zebrafish), and other SNP-based demuxers have been used successfully for demuxing cells from closely related individuals including human siblings (scSplit) and human maternal/fetal pairs (souporcell). We will expand our Discussion to address the potential drawbacks of pooling cells from different species or tissue types including the possibility of a bias in scRNA-seq sample preparation methods. We expect that variations in cell or nuclei sizes between species could cause biases in cell capture depending on the scRNA-seq library preparation method, especially with microfluidic based scRNA-seq preparation methods. We will search for a dataset that would allow for synthetic pooling of inbred mouse data and, if available, put this through our synthetic pooling and demuxing pipeline. While other papers have reported this does not work with other SNP demux tools, and on comments on the souporcell github (https://github.com/wheaton5/souporcell/issues/154) it does not seem to be working, we feel this would be a nice test/reference for showing the limitations for SNP-based demuxing in highly genetically similar individuals.

(Reviewer #2)* *

“3. What is the upper limit number of samples when using this model. Please make some estimation or discussion about it.”

__Our response: __We think this is a pressing question for the future of SNP-based demuxing and deserves further discussion in this manuscript. This is directly addressed by the authors of souporcell in a github thread with regard to human samples (worked on 21 human samples, may work in up to 40). At this point, we have no reason to believe that the limit on sample numbers should be different in other species.

Planned revisions:

We will include discussion about potential limits for the maximum number of samples that can be pooled and demuxed using this approach. As discussed below in response to reviewer 3, we will quantify the genetic differences in pooled datasets in this manuscript in order to give readers an improved prediction of how well SNP-based demuxers are likely to work on their animals of interest. We will look for previously published pooled dataset from zebrafish that includes multiple dozens of samples and attempt to SNP-demultiplex this pool. While we will be unable to validate the accuracy, given how well SNP-based demuxing has performed we can at least determine if cell origins are assigned.

Reviewer #2: Minor comments:

“1. Please add an algorithm principle of this model.”

__Our response: __Thanks for the suggestion, we will do so.

Planned revision:

We will direct readers to the algorithm principle of souporcell in the original paper and include a flowchart of our workflow for running souporcell piece by piece as we have done in the manuscript. As mentioned above, we will make clear how we made the necessary adjustments to the original souporcell pipeline to successfully apply it to datasets with various resources available in these species.

Reviewer #2:

“2. Give a clear definition of doublets including the ground truth and Souporcell result.”

__Our response: __We appreciate this recommendation. For the purposes of this paper our definition of a ‘doublet’ is a dataset represented by a single cell barcode that actually contains more than one cell. However, true doublets can be identified with absolute certainty only in our synthetically pooled datasets, because no demultiplexing approach used for benchmarking is 100% accurate. Therefore, ‘true doublet’ will refer to known doublets based on synthetically pooled dataset ground truths. Further, for our experimental datasets we will also use ‘confirmed doublet’ to refer to cells that were called doublets by both the ground truth and souporcell. And we will use ‘contested doublet’ to refer to cells in which the experimentally derived ground truth and souporcell result disagree about a potential doublet.

Planned revision:

We will insert a clear definition of doublets used in this paper as described above, including the complexity in identifying which doublets are real given the relationship between ground truth and the souporcell results for each experiment.

Reviewer #2:

“3. Authors should indicate the time cost of running one round of such analysis, the minimal computational requirements?”

__Our response: __This is an important point and will be helpful to readers.

Planned revision:

We will add to the manuscript information on the required time, RAM consumption, and computational requirements for running various setups for souporcell.

Reviewer #3: Major comments:

“The manuscript makes a convincing case for the ability of a preexisting SNP-based demultiplexing tool, called souporcell, to demultiplex pooled samples. The study uses three methods for validation: 1. In silico data pooling; 2. Pooling of transgenic lines; 3. Pooling of cells tagged with CMOs (10x genomics). The results are consistent across experiments.

The authors propose that souporcell is a solution for demultiplexing pooled samples whenever sample tagging methods are not feasible. Although the authors test this approach in several species and conditions, the validation does not cover all possible cases and situations, obviously. Indeed, the authors recommend potential users to run pilot validation experiments with a secondary demultiplexing methods.

However, the manuscript would become more useful if the following points are addressed:

First, what is the genetic relatedness of the individuals pooled in the experiments? What is the SNP frequency in the samples analyzed, and how does that compare to SNP frequency in mouse strains? (The number of SNPs in the VCF is reported in a supplementary table but not discussed in the main text). This point is extremely important: as the authors mention, it is not possible to demultiplex samples from the same mouse strain. Inbreeding is relatively common in laboratory species, even unconventional ones; therefore, information on genetic relatedness and SNP rate would help readers assess whether SNP-based demultiplexing has a good chance to work in their systems. Addressing this point does not require any additional experiments, and computing from the single-cell reads how many SNPs distinguish the individuals pooled here should be straightforward.”

__Our response: __We appreciate the comments raised by reviewer #3.These are valuable critiques and will greatly improve the manuscript.

Planned revisions:

We will expand our Discussion with a paragraph on the limits for genetic differences required for SNP-based demuxing to work, as mentioned in response to Reviewer 2. This will include references to Table 1 values on SNP numbers utilized in each analysis, and hypotheses on the absolute limits for genetic relatedness. We will expand Table 1B to include green monkey. As mentioned in response to Reviewer 2, if previously published data we will also try applying souporcell to data from an inbred mouse line to test run an extreme case of applying SNP-based demuxing to data from very inbred animals. We will more clearly annotate the known relationship between individuals in our experiments, and will discuss this within our Discussion. We will contact the zebrafish and axolotl authors and ask if these animals were siblings. We will identify and apply a method for quantifying the genetic relationship between individuals in each scRNA-seq experiment in this study, to enable us to provide readers with a quantitative measure of genetic diversity present in each experiment. This analysis should shed some light on the requirements for genetic variability in order for SNP-based demultiplexers to succeed.

Reviewer #3:____

“Moreover, the relatively limited number of samples pooled does not validate the use of souporcell with a larger number of samples. For example: in developmental studies, often dozens of embryos are collected and pooled. What are the potential caveats of using souporcell for demultiplexing larger number of samples? The Discussion would be a good place to warn potential users of the limitations of the approach.”

__Our response: __We agree this could still be a limitation, and for developmental studies with multiple dozens of samples, further exploration of optimal demultiplexing methods or the combination of computational and wet-lab based demux methods may be required.

Planned revision:

We will expand our Discussion on predicted limits for SNP-based demuxing of high sample pools, as discussed in response to Reviewer 2. We agree that developmental projects often involve pooling large numbers of samples, so it is worth clearly outlining the benefits and risks of planning to use SNP-based demultiplexing on such high sample pools, and to outline the limits as discussed by the developer of souporcell. As stated above, we will work to identify a previously published pooled zebrafish dataset with multiple dozens of samples and run souporcell on it. While this will not provide any validation it will at the least determine if we are able to assign cell origins, which have thus far been very reliable when assignments have been made.

Reviewer #3: Minor comments:

“- is the accuracy of doublet detection rate a function of number of samples? This can be tested by repeating the monkey in silico experiment with three individuals.”

__Our response: __This is a good question. We do not thing that the number of samples substantially affects the accuracy of doublet detection by souporcell, but we will test this.

Planned revision:

As suggested, we will repeat the monkey analysis with 3 samples to see how this changes doublet detection. Overall, due to the low quality of doublet detection by souporcell found in this manuscript, we will expand our Discussion of doublet detection to propose some potentially useful recommendations for making conservative doublet calls with souporcell external programs (addressed above in response to Reviewer 2. We expect that the more substantial filtering of the monkey datasets relative to the zebrafish dataset prior to pooling contributed to this question. To make these differences more obvious we will more deliberately emphasize the differences in dataset filtering for each experiment.

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

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

From Reviewer 1:

“More generally, showing more direct evidence for the variability of different cell types (not just the immune system) could be informative for scRNA-seq users.”

__Our response: __We do not plan to conduct extensive analyses of other published single cell datasets to provide a further reason for why it is important to have biological replicates for single cell experiments. When building this manuscript, we chose not to pursue the option of publishing an analysis of published single cell datasets in which we could identify artifactual results and animal to animal variability, because we worried that this would be harmful to future open science efforts, and therefore, counterproductive. Further, past papers have already demonstrated the issue of batch effects and animal to animal variability in scRNA-seq datasets, and the requirement for biological replicates to facilitate differential expression analysis. As mentioned above, we will do a better job citing the papers that address these points.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this manuscript, Cardiello and colleagues address the problem of demultiplexing pooled samples in single-cell RNA sequencing (scRNAseq) experiments. The manuscript benchmarks the use of a preexisting SNP-based demultiplexing tool, called souporcell, in pooled samples from non-conventional laboratory species. The validation includes computational pooling of published data from different individuals (zebrafish, green monkey), and generation of new pooled data with independent ground-truth information available (with frogs and three salamander species). The authors conclude that souporcell is suitable for demultiplexing scRNAseq data collected as pools from different individuals. The authors propose that SNP-based demultiplexing can be used to monitor and correct for batch effects, whenever data need to be collected as pools (for example: small sample sizes, developmental datasets etc).

Major comments:

The manuscript makes a convincing case for the ability of a preexisting SNP-based demultiplexing tool, called souporcell, to demultiplex pooled samples. The study uses three methods for validation: 1. In silico data pooling; 2. Pooling of transgenic lines; 3. Pooling of cells tagged with CMOs (10x genomics). The results are consistent across experiments.

The authors propose that souporcell is a solution for demultiplexing pooled samples whenever sample tagging methods are not feasible. Although the authors test this approach in several species and conditions, the validation does not cover all possible cases and situations, obviously. Indeed, the authors recommend potential users to run pilot validation experiments with a secondary demultiplexing methods.

However, the manuscript would become more useful if the following points are addressed:

First, what is the genetic relatedness of the individuals pooled in the experiments? What is the SNP frequency in the samples analyzed, and how does that compare to SNP frequency in mouse strains? (The number of SNPs in the VCF is reported in a supplementary table but not discussed in the main text). This point is extremely important: as the authors mention, it is not possible to demultiplex samples from the same mouse strain. Inbreeding is relatively common in laboratory species, even unconventional ones; therefore, information on genetic relatedness and SNP rate would help readers assess whether SNP-based demultiplexing has a good chance to work in their systems. Addressing this point does not require any additional experiments, and computing from the single-cell reads how many SNPs distinguish the individuals pooled here should be straightforward.

Moreover, the relatively limited number of samples pooled does not validate the use of souporcell with a larger number of samples. For example: in developmental studies, often dozens of embryos are collected and pooled. What are the potential caveats of using souporcell for demultiplexing larger number of samples? The Discussion would be a good place to warn potential users of the limitations of the approach.

Minor comments:

• is the accuracy of doublet detection rate a function of number of samples? This can be tested by repeating the monkey in silico experiment with three individuals.

Significance

The manuscript presents a technical advance, by validating the use of souporcell for demultiplexing scRNAseq data collected from non-conventional animal species.

The audience potentially interested in this paper is relatively broad. Potential readers include biologists that collect and analyze scRNAseq data from pooled samples, for instance scientists working in the fields of embryonic development and evolutionary developmental biology, but also clinical researchers. The manuscript will be particularly interesting for scientists working on amphibians, because souporcell is validated experimentally in three amphibian species.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.

This study provided a SNP-based demuxers to facilitate effective experimental design of scRNA-seq. This model used discrepancies in SNPs across species or individuals to trace back the source of cells in scRNA-seq experiments. Benchmarking the performance of demultiplexing, this study analyzed in silico or experimentally pooled scRNA-seq data from species including zebrafish, African green monkeys, Xenopus laevis, axolotl, Pleurodeles waltl, and Notophthalmus viridescens. It demonstrated that high accurately demultiplex can be achieved regardless of existence of genome and a common SNP set. Overall, this study provided an economical, powerful, and less-biased pooled scRNA-seq data analysis method, depending minimally on the availability of genomic resources.

Major comments:

1. SNP-based demultiplexing performed well on some species, such as zebrafish and Africa green monkey, from which over 90% of the cells analyzed were correctly identified. However, this accuracy decreases in Pleurodeles samples when a common SNPs VCF file is absent (Fig.3). It showed that cell identity can be more precisely defined with the increase of average read depth (Fig 3B). So, I am wondering whether the mis-defined cells shown in Fig. 3E, actually are cells with lower reads. It is better if the authors can test such a correlation between the cell identity and the depth of reads using the data from Fig. 3E.
2. Please discuss limitations of this approach in the manuscript. (1) To which extent, when SNPs are roughly present in the individuals of same species, SNP-based demultiplexing can be applied, e.g., individuals from an inbred strain (c57bl6 mice) would not work.(2) The authors experimentally tested two newt species using SNP-based demultiplexing. When multiple species are experimentally applied, may the cell/nuclei size variation cause problem?
3. What is the upper limit number of samples when using this model. Please make some estimation or discussion about it.

Minor comments:

1. Please add an algorithm principle of this model.
2. Give a clear definition of doublets including the ground truth and Souporcell result.
3. Authors should indicate the time cost of running one round of such analysis, the minimal computational requirements?

Significance

1. Accurate demultiplexing of pooled data can reduce the batch effect between data and experimental costs.
2. This model will achieve good results in analyzing cell evolution between different species, or individuals of same species carrying sufficient SNPs.
3. It is sufficient to run this analysis only with a de novo transcriptome, opened the possibility of using pooled sc-RNA analysis on less-investigated species.

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Referee #1

Evidence, reproducibility and clarity

Cardiello et al tested if souporcell (https://pubmed.ncbi.nlm.nih.gov/32366989/) can be used to demultiplex samples for some model organisms, based on identified SNPs. For this, they used synthetic multiplexed data, publicly available datasets and some new datasets, spanning samples from five model organisms. Their analysis indicates that souporcell could be used to demultiplex scRNA-seq experiments for multiple species, which offers a cost-beneficent approach.

The manuscript reads well and shows this approach can work for different model organisms. However, unfortunately, I am confused about the amount of novelty in this manuscript. The method, souporcell, is already published. The authors indicate souporcell is not validated in non-human samples, but the original paper states that their method works with malaria parasite data (Fig 3b, FigS4). Adapting and using an available tool for different model organisms is good and groups working on different model organisms may find this manuscript useful, but the same could be said for the original article. Due to these reasons, I am not sure whether this manuscript has novelty sufficient for publication. I also wrote down two minor points below:

1. Doublets assigned by souporcell compared to the fluor-based assignment look random. In Fig 2 doublet recovery rate looks smaller, and in fig 3 doublet rate prediction looks more random. This is a bit confusing. Is there any explanation for this?
2. The authors discussed the immune system cells might show some variability in their discussion (referring to fig 3), but this is not clearly shown in the figures as data. Having a percentage bar graph could make it clearer for the readers. More generally, showing more direct evidence for the variability of different cell types (not just the immune system) could be informative for scRNA-seq users.

Significance

scRNA-Seq is becoming a routine approach to assay gene expression profiling. However, it remains costly. There are new approaches to multiplex and demultiplex samples to decrease the cost. Thus, it is good to see that one available tool works for five different model organisms.

Although it is good to see an available tool works for 5 different species, I am not sure about the novelty presented in this manuscript. Technical advances are not clear to this reviewer, as the method is already published. Moreover, this is a technical report manuscript and there is no biological conceptual advance. As a developmental biologist using single-cell mRNA sequencing, someone more directly from the single-cell field may have further comments on novelty, recommendations for references, and could comment on computational aspects in more detail.

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Reply to the reviewers

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

The manuscript "An Sfi1-like centrin interacting centriolar plaque protein affects nuclear microtubule homeostasis" by Wenz and co-authors describes the detection and analysis of the Sfi1-like protein in apicomplexan parasite Plasmodium falciparum. The authors examined the protein localization and function in asexual stages during parasite replication in the red blood cells. The authors detected PfSlp in the PfCentrin1 pulldown, created PfSlp conditional knockdown strain, and evaluated growth and morphological deficiencies associated with the PfSlp deficiency. The study's primary finding is that PfSlp inhibits the extension of nuclear MTs.

Major comments

The key conclusion is appropriate but is poorly supported by experimental evidence. The transitional, experiment-to-experiment conclusions are preliminary and may require additional experiments. The authors did not present a convincing model of the PfSlp1 function in mitosis.

We appreciate the reviewer’s evaluation that our key conclusions are appropriate, but also have taken some of the valid comments below into account and added some conclusive experimental data and partly modified the choice of words when interpreting the data. We are now fully convinced that our conclusions are appropriate and supported by experimental evidence. To understand the function of PfSlp, which was described for the first time in this study, precisely will require a more detailed model of the still very much understudied malaria parasite centrosome and will be the subject of future inquiries.

If PfSlp inhibits the MT polymerization, then the PfSlp reduction should lead to an extension of the bipolar spindle, which is partly supported by longer MTs in the hemispindles. How is the excess of the nuclear MTs prevent the spindle resolution in anaphase?

Intranuclear hemispindle microtubules are indeed elongated. Increased microtubule polymerization does not necessary lead to an increased spindle length but could just as well promote the nucleation of multiple short microtubules or increase overlap between antiparallel microtubules. We, however, want to emphasize that our key conclusion is that PfSlp is implicated in the regulation of nuclear tubulin levels, rather than “inhibits extension of nuclear MT”. In our view this is an important distinction since microtubule misorganization is merely a consequence of changing nuclear tubulin levels. At no point we want to suggest that PfSlp somehow directly inhibits polymerization of microtubules and therefore did not provide any specific evidence. The fact that PfSlp and microtubules are in different compartments underlines this. Yet, we have noted that our abstract uses the word polymerization. Although we mention that it occurs as a consequence of increased tubulin concentration, which thermodynamically favors microtubule polymerization, we acknowledge that this could be misleading and removed this term (line 30). Concerning how the excess nuclear MTs prevent anaphase spindle resolution we propose several explanations in the discussion (lines 381ff). All line numbers refer to document with “tracked changes”.

Fig 4C misrepresents mitotic phases: bipolar spindle should be broken into two in anaphase, while the drawing shows one elongated spindle connecting two poles.

Indeed, we frequently observed, anaphase spindles being “split” ourselves (Simon et al. LSA, 2021, Fig. 2A). Although sometimes we would see one elongated spindle and sometimes more than two as in Liffner et al. 2021 Fig. 3A. For simplicity we only drew one elongated interpolar microtubule bundle but have now corrected this for more accurate representation.

The authors should correct the use of terminology. Throughout the manuscripts, the parasite division stages are called life stages. Life stages are merozoites, gametocytes, ookinetes, sporozoites, etc. The division stages apply to a single life stage and, in the case of schizogony, are rings, trophozoites, and schizonts.

We once falsely referred to life cycle in line 182 when we should have referred to the intraerythrocytic development cycle. The paragraph using the incorrect wording was removed in the revision.

Please, note that schizogony does not follow the ring and trophozoite stages (line 119); it includes them as the distinctive morphological stages of one round of schizogony. The cell cycle terminology is incorrectly applied.

We have the impression that the usage of the term schizogony is rather “fluid” in that it is occasionally also employed to just the describe the phase where DNA replication, nuclear division, and cytokinesis occur (hence schizont stage), but we clearly note the more canonical use as equivalent of the asexual intraerythrocytic development cycle as whole. We modified the terminology accordingly (e.g. by employing “schizont stage”) lines 43, 142, 184, 238, 265.

What is the "mitotic spindle stage," "mitotic spindle nuclei, "or "mitotic spindle duration" (Fig. 4B)?

It has now been conclusively demonstrated that nuclei go through independent nuclear cycles with different morphological stages (Simon et al. 2021 LSA, Klaus et al. 2022 Sci Advances). Hence, we use the term “mitotic spindle stage” to contrast it with the “hemispindle stage”, which can be morphologically distinguished using microtubules as a marker and occurs just prior to S-Phase. Consequently, “mitotic spindle nuclei” are nuclei in the “mitotic spindle stage”. “mitotic spindle duration” designates the time nuclei spend in that stage i.e. from hemispindle collapse until anaphase spindle elongation. We have adjusted and more accurately defined the terminology throughout the text and complemented Fig. 1A for clarity.

Minor comments

The PfSlp knockdown is inefficient: the 55% reduction at the RNA level translates into a minor change at the protein level (Fig.2 and S4). The evaluation of the protein changes should be done by western blot analysis with appropriate controls. The intensity of the IFA signal (used in the study) changes depending on the focal plane, as seen in Fig 1D.

Due to the exceptionally big size of PfSlp of around 407 kDa and the low expression levels western blot analysis was not feasible in our hands. For quantification of the IFA signal we used image projections and background subtraction to integrate the signal of the full z-stack containing the entire cell and our measurement was therefore independent of the focal plane. We have now described this a bit more thoroughly in the methods section (lines 620ff). The change in signal as measured by IFA is still clearly significant and shows a reduction of about 45%, which is coherent with the reduction of 55% found by RNA analysis and ultimately results in a specific phenotype.

Growth defects of the PfSlp KD: It is unclear what causes the reduced parasitemia of the GlcN untreated Slp parasites (Fig. 2C and D).

A likely explanation is that the C-terminal tagging of PfSlp already slightly impairs the function of the protein causing a mild growth phenotype that is not observed in wild type although it is not statistically significant (Fig. 2C). Importantly, the reproduced analysis of parasite growth, shown as multiplication rate in Fig. 2C (and growth curve in Fig. S6) now more clearly demonstrates that when normalizing for GlcN treatment and GFP-glms tagging (“3D7 corr.”) the growth defect is still significant and can therefore be attributed to Slp KD and not to tagging or GlcN treatment addition, which on their own do not cause a significant phenotype.

To conclude that the kinetics of DNA replication is affected, the authors will need to perform the real-time measurements of DNA replication forks.

We thank the reviewer for pointing this out and removed the term “kinetics” (line 182, 269).

The presented data supports that fewer S/M rounds were performed by PfSlp lacking parasites but gives no way to determine whether the S or the M phase was affected.

We thank the reviewer for this valuable comment. Our data so far showed that the very first spindle extension, and therefore M-Phase, is clearly affected (Fig. 4A-B). If the first division fails all subsequent S phases and M phases might be affected at the population level. To test whether S-phase is affected we now acquired time lapse imaging of single cells labeled with the quantitative DNA dye 5-SiR-Hoechst and saw no difference in DNA signal increase for PfSlp KD parasites, while nuclear number was reduced, showing directly that M phase rather than S-Phase is affected (Fig. 4C, lines 280ff).

DNA quantification graph (Fig. 2D) is confusing and does not correlate with the quantification of merozoites (Fig. 2E). Why is the DNA intensity of Slp- parasites lower than the DNA intensity of the Slp+ parasites, even though Slp deficient line produces less progeny? Is it possible that you missed the actual peak of DNA replication? Authors may consider more tight time courses with a few additional time points.

This is a good point. We have repeated this experiment with longer sampling time and shorter intervals. We now plot the fraction of cells with DNA content above 2N (also to exclude double infections and cells that arrest prior to the schizont stage) as a measure to see how many cells are replicating (Fig. 2D, lines 175ff). Although the replication peak was, as observed before, shifted by GlcN treatment we found no significant differences in height. Although the lack of PfSlp tagging and GlcN treatment in the 3D7- control might favor the slightly more productive replication. We complement this analysis by plotting the average DNA fluorescence intensity over time (Fig. S7A) and the area under the curve (see below), as an approximation of “total replication activity” and still found no significant differences (Fig. S7B). The fact that the DNA fluorescence intensity peak does not correlate with the slightly reduced merozoite number observed in Fig. 2E is not very surprising as the fixed time point sampling for DNA quantification can’t differentiate between cells slowing or even halting progression and thereby confounding the averages. This limitation of single timepoint population analysis specifically highlight the importance of our time resolved single cell analysis presented later in Fig. 4, which clarifies the phenotype. Further, merozoite number counting does not give any insight about ploidy of the individual merozoites. Considering the significant nuclear division defect we also show in Fig. 4 it is plausible that some merozoites in the Slp KD could be polyploid, while globally replication is not strongly affected.

Given the main claim, the study lacks the spatial-temporal analysis of tubulin described only in words. The tubulin quantifications by WB (Fig. S6) are not convincing, as well as the resulting conclusion of the cell cycle retardation.

We are not completely sure what the reviewer is indicating by a lack of spatial-temporal analysis of tubulin given that we show time-resolved imaging data of tubulin organization in dividing cells and quantify intranuclear tubulin levels. Those data (particularly Fig. 4A) clearly show a retardation in the mitotic spindle stage. We, however, acknowledge that the data on tubulin quantification via western blot could, as Reviewer 2 also points out, be improved through the addition of biological replicates. We have repeated those experiments twice and can now confirm by statistical analysis that total tubulin, aldolase, and centrin protein levels are not affected by Slp KD at 24, 30, and 36 hpi (Fig. 3E, Fig. S8, lines 232ff). This indicates that the increase in intranuclear tubulin is not a consequence of globally increased tubulin expression.

It is unclear how the authors arrived at the conclusion that the mitotic spindle is deficient in PfSlp KD parasites. Fig. 3C does not show visible differences in GlcN treated and untreated parasites.

PfSlp KD parasites show unusual microtubule protrusions branching of the main microtubule mass, which have never been observed in wild type parasites. This should have been indicated more clearly by adding an arrow in Fig. 3C. We further think our observation that the tubulin content in mitotic spindles is almost three times higher on average than in wild type spindles (Fig. 3D) and that those spindles do not properly extend (Fig. 4A-B) justifies this claim.

How many nuclei are in the cells shown in figure 4 and supplemental movies? It seems as if GlcN treated Slp parasites form one long spindle.

In a previous study (Simon et al. 2021, LSA, Fig. 1B) we have demonstrated that the number of distinct microtubule foci, i.e. mitotic spindles, observed in cells corresponds directly to the number of nuclei. Hence we can assume that prior to successful spindle extension in the PfSlpKD there is one nucleus or two nuclear masses that are in the process of separation. We now added some new time-lapse microscopy data of DNA- and tubulin-stained parasites that confirms that arrested Slp KD parasites fail to properly divide their nuclei (Fig. 4C, Mov. S4-5) and confirms our previously published findings about nuclear number.

A majority of PfSlpKD parasites indeed seem to form one long spindle. However, this “long spindle” appears only after a significant time delay during which wild type parasites already have undergone multiple nuclear divisions and could be a downstream effect of this retardation through e.g. increase of total tubulin levels over time (Fig. 3E).

The conclusion of anaphase block is unsupported: the authors need to demonstrate the accumulation of the metaphase nuclei with a bipolar spindle.

Anaphase describes the phase of chromosome segregation and includes the full extension of the spindle, as discussed above, both of which fails in more than half of the PfSlpKD parasites (Fig. 4A, Mov. S3, S5) and is therefore interpreted as “failure to properly progress through anaphase” for the first time in the discussion (line 381). We currently can’t think about a more direct way to demonstrate this than by time lapse imaging of the very first mitosis in individual parasites. Any analysis of populations at later time point or using fixed cells will be skewed by the phenotype occurring in the very early stages of nuclear division.

Reviewer #1 (Significance (Required)):

The eukaryotic centrosome is a microtubule organizing center that guides the segregation of duplicated chromosomes. Despite being an essential regulator of the parasite division, the apicomplexan centrosome remains poorly understood. Recent studies in Toxoplasma gondii (Suvorova et al., 2015) and Plasmodium species (Simon et al., 2021) demonstrated high diversity of the centrosome organization making the studies of microtubule organizing centers in apicomplexans, particularly challenging. Examining the protein composition is one of the ways to uncover organelle function. The current study would help to understand the evolution of the MTOC and mechanisms of cell division in understudied eukaryotic models.

The focus of my research is the apicomplexan cell cycle. I previously showed the bipartite organization of the Toxoplasma centrosome and identified and characterized several centrosomal constituents, including centrin partner Sfi1. Our most recent study presented evidence of the functional spindle assembly checkpoint in Toxoplasma tachyzoites.

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

Summary:

Plasmodium falciparum parasites undergo several rounds of asynchronous nuclear divisions to produce daughter cells. This process is controlled by the centriolar plaque, a non-canonical centrosome that functions to organize intranuclear spindle microtubules. The organization and composition of this microtubule organizing center is not well understood. Here, Wenz et al. identify a novel centrin-interacting protein, PfSlp, that, following knockdown, leads to fewer daughter cells and aberrant intranuclear microtubule homeostasis and organization.

Wenz et al. identify PfSlp via co-immunoprecipitation of P. falciparum 3D7 strain with an episomally expressed PfCen1-GFP, noting PfSlp as a gene of interest based on the presence of several centrin-binding motifs. The authors go forward to generate a transgenic 3D7 strain, equipping PfSlp with GFP and glmS ribozyme, to localize and evaluate the function of PfSlp in asexual blood stage parasites. PfSlp appears to, using immunofluorescence and STED microscopy, localize to the outer centriolar plaque in schizonts, based on its colocalization with PfCen3. The authors show, utilizing the inducible glmS ribozyme knockdown system, that PfSlp is required for proper parasite growth, noting a defect following addition of GlcN. This defect is noted to cause a delay in the initiation of nuclear division, or schizogony. Analysis of intranuclear microtubule dynamics reveal abnormal microtubule organization, specifically an increase in nuclear microtubule abundance and length following PfSlp knockdown. Together, these findings characterize the role of a novel protein, PfSlp, that contributes to nuclear tubulin homeostasis and organization during schizogony.

Major comments:

The major claims made by Wenz et al. are largely convincing with the data provided.

1. One area that requires additional attention is the following: Wenz et al. claim PfSlp and centrin to be interacting partners based on 1) co-immunoprecipitation (without prior protein crosslinking), 2) the presence of centrin-binding motifs in PfSlp and 3) colocalization of PfSlp and PfCen3. This interaction is not interrogated fully and claims specific to this point need to be clarified and described as preliminary. As it is written, Wenz et al. claim PfSlp is required for centrin recruitment to the centriolar plaque but this is not investigated fully. The data show lower levels of endogenous centrin at the centriolar plaque in PfSlp knockdown parasites but centrin protein levels are similar in wildtype and knockdown PfSlp parasites. As is, the phenotype attributed to PfSlp knockdown could be attributed to PfSlp or aberrant centrin recruitment to the centriolar plaque. Experiments manipulating PfSlp centrin-binding motifs would strengthen these claims and elucidate the role of PfSlp apart from centrin. If not included, less emphasis should be placed here.

We agree with the reviewer that additional evidence to demonstrate the direct interaction between PfSlp and centrin would be adequate. Due to the presence of multiple widely spaced centrin binding motifs in PfSlp, which would require multiple highly challenging rounds of genome editing to be modified, we have opted for reciprocal co-IP using PfSlp-GFP (line 139, Fig. S3, see below). The exceptionally large size of PfSlp of 407 kDa and low expression prevented us from detecting it directly on the western blot, but we found a clear centrin band in the Slp IP that was absent in the control.

We have also further qualified our formulation about centrin recruitment depending on PfSlp (lines 138, 146). Finally, we agree that there are many factors downstream of PfSlp that can contribute to the observed phenotype, which might include centrins and will be subject of future investigations.

The 3.5 mM glucosamine has some toxicity in the parental 3D7. Is it possible to use a lower concentration so the growth of 3D7 is unaffected but the grow of the Slp-GFP GlmS parasites is still reduced?

We acknowledge that the used Glucosamine concentration is on the higher end of the classically used range. The slight toxicity of Glucosamine is dose-dependent and only vanishes at submillimolar concentrations. During initial experiments we have found to generate a robust phenotype with 3.5 mM and decided to carry out all experiments at this concentration. We think that the added effect of PfSlpKD over GlcN treatment alone is sufficiently show as e.g. the merozoite number phenotype (Fig. 2E) and the mitotic delay (Fig. 4B) only occurs in Slp+ parasites.

Fig 3E - the quantification of tubulin levels requires biological replicates to have means and error bars.

We fully agree with reviewer 2 (and reviewer 1 who commented along the same lines) and now generated two more biological replicates that allow us to confirm by statistical analysis that total tubulin, aldolase, and centrin protein levels are not affected by Slp KD at 24, 30, and 36 hpi (Fig. 3E, Fig. S8, lines 235ff).

The use of "centrin" is somewhat imprecise throughout. The authors should specific which centrin (PfCentrin1 or PfCentrin3 or others) they are referring to each time in the text.

Thank you for requesting this clarification. We have used “centrin” on purpose but have failed to properly explain our terminology in the text. For the detection of endogenous centrin we use a polyclonal antibody raised against PfCentrin3 (Simon et al. 2021). Due to the very high sequence identity between PfCentrin1-4 we can’t exclude cross-reactivity of any polyclonal antibody. Throughout the field so far polyclonal antibodies raised against Chlamydomonas centrin and Toxoplasma centrin 1 have been successfully used to label centrin pool at the centriolar plaque. Since we can’t distinguish with certainty which of the centrins (PfCen1-4) is targeted we chose the general description “centrin”. We were however able to show that all four centrins (PfCen1-4) colocalize at the centriolar plaque (Voss et al. biorxiv, /10.1101/2022.07.26.501452) and that Plasmodium centrins interact with each other was demonstrated previously (Roques et al. 2019) while the interaction between PfCen1 and PfCen3 was shown in this study. Therefore, this will not limit our conclusions. We now explain this better in the text (lines 132ff) and adjusted the labeling in Fig. 1E.

The mention of the cell cycle checkpoint is an interesting and appropriate point in the discussion. However, the discussion of it in the last sentence of the introduction is less appropriate. It should be removed from line 92-93.

We are excited by the prospects of this study to finally be able to investigate the presence of checkpoint induced delays using time-lapse microscopy, but absolutely agree with the reviewer and have removed the statement in the introduction.

Minor comments:

1. Line 50 - "are remaining unclear" should "remain unclear"

Has been corrected.

Line 65 - "players" is quite informal. A better word should be selected.

Was replaced with “factors”.

Line 223 - "were" should be "where"

Has been corrected.

The delay in schizogony which is observed following addition of GlcN (Figure S5) may be made more convincing if the experiment is performed hours post invasion rather than hours post treatment. The synchronization of the parasites is in question as it is described in the methods.

We have included this data from our initial exploratory analyses and since it was not central to our argumentation, we choose to add it as supplemental figure. After producing further data, we came to realize that the classical morphological characterization using Giemsa-staining partly mispresents the relevant transition from the pre-mitotic to mitotic stages as the onset of first spindle formation and DNA replication can’t be detected. Previous studies have also indicated that parasites which were drug arrested at the trophozoite to schizont transition were morphologically similar to mid- to late schizonts (Naughton and Bell, 2007). In a context that investigates nuclear division phenotypes we feel that this analysis might rather be misleading and that the provided growth assays, DNA replication quantification, and time lapse movies are significantly more informative. Therefore, we have decided to remove the figure altogether. However, we have moved Fig. S7 to Fig. 4 to show the results of the 3D7+GlcN movie quantification in the context of the Slp+/-GlcN results.

In general, data presentation is clear and readable. The growth defect observed following GlcN treatment (Figure 2C) could be made more clear with data normalization to emphasize that which can be attributed to PfSlp knockdown and not GlcN.

This is a good suggestion and we have reproduced the initial dataset (Fig. 2C, Fig. S6, see below) and normalized the 3D7 multiplication rate, which shows the effect more directly than the growth curves displayed before, for Slp-tagging and GlcN treatment (“3D7 corr.”). We still found Slp +GlcN to be the only condition to have a significant reduction in multiplication rate in the first cycle after treatment (24-72hpi) with respect to 3D7 control as well as the normalized 3D7 value (“3D7 corr”).

Line 276 - Why is nuclear tubulin homeostasis more relevant for closed mitosis? This is difficult to understand. It should be phrased differently or provided with additional explanation.

We thank the reviewer for the comment and agree that this is poorly formulated. We were meaning to express that in e.g. mammalian organisms the nuclear envelope gets disassembled during mitosis and thereby removes the need to regulate import of tubulin into the nucleus for spindle assembly. This is a self-evident statement and has been removed for clarity.

Line 316 - "were" should be "was"

Has been corrected.

The identity, source, and dilution for each antibody must be reported for each use in the methods.

We noticed that we had not fully referenced Table S3, where we listed all used antibodies and dilutions, which we have now done throughout the methods section.

Reviewer #2 (Significance (Required)):

The mechanisms by which intranuclear microtubule dynamics are regulated by Plasmodium falciparum parasites are not well understood. Furthermore, the proteins that are present near the centriolar plaque remain mostly unknown. Understanding the role of the Plasmodium centriolar plaque and its members is critical to describing these dynamics and contributes to our growing understanding of schizogony, an atypical mode of cell division mode with several rounds of nuclear division lacking cytokinesis. Therefore, the identification and initial characterization of PfSlp1 is useful for malaria parasite cell division community.

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

The work by Wenz and Simon approaches the function of a novel component of the malaria parasite centriolar plaque, a structure whose complexity has begun to be unraveled only recently__, greatly by the same group. __The authors identify a homolog of Sfi1, a centrin binding protein highly conserved in eukaryotes. Sfi1 homologues usually co-localize with centrioles.

As a tool to characterize its function, the authors uses a conditional knock down strategy, based on GlcN addition, to downregulate PfSfi1-like protein (PfSlp). The authors analyze the impact of pfSlp downregulation on cell division progression, and go on detailly characterizing the progression of mitotic nuclear division. In sum the study finds that expression of Slp1 is required for proper progression of cell division in Plasmodium parasites.

The study is well conducted, and the manuscript clearly written. In general terms I found the data shown to support the author's claims. However, I do have a few points of concern to raise, particularly pertaining overinterpretation of the data, and points that need clarification before the manuscript is fit for publication. In particular the authors should explain more clearly how the data based on fluorescence intensity quantifications was acquired and processed, and how this information is intertwined with the expected kinetics of structures measured, along the cell cycle.

We appreciate the positive feedback and the constructive comments made by the reviewer and now adapted our interpretation of the data or provide additional experimental data to strengthen our argumentation as outlined below. Further we have added some detail to the description of our experimental approaches in the methods section.

I outline below major and minor points that require attention,

Major Points

The manuscript stems off the premise that PfSlp interacts with PfCen1. Despite the fact that Sfi1 is a known interactor of centrin, that the identified protein in Plasmodium has centrin binding motifs, and these proteins co-localize, the support for the direct interaction between the two proteins is based solely on the IP/MS result. No reciprocal IP results are shown.

We thank the reviewer for the suggestion and have now added the reciprocal co-IP, which shows a specific interaction between PfSlp and centrin without need for cross-linking (Fig. S3, see also reply to comment 1 by reviewer 2).

Line 118 specifies that co-localization of Slp-GFP with centrin "corroborates their direct interaction." Co-localization most certainly does not show direct interaction. In addition, Figure 1D shows co-localization with Cen3, not with Cen1, which was the only protein shown to have a physical interaction with Slp via immunoprecipitation. Hence, the claim is unplaced and this section should be reworded for clarity.

The reviewer is correct to point out that co-localization even at STED nanoscale resolution does not demonstrate interaction. We have reworded this statement. Cen3 was the only other specific protein found in the Cen1 immunoprecipitation (Table S1) and the interaction between the four centrins Cen1-4 was shown in an earlier study in P. berghei (Rogues et al. 2019). However, as the Reviewer 2 also indicated, we did not clearly communicate what the targets of our centrin antibody are. We, indeed used an antibody raised against PfCen3. Due to the very high sequence identity between centrins it is, however, unrealistic to exclude cross-reactivity between centrins for a polyclonal antibody (as explained in more detail in our response to Reviewer 2). We have added an explanatory statement in the main text (lines 132ff). Our recent finding that GFP-tagged PfCen1-4 all colocalize at the same position in the centriolar plaque (Voss et al. biorxiv, /10.1101/2022.07.26.501452) and our previously published study of the centriolar plaque (Simon et al. 2021) gives us additional confidence that the antibody specifically labels the compartment of interest.

I was surprised to see how little recovery of PfCen1-GFP the authors obtained from their IP experiments. Whilst I understand that a western blot is not quantitative, I wonder, were the amounts of protein loaded onto each lane normalized for comparative purposes in any way? Please comment on this at least in the figure legend so the reader can gage whether the little PfCen1-GFP recovery was a consequence of the IP experiment, or whether the WB is not representative of the actual IP results but rather show a fraction of the recovered material.

We did not determine the total protein concentration (by e.g. Bradford assay) and therefore did not normalize for protein amounts per lane. Instead, we determined the number of infected red blood cells per ml before Saponin-lysis of the red blood cells and loaded protein lysate equivalent to 1 x 107 cells per lane. We now explain this more clearly in the legend for Fig. S1. During the IP, much of the total protein amount might got lost during the washing steps, which might explain the weak Centrin1-GFP band and the absence of a protein signal in the eluate lane by Ponceau staining (neither a signal for Centrin1-GFP nor unspecific protein signal in the Ponceau). We would conclude that the WB, or at least the lane with the eluate, shows a fraction of the recovered material.

If the WB is indeed representative of the actual PfCen1-GFP recovery rates, I suggest you discuss the possible outcomes of having pulled down so little from the total cell lysate - could it be that the recovered proteins are representative of interactions happening only for a subset of soluble PfCen1 molecules? Can the little protein recovery be explained by Cen1 interactions with insoluble cell components such as the cytoskeleton?

As described above, the eluate lane does likely not represent the actual amount of Cen1-GFP that was pulled down and therefore the WB is not representative of the PfCentrin1-GFP recovery rates. Based on our previous studies we are not aware of any cellular PfCen1 pool beside the cytoplasm and the centriolar plaque. Although they might be below the detection limit. The reviewer raises an interesting hypothesis but we don’t have sufficient data to assume an association with the cytoskeleton and verifying this would require extended further studies.

Were other IP conditions tested? Were the same results obtained?

We carried out three PfCen1-GFP IPs. Once without cross-linking as shown in the study and twice with cross-linking. The two IPs with crosslinking had different amounts of targets identified (24 vs 162). While we did not detect PfSlp in the one with the low number of peptides we detected PfSlp in the second IP. In both IPs we additionally detected PfCen2 and PfCen3.

Do you get the same interactors if the IP is done using anti-Centrin instead of anti-GFP?

We did not test an anti-Centrin antibody for IPs as the protocol from the Brochet group was optimized for the highly specific bead-coupled anti-GFP antibody.

Please define how you identified "specific hits." This is, please describe your criteria for determining "specificity." Was it an all or nothing selection approach? Are Cen1, Cen3 and PfSlp significantly enriched? And if so, how did you define "enriched for" in the context of your experiment?

We thank the reviewer for given us the chance to clarify our candidate selection. We specifically selected the Cen1-GFP IP targets without cross-linking since it produced a short list of hits detected by mass spectrometry. We used an all or nothing approach in that we subtracted from that list any protein that was ever identified in a GFP control IP analysis by the Brochet lab using the same protocol (Balestra et al. 2021). This left only three proteins Cen1, Cen3, and Slp, as our “specific” hits. We have modified the text to explain our selection criteria more explicitly (lines 112ff) while avoid using the term “enrichment” since this is an all or nothing selection.

I'm not at all suggesting here that you repeat this experiment. I understand that the focus of the manuscript is the description of PfSlp, and this stands regardless of the IP results. However, I suggest you include a lengthier discussion of the results shown in SFig1 and Fig1, and the limitations of the approach.

We appreciate the assessment by the reviewer that the focus of the manuscript is otherwise and acknowledge that this is not an extensive analysis of PfCen1 interaction partners. We have, as requested, added a comment addressing this limitation in the discussion (lines 331ff).

Line 123 mentions that Cen3 and Slp1 are recruited together only because they co-localize in most cells showcasing hemi-spindles. Please simply keep "simultaneously" here, as this is the only thing you can conclude from your quantification data. Being recruited "together" implicitly means by "the same mechanism", which is not shown by your data.

We agree that simultaneously is more accurate and we have modified the text (line 146).

Please specify which statistical test was used for determining significance in Figure S4, and what *** refers to in this case. It is hard to judge really how different these data sets are in light of the overlapping error bars. Also, what is quantified here? Integrated density from an immunofluorescence assay? How are the data normalized to be comparable? How many replicates did you quantify? Or are the data shown representative of a single experiment? I could not find these details in the M&M section or the figure legend.

We have revisited all figure legends and consistently defining the p-value and number of replicates (usually N=3) and briefly explain the measurement. Further we have extended the methods section to make our image quantification approach clearer.

Also, on the interpretation of these data; If Slp1 causes a delay in cell cycle progression, and taking into account that the fluorescence intensity of Slp1 varies along the cell cycle, with Slp1 intensity increasing as cell cycle progresses from the ring stages onwards, are these comparable measurements? In other words, are you selecting the same stages whereby the same Slp1 intensities at the centriolar plaque would be expected?

If I understand correctly these measurements are carried out at 55hs post GlcN addition (when the growth phenotype starts evidencing itself?). At this time point, the relative abundance of ring and trophozoite stages (stages at which Slp1 is not expected to be detectable at the CP) is considerable higher than that of the control condition, hence a reduction in Slp1 is expected, and a mechanistic claim about recruitment or stability would be incorrect. Please clarify.

As the reviewer correctly points out it is important to normalize for the stages when quantifying the PfSlp intensities. To achieve this, we only selected schizont stage parasites with a similar distribution of cells containing 3-10 nuclei between the conditions to ensure we are looking at comparable stages. We then quantified the integrated density at each individual centriolar plaque, designated by the presence of a centrin signal. Outside of centriolar plaques no PfSlp signal can be detected. As for ring and trophozoites stages, they do not have a discernable centriolar plaque, or at least not with the markers available in the field, and likely do not express PfSlp based on published transcriptomics data (Plasmodb.org). We have revisited the text to make our quantification strategy clearer (line 170, 621ff).

To understand the relative contribution of Slp1 to the growth delay phenotype, please include 3D7+GlcN control in the quantification of stages shown in Fig. S5. Please check how the data shown in Fig S5 was normalized; the 49 and 73hs bars in the -GlcN condition exceed 100%.

As indicated in our reply to Reviewer 2 we only included this data from our initial exploratory analyses and since it was not central to our argumentation, we chose to add it as supplemental figure. After producing further data, we came to realize that the classical morphological characterization using Giemsa-staining partly mispresents the relevant transition from the pre-mitotic to mitotic stages as the onset of first spindle formation and DNA replication can’t be detected. Previous studies have also indicated that parasites which were drug-arrested at the trophozoite to schizont transition were morphologically similar to mid- to late schizonts (Naughton and Bell, 2007). In a context that investigates nuclear division phenotypes we feel that this analysis might rather be misleading and that the provided growth assays, DNA replication quantification, and time lapse movies are significantly more informative. Therefore, we have decided to remove the figure altogether. However, we have moved Fig. S7 to Fig. 4 to show the results of the 3D7+GlcN movie quantification in the context of the Slp+/-GlcN results.

What is "centrin signal" shown in Figure 2B? Centrin1? Centrin 3? Please clarify which centrin protein you are referring to throughout the manuscript, or provide evidence that they could be interchangeably used for localization and intensity measurement experiments.

We thank the reviewer for pointing out this vagueness. As explained above in the second major point and in the reply to reviewer 2 we use the term “centrin” to emphasize that we cannot be certain to which degree PfCen1,2,3 or 4 contribute to the signal. Our recent preprint (Voß et al. 2022) and Roques et al. 2019 and Simon et al. 2021 however suggest that all centrins co-localize and interact at the outer centriolar plaque. As mentioned we now discuss this in the text (lines 130ff).

Line 149 outlines that Slp1 and centrin intensities are simultaneously reduced, and that this fact alone "affirms" they are part of one complex, and that this implies that Spl1 is somehow involved in centrin recruitment. This claim is not supported by the data shown. There are multiple possible explanations as to how the intensities of both proteins could simultaneously decrease without them conforming the same structure, the same complex or even directly interacting. For example, if the centriolar plaque homeostasis is altered, or the "intensities" are simultaneously dependent on cell cycle progression, they will both be affected without necessarily ever interacting. In fact, if the centrin intensity monitored is that of Cen3, a direct interaction between Slp1 and Cen3 is not demonstrated at any time. At best, the authors could argue that both proteins are directly interacting with Cen1. Again, even this is no definitive proof that they form the same complex.

The reviewer is correct to point out that there are multiple explanations for the decrease of centrin and Slp signal and we have phrased some of the relevant statements more carefully (lines 138, 146, 172). We, however, think that our new reciprocal co-IP data (Fig. S3) in combination with the already provided evidence now significantly strengthens our claim about the interaction between centrin and Slp.

Measurements of DNA content, shown in Figure 2D, show that +GlcN Slp1 knockdown parasites exhibited reduced DNA amounts at 42hs post induction. These results are interpreted as "defects in nuclear division," however, 1. Nuclear division is not analyzed directly, but rather approximated by measuring DNA content. 2. Even in the presence of perfectly normal nuclear division, the DNA content reduction for these parasites at this time point is expected, as cell cycle progression is affected.

Line 160 states that a reduction in merozoite number corroborates a defect in nuclear division. However, the data shown only quantifies merozoites per schizont. As mentioned above, nuclear division is not directly assayed.

We thank the reviewer for emphasizing this important distinction (alongside Reviewer 1). Making the conclusion about nuclear division based on the reduced number of merozoites was premature and we now phrased this more carefully (line 198). Even our data showing inhibition of spindle extension (Fig. 4A-B), although being a strong indicator, do not strictly speaking observe nuclear division. Hence, we have added time-lapse imaging data of nuclear number in KD vs control conditions using the quantitative live cell DNA dye 5-SiR-Hoechst (Fig. 4C. Mov. 4-5). These data now clearly show that the nuclear division or M-phase is affected, while the increase of DNA signal, which represents replication, is not distinguishable from the control. This confirms that nuclear division is the initial and relevant phenotype.

What the nuclear division defects observed are is unclear. Is there fusion, fission? loss of nuclear content? defects in mitosis completion? defects in DNA replication? A reduction in merozoites per schizont, with a concomitant reduction in overall DNA levels could also be explained by a general arrest in the final stages of division. Do other processes linked to nuclear division progress normally? For example, is there daughter cell formation during schizogony without the expected accompanying nuclear division? Are daughters forming in the correct number and position? Are there more daughter cells than nuclei? Or are parasites dying before completing schizogony and producing merozoites? These possibilities need to be carefully teased out before a nuclear division defect can be assigned as the sole causing factor of the division phenotypes observed.

These are all very pertinent questions some of which go beyond the scope of this very first characterization of PfSlp function but we are keen to include those in our future analysis. Some of them we can answer while I will try to offer an interpretation for the remaining ones:

It isn’t fully clear to us what is meant by “Is there fusion, fission”. We will assume that the reviewer refers to the process of karyofission where the nuclear membrane is constricted and fused between the segregating chromatin masses. The field is still lacking a nuclear membrane marker, which makes a direct analysis of this question difficult. Under normal circumstances it has been demonstrated that mitosis is fully closed and the nuclei are completely surrounded by membrane right after division (Klaus et al. 2021). To maybe clarify further we use the term nuclear division to designate the formation of two physically distinct nuclei from one progenitor. We can’t and don’t comment on the integrity of the nuclear membrane and if we had to speculate, it is probably not affected.

Our new data on DNA dynamics (Fig. 4C) shows a delay in nuclear division while DNA replication seems unaffected in the early division stages. The failure to complete mitosis is also shown by the lack of proper spindle extension. It is possible that PfSlp KD affects final stages of division, but since we treat parasites at ring stages and detect a strong phenotype already at the very first division which occurs only a couple of hours after centrin/Slp recruitment one must assume that this is the defining phenotype, which likely has repercussion on later rounds of division. This makes it virtually impossible to clearly define late phenotypes. We actually have to assume that parasites that proceed to later stages of division do so because PfSlp KD was less efficient.

Our data directly shows that more than half of our PfSlp KD parasites “fail to properly divide their nucleus” in the first round of mitosis and therefore can’t construe any other way than to designate this as a “nuclear division phenotype”. We purposefully don’t comment on potential later phenotypes and an impact on cytokinesis (budding) but look forward to investigating this in the future.

Minor Points

• Line 49: consider "...mechanisms remain unclear" instead of "... mechanisms are remaining unclear"

We have corrected this sentence as suggested.

• Readers not familiar with Plasmodium cell division would benefit from having the different stages shown schematically in Figure 1A labeled (ring, merozoite, trophozoite, etc.)

Good suggestion. We have expanded the labeling in Fig. 1A, but still choose to focus on the division stage, which is relevant for the presented data.

• Figure 1 legend: Please specify that "centrin" staining is approximated by centrin 3 specifically. Figure 1E is missing a legend in Figure 1's legend.

Thank you for pointing this out. We have expanded the figure legend accordingly.

• To ease the reader's interpretation of the data, please consider using a different color for 3D7 +GlcN in the plots shown in Figure 2. It is difficult to distinguish the light magenta from the red color at first glance, especially when the lines are partially overlapping.

We explored many different color combinations and consulted with several colleagues and concluded that the chosen color combination is most suitable to convey the logic of the strains (while accounting for green-red blindness).

• Please clarify how long after GlcN addition are phenotypes assessed - ex. Microtubule cumulative length measurements shown in Figure 3.

We mentioned in the previous Fig. 2 that we add GlcN at the ring stage preceding the schizont stage we analyze but failed to specify that we consistently do so for all experiments. We have added more information in the results (line 221) and to the methods section in more detail.

• For Figure 3C please provide a separate image for the Slp channel alone. The overlay of the green centrin signal and the magenta from the tubulin staining render a yellow signal. It is difficult to appreciate the level of Slp knockdown in these cells. Moreover, in the inset, the label "zoom in" is on top of the centrin signal in green, precluding the proper assessment/observation of any yellow signal left over.

Thank you for this remark. We have removed the centrin signal, which is clearly shown in the main panel, from the zoom ins to render the residual PfSlp signal clearly visible.

• When describing Sf1 in T. gondii, please also cite PMID: 36009009 PMCID: PMC9406199 DOI: 10.3390/biom12081115

When submitting our manuscript this study was not yet published, but we are happy to now include it in the introduction (line 92).

The notion of "checkpoint" is mentioned in the introduction and revisited in the discussion. This is a topic under current discussion/evaluation in the field. As mentioned by the authors, demonstration of a checkpoint implies demonstrating reversibility of the putative checkpoint. Though the authors do not make claims about Slp1 or the phenotypes observed activating a specific checkpoint, the manuscript could be further strengthened if the authors showed that the anaphase arrest is reversible upon wash out of GlcN and restored levels of PfSlp expression. I'm including this comment as a "minor points" because it is a only suggestion. I understand that carrying out these experiments is not within the scope of this work. However, if the authors decided to pursue this, it would certainly strengthen the manuscript.

We highly appreciate the suggestion made by the reviewer and already considered ways to inactivate the putative spindle assembly checkpoint or reverse the phenotype. Wash out of GlcN would theoretically be an option although we are unsure that the kinetics of the subsequent protein synthesis would unfold on a short enough time scale. As suggested by Reviewer 2 we try to remain cautious about directly addressing the checkpoint issue, since e.g. PfSlp due to its localization can’t be a direct component of the checkpoint itself. The mention of “checkpoints” has also been removed from the introduction. We are, however, excited that using our time lapse microscopy protocols there now is a framework to investigate this in more depth in the future.

Reviewer #3 (Significance (Required)):

Plasmodium species lack centrioles, and display a divergent mitosis. It is therefore of interest and relevance to understand the peculiarities of the centriolar plaque, as it likely underlies the ability of Plasmodium to upscale its numbers.

Our molecular understanding of the underpinning factors controlling nuclear and cell division in Plasmodium is limited to a few recent publications. The data presented herein is novel and contributes to the body of work with molecular insight and high resolution microscopy coming on for the malaria field.

My expertise is in cell division in Apicomplexan parasites

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Referee #3

Evidence, reproducibility and clarity

The work by Wenz and Simon approaches the function of a novel component of the malaria parasite centriolar plaque, a structure whose complexity has begun to be unraveled only recently, greatly by the same group. The authors identify a homolog of Sfi1, a centrin binding protein highly conserved in eukaryotes. Sfi1 homologues usually co-localize with centrioles. As a tool to characterize its function, the authors uses a conditional knock down strategy, based on GlcN addition, to downregulate PfSfi1-like protein (PfSlp). The authors analyze the impact of pfSlp downregulation on cell division progression, and go on detailly characterizing the progression of mitotic nuclear division. In sum the study finds that expression of Slp1 is required for proper progression of cell division in Plasmodium parasites.

The study is well conducted, and the manuscript clearly written. In general terms I found the data shown to support the author's claims. However, I do have a few points of concern to raise, particularly pertaining overinterpretation of the data, and points that need clarification before the manuscript is fit for publication. In particular the authors should explain more clearly how the data based on fluorescence intensity quantifications was acquired and processed, and how this information is intertwined with the expected kinetics of structures measured, along the cell cycle.

I outline below major and minor points that require attention,

Major Points

• The manuscript stems off the premise that PfSlp interacts with PfCen1. Despite the fact that Sfi1 is a known interactor of centrin, that the identified protein in Plasmodium has centrin binding motifs, and these proteins co-localize, the support for the direct interaction between the two proteins is based solely on the IP/MS result. No reciprocal IP results are shown. Line 118 specifies that co-localization of Slp-GFP with centrin "corroborates their direct interaction." Co-localization most certainly does not show direct interaction. In addition, Figure 1D shows co-localization with Cen3, not with Cen1, which was the only protein shown to have a physical interaction with Slp via immunoprecipitation. Hence, the claim is unplaced and this section should be reworded for clarity.

• I was surprised to see how little recovery of PfCen1-GFP the authors obtained from their IP experiments. Whilst I understand that a western blot is not quantitative, I wonder, were the amounts of protein loaded onto each lane normalized for comparative purposes in any way? Please comment on this at least in the figure legend so the reader can gage whether the little PfCen1-GFP recovery was a consequence of the IP experiment, or whether the WB is not representative of the actual IP results but rather show a fraction of the recovered material. If the WB is indeed representative of the actual PfCen1-GFP recovery rates, I suggest you discuss the possible outcomes of having pulled down so little from the total cell lysate - could it be that the recovered proteins are representative of interactions happening only for a subset of soluble PfCen1 molecules? Can the little protein recovery be explained by Cen1 interactions with insoluble cell components such as the cytoskeleton? Were other IP conditions tested? Were the same results obtained? Do you get the same interactors if the IP is done using anti-Centrin instead of anti-GFP?

• Please define how you identified "specific hits." This is, please describe your criteria for determining "specificity." Was it an all or nothing selection approach? Are Cen1, Cen3 and PfSlp significantly enriched? And if so, how did you define "enriched for" in the context of your experiment?

• I'm not at all suggesting here that you repeat this experiment. I understand that the focus of the manuscript is the description of PfSlp, and this stands regardless of the IP results. However, I suggest you include a lengthier discussion of the results shown in SFig1 and Fig1, and the limitations of the approach.

• Line 123 mentions that Cen3 and Slp1 are recruited together only because they co-localize in most cells showcasing hemi-spindles. Please simply keep "simultaneously" here, as this is the only thing you can conclude from your quantification data. Being recruited "together" implicitly means by "the same mechanism", which is not shown by your data.

• Please specify which statistical test was used for determining significance in Figure S4, and what *** refers to in this case. It is hard to judge really how different these data sets are in light of the overlapping error bars. Also, what is quantified here? Integrated density from an immunofluorescence assay? How are the data normalized to be comparable? How many replicates did you quantify? Or are the data shown representative of a single experiment? I could not find these details in the M&M section or the figure legend.

• Also, on the interpretation of these data; If Slp1 causes a delay in cell cycle progression, and taking into account that the fluorescence intensity of Slp1 varies along the cell cycle, with Slp1 intensity increasing as cell cycle progresses from the ring stages onwards, are these comparable measurements? In other words, are you selecting the same stages whereby the same Slp1 intensities at the centriolar plaque would be expected? If I understand correctly these measurements are carried out at 55hs post GlcN addition (when the growth phenotype starts evidencing itself?). At this time point, the relative abundance of ring and trophozoite stages (stages at which Slp1 is not expected to be detectable at the CP) is considerable higher than that of the control condition, hence a reduction in Slp1 is expected, and a mechanistic claim about recruitment or stability would be incorrect. Please clarify.

• To understand the relative contribution of Slp1 to the growth delay phenotype, please include 3D7+GlcN control in the quantification of stages shown in Fig. S5. Please check how the data shown in Fig S5 was normalized; the 49 and 73hs bars in the -GlcN condition exceed 100%.

• What is "centrin signal" shown in Figure 2B? Centrin1? Centrin 3? Please clarify which centrin protein you are referring to throughout the manuscript, or provide evidence that they could be interchangeably used for localization and intensity measurement experiments.

• Line 149 outlines that Slp1 and centrin intensities are simultaneously reduced, and that this fact alone "affirms" they are part of one complex, and that this implies that Spl1 is somehow involved in centrin recruitment. This claim is not supported by the data shown. There are multiple possible explanations as to how the intensities of both proteins could simultaneously decrease without them conforming the same structure, the same complex or even directly interacting. For example, if the centriolar plaque homeostasis is altered, or the "intensities" are simultaneously dependent on cell cycle progression, they will both be affected without necessarily ever interacting. In fact, if the centrin intensity monitored is that of Cen3, a direct interaction between Slp1 and Cen3 is not demonstrated at any time. At best, the authors could argue that both proteins are directly interacting with Cen1. Again, even this is no definitive proof that they form the same complex.

• Measurements of DNA content, shown in Figure 2D, show that +GlcN Slp1 knockdown parasites exhibited reduced DNA amounts at 42hs post induction. These results are interpreted as "defects in nuclear division," however, 1. Nuclear division is not analyzed directly, but rather approximated by measuring DNA content. 2. Even in the presence of perfectly normal nuclear division, the DNA content reduction for these parasites at this time point is expected, as cell cycle progression is affected.

• Line 160 states that a reduction in merozoite number corroborates a defect in nuclear division. However, the data shown only quantifies merozoites per schizont. As mentioned above, nuclear division is not directly assayed. What the nuclear division defects observed are is unclear. Is there fusion, fission? loss of nuclear content? defects in mitosis completion? defects in DNA replication? A reduction in merozoites per schizont, with a concomitant reduction in overall DNA levels could also be explained by a general arrest in the final stages of division. Do other processes linked to nuclear division progress normally? For example, is there daughter cell formation during schizogony without the expected accompanying nuclear division? Are daughters forming in the correct number and position? Are there more daughter cells than nuclei? Or are parasites dying before completing schizogony and producing merozoites? These possibilities need to be carefully teased out before a nuclear division defect can be assigned as the sole causing factor of the division phenotypes observed.

Minor Points

• Line 49: consider "...mechanisms remain unclear" instead of "... mechanisms are remaining unclear"

• Readers not familiar with Plasmodium cell division would benefit from having the different stages shown schematically in Figure 1A labeled (ring, merozoite, trophozoite, etc.)

• Figure 1 legend: Please specify that "centrin" staining is approximated by centrin 3 specifically. Figure 1E is missing a legend in Figure 1's legend.

• To ease the reader's interpretation of the data, please consider using a different color for 3D7 +GlcN in the plots shown in Figure 2. It is difficult to distinguish the light magenta from the red color at first glance, especially when the lines are partially overlapping.

• Please clarify how long after GlcN addition are phenotypes assessed - ex. Microtubule cumulative length measurements shown in Figure 3.

• For Figure 3C please provide a separate image for the Slp channel alone. The overlay of the green centrin signal and the magenta from the tubulin staining render a yellow signal. It is difficult to appreciate the level of Slp knockdown in these cells. Moreover, in the inset, the label "zoom in" is on top of the centrin signal in green, precluding the proper assessment/observation of any yellow signal left over.

• When describing Sf1 in T. gondii, please also cite PMID: 36009009 PMCID: PMC9406199 DOI: 10.3390/biom12081115

The notion of "checkpoint" is mentioned in the introduction and revisited in the discussion. This is a topic under current discussion/evaluation in the field. As mentioned by the authors, demonstration of a checkpoint implies demonstrating reversibility of the putative checkpoint. Though the authors do not make claims about Slp1 or the phenotypes observed activating a specific checkpoint, the manuscript could be further strengthened if the authors showed that the anaphase arrest is reversible upon wash out of GlcN and restored levels of PfSlp expression. I'm including this comment as a "minor points" because it is a only suggestion. I understand that carrying out these experiments is not within the scope of this work. However, if the authors decided to pursue this, it would certainly strengthen the manuscript.

Significance

Plasmodium species lack centrioles, and display a divergent mitosis. It is therefore of interest and relevance to understand the peculiarities of the centriolar plaque, as it likely underlies the ability of Plasmodium to upscale its numbers.

Our molecular understanding of the underpinning factors controlling nuclear and cell division in Plasmodium is limited to a few recent publications. The data presented herein is novel and contributes to the body of work with molecular insight and high resolution microscopy coming on for the malaria field.

My expertise is in cell division in Apicomplexan parasites

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Referee #2

Evidence, reproducibility and clarity

Summary:

Plasmodium falciparum parasites undergo several rounds of asynchronous nuclear divisions to produce daughter cells. This process is controlled by the centriolar plaque, a non-canonical centrosome that functions to organize intranuclear spindle microtubules. The organization and composition of this microtubule organizing center is not well understood. Here, Wenz et al. identify a novel centrin-interacting protein, PfSlp, that, following knockdown, leads to fewer daughter cells and aberrant intranuclear microtubule homeostasis and organization.

Wenz et al. identify PfSlp via co-immunoprecipitation of P. falciparum 3D7 strain with an episomally expressed PfCen1-GFP, noting PfSlp as a gene of interest based on the presence of several centrin-binding motifs. The authors go forward to generate a transgenic 3D7 strain, equipping PfSlp with GFP and glmS ribozyme, to localize and evaluate the function of PfSlp in asexual blood stage parasites. PfSlp appears to, using immunofluorescence and STED microscopy, localize to the outer centriolar plaque in schizonts, based on its colocalization with PfCen3. The authors show, utilizing the inducible glmS ribozyme knockdown system, that PfSlp is required for proper parasite growth, noting a defect following addition of GlcN. This defect is noted to cause a delay in the initiation of nuclear division, or schizogony. Analysis of intranuclear microtubule dynamics reveal abnormal microtubule organization, specifically an increase in nuclear microtubule abundance and length following PfSlp knockdown. Together, these findings characterize the role of a novel protein, PfSlp, that contributes to nuclear tubulin homeostasis and organization during schizogony.

Major comments:

The major claims made by Wenz et al. are largely convincing with the data provided.

1. One area that requires additional attention is the following: Wenz et al. claim PfSlp and centrin to be interacting partners based on 1) co-immunoprecipitation (without prior protein crosslinking), 2) the presence of centrin-binding motifs in PfSlp and 3) colocalization of PfSlp and PfCen3. This interaction is not interrogated fully and claims specific to this point need to be clarified and described as preliminary. As it is written, Wenz et al. claim PfSlp is required for centrin recruitment to the centriolar plaque but this is not investigated fully. The data show lower levels of endogenous centrin at the centriolar plaque in PfSlp knockdown parasites but centrin protein levels are similar in wildtype and knockdown PfSlp parasites. As is, the phenotype attributed to PfSlp knockdown could be attributed to PfSlp or aberrant centrin recruitment to the centriolar plaque. Experiments manipulating PfSlp centrin-binding motifs would strengthen these claims and elucidate the role of PfSlp apart from centrin. If not included, less emphasis should be placed here.

2. The 3.5 mM glucosamine has some toxicity in the parental 3D7. Is it possible to use a lower concentration so the growth of 3D7 is unaffected but the grow of the Slp-GFP GlmS parasites is still reduced?

3. Fig 3E - the quantification of tubulin levels requires biological replicates to have means and error bars.

4. The use of "centrin" is somewhat imprecise throughout. The authors should specific which centrin (PfCentrin1 or PfCentrin3 or others) they are referring to each time in the text.

5. The mention of the cell cycle checkpoint is an interesting and appropriate point in the discussion. However, the discussion of it in the last sentence of the introduction is less appropriate. It should be removed from line 92-93.

Minor comments:

1. Line 50 - "are remaining unclear" should "remain unclear"

2. Line 65 - "players" is quite informal. A better word should be selected.

3. Line 223 - "were" should be "where"

4. The delay in schizogony which is observed following addition of GlcN (Figure S5) may be made more convincing if the experiment is performed hours post invasion rather than hours post treatment. The synchronization of the parasites is in question as it is described in the methods.

5. In general, data presentation is clear and readable. The growth defect observed following GlcN treatment (Figure 2C) could be made more clear with data normalization to emphasize that which can be attributed to PfSlp knockdown and not GlcN.

6. Line 276 - Why is nuclear tubulin homeostasis more relevant for closed mitosis? This is difficult to understand. It should be phrased differently or provided with additional explanation.

7. Line 316 - "were" should be "was"

8. The identity, source, and dilution for each antibody must be reported for each use in the methods.

Significance

The mechanisms by which intranuclear microtubule dynamics are regulated by Plasmodium falciparum parasites are not well understood. Furthermore, the proteins that are present near the centriolar plaque remain mostly unknown. Understanding the role of the Plasmodium centriolar plaque and its members is critical to describing these dynamics and contributes to our growing understanding of schizogony, an atypical mode of cell division mode with several rounds of nuclear division lacking cytokinesis. Therefore, the identification and initial characterization of PfSlp1 is useful for malaria parasite cell division community.

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Referee #1

Evidence, reproducibility and clarity

The manuscript "An Sfi1-like centrin interacting centriolar plaque protein affects nuclear microtubule homeostasis" by Wenz and co-authors describes the detection and analysis of the Sfi1-like protein in apicomplexan parasite Plasmodium falciparum. The authors examined the protein localization and function in asexual stages during parasite replication in the red blood cells. The authors detected PfSlp in the PfCentrin1 pulldown, created PfSlp conditional knockdown strain, and evaluated growth and morphological deficiencies associated with the PfSlp deficiency. The study's primary finding is that PfSlp inhibits the extension of nuclear MTs.

Major comments

• The key conclusion is appropriate but is poorly supported by experimental evidence. The transitional, experiment-to-experiment conclusions are preliminary and may require additional experiments. The authors did not present a convincing model of the PfSlp1 function in mitosis. If PfSlp inhibits the MT polymerization, then the PfSlp reduction should lead to an extension of the bipolar spindle, which is partly supported by longer MTs in the hemispindles. How is the excess of the nuclear MTs prevent the spindle resolution in anaphase? Fig 4C misrepresents mitotic phases: bipolar spindle should be broken into two in anaphase, while the drawing shows one elongated spindle connecting two poles.

• The authors should correct the use of terminology. Throughout the manuscripts, the parasite division stages are called life stages. Life stages are merozoites, gametocytes, ookinetes, sporozoites, etc. The division stages apply to a single life stage and, in the case of schizogony, are rings, trophozoites, and schizonts. Please, note that schizogony does not follow the ring and trophozoite stages (line 119); it includes them as the distinctive morphological stages of one round of schizogony. The cell cycle terminology is incorrectly applied. What is the "mitotic spindle stage," "mitotic spindle nuclei, "or "mitotic spindle duration" (Fig. 4B)?

Minor comments

• The PfSlp knockdown is inefficient: the 55% reduction at the RNA level translates into a minor change at the protein level (Fig.2 and S4). The evaluation of the protein changes should be done by western blot analysis with appropriate controls. The intensity of the IFA signal (used in the study) changes depending on the focal plane, as seen in Fig 1D.

• Growth defects of the PfSlp KD: It is unclear what causes the reduced parasitemia of the GlcN untreated Slp parasites (Fig. 2C and D). To conclude that the kinetics of DNA replication is affected, the authors will need to perform the real-time measurements of DNA replication forks. The presented data supports that fewer S/M rounds were performed by PfSlp lacking parasites but gives no way to determine whether the S or the M phase was affected.

• DNA quantification graph (Fig. 2D) is confusing and does not correlate with the quantification of merozoites (Fig. 2E). Why is the DNA intensity of Slp- parasites lower than the DNA intensity of the Slp+ parasites, even though Slp deficient line produces less progeny? Is it possible that you missed the actual peak of DNA replication? Authors may consider more tight time courses with a few additional time points.

• Given the main claim, the study lacks the spatial-temporal analysis of tubulin described only in words. The tubulin quantifications by WB (Fig. S6) are not convincing, as well as the resulting conclusion of the cell cycle retardation.

• It is unclear how the authors arrived at the conclusion that the mitotic spindle is deficient in PfSlp KD parasites. Fig. 3C does not show visible differences in GlcN treated and untreated parasites.

• How many nuclei are in the cells shown in figure 4 and supplemental movies? It seems as if GlcN treated Slp parasites form one long spindle.

• The conclusion of anaphase block is unsupported: the authors need to demonstrate the accumulation of the metaphase nuclei with a bipolar spindle.

Significance

The eukaryotic centrosome is a microtubule organizing center that guides the segregation of duplicated chromosomes. Despite being an essential regulator of the parasite division, the apicomplexan centrosome remains poorly understood. Recent studies in Toxoplasma gondii (Suvorova et al., 2015) and Plasmodium species (Simon et al., 2021) demonstrated high diversity of the centrosome organization making the studies of microtubule organizing centers in apicomplexans, particularly challenging. Examining the protein composition is one of the ways to uncover organelle function. The current study would help to understand the evolution of the MTOC and mechanisms of cell division in understudied eukaryotic models.

The focus of my research is the apicomplexan cell cycle. I previously showed the bipartite organization of the Toxoplasma centrosome and identified and characterized several centrosomal constituents, including centrin partner Sfi1. Our most recent study presented evidence of the functional spindle assembly checkpoint in Toxoplasma tachyzoites.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

In an interesting study, Leboutet et al. here make excellent use of CRISPR techniques to investigate the role of C-terminal di-glycine sequences important for lipidation in the two Atg8 orthologs LGG-1 and LGG-2 in the autophagy process and in C. elegans development. A main finding from the study is that the LGG-1(G116A) variant, except for visible LGG-1 punctae staining (likely representing autophagosomes), can perform all functions compared to WT LGG-1, suggesting that these can potentially be uncoupled from membrane conjugation, an unexpected concept.

1) To this end, an unaddressed concern in this study is that it has not been ruled out if LGG-1(G116A) perhaps can still trigger an unspecified entity to associate with membranes. Specifically, the authors identify a lower band (referred to as an unexpected, minor band) in Fig 1C for G116A and G116AG117A, but do not investigate the nature of this band (noting, importantly, that these two mutants show normal development). Immuno-EM could be very useful here.

2) Importantly, all of the different LGG-1 mutants are not equally investigated (as done in Fig 1F-K), which is a missed opportunity for the study overall (eg G116AG117* in Fig. 2M and 4). In particular, comparative Western blots are missing for all of the different proteins.

3) Lastly, the study is missing a discussion of the ability of LGG proteins to dimerize (not mentioned at all), a deeper analysis of LGG-2 (later stages than 15 cells, Fig 5D, and of even more significance, EM of double mutants - are there really autophagosomes formed in these?), as well as a more in-depth investigation of ATG-4 interactions (atg-4 investigated only in Fig. 1N, but then never again), which could also help address possible mechanisms involving differentially interacting binding partners.

4) Other discussion points worth further elaboration includes how removal of paternal mitochondria in the absence of autophagosomes without LGG-1 and LGG-2 could take place (again, what does EM look like? This is a particularly important implication of this study, which warrants further study), as well as how the new study's finding possibly impact the use of transgenic C. elegans GFP::LGG-1 markers, including a G116A marker that the authors have published and used as a negative control.

Other relevant points:

1) Fig 2N and S2D are replicated.

2) Error bars are missing in Fig 3I.

3) Fig. 5K should be quantified over multiple repeats.

4) Fig 7 feels like almost 'walking' backwards, may be more efficiently integrated elsewhere in the manuscript (it is also not clear why lgg-2 RNAi is used here, instead of the mutants that are used everywhere else in the study?). Moreover, the authors may want to consider discussing Fig 3/development first (considering the reader has been informed that lgg-1 is an essential gene,- to this point, it is only later made clear that the lethal allele has 8% 'breakthroughs - are these the animals analyzed?) and Fig. 6/EM together with Fig. 1.

5) The yeast section is highlighted in the abstract whereas all data are in supplements; overall it could be better integrated. In particular, sequence alignments and Western blots are missing here.

6) Result section should be revisited for clarity and language, including written in past tense.

Significance

Insights into the role of a C-terminal di-glycine sequences in Atg8 is useful for our understanding of how especially Caenorhabtidis species may engage different precursor vs cleaved Atg8 isoforms for various biological functions. In particular, the interesting and novel concept proposed by the authors in this study is that LGG-1 possesses functions independently of membrane conjugation, which may have potential implications the use of lipidated Atg8 reporters as markers, but also how autophagosomes are formed and function more broadly. However, further evidences is needed to support this 'negative' finding, as commented on above.

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Referee #2

Evidence, reproducibility and clarity

Leboutet et al. use a clever strategy to test the role of LC3 modifications in animal cells. They generate an allelic series of cleavage site mutants of the major LC3 isoform in C. elegans, LGG-1. They convincingly demonstrate that a non-cleavable precursor form of LC3(AA) is unable to localize or function during various forms of macroautophagy, embryonic development, adult survival, or cell death/corpse clearance. A pre-cleaved intermediate form of LC3(A*) is also unable to localize or function during various forms of macroautophagy and has neomorphic characteristics visualized by EM and corpse clearance, but fully functions to promote embryonic development. Surprisingly, mutating the predicted cleavage site of LC3(AG) results in defects in localization, but only a mild delay in autophagic flux. Similarly, LC3(AG) mutants show no defects in viability or embryonic development, which the authors show is partially due to the function of the other LC3 isoform, LGG-2.

Major comments:

What is the new LGG form * in Fig. 1C? Does the Mass Spec data give any hints? The authors imply that this is not lipidated, but show no direct evidence for this statement. There are reports of LC3 conjugation to lipids beside PE, such as PS. Could this represent a switch form LC3-PE to LC3-PS? Or simply cleavage and lipidation at G117? The lack of localization to autophagosomes convincingly demonstrates that this form * does not act like the classic form II, which was thought to be the functional form of LC3, but more information about this isoform would be needed to convincingly make the author's conclusions about lipidation.

The text compares the number of omegasomes vs phagophores vs autophagosomes and refers to Fig. 7E-G, but these graphs do not clearly identify the number of double-positive and single-positive populations, making it impossible to interpret this data. A graph similar to Fig. S5A should replace 7E-G to clearly convey this data.

Fig. 7E vs 7P - Why are there twice as many ATG-18 dots in 7P controls? Is one OP50-fed and the other HT115-fed? Or are the strains different? Why this is different isn't clear from the methods and is missing from the worm strain list.

Fig. S4F - I'm not sure of the utility of the LGG-1 rescue experiments in yeast. WT LGG-1 expression doesn't appear to significantly rescue atg8∆ mutants and it's not clear that there is any significant difference between different LGG-1 isoforms, especially given the broken y-axis. Also showing n=1 and missing statistics. The other yeast experiments are more interpretable and these findings do not significantly add to the paper.

Minor comments:

First half of the first paragraph of the introduction is under-referenced. Please cite relevant review articles. Introduction could also be shortened and more to the point.

Missing statistics in Fig. 1L right. Can't conclude it's increased if not significant.

Fig. 1N is not discussed in the manuscript.

Fig. 3 would be improved by maintaining the color scheme from Fig. 2

Fig. 3H and Fig. 4D are showing similar data in opposite ways (viability vs. lethality). For your reader's sake, please use the same measure for the same assay.

There is no 5-cell stage. C. elegans early embryonic stages are 1, 2, 3, 4, 6, 7, 8, 12, 14, 15.

The relative prevalence of LGG-2-I vs LGG-2-II should be presented in Fig. 5K, similar to the analysis of LGG-1 isoforms in Fig. 1C. It appears that LGG-2 conjugation is being altered in various lgg-1 alleles.

Fig. 6H - EM counts are typically represented as number per section area, not section. The size of cell sections can vary by a large amount.

The authors refer to G116AG117 as gain-of-function, but this is confusing given all the LGG-1 functions lost. A more accurate term could be neomorphic, although the authors haven't performed the genetics to test whether the allele is antimorphic (i.e. G116AG117/null).

Why wasn't the double alanine mutant used in any assays past Fig. 3?

Fig. 7R right model - Phagophore membranes need to be connected at the ends - What are the light green circles representing? - Why does the blue G116A mutant localize to the cargo in the model? The author's said they didn't observe any localization.

Why is Fig. 2N identical to Fig. S3D? There's no need to include the same data twice. Also, both contain an error on the y-axis (15 instead of 5).

Discussion - P. 12 - "Our genetic data indicate that form I of LGG‐1 is sufficient for initiation, elongation and closure of autophagosomes". Indicate is an overstatement. The authors do not perform assays for initiation, elongation or closure.

Discussion - P. 12 - "paternal mitochondria could be degraded by autophagosomes devoid of both LGG‐1 and LGG‐2 " - I couldn't find data in this paper where paternal mitochondria are shown to never have LGG-1 or LGG-2 on them. A single time point analysis isn't sufficient to demonstrate that for molecules that dynamically associate and disassociate with membranes.

Significance

This study shatters our existing models of LC3 function. That LC3 could show any function without localizing to autophagosomes or other structures goes against our current understanding of LC3 function, making this study incredibly important for the autophagy field and the myriad autophagy-relevant clinical fields.

My expertise is C. elegans genetics, embryonic development, membrane trafficking, and non-canonical autophagy.

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Laboutet et al., titled: "LGG-1/GABARAP lipidation is dispensable for autophagy and development in C. elegans," describes the potential function of a nonlipidated LGG-1 mutant containing a G116A mutation. Comparison of a G116A missense mutation to the lgg-1 null mutation or a lgg-1(G116A>G117*) suggests that there is some function retained in the G116A missense mutation. The authors claim that no foci form in the lgg-1(G116A) mutants and take this to mean that there is no lipidation. Assays for autophagy function are carried out, such as the degradation of paternal mitochondria in the 1-cell and 15-cell embryo, survival after L1 starvation, normal lifespan, and the presence of apoptotic corpses. In all cases, the lgg-1(G116A) mutant clearly shows function. However, how can we be sure that there is no lipidated form? The authors state that not seeing LGG-1 positive dots in the embryos with an LGG-1 antibody is enough to state that this is not a lipidated form of LGG-1. However, this should be confirmed biochemically. If there were absolutely no lipidated form, the authors also would have to confirm that the function that they see in their assays, for example in survival after starvation, or in degradation of paternal mitochondria is indeed autophagy-dependent. Double mutants with the lgg-1(G116A) and a degradation mutant, like epg-5, should eliminate the activity seen in their assays. Otherwise, this activity may be due to another function of LGG-1 that is not autophagy-dependent.

Major questions:

1.Can we be sure that there is no lipidated form? What if another amino acid can be lipidated to a lower extent? If it is not lipidated, how do the authors propose that this LGG-1 mutant is functioning? In the G116A mutants, and G116AG117* mutant, a new band shows in between the LGG-1 I and LGG-1 II forms, does this band have any activity?

2.In Fig. 1, functional assays for LGG-1 dependent autophagy function in the manuscript are the degradation of paternal mitochondria in the 1-cell and 15-cell embryo, survival after L1 starvation, normal lifespan, and the presence of apoptotic corpses. In Fig. 4, the authors show that most of this activity may be due to redundancy with LGG-2, as the starvation survival of lgg-1(G116A) mutants is mostly abolished by the lgg-2 null mutation. Three assays are done to compare the lgg-1(G116A) single to the lgg-1(G116A); lgg-2 null double, and in 2/3 assays there is still activity conferred by the lgg-1(G116A) mutant observed in the double mutants. What if this activity is not autophagy-dependent?

3.In Figures 1F (100 cell embryo) with lgg-1(G116A) mutant, there are light foci visible, clearly not as bright as in the wild-type, but could these be some less lipidated form of LGG-1 with some remaining function? Again, in figure 4J with the lgg-1(G116A); lgg-2 null (15 cell embryo), very light foci accumulate. What are these?

4.In Figure S4F there is a small difference between the atg8 +empty vector and the atg8 +LGG-1(G116A), however there are no statistics shown.

5.There is evidence that the efficiency of degradation by autophagy in aggrephagy is modulated by the composition of the aggregates (Zhang et al 2017). A model has been proposed where PGL-1, PGL-3 and SEPA-1 are mainly degraded via an EPG-2 mediated pathway, however an EPG-2 independent pathway also exists. Which pathway is being used in the LGG-1(G116A) mutant?

Minor points:

1. The manuscript would benefit from some language editing. In page 2, line 5, it reads: "The general scheme is successive recruitment of a series of protein complexes involved in the dynamic of the process through several steps implicating the phosphorylation of lipids..." Here, it should read "dynamics." The authors use this term often and they should refer to "dynamics".
2. The label "1-cell" are missing in Fig. 1B showing the lgg-1() mutant on the left.

Significance

The manuscript reports a truly novel finding and could be potentially interesting to the autophagy research community. Because the authors make a claim that something is not required, the burden of proof is higher and the authors have to unequivocally show that the LGG-1(G116A) mutant is not lipidated.

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Reply to the reviewers

Reviewer 1.

Major point:

(1) The authors rely upon the redistribution of RNA to measure the inheritance of extant RNAs following cell cycle release. Blocking transcription nicely shows new synthesis is not required for this inheritance. This is also consistent with the idea any newly synthesized RNA would be 'dark,' or not EU labeled, but the transcription inhibitor experiments are critical controls and nicely done. As hinted at the end of their discussion, however, a lack of RNA localizing to G1 chromosomes could be formally attributable to differential RNA stability. Might altered RNA stability of NEAT1, MALAT1, or U2 also contribute to the observed altered localizations upon interphase reentry? The authors could use qPCR or measure RNA half-life to test this possibility. These data would nicely compliment the authors' existing FISH experiments and allow them to specifically argue for differential RNA localization.

We have addressed this point by measuring the stability of MALAT1, U2, and NEAT1 in G2 cells after transcription inhibition using RNA FISH. We find that U2 and MALAT1 exhibit very little RNA degradation after 2.5 hours of transcription inhibition, which is consistent with the reported half-lives for each of these transcripts (10 hours for MALAT1 and >24hrs for U2; PMC3337439). We conclude that differential RNA stability cannot account for differential RNA import observed for these two transcripts. In contrast, NEAT1 transcript is almost undetectable after 1.5 hours of transcription inhibition, which is also consistent with the reported half-life of this transcript (22406755, 3337439). Therefore, RNA degradation during mitosis could contribute to a lack of NEAT1 nuclear import in G1. We have included this new data in a modified Figure 2E (text p5 lines 154-166).

Minor Points:

(1) The authors examine published datasets identifying RNA associated with chromatin and state the reason why these data show little overlap is "primarily attributable to purification methodology." This statement seems speculative, and its basis seems unclear.

We have changed the wording of this section to remove unwarranted speculation (p4-5 lines 116-129).

(2) The SAF-A-AA experiments failed to reveal insight into mechanisms of RNA sorting, although they do suggest the AA construct functions as a gain-of-function due to a) increased RNA reincorporated into chromosomes b) dramatic increase of chromosome targeting of SAF-A. These effects make it difficult to interpret the SAF-A-AA data. Related to this point, the analysis of altered RNA distributions relative to SAF-A is underdeveloped. Because the authors only examined one lncRNA (MALAT1), the conclusion that “forced retention of SAF-A on mitotic chromatin does not lead to an increase in the nuclear inheritance of specific transcripts” seems like an overstatement.

We have reworded this conclusion about the role of SAF-A-AA on mitotic chromatin retention to more accurately reflect our findings (p6 line 197). (3) The authors find the U2 spliceosomal RNA is preferentially inherited. Might they speculate why this would be advantageous?

We have added a sentence to the discussion speculating about the importance of U2 inheritance (p8 line 269-271). (4) Optional: it would be exciting to test the significance of U2 RNA inheritance

We agree with the reviewer that this would be an exciting future direction to test. We envision that testing this idea rigorously would require the development of several new degron cell lines and is outside the scope of this study. (5) For Figure 1, please add statistics to figures and legend; add N=cells examined.

We have added a new supplemental Excel spreadsheet that contains the N of cells measured for each experiment and added statistics to figure legends and figures where tests were significant. (6) For Figure 2, single channel panel of U2 RNA should be added. Figure 2E seems to reproduce the same data shown in Figure 2D (right-most columns) shown with different axes.

We have added a single channel image of U2 to Figure 2 and replaced panel 2E with analysis of MALAT1, NEAT1, and U2 stability after transcription inhibition. (7) Figure 3, it is unclear why the authors selected MALAT1 for analysis, but not NEAT1 (or the single (unlabeled) antisense RNA also enriched in the SAF-A IP (figure 2C).

We examined MALAT1 in greater detail because it is the most abundant lncRNA bound by SAF-A and most robust RNA FISH probe. The unlabeled antisense transcript is hnRNPUas1 and was not detectable in DLD1 cells by RNA FISH. (8) Figure 4B, please add statistics to figure and legend.

For this experiment we prefer not to add statistics to the figure. This experiment was performed on a limited number of cells (21 and 8 respectively) and we do not believe that it is statistically appropriate to treat each cell as an independent N. The data confirms results in our previously published work (Sharp et al 2020) using live cell imaging. (9) Methods: in their description of the published lists of chromatin-bound RNAs, the authors should cite those works and provide a data availability statement with the associated GEO

We have cited these works in the text and methods sections and added GEO accession numbers associated with these studies. (p21 line 442).

Reviewer 2

Major comments:

The authors pose an interesting question -- how does nuclear RNA segregate following mitosis. In many ways, the results presented in this manuscript are rather preliminary. Key controls and validation are missing. Because of this, it is difficult to assess the validity of the main conclusions of the study. More specifically:

1. The main conclusion of the manuscript ("about half of nuclear RNA is inherited by G1 cells following division") is primarily dependent on the experiment described in Fig 1A-B. The authors labeled synchronized cells with EU and quantified nuclear signal after release from synchronization. However, key controls are missing. What is the synchronization efficiency of the RO3306 treatment? How many cells in their acquired fields of cells are in G2 vs in other cell cycle stages? Following their drug release, what percentage of the synchronized cells have undergone telophase? What is the potential error rate in identifying the cell cycle stage using their visual imaging analysis? Without these key controls, it is unclear how to interpret the data presented in Fig 1B.

One reason that nuclear inheritance has not been properly addressed in the literature is the difficulty in obtaining pure populations of cells synchronized in telophase or recently divided cells in early G1. There are no drugs available which can uniquely target these cell stages. In addition, the ability of human cells to all release perfectly synchronously from a drug-induced arrest can vary with cell type. For this reason we used a strategy employing synchronization methods designed to enrich cell populations for telophase or early G1 events, combined with single cell analysis of events with the distinct cytological features of each stage. Cells that have recently divided are extremely distinctive and easily identified using a combination of DAPI morphology to assess nuclear size and condensation state and the presence of Aurora-B/Midbody staining to indicate a recent cytokinesis. Our approach of using single cell analysis coupled with quantitative imaging therefore does not require a high efficiency of synchronization in cell populations. To gain confidence that our observations were reproducible we analyzed a large number of cells, performed multiple experimental replicates, and applied statistical tests to the data.

To clarify these important points we have added text to the descriptions of how these experiments were performed (p3 line 72) and added information about the number of biological replicates to all figure legends and number of cell analyzed in each experiment to Supplementary Table 1.

1. The use of transcriptional inhibitors in Fig 1 is really nice and is important for showing that it's not due to new transcription following mitosis. Well done!

2. One potential mechanism that could explain the observed 25% relocalized nuclear RNA is through passive diffusion. That is, a proportion of molecules that are randomly diffusing during mitosis get trapped inside the newly formed nuclear membrane in early G1. This would be considered noise, and not a specific process that actively relocalizes nuclear RNA back into the nucleus. However, the authors' assay does not have a measure of the noise in their system. One potential experiment that may help quantify this noise is to express GFP in their cells, perform the experiment described in Fig 1A, and quantify the nuclear signal after telophase. This quantification would be the lower bound of the random process. A similar experiment with GFP-NLS could be performed to assess the upper bound of the 'inherited' molecules after mitosis. Without this type of control to quantify noise/random diffusion levels, it is unclear how much of the 25% EU signal that the authors detect is specific to the process they are testing.

We appreciate the point that the reviewer has raised. To address this concern we examined the localization of the abundant mRNA b-actin. We examined the fraction of all b-actin FISH signal that is present in the nucleus in G2 and G1 cells following division. If a significant fraction of RNA is trapped in the reforming nucleus then we would have expected the fraction of b-actin in the G1 nucleus to increase. We observed that less b-actin RNA was present in the G1 nucleus, suggesting that passive entrapment of RNA is unlikely to be a mechanism of RNA inheritance. This is consistent with a lack of inheritance of MALAT1 and NEAT1 lncRNAs following mitosis. We have added these results to a new Supplemental Figure 2 and added text describing the results to the Results section of the manuscript (p4 lines 101-113). Additionally, this result is consistent with recent work showing that mitotic chromosomes condense through histone deacetylation and exclude negatively charged macromolecules (PMID: 35922507) and that chromosome clustering by Ki67 in early G1 phase excludes the cytoplasm from the new nucleus (PMID: 32879492). These references and ideas are now included in the results section of the manuscript.

Related to the comment 1 and 2, EU labeling for 3 hrs in G2 cells would label ALL transcribed RNA, which would include mature mRNAs that will be translated in the cytoplasm. That is, this method is not specific to labeling nuclear RNAs only. How much of their signal is from mRNAs that got trapped inside the newly formed nuclear membrane? One way to test this is to measure the nuclear EU signal at later time points following telophase. Presumably, the nuclear transport mechanism would lead to export of non-nuclear RNAs and only the retained nuclear RNAs would contribute to the signal.

Please see our response to point 3 with regard to entrapment. The laboratory that originally described EU RNA labeling demonstrated a 3 hour EU labeling period results in labeling nuclear RNA, and that longer labeling periods are required to visualize EU labeling of cytoplasmic RNAs after export (18840688). We have also observed in our previously published work that the 3 hour period labels nuclear RNA during interphase (33053167, 32035037). The nuclear EU signal reflects RNAs undergoing transcription, nuclear retained RNAs, and mature mRNAs prior to nuclear export.

To identify nuclear RNAs that could be relocalized following mitosis, the authors analyzed data from "two different studies using different methodologies and a total of three different cell lines". From this analysis, the authors "found very little overlap in the chromatin-bound RNAs identified in these studies (Fig 2A)". This analysis seems fraught with problems. What is the rationale for using these studies? How valid is it to compare results from different methodologies and from different cell lines from the DLD-1 cells used in this study?

We analyzed the data from these two studies because they were the only published studies that identified RNAs that were tightly linked to chromatin. We chose to compare the results from three different human cell lines because we sought to identify nuclear RNAs that were cell type-independent, so that we could analyze the transcripts behavior in DLD1 cells. In support of using these two studies all the RNAs that we analyzed were nuclear in our RNA FISH assays.

A known problem of assessing chromatin-bound RNAs is that the level of contamination from cytoplasmic RNAs is highly variable and highly dependent on the assay. Indeed some of the most common contaminants of nuclear RNA assays are sn-, and sno-RNAs, and these are the main classes of RNA that the authors identified as common among the three data sets. What validation was used to assess whether these are the common noise/contaminants in the data?

Our goal in using the two previously published studies was to identify cell type-independent nuclear RNAs that could be studied in detail using FISH. For validation in our study we performed RNA FISH on MALAT1, NEAT1, and U2. We found that each of these RNAs are highly enriched in the nucleus, consistent with previous publications. Since snRNAs function in splicing and snoRNA primarily function in the modification of tRNA and rRNA in the nucleolus it seems unlikely that these are contaminants of nuclear preparations. Each of the published studies performed their own validations of their purification and sequencing methodology. For the purpose of our work nuclear enrichment of a transcript by RNA FISH satisfied our requirements.

One experimental validation that can be performed is biochemical fractionation of EU labeled cells, which would allow for fractionating nuclear from cytoplasmic RNA. The same problems arise with the analysis shown in Fig 3C when comparing SAF-A RIP-seq with this merged list of chromatin bound RNAs.

In support of the nuclear enrichment of each of the transcripts that we examined RNA-FISH analysis demonstrated significant nuclear enrichment. Additionally, many previous studies have shown that each of these transcripts are enriched in the nucleus (U2: 11489914, 10021385, 7597053; NEAT1: 17270048; MALAT1: 12970751, 17270048). New text describing our use of these studies is present in the results section (p4-5 lines 117-129).

Throughout the manuscript, the authors pose their findings as "RNA inheritance" following mitosis. However, this terminology is misleading. In fact, unless RNAs are lost/kicked out of the cell as they divide, aren't all RNAs inherited following cell division since they are present in the new daughter cells? Instead, what the authors mean is that some nuclear RNAs retain their function following cell division by relocalizing back into the nucleus in the new G1 cells, whereas other nuclear RNAs are unable to relocalize into the nucleus, and then presumably turned over by degradation process. The authors should take better care of their terminology throughout the manuscript.

Thank you for pointing this out to us. As the reviewer stated most nuclear RNAs are removed from chromatin during mitosis. Only a subset are reimported into the nucleus. We have modified our wording to clearly state that we are discussing nuclear RNA inheritance by daughter cell nuclei rather than inheritance into daughter cells in general. These text changes can be found throughout the manuscript.

Minor comments: 1. In all of the figures showing quantification of nuclear EU/FISH signal, the colors (red v blue) are not described (not found in the legend or methods). Presumably they are biological replicates, but this should be clearly stated.

We have modified the plots and figure legends to more clearly explain what is plotted (See text in Figure Legends). 2. Is figure 2E the same data presented in Fig 2D but in different y-axis? If so, state clearly

We have removed the data in the previous version of Figure 2E and replaced it with new data examining stability of MALAT1, NEAT1, and U2 in response to Reviewer 1 (p5 lines 154-166).

Figure 3A. This experiment is using the SAF-A-AID-mCherry system. Therefore the label in Fig 3A should be SAF-A-KD (Knockdown) instead of KO (knockout)

We have corrected this in Figure 3. 4. Typo in Fig 4B y-axis. It should be "Chromatin-localized SAF-A" instead of "Chromain-localized SAF-A"

Thank you for pointing this out, we have corrected it. 5. The methods section indicate the "precise N or replicates in indicated figure legends" but none of the figure legends have the N values listed.

We have listed number of biological replicates in all figure legends and included a new Supplemental Table 1 that contains the number of cells measured for each experiment.

Reviewer 3

The authors investigate an interesting question focussed on whether nuclear RNA from the previous cell cycle is present in the subsequent G1. It turns out that this is more complex than expected with some classes of RNA being inherited whilst others are not. SAF-A or HNRNPU had been implicated in this process but the authors suggest that its role is limited.

Figure 1 In panel A the authors write on image SAF-A-mCh. What does this refer to?

We have added information to the Figure legends indicating that this refers to SAF-A-AID-mCherry knocked-in to the endogenous SAF-A locus (see Figure Legends).

Panel B and other panels can the authors present this data as a boxplot or distribution plot to get a better feel of the data distribution spread.

We have modified all the plots in the manuscript to the Superviolin form to provide a clearer depiction of experimental replicates, mean, and standard deviation.

Presumably labelled RNAs are naturally turned over. Have the authors considered that some loss of signal could be because of this?

We have addressed the stability of specific RNAs using RNA FISH. We find that U2 and MALAT1 show essentially no degradation during the time course of our experiments. This data has now been included in an updated Figure 2. We have also modified our text to address this point more clearly (Figure 2E and p5 lines 154-166).

Panel E, have the authors considered labelling RNA before RO3306 treatment? What effect would this have?

We have performed this experiment in RPE1 cells and the presence of RO3306 did not affect cytological detection of transcript labeling. We have not included this experiment in the manuscript because it is performed in a different cell line than we use for the remainder of these studies.

Shouls TI be added before RO3306 washout?

We added transcription inhibitors after RO washout and entry into mitosis because transcription is naturally suppressed during mitosis. We were concerned that transcriptional inhibition in late G2 could lead to failure to properly enter into M phase.

Also, it is unclear what the arrows are pointing at. In panel F there is a difference between the red and blue experiments. In the methods the authors say that inhibition was for either 1.5 or 2 h. Is this the source of the difference?

We have modified the figure legends to state clearly that different colors indicate biological replicate experiments (See Figure Legends). Figure 2 In panel A there are clear differences between the cell lines. Is it right to compare them? Particularly the GRID-seq vs diMARGI? B, how relevant is it focussing on the "42" overlapping RNAs? In my mind this is not very informative.

Our goal with this analysis was to identify cell type-independent chromatin bound RNAs to analyze in greater detail. Therefore, we analyzed three different cell lines because we planned to analyze transcript behavior in DLD1 cells, which were not included in either study. We have explained this rationale in greater detail in a revised version of the text (p4-5 lines 116-129).

D-E, at a glance it is not clear that E is an expanded view of D. It might be easier if the panels were at same height.

We have removed Panel E and replaced it with a new experiment examining the stability of NEAT1, MALAT1, and U2 after transcription inhibition (p5 lines 154-166). Figure 3 Is it correct to describe IAA treated degron cells as a KO? I also could not see a WB showing how complete SAF-A KD was.

We previously characterized these cell lines in great detail (Sharp et al. JCB 2020). We have now provided quantitative measurement of SAF-A-mCherry fluorescence after different times of auxin addition to provide a quantitative estimate of SAF-A depletion (Supplemental Figure 3C).

2 h treatment seems quite short, is this enough time to obtain sufficient knock down? How heterogenous is SAF-A KD in the cell population?

We examined SAF-A depletion by auxin addition at 2 hours and 24 hours and achieve comparable depletion levels. This data in now included in Supplementary Figure 3C. There is some heterogeneity in the KD as is evident in Figure S3C, but these cells are easily identifiable by the presence of SAF-A-AID-mCherry fluorescence.

Previous studies have shown that SAF-A does not like being tagged. How certain are the authors that these cells behave typically?

We have generated two different cell lines (DLD1 and RPE1) where a C-terminal tag is inserted into the both copies of the endogenous SAF-A gene. SAF-A is one of the common essential genes (https://depmap.org/portal/gene/HNRNPU?tab=overview), however each of our cell lines exhibits no growth defects. We have recently shown that C-terminally tagged SAF-A fully rescues SAF-A knockout phenotypes (Sharp et al. JCB. 2020). Additionally, we have also performed RNA-seq (not published) on RPE1, RPE1 with endogenously tagged SAF-A and RPE-1 depleted of SAF-A and rescued with WT SAF-A-GFP and observed no changes in gene expression or mRNA splicing. Based on these assays we are confident that C-terminally tagged SAF-A expressed at endogenous levels functions normally. Figure 4 I'm struggling with the heading, and wonder if this is not supported by the data. Similarly the final sentence "The highly dynamic exchange of SAF-A:RNA complex" does not really provide an explanation.

We have expanded the text in this section to explain this phenotype in greater detail (p7 lines 216-218).

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Referee #3

Evidence, reproducibility and clarity

The authors investigate an interesting question focussed on whether nuclear RNA from the previous cell cycle is present in the subsequent G1. It turns out that this is more complex than expected with some classes of RNA being inherited whilst others are not. SAF-A or HNRNPU had been implicated in this process but the authors suggest that its role is limited.

Figure 1

In panel A the authors write on image SAF-A-mCh. What does this refer to? Panel B and other panels can the authors present this data as a boxplot or distribution plot to get a better feel of the data distribution spread. Presumably labelled RNAs are naturally turned over. Have the authors considered that some loss of signal could be because of this? Panel E, have the authors considered labelling RNA before RO3306 treatment? What effect would this have? Shouls TI be added before RO3306 washout? Also, it is unclear what the arrows are pointing at. In panel F there is a difference between the red and blue experiments. In the methods the authors say that inhibition was for either 1.5 or 2 h. Is this the source of the difference?

Figure 2

In panel A there are clear differences between the cell lines. Is it right to compare them? Particularly the GRID-seq vs diMARGI? B, how relevant is it focussing on the "42" overlapping RNAs? In my mind this is not very informative. D-E, at a glance it is not clear that E is an expanded view of D. It might be easier if the panels were at same height.

Figure 3

Is it correct to describe IAA treated degron cells as a KO? I also could not see a WB showing how complete SAF-A KD was. 2 h treatment seems quite short, is this enough time to obtain sufficient knock down? How heterogenous is SAF-A KD in the cell population? Previous studies have shown that SAF-A does not like being tagged. How certain are the authors that these cells behave typically?

Figure 4

I'm struggling with the heading, and wonder if this is not supported by the data. Similarly the final sentence "The highly dynamic exchange of SAF-A:RNA complex" does not really provide an explanation.

Significance

This is a clear well undertaken study that has made some interesting observations, which will inform future studies.

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Referee #2

Evidence, reproducibility and clarity

Peer review of manuscript "Differential nuclear import determines lncRNA inheritance following mitosis" by Blower et al.

Section A: Evidence, reproducibility, and clarity

Summary:

In this manuscript, Blower and colleagues examine the fate of nuclear RNAs following cell division. Using cell synchronization methods combined with RNA labeling with EU, the authors show that some of the nuclear RNAs synthesized in the previous cell cycle are relocalized to the nucleus of the new daughter cells in G1. To assess which classes of nuclear RNAs could be 'inherited' after cell division, they used bioinformatic analyses of previous studies, and found a small group of non-coding RNAs in common. To validate a few of these, the authors used RNA FISH to quantify nuclear signals of U2, MALAT1, and NEAT1 in G2 and subsequent G1 stages, and found that only U2 seems to be relocalized to the nucleus following division. The authors then tested whether RNA inheritance could be driven by SAF-A by examining the localization of U2, MALAT1, and NEAT1 in G1 when SAF-A WT or SAF-Amutant (retained in mitosis) is present. But they found that SAF-A plays a minor role in this process. Finally, they found that for U2, nuclear import is required for its relocalization to the nucleus in G1.

Major comments:

The authors pose an interesting question -- how does nuclear RNA segregate following mitosis. In many ways, the results presented in this manuscript are rather preliminary. Key controls and validation are missing. Because of this, it is difficult to assess the validity of the main conclusions of the study. More specifically:

1. The main conclusion of the manuscript ("about half of nuclear RNA is inherited by G1 cells following division") is primarily dependent on the experiment described in Fig 1A-B. The authors labeled synchronized cells with EU and quantified nuclear signal after release from synchronization. However, key controls are missing. What is the synchronization efficiency of the RO3306 treatment? How many cells in their acquired fields of cells are in G2 vs in other cell cycle stages? Following their drug release, what percentage of the synchronized cells have undergone telophase? What is the potential error rate in identifying the cell cycle stage using their visual imaging analysis? Without these key controls, it is unclear how to interpret the data presented in Fig 1B.
2. The use of transcriptional inhibitors in Fig 1 is really nice and is important for showing that it's not due to new transcription following mitosis. Well done!
3. One potential mechanism that could explain the observed 25% relocalized nuclear RNA is through passive diffusion. That is, a proportion of molecules that are randomly diffusing during mitosis get trapped inside the newly formed nuclear membrane in early G1. This would be considered noise, and not a specific process that actively relocalizes nuclear RNA back into the nucleus. However, the authors' assay does not have a measure of the noise in their system. One potential experiment that may help quantify this noise is to express GFP in their cells, perform the experiment described in Fig 1A, and quantify the nuclear signal after telophase. This quantification would be the lower bound of the random process. A similar experiment with GFP-NLS could be performed to assess the upper bound of the 'inherited' molecules after mitosis. Without this type of control to quantify noise/random diffusion levels, it is unclear how much of the 25% EU signal that the authors detect is specific to the process they are testing.
4. Related to the comment 1 and 2, EU labeling for 3 hrs in G2 cells would label ALL transcribed RNA, which would include mature mRNAs that will be translated in the cytoplasm. That is, this method is not specific to labeling nuclear RNAs only. How much of their signal is from mRNAs that got trapped inside the newly formed nuclear membrane? One way to test this is to measure the nuclear EU signal at later time points following telophase. Presumably, the nuclear transport mechanism would lead to export of non-nuclear RNAs and only the retained nuclear RNAs would contribute to the signal.
5. To identify nuclear RNAs that could be relocalized following mitosis, the authors analyzed data from "two different studies using different methodologies and a total of three different cell lines". From this analysis, the authors "found very little overlap in the chromatin-bound RNAs identified in these studies (Fig 2A)". This analysis seems fraught with problems. What is the rationale for using these studies? How valid is it to compare results from different methodologies and from different cell lines from the DLD-1 cells used in this study? A known problem of assessing chromatin-bound RNAs is that the level of contamination from cytoplasmic RNAs is highly variable and highly dependent on the assay. Indeed some of the most common contaminants of nuclear RNA assays are sn-, and sno-RNAs, and these are the main classes of RNA that the authors identified as common among the three data sets. What validation was used to assess whether these are the common noise/contaminants in the data? One experimental validation that can be performed is biochemical fractionation of EU labeled cells, which would allow for fractionating nuclear from cytoplasmic RNA. The same problems arise with the analysis shown in Fig 3C when comparing SAF-A RIP-seq with this merged list of chromatin bound RNAs.
6. Throughout the manuscript, the authors pose their findings as "RNA inheritance" following mitosis. However, this terminology is misleading. In fact, unless RNAs are lost/kicked out of the cell as they divide, aren't all RNAs inherited following cell division since they are present in the new daughter cells? Instead, what the authors mean is that some nuclear RNAs retain their function following cell division by relocalizing back into the nucleus in the new G1 cells, whereas other nuclear RNAs are unable to relocalize into the nucleus, and then presumably turned over by degradation process. The authors should take better care of their terminology throughout the manuscript.

Minor comments:

1. In all of the figures showing quantification of nuclear EU/FISH signal, the colors (red v blue) are not described (not found in the legend or methods). Presumably they are biological replicates, but this should be clearly stated.
2. Is figure 2E the same data presented in Fig 2D but in different y-axis? If so, state clearly
3. Figure 3A. This experiment is using the SAF-A-AID-mCherry system. Therefore the label in Fig 3A should be SAF-A-KD (Knockdown) instead of KO (knockout)
4. Typo in Fig 4B y-axis. It should be "Chromatin-localized SAF-A" instead of "Chromain-localized SAF-A"
5. The methods section indicate the "precise N or replicates in indicated figure legends" but none of the figure legends have the N values listed.

Significance

General assessment:

The authors pose an interesting question -- how does nuclear RNA segregate following mitosis. However, the results presented in this manuscript are rather preliminary. Key controls and validation are missing. Because of this, it is difficult to assess the validity of the main conclusions of the study.

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Referee #1

Evidence, reproducibility and clarity

This study follows up on the authors' recent work showing entry into mitosis is associated with RNA removal from condensing chromosomes and investigates whether the same RNAs that were removed are reincorporated into G1 chromosomes. The study is timely and simple yet elegant. The authors use pulse chase experiments to follow the distribution of bulk RNAs in synchronized then released cells, showing a subset of labeled RNAs are reincorporated into chromatin. This process is selective, as the authors demonstrate some RNAs are more likely to reassociate with G1 chromosomes than others. The authors go on to use various small molecule inhibitors to provide mechanistic insights showing transcription is not required for RNA inheritance, but nuclear pore components (importin-B) are.

The study is well conceived and executed. I recommend the following relatively minor revisions.

Major point:

1. The authors rely upon the redistribution of RNA to measure the inheritance of extant RNAs following cell cycle release. Blocking transcription nicely shows new synthesis is not required for this inheritance. This is also consistent with the idea any newly synthesized RNA would be 'dark,' or not EU labeled, but the transcription inhibitor experiments are critical controls and nicely done. As hinted at the end of their discussion, however, a lack of RNA localizing to G1 chromosomes could be formally attributable to differential RNA stability. Might altered RNA stability of NEAT1, MALAT1, or U2 also contribute to the observed altered localizations upon interphase reentry? The authors could use qPCR or measure RNA half-life to test this possibility. These data would nicely compliment the authors' existing FISH experiments and allow them to specifically argue for differential RNA localization.

Minor Points:

1. The authors examine published datasets identifying RNA associated with chromatin and state the reason why these data show little overlap is "primarily attributable to purification methodology." This statement seems speculative, and its basis seems unclear.
2. The SAF-A-AA experiments failed to reveal insight into mechanisms of RNA sorting, although they do suggest the AA construct functions as a gain-of-function due to a) increased RNA reincorporated into chromosomes b) dramatic increase of chromosome targeting of SAF-A. These effects make it difficult to interpret the SAF-A-AA data. Related to this point, the analysis of altered RNA distributions relative to SAF-A is underdeveloped. Because the authors only examined one lncRNA (MALAT1), the conclusion that "forced retention of SAF-A on mitotic chromatin does not lead to an increase in the nuclear inheritance of specific transcripts" seems like an overstatement.
3. The authors find the U2 spliceosomal RNA is preferentially inherited. Might they speculate why this would be advantageous?
4. Optional: it would be exciting to test the significance of U2 RNA inheritance
5. For Figure 1, please add statistics to figures and legend; add N=cells examined.
6. For Figure 2, single channel panel of U2 RNA should be added. Figure 2E seems to reproduce the same data shown in Figure 2D (right-most columns) shown with different axes.
7. Figure 3, it is unclear why the authors selected MALAT1 for analysis, but not NEAT1 (or the single (unlabeled) antisense RNA also enriched in the SAF-A IP (figure 2C).
8. Figure 4B, please add statistics to figure and legend.
9. Methods: in their description of the published lists of chromatin-bound RNAs, the authors should cite those works and provide a data availability statement with the associated GEO

Significance

General assessment: (strengths) The study is timely and simple yet elegant. It is well conceived and executed. In general, the conclusions are supported by the data. The study provides insight into a fundamental basic science process likely of interest to a broad readership. (weaknesses) The authors do provide some mechanistic insight into how RNAs are inherited, although this mechanism will need to be further developed in future studies. The role of SAF-A is unclear. Because those experiments did not produce a clear effect, they seem distracting. The idea that specific RNAs are sorted is exciting, but as yet we do not know how this sorting happens. Future work examining the importance of RNA inheritance should be prioritized. Measuring RNA abundance of the different analyzed RNAs (NEAT1, MALAT1, vs U2) should be added to the current manuscript.

Advance: This study follows up on the authors' recent work showing entry into mitosis is associated with RNA removal from condensing chromosomes and investigates whether the same RNAs that were removed are reincorporated into G1 chromosomes.

Audience: The study provides insight into a fundamental basic science process likely of interest to a broad readership.

Describe your expertise: basic cell biology, RNA localization

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

The manuscript presented the Cryo-EM structure of HECT E3 UBR5. Using Alphafold2 model of UBR5, the authors were able to dock and refine the structure model of full length UBR5. Interestingly, UBR5 exists as a homodimer and could potentially assemble into a larger oligomer based on SEC and Cryo-EM data. The antiparallel arrangement of the homodimer suggests that the C-terminal HECT domain could transfer ubiquitin in trans or in cis configuration. The tetrameric model reveals a ring-like structure with a large central cavity, presumably to accommodate large proteins/complexes. Using AKIRIN2 as a substrate, the authors demonstrated that UBR5 did not ubiquitinate AKIRIN2, but prefers ubiquitin modified AKIRIN2 as the substrate for ubiquitin chain elongation. Indeed, they observed that UBR5 preferentially ubiquitinates pre-ubiquitinated non-cognate substrate and free ubiquitin hinting that UBR5 is a chain elongating E3. Lastly they showed that UBR5 HECT contains a plug-loop that blocks C-lobe rotation and suggested that conformational change is necessary for ubiquitin transfer.

Comments:

1. Homodimerization and oligomerization are the novel aspect of this study but the manuscript lacks validation of the structure. The authors should provide biochemical/mutagenesis analysis to support the dimerization interface observed in the structure. Also the model showed that SBB2 is involved in the tetramerization interface, could the authors verify this by designing a SBB2 deletion mutant?
2. Would be useful to show the docking of Alphafold2 model onto the Cryo-EM map prior to further model building and refinement in the supplementary data.
3. Please show the SDS-PAGE of purified UBR5 used for Cryo-EM study.
4. Figure referencing is not in order. For example Figure 1J was described before Figure 1I. Figure 2A,B mentioned after Figure 2C. Also some Figures are not properly referenced in the main text, e.g. p10 when describing ubiquitin chain formation of UbAKIRIN2 and UbSecurin. Please check throughout the manuscript.
5. Figure legends are missing for Figure 1H-1J
6. It was stated in p10 that there was no binding between UBR5 and UbSecurin in Figure S3C, but Figure S3C showed faint FAM-UbSecurin across the fractions. It would be useful to repeat this with FAM-UbSecurin alone to ensure the faint bands are background signal.
7. In p10, it was stated that UBR5 and UbAKIRIN2 interaction was enhanced in the presence of ubiquitin. How did the authors come to this observation? The sucrose gradients (Figure 3B) showed that UBR5 co-elutes with both AK2 and UbAK2. This reviewer is unclear whether the intensity of the bands can be used to evaluate the strength of the binding affinity. Was the experiment performed at the same protein component concentration/condition? The UBR5 appears to elute at different fractions from the two experiments.
8. The authors suggested that the UBA domain might bind ubiquitin and promote ubiquitination of UbAKIRIN2. It is noteworthy that prior studies on several HECT E3s showed that HECT domain alone can catalyze free ubiquitin chain assembly. Could it be possible that the HECT domain of UBR5 alone could catalyze the extension of UbAKIRIN2, UbSecurin or UbdeltaGG?
9. In Figures 3A and S3B, does the ubiquitin chain elongation occur only on the fused ubiquitin?
10. Figure 4E mentioned in the main text but is missing.
11. It is not clear from Figure 4 whether the plug-loop is blocking the rotation of C-lobe. The overlaid Figure 4 is quite busy, would be useful to show the UBR5 HECT domain alone with other HECT domain presented in the same orientation but in separate panels. With the plug-loop in the current configuration, does it block E2 binding and transthiolation reaction? Figure 5B seemingly suggested that plug-loop is blocking transthiolation but it is hard to visualize. The authors could enlarge Figure 5B and color the plug-loop differently.

Significance

The manuscript provides the structural insight into the organization of UBR5. While the Cryo-EM data largely agrees with the Alphafold2 UBR5 model, this should not take away the significant effort in obtaining the large UBR5 protein structure. The structure reveals an unexpected homodimerization of UBR5 and in the assembly of larger oligomer. The biochemical analyses suggest that UBR5 is proficient in ubiquitin chain elongation. Overall the study provides a structural framework for understanding how UBR5 could function as an ubiquitin ligase. The findings will be of interest to scientist in the ubiquitin field and in understanding UBR5 biology.

Limitation: aside from the structure, the study lacks detailed mechanisms of how UBR5 catalyzes ubiquitin transfer. While few models for ubiquitin transfer were proposed, the study lacks suitable substrates to investigate the mechanism. The study showed that UBR5 can elongate ubiquitin chain, but it is not clear whether UBR5 could transfer ubiquitin to substrate.

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Referee #2

Evidence, reproducibility and clarity

This manuscript reported the CryoEM ring-like structure of the full-length human E3 assembly ligase UBR5, showing its assembly into a tetramer. The authors identified critical determinants for antiparallel homodimer and tetrameric assembly. They further described AKIRIN2 as UBR5 substrate and provided evidences of a preferential interaction and activity of UBR5 towards monoubiquitinated proteins. Based on these findings, they proposed UBR5 as chain-elongating E3 ligase.

CryoEM data are solid, and the model interpretation of the tetrameric structure provides a precise description of the domain composition of the protein that well fit with biochemical data. Additional experiments are suggested to corroborate few statements of the authors.<br /> We believe they are realistic in terms of time and resource.

1. Authors should address the importance of tetramerization by mutating SBB2 at the tetramerization interface and comparing the mutant with wild type in mass photometry and ubiquitination assays. In silico analysis of the interaction interfaces (e.g by using PISA software) could be useful to select amino acids to be mutated. The authors suggested a role for oligomerization in catalysis and mutants are needed in order to define the real "functional unit" of the enzyme.
2. The authors used sucrose gradient sedimentation assay to prove UBR5 and substrate interaction (Fig. 3). Control experiment that showed UBR5 protein sedimentation in presence of GFP only is instead in Supplementary Fig. 3D. Unfortunately, in that panel the signal of UBR5 is not visible. Main figure should be revised showing proper controls of the experiment.
3. The authors need to better clarify the features of the AKIRIN-UBR5 interaction. According to the data, the enzyme is equally active on both AKIRIN-Ub and Securin-Ub, suggesting a Ub-specific engagement. What would be a correct explanation of these results? Is the UBA domain directly involved in this process? Testing the activity of a UBA-impaired mutant should help to solve this issue.
4. The authors identified a 25 aa sequence, called Plug loop, preceding the HECT domain. In the structure it is inserted between N and C-lobe subdomains of the HECT and appears to lock the enzyme in an open L-conformation. These structural findings are interesting, but no supported by experimental data. Which is the effect of the Plug loop deletion in a ubiquitination assay? Without further validation the last chapter of the results remains purely speculative and may better fit in the discussion.
5. The datasets are clearly affected by preferential orientation as showed by the angular distribution and 2D classes (reason why the authors correctly performed data collection with tilt). A comment on this is required in the experimental section. In addition, it is not clear whether the presented maps (Fig 1 and 2) derive from merging of the two datasets or only the model has been built using the two different datasets.
6. As a general comment, authors should enlarge panels in which structural details are described, highlighting the side chain residues involved in binding interfaces. Fig. 5 and Fig. 6 are particularly small and incomplete. Most of the structural figures miss key labels needed for a proper understanding. E.g. among the others, numbering of the helix composing the armadillo domain.
7. The overall organization of the figures is quite confusing. Pag. 7 Figure 2C should represent a "box stabilized by three zinc ions mediated by two histidine and seven cysteine residues" according to text citation, but none of these details is highlighted in the corresponding figure. The eye in Figure 1,2,4 does not mean much if a proper box is not linked to the actual site to be seen. In addition, arrows indicating the rotation axis is hard to interpret. Few panels miss the legend. Figure 1A and many other panels miss the reference in the text. More details below.

Additional points:

• Mass Photometry data need additional comments and labels. Please comment on the MP concentration used to analyze the samples. Being a dynamic system, you are probably seeing an equilibrium of species at 10 nM in MP. For better completeness of MP figures, labels that includes counts, % of species and sigma should be added to the nice representation of oligomers. Which condition/fraction represent the MP data showed in 1B?
• If Alphafold models are mentioned and used for model building, it would be nice to provide at least a pLDDTscore and ptm score. Since some details of the AF model are described in the text, an additional superposition of the AF model with the final model derived by EM would be useful to the community.
• A simple workflow describing the cryoEM data processing that includes how many particles have been used in each step is required, at least in the methods section. The authors need to show the cryoEM 2D classes of the dimer as well.
• Please add the domain boundaries in Figure 1A and highlight the domains on the alignment included in Supplemental Table 1.
• Pag. 8 please decide which abbreviation to use, either UBR or Ubr.
• Page 8, line 192. I found annoying to find the same sentence used by competitors who posted a bioRxiv paper 3 days before the one we are reviewing (doi.org/10.1101/2022.10.31.514604 page 4, line 135).
• In supp. 1C legend, "high concentration of NaCl" is a bit vague
• Complementary to Supp Fig 2A, a zoom in of the density map with traced model would be beneficial to show the actual map quality obtained.
• Pag. 6 lines 133-134, the helix residues involved in homodimerization are cited in the text, but not highlighted in the Figure 1.
• Figure 1 legend, panels H-I-J description are missing.
• Figure 3, panel B, meaning of the asterisk is not reported in the figure legend.
• Figure 4, 5 panels from A to E are cited in the text while figure reported only 4.

Referees cross-commenting

I think all the reviews are fairly consistent and agree with the comments raised by my colleagues with the one exception of Point 3 of Reviewer 1. The issue is certainly important yet the experiment suggested is not clear. I personally have troubles designing an informative experimental set-up.

Significance

This paper presents the intriguing Cryo-EM structure of the full-length HECT E3 ligase UBR5. As it stands, this work provides evidence of the existence of a tetrameric RING-like conformation that could represent the functional unit of the catalysis. Very little validation of the features identified in the Cryo-EM structure is given, thus the paper remains quite descriptive, but in any case interesting and informative for the ubiquitin field.

Considering that UBR5 is a quite competitive subject in these days (e.g. at least one additional Cryo-EM structure was posted in BioRxiv, doi.org/10.1101/2022.10.31.514604), I would positively consider this manuscript for publication if the authors reply in full to the issues raised.

My field of expertise: Ubiquitin regulation and interactions, biochemistry, biophysics and Cryo-EM.

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Referee #1

Evidence, reproducibility and clarity

This manuscript describes cryo EM structural analyses of human E3 ligase Ubr5 in dimeric and tetrameric states. Ubr5 belongs to structurally poorly characterized family of Hect E3 ligases and has important biological functions, e.g. in targeting transcription factors for proteasomal degradation. The manuscript therefore addresses an important subject of basic science and biomedical interest. Using in vitro ubiquitylation assays the authors show that purified Ubr5 forms ubiquitin chains on a substrate (akirin2), but also on a non-substrate (securin), provided the proteins are covalently fused to ubiquitin. The authors interpret this as a preference of Ubr5 for ubiquitin chain elongation over initiation. Consistently they show that Ubr5 forms free ubiquitin chains linked by Lys48 in vitro. Whilst the structures of full-length Ubr5 are very interesting and important, this manuscript appears to be at a premature stage. The structural interpretation and models lack experimental validation and remain speculative. The presented activity assays are interesting but do not quite link up with the structural part which leaves the manuscript somewhat disconnected. In my view this manuscript requires considerably more work to correlate structure with function, as suggested below, but holds the potential of turning into a highly insightful story.

Conceptual comments:

1. Key observation is that a Ubr5 dimer assembles into higher-order oligomers. The authors speculate that this is functionally relevant, e.g. by the possibility of substrate ubiquitylation occurring within the central cavity of the ring shaped tetramer or ubiquitylation in cis and trans. However, neither significance of Ubr5 oligomerisation nor dynamics/determinants in solution is investigated.
   • In line 100, the authors state that individual oligomeric species could not be separated but do not show data. Why can the species not be separated? Do they exchange? Can exchange be controlled by ionic strength/pH/temperature...?<br /> The authors also suggest that the tetramer is transient (e.g. line 165). What is the evidence for this? Subunit exchange may be tested by mixing different species, e.g, containing labels/tags etc.
   • The authors should design structure-based mutations, particularly within the small, tetrameric interface, and measure oligomerisation state of the mutants to correlate their cryo EM analyses with oligomeric states observed in solution.
   • The authors should also subject individual oligomers (if required, by mutational stabilization of particular states) in activity assays to test their hypotheses.
2. Lines 160-163: "We performed extended 3D classification of the tetramer, which allowed us to confidently dock two models of UBR5 dimers. This revealed that the tetrameric assembly of UBR5 is formed by SBB2 domains of two opposite dimers (Figure 1I)." The idea that the SBB2 domains make up tetrameric interface should be experimentally validated.
3. Lines 311 onward: The authors speculate that oligomeric arrangements of Ubr5 allow for substrate modification in trans and cis, expanding the substrate repertoire. As part of results section, this should be experimentally addressed. Depending on the exchange behaviour of subunits within oligomers, it may be possible to use mixing experiments. Alternatively, they authors may consider comparing Ubr5 ubiquitylation efficiency towards substrates of different sizes, which may allow for better interpretation of the distance restraints they defined.
4. Figure 1J suggests that MLLE domain was modelled, yet the authors note in the text (line 190) that this domain is disordered.
5. The interpretation of the in vitro experiments comparing ubiquitylation of ubiquitin fused akirin2 (substrate) and ubiquitin fused securin (non-substrate) require a re-evaluation: The fact that even ubiquitin fused securin is efficiently ubiquitylated by Ubr5 in vitro shows that a fused ubiquitin molecule is sufficient to recruit a protein for modification by Ubr5 (likely via the UBA domain) in vitro. The specificity of Ubr5 for certain substrates in the cell must therefore follow different (unknown) mechanisms. It is possible that observed, additional affinity between Ubr5 and akirin2 (which is independent of ubiquitin) contributes to this. The available data, however, are insufficient to suggest a hierarchy of interactions (suggested in line 235-237). The interpretation should also be adapted in Figure 6 in which an order of binding events is postulated.
6. Fluorescently labelled deltaGG-ubiquitin is used to monitor free chain formation by Ubr5. Why is this setup used rather than a simple assay with full-length ubiquitin? The rationale/benefit should be clarified.
7. Conformation/functional significance of the plug loop should be validated by mutagenesis.
8. The mass spec data should be presented in a compact supplementary figure or table, in addition to comprehensive data table in Suppl. Table 2.
9. Statements without data backup should be phrased as hypotheses or experimentally validated, e.g.,
   • line 27:"Using cryo-EM processing tools, we observe the dynamic nature of the domain movements of UBR5, which allows the catalytic HECT domain to reach engaged substrates."
   • line 31:"This preference for ubiquitinated substrates permits UBR5 to function in several different signalling pathways and cancers".
   • line 78: "This striking feature allows the positioning of substrate binding sites in close proximity to the catalytic HECT domain in cis or trans, expanding its substrate-recruiting capacities."

Methods section:

• The authors should provide information on how Ubr5 sequence was optimized (line 477).
• The authors should explain why different versions of Ubr5 were used for cryo-EM and activity assays.
• Given the importance of oligomerisation, the authors should explain which fraction of the gel filtration was used for activity assays. Depending on the reversibility of oligomerisation and if oligomerisation impacts activity, the authors should also specify what concentrations these fractions had and/or which concentration they were concentrated to.
• The authors should specify how and at which position cysteine was introduced for labelling of the deltaGG-ubiquitin version.
• The authors should specify which concentrations mass photometry measurements were performed at.
• A description of mass spectrometry measurements is missing.

Results/figures etc:

• Lines 88-91 require more precision: "...highly conserved" - compared to what? Some quantification would help here. "...the length is an average across all species". What is meant by "all species" (all species shown, all metazoans?)?
• Figure 1B shows appears to also show a monomer peak. The authors should label it and comment on it. Can the authors comment on the right shoulder of tetramer peak which was not fitted?
• Figure Legends 1H, 1I, and 1J are missing
• In Figures 1G, 2A and 2B, colour labelling positive/negative is swapped.
• Line 215: "... have observed the formation of free chains". Here, a figure/figure reference is needed.
• Figure 5 C, D, Figure 6: The way models are drawn is very difficult to understand. Maybe the authors could find clearer way to illustrate their hypotheses?

Referees cross-commenting

Reviewer 2 noticed that the manuscript contains a sentence that, in paraphrased form, may have been adopted from a competing manuscript published on Biorxiv some days earlier. According to the conventions of good scientific practice, the competing manuscript should be cited here.

Significance

see above

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Reply to the reviewers

1. General Statements [optional]

We would like to thank the reviewers for taking time in reviewing and commenting on our paper. The comments were very constructive and conscientious, thanks to their expertise in the field. These comments and the revisions would surely make this paper a better and more robust finding in the field.

The comments were about clearer explanations, increasing the quality of the data and additional experiments for a stronger conclusion, all of which we are eager to accomplish. Now we have sorted out the problems and planned the experiments required in the revision, as detailed below.

2. Description of the planned revisions

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

Summary In this manuscript, Komori et al. examined the role of the LRRK2 substrate and regulator Rab29 in the lysosomal stress response. Briefly, in chloroquine (CQ)-treated HEK293 cells the authors observed an apparent LRRK2-independent increased in Rab29 phosphorylation which was accompanied by translocation of Rab29 to lysosomes. Intriguingly, the authors detected a similar increase in Rab29 phosphorylation when Rab29 was tethered to lysosomes in the absence of CQ treatment. Using mass spectrometry, mutagenesis and a phospho-specific anti-body, the authors mapped the CQ-induced phosphorylation site to S185 and demonstrated its independence from LRRK2. Next, the authors found that PKCa was the kinase responsible for S185 phosphorylation and lysosomal translocation of Rab29. Lastly, the authors showed that in addition to PKCa the lysosomal translocation of Rab29 was also regulated by LRRK2. Overall, Komori and colleagues provide interesting new insights into the phosphorylation-dependent regulation of Rab29. However, there are. Number of technical and conception concerns which should be addressed.

Major points 1) Figure 1F: the localization of Rab29 to lysosomes is not convincing at all. The authors should either provide more representative image examples or image the cells at a higher resolution. The authors should also confirm the CQ-induced lysosomal localization of Rab29 in a different cell type (e.g., HEK293).

We will replace Fig 1F pictures with slightly more magnified images with higher resolution. We will also include additional cell types (HEK293, and other cells that are predicted to express endogenous Rab29); Reviewer #2 also raised this point (see Reviewer #2 comment on Significance).

Moreover, the authors should show that prenylation of Rab29 is required for its CQ-induced phosphorylation.

We will test the effect of lovastatin, a HMG-CoA reductase inhibitor that causes the depletion of the prenylation precursor geranylgeranyl diphosphate from cells (Binnington et al., Glycobiology 2016, Gomez et al, J Cell Biol 2019), or 3-PEHPC, a GGTase II specific inhibitor that also causes the inhibition of Rab prenylation (Coxon FP et al, Bone 2005).

2) The rapalog-induced increase in Rab29 phosphorylation in Figure 2D is not convincing since there is at least 2-3-fold more Rab29 in FRB-LAMP1 expressing cells compared to their FRB-FIS1 counterparts. An independent loading control is also missing. This is a key experiment and should be properly controlled and quantified. In addition, can CQ treatment drive 2xFKBP GFP-Rab29 from mitochondria to lysosomes (in the presence of rapalog and FRB-Fis1)?

We will carefully examine another round of rapalog-induced phosphorylation of Rab29, with an independent loading control such as alpha-tubulin. The immunoblot analysis will be made against the intensity of non-p-Rab29. The response to the latter question was described in the section 4 below.

3) Figure 4A-C: Are these stable Rab29 expressing cells? If not, the quantification of "the size of largest lysosome in EACH cell" becomes very problematic. This analysis should be repeated with stable Rab29 variant cells in a background lacking endogenous Rab29. Furthermore, the LAMP1 signal is too dim to see any convincing colocalization (e.g., with WT) or the lack thereof (e.g., in the case of S185D).

The cells shown in Figure 4 are HEK293 cells transiently expressing Rab29, and the issue of quantification was described in the section 3 below. We agree that the signal of LAMP1 was dim, and it turned out that the confocal microscope we used had problems with the sensitivity of the red channels. We will be taking another round of these images with a new confocal microscope.

Lastly, the authors should corroborate their findings with an ultrastructural analysis since the electron microscopy would definitively be more suitable for this type of measurements.

We are planning to obtain electron microscopic images, according to this reviewer’s request. We plan to invite an expert in electron microscopy analysis as a co-author.

4) The lysosomal colocalization of Rab29 in Figure 5C is again not convincing. This analysis needs to be repeated with high resolution imaging.

Again, we will repeat this experiment with a new confocal microscope, with the hope that it would yield better images.

5) The authors need to show the level of LRRK2 depletion (Figure 6). Given the role of LRRK2 in driving lysosomal Rab29 translocation, the importance of the LRRK2 independent pS185 for this process remains unclear.

We will add the level of LRRK2 on its knockdown; we have experienced that LRRK2 knockdown usually occurs with more than 50% efficiency every time. The response to the latter comment was described in the section 3 below.

6) In general, the authors employ an alternative, biochemical assay (e.g., LysoIP) for the lysosomal translocation of Rab29. This would in particular help to clarify the effect of the Rab29 variants and LRRK2 inhibition.

We have previously shown that the overexpressed Rab29 (and LRRK2) is enriched in the lysosomal fraction from CQ-treated cells, which was performed using dextran-coated magnetite (Eguchi et al, PNAS 2018). Using the same biochemical method, we will show the enrichment of endogenous Rab29 in the lysosomal fraction.

Minor points

9) Figure 2C is lacking the control IF staining for mitochondria (to which 2xFKBP-GFP-Rab29 is assumed be recruited upon co-expression with FRB-FIS1).

We will stain the cells with MitoTracker to ensure that anchoring away of 2xFKBP-GFP-Rab29 by FRB-Fis1 results in mitochondrial localization.

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

The data in the manuscript convincingly demonstrates that lysosomal overload by Chloroquine treatment induces Rab29 localisation to the lysosomes and that this membrane association is dependent on PKCalpha-dependent phosphorylation at Ser185.

We have a number of rather minor comments listed below:

Figure 2

The increasing levels of non-phosphorylated Rab29 over the indicated time course of AP21967 treatment in Figure 2B are concerning. First, could you provide an explanation for this clear increase in both non-p-Rab29 and p-Rab29 in the phostag but not the normal gel? Second, could all quantifications of p-Rab29 be made relative to the non-p-Rab29?

We will try another round of rapalog-induced phosphorylation of Rab29, with an independent loading control. The immunoblot analysis will be made against the intensity of non-p-Rab29. Reviewer #1 raised a similar concern on Figure 2D.

Figure 5

To further demonstrate that PKCalpha phosphorylates endogenous Rab29 at Ser185, we recommend reperforming the Go3983/PMA treatment in figure B with the anti-p-Ser185 antibody. It may be sufficient to perform the treatment only at 4 or 8 hours, simply to provide stronger evidence regarding the phosphorylation of endogenous Rab29.

We will give a try, although the anti-phosphorylated protein antibodies that we tried never worked for phos-tag SDS-PAGE. With the conventional western blot, we will be able to try this experiment.

It is not clear whether the activity of PMA in the assay is due to inhibition of PKCalpha. Are the effects ablated by PKCalpha KD

We will test the knockdown of PKCalpha, beta, gamma and delta by siRNAs to further narrow down the effects of PKC-dependent phosphorylation of Rab29.

Reviewer #2 (Significance (Required)):

These cell biology findings are important in the field as both Rab29 and LRRK2 are implicated in the pathogenesis of Parkinson disease. The phosphorilation of Ser185 of Rab29 by PKCalpha is novel and contributes to our understanding of Rab29 and LKRR2 regulation. One limitation of the study is that is conducted in only two cell types quite unrelated to the disease, so how general and disease relevant are the findings it is not clear. Most of the data are solid. There are two experiments whose results are difficult to interpret and a few controls missing. Also a few issues with quantifications, all of which is described in details above and will need to be fixed prior to publication. My expertise for this paper is in the cell biology of lysosomal function.

The issue that only two cell types were analyzed was also raised by reviewer #1, so we will examine additional cell types, especially those that are predicted to express endogenous Rab29. Our responses to other issues raised are described elsewhere. Thank you for these insightful comments.

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

Figure 4A-C: Are these stable Rab29 expressing cells? If not, the quantification of "the size of largest lysosome in EACH cell" becomes very problematic. This analysis should be repeated with stable Rab29 variant cells in a background lacking endogenous Rab29. (Reviewer #1)

As described in the section 2 above, the cells shown in Figure 4 are HEK293 cells transiently expressing Rab29. We are sorry that the description “the size of largest lysosome in each cell” was misleading. As we analyzed only cells overexpressing GFP-Rab29 that were marked with GFP fluorescence, we believe that transient expression should not be a problem. To avoid any misunderstandings, we have described in Figure 4 legends that only lysosomes in Rab29-positive cells (and all cells expressing Rab29) were included in the analysis of the largest lysosome of each cell.

Regarding the effect of endogenous Rab29 in Figure 4 experiments, Reviewer #2 similarly raised the issue on whether Rab29 phosphomimetics are acting as dominant active, preventing lysosomal enlargement. On this point, we have previously reported that knockdown of endogenous Rab29 causes the enhancement of lysosomal enlargement upon CQ treatment (Figure 5I,J of Eguchi et al, PNAS 2018), suggesting that the lysosome-deflating effect by phosphomimetics is a dominant active effect rather than dominant negative suppressing endogenous Rab29. This point is considered significant, and thus has been explained in the results section (page 7, lines 168-171).

Along similar lines: why not all cells in Figure 5E and Figure 5G show Rab29- and LRRK2-positive structures? How do the authors know which of these phenotypes is the prevalent one? (Reviewer #1)

As for the ratio of cells with Rab29- and LRRK2-positive structures, it seems reasonable given that different cells have different levels of exposure to lysosomal stress and that the response is transient and does not occur simultaneously. The ratio of these positive cells may also vary depending on the cell culture conditions. Since Rab29- and LRRK2-positive structures are rarely seen in control cells, we think this would be a meaningful phenotype even if only 20-30% of cells show such structures. The result that the ratio of localization changes is not 100% is now noted in the results section explaining Figure 1G (page 4-5, lines 108-110) where the immunocytochemical data first appears.

Given the role of LRRK2 in driving lysosomal Rab29 translocation, the importance of the LRRK2 independent pS185 for this process remains unclear. (Reviewer #1)

Our data suggested that Rab29 is stabilized on lysosomes only when LRRK2-mediated phosphorylation and S185 phosphorylation both occur on Rab29 molecule (as shown in Figure 7 scheme), so we believe there is no contradiction. We have now described more clearly about this notion at the end of the results section (page 9, lines 235-236).

It is not clear what the authors mean by "lysosomal overload stress". Since mature lysosomal incoming pathways such as autophagy or endocytosis are disrupted by CQ, it is difficult to picture an overload. Maybe rephrasing would help to clarify this. (Reviewer #1)

Chloroquine (CQ) is known as a lysosomotropic agent that accumulates within acidic organelles due to its cationic and amphiphilic nature, causing lysosome overload and osmotic pressure elevation, and this is what we call “lysosomal overload stress”. The well-known effects of CQ to disrupt lysosomal incoming pathways are ultimately caused by the above consequences. Also, we have previously reported that lysosomal recruitment of LRRK2 is caused by CQ but not by bafilomycin A1, the latter being an inducer of lysosomal pH elevation, or by vacuolin-1 that enlarges lysosomes without inducing lysosomal overload/pH elevation (Eguchi et al, PNAS 2018), and further found that not only CQ but also other lysosomotropic agents commonly induced LRRK2 recruitment (Kuwahara et al, Neurobiol Dis 2020). We thus have described the effect of CQ as “overload”. However, it is true that we have not provided a clear explanation for readers, so we have added some notes for lysosomal overload stress in the introduction section (page 3, lines 69-71).

Which cell type is used for the IF analysis in Figure 2C? This information is in general quite sparse. The authors should clearly state the cell type for each experiment/Figure. (Reviewer #1)

We have added cell type information that was missing in several places in the manuscript. We are very sorry for the inconveniences. For clarification, HEK293 cells were used in Figure 2C.

Are the images in figure 1F representative? i.e. does Rab29 always colocalise to such enlarged lysosomes upon CQ treatment and does CQ treatment always drastically alter the cellular distribution of Rab29? (Reviewer #2)

The images in Figure 1F are representative of when Rab29 is recruited, but it is not seen in all cells, and the ratio of recruitment (~80%) is shown in Figure 1G. Reviewer #1 also asked why Rab29 recruitment is not seen in all cells, and we gave the same answer above. It may be reasonable to speculate that different cells have different levels of exposure to lysosomal stress and that the response is transient and does not occur simultaneously. The ratio of these positive cells may also vary depending on the cell culture conditions. For the readers’ clarity, we have added that the ratio of localization change of Rab29 is not 100% and is comparable to that of LRRK2 previously reported (page 4-5, lines 108-110).

Considering that the "forced localisation technique" induces a non-physiological colocalization of non-endogenous Rab29 to lysosomes, it may be an overestimation to conclude just from these data that phosphorylation of Rab29 occurs on the lysosomal surface. This is also quite in contrast with the later finding that phosphorylation by PKCalpha promotes lysosome localization of Rab29. It seems more reasonable to conclude that Rab29 can be phosphorylated when localised at the lysosomes (as opposed to other organelles such as mitochondria). If the authors feel strongly about this point they might need to find a less non-physiological assay. (Reviewer #2)

Yes, it could be an overestimation, and as we do not have better means to conduct a less non-physiological assay, we have modified the description from “occurred on the lysosomal surface” to “could occur on the lysosomal surface” (page 5, line 112 (subtitle) and line 128).

Regarding the comparison with the later finding that phosphorylation by PKCalpha promotes lysosome localization of Rab29, these data (Figure 2 and 5) could be explained with a single speculation: phosphorylation of Rab29 on lysosomal membranes could retain Rab29 on the membranes for a longer time. It is not easy to decipher which comes first, association with membranes or phosphorylation of Rab29, in a physiological assay, but considering reports that show PKCalpha activation happens on membranes (Prevostel et al., J Cell Sci 2000), at least the data favor our conclusion over the idea of PKCalpha phosphorylating Rab29 in the cytoplasm and then promoting lysosomal localization. This point is now clearly described in the discussion (page 10, lines 248-251).

It is not clear how the Rab29 phosphomimetics are acting as dominant active preventing lysosomal enlargement. Authors should speculate or repeat the experiments in absence of endogenous Rab29 to clarify the matter. (Reviewer #2)

A similar concern about the effect of endogenous Rab29 was also raised by Reviewer #1 (see above). We have previously reported that knockdown of endogenous Rab29 causes the enhancement of lysosomal enlargement upon CQ treatment (Figure 5I,J of Eguchi et al, PNAS 2018), suggesting that the lysosome-deflating effect by phosphomimetics is a dominant active effect rather than dominant negative suppressing endogenous Rab29. This point is considered important and thus has been explained in the results section (page 7, lines 168-171).

Overall, there is some missing information regarding repeats for Western blots, such as those in figure 3C, 3D and 3E. Please add indications about repeats in the figure legend or methods. (Reviewer #2)

We have added the repeat information to each figure legend where it was missing. We are very sorry for the inconveniences.

The model in figure 7 however seems to suggest that Rab29 associates to lysosomal membranes independently, and is then stabilised at the membranes by LRRK2 and PKCalpha - a point which is not directly supported by the data. (Reviewer #2)

As noted earlier, we consider that phosphorylation of Rab29 on lysosomal membranes could retain Rab29 on the membranes for a longer time, given the present data and previous reports that phosphorylation of Rab29 is more likely to happen on the lysosomal membrane than in the cytosol. Also, as inhibition of either of the two phosphorylations ends up in disperse Rab29 localization, we have made this figure as a model of what is plausible right now. This explanation is now added in the discussion (page 10, lines 248-251).

English proofreading should be improved: "CQ was treated to HEK293" (page 4), "As we assumed that this phosphorylation is independent of LRRK2" as an opening line (page 5) (Reviewer #2)

Thank you for pointing out these incorrect wordings. They were corrected.

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

In addition, can CQ treatment drive 2xFKBP GFP-Rab29 from mitochondria to lysosomes (in the presence of rapalog and FRB-Fis1)? (Reviewer #1)

We do not think that a comparison between the affinities of FKBP-rapalog-FRB and Rab29-[unknown factor that directs Rab29 to lysosomes] is necessary, as the former has a Kd in the single digit nM range (Banaszynski et al, JACS 2005), whereas the latter (based on estimations from related PPIs) is estimated to be in the μM range, which shows a much weaker affinity than the former (McGrath et al, Small GTPases 2019). Furthermore, even if Rab29 appears to have migrated from mitochondria to lysosomes as a result of this experiment, one cannot rule out the possibility that a small portion of the mitochondrial membrane was incorporated into the lysosomal membrane that was enlarged by CQ treatment.

Molecular weight markes should be provided for all immunoblot experiments. (Reviewer #1)

The immunoblot pictures without molecular weight markers in our paper are all Phos-tag SDS-PAGE blot analyses. Phos-tag SDS-PAGE results in band shifts of phosphorylated proteins, and writing in markers would be misleading. Moreover, previous representative studies heavily using Phos-tag (e.g., Kinoshita et al, Proteomics 2011, Ito et al, Biochemical Journal 2016) also did not show the molecular weight markers. Here we performed phos-tag SDS-PAGE analysis only to find differences in the phosphorylation state of Rab proteins.

The use of the quantification ratio of cells with Rab29-positive lysosomes in figure 1G might be slightly misleading as it does not allow the reader to understand to what extent Rab29 localisation at lysosomes upon CQ treatment. We recommend using a simpler quantification, such as by measuring the average colocalisation of Rab29 and LAMP1 per cell. (Reviewer #2)

For figure 5D and 5F, As with figure 1G, we recommend using a more straightforward and impartial method of quantification such as simply measuring the colocalisation of Rab29 with LAMP1. (Reviewer #2)

Popular colocalization analyses using Pearson’s or Mander’s coefficients would be a good choice if the amounts of Rab29 varied greatly between lysosomes. However, this may not apply in this case; the amount of Rab29 or LRRK2 on each lysosome is considered to saturate quickly and a relatively low amount of them may not be detected on immunofluorescence observations, whereas the probability of finding these structures has been shown to exhibit a moderate sigmoid curve (as seen in Figure 1E or 2H of Eguchi et al., PNAS 2018). Therefore, the amount of Rab29 or LRRK2 could be approximated to a Bernoulli distribution in terms of colocalization with lysosomes, and this is the reason why we chose to quantify “the ratio of cells with Rab29-positive lysosomes”.

We recommend using a more transparent and simple quantification method, such as average size of lysosomes per cell. (Reviewer #2)

As one can see in the inset of Figure 4B, unenlarged lysosomes are unfortunately too small for the quantification of their size, much less tell two small lysosomes apart in our experimental settings and laboratory resources, so we decided to analyze the largest lysosome in each cell as a representative of the cells to minimize measurement errors. This measurement only includes GFP-Rab29 positive cells, and by comparing against CQ-untreated cells we intended to increase the validity of this analysis. This quantification method was also used in our previous report (Eguchi et al, PNAS 2018).

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Referee #2

Evidence, reproducibility and clarity

The data in the manuscript convincingly demonstrates that lysosomal overload by Chloroquine treatment induces Rab29 localisation to the lysosomes and that this membrane association is dependent on PKCalpha-dependent phosphorylation at Ser185.

We have a number of rather minor comments listed below:

Figure 1

• Are the images in figure 1F representative? i.e. does Rab29 always colocalise to such enlarged lysosomes upon CQ treatment and does CQ treatment always drastically alter the cellular distribution of Rab29?
• The use of the quantification ratio of cells with Rab29-positive lysosomes in figure 1G might be slightly misleading as it does not allow the reader to understand to what extent Rab29 localisation at lysosomes upon CQ treatment. We recommend using a simpler quantification, such as by measuring the average colocalisation of Rab29 and LAMP1 per cell.

Figure 2

• Considering that the "forced localisation technique" induces a non-physiological colocalization of non-endogenous Rab29 to lysosomes, it may be an overestimation to conclude just from these data that phosphorylation of Rab29 occurs on the lysosomal surface. This is also quite in contrast with the later finding that phosphorylation by PKCalpha promotes lysosome localization of Rab29. It seems more reasonable to conclude that Rab29 can be phosphorylated when localised at the lysosomes (as opposed to other organelles such as mitochondria). If the authors feel strongly about this point they might need to find a less non-physiological assay.
• The increasing levels of non-phosphorylated Rab29 over the indicated time course of AP21967 treatment in Figure 2B are concerning. First, could you provide an explanation for this clear increase in both non-p-Rab29 and p-Rab29 in the phostag but not the normal gel? Second, could all quantifications of p-Rab29 be made relative to the non-p-Rab29?

Figure 3

• It is not clear how the Rab29 phosphomimetics are acting as dominant active preventing lysosomal enlargement. Authors should speculate or repeat the experiments in absence of endogenous Rab29 to clarify the matter.
• Overall, there is some missing information regarding repeats for Western blots, such as those in figure 3C, 3D and 3E. Please add indications about repeats in the figure legend or methods.

Figure 4

• We recommend using a more transparent and simple quantification method, such as average size of lysosomes per cell.

Figure 5

• To further demonstrate that PKCalpha phosphorylates endogenous Rab29 at Ser185, we recommend reperforming the Go3983/PMA treatment in figure B with the anti-p-Ser185 antibody. It may be sufficient to perform the treatment only at 4 or 8 hours, simply to provide stronger evidence regarding the phosphorylation of endogenous Rab29.
• It is not clear whether the activity of PMA in the assay is due to inhibition of PKCalpha. Are the effects ablated by PKCalpha KD
• For figure 5D and 5F, As with figure 1G, we recommend using a more straightforward and impartial method of quantification such as simply measuring the colocalisation of Rab29 with LAMP1.

Figure 6

• Again, we recommend altering the methods of quantification

Figure 7

• The model in figure 7 however seems to suggest that Rab29 associates to lysosomal membranes independently, and is then stabilised at the membranes by LRRK2 and PKCalpha - a point which is not directly supported by the data.

English proofreading should be improved: "CQ was treated to HEK293" (page 4), "As we assumed that this phosphorylation is independent of LRRK2" as an opening line (page 5),

Significance

These cell biology findings are important in the field as both Rab29 and LRRK2 are implicated in the pathogenesis of Parkinson disease. The phosphorilation of Ser185 of Rab29 by PKCalpha is novel and contributes to our understanding of Rab29 and LKRR2 regulation. One limitation of the study is that is conducted in only two cell types quite unrelated to the disease, so how general and disease relevant are the findings it is not clear. Most of the data are solid. There are two experiments whose results are difficult to interpret and a few controls missing. Also a few issues with quantifications, all of which is described in details above and will need to be fixed prior to publication. My expertise for this paper is in the cell biology of lysosomal function.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this manuscript, Komori et al. examined the role of the LRRK2 substrate and regulator Rab29 in the lysosomal stress response. Briefly, in chloroquine (CQ)-treated HEK293 cells the authors observed an apparent LRRK2-independent increased in Rab29 phosphorylation which was accompanied by translocation of Rab29 to lysosomes. Intriguingly, the authors detected a similar increase in Rab29 phosphorylation when Rab29 was tethered to lysosomes in the absence of CQ treatment. Using mass spectrometry, mutagenesis and a phospho-specific anti-body, the authors mapped the CQ-induced phosphorylation site to S185 and demonstrated its independence from LRRK2. Next, the authors found that PKCa was the kinase responsible for S185 phosphorylation and lysosomal translocation of Rab29. Lastly, the authors showed that in addition to PKCa the lysosomal translocation of Rab29 was also regulated by LRRK2. Overall, Komori and colleagues provide interesting new insights into the phosphorylation-dependent regulation of Rab29. However, there are. Number of technical and conception concerns which should be addressed.

Major points

1. Figure 1F: the localization of Rab29 to lysosomes is not convincing at all. The authors should either provide more representative image examples or image the cells at a higher resolution. The authors should also confirm the CQ-induced lysosomal localization of Rab29 in a different cell type (e.g., HEK293). Moreover, the authors should show that prenylation of Rab29 is required for its CQ-induced phosphorylation.
2. The rapalog-induced increase in Rab29 phosphorylation in Figure 2D is not convincing since there is at least 2-3-fold more Rab29 in FRB-LAMP1 expressing cells compared to their FRB-FIS1 counterparts. An independent loading control is also missing. This is a key experiment and should be properly controlled and quantified. In addition, can CQ treatment drive 2xFKBP GFP-Rab29 from mitochondria to lysosomes (in the presence of rapalog and FRB-Fis1)?
3. Figure 4A-C: Are these stable Rab29 expressing cells? If not, the quantification of "the size of largest lysosome in EACH cell" becomes very problematic. This analysis should be repeated with stable Rab29 variant cells in a background lacking endogenous Rab29. Furthermore, the LAMP1 signal is too dim to see any convincing colocalization (e.g., with WT) or the lack thereof (e.g., in the case of S185D). Lastly, the authors should corroborate their findings with an ultrastructural analysis since the electron microscopy would definitively be more suitable for this type of measurements.
4. The lysosomal colocalization of Rab29 in Figure 5C is again not convincing. This analysis needs to be repeated with high resolution imaging. Along similar lines: why not all cells in Figure 5E and Figure 5G show Rab29- and LRRK2-positive structures? How do the authors know which of these phenotypes is the prevalent one?
5. The authors need to show the level of LRRK2 depletion (Figure 6). Given the role of LRRK2 in driving lysosomal Rab29 translocation, the importance of the LRRK2 independent pS185 for this process remains unclear.
6. In general, the authors employ an alternative, biochemical assay (e.g., LysoIP) for the lysosomal translocation of Rab29. This would in particular help to clarify the effect of the Rab29 variants and LRRK2 inhibition.

Minor points

1. It is not clear what the authors mean by "lysosomal overload stress". Since mature lysosomal incoming pathways such as autophagy or endocytosis are disrupted by CQ, it is difficult to picture an overload. Maybe rephrasing would help to clarify this.
2. Which cell type is used for the IF analysis in Figure 2C? This information is in general quite sparse. The authors should clearly state the cell type for each experiment/Figure.
3. Figure 2C is lacking the control IF staining for mitochondria (to which 2xFKBP-GFP-Rab29 is assumed be recruited upon co-expression with FRB-FIS1).
4. Molecular weight markes should be provided for all immunoblot experiments.

Significance

Please see above.

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Reply to the reviewers

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

Basier and Nurse revisit the fundamental question of how the rates of RNA and protein synthesis scale with cell size. The strong null hypothesis is that synthesis scales linearly with cell size: cells that are twice as big should make stuff twice as fast. This hypothesis has been tested many times, in many systems, using many approaches over the past century and, in general, the null hypothesis has been sustained. However, there have been many examples of evidence for more complicated synthetic patterns. Whether these results indicate that biosynthesis rates vary across the cell cycle, or in response to other factors, in addition to increasing with cell size, or whether observed deviations from the predictions of the null hypothesis has been due to artifacts of cell synchronization and labeling, is thus an open, interesting and, because biosynthesis rates have critical implications in cellular function and metabolic robustness, important question.

The authors address the question in fission yeast using metabolic pulse labeling with a ribonucleoside or amino acid analog in asynchronous cells and single cell analysis to directly compare incorporation levels with cell size and cell cycle stage. The experiments are well designed, well executed and well controlled. Furthermore, the data is well presented and appropriately interpreted. In particular, the presentation of the size-v.-label data in Figures 2A and D, with the averages and variances in 2B and E and the normalized data in 2C and F are easy understand and interpret. It is thus notable that the size-v.-label data for the longer (cdc22-22) cells is omitted in favor of just the average (2H,J) and normalized (2I,K) data. This size-v.-label data should be added to Figure 2.

We added two panels to the Figure supplementary 2 showing the requested data, the size-v.-global translation (S2E) and size-v.-global transcription (S2F).

The authors should also explicitly state how they chose 15 µm as the inflection point in 2H; 16-17 µm seems like it would give a horizontal plateau, which would better fit their saturation explanation.

This comment relates to the second comment of reviewer 4, see below for the detailed answer.

The authors measure DNA content with a DNA-binding dye, the signal from which should linearly scale with DNA content. However, instead of reporting and analyzing total signal from the DNA-binding dye (or better yet, total signal in the nucleus, which they could do, having segmented the nucleus in their images), they report max signal. Using max signal is complicated because, as cells and thus nuclei increase in size the concentration of DNA and thus the max (but not total) DNA-binding-dye signal in in the nucleus decreases, requiring two-dimensional dye/size analysis (such as shown in Figure 3B) to distinguish G1 and G2 cells. The authors should use the more straight forward measure of total nuclear DNA-binding dye signal, or explicitly explain why they can't or prefer not to do so.

The total fluorescence intensity signal of the DNA-binding dye is noisy because we had to use a low concentration of the dye. This was necessary as it allows a clearer distinction between cells with a one 1C DNA content and cells with a 2C DNA content that higher concentrations did not. The maximum signal per cell-v.-cell length produces distinct populations of cells in G1, or G2/M phase (see Figure 3H, and Figure 4B), and populations identified in this way have the distributions of total fluorescence intensity expected from cells in G1 and G2 or M phase (see Figure 3I and Figure S4D). We added one extra panel to Supplementary Figure 4 showing the distributions of the total fluorescence intensity signal of the DNA-binding dye for the G1, S, and G2 or M populations (S4D) for comparison.

The authors should state in figure legends the strain numbers used for all experiments.

We have modified all the figure legends to include the strain numbers.

They should also cite the source of all the constituent parts (e.g. hENT1, hsvTK, EGFP-pcn1, and synCut3-mCherry) of their strains.

The missing reference for the source of hENT1 and hsvTK (Sivakumar et al. 2004) has been added, the references for EGFP-pcn1 (Meister et al., 2003) and synCut3-mCherry (Patterson et al., 2021) were already present.

CROSS-CONSULTATION COMMENTS My colleagues make constructive points. I agree with all of them, although I am less concerned about the use of cdc2-22 and CCP∆ to alter cell length and cell cycle distribution. Although these mutations alter CDK specific activity (and thus length and distribution) and could alter specific patterns of translation, the fact that they double at normal rates makes it seem unlikely that they could be significantly changing bulk synthesis rates.

Reviewer #1 (Significance (Required)):

As noted above, this work addresses an open, interesting and important question. Moreover it provides useful data in a specific system and a useful example of a general experimental approach to the problem. However, it does not settle the question of how biosynthesis scales with size, even in the specific case of fission yeast. In particular, it shows that protein synthesis plateaus just above normal cell size, whereas RNA synthesis scales up to twice normal cell size. This observation is striking, because there is no obvious mechanism that would (and the authors offer no suggestion of how to) explain how protein synthesis could be limited if RNA synthesis is not. Therefore, the strength of the paper is that it identifies an intriguing phenomena and its limitation is that it does not provide any testable hypotheses to explain that phenomena.

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

Summary: Basier and Nurse investigate how "cell size, the amount of DNA, and cell cycle events affect the global cellular production of proteins and RNA molecules". Both transcription and translation, driving the production of biomass, have been shown to increase as a function of cell size in various systems. However, whilst cell size generally correlates with cell cycle progression there are inconsistent results in the literature if global cellular translation and transcription is affected by cell cycle state. They argue that this might be due to perturbations induced by different synchronisation methods used in the various studies.

Therefore, in this study, to avoid potential perturbation from synchronisation methods, they developed a system that allows to assay unperturbed exponentially growing populations of fission yeast cells. The assay is based on single-(fixed)cell measurements of cell size, cell cycle stage, and the levels of global cellular translation and transcription. This allows them to correlate cell cycle state, cell size and global cellular translation and transcription levels at the single cell level under unperturbed conditions.

Their results show that translation and transcription steadily increase with cell size, but that the rate of translation, but not transcription, becomes rapidly restricted when cells become larger than wild type dividing cells. This suggests that it is unlikely that the synthesis of RNA is the limiting factor for translation rate in large cells. In addition, their data indicates that translation scales with size, but that the rate increases faster at late S-phase/early G2 and even faster in early in mitosis before decreasing in mitosis and return to interphase. Transcription, on the other hand, increases as a combination of size and the amount of DNA. Overall, this suggests that cell cycle control affects global cellular translation and transcription, which is in line with some studies, but not others. As far as I can tell the assays and data analysis are robust and the data supports the general conclusions.

Major comments I agree that inconsistent results published on this topic might be due to perturbations induced by different synchronisation methods used in the various studies. However, but much less emphasised in the paper, it also likely depends on the model system used. For example, in budding yeast there is strong evidence for gene expression homeostasis, i.e. gene expression increases as a function of size, independent of gene copy number. Do the authors believe this is a budding yeast specific phenomenon or is this a consequence of specific synchronisation methods used in budding yeast?

Gene expression homeostasis has been suggested for budding yeast, but in contrast recent work in budding yeast also suggests that gene expression increases with the genome copy number and therefore the gene copy number in addition to cell size (Swaffer et al., 2022 – currently on bioRxiv). The differences that have been reported might be due to perturbations such as synchronisation methods as well as differences between yeast species.

Whether growth rate increases linearly or exponentially has been the topic of decade long debates. Their data indicates that the translation rate increases faster at late S-phase/early G2 and even faster early in mitosis before decreasing in mitosis and return to interphase, 'resetting' the growth rate. This suggest an exponential, rather than linear, increase in biomass (i.e. growth rate?), but this is not explicitly pointed out. It would be good to get the authors opinion on this in the discussion.

Assuming that protein degradation remains constant throughout growth, the increase of translation with cell size suggests that the growth rate increases as cells grow in size, possibly exponentially. In addition, our data showing that the translation rate increases from G1 to G2 for the same cell size, suggests that for cells of a given size the growth rate is faster in G2 than in G1. Thus, growth could be basically exponential but the speed of increase accelerates at the transition between S and G2, and early in mitosis, slowing down later in mitosis. We added the following sentence to the discussion section “Global transcription and translation increase with cell size possibly exponentially, but the changes in global translation during transitions through cell cycle stages suggest that the speed of growth is modulated by cell cycle progression, increasing between S and G2, and early in mitosis, and slowing down later in mitosis.”.

The authors state that their approach has allowed them to determine how cellular changes are arising from progression through the cell cycle. However, they use fixed cells, rather than live cell imaging, so can't claim to have established changes during cell cycle progression, but only a correlation with cell cycle state/phase. Whilst this could be used as a proxy for progression it should be clearly stated in the abstract and elsewhere to prevent confusion. I for one, based on the abstract, thought they developed a live cell imaging strategy to look at this.

We have modified the abstract to reflect the fact that the cells were fixed in our assays (line 36).

In reference to the Stonyte, et al., study, in addition to different conditions (temperature shift and isoleucine medium), why do the authors think their findings are different? Is it the lack of correlation to cell size in the Stonyte paper or something else? For example, would using different growth conditions (as in the Stonyte paper). where fission yeast cells spend more time in G1, be used instead of the CCP mutant? Can the authors exclude that the lack of G1-S/cyclin-CDKs is not at the basis of a lower rate of translation in G1 and S phase cells? Either these experiments should be carried out or this should be discussed in more detail.

In the study carried out by Stonyte et al., the relative translation rate per cell (a measurement related to our measurement of translation normalised per unit of length) of wild type fission yeast cells grown asynchronously in isoleucine minimal medium is constant between the G1 and the S phase cell populations, and is higher in the G2 population compared to the S phase population (Figure 2D of Stonyte et al., 2018). This is consistent with the lack of increase that we observe for a given cell size from G1 to G2, and the increase we observe from S to G2 in Figure 3K. In the same figure, Stonyte at al., find no difference between the G2 and the M-G1 populations but are not able to distinguish cells at different stages of mitosis or in early G1. Our study suggests that translation increases early in mitosis before decreasing after anaphase A, thus in the Stonyte et al study, pooling all stages of mitosis and early G1 cells might mask the dynamics of what is happening during mitosis. The lack of G1-S/cyclin-CDK could be the basis for the lower rate of translation in G1 and S-phase. We discuss this further in a reply to the first question of the significance part of reviewer 2 and have added a section to the discussion of the paper (see below for details).

If the signal to noise signal is reduced by 20 minutes EU incubation (rather than 10 minutes) why wasn't it used in all experiments?

To measure RNA production as closed as possible to the instantaneous rate of RNA synthesis, we sought to use the shortest pulse possible. We did this because the half-lives of some RNA species are short, in particular, the half-life of the pool of mRNA has been reported to be around 13.1 minutes in budding yeast (Chan et al., 2017). In longer pulses, some RNA molecules that have been synthesised after addition of EU will therefore have been degraded before cells are fixed, producing a measurement that underestimates the rate of RNA synthesis. We chose to incubate cells for 10 minutes as we estimated it to be the shortest time generating a signal to noise ration above 1 (Figure 1F). The one exception to this was with the pulsing of the CCP∆ EGFP-pcn1 hENT1 hsvTK mutant cells which incorporates less EU during the same time frame so we incubated this strain for 20 minutes to generate enough signal to be quantifiable (see line 237, “we assayed CCP∆ EGFP-pcn1 hENT1 hsvTK cells for global transcription using a 20-minute EU incubation to compensate for their lower signal production”).

And the conclusion that the increase in transcription is not showing any discontinuities, are they referring to the triplicates in the supplementary figure 2?

We think there might be a misunderstanding. We conclude that the increase in transcription shows no discontinuity because the median transcription increases steadily with cell length in Figure 2E. We have added “since global transcription increases smoothly with cell length (Figure 2E)” to clarify the text.

Minor comments Lines 168-169: should be Figure 2F, S2C, S2D rather than Figure 2C, S2A, S2B.

The figure numbers have been corrected in the manuscript.

Line 179: doubling time instead of growth rate?

The mention of “growth rate” has been changed to “doubling time” in the manuscript.

Lines 184-186: There is an overall trend of slight decrease in transcription per length in cdc25-22 cells but a slight increase in wild-type cells. How does this differ to wild-type cells? Are these non-significant changes and could these be attributed to the low signal to noise ratio?

These changes may be due to the low signal to noise ratio in the cdc25-22 transcription assay. We have added “The decrease with cell length in transcription that we observe in the cdc25-22 hENT1 hsvTK (Figure 2K) cells but not in the hENT1 hsvTK cells (Figure 2F) may be due to the low signal to noise ratio”.

There is no cell size that is specific to S phase, it falls within the range of G1 and G2 cells. Since this strain has a variable onset of S phase, the phase durations could differ. Therefore, could time spent in each phase affect the translation rate (live cell imaging, i.e. progression, could address this, but not fix cell correlation)?

It is possible that the phase duration of G1 and G2 could differ from one cell to another. There is no evidence that the length of S-phase varies in these cells. It would be interesting to measure how the phase length influences translation, but our techniques do not allow for the measurement of global translation in living cells.

The data reflects translation/transcription in single cells at a specific cell cycle phase, not during the transition between cell cycle phases. Therefore, it would be more appropriate to only use G1, S, G2 and M rather than S/G2 transition or early G2.

Our data represents cells at fixed cell cycle phases and we do not monitor the transition themselves directly. However, the discontinuity in signal for cells of the same size in consecutive stages of the cell cycle (for instance the discontinuity in translation between S and G2 cells of the same size in Figure 3J) is indicative that the transition between the two cell cycle phases is a consequence of a rate change.

In figure 4C, there is a decrease in global transcription after 13 um (black line showing all cells), which they don't see in cdc25-22 mutants. Their conclusion that global transcription is constantly increasing with cell size is based on cdc-25 cells but the experiment in CCP mutant cells shows a decrease in the median of transcription. Are there replicates for these experiments as in figure 2 and supplementary figure S2? Maybe an average trend can be plotted too? Apart from the first set of experiments (figure 2 and supplementary figure 2), they don't show replicates for other strains. Maybe they can include another graph as in figure 3D and 3K of average replicate values?

The apparent decrease in transcription on Figure 4C in long cells is seen in only one length bin (13.5 µm), which has a smaller number of cells compared to the ones directly before (89 cells, compared to 216 cells for the 12.5 µm bin and 316 cells for the 11.5 µm bin). This might have resulted in a higher variability in the measurement of the population median. We do not see the same decrease at 13.5 µm in the wild type (Fig 5G), the cdc25-22 mutant (Fig 2J), or the CCP∆ strain (Fig S4B) so on balance we favour the interpretation that the decrease observed in the longer length bin of Figure 3J is due to variability caused by the lower number of cells in that bin.

CROSS-CONSULTATION COMMENTS I believe that since the whole premise of this study is that by using unperturbed conditions their findings are different from previously published work they should either clearly point out that this difference might be due to using mutations affecting CDK activity or carry out an experiment in media that induces a G1 population. CDK has been strongly implicated in promoting translation. Using a strain that lacks the G1 and S cyclin CDKs or compromised M-CDK is therefore likely to have an effect on translation, which could be at the basis of the increase in translation during the G2 (and S) phase of the cell cycle.

This is addressed in the next comment.

Reviewer #2 (Significance (Required)):

As far as I can tell the assays and data analysis are robust and the data supports the general conclusions. However, whilst the cells are assessed in unperturbed conditions, they do use CDK mutants and the cdc25ts mutant to establsih gene expression during the different phases of the cell cycle, which could affect translation/transcription rates. This should either be clearly pointed out or complemented with an experiment where WT cells are grown in conditions that induces distinct G1-S-G2 populations of cells.

The cell cycle stage and CDK activity are intrinsically linked. CDK activity defines the cell cycle stage so that an increase in CDK activity through the cell cycle is responsible for cells progressing through G1, S, G2, and mitosis (Coudreuse and Nurse, 2010, Swaffer et al., 2016). Nutritional conditions that induce a G1 also rely on repression of CDK activity through increased production of the Rum1 inhibitor (Rubio et al., 2018) to generate a G1 population. Therefore, uncoupling CDK activity from the cell cycle would not be possible in an unperturbed cell population. We have added the following paragraph to the discussion to address the comment “The cell cycle stage of a cell and the activity of its CDK molecules are intrinsically linked since CDK activity defines the cell cycle stage of a cell. CDK activity increases through the cell cycle and is responsible for cells progressing through G1, S, G2, and mitosis [44,53] so that an unperturbed asynchronous population of cells in G1 is achieved by a low CDK activity. Thus our results reflect changes happening through the cell cycle as the CDK regulation network undergoes modifications, and an unperturbed cell cycle therefore cannot be uncoupled from CDK activity.”.

Overall, the work presented suggests that cell cycle control affects global cellular translation and transcription, which is in line with some studies, but not others. Whilst the study falls short of testing/establishing the (potential) mechanisms involved, these are important findings, which can be used to guide new studies into how the production of biomass is controlled as cells proceed through the cell cycle.

The cell size field, which is considerable and growing, will be interested in this work.

I have expertise in cell cycle control and genome stability, with a focus on the G1-to-S transition and cell cycle checkpoints during interphase.

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

Summary Basier and Nurse use fission yeast as a model system to investigate how transcription and translation are coupled to cell-cycle progression. They use metabolic labeling in exponentially growing cells and analyze single cells by microscopy. They find that translation scales with size and increases at S/G2 and early mitosis while transcription increases with both size and the amount of DNA. They suggest that changes in CDK activity regulate changes in global translation rates.

Major comments: 1) The paper addresses a much-disputed question in the field. The approach makes the most of the fission-yeast model system and the experiments are beautifully performed. The conclusions are well supported by the data. The experiments are replicated adequately and the statistical analyses are appropriate.

2) The use of cdc25 and in particular the cig1Δ cig2Δ puc1Δ mutants to manipulate cell size is not without challenges when monitoring translation rates. A number of reports in different model organisms suggest that CDK activity can regulate translation. Work from the Nurse lab identified translation factors as CDK substrates (Swaffer et al, 2016), RNApolIII activity and thus tRNA levels are regulated in the cell cycle by CDK in budding yeast (Herrera et al, 2018), phosphorylation of the ribosomal protein RPL12 by CDK1 is required for translation of at least some proteins in mitosis in human cells (Imami et al, 2018), as is phosphorylation of DENR (Clemm von Hohenberg et al, 2022). The authors also suggest that changes in CDK activity might be responsible for the observed changes in global translation rates. It is important to consider whether using mutants impinging on CDK activity might lead to under- or overestimating cell-cycle dependent translation. The authors should either discuss this issue and tune down the hypothesis that CDK activity regulates changes in global translation rates, or use another approach to address the issue. One could use a replication mutant such as cdc17 or cdc20 to alter cell size without interfering with CDK activity. These experiments would strengthen the conclusions and might support the idea that CDK activity regulates changes in global translation rates. References Clemm von Hohenberg K, Müller S, Schleich S, Meister M, Bohlen J, Hofmann TG, Teleman AA (2022) Cyclin B/CDK1 and Cyclin A/CDK2 phosphorylate DENR to promote mitotic protein translation and faithful cell division. Nat Commun 13: 668 Herrera MC, Chymkowitch P, Robertson JM, Eriksson J, Bøe SO, Alseth I, Enserink JM (2018) Cdk1 gates cell cycle-dependent tRNA synthesis by regulating RNA polymerase III activity. Nucleic Acids Res 46: 11698-11711 Imami K, Milek M, Bogdanow B, Yasuda T, Kastelic N, Zauber H, Ishihama Y, Landthaler M, Selbach M (2018) Phosphorylation of the Ribosomal Protein RPL12/uL11 Affects Translation during Mitosis. Mol Cell 72: 84-98 e89 Swaffer MP, Jones AW, Flynn HR, Snijders AP, Nurse P (2016) CDK Substrate Phosphorylation and Ordering the Cell Cycle. Cell 167: 1750-1761 e1716

As discussed above in the reply to reviewer 2, the cell cycle stage and CDK activity are intrinsically linked, CDK activity defines the cell cycle stage so that an increase in CDK activity through the cell cycle is responsible for cells progressing through G1, S, G2, and mitosis (Coudreuse and Nurse, 2010, Swaffer et al., 2016). Therefore, uncoupling CDK activity from the cell cycle is not possible in an unperturbed population. Temperature sensitive mutants of cdc20 (Ramirez et al., 2015, Win et al., 2002) and cdc17 (Jimenez et al., 1992) cause loss of viability when cells are shifted to the restrictive temperature so it cannot be assumed that they are in unperturbed conditions which makes results hard to interpret. It should be noted as far as possible in these experiments we have tried to avoid perturbations. In addition, the fraction of cells permeabilised in our assay decreases significantly when cells are grown above 30 °C, making it difficult to assay such temperature shifts.

Minor comments: 1) The figures are beautifully presented, easy to understand and the cartoons present the experimental strategies very clearly.

2) A major feature of the approach is that translation and transcription are monitored in exponentially growing cells, which are not exposed to any stress such as cell-cycle synchronization. However, one could argue that the analogues used for labeling impose some kind of stress, even if this is not very likely at the labeling times employed. A simple control experiment where the growth rates of labeled and unlabeled cells are compared would strengthen the claim that these are indeed happily growing cells.

It is possible that incubating cells with the analogues could impose some kind of stress on the cell although that could be said about almost any experimental procedure. We have added two supplementary figures with the suggested experiments, showing that incubating cells with EU has little or no impact on their doubling time (we see at most a 2.4 % increase in doubling time in hENT1 hsvTK cells incubated with 20 µM EU, Figure S1I) and that incubating cells with HPG has little impact on their doubling time (we see a 8.6 % increase in doubling time in wild type cells incubated with 10 µM HPG, Figure S1H). Considering the small impact of analogue incubation on the doubling time of the population, and the fact that cells are only exposed to the analogue for a short time in our assays (compared to continuous growth in the presence of the analogue in the growth curves presented in Figure S1H and I), we conclude that the stress imposed is low.

3) Please comment why the length of the EU labeling differs from figure to figure. In fig 2C, S2C and S2D the labeling on the y axes states 10 min, in Fig 4C it says 20 min.

Please refer to the reply to reviewer 2 on the same topic.

4) Lines 118-119 "The pulse signal was five times the background signal." Figure S2A,B show large variation in signal intensity after 5 min labelling. It is not clear how the pulse signal was estimated to be five times the background signal.

We have added two panels for the supplementary figure 2 showing how the signal to noise ratio was computed for the HPG assay after 5 minutes of incubation (Figure S2G) and for the EU assay after 10 minutes of incubation (Figure S2H).

5) In Fig S4C transcription is up by ca 60 % from G1 to G2, while in Fig 4D transcription is up by ca 25-30%, also from G1 to G2. The only difference I can see is the use of PCNA-GFP. Please comment what the reason might be.

In Figure 4D, transcription is up 33 % from G1 to G2 and in Figure S4C, transcription is up 62 % from the 1C to the 2C population. It is possible that the EGFP-pcn1 strain might have a small growth defect which could possibly explain its lower signal production, the slower growth rate might mean that the concentration of RNA polymerase could be lower in this strain and the dynamic equilibrium model predicts that this would results in a smaller increase from G1 to G2 compared to cells with a higher concentration of RNA polymerase. But obviously this is speculative.

6) Fig 1 B images of unlabeled control cells should also be shown.

We have added 2 panels to the supplementary figure 1 showing the background controls in which cells are fixed immediately after addition of the analogue for the HPG assay (Figure S1F) and for the EU assay (Figure S1G).

7) Lines 156 "to investigate how global cellular translation and transcription are affected by cell size, and by progression through the cell cycle" should be amended. Throughout the description of data in figure 2 binucleated and septated cells were excluded from the analyses, meaning that the data only represent cells in G2. The text should make this clear.

"to investigate how global cellular translation and transcription are affected by cell size, and by progression through the cell cycle" has been changed to "to investigate how global cellular translation and transcription are affected by cell size and by progression through G2" to reflect the fact that binucleated and septated cells are excluded from the analysis on this figure.

8) Lines 241-243 "the S-phase subpopulation was found to have an intermediary global transcription value between the G1 and G2/M subpopulations of around 20-25 %." And Lines 310-313 "the rate of transcription is increased in cells undergoing S-phase by 20 % and is 35 % higher in G2 cells which have completed S-phase, indicating that DNA content is limiting the global rate of transcription." It is unclear what the percentage values refer to and which populations exactly are being compared.

"the S-phase subpopulation was found to have an intermediary global transcription value between the G1 and G2/M subpopulations of around 20-25 %" has been changed to “the S-phase subpopulation was found to have an intermediary global transcription value between the G1 and G2/M subpopulations with an increase of around 20-25 % compared to the G1 subpopulation” and “the rate of transcription is increased in cells undergoing S-phase by 20 % and is 35 % higher in G2 cells which have completed S-phase, indicating that DNA content is limiting the global rate of transcription” has been changed to “the rate of transcription is increased in cells undergoing S-phase by 20 % compared to G1 cells and is 35 % higher in G2 cells which have completed S-phase compared to G1 cells, indicating that DNA content is limiting the global rate of transcription”. These changes hopefully will clarify what populations comparisons the percentage values are referring to.

9) Line 85 "Asynchronous cultures ... have not detected" rephrase; change detected to displayed or similar.

“detected” has been changed to “displayed”

10) Line 243 Figure 4J, K should read Figure 4C, D.

“Figure 4j, K” has been changed to “Figure 4D, C”

CROSS-CONSULTATION COMMENTS

I also agree with the comments made by the colleagues. As for the use of the cyclin and cdc25 mutants: I agree with Reviewer #1 that it is unlikely that bulk synthesis rates are conisedarably different, since these strains are going at more or lass normal rates. However, I also agree with reviewer #2 that these mutants cannot be considered as unperturbed conditions. I suspect subtle regulation and in particular cell-cycle dependent regulation might well be lost. At the very least the focus of the interpretation should be on translation/transcription as a function of size, rather than in terms of cell-cycle regulation.

Reviewer #3 (Significance (Required)):

Basier and Nurse address a long-standing question in the cell-cycle field, namely how/whether transcription and translation are coupled to cell-cycle progression. This is technically challenging to address, and many previous studies were hampered by the necessity to synchronize the cells in the cell cycle. The approach of this study of using metabolic labeling in non-synchronized cells is not novel in itself. However, the analysis by microscopy is superior to previous flow-cytometry based strategies in that it allows the use of cell-cycle markers and thereby precise identification of cells in each cell-cycle phase. In addition, it allows accurate measurements of cell size and thus addressing questions of correlations between cell size and transcription / translation rates. A further strength of the study design is that they investigate both transcription and translation in parallel. The authors very nicely review the existing literature and point out the likely reasons for conflicting conclusions (synchronization methods, choice of model system). The advantages of their approach, such as single-cell analyses in non-synchronized cells and the use of cell-cycle markers make their conclusions less likely to be flawed and thus represent an important advance in the field. These findings are of interest for researchers working on the cell-cycle field and on the translation field. There have been significant technical advances in the translation field in recent years, allowing studying not only global translation but also translation of specific mRNAs. I expect that the old questions of coupling cell cycle and cell growth will be revisited also by others, exploiting these new approaches. My field of expertise extends to the cell-cycle field and the regulation of translation and the use of fission yeast.

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

Summary Single cell measurements (flow cytometry and imaging) from unperturbed cells are obtained to investigate scaling of transcription and translation in fission yeast. A key finding is that translation and transcription are somewhat differentially responding to changes in cell size and cell cycle. Perhaps the most central finding of this manuscript is that transcription is not a limiting factor to translation and suggests that transcription is not limiting growth (increase in biomass).

Major comments: What I like in this manuscript is that the translation and transcription measurements have been carefully checked to reflect the initial rates before the HPG and EU signals lose their linearity. More generally, experiments have been conducted with appropriate controls, and the analysis of unperturbed cells in each cell cycle phase is likely to be highly relevant for resolving some of the controversies in the field. Most claims and the conclusions are well supported by the data. Although it is encouraging that the results for translation match the single cell mass measurements in mammalian cells (e.g., ref 18), I would have liked to see some more discussion about the potential caveats of the performed analyses such as the low signal to noise ratio in EU incorporation and other potential technical issues, which might have confounded the results. As an example, looking at Figs 1B and E, most of the protein and RNA synthesis signal is nuclear localized. Is this due to nucleolar staining and incorporation of the labels into nascent ribosomes? Yet the manuscript mentions that roughly half of RNA is for rRNA and for ribosomal proteins the fraction of HPG incorporation might be even lower. This statement does not sound entirely consistent with the experimental images shown in Fig 1. Please clarify.

We had initially performed modelling to estimate the proportion of rRNA in transcription but after reconsideration we agree that is difficult to assess whether the special pattern we observe is consistent with the statement that roughly half of the nascent RNA is rRNA. There is signal in the cytoplasm indicating that within the pulse time some RNA are exported from the nucleus, thus the localisation of the RNA signal is not necessarily an accurate indication of the fractions of the different RNA types in global transcription. We have removed the statement “Although the precise fractions of the different types of RNA in global transcription have not been fully characterised, recent work indicated that only half of the newly synthesised RNA consists of ribosomal RNA molecules, suggesting that a significant portion of transcription is dedicated to the production of messenger and other RNA molecules [27].” It cannot be concluded that most of the protein synthesis is nuclear located in Figure 1B. As mentioned in the text we cannot differentiate between proteins being synthesised in the nucleus and proteins being rapidly imported, we also cannot say what fraction of the proteins synthesised are related to ribosome biogenesis.

A curious thing that has been glossed over is that the transcription and translation seem not to be completely linear but to display opposite patterns (translation slightly reducing, transcription slightly overshooting with cell size compared to a linear model). It remains possible that this could be experimental noise and a visual pattern that is not real, but it could also be relevant for growth control. For example, my interpretation from Fig. 2B is that the signal is not linear and starts to saturate around 10.5 um cell length as seen from the upper IQR. Related to this, I think it is oversimplification to force the data to appear as a discontinuous linear trend by splitting the data in 2H into two segments. Such a treatment will obviously match the data better than a single linear regression, but perhaps some nonlinear model would be actually much more accurate, unless you can point out some kind of regulatory event at the intersection of these two linear segments. In my opinion the current data looks more like a typical (logarithmic) growth curve of the cell population reaching saturation. Please comment.

We agree that fitting two linear regressions for cells shorter and longer than 15 µm is in Figure 2H and 2I was an oversimplification which could result in a false discontinuity in the data. This echoes a comment from reviewer 1 pointing out that 15 µm might not be the length at which the transition occurs. We have removed the linear regressions and added a locally estimated scatterplot smoothing (LOESS) function which capture the nonlinear transition between the increase of translation with size and the saturation, and we have changed the cell length at the estimated saturation from 18 to 19 µm in the text to better reflect the trend.

The main conclusion presented in the abstract is that scaling in transcription may result from dynamic equilibrium between RNA polymerases and available DNA template. This is a bit of speculative part, which I was not too fond of. The dynamic equilibrium idea has been suggested also elsewhere (refs 47) and is not well developed in this manuscript. There is a lack of mechanistic understanding and no formal (mathematical) model to support this idea. For example, global transcription increases much less (1.3-1.4x) than expected based on the increase in DNA content from G1 to G2 (2x). Is this expected based on the dynamic equilibrium model?

The dynamic equilibrium model has been proposed and developed by Swaffer et al. (2022 – currently on bioRxiv) based on mass action kinetics describing the interaction between RNA polymerases and DNA. The model predicts that transcription increases with cell size and with the amount of DNA. With this model, the increase in transcription with DNA for a given cell size is also a function of cell size. Smaller cells are predicted to have a smaller relative increase in transcription from 1C to 2C DNA content than larger cells. This implies that depending on the cell size to DNA ratio of a cell, the span increase in transcription produced by a doubling in the amount of DNA goes from a small increase (at small cell size to DNA ratio) to a doubling (at large cell size to DNA ratio). Thus, in our view the 1.3-1.4x increase in transcription we observe from G1 to G2 is consistent with the dynamic equilibrium model.

I am somewhat concerned about the interpretation of the S phase data in the global transcription measurement. The quantification in Fig. 4D shows S phase being intermediate between G1 and G2. Yet, when you look at the data in Fig. 4C, the S phase median is clearly discontinuous, with higher transcription in smaller S cells. I believe this could affect the normalized data in Fig. 4D and result in the apparent increase in transcription in S phase cells. Having said that, I am not sure if this small S phase transcription is noise (low cell counts?) or a real S phase specific regulation of transcription which is not DNA content dependent. This results is one of the most central ones in this paper to differentiate between transcriptional and translational scaling. Therefore, additional data or insights would be highly appreciated.

It is possible that the discontinuity in the medians of the S phase population in Figure 4C could be the result of noise due to the low cell count in the short size bins (115 cells at 6.5 µm, 404 at 7.5 µm). In addition, because we cannot measure DNA with a degree of accuracy high enough to identify how advanced in S-phase each cell is, we do not know the distribution of the advancement into S-phase of cells for each length bin. This is complicated by the fact that some cells of the CCP∆ mutant start S-phase whilst still septated and might be in a late S-phase stage by the time the cell splits so the median global transcription of the shorter length bin does not necessary reflect the median of early S-phase cells. Hence the discontinuity observed with cell length does not necessarily suggest that there is a discontinuity happening through S-phase. We suggest that since the mean global transcription per cell length of cells in S-phase is in between the mean global transcription per cell length of cells in G1 and in G2, the increase happens through S-phase. To reflect this possibility we have added “It is also possible that the increase happens at a certain stage of S-phase independent of the amount of DNA since we do not know the extent of S-phase of each cell.”

Minor comments: Line 61: "patterns of protein RNA". I guess this refers to patterns of protein/RNA synthesis?

“patterns of protein and RNA” has been changed to “patterns of protein and RNA synthesis”.

Line 248: typo "Tanslation"

“Tanslation” has been changed to “Translation”.

Line 410 and 416: Move interquartile ranges from line 416 to line 410 as this is the first occurrence of the IQR abbreviation.

“Interquartile ranges” has been moved from line 416 to line 410.

Line 473: "Almost linear". This is a subjective expression, please provide some measure such as the R2 value to quantitatively evaluate linearity in this strain.

We have added a measure of the deviation from linearity in the text “, 15 % deviation from the OLS linear regression shown in Figure 1F”. Line 547: Is there a reason to stress in this experiment that the AREA of the fluorescence signal was measured as the area indicates the total fluorescence intensity?

“area of the” has been replaced by “total” so the sentence refers to the total fluorescence intensity signal of Sytox Green. Fig1A: The schematic mentions peptides, shouldn't it be more accurate to use "polypeptides" or "proteins" when discussing protein synthesis?

“Peptides has been changed to Polypeptides”

Fig 5G: Y axis scale has a typo in the word transcription.

“Trancription” has been changed to “Transcription”

CROSS-CONSULTATION COMMENTS I also agree with the points raised by the colleagues. There will always be some technical or interpretation issues related to every experimental technique, every model system and every mutant strain used. I believe after addressing these limitations as pointed out in the reviews, most of those issues have been clarified for the readers.

Reviewer #4 (Significance (Required)):

Basier and Nurse revisit the classic question regarding growth and cell size control by examining scaling of global translation and transcription in fission yeast. Knowing how cells alter their transcription and translation has important consequences in cellular functions during proliferation and cellular aging and is of broad general interest. The main driver for this current work is that previous experiments both in fission yeast and other model organisms have yielded conflicting results, possibly due to different cell cycle synchronization methods. The strength of the paper is indeed in the single cell analyses of well defined yeast strains which allow accurate assessment of the cell cycle dependent changes and accurate measurements of cell size using the cell length.

Reassuringly, the single cell analyses from unperturbed yeast cells resemble those recently obtained from unperturbed growth of individual mammalian cells. The main conclusion that transcription is not limiting translation, and consequently not limiting growth of the cells, is interesting as it is not consistent with some of the prevailing ideas in the cell size field. These ideas include ploidy dependent gene expression where DNA content is thought to be limiting growth or the model for minimal gene expression which assumes RNA polymerases are limiting gene expression and growth. In this regard, this manuscript provides important insights for future thinking of how growth is controlled.

keywords: cell cycle, cell size control

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 #4

Evidence, reproducibility and clarity

Summary

Single cell measurements (flow cytometry and imaging) from unperturbed cells are obtained to investigate scaling of transcription and translation in fission yeast. A key finding is that translation and transcription are somewhat differentially responding to changes in cell size and cell cycle. Perhaps the most central finding of this manuscript is that transcription is not a limiting factor to translation and suggests that transcription is not limiting growth (increase in biomass).

Major comments:

What I like in this manuscript is that the translation and transcription measurements have been carefully checked to reflect the initial rates before the HPG and EU signals lose their linearity. More generally, experiments have been conducted with appropriate controls, and the analysis of unperturbed cells in each cell cycle phase is likely to be highly relevant for resolving some of the controversies in the field. Most claims and the conclusions are well supported by the data.

Although it is encouraging that the results for translation match the single cell mass measurements in mammalian cells (e.g., ref 18), I would have liked to see some more discussion about the potential caveats of the performed analyses such as the low signal to noise ratio in EU incorporation and other potential technical issues, which might have confounded the results. As an example, looking at Figs 1B and E, most of the protein and RNA synthesis signal is nuclear localized. Is this due to nucleolar staining and incorporation of the labels into nascent ribosomes? Yet the manuscript mentions that roughly half of RNA is for rRNA and for ribosomal proteins the fraction of HPG incorporation might be even lower. This statement does not sound entirely consistent with the experimental images shown in Fig 1. Please clarify.

A curious thing that has been glossed over is that the transcription and translation seem not to be completely linear but to display opposite patterns (translation slightly reducing, transcription slightly overshooting with cell size compared to a linear model). It remains possible that this could be experimental noise and a visual pattern that is not real, but it could also be relevant for growth control. For example, my interpretation from Fig. 2B is that the signal is not linear and starts to saturate around 10.5 um cell length as seen from the upper IQR. Related to this, I think it is oversimplification to force the data to appear as a discontinuous linear trend by splitting the data in 2H into two segments. Such a treatment will obviously match the data better than a single linear regression, but perhaps some nonlinear model would be actually much more accurate, unless you can point out some kind of regulatory event at the intersection of these two linear segments. In my opinion the current data looks more like a typical (logarithmic) growth curve of the cell population reaching saturation. Please comment.

The main conclusion presented in the abstract is that scaling in transcription may result from dynamic equilibrium between RNA polymerases and available DNA template. This is a bit of speculative part, which I was not too fond of. The dynamic equilibrium idea has been suggested also elsewhere (refs 47) and is not well developed in this manuscript. There is a lack of mechanistic understanding and no formal (mathematical) model to support this idea. For example, global transcription increases much less (1.3-1.4x) than expected based on the increase in DNA content from G1 to G2 (2x). Is this expected based on the dynamic equilibrium model?

I am somewhat concerned about the interpretation of the S phase data in the global transcription measurement. The quantification in Fig. 4D shows S phase being intermediate between G1 and G2. Yet, when you look at the data in Fig. 4C, the S phase median is clearly discontinuous, with higher transcription in smaller S cells. I believe this could affect the normalized data in Fig. 4D and result in the apparent increase in transcription in S phase cells. Having said that, I am not sure if this small S phase transcription is noise (low cell counts?) or a real S phase specific regulation of transcription which is not DNA content dependent. This results is one of the most central ones in this paper to differentiate between transcriptional and translational scaling. Therefore, additional data or insights would be highly appreciated.

Minor comments:

Line 61: "patterns of protein RNA". I guess this refers to patterns of protein/RNA synthesis?

Line 248: typo "Tanslation"

Line 410 and 416: Move interquartile ranges from line 416 to line 410 as this is the first occurrence of the IQR abbreviation.

Line 473: "Almost linear". This is a subjective expression, please provide some measure such as the R2 value to quantitatively evaluate linearity in this strain.

Line 547: Is there a reason to stress in this experiment that the AREA of the fluorescence signal was measured as the area indicates the total fluorescence intensity?

Fig1A: The schematic mentions peptides, shouldn't it be more accurate to use "polypeptides" or "proteins" when discussing protein synthesis?
Fig 5G: Y axis scale has a typo in the word transcription

Referees cross-commenting

I also agree with the points raised by the colleagues. There will always be some technical or interpretation issues related to every experimental technique, every model system and every mutant strain used. I believe after addressing these limitations as pointed out in the reviews, most of those issues have been clarified for the readers.

Significance

Basier and Nurse revisit the classic question regarding growth and cell size control by examining scaling of global translation and transcription in fission yeast. Knowing how cells alter their transcription and translation has important consequences in cellular functions during proliferation and cellular aging and is of broad general interest. The main driver for this current work is that previous experiments both in fission yeast and other model organisms have yielded conflicting results, possibly due to different cell cycle synchronization methods. The strength of the paper is indeed in the single cell analyses of well defined yeast strains which allow accurate assessment of the cell cycle dependent changes and accurate measurements of cell size using the cell length.

Reassuringly, the single cell analyses from unperturbed yeast cells resemble those recently obtained from unperturbed growth of individual mammalian cells. The main conclusion that transcription is not limiting translation, and consequently not limiting growth of the cells, is interesting as it is not consistent with some of the prevailing ideas in the cell size field. These ideas include ploidy dependent gene expression where DNA content is thought to be limiting growth or the model for minimal gene expression which assumes RNA polymerases are limiting gene expression and growth. In this regard, this manuscript provides important insights for future thinking of how growth is controlled.

keywords: cell cycle, cell size control

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

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary

Basier and Nurse use fission yeast as a model system to investigate how transcription and translation are coupled to cell-cycle progression. They use metabolic labeling in exponentially growing cells and analyze single cells by microscopy. They find that translation scales with size and increases at S/G2 and early mitosis while transcription increases with both size and the amount of DNA. They suggest that changes in CDK activity regulate changes in global translation rates.

Major comments:

1. The paper addresses a much-disputed question in the field. The approach makes the most of the fission-yeast model system and the experiments are beautifully performed. The conclusions are well supported by the data. The experiments are replicated adequately and the statistical analyses are appropriate.
2. The use of cdc25 and in particular the cig1Δ cig2Δ puc1Δ mutants to manipulate cell size is not without challenges when monitoring translation rates. A number of reports in different model organisms suggest that CDK activity can regulate translation. Work from the Nurse lab identified translation factors as CDK substrates (Swaffer et al, 2016), RNApolIII activity and thus tRNA levels are regulated in the cell cycle by CDK in budding yeast (Herrera et al, 2018), phosphorylation of the ribosomal protein RPL12 by CDK1 is required for translation of at least some proteins in mitosis in human cells (Imami et al, 2018), as is phosphorylation of DENR (Clemm von Hohenberg et al, 2022). The authors also suggest that changes in CDK activity might be responsible for the observed changes in global translation rates. It is important to consider whether using mutants impinging on CDK activity might lead to under- or overestimating cell-cycle dependent translation. The authors should either discuss this issue and tune down the hypothesis that CDK activity regulates changes in global translation rates, or use another approach to address the issue. One could use a replication mutant such as cdc17 or cdc20 to alter cell size without interfering with CDK activity. These experiments would strengthen the conclusions and might support the idea that CDK activity regulates changes in global translation rates.

References

Clemm von Hohenberg K, Müller S, Schleich S, Meister M, Bohlen J, Hofmann TG, Teleman AA (2022) Cyclin B/CDK1 and Cyclin A/CDK2 phosphorylate DENR to promote mitotic protein translation and faithful cell division. Nat Commun 13: 668

Herrera MC, Chymkowitch P, Robertson JM, Eriksson J, Bøe SO, Alseth I, Enserink JM (2018) Cdk1 gates cell cycle-dependent tRNA synthesis by regulating RNA polymerase III activity. Nucleic Acids Res 46: 11698-11711

Imami K, Milek M, Bogdanow B, Yasuda T, Kastelic N, Zauber H, Ishihama Y, Landthaler M, Selbach M (2018) Phosphorylation of the Ribosomal Protein RPL12/uL11 Affects Translation during Mitosis. Mol Cell 72: 84-98 e89

Swaffer MP, Jones AW, Flynn HR, Snijders AP, Nurse P (2016) CDK Substrate Phosphorylation and Ordering the Cell Cycle. Cell 167: 1750-1761 e1716

Minor comments:

1. The figures are beautifully presented, easy to understand and the cartoons present the experimental strategies very clearly.
2. A major feature of the approach is that translation and transcription are monitored in exponentially growing cells, which are not exposed to any stress such as cell-cycle synchronization. However, one could argue that the analogues used for labeling impose some kind of stress, even if this is not very likely at the labeling times employed. A simple control experiment where the growth rates of labeled and unlabeled cells are compared would strengthen the claim that these are indeed happily growing cells.
3. Please comment why the length of the EU labeling differs from figure to figure. In fig 2C, S2C and S2D the labeling on the y axes states 10 min, in Fig 4C it says 20 min.
4. Lines 118-119 "The pulse signal was five times the background signal." Figure S2A,B show large variation in signal intensity after 5 min labelling. It is not clear how the pulse signal was estimated to be five times the background signal.
5. In Fig S4C transcription is up by ca 60 % from G1 to G2, while in Fig 4D transcription is up by ca 25-30%, also from G1 to G2. The only difference I can see is the use of PCNA-GFP. Please comment what the reason might be.
6. Fig 1 B images of unlabeled control cells should also be shown.
7. Lines 156 "to investigate how global cellular translation and transcription are affected by cell size, and by progression through the cell cycle" should be amended. Throughout the description of data in figure 2 binucleated and septated cells were excluded from the analyses, meaning that the data only represent cells in G2. The text should make this clear.
8. Lines 241-243 "the S-phase subpopulation was found to have an intermediary global transcription value between the G1 and G2/M subpopulations of around 20-25 %." And Lines 310-313 "the rate of transcription is increased in cells undergoing S-phase by 20 % and is 35 % higher in G2 cells which have completed S-phase, indicating that DNA content is limiting the global rate of transcription." It is unclear what the percentage values refer to and which populations exactly are being compared.
9. Line 85 "Asynchronous cultures ... have not detected" rephrase; change detected to displayed or similar.
10. Line 243 Figure 4J, K should read Figure 4C, D.

Referees cross-commenting

I also agree with the comments made by the colleagues. As for the use of the cyclin and cdc25 mutants: I agree with Reviewer #1 that it is unlikely that bulk synthesis rates are conisedarably different, since these strains are going at more or lass normal rates. However, I also agree with reviewer #2 that these mutants cannot be considered as unperturbed conditions. I suspect subtle regulation and in particular cell-cycle dependent regulation might well be lost. At the very least the focus of the interpretation should be on translation/transcription as a function of size, rather than in terms of cell-cycle regulation.

Significance

Basier and Nurse address a long-standing question in the cell-cycle field, namely how/whether transcription and translation are coupled to cell-cycle progression. This is technically challenging to address, and many previous studies were hampered by the necessity to synchronize the cells in the cell cycle. The approach of this study of using metabolic labeling in non-synchronized cells is not novel in itself. However, the analysis by microscopy is superior to previous flow-cytometry based strategies in that it allows the use of cell-cycle markers and thereby precise identification of cells in each cell-cycle phase. In addition, it allows accurate measurements of cell size and thus addressing questions of correlations between cell size and transcription / translation rates. A further strength of the study design is that they investigate both transcription and translation in parallel.

The authors very nicely review the existing literature and point out the likely reasons for conflicting conclusions (synchronization methods, choice of model system). The advantages of their approach, such as single-cell analyses in non-synchronized cells and the use of cell-cycle markers make their conclusions less likely to be flawed and thus represent an important advance in the field.

These findings are of interest for researchers working on the cell-cycle field and on the translation field. There have been significant technical advances in the translation field in recent years, allowing studying not only global translation but also translation of specific mRNAs. I expect that the old questions of coupling cell cycle and cell growth will be revisited also by others, exploiting these new approaches. My field of expertise extends to the cell-cycle field and the regulation of translation and the use of fission yeast.

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Referee #2

Evidence, reproducibility and clarity

Summary: Basier and Nurse investigate how "cell size, the amount of DNA, and cell cycle events affect the global cellular production of proteins and RNA molecules". Both transcription and translation, driving the production of biomass, have been shown to increase as a function of cell size in various systems. However, whilst cell size generally correlates with cell cycle progression there are inconsistent results in the literature if global cellular translation and transcription is affected by cell cycle state. They argue that this might be due to perturbations induced by different synchronisation methods used in the various studies.

Therefore, in this study, to avoid potential perturbation from synchronisation methods, they developed a system that allows to assay unperturbed exponentially growing populations of fission yeast cells. The assay is based on single-(fixed)cell measurements of cell size, cell cycle stage, and the levels of global cellular translation and transcription. This allows them to correlate cell cycle state, cell size and global cellular translation and transcription levels at the single cell level under unperturbed conditions.

Their results show that translation and transcription steadily increase with cell size, but that the rate of translation, but not transcription, becomes rapidly restricted when cells become larger than wild type dividing cells. This suggests that it is unlikely that the synthesis of RNA is the limiting factor for translation rate in large cells. In addition, their data indicates that translation scales with size, but that the rate increases faster at late S-phase/early G2 and even faster in early in mitosis before decreasing in mitosis and return to interphase. Transcription, on the other hand, increases as a combination of size and the amount of DNA. Overall, this suggests that cell cycle control affects global cellular translation and transcription, which is in line with some studies, but not others. As far as I can tell the assays and data analysis are robust and the data supports the general conclusions.

Major comments

I agree that inconsistent results published on this topic might be due to perturbations induced by different synchronisation methods used in the various studies. However, but much less emphasised in the paper, it also likely depends on the model system used. For example, in budding yeast there is strong evidence for gene expression homeostasis, i.e. gene expression increases as a function of size, independent of gene copy number. Do the authors believe this is a budding yeast specific phenomenon or is this a consequence of specific synchronisation methods used in budding yeast?

Whether growth rate increases linearly or exponentially has been the topic of decade long debates. Their data indicates that the translation rate increases faster at late S-phase/early G2 and even faster early in mitosis before decreasing in mitosis and return to interphase, 'resetting' the growth rate. This suggest an exponential, rather than linear, increase in biomass (i.e. growth rate?), but this is not explicitly pointed out. It would be good to get the authors opinion on this in the discussion.

The authors state that their approach has allowed them to determine how cellular changes are arising from progression through the cell cycle. However, they use fixed cells, rather than live cell imaging, so can't claim to have established changes during cell cycle progression, but only a correlation with cell cycle state/phase. Whilst this could be used as a proxy for progression it should be clearly stated in the abstract and elsewhere to prevent confusion. I for one, based on the abstract, thought they developed a live cell imaging strategy to look at this.

In reference to the Stonyte, et al., study, in addition to different conditions (temperature shift and isoleucine medium), why do the authors think their findings are different? Is it the lack of correlation to cell size in the Stonyte paper or something else? For example, would using different growth conditions (as in the Stonyte paper). where fission yeast cells spend more time in G1, be used instead of the CCP mutant? Can the authors exclude that the lack of G1-S/cyclin-CDKs is not at the basis of a lower rate of translation in G1 and S phase cells? Either these experiments should be carried out or this should be discussed in more detail.

If the signal to noise signal is reduced by 20 minutes EU incubation (rather than 10 minutes) why wasn't it used in all experiments? And the conclusion that the increase in transcription is not showing any discontinuities, are they referring to the triplicates in the supplementary figure 2?

Minor comments

Lines 168-169: should be Figure 2F, S2C, S2D rather than Figure 2C, S2A, S2B.

Line 179: doubling time instead of growth rate?

Lines 184-186: There is an overall trend of slight decrease in transcription per length in cdc25-22 cells but a slight increase in wild-type cells. How does this differ to wild-type cells? Are these non-significant changes and could these be attributed to the low signal to noise ratio?

There is no cell size that is specific to S phase, it falls within the range of G1 and G2 cells. Since this strain has a variable onset of S phase, the phase durations could differ. Therefore, could time spent in each phase affect the translation rate (live cell imaging, i.e. progression, could address this, but not fix cell correlation)?

The data reflects translation/transcription in single cells at a specific cell cycle phase, not during the transition between cell cycle phases. Therefore, it would be more appropriate to only use G1, S, G2 and M rather than S/G2 transition or early G2.

In figure 4C, there is a decrease in global transcription after 13 um (black line showing all cells), which they don't see in cdc25-22 mutants. Their conclusion that global transcription is constantly increasing with cell size is based on cdc-25 cells but the experiment in CCP mutant cells shows a decrease in the median of transcription. Are there replicates for these experiments as in figure 2 and supplementary figure S2? Maybe an average trend can be plotted too? Apart from the first set of experiments (figure 2 and supplementary figure 2), they don't show replicates for other strains. Maybe they can include another graph as in figure 3D and 3K of average replicate values?

Referees cross-commenting

I believe that since the whole premise of this study is that by using unperturbed conditions their findings are different from previously published work they should either clearly point out that this difference might be due to using mutations affecting CDK activity or carry out an experiment in media that induces a G1 population. CDK has been strongly implicated in promoting translation. Using a strain that lacks the G1 and S cyclin CDKs or compromised M-CDK is therefore likely to have an effect on translation, which could be at the basis of the increase in translation during the G2 (and S) phase of the cell cycle.

Significance

As far as I can tell the assays and data analysis are robust and the data supports the general conclusions. However, whilst the cells are assessed in unperturbed conditions, they do use CDK mutants and the cdc25ts mutant to establsih gene expression during the different phases of the cell cycle, which could affect translation/transcription rates. This should either be clearly pointed out or complemented with an experiment where WT cells are grown in conditions that induces distinct G1-S-G2 populations of cells.

Overall, the work presented suggests that cell cycle control affects global cellular translation and transcription, which is in line with some studies, but not others. Whilst the study falls short of testing/establishing the (potential) mechanisms involved, these are important findings, which can be used to guide new studies into how the production of biomass is controlled as cells proceed through the cell cycle.

The cell size field, which is considerable and growing, will be interested in this work.

I have expertise in cell cycle control and genome stability, with a focus on the G1-to-S transition and cell cycle checkpoints during interphase.

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Referee #1

Evidence, reproducibility and clarity

Basier and Nurse revisit the fundamental question of how the rates of RNA and protein synthesis scale with cell size. The strong null hypothesis is that synthesis scales linearly with cell size: cells that are twice as big should make stuff twice as fast. This hypothesis has been tested many times, in many systems, using many approaches over the past century and, in general, the null hypothesis has been sustained. However, there have been many examples of evidence for more complicated synthetic patterns. Whether these results indicate that biosynthesis rates vary across the cell cycle, or in response to other factors, in addition to increasing with cell size, or whether observed deviations from the predictions of the null hypothesis has been due to artifacts of cell synchronization and labeling, is thus an open, interesting and, because biosynthesis rates have critical implications in cellular function and metabolic robustness, important question.

The authors address the question in fission yeast using metabolic pulse labeling with a ribonucleoside or amino acid analog in asynchronous cells and single cell analysis to directly compare incorporation levels with cell size and cell cycle stage. The experiments are well designed, well executed and well controlled. Furthermore, the data is well presented and appropriately interpreted. In particular, the presentation of the size-v.-label data in Figures 2A and D, with the averages and variances in 2B and E and the normalized data in 2C and F are easy understand and interpret. It is thus notable that the size-v.-label data for the longer (cdc22-22) cells is omitted in favor of just the average (2H,J) and normalized (2I,K) data. This size-v.-label data should be added to Figure 2. The authors should also explicitly state how they chose 15 µm as the inflection point in 2H; 16-17 µm seems like it would give a horizontal plateau, which would better fit their saturation explanation.

The authors measure DNA content with a DNA-binding dye, the signal from which should linearly scale with DNA content. However, instead of reporting and analyzing total signal from the DNA-binding dye (or better yet, total signal in the nucleus, which they could do, having segmented the nucleus in their images), they report max signal. Using max signal is complicated because, as cells and thus nuclei increase in size the concentration of DNA and thus the max (but not total) DNA-binding-dye signal in in the nucleus decreases, requiring two-dimensional dye/size analysis (such as shown in Figure 3B) to distinguish G1 and G2 cells. The authors should use the more straight forward measure of total nuclear DNA-binding dye signal, or explicitly explain why they can't or prefer not to do so.

The authors should state in figure legends the strain numbers used for all experiments. They should also cite the source of all the constituent parts (e.g. hENT1, hsvTK, EGFP-pcn1, and synCut3-mCherry) of their strains.

Referees cross-commenting

My colleagues make constructive points. I agree with all of them, although I am less concerned about the use of cdc2-22 and CCP∆ to alter cell length and cell cycle distribution. Although these mutations alter CDK specific activity (and thus length and distribution) and could alter specific patterns of translation, the fact that they double at normal rates makes it seem unlikely that they could be significantly changing bulk synthesis rates.

Significance

As noted above, this work addresses an open, interesting and important question. Moreover it provides useful data in a specific system and a useful example of a general experimental approach to the problem. However, it does not settle the question of how biosynthesis scales with size, even in the specific case of fission yeast. In particular, it shows that protein synthesis plateaus just above normal cell size, whereas RNA synthesis scales up to twice normal cell size. This observation is striking, because there is no obvious mechanism that would (and the authors offer no suggestion of how to) explain how protein synthesis could be limited if RNA synthesis is not. Therefore, the strength of the paper is that it identifies an intriguing phenomena and its limitation is that it does not provide any testable hypotheses to explain that phenomena.

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Reply to the reviewers

1. General Statements

We thank the reviewers for their critical analysis of our manuscript. We have addressed all reviewer concerns and questions in our revised version. Along with other improvements requested by the reviewers, we added an MTT assay to validate our flow cytometry assays, normalized binding to surface area to better compare toxin binding between Leishmania and HeLa cells, and revised the discussion. We believe the revised contribution provides important novel insights into membrane integrity in a non-standard organism that will appeal to a broad audience.

Reviewer comments below are in italics.

Point-by-point description of the revisions

Reviewer 1

*Major Comments. The experimental work has been carried out carefully, including multiple biological replicates, convincing statistical analysis. Data presentation is extensive, including 6 supplementary figures. It is likely that the experiments could be reproduced by others, as the approaches do not seem to be especially difficult, and the methods are well documented. *

We thank the reviewer for this assessment.

*My major comment regarding revision is that this paper is quite long and extensive given the relatively restricted body of experiments and discrete conclusions. The principal discovery is that sphingolipids protect Leishmania parasites against somewhat artificial treatment with bacterial sterol-binding pore forming toxins, but they do not do so by obstructing toxin binding to sterols. A similar effect is seen for the antileishmanial drug amphotericin B, the most important agent studied. No further mechanistic insights are provided regarding the process whereby sphingolipids blunt toxicity of either the CDCs or amphotericin B. In addition, the experimental approach relies largely upon one methodology, dose-response curves. A report with such highly focused scope should be presentable with considerably more economy. In particular, the Discussion is long and diffuse, obscuring the presentation of the major conclusions. It could probably be cut in half and would in the process present the major deliverables of the paper with higher impact. *

We have condensed the discussion as requested, and to address Reviewer 2’s concerns, we provided a summary articulating the significance.

Significance

*The most notable advance is the observation that sphingolipids protect Leishmania parasites from the cytotoxic activity of the first line antileishmanial drug amphotericin B that binds to the major sterol in the parasite plasma membrane, ergosterol, and induces pore formation. This discovery suggests that parallel treatments with agents that selectively reduce sphingolipid levels in the parasite might act synergistically with amphotericin B, potentially allowing treatment with lower doses of this inherently toxic drug. This work will likely be of most interest to those with a focus on pharmacology and drug development for this and related parasites, but it will also be of some interest to those working on the basic biochemistry of these organisms. The senior authors are major workers in sphingolipid biochemistry in Leishmania parasites and thus are well positioned to address the relevant background in the field, much of which has come out of their laboratories.

The major limitation of this study is its relatively circumscribed scope, resulting in one principal conclusion: Leishmania sphingolipids blunt the potency of toxins or drugs that target sterols for pore formation, but they do not do so by impairing binding of these agents to sterols, as they do in mammalian cells. The work would be of higher impact if it addressed mechanistically how sphingolipids do decrease toxicity, e.g., do they prevent these agents from oligomerizing or from intercalating into the membrane to form pores. Such studies would require the application of an expanded repertoire of experimental methodologies going beyond the measurement of dose-response curves with various mutants and drugs.*

We agree with the reviewer that next steps include determining if Leishmania sphingolipids interfere with oligomerization or pore-insertion. One challenge is that these tools need to first be validated in Leishmania.

To address the reviewer concern about the limited range of experimental methodologies, we added an MTT assay (Supplementary Fig S2E) as validation of our flow cytometry assays. We have better summarized the significance and broad impact of our work in lines 466-476.

Reviewer 2

*In the abstract the authors describe that the pore-forming toxins engage with ceramide and other lipids and while it's clear that the levels of sphingolipids are important for the effect of these toxins there is limited evidence to show they physically interact as the word engage suggests. *

We agree with the reviewer that we do not show physical interaction. In the abstract, we are careful to only use the word “engage” in association with our proposed model. Our proposed model both explains our data, and uses those data to open new horizons by making falsifiable predictions that can be tested in the future. Direct engagement of toxins with lipids is one such prediction. For these reasons, we prefer to retain the word “engage” in the abstract.

*The authors conclude that the ergosterol on the Leishmania cell membrane is less accessible to the CDCs as it does not bind as much CDCs as a HeLa cell. What is the relative abundance of sterols in the HeLa membrane in comparison to a Leishmania cell. A HeLa cell is much bigger than a Leishmania cell and will therefore be able to bind a lot more CDC, was the MFI normalised for cell size? This would be important to know as the difference in intensity may be purely related to the difference in cell size. *

We thank the reviewer for this insight. We had not normalized MFI by cell surface area. We added MFI normalized to cell size (described on lines 573-577) and found that when area was accounted for, the promastigotes bound more toxin than HeLa cells. These data are now included as Supplementary Fig S1A, and discussed on lines 187-189.

*The authors are keen to prosecute that ceramide is important for differences between PFO and SLO action as the inhibitor has a much greater effect on the PFO treatment of ipcs- cells than SLO, as ceramide will accumulate in these cells. But for the SLO analysis they stated that the treatment of spt2- with myriocin had no change on the LC50 as the target of myriocin was spt2 while they noted was there a drop in the LC50 with PFO. Based on this I think the importance of ceramide is being overstated here, as spt2- cells have little ceramide in them. Moreover the authors also suggest that changes to the lipid environment rather than a single species might be important. Are there alternative targets the myriocin might inhibit when there is no spt2-, it is intriguing that there is a decrease in LC50 for PFO on spt2- myriocin treated cells. *

Clearly, IPC is very important for determining the cytotoxicity for the CDCs in Leishmania but I think the evidence for the role of ceramide and the sensing of it is less clear cut and the strength of the conclusions about this should be modified. In the results the authors conclude that the L3 loop is sensing ceramide and the data shows that the L3 loop is important but in the discussion they are more circumspect about the moieties L3 can detect. The authors should qualify these conclusions in the results a bit more.

As requested by the reviewer, we have qualified our statements in the results, lines 282, 297, 315.

*Minor comments *

*It would be helpful for the review process to include line and page numbers to highlight areas that I have concerns about. *

We agree with the reviewer and have added line numbers.

*In the first paragraph of the results is there a reference for the spt2- cell line that was used here. *

We have added the Zhang 2003 reference to the first paragraph of the results, line 161.

*In the second paragraph there is a disconnect between the statements about the phenotype of the ipcs- cells and the reference/evidence for it. *

We have added the reference to the earlier mention of the ipcs cells, and in the introduction, lines 118-120 and 167-169.

*On many of the graphs the letters a, b, c are alongside many of the symbols but it was unclear what they represented. *

The letters represent statistically distinct groups. These are used instead of stars and bars to reduce clutter on the figure. We have now explained the difference in the first figure legend in which they are used, lines 818-823.

*The colour scheme for figure 4 was confusing - yellow diamonds in A/B are spt2-/+spt2 but in C/D are iscl-, this makes it hard to compare between them. *

We have changed the color and symbols for the iscl- mutant in Fig 4 and Fig S6.

*The methodology states that various tests were used to define whether differences were significant but it was not clear from the figures when these were being applied only a few graphs had '*' associated with them. *

We have clarified this in the figure legends.

*There is no overall conclusion to the study at the end of the discussion just a series of limitations of the study, which is good to acknowledge but feels an odd way to finish the manuscript. *

We have revised the discussion in response to Reviewer 1, and included a summary to tie everything together, lines 466-476.

*Significance: *

Overall this is a strong manuscript with a set of experiments that have a clear strategy and purpose that was well written. This paper outlines the importance of the lipid composition for the cytotoxicity of both sterol specific toxins and amphotericin B in Leishmania, which will have significant implications for their study for other pathogens but also for the development of combination therapies to enhance the potency of amphotericin B, as such I think this will be of interest to both researchers interested in drug discovery and those interested in lipid metabolism.

We thank the reviewer for this assessment.

Reviewer 3

Major comments: 1) The idea that sphingolipids do not block toxin access relies on the work of CDC-based probes binding the accessible pool of cholesterol in mammalian membranes. The authors make the observation that ergosterol is not shielded by sphingolipids because the presence of them does not prevent CDC binding. Is it possible to show that Leishmania sphingolipids are able to actually sequester ergosterol or would it all be considered free and available to toxin binding?

Our interpretation of the binding data is that the Leishmania sphingolipids fail to sequester ergosterol from toxins, so ergosterol accessibility is independent of sphingolipids. Similar to mammalian cells, there could be an “essential” pool of ergosterol bound to other proteins/lipids that is inaccessible to toxins. However, detecting that pool is technically challenging.

We have revised the manuscript to clarify this, lines 454-456.

* 2) The statistical analysis applied to each experiment, while defined in the figure legends, are presented mostly using uncommon methods of presentation, making it difficult to determine if the correct analysis was applied.*

We have clarified the statistics and use of letters. The letters represent statistically distinct groups. These are used instead of stars and bars to reduce clutter on the figure. We have now explained the difference in the first figure legend in which they are used, lines 818-823.

* 3) The binding of these toxins to Leishmania cells appears to be independent of their lipid composition, but Figure 1A-D suggests that these toxins do not bind very well to Leishmania; a ~65 fold increase in toxin added only results in a maximal 3 fold change in amount of toxin bound. Therefore, the authors need to demonstrate that this increase in binding is not simply the result of adding more ug of each CDC. *

Leishmania are smaller than HeLa cells, which accounts for the apparent reduced binding. We added Supplementary Fig S1A, which normalized MFI to estimated surface area. When normalized to surface area, Leishmania bound to toxin better than HeLa cells. We further note that the dose-dependent increase in cytotoxicity argues against non-specificity of increased toxin.

* 4) The authors use HeLa cells to compare the ability of these toxins to bind to sterol containing membranes, but it is unclear how a mammalian cell line, which lacks ergosterol, can inform upon the differences in binding to Leishmania membranes when their data shows almost no cholesterol is found in the Leishmania membrane. The use of HeLa cells to compare the toxicity of these CDCs is simply a control experiment for the lytic activity of these proteins, and should not be used as a direct comparison of their LC50s, as a mammalian plasma membrane lipid composition is significantly different from that of Leishmania. If the authors want to use HeLa cells as a direct comparison to show that sphingolipids in mammalian cells also protect them from CDC pore formation, they must demonstrate the HeLa cells which have genetic defects in sphingolipid biology or which have been treated with sphingomyelinases are more sensitive to these CDCs. *

We agree with the reviewer that to argue sphingolipids in mammalian cells are protective would require additional data beyond the scope of this manuscript. We are not making any statements about the role of sphingolipids in mammalian cells, which have a controversial role in CDC damage and membrane repair (see e.g. Schoenauer et al 2019. PMID: 29979630). Since the head group of sphingomyelin interacts with cholesterol (Endapally et al 2019), but the IPC head group is not expected to interact similarly with ergosterol, we choose to remain focused on Leishmania sphingolipids.

Given our focus on Leishmania, why include HeLa cells at all? We think including HeLa cells provides an important and relevant point of reference because there are situations where both human cells and Leishmania promastigotes could encounter pore-forming toxins. This comparison provides insight to the following question: “In a mix of promastigotes and human cells (for example during a blood meal), which cells would die first from the bacterial PFT?” Comparing cytotoxicity to HeLa cells provides a point of reference in judging how cytotoxic CDCs are to Leishmania promastigotes, and how sensitive the spt- promastigotes become.

We have rephrased the manuscript (lines 208-209) to better clarify that HeLa cells are a reference point so readers can evaluate the relative sensitivity of sphingolipid-deficient promastigotes.

* 5) The authors need to demonstrate that the mutant cholesterol recognition motif (CRM) and the glycan binding mutant proteins can still bind to both Leishmania and Hela cell membranes to serve as controls for their lack of lytic activities. Without this, they cannot conclude that "Leishmania membranes engage the same binding determinants used by CDCs to target mammalian cells". *

The glycan binding and ΔCRM mutants are unable to bind to HeLa cells. These toxin mutations were previously characterized (Mozola & Caparon, 2015 and Farrand et al 2010), showing that their defect lies in binding to cells, but not oligomerization or pore-formation. Since their defect lies solely in binding, if these toxins were able to bind to spt2- cells, they would kill the spt2- cells. This enables us to use these toxin mutants to ask if the CRM or glycan-binding is essential for toxin binding to Leishmania. Since the only defect in these mutant toxins is binding (either to glycans or cholesterol), the failure of these mutants to kill allows us to conclude that both of these binding surfaces on the toxin are essential for cytotoxicity in L. major.

We have clarified the manuscript, lines 236-240. *

Minor comments: 6) Multiple figures lack adequately defined axes. Examples include, but are not limited to: Figure 1A-D where the X-axis is plotted as logarithmic based 2 but this is not defined. Figure 2 the Y axis is plotted as logarithmic based 10 but is not defined. *

We have updated the figure legends to indicate where log axes are used.

7) The authors state that "Promastigotes with inactivated de novo sphingomyelin synthesis has a significant increase in total sterols" in reference to Figure 1E. Not only is there no significance indicated for the spt2-/-, the authors only indicate a significance point for the Myr (not yet defined) + WT sample in "Other sterols".

We have rephrased this to indicate a trend, line 181.

8) The authors use increases in membrane permeability as a read out for specific lysis using PI uptake, however, they then refer to this read out as killing of Leishmania, without measuring the viability of these cells. Therefore, the authors should provide additional experiments that demonstrate the death of the different Leishmania strains treated with the cytolysins.

As requested, we have now provided an additional experiment to validate Leishmania death. We have now added MTT assay as Fig S2E, and discussed in the results, lines 202-205.

9) It is not clear how the authors calculated their LC50 values in Figure 2. According to the figure legends, the authors used HU/ml ranges that would be sub lethal or not completely lysed within this range to most of the Leishmania strains tested. The data presented in Figure are not clear that the correct LC50 calculations were used as none of the Specific Lysis curves do not reach saturation with the concentrations presented, and one does not even reach 50% Lysis.

We thank the reviewer for catching this discrepancy. The legend in Fig 2 did not include the correct ranges of toxin dose used for PFO. We have corrected the legend to indicate the toxin range used. To calculate LC50, we used linear regression on the linear portion of the death curve to determine the concentration at 50% lysis. This gives us a way to determine LC50 even without the use of very large (and costly) amounts of toxin to get extensive saturation on the kill curve.

* 10) Figure 4 and Figure S6 are very difficult to interpret. Figure S6 would benefit by breaking up each graph into multiple graphs that would allow the reader to see more of the curves individually. Additionally, there are multiple conditions were it appears that a different number of experiments (2-4 totals) were preformed but statistical analysis was applied to these data. *

We updated the labels on Fig 4 for improved readability. We broke Fig S6 up into multiple graphs. We have removed unpaired data (eg the n of 4 noted by the reviewer), and re-checked our stats. This change did not alter our conclusions. The apparent n of 2 was overlap of data points due to poor jittering of the datapoints. We have increased the jitter on the data points to make all three reps more distinct.

* 11) The authors state "In contrast to myriocin-treated ipcs- L. major, which contain low levels of ceramide, myriocin treated iscl- L. major contain low levels of IPC" but do not provide a reference or point to data to support this claim. *

We have qualified these statements to say ‘are expected to’ on lines 306-307.

* 12) Figure 5 E would benefit in presentation by being broken up into 4 separate graphs based on the toxin used, as it is difficult to determine which data points are being compared. *

We compare by toxin used in Fig 5A-D. The purpose of Fig 5E is to compare between toxins. We included all of the data points (including resistant control strains) for completeness. The main focus is the spt2- and ipcs- parts of Fig 5E.

* 13) The authors state that "myriocin did not inhibit growth more than 25% promastigotes at 10 μM" but this data is not presented. *

We have now added these data as Fig 6A.

14) Multiple graphs lack legends or have axis that are not defined.

In order to improve readability and avoid cluttering the figures, where the legends and axes are the same across multiple graphs, they are included only once for a given row and/or column.*

Significance:

Overall, the experiments presented were conducted to analyze each question, but many of the results are observational, without considering the impact of altered lipid species on the findings. The data suggests an existence of a protective mechanism for the parasite from CDCs, but it unclear how these finding inform upon the CDC or Leishmania fields. CDCs have been known to target sterols within membranes and that altered local membrane environments can have substantial impacts on CDC binding. This work suggests that the altered lipid species of Leishmania membranes, compared to a mammalian membrane, could dramatically effect the sequestering power of sphingolipids or other lipids, and thus change how CDCs bind to them. This work advances is likely to have specialized audience of Leishmania researchers looking at the dynamics of their membranes.*

We believe this work will be valuable to a broad audience because it will be of interest to researchers studying membranes in general, pathogenic eukaryotes and pore-forming toxins. Most membrane biology work is done either in opisthokonts or in model liposomes, so there are few studies on biomembranes in other taxonomic groups, including many different human pathogens. We provide a blueprint for examining the membranes of non-standard organisms, establish L. major as a pathogenically relevant model system, and report on key differences in sterol sequestration compared to mammalian cells. These findings provide important perspectives for the generalization of biomembranes, especially when compared to prior work in opisthokonts.

We have clarified our significance in lines 466-476.

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Referee #3

Evidence, reproducibility and clarity

Haram & Moitra et al. report a mechanism by which the lipid environment of the Leishmania membrane determines the effects of two different pore forming toxins. They demonstrate that sphingolipids protect Leishmania from toxin-induced cytotoxicity without effecting the proteins ability to bind to the membrane. They further demonstrate that ceramide can reduce the cytotoxicity of the toxins through the sensing of the local lipids, and that this composition can protect Leishmania from first line antibiotics. The manuscript follows a model for explaining this protection, but several important questions and controls remain and need to be addressed.

Major comments:

1. The idea that sphingolipids do not block toxin access relies on the work of CDC-based probes binding the accessible pool of cholesterol in mammalian membranes. The authors make the observation that ergosterol is not shielded by sphingolipids because the presence of them does not prevent CDC binding. Is it possible to show that Leishmania sphingolipids are able to actually sequester ergosterol or would it all be considered free and available to toxin binding?
2. The statistical analysis applied to each experiment, while defined in the figure legends, are presented mostly using uncommon methods of presentation, making it difficult to determine if the correct analysis was applied.
3. The binding of these toxins to Leishmania cells appears to be independent of their lipid composition, but Figure 1A-D suggests that these toxins do not bind very well to Leishmania; a ~65 fold increase in toxin added only results in a maximal 3 fold change in amount of toxin bound. Therefore, the authors need to demonstrate that this increase in binding is not simply the result of adding more ug of each CDC.
4. The authors use HeLa cells to compare the ability of these toxins to bind to sterol containing membranes, but it is unclear how a mammalian cell line, which lacks ergosterol, can inform upon the differences in binding to Leishmania membranes when their data shows almost no cholesterol is found in the Leishmania membrane. The use of HeLa cells to compare the toxicity of these CDCs is simply a control experiment for the lytic activity of these proteins, and should not be used as a direct comparison of their LC50s, as a mammalian plasma membrane lipid composition is significantly different from that of Leishmania. If the authors want to use HeLa cells as a direct comparison to show that sphingolipids in mammalian cells also protect them from CDC pore formation, they must demonstrate the HeLa cells which have genetic defects in sphingolipid biology or which have been treated with sphingomyelinases are more sensitive to these CDCs.
5. The authors need to demonstrate that the mutant cholesterol recognition motif (CRM) and the glycan binding mutant proteins can still bind to both Leishmania and Hela cell membranes to serve as controls for their lack of lytic activities. Without this, they cannot conclude that "Leishmania membranes engage the same binding determinants used by CDCs to target mammalian cells".

Minor comments:

1. Multiple figures lack adequately defined axes. Examples include, but are not limited to: Figure 1A-D where the X-axis is plotted as logarithmic based 2 but this is not defined. Figure 2 the Y axis is plotted as logarithmic based 10 but is not defined.
2. The authors state that "Promastigotes with inactivated de novo sphingomyelin synthesis has a significant increase in total sterols" in reference to Figure 1E. Not only is there no significance indicated for the spt2-/-, the authors only indicate a significance point for the Myr (not yet defined) + WT sample in "Other sterols".
3. The authors use increases in membrane permeability as a read out for specific lysis using PI uptake, however, they then refer to this read out as killing of Leishmania, without measuring the viability of these cells. Therefore, the authors should provide additional experiments that demonstrate the death of the different Leishmania strains treated with the cytolysins.
4. It is not clear how the authors calculated their LC50 values in Figure 2. According to the figure legends, the authors used HU/ml ranges that would be sub lethal or not completely lysed within this range to most of the Leishmania strains tested. The data presented in Figure are not clear that the correct LC50 calculations were used as none of the Specific Lysis curves do not reach saturation with the concentrations presented, and one does not even reach 50% Lysis.
5. Figure 4 and Figure S6 are very difficult to interpret. Figure S6 would benefit by breaking up each graph into multiple graphs that would allow the reader to see more of the curves individually. Additionally, there are multiple conditions were it appears that a different number of experiments (2-4 totals) were preformed but statistical analysis was applied to these data.
6. The authors state "In contrast to myriocin-treated ipcs- L. major, which contain low levels of ceramide, myriocin treated iscl- L. major contain low levels of IPC" but do not provide a reference or point to data to support this claim.
7. Figure 5 E would benefit in presentation by being broken up into 4 separate graphs based on the toxin used, as it is difficult to determine which data points are being compared.
8. The authors state that "myriocin did not inhibit growth more than 25% promastigotes at 10 μM" but this data is not presented.
9. Multiple graphs lack legends or have axis that are not defined.

Significance

Overall, the experiments presented were conducted to analyze each question, but many of the results are observational, without considering the impact of altered lipid species on the findings. The data suggests an existence of a protective mechanism for the parasite from CDCs, but it unclear how these finding inform upon the CDC or Leishmania fields. CDCs have been known to target sterols within membranes and that altered local membrane environments can have substantial impacts on CDC binding.

This work suggests that the altered lipid species of Leishmania membranes, compared to a mammalian membrane, could dramatically effect the sequestering power of sphingolipids or other lipids, and thus change how CDCs bind to them.

This work advances is likely to have specialized audience of Leishmania researchers looking at the dynamics of their membranes.

Expertise: I study host-pathogen interactions with a focus on plasma membrane lipids and cholesterol.

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Referee #2

Evidence, reproducibility and clarity

Summary

One of the major treatments of the parasitic disease Leishmaniasis is the drug amphotericin B, which targets ergosterol and a route to increasing its potency would be to increase the accessibility ergosterol on the surface of the parasite. With that in mind the authors investigated sterol-binding ability of two cytotoxic toxins PFO and SLO. The authors clearly show that these toxins are readily able to bind to the surface of the parasite regardless of the levels of sphingolipids present, yet the presence of inositol phosphorylceramide and to a lesser extent ceramide affect overall cytotoxicity. The L3 loop of the toxin was shown to be important for the sensitivity of the different toxins to the lipid composition of the membrane.

Major comments

In the abstract the authors describe that the pore-forming toxins engage with ceramide and other lipids and while it's clear that the levels of sphingolipids are important for the effect of these toxins there is limited evidence to show they physically interact as the word engage suggests.

The authors conclude that the ergosterol on the Leishmania cell membrane is less accessible to the CDCs as it does not bind as much CDCs as a HeLa cell. What is the relative abundance of sterols in the HeLa membrane in comparison to a Leishmania cell. A HeLa cell is much bigger than a Leishmania cell and will therefore be able to bind a lot more CDC, was the MFI normalised for cell size? This would be important to know as the difference in intensity may be purely related to the difference in cell size.

The authors are keen to prosecute that ceramide is important for differences between PFO and SLO action as the inhibitor has a much greater effect on the PFO treatment of ipcs- cells than SLO, as ceramide will accumulate in these cells. But for the SLO analysis they stated that the treatment of spt2- with myriocin had no change on the LC50 as the target of myriocin was spt2 while they noted was there a drop in the LC50 with PFO. Based on this I think the importance of ceramide is being overstated here, as spt2- cells have little ceramide in them. Moreover the authors also suggest that changes to the lipid environment rather than a single species might be important. Are there alternative targets the myriocin might inhibit when there is no spt2-, it is intriguing that there is a decrease in LC50 for PFO on spt2- myriocin treated cells.

Clearly, IPC is very important for determining the cytotoxicity for the CDCs in Leishmania but I think the evidence for the role of ceramide and the sensing of it is less clear cut and the strength of the conclusions about this should be modified. In the results the authors conclude that the L3 loop is sensing ceramide and the data shows that the L3 loop is important but in the discussion they are more circumspect about the moieties L3 can detect. The authors should qualify these conclusions in the results a bit more.

Minor comments

It would be helpful for the review process to include line and page numbers to highlight areas that I have concerns about.

In the first paragraph of the results is there a reference for the spt2- cell line that was used here.

In the second paragraph there is a disconnect between the statements about the phenotype of the ipcs- cells and the reference/evidence for it.

On many of the graphs the letters a, b, c are alongside many of the symbols but it was unclear what they represented.

The colour scheme for figure 4 was confusing - yellow diamonds in A/B are spt2-/+spt2 but in C/D are iscl-, this makes it hard to compare between them.

The methodology states that various tests were used to define whether differences were significant but it was not clear from the figures when these were being applied only a few graphs had '*' associated with them.

There is no overall conclusion to the study at the end of the discussion just a series of limitations of the study, which is good to acknowledge but feels an odd way to finish the manuscript.

Significance

Overall this is a strong manuscript with a set of experiments that have a clear strategy and purpose that was well written. This paper outlines the importance of the lipid composition for the cytotoxicity of both sterol specific toxins and amphotericin B in Leishmania, which will have significant implications for their study for other pathogens but also for the development of combination therapies to enhance the potency of amphotericin B, as such I think this will be of interest to both researchers interested in drug discovery and those interested in lipid metabolism.

Expertise in the molecular cell biology of trypanosomes and leishmania.

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Referee #1

Evidence, reproducibility and clarity

Summary.

In this manuscript the authors demonstrate that sphingolipids protect Leishmania major promastigotes from the toxic effects of two bacterial cholesterol-binding cytolysins (CDCs), polypeptide toxins that first bind to sterols in cellular membranes and then oligomerize to form pores. More significantly for Leishmania biology, sphingolipids also protect the parasites against similar pore forming activity by the first line antileishmanial drug amphotericin B, suggesting that treatments that reduced the levels of sphingolipids, especially ceramide and inositol-phosphoceramide (IPC), might enhance selective potency of amphotericin for the parasite and thus allow lower doses of this inherently toxic drug to be applied. The experimental work is based largely on dose-response curves of wild type, spt2- mutants (fail to make sphingolipids), ipcs- mutants (do not synthesize IPC and build up the precursor ceramide), and the respective add-back lines with and without treatment with myriocin that inhibits the SPR enzyme and thus blocks sphingolipid biosynthesis. These bacterial toxins, although not directly relevant to Leishmania biology, are used as a model to investigate sensitivity to sterol-binding pore forming agents, and sensitivity to the antileishmanial drug amphotericin B, which parallels the bacterial toxins in binding to ergosterol and forming membrane pores, is also found to be enhanced when sphingolipid levels are reduced. Notably, sphingolipids do not reduce the binding of CDCs to ergosterol in the parasite membrane, as they do in mammalian cells, but rather act downstream of sterol binding, possibly by reducing pore forming activity by some unknown mechanism.

Major Comments.

The experimental work has been carried out carefully, including multiple biological replicates, convincing statistical analysis. Data presentation is extensive, including 6 supplementary figures. It is likely that the experiments could be reproduced by others, as the approaches do not seem to be especially difficult, and the methods are well documented.

My major comment regarding revision is that this paper is quite long and extensive given the relatively restricted body of experiments and discrete conclusions. The principal discovery is that sphingolipids protect Leishmania parasites against somewhat artificial treatment with bacterial sterol-binding pore forming toxins, but they do not do so by obstructing toxin binding to sterols. A similar effect is seen for the antileishmanial drug amphotericin B, the most important agent studied. No further mechanistic insights are provided regarding the process whereby sphingolipids blunt toxicity of either the CDCs or amphotericin B. In addition, the experimental approach relies largely upon one methodology, dose-response curves. A report with such highly focused scope should be presentable with considerably more economy. In particular, the Discussion is long and diffuse, obscuring the presentation of the major conclusions. It could probably be cut in half and would in the process present the major deliverables of the paper with higher impact.

Minor Comments:

Except for my comment about the length of the manuscript (which I consider to be a major comment for this paper), I have no further suggestions on this topic.

Significance

The most notable advance is the observation that sphingolipids protect Leishmania parasites from the cytotoxic activity of the first line antileishmanial drug amphotericin B that binds to the major sterol in the parasite plasma membrane, ergosterol, and induces pore formation. This discovery suggests that parallel treatments with agents that selectively reduce sphingolipid levels in the parasite might act synergistically with amphotericin B, potentially allowing treatment with lower doses of this inherently toxic drug. This work will likely be of most interest to those with a focus on pharmacology and drug development for this and related parasites, but it will also be of some interest to those working on the basic biochemistry of these organisms. The senior authors are major workers in sphingolipid biochemistry in Leishmania parasites and thus are well positioned to address the relevant background in the field, much of which has come out of their laboratories.

The major limitation of this study is its relatively circumscribed scope, resulting in one principal conclusion: Leishmania sphingolipids blunt the potency of toxins or drugs that target sterols for pore formation, but they do not do so by impairing binding of these agents to sterols, as they do in mammalian cells. The work would be of higher impact if it addressed mechanistically how sphingolipids do decrease toxicity, e.g., do they prevent these agents from oligomerizing or from intercalating into the membrane to form pores. Such studies would require the application of an expanded repertoire of experimental methodologies going beyond the measurement of dose-response curves with various mutants and drugs.

Reviewer's areas of expertise: Biochemistry, cell, and molecular biology of parasitic protozoa.

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Reply to the reviewers

Comments received from the Reviewers on 18th of November 2022 are included in plain font, followed point-by-point by the authors’ comments in bold.

Reviewer #1 (Evidence, reproducibility and clarity):

The endothelial release of NRG (neuregulin)-1 is a paracrine growth factor activating ErbB (erythroblastic leukaemia viral oncogene) receptor tyrosine kinases on various targets cells epicardial, endocardial/endothelial (autocrine stimulation) and myocardiocytes. It is well known, after the manuscript of the K.D.Poss group that Neuregulin1 induced perivascular cells after injury to the adult zebrafish heart as well as mammalian cells. Inhibition of the Erbb2 co-receptor, disrupts cardiomyocyte proliferation in response to injury, whereas myocardial NRG1 overexpression enhances this proliferation. Thus, seems to be clear, that NRG-1 could stimulate regenerative, inflammatory, fibrotic, and metabolic processes In uninjured zebrafish, the reactivation of Nrg1 expression induces cardiomyocyte dedifferentiation, overt muscle hyperplasia, epicardial activation, increased vascularization, and causes cardiomegaly through persistent addition of wall myocardium (review cited by authors in this MS). In light of the large amount of these research deals with NRG1, the molecular mechanisms linked and focused to understand the multivariate effects of the NRG1 cascade are welcome. The authors have focused the manuscript on a comparative and translational demonstration of STAT5b involvement in the signal transduction of NRG1.

Although the authors have done several experiments they have presented these in a chaotic way among mice, zebrafish and human biopsies. I can suggest rewriting partially the results section in a way more ordered and readable. Even the discussion is a little bit chaotic and lacks some aspects. For example, who is the stimulator of NRG1 release? Moreover, the literature cited is partially or not correctly reported (in the references section). In my opinion, the authors should revise the manuscript in the light of following suggestions.

MAJOR:

Title: line 1. The title does not explain the translational aspects of the manuscript. I suggest something like: "STAT5b is a key effector of NRG1/ERBB4-mediated cardiomyocyte growth: a translational approach".

We have now modified the title as suggested by the Reviewer.

Abstract. Lines 20-25. The authors should rewrite this section by removing their previous finding ("we") reporting. In lines 25-36 the reported data is not well explained and is not clear who did that. The authors, previously? It is not well explained.

Lines 35-37 Please, specify in which type of hypertrophic heart samples (is it from humans?) have been observed the NRG1 pathway.

We have now rewritten this section of the abstract and changed the tense to present to better indicate which observations are presented in the manuscript.

Introduction. Lines 44-46. All the cited papers consist of the preprint, thus they could be inserted in the discussion and not in the introduction. In the alternative, they could be inserted here following a sentence "Preliminary study seems to indicate...."

We apologize for the mistake, the references are not preprints but articles published in Nature. We have amended the reference section so this is now indicated more clearly.

Lines 50-51. The references cited are relative only to murine research and not to other animals. Thus, only murine models have been demonstrated. Thus, the assertion could be not valid for zebrafish or humans, thus the authors should point out this.

The reference number 8 in the original submission refers to experiments conducted in embryonic zebrafish. We have now rewritten this section by separating the previous observations in mice and in zebrafish into separate sentences.

Material and Methods.

Lines 148-150. The authors should explain in which way they have treated the embryos. For example, have they treated the embryos in an immersion way of what?

We have now added the requested information in the Materials and Methods section.

Lines 159-160. Vanadates serve as structural mimics of phosphates. Thus it acts as a competitive inhibitor of ATPases, alkaline and acid phosphatases, and protein-phosphotyrosine phosphatases. The authors should explain the reason for the use of this chemical as a control.

This chemical was not used as a control, but included in both the NRG-1 and control injections. This is now more clearly indicated in the Materials and Methods section. Pervanadate was added to the injections to ensure that pSTAT5 signal is not lost during sample preparation during which the larvae were kept in room temperature for 20 minutes after injection. The peak for pSTAT5 signal according to our previous research after NRG-1 stimulation is already at around 1-2 minutes and after 15 minutes the signal is already fading. We did attempt the experiment without the pervanadate and the results indeed were similar although the difference between the control and NRG-1 injections was less pronounced.

Lines 179-181. The fish cells are partially autofluorescent. The authors did not use any system to remove the autofluorescence or, perhaps they lack to indicate in the text (i-e- pre-exposure under UV or Sudan black treatment).

While the authors agree on the potential contribution of autofluorescence on the background signal, this was not considered a significant confounding factor in the experimental conditions described in the manuscript. Indeed, the myosin heavy chain antibody gave a clear bright signal and the STAT5 stainings were validated with the loss of signal in the Stat5b targeting CRISPR/Cas9 treated zebrafish.

Results. This section should be rewritten in order by differentiating the data from zebrafish, murine and humans. For example, I can guess that the title on line 330 is referred to zebrafish, but it is not indicated in the title and the text.

We have now more clearly separated the results from mice and zebrafish.

Discussion. This section should be revised in light of the previous suggestion because not bring the reader to have a clear idea of the importance of this research. Thus, I suggest preparing a clear discussion on 1) who or what can stimulate the NRG1 release by endothelial cells (or also from other activated cells, i.e. endocardial); 2) if this release is similar in vertebrates studied models; 3) If the pathway studied is similar and when it is different. All these points should be documented by references. Moreover, the authors could correlate the manuscript with a draw that explains the signalling pathway that they suggest.

We have now added the suggested information on NRG-1 release in the Introduction and added a new paragraph to the Discussion where we compare the reported differences of the NRG-1/ERBB4 pathway in mice and in zebrafish. In addition, we have included a new figure (Figure 7) that explains the signaling pathway, as recommended by the Reviewer.

References:

The molecular pathways that direct the process of reactivation of proliferation processes and hypertrophy are beginning to be elucidated with evidence that fibroblast growth factors, and microRNAs involvement that can be the starting time before NRG1 expression. Thus, in Mammals as well as fish the authors should read and mention some of the linked previous research (J Physiol 596.23 (2018) pp 5625-5640; Nat Med. 2007 May;13(5):613-8. doi: 10.1038/nm1582; Cardiovasc Res (2015) 107, 487-498 doi:10.1093/CVR/cvv190; Cell Death Discovery 4, 41 (2018) https://doi.org/10.1038/s41420-018-0041-x)

We have added a new paragraph to the discussion section where we discuss the interplay of the NRG-1 signaling pathway with other pathways reported to induce cardiomyocyte growth.

MINOR:

References 4 and 5 are pre-print, please indicate the correct final reference

We again apologize for the mistake. The references are now amended to refer to the correct articles published in Nature.

Reference 14 resulted in an invalid URL address. The manuscript resulted non-existent

We apologize for the mistake. The URL had accidentally doubled in the reference. We have now amended the URL.

Reference 19 is incomplete

Reference 20 not exhaustive is a personal communication, thus should be removed from here and reported in the text as "personal communication"

Reference 27 is incomplete

Reference 42 is incomplete

Reference 69 is incomplete

We apologize for the mistakes. We have amended the shortcomings in the reference section.

Reviewer #1 (Significance):

The Manuscript could be interesting for the readers, but need a deeper revision in the presentation and mainly in the Result-Discussion section.

The Results and Discussion sections have now been revised as recommended by the Reviewer.

After the revision, the manuscript will be of marked interest to cardiologists.

The authors agree and wish to thank the Reviewer for valuable comments.

Reviewer #2 (Evidence, reproducibility and clarity):

RC-2022-01698 Review

The authors report that NRG-1/ERBB4 signaling regulates activation of STAT5b and its target genes Igf1, Myc and Cdkn1a in murine cardiomyocytes, both in vitro and in vivo. STAT5b is a key activator in mediating NRG-1-induced cardiac hypertrophy in rodents. The NRG-1-ERRB4-STAT5 signaling axis is conserved in vertebrates since it regulates cardiomyocyte hyperplasia in zebrafish and is active in human hearts with pathological hypertrophy. Mechanistically, dynamin-2 was shown to control the cell surface localization of ERBB4 and its inhibition downregulates the NRG-1-ERRB4-STAT5 signaling pathway in hypertrophic and hyperplastic cardiomyocyte growth.

The study is reasonably well conducted, experiments are controlled and quantified and most conclusions drawn by the authors are supported by their own data.

This work is of high significance since it uncovers a mechanism responsible for cardiomyocyte hypertrophy which is perturbed in pathological cardiac hypertrophy. This finding opens the possibility that targeting STAT5 activation in patients with cardiomyopathy and heart failure might ameliorate the disease. It is of special note that NRG-1-ERRB4-STAT5 signaling promotes cardiomyocyte proliferation in zebrafish, a species known for its high cardiac repair capacity. This suggests the intriguing possibility that Stat5 could play an important role also in heart regeneration.

A major shortcoming is the presentation of the scientific questions and what this paper is abou could be improved.

As noted also above, the Introduction, Results and Discussion sections have now been revised as recommended by the Reviewers.

Major comments:

1. The use of the NRG1 scavenger should be validated. The provided reference #28 does not validate it. In the reference ____#28, it is reported that ERBB4 phosphorylation is downregulated with the scavenger in mice treated with AAV-VEGFB. The effect of the expression of the NRG-1 scavenger is detectable by western analysis in the mice treated with the AAV-VEGFB (Figure 6A,E) since the treatment induces prolonged activation of ERBB4 by upregulating the release and synthesis of ERBB4 ligands. Additionally, in a control pull-down experiment, the NRG-1 scavenger did bind NRG-1 from mouse serum (please see Rebuttal Figure 1).

It is conceivable that direct manipulation of STAT5 might have effects independent from NRG-1/ERBB4 signaling and therefore might affect also hyperplasia in addition to hypertrophy. This study would benefit from additional experiments showing the proliferation rate (quantified with e.g. H3P or Aurora B) upon Stat5 knockdown or ERBB inhibition in murine cardiomyocytes.

The proliferation rate of adult cardiomyocytes in uninjured models has been reported to be very low, less than 1% (Bergmann et al., 2009. Science. 324:98-102). For this reason we employed an immortalized dividing murine adult cardiomyocyte cell line, HL-1 Claycomb et al., 1998. Proc Natl Acad Sci U S A. 17:95:2979-84.), to address the question raised by the Reviewer. HL-1 cells were transduced with control and Stat5b-targeting shRNAs and cultured on 24-wells in the presence of NRG-1. The amount of HL-1 cells treated with Stat5b-targeting shRNAs was significantly smaller as compared to cells treated with control shRNA 72 hours after transduction (Rebuttal Figure 2A). However, it seems that increased cell death significantly contributed to the observed decrease in cell number as an increase of dead cells was observed in wells treated with Stat5b shRNAs when the cells were stained with a cell permeable (blue) and cell-impermeable (green) nuclear stain (Rebuttal Figure 2B-C). In accordance, the expression of the proliferation marker PCNA was unaltered by the Stat5b shRNA treatment in western analyses (Rebuttal Figure 2D). Thus, it seems that STAT5b does not control the proliferation rate but the viability of dividing adult mouse cardiomyocytes. The observation is not surprising since STAT5b has been reported to control the expression of the anti-apoptotic BCL2 and BCL-xL in adult cardiomyocytes (Chen et al. 2018. Cardiovasc. Res. 114:679-689).

In lines 504-507, the authors write: "In accordance, the target genes of STAT5b IGF1 and MYC have been associated with hypertrophic and hyperplastic growth, respectively, implying that the STAT5b mediated hypertrophic growth may be mediated by the expression of MYC and the hyperplastic growth by the expression of IGF1". The authors cannot make this conclusion because both MYC and IGF1 are downregulated in murine cardiomyocytes. If hyperplastic growth is not regulated by NRG-1-ERRB4-STAT5 signaling in mice, then IGF1 expression should be unaffected by any manipulation of the pathway. It is suggested that the authors modify this statement to accurately reflect their data.

We have decided to remove this conjecture from the discussion as suggested by the Reviewer.

In lines 564-566, the authors write: "In accordance with clinical applicability, administration of NRG-1 and the protein product of the STAT5b target gene IGF1 has demonstrated success in attenuating dilated cardiomyopathy in clinical trials". STAT5 is already activated in pathological cardiac hypertrophy as shown in Figure 6C-D. How can the authors explain how administration of the STAT5 target gene IGF1, thus potentiating the effect of STAT5 activation, could reduce hypertrophy and thus ameliorate the disease?

Left-ventricular hypertrophy is considered a compensatory response to increased pressure-overload and only if left unattended can lead to dilated cardiomyopathy. There is growing evidence that NRG-1/ERBB4 signaling is lost when the compensatory hypertrophic growth advances to dilated cardiomyopathy (Rohrbach et al., 2005. Basic Res. Cardiol. 100:240-9; Rohrbach et al., 1999. Circulation. 100:407-12.) which may suggest that the loss of the NRG-1/ERBB4 signaling is one of the factors that contributes to the progression of the disease. Therefore it would be feasible that the reactivation of the signaling pathway that was active during the compensatory response could ameliorate the disease. As we also indicate that the NRG-1/ERBB4/STAT5 pathway is involved in the proliferative growth of cardiomyocytes at embryonic stage in zebrafish, an organism known for its high cardiac regenerative capability (Poss et al., 2002. Science. 298:2188-90.), it is also feasible that the NRG-1/ERBB4/STAT5 pathway is involved in cardiac regeneration. Lastly, there is growing evidence that NRG-1, IGF-1 and STAT5b are also involved in cardiomyocyte survival (Mehrhof et al., 2001. Circulation. 104:2088-94; De Keulenaer et al., 2019. Circ. Heart Fail. 12:e006288; Chen et al., 2018. Cardiovasc. Res. 114:679-689) and therefore could alleviate the symptoms of dilated cardiomyopathy by compelling the cardiomyocytes more resistant to cell death. There are reports that suggest that cardiomyocyte apoptosis is increased and putatively even has a causal role in dilated cardiomyopathy (Yamamoto et al., 2003. J. Clin. Invest. 111:1463-74; Wencker et al., 2003. J. Clin. Invest. 111:1497-504.).

Minor comments:

1. Increase in cardiomyocyte numbers is better evidence for NRG-1-induced hyperplasia in zebrafish compared to increase in ventricle area. It is recommended that the authors swap Figure 3A-B with Supplementary Figure 3 in order to show more clearly the different role of NRG-1 in zebrafish compared to rodents. We have now moved original Supplementary Figure 3 into new Figure 3A as recommended by the Reviewer.

The authors should report the p-value for all experiments including those considered not statistically significant.

The non-significant P-values have now been added to the figures.

Reviewer #2 (Significance):

General significance of the reserach ms is high.

The authors wish to thank the Reviewer for valuable comments.

However, the ms is written and the study is conducted with little direction. Perhaps the authors could spend more effort on clearly explaining what the direction of their paper is.

We have now reconstructed the manuscript and paid extra attention into explaining the direction and aim of the research.

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Referee #2

Evidence, reproducibility and clarity

The authors report that NRG-1/ERBB4 signaling regulates activation of STAT5b and its target genes Igf1, Myc and Cdkn1a in murine cardiomyocytes, both in vitro and in vivo. STAT5b is a key activator in mediating NRG-1-induced cardiac hypertrophy in rodents. The NRG-1-ERRB4-STAT5 signaling axis is conserved in vertebrates since it regulates cardiomyocyte hyperplasia in zebrafish and is active in human hearts with pathological hypertrophy. Mechanistically, dynamin-2 was shown to control the cell surface localization of ERBB4 and its inhibition downregulates the NRG-1-ERRB4-STAT5 signaling pathway in hypertrophic and hyperplastic cardiomyocyte growth.

The study is reasonably well conducted, experiments are controlled and quantified and most conclusions drawn by the authors are supported by their own data.

This work is of high significance since it uncovers a mechanism responsible for cardiomyocyte hypertrophy which is perturbed in pathological cardiac hypertrophy. This finding opens the possibility that targeting STAT5 activation in patients with cardiomyopathy and heart failure might ameliorate the disease. It is of special note that NRG-1-ERRB4-STAT5 signaling promotes cardiomyocyte proliferation in zebrafish, a species known for its high cardiac repair capacity. This suggests the intriguing possibility that Stat5 could play an important role also in heart regeneration.

A major shortcoming is the presentation of the scientific questions and what this paper is abou could be improved. Major comments:

1. The use of the NRG1 scavenger should be validated. The provided reference #28 does not validate it.
2. It is conceivable that direct manipulation of STAT5 might have effects independent from NRG-1/ERBB4 signaling and therefore might affect also hyperplasia in addition to hypertrophy. This study would benefit from additional experiments showing the proliferation rate (quantified with e.g. H3P or Aurora B) upon Stat5 knockdown or ERBB inhibition in murine cardiomyocytes.
3. In lines 504-507, the authors write: "In accordance, the target genes of STAT5b IGF1 and MYC have been associated with hypertrophic and hyperplastic growth, respectively, implying that the STAT5b mediated hypertrophic growth may be mediated by the expression of MYC and the hyperplastic growth by the expression of IGF1". The authors cannot make this conclusion because both MYC and IGF1 are downregulated in murine cardiomyocytes. If hyperplastic growth is not regulated by NRG-1-ERRB4-STAT5 signaling in mice, then IGF1 expression should be unaffected by any manipulation of the pathway. It is suggested that the authors modify this statement to accurately reflect their data.
4. In lines 564-566, the authors write: "In accordance with clinical applicability, administration of NRG-1 and the protein product of the STAT5b target gene IGF1 has demonstrated success in attenuating dilated cardiomyopathy in clinical trials". STAT5 is already activated in pathological cardiac hypertrophy as shown in Figure 6C-D. How can the authors explain how administration of the STAT5 target gene IGF1, thus potentiating the effect of STAT5 activation, could reduce hypertrophy and thus ameliorate the disease?

Minor comments:

1. Increase in cardiomyocyte numbers is better evidence for NRG-1-induced hyperplasia in zebrafish compared to increase in ventricle area. It is recommended that the authors swap Figure 3A-B with Supplementary Figure 3 in order to show more clearly the different role of NRG-1 in zebrafish compared to rodents.
2. The authors should report the p-value for all experiments including those considered not statistically significant.

Significance

General significance of the reserach ms is high. However, the ms is written and the study is conducted with little direction. Perhaps the authors could spend more effort on clearly explaining what the direction of their paper is.

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Referee #1

Evidence, reproducibility and clarity

The endothelial release of NRG (neuregulin)-1 is a paracrine growth factor activating ErbB (erythroblastic leukaemia viral oncogene) receptor tyrosine kinases on various targets cells epicardial, endocardial/endothelial (autocrine stimulation) and myocardiocytes. It is well known, after the manuscript of the K.D.Poss group that Neuregulin1 induced perivascular cells after injury to the adult zebrafish heart as well as mammalian cells. Inhibition of the Erbb2 co-receptor, disrupts cardiomyocyte proliferation in response to injury, whereas myocardial NRG1 overexpression enhances this proliferation. Thus, seems to be clear, that NRG-1 could stimulate regenerative, inflammatory, fibrotic, and metabolic processes In uninjured zebrafish, the reactivation of Nrg1 expression induces cardiomyocyte dedifferentiation, overt muscle hyperplasia, epicardial activation, increased vascularization, and causes cardiomegaly through persistent addition of wall myocardium (review cited by authors in this MS).

In light of the large amount of these research deals with NRG1, the molecular mechanisms linked and focused to understand the multivariate effects of the NRG1 cascade are welcome. The authors have focused the manuscript on a comparative and translational demonstration of STAT5b involvement in the signal transduction of NRG1.

Although the authors have done several experiments they have presented these in a chaotic way among mice, zebrafish and human biopsies. I can suggest rewriting partially the results section in a way more ordered and readable. Even the discussion is a little bit chaotic and lacks some aspects. For example, who is the stimulator of NRG1 release? Moreover, the literature cited is partially or not correctly reported (in the references section). In my opinion, the authors should revise the manuscript in the light of following suggestions. MAJOR: Title: line 1. The title does not explain the translational aspects of the manuscript. I suggest something like: "STAT5b is a key effector of NRG1/ERBB4-mediated cardiomyocyte growth: a translational approach".

Abstract.

Lines 20-25. The authors should rewrite this section by removing their previous finding ("we") reporting. In lines 25-36 the reported data is not well explained and is not clear who did that. The authors, previously? It is not well explained. Lines 35-37 Please, specify in which type of hypertrophic heart samples (is it from humans?) have been observed the NRG1 pathway.

Introduction.

Lines 44-46. All the cited papers consist of the preprint, thus they could be inserted in the discussion and not in the introduction. In the alternative, they could be inserted here following a sentence "Preliminary study seems to indicate...." Lines 50-51. The references cited are relative only to murine research and not to other animals. Thus, only murine models have been demonstrated. Thus, the assertion could be not valid for zebrafish or humans, thus the authors should point out this.

Material and Methods.

Lines 148-150. The authors should explain in which way they have treated the embryos. For example, have they treated the embryos in an immersion way of what? Lines 159-160. Vanadates serve as structural mimics of phosphates. Thus it acts as a competitive inhibitor of ATPases, alkaline and acid phosphatases, and protein-phosphotyrosine phosphatases. The authors should explain the reason for the use of this chemical as a control. Lines 179-181. The fish cells are partially autofluorescent. The authors did not use any system to remove the autofluorescence or, perhaps they lack to indicate in the text (i-e- pre-exposure under UV or Sudan black treatment).

Results. This section should be rewritten in order by differentiating the data from zebrafish, murine and humans. For example, I can guess that the title on line 330 is referred to zebrafish, but it is not indicated in the title and the text.

Discussion. This section should be revised in light of the previous suggestion because not bring the reader to have a clear idea of the importance of this research. Thus, I suggest preparing a clear discussion on 1) who or what can stimulate the NRG1 release by endothelial cells (or also from other activated cells, i.e. endocardial); 2) if this release is similar in vertebrates studied models; 3) If the pathway studied is similar and when it is different. All these points should be documented by references. Moreover, the authors could correlate the manuscript with a draw that explains the signalling pathway that they suggest.

References:

The molecular pathways that direct the process of reactivation of proliferation processes and hypertrophy are beginning to be elucidated with evidence that fibroblast growth factors, and microRNAs involvement that can be the starting time before NRG1 expression. Thus, in Mammals as well as fish the authors should read and mention some of the linked previous research (J Physiol 596.23 (2018) pp 5625-5640; Nat Med. 2007 May;13(5):613-8. doi: 10.1038/nm1582; Cardiovasc Res (2015) 107, 487-498 doi:10.1093/CVR/cvv190; Cell Death Discovery 4, 41 (2018) https://doi.org/10.1038/s41420-018-0041-x)

Minor

References 4 and 5 are pre-print, please indicate the correct final reference

Reference 14 resulted in an invalid URL address. The manuscript resulted non-existent

Reference 19 is incomplete

Reference 20 not exhaustive is a personal communication, thus should be removed from here and reported in the text as "personal communication"

Reference 27 is incomplete

Reference 42 is incomplete

Reference 69 is incomplete

Significance

The Manuscript could be interesting for the readers, but need a deeper revision in the presentation and mainly in the Result-Discussion section.

After the revision, the manuscript will be of marked interest to cardiologists.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Modulation of protein phosphorylation level is a critical mechanism in the regulation of different cellular processes, whose dysregulation is associated with disease, including cancers. Protein phosphatase (PP)2A is a central phosphatase involved in multiple cellular pathways, including cell cycle, metabolism, and regulation of gene expression. In addition, inactivation of PP2A is required for RAS-mediated human cell transformation, making reactivation of PP2A as a potential therapeutic approach. Aakula, Sharma et al investigated whether RAS and PP2A could co-regulate cellular processes involved in tumorigenesis via the modulation of protein phosphorylation. The authors re-analysed their previously published phosphoproteomics datasets performed after knockdown with siRNAs of RAS (H/K/N), PP2A-A, or PP2A inhibitory proteins (CIP2A, PME1, SET). The authors found a set of phosphosites commonly regulated by RAS and PP2A, which is enriched for proteins involved in the epigenetic regulation of gene expression, including DNA methylation, chromatin remodelling, and chromatin modifications. The authors then investigated how modulating RAS and PP2A activities (by siRNA or small molecule inhibitors) affect the chromatin recruitment of the HDAC1 and HDAC2 proteins, which are part of the NuRD chromatin-remodelling complex. Modulation of RAS and PP2A activities also affects transcription, both with a single GFP gene construct and by RNA-seq, with knockdown of RAS mostly decreasing gene expression while knockdown of PP2A generally associated with increased gene expression. The authors then investigated the genome-wide effects of knocking PP2A on DNA methylation and chromatin accessibility (ATAC-seq) and found a limited number of sites affected.

Major comments:

1. The investigation and characterization of the phosphosites that are common to both RAS and PP2A is an important question, as stated by the authors. However, the authors hardly investigated the potential roles of these common phosphosites (only CHD3 S713 has been partially investigated) but rather relied on knockdown by siRNAs of the factors, which limits the conclusions of the manuscript as it remains unknown whether these phosphosites have any effect on protein activity and/or interactions.
2. The major technical limitation of the manuscript is the dependence on siRNAs to investigate RAS and PP2A. Knockdown by siRNAs takes a long time, which limits the conclusions that can be drawn as the results are going to be a mixture of direct (loss of RAS/PP2A) and indirect (cellular responses to the direct effects) effects. Typically, changes in gene expression, DNA methylation, and chromatin accessibility could be explained, at least in part, by indirect effects of the knockdown (changes in cell cycle, cellular responses to stress induced by the knockdown...). I think it will be important to confirm on some target genes that the main results of the manuscript are direct effects by using known small molecule inhibitors with short treatment time.
3. The genome-wide data do not seem to have been submitted to the GEO (or I could not find the information), which also means that it is not clear how many biological replicates have been performed.
4. Generally, the authors should put more information in the Legends/Methods as several key information are missing (see Minor Comments).
5. The authors should integrate more their RNA-seq, RRBS, and ATC-seq data as these datasets have been generated in the same cell line (I suppose RRBS is also in HeLa, see Minor Comment 2). Do the authors see consistent changes on RRBS/ATAC-seq for the upregulated/downregulated genes?

Minor comments:

1. Did the authors performed a total (with rRNA depletion) or a poly(A)+ RNA-seq?
2. In the Methods section for the RRBS, it is written that the DNA was isolated from the same samples. Is it the same samples as the RNA-seq? More precision is required.
3. It would also be useful to put in the legends the cell line used in each experiment.
4. Figure 3, Figure 4, and Figure S5: I could not find any information on the treatment time and the concentrations of the small molecule inhibitors used. These information need to be added to the legends.
5. Figure 3B: the authors need to performed qRT-PCR to show that the overexpression is similar between the different conditions. Right now, the differences could be explained by a difference in transcription between the different constructs.
6. Also, do the mutations affect CHD3 chromatin association or interaction with other NuRD components? This kind of straightforward experiments would clearly improve the interest of the manuscript as it will provide information on the potential roles of phosphosites.
7. Figure 3C, E, G, and I: A nuclear loading control is required for each experiment. Also, western blots on whole cell extracts are required to see if the changes in nuclear/chromatin level are not just explained by a change in the total expression of HDAC1 and HDAC2 following siRNA treatment.
8. Lines 552-555: I am not convinced that the presence of DOT1L among the regulators associated with open promoter regions provides a direct link between the phosphoproteome and ATAC-seq data. DOT1L is a methyltransferase associated with transcription initiation and transcription elongation and therefore it is not surprising to find this protein in open promoter regions. In addition, to claim a direct link would require data showing that protein phosphorylation of DOT1L regulates its recruitment to promoter regions.
9. Figure 7F/G: Are the overlaps significantly enriched?

Referees cross-commenting

If the manuscript is clearly presented as a ressource paper, I agree with reviewer 1. My major comments 1 and 2 (knockdown of total proteins rather than looking at phosphoresidues, RNAi) can be addressed in the discussion rather than experimentally.

Significance

The mechanistic roles of phosphosites remain generally an understudied area of research while kinases and phosphatases are known to be frequently dysregulated in disease. The generation of a list of phosphosites common to RAS proteins and PP2A is therefore of interest as this will provide targets for further investigation. The authors tested some of the targets by using a siRNA approach, which confirmed the involvement of PP2A in the regulation of gene expression, DNA methylation, and chromatin remodelling/accessibility and of RAS in the regulation of gene expression and chromatin accessibility. However, the authors focused on the proteins rather than the phosphosites, which limits the significance of the work as it remains unclear whether the effects the authors are observing are mediated by changes in phosphorylation level (in addition to the potential issues of indirect effects due to the siRNA approach).

Context:

Loss of PP2A phosphatase activity is required for human cell transformation while RAS is a known oncogene (Chen et al, 2004; Rangarajan et al, 2004). The manuscript investigated which proteins were commonly phospho-regulated by RAS and PP2A activities and found an overrepresentation of proteins involved in transcriptional regulation and epigenetics, which confirms and expands previous observations. PP2A, as part of the INTAC complex that is composed of Integrator and PP2A, has been found to regulate nascent transcription (Vervoort et al, 2021; Zheng et al, 2020). In addition, PP2A activity has also been linked to DNA methylation (Hausser et al, 2006; Kundu et al, 2020; Sunahori et al, 2013) and nuclear localization of several histone deacetylases (HDAC) (Tinsley & Allen-Petersen, 2022).

Audience:

The reported findings will be of interest to people working on the RAS/PP2A-associated cancers, and more generally in the fields of regulation of gene expression, chromatin remodelling, and epigenetics.

Field of expertise:

transcription, chromatin, RNA polymerase II, transcriptional kinases and phosphatases.

References

Chen W, Possemato R, Campbell KT, Plattner CA, Pallas DC, Hahn WC (2004) Identification of specific PP2A complexes involved in human cell transformation. Cancer Cell 5: 127-136

Hausser A, Link G, Hoene M, Russo C, Selchow O, Pfizenmaier K (2006) Phospho-specific binding of 14-3-3 proteins to phosphatidylinositol 4-kinase III beta protects from dephosphorylation and stabilizes lipid kinase activity. J Cell Sci 119: 3613-3621

Kundu A, Shelar S, Ghosh AP, Ballestas M, Kirkman R, Nam H, Brinkley GJ, Karki S, Mobley JA, Bae S et al (2020) 14-3-3 proteins protect AMPK-phosphorylated ten-eleven translocation-2 (TET2) from PP2A-mediated dephosphorylation. J Biol Chem 295: 1754-1766

Rangarajan A, Hong SJ, Gifford A, Weinberg RA (2004) Species- and cell type-specific requirements for cellular transformation. Cancer Cell 6: 171-183

Sunahori K, Nagpal K, Hedrich CM, Mizui M, Fitzgerald LM, Tsokos GC (2013) The catalytic subunit of protein phosphatase 2A (PP2Ac) promotes DNA hypomethylation by suppressing the phosphorylated mitogen-activated protein kinase/extracellular signal-regulated kinase (ERK) kinase (MEK)/phosphorylated ERK/DNMT1 protein pathway in T-cells from controls and systemic lupus erythematosus patients. J Biol Chem 288: 21936-21944

Tinsley SL, Allen-Petersen BL (2022) PP2A and cancer epigenetics: a therapeutic opportunity waiting to happen. NAR Cancer 4: zcac002

Vervoort SJ, Welsh SA, Devlin JR, Barbieri E, Knight DA, Offley S, Bjelosevic S, Costacurta M, Todorovski I, Kearney CJ et al (2021) The PP2A-Integrator-CDK9 axis fine-tunes transcription and can be targeted therapeutically in cancer. Cell 184: 3143-3162 e3132

Zheng H, Qi Y, Hu S, Cao X, Xu C, Yin Z, Chen X, Li Y, Liu W, Li J et al (2020) Identification of Integrator-PP2A complex (INTAC), an RNA polymerase II phosphatase. Science 370

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Referee #1

Evidence, reproducibility and clarity

In this resource manuscript by Aakula et al., the authors reanalyze existing phosphoproteomic datasets to find areas of convergence of proteins and sites regulated by RAS activation on the one hand, and PP2A inhibition on the other. They identify a number of such sites. The validation is relatively modest, showing effects on HDAC1/2 localization, the silencing of an artificial promoter, and then focus on epigenetic regulation in more general experiments. They provide plentiful data and correlations that will be useful for others interested in mechanism of regulation by protein phosphorylation. The major limitation, acknowledged by the authors, is that this is a resource rather than a deep validation of the overlaps.

I have only a few minor specific comments.

The overlap of PP2A and RAS regulated phosphoproteins in the gene ontology networks is made up of small numbers - 3/6 in term 0070087. When only 3 genes are in a category, given the reliability of GO terms, it doesn't generate much excitement.

Likewise the effect of knockdowns of putative targets in NSCLC cells was modest, with 10- 20% decrease in cell viability. I suspect many gene knockdowns might give a similar effect.

Line 299 starts a >1.5 pages long paragraph about CHD3 and HDAC1/2; it would be easier to read if this were two or three shorter paragraphs.

The pulldown data (S5A) is done with over-expressed proteins and shows a weak interaction. Without evidence for endogenous protein interaction, the conclusion that there is a substantial in vivo physiologic interaction between B56α and HDAC1 must be qualified.

Referees cross-commenting

I agree with reviewer 2 that there are shortcomings. If this is viewed as a resource, and not a strong conclusion paper, my feeling is that additional confirmation experiments would not add much. I agree they should be careful to discuss the limitations of the RNAi approach.

Significance

The data suggest that two major cancer mutations converge to influence epigenetic regulators. The data is correlative and will assist future mechanistic studies.

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Reply to the reviewers

[Reviewer's comments]

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

Summary In this article Roure et al address the role of BMP during formation of the ascidian palps, using Ciona intestinalis. Overexpression of BMP (specifically ADMP) from early stages of development results in complete suppression of palp formation, and early loss of the palp forming region (also called anterior neural border ANB). Using p-Smad1/5/8 antibody staining they show a marker of the ANB (FoxC) is expressed in a region negative for BMP signals. Inhibition of BMP signals is not sufficient to produce ectopic ANB. However, treatment with FGF protein from very early stages (8-cell stage) plus inhibition of BMP signaling (from 8-cell stage) increased FoxC expression. Looking at later stages of development the authors show that in a U-shaped expression domain of Foxg, Smad1/5/8 is active in the ventral-most part, which is expected to form the ventral-most palp. BMP2 treatment from gastrula stages results in loss of the ventral most palp expression of Isl and repression of ventral Foxg expression. Inhibition of BMP signaling from gastrula or neurula stages results in failure of a U-shaped pattern of Isl expression to resolve into the three palp expression domains, and by late tailbud stages, Sp6/7/8/9 (proposed as a repressor of Foxg in the inter-palp territory) expression is reduced and the numbers of specific cell-types making up the palps is increased. These cells are present in a single large palp of dorsal identity. Thus, inhibition of BMP from early gastrula stages results in a single palp made of more cells than the three palps of control larvae, presumably due to recruitment of cells usually present between the palps. The authors then show a similar phenotype in another ascidian species Phallusia mammillata. Using their previous RNA-Seq data of embryos treated with BMP4, they looked for potential novel palp markers and identify a further eight novel markers of the palps. Looking further into this data and at a list of 68 genes expressed in palps (but not exclusively) they find that in whole embryo RNA-Seq data 70% were regulated by BMP signaling, mostly repressed, but some activated by BMP. 30 of these genes were regulated by Notch. Apart from the confusion I explained in my comments below, the data seems to be carefully presented and interpreted. Overall, this manuscript presents a more detailed analysis of the role of BMP signaling during ascidian palp formation, but it remains to be precisely understood.

[Response]

We thank the reviewer for the evaluation of our work.

Major comments

1) I am a little confused about the timing of the protein treatments. In Figure 2, the authors show nicely that at the neurula stages, P-Smad1/5/8 staining abuts the FoxC ANB territory. Then at late neurula P-Smad1/5/8 is detected in the ventral-most part of the Foxg U-shaped part of the palp forming region, presumably the ventral most palp. However, the protein treatments with BMP (and FGF) are carried out from the 8-cell stage, which seems a bit drastic and embryos look difficult to orientate (e.g. Fig. 3D).

[Response]

We first would like to clarify the issue raised from Figure 3. Actually, Figure 3D was the only case where the embryo was shown from the side (the description as a lateral view was inadvertently omitted in the legend). We have now modified Figure 3 by properly showing only dorsal (neural plate) views and lateral views in insets when necessary. In addition, we have added schemes of embryos depicting the main tissues we have examined (palps, CNS and epidermis) and their localization depending on the treatments.

Regarding the timing of treatments, we performed them at the 8-cell stage to make them manageable to perform. At the latest, bFGF treatment should be performed at the 16-cell stage (before neural induction at the 32-cell stage), while BMP2 treatment should be performed at the 64-cell stage (before the onset of Foxc/partial effect at early gastrula (St. 10)). In principle, sequential treatment (first bFGF, then BMP2) could thus be performed. Since earlier treatments, produce the same effects, we reasoned that combined treatments from the 8-cell stage should be equivalent and would avoid fastidious repeated manipulation of the embryos that could negatively impact their development. We are convinced that the way we performed the treatment has no impact on our results (except for the treatment by bFGF alone on Foxc as already discussed in the text) and conclusions.

While BMP-treatment from early stages inhibits all palp gene expression and any sign of palp formation (Figure 1), treatment with BMP from the early gastrula stage, when Smad1/5/8 is detected only in mesendoderm cells and before it is detected in any ectoderm, is sufficient only to block ventral palp formation and cause a partial down-regulation of FoxC expression in the ANB. Thus, there seems to be a discrepancy between the roles proposed for BMP during ANB and palp formation as judged by P-Smad1/5/8 staining and the temporal evidence from BMP- and BMP-inhibitor treatment. Do the authors have some explanation for why they need to treat at least one hour before the BMP-mediated patterning mechanism (as indicated from the P-Smad1/5/8 staining) is taking place? For example, could the authors check how long it takes DMH1 to inhibit P-Smad1/5/8 positive staining? Or BMP to strongly induce P-Smad1/5/8? This seems to be a simple experiment and might go some way to explaining why they need to treat embryos much earlier than I would have thought necessary.

[Response]

We understand the reviewer's concerns, but we do not think that there are major discrepancies in the timing of events. The main rationale is to consider the onset of expression for the main genes of interest. We have examined their dynamics of expression in details, but we do not show them since our conclusions are in agreement with a previous report (Figure 1 from Liu and Satou, 2019). We have summarized the data in the modified Figure 2. Foxc can be detected from early gastrula stages (St. 10) when the palp precursors consist of a single row of 4 cells. This is the exact developmental time when the treatment with BMP2 has partial effects (Figure 4). Once the cells divide to make 2 rows of 4 cells robustly expressing Foxc (St. 12), BMP2 treatment has no effect on Foxc. Similarly, DMH1 treatment has no effect from late neurula stage (St. 16) (Figure 4) that corresponds to the onset of Sp6/7/8/9 expression. We thus consider that modulating BMP pathway has no effect once key regulatory genes have acquired a robust expression in their normal domains. We have enhanced these points in the main text (lines 205-208, lines 228-229).

We think the above discussion should address the points raised by the reviewer. In the contrary, we are willing to perform the suggested experiments.

2) It does not make sense to me that BMP treatment from gastrula stage blocks only ventral palp formation (Figure 4) and ventral Foxg expression (Fig. 5G). In particular, it is the ventral palp region which is positive for P-Smad1/5/8 (Fig.2I,J) so I would not expect the ventral palp to be the most sensitive to BMP-treatment.

[Response]

We were, like the reviewer, surprised by the phenotype. The time window to obtain this phenotype is quite narrow, and most likely deals with the full acquisition of the palp fate ('consolidation' of Foxc expression, onset of Foxg). This is actually a phenotype that we have not characterized in details. And such a characterization may help clarify the role of BMP: does BMP regulate papilla/inter-papilla fates only for the ventral palp or for all three palps? Does BMP 'only' regulate the dorso-ventral identities of the palps?

To better understand the role of BMP in palp formation, we propose to describe this specific phenotype: loss of ventral palp induced by BMP2 treatment at St. 10. We propose to test the following hypotheses. What is the fate of the ventral palp? Conversion into epidermis (more ventral fate)? Conversion into inter-papillar fate? What is the identity of the 2 remaining presumptive palps? Do they still have a dorsal identity? Are they converted into ventral palps? This is part of the proposed experiments for a revision.

Minor comments line 185 I see what the authors are trying to say but I don't agree that BMP limits the domain of FoxC expression as inhibition of BMP has no effect on FoxC. Rather BMP has to be kept out of the ANB in order to allow ANB formation.

[Response]

We have modified the sentence (lines 195-196).

The relationship between Foxg and Sp6/7/8/9 expression is not really clear and it would be better to do this with double ISH if the authors want to show mutually exclusive expression domains, or at least provide a summary figure.

[Response]

We have modified Figure 5 by adding schematic representations of our understanding of the expression patterns in relation to the different precursors of the palp lineage.

In case the reviewer does not find this clarification sufficient, we propose to perform the double fluorescent in situ hybridizations as part of the revision plan.

Line 218, I do not see the data showing that Isl is expressed at a U-shape at st. 23, it seems to be expressed in three dots, unless embryos are treated with DMH1.

[Response]

We apologize for the misunderstanding since the sentence was not clear. We referred to the U-shaped Isl expression under BMP inhibition. Indeed, Isl starts to be expressed in 3 separate domains in the palp forming region, and not following a U-shape as its upstream regulator Foxg (Liu and Satou, 2019). We amended the sentence (lines 234-235).

Figure 6B, G. It could be nice to show a close up of the palps to see elongated cells.

[Response]

The close up pictures have now been added in the modified Figure 6.

Figure 6K. It is better to use a statistical test to support the authors conclusions.

[Response]

As suggested, we have performed a statistical evaluation (Mann-Whitney U test) of the cell counts. The p-values are presented in Figure 6Q. The slight increase of Celf3/4/5/6 is not statistically significant, but it does not impact our conclusion that the number of papilla cells increases following BMP inhibition.

It could be nice to provide a timeline for Smad1/5/8 signaling and the role for BMP signals that are proposed in this manuscript as a summary diagram.

[Response]

Following the suggestion, we have added summary diagrams in Figure 2 for BMP signaling in relation to lineages and gene expression.

lines 66-74 is lacking references.

[Response]

This is now corrected (lines 70-80).

Reviewer #1 (Significance (Required)):

Significance While it is still not clear how BMP signals are established (which ligands for example) and their precise role in palp formation, this manuscript adds more information to our current understanding of the role of BMP signaling during palp formation. In particular it shows that BMP signals need to be kept out of the ANB for its formation and that it is required to resolve the later forming palp territory into three discrete palp regions. However, there is some way to go before this is fully understood. This article will certainly be of interest to ascidian developmental biologists trying to understand the formation and patterning of the larval PNS. It may also be of some interest to evolutionary biologists trying to understand the relationship between the telencephalon territory of vertebrates and the palp forming territory of ascidians as some links have been proposed between these two developmental territories (e.g. line 78).

[Reviewer's comments]

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

Summary. The manuscript presents a detailed examination of how dynamic changes in BMP signaling during the development of the ascidian larval palps. Early in development BMP inhibition is responsible for the formation of a large field within the neuroectoderm that includes, among other fates, the presumptive palps. As development progresses, the territories of BMP activity/inhibition appear to be spatially refined within the palp-forming territory to specify palp versus interpalp fate. The experiments are presented with sufficient replication and statistical rigor.

[Response]

We thank the reviewer for the evaluation of our work.

Major Comments.

1. The researchers should look at otx expression in pFOG>Admp overexpressing embryos. It is difficult to assess from Figure 1, but it appears possible the the entire anterior sensory vesicle (not just the palps) are absent in the pFOG>Admp embryos (can the authors say briefly whether other ectodermal structures such as the atrial primordia or the oral siphon are still present?). Thus, is it possible that the entire a-lineage is disrupted? This would be an important distinction to make: are the defects attributed to experimental BMP activation specific to the palps, or are they more widespread in the anterior neuroectoderm? If the entire a-lineage is mis-fated, might this change the interpretation of the role of BMP inhibition? For example, might the formation of the palps depend on the proper development of the neighboring anterior neural plate? To address this concern, the authors should use a different driver to restrict Admp overexpression only to the palp forming region.

[Response]

In Figure 1, we show that Celf3/4/5/6, a general neural marker was still expressed in pFog>Admp embryos. We explain, in the Figure 1 legend, that this most likely corresponds to the CNS. It does not demonstrate that the anterior sensory vesicle (a-line induced CNS lineage) is still present. Unfortunately, Otx cannot be used as a suitable marker since it is also expressed in the posterior sensory vesicle (A-line lineage) (Hudson et al., 2003). Other a-line markers do exist. However, determining their expression at tailbud stages may not be conclusive since it is most likely that the patterning of the sensory vesicle (hence the expression of these markers) is modified after BMP activation. We have presented in former Figure 3 and Figure S1, strong evidence that the a-line neural lineage is intact at the neural plate stage. To better communicate these data, we have combined then in a modified Figure 3 that includes all markers examined and interpretative embryonic schemes. We show that, following BMP2 treatment, Otx and Celf3/4/5/6 were downregulated in the palp lineage but otherwise normal. Consequently, the a-line CNS lineage is most likely not affected by BMP pathway activation. This does not mean that its later derivatives form normally, but this is an issue that we have not addressed. A previous report indicates that BMP activation leads to Six1/2 repression and, possibly, the absence of oral siphon primordium (based on the images, no description in this paper) (Figure 1 from Abitua et al., 2015).

We think that we have addressed the concern of the reviewer, but would like to comment on the suggested experiment. It is very difficult to find a driver that would allow BMP activation only in the palp lineage (by overexpressing a constitutive active BMP receptor for example). a-line neural linage and palp lineage are intimately linked and separate at gastrula stages (St. 10). The regulatory sequences of Foxc, the first palp specific gene that we know, would thus be interesting. But it is most likely too late according to our whole embryo protein treatments (Figure 4). In agreement with this assumption, overexpressing Bmp2/4 (another BMP ligand) using the regulatory sequences of Dmrt (a master regulator of the palp+a-line CNS lineage expressed just before Foxc) does not apparently abolish palp formation (Extended Data Figure 5 from Abitua et al., 2015).

1. The authors hypothesize that papilla versus inter-papilla fate is controlled by differential BMP signaling. Is it possible to show differential P-Smad staining in papilla versus inter-papilla territories, as in Figure 2 for earlier gastrula-stage embryos? This data would make the authors hypothesis much more compelling. It appears that the authors have the necessary reagents.

[Response]

The actual lineage and fate segregation of papilla and inter-papilla lineage has not been determined as far as we know. Our current understanding comes from indirect evidence from gene expression and gene function, in particular from the study of Foxg and Sp6/7/8/9 by Liu and Satou (2009). Papillae originate from the 3 Foxg/Isl positive spots that are visible at very early tailbud stages. At earlier stages, Isl is not expressed and Foxg is expressed with a U-shape (Figure 5). Within this U, it is most likely that the segregation of papilla and inter-papilla fates takes place when Sp6/7/8/9 starts being expressed at late neurula stages. It is thought that Sp6/7/8/9+/Foxg+ cells will become inter-papilla cells while Sp6/7/8/9-/Foxg+ will become papilla. Our data indicate that BMP signaling is active in the future ventral papilla. We have mapped these data on schematics in the modified Figure 2.

Minor Comments.

1. There is no mention of panels Figure 1 U and V in the text. In the figure legend they are misidentified as panels S and T.

[Response]

This has been corrected.

Very small issue with English usage that occurs throughout the manuscript. The authors should check the use of "palps" versus "palp", particularly when expressions such as the following are used: "palps formation", "palps network", "palps lineage", "palps differentiation", "palps molecular markers", "palps neuronal markers", "palps phenotypes", etc . For example, the sentence, "Here, we show that BMP signaling regulates two phases of palps formation in Ciona intestinalis", should read instead "Here, we show that BMP signaling regulates two phases of palp formation in Ciona intestinalis".

[Response]

Thank you, we have corrected these mistakes.

It would be worth mentioning possible relationships between the tunicate palps and the adhesive glands for larval fish and amphibians. Are there common mechanisms? All of these are anterior ectoderm derivatives.

[Response]

Thank you for the suggestion. We have added a section on that topic in the discussion (line 358).

Please consider providing references in the Introduction for the sentences which end on the following lines of text: 36 ( . . . is the sister group of vertebrates), 46 ( . . . and sensory properties), 48 ( . . . the secretion of adhesive materials), 57 ( . . . on the nervous system in chordates), 68 ( . . . also known as Ap2-like), 74 ( . . . anterior neural territories)

[Response]

References have now been added.

To provide extra emphasis and to help the figures to stand alone with their respective legends, can you mention in the legend for Fig. 2 that D and E are controls? Also, can a brief legend be provided for S2 to give overall indication of staging, scale, orientation, etc.?

[Response]

Actually, the original Fig 2D and 2E correspond to treated embryos as explained in the legend. For clarity, these embryos have been separated from control embryos in the modified Figure 2.

Figure S2 has modified and a legend has been added.

Reviewer #2 (Significance (Required)):

Significance.

This study presents an advance in our understanding of the fine-structure regulation of BMP signaling in sculpting neuroectoderm derivatives. While this study is potentially of broad interest, the authors fail to fully discuss the comparative aspects of this study in the context of conserved chordate developmental mechanisms. This could be remedied without too much difficulty in the Discussion section.

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

Summary: This paper explores the role of BMP signaling for palp formation in ascidians using gain and loss of function approaches. The paper shows that while BMP at early (gastrula) stages prevents formation of the Foxc-positive palp ectoderm in Ciona, at later stages it appears to be essential for separation of the palps (possibly by promoting differentiation of interpapillary cells). The paper further shows that BMP plays similar roles in a different ascidian, Phallusia mammillata. Using previously published RNA-Seq results for the latter species after BMP up-regulation, the authors were able to identify additional BMP-responsive genes expressed in the palp region of ascidians.

[Response]

We thank the reviewer for the evaluation of our work.

Major comments: However, while the effect of BMP overexpression at early stages has been confirmed by two independent strategies (electroporation of the BMP agonist ADMP and BMP2 treatment), the effects of late BMP activation as well as the effects of BMP inhibition at both early and late stages have been studied exclusively by pharmacological treatments with a single BMP signaling agonist (BMP2) and antagonist (DMH1). To substantiate these findings and rule out unspecific side effects, it would have been desirable to verify them with alternative strategies.

[Response]

The reviewer may have missed some of our data. We have shown that BMP inhibition through overexpression of the secreted antagonist Noggin via electroporation using the early ectodermal driver pFog gives the same phenotypes as DMH1 treatment. The effects on Foxc * were presented in Figure S1, and are now presented in the modified Figure 3 (line 170). We also showed that the morphological Cyrano phenotype was observed with Noggin overexpression (modified Figure 6H). We now present a novel Figure S1 with expression of Isl and Celf3/4/5/6* following Noggin overexpression, and stress the use of this independent way of inhibiting BMP (lines 260-264). Given that early or late BMP inhibition lead to the same phenotype, we do not consider that overexpressing Noggin at gastrula stages is necessary.

Regarding BMP activation from gastrula stages, we have only used BMP2 treatment. It may be possible to overexpress Admp using promoters active in the palp lineage such as the ones of Dmrt, Foxc or Foxg. However, it may be difficult to phenocopy the phenotype obtained using BMP2 protein (loss of ventral palp), for two reasons. First, the precise timing to reach high BMP activation is not tightly controlled using such a method. Hence, all drivers should be tested. Second, the different promoters are active progressively later in development and in more and more restricted regions. Consequently, we consider that this requires a huge effort to validate a method (BMP protein treatment) that we already validated for the early effects and that has been used in several publications.

Therefore, while this study provides some new insights into the role of BMP in the specification of the palp forming region and subsequent palp development in ascidians, the evidence provided is relatively weak. Moreover, the scope of the study is quite limited. While identifying some BMP-responsive genes expressed in the palp region and describing the effects of BMP dysregulation on palp morphology, the study does not provide further insights into the underlying mechanisms how BMP patterns this region or affects subsequent palp formation.

[Response]

We are surprised by the appreciation of the reviewer describing our work as 'some new insights'. To our knowledge, this is the first report addressing the role of BMP signaling in palp formation at the molecular level. The only previous report by Darras and Nishida (2001) describes solely the morphology of the palps following overexpression of Bmp2/4 and Chordin overexpression by mRNA injection. We have brought significant novel findings 1) two important steps in palp formation with a precise description of the cellular and molecular actors, and a proposed function for BMP at each step, 2) evidence for conservation of this process in different ascidian species and 3) significant enrichment in the molecular description of this process. Moreover, the reviewer does not ask for specific items, we thus feel in the impossibility to offer satisfaction.

Minor comments:

• 63: ...as the anterior...

[Response]

Corrected.

• 68, 71, 74: references missing

[Response]

References have now been added.

• 73: better: anterior neural territories and placodes

[Response]

Corrected.

• 76: palp territories also share molecular signature with anterior (eg. olfactory) placodes, not only telencephalon

[Response]

Corrected.

• 106: awkward sentence

[Response]

Corrected.

• 114: at what stage was ADMP electroporated?

[Response]

Electroporation of plasmid DNA is performed in the fertilized egg. Transcription of the transgene is controlled by the driver. In this case, with pFog, it occurs from the 16-cell stage. This precision has been added in line 121.

• 134: to facilitate comparison between stages it would be useful to label cells in Fig. 2(eg. which are a-line and b-line cells? Where is the border between them?)

[Response]

As suggested by the reviewer, we have modified Figure 2 with embryo outlines and schemes to better appreciate where BMP signaling is active.

• 152: since Foxc and Foxg overlap with pSMAD1/5/8 at neurula but not gastrula stages, do you know whether this is due to a dorsal expansion of BMP activity or a ventral expansion of Foxc/Foxg expression? Again, labeling of the nuclei would help

[Response]

The change corresponds to a dorsal expansion of P-Smad1/5/8. Our conclusion comes from combining nuclear staining (not shown for simplicity) and available fate maps. The results are presented in schematic diagrams of embryos in frontal views in the modified Figure 2.

• 174: the description is not clear here; what proportion of embryos did show reduction versus expansion of expression?. Why is the reduction shown in Fig.3 D asymmetrical?

[Response]

The proportions are now indicated in line 184.

We apologize for the impression led by Fig 3D. Actually, it was the only case where the embryo was shown from the side (the description as a lateral view was inadvertently omitted in the legend). It did not show an asymmetric repression but an ectopic expression. We have now modified Figure 3 by properly showing only dorsal (neural plate) views and lateral views in insets when necessary. In addition, we have added schemes of embryos depicting the main tissues we have examined (palps, CNS and epidermis) and their localization depending on the treatments. We hope that the results are now clearly presented.

• 198: ... of endogenous...

[Response]

Corrected (line 213).

• 208: I suggest to highlight the regions of changes in Fig. with asterisks/arrows etc.

[Response]

We have added schematic embryos to highlight expression changes in the modified Figure 5.

• 218: contrary to what is stated here, there is no depiction of u-shaped Isl1 expression in control embryos of Fig. 4

[Response]

As also pointed by reviewer 1, we apologize for the misunderstanding since the sentence was not clear. We referred to the U-shaped Isl expression under BMP inhibition. Indeed, Isl starts to be expressed in 3 separate domains in the palp forming region, and not following a U-shape as its upstream regulator Foxg (Liu and Satou, 2019). We amended the sentence (lines 234-235).

• 220: the cell shapes referred to here cannot be seen in Fig. 4 (too small)

[Response]

We have modified Figure 6 to include close up of the palps.

• 271: the description here is confusing: first you talk about 53 genes and the mention palp expression of 12/26. Where does number 26 come from? And why was in situ done then for 27 additional genes? Also, while the comparison with previously published RNA-Seq data was valuable in uncovering additional BMP-sensitive palp markers, it does not provide any substantial new insights into how BMP patterns this territory.

[Response]

We have amended the sentence to make it clearer (lines 291-295).

• line 624: where

[Response]

Thank you. Corrected line 731.

• Fig. 2: to facilitate comparison between stages it would be useful to label cells (eg. which are a-line and b-line cells? Where is the border between them?)

[Response]

Already responded above.

-Fig. 3: Why is the expression in D asymmetrical? In the main text you write that expression is expanded in some embryos but reduced in others - Please show examples also of the expanded phenotype and give numbers

[Response]

Already responded above.

• Fig. 6: small panels in I, L, N need to be explained (single channels), white signal needs to be explained (overlap ?)

[Response]

We used white for better display of separate single channels. Given the confusion and the good quality of the 2 color fluorescent in situ images, we removed these panels in the modified Figure 6.

White in K and L correspond to overlap (explained in the legend).

• Fig. S2: legend is missing

[Response]

This has been amended.

Reviewer #3 (Significance (Required)):

Since the study does not provide substantial new insights into the mechanisms how BMP patterns the palp forming region or affects subsequent palp formation in ascidians, it will be of interest mostly for a specialized audience in the field of developmental biology.

[Response]

We do not agree with the reviewer as discussed above. The description of the role of BMP signaling in the specification of the ANB and its subsequent patterning in ascidians has interesting evolutionary implications and should be of interest for a broader audience.

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Referee #3

Evidence, reproducibility and clarity

Summary:

This paper explores the role of BMP signaling for palp formation in ascidians using gain and loss of function approaches. The paper shows that while BMP at early (gastrula) stages prevents formation of the Foxc-positive palp ectoderm in Ciona, at later stages it appears to be essential for separation of the palps (possibly by promoting differentiation of interpapillary cells). The paper further shows that BMP plays similar roles in a different ascidian, Phallusia mammillata. Using previously published RNA-Seq results for the latter species after BMP up-regulation, the authors were able to identify additional BMP-responsive genes expressed in the palp region of ascidians.

Major comments:

However, while the effect of BMP overexpression at early stages has been confirmed by two independent strategies (electroporation of the BMP agonist ADMP and BMP2 treatment), the effects of late BMP activation as well as the effects of BMP inhibition at both early and late stages have been studied exclusively by pharmacological treatments with a single BMP signaling agonist (BMP2) and antagonist (DMH1). To substantiate these findings and rule out unspecific side effects, it would have been desirable to verify them with alternative strategies.

Therefore, while this study provides some new insights into the role of BMP in the specification of the palp forming region and subsequent palp development in ascidians, the evidence provided is relatively weak. Moreover, the scope of the study is quite limited. While identifying some BMP-responsive genes expressed in the palp region and describing the effects of BMP dysregulation on palp morphology, the study does not provide further insights into the underlying mechanisms how BMP patterns this region or affects subsequent palp formation.

Minor comments:

• 63: ...as the anterior...
• 68, 71, 74: references missing
• 73: better: anterior neural territories and placodes
• 76: palp territories also share molecular signature with anterior (eg. olfactory) placodes, not only telencephalon
• 106: awkward sentence
• 114: at what stage was ADMP electroporated?
• 134: to facilitate comparison between stages it would be useful to label cells in Fig. 2(eg. which are a-line and b-line cells? Where is the border between them?)
• 152: since Foxc and Foxg overlap with pSMAD1/5/8 at neurula but not gastrula stages, do you know whether this is due to a dorsal expansion of BMP activity or a ventral expansion of Foxc/Foxg expression? Again, labeling of the nuclei would help
• 174: the description is not clear here; what proportion of embryos did show reduction versus expansion of expression?. Why is the reduction shown in Fig.3 D asymmetrical?
• 198: ... of endogenous...
• 208: I suggest to highlight the regions of changes in Fig. with asterisks/arrows etc.
• 218: contrary to what is stated here, there is no depiction of u-shaped Isl1 expression in control embryos of Fig. 4
• 220: the cell shapes referred to here cannot be seen in Fig. 4 (too small)
• 271: the description here is confusing: first you talk about 53 genes and the mention palp expression of 12/26. Where does number 26 come from? And why was in situ done then for 27 additional genes? Also, while the comparison with previously published RNA-Seq data was valuable in uncovering additional BMP-sensitive palp markers, it does not provide any substantial new insights into how BMP patterns this territory.
• line 624: where
• Fig. 2: to facilitate comparison between stages it would be useful to label cells (eg. which are a-line and b-line cells? Where is the border between them?) -Fig. 3: Why is the expression in D asymmetrical? In the main text you write that expression is expanded in some embryos but reduced in others - Please show examples also of the expanded phenotype and give numbers
• Fig. 6: small panels in I, L, N need to be explained (single channels), white signal needs to be explained (overlap ?)
• Fig. S2: legend is missing

Significance

Since the study does not provide substantial new insights into the mechanisms how BMP patterns the palp forming region or affects subsequent palp formation in ascidians, it will be of interest mostly for a specialized audience in the field of developmental biology.

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Referee #2

Evidence, reproducibility and clarity

Summary.

The manuscript presents a detailed examination of how dynamic changes in BMP signaling during the development of the ascidian larval palps. Early in development BMP inhibition is responsible for the formation of a large field within the neuroectoderm that includes, among other fates, the presumptive palps. As development progresses, the territories of BMP activity/inhibition appear to be spatially refined within the palp-forming territory to specify palp versus interpalp fate. The experiments are presented with sufficient replication and statistical rigor.

Major Comments.

1. The researchers should look at otx expression in pFOG>Admp overexpressing embryos. It is difficult to assess from Figure 1, but it appears possible the the entire anterior sensory vesicle (not just the palps) are absent in the pFOG>Admp embryos (can the authors say briefly whether other ectodermal structures such as the atrial primordia or the oral siphon are still present?). Thus, is it possible that the entire a-lineage is disrupted? This would be an important distinction to make: are the defects attributed to experimental BMP activation specific to the palps, or are they more widespread in the anterior neuroectoderm? If the entire a-lineage is mis-fated, might this change the interpretation of the role of BMP inhibition? For example, might the formation of the palps depend on the proper development of the neighboring anterior neural plate? To address this concern, the authors should use a different driver to restrict Admp overexpression only to the palp forming region.
2. The authors hypothesize that papilla versus inter-papilla fate is controlled by differential BMP signaling. Is it possible to show differential P-Smad staining in papilla versus inter-papilla territories, as in Figure 2 for earlier gastrula-stage embryos? This data would make the authors hypothesis much more compelling. It appears that the authors have the necessary reagents.

Minor Comments.

1. There is no mention of panels Figure 1 U and V in the text. In the figure legend they are misidentified as panels S and T.
2. Very small issue with English usage that occurs throughout the manuscript. The authors should check the use of "palps" versus "palp", particularly when expressions such as the following are used: "palps formation", "palps network", "palps lineage", "palps differentiation", "palps molecular markers", "palps neuronal markers", "palps phenotypes", etc . For example, the sentence, "Here, we show that BMP signaling regulates two phases of palps formation in Ciona intestinalis", should read instead "Here, we show that BMP signaling regulates two phases of palp formation in Ciona intestinalis".
3. It would be worth mentioning possible relationships between the tunicate palps and the adhesive glands for larval fish and amphibians. Are there common mechanisms? All of these are anterior ectoderm derivatives.
4. Please consider providing references in the Introduction for the sentences which end on the following lines of text: 36 ( . . . is the sister group of vertebrates), 46 ( . . . and sensory properties), 48 ( . . . the secretion of adhesive materials), 57 ( . . . on the nervous system in chordates), 68 ( . . . also known as Ap2-like), 74 ( . . . anterior neural territories)
5. To provide extra emphasis and to help the figures to stand alone with their respective legends, can you mention in the legend for Fig. 2 that D and E are controls? Also, can a brief legend be provided for S2 to give overall indication of staging, scale, orientation, etc.?

Significance

This study presents an advance in our understanding of the fine-structure regulation of BMP signaling in sculpting neuroectoderm derivatives. While this study is potentially of broad interest, the authors fail to fully discuss the comparative aspects of this study in the context of conserved chordate developmental mechanisms. This could be remedied without too much difficulty in the Discussion section.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this article Roure et al address the role of BMP during formation of the ascidian palps, using Ciona intestinalis. Overexpression of BMP (specifically ADMP) from early stages of development results in complete suppression of palp formation, and early loss of the palp forming region (also called anterior neural border ANB). Using p-Smad1/5/8 antibody staining they show a marker of the ANB (FoxC) is expressed in a region negative for BMP signals. Inhibition of BMP signals is not sufficient to produce ectopic ANB. However, treatment with FGF protein from very early stages (8-cell stage) plus inhibition of BMP signaling (from 8-cell stage) increased FoxC expression. Looking at later stages of development the authors show that in a U-shaped expression domain of Foxg, Smad1/5/8 is active in the ventral-most part, which is expected to form the ventral-most palp. BMP2 treatment from gastrula stages results in loss of the ventral most palp expression of Isl and repression of ventral Foxg expression. Inhibition of BMP signaling from gastrula or neurula stages results in failure of a U-shaped pattern of Isl expression to resolve into the three palp expression domains, and by late tailbud stages, Sp6/7/8/9 (proposed as a repressor of Foxg in the inter-palp territory) expression is reduced and the numbers of specific cell-types making up the palps is increased. These cells are present in a single large palp of dorsal identity. Thus, inhibition of BMP from early gastrula stages results in a single palp made of more cells than the three palps of control larvae, presumably due to recruitment of cells usually present between the palps.

The authors then show a similar phenotype in another ascidian species Phallusia mammillata. Using their previous RNA-Seq data of embryos treated with BMP4, they looked for potential novel palp markers and identify a further eight novel markers of the palps. Looking further into this data and at a list of 68 genes expressed in palps (but not exclusively) they find that in whole embryo RNA-Seq data 70% were regulated by BMP signaling, mostly repressed, but some activated by BMP. 30 of these genes were regulated by Notch.

Apart from the confusion I explained in my comments below, the data seems to be carefully presented and interpreted. Overall, this manuscript presents a more detailed analysis of the role of BMP signaling during ascidian palp formation, but it remains to be precisely understood.

Major comments

1. I am a little confused about the timing of the protein treatments. In Figure 2, the authors show nicely that at the neurula stages, P-Smad1/5/8 staining abuts the FoxC ANB territory. Then at late neurula P-Smad1/5/8 is detected in the ventral-most part of the Foxg U-shaped part of the palp forming region, presumably the ventral most palp. However, the protein treatments with BMP (and FGF) are carried out from the 8-cell stage, which seems a bit drastic and embryos look difficult to orientate (e.g. Fig. 3D). While BMP-treatment from early stages inhibits all palp gene expression and any sign of palp formation (Figure 1), treatment with BMP from the early gastrula stage, when Smad1/5/8 is detected only in mesendoderm cells and before it is detected in any ectoderm, is sufficient only to block ventral palp formation and cause a partial down-regulation of FoxC expression in the ANB. Thus, there seems to be a discrepancy between the roles proposed for BMP during ANB and palp formation as judged by P-Smad1/5/8 staining and the temporal evidence from BMP- and BMP-inhibitor treatment. Do the authors have some explanation for why they need to treat at least one hour before the BMP-mediated patterning mechanism (as indicated from the P-Smad1/5/8 staining) is taking place? For example, could the authors check how long it takes DMH1 to inhibit P-Smad1/5/8 positive staining? Or BMP to strongly induce P-Smad1/5/8? This seems to be a simple experiment and might go some way to explaining why they need to treat embryos much earlier than I would have thought necessary.
2. It does not make sense to me that BMP treatment from gastrula stage blocks only ventral palp formation (Figure 4) and ventral Foxg expression (Fig. 5G). In particular, it is the ventral palp region which is positive for P-Smad1/5/8 (Fig.2I,J) so I would not expect the ventral palp to be the most sensitive to BMP-treatment.

Minor comments

line 185 I see what the authors are trying to say but I don't agree that BMP limits the domain of FoxC expression as inhibition of BMP has no effect on FoxC. Rather BMP has to be kept out of the ANB in order to allow ANB formation.

The relationship between Foxg and Sp6/7/8/9 expression is not really clear and it would be better to do this with double ISH if the authors want to show mutually exclusive expression domains, or at least provide a summary figure.

Line 218, I do not see the data showing that Isl is expressed at a U-shape at st. 23, it seems to be expressed in three dots, unless embryos are treated with DMH1.

Figure 6B, G. It could be nice to show a close up of the palps to see elongated cells.

Figure 6K. It is better to use a statistical test to support the authors conclusions.

It could be nice to provide a timeline for Smad1/5/8 signaling and the role for BMP signals that are proposed in this manuscript as a summary diagram.

lines 66-74 is lacking references.

Significance

While it is still not clear how BMP signals are established (which ligands for example) and their precise role in palp formation, this manuscript adds more information to our current understanding of the role of BMP signaling during palp formation. In particular it shows that BMP signals need to be kept out of the ANB for its formation and that it is required to resolve the later forming palp territory into three discrete palp regions. However, there is some way to go before this is fully understood. This article will certainly be of interest to ascidian developmental biologists trying to understand the formation and patterning of the larval PNS. It may also be of some interest to evolutionary biologists trying to understand the relationship between the telencephalon territory of vertebrates and the palp forming territory of ascidians as some links have been proposed between these two developmental territories (e.g. line 78).

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Reply to the reviewers

Reviewer 1

This paper identifies a role for the hereditary spastic paraplegia protein spatacsin in lysosome morphology, positioning and dynamics, and undertakes detailed mechanistic studies to try to identify the mechanism for this effect. In doing so the paper elucidates further mechanistic information about the properties of two other hereditary spastic paraplegia proteins, spastizin and AP5Z1. The work is done in mammalian cells and uses a combination of over-expression, depletion and biochemical studies. The main findings are:

1. The authors present evidence that spatacsin is an ER-localised protein.
2. Murine embryonic fibroblasts lacking spatacsin have a reduced number of tubular lysosomes and the remaining lysosomes are less motile. In general, a relationship between tubular lysosome morphology and lysosome motility, often in association with the endoplasmic reticulum (ER), is demonstrated. These tubular lysosomes are catalytically active and acidic.
3. In terms of mechanism of this effect, by combining a yeast-two hybrid and siRNA phenotypic screen, the authors identify a number of spatacsin-interacting proteins that also regulate lysosomal tubulation. The most important of these for the purposes of this paper is UBR4, an E3 ubiquitin ligase.
4. The authors show that spatacsin and UBR4 promote degradation of AP5Z1, and that this property required the ability of spatacsin to interact with UBR4. Somewhat surprisingly, as AP5Z1 is a coat protein, this degradation appeared to occur within the lumen of the lysosome - the authors speculate how this could be in the discussion.
5. The authors then demonstrate that AP5Z1 and spastizin, both hereditary spastic paraplegia proteins, compete for binding with spatacsin.
6. The relationship between spatacsin, spastizin, AP5Z1 and motor proteins in then examined. There is a known interaction between spastizin and KIF13A and expression of a dominant negative KIF13A protein reduced lysosomal tubulation. The authors then demonstrate an interaction between AP5Z1 and the p150Glued dynein/dynactin complex member, then showed that expression of a dominant negative p150Glued protein reduced lysosomal tubulation.
7. Finally, that authors demonstrate the relevance of these findings to neurons, the target cells of hereditary spastic paraplegia, by showing that lysosomal tubulation and axonal transport are reduced in mouse neurons lacking spastacsin, and that depletion of UBR4 or AP5Z1 affected these as expected from the experiments above.

Major comments:

Overall I believe that the key conclusions of this paper are generally convincing and that the work is of high quality. However, I do have some reservations:

1. The localisation of spatacsin on the ER. It is always difficult to be convinced about colocalization of a diffuse punctate marker and the ER. From the STED experiments in figure 1, while it definitely seems that there is some spatacsin on the ER, there also appears to be some spatacsin puncta that are not. I'd like to know if these puncta represent lysosome-associated spatacsin. This is important for interpretation of the subsequent experiments (see point 3 below). I also think quantification of these co-localisation will increase confidence in the results. In addition, a caveat of the immunofluorescence studies is that they use over-expressed spatacsin. I appreciate that there are no good antibodies to endogenous spatacsin, but I don't think this limitation is sufficiently acknowledged. As the claim of ER-localisation is critical for the proposed mechanistic model, and in the absence of experiments with endogenously tagged spatacsin, this makes the biochemical fractionation studies of figure 1C very important. To make these more convincing I would prefer to see additional control markers to verify the separation of lysosomal and ER compartments - e.g. lamp1, lamp2, an ER tubular marker such as a REEP5 or a reticulon.

Authors response : We agree with the reviewer that the localization of spatacsin is critical, and we appreciate the knowledge of the reviewer concerning the lack of good antibodies to endogenous spatacsin. We better acknowledged this limitation in our revised manucript (p. 5 and p. 15). We performed extra experiments to convincingly show that spatacsin is indeed localized at the ER. First, we performed 3-color STED experiments to visualize in the same cell spatacsin, the ER and lysosomes. The preliminary data seem to indicate that some spatacsin is associated with lysosomes at ER-lysosomes contact site. We plan to add quantifications of colocalization between spatacsin and ER staining at STED resolution to better support the fact that spatacsin is a protein of the ER.

Moreover, as requested, we have performed a western blot with Lamp2 and REEP5 antibodies on the ER- and lysosome-enriched fractions (New Figure 1B). This western blot shows that a significant proportion of Lamp2 is present in the ER-enriched fraction, which may be explained by the strong association of ER with late endosomes and lysosomes. Yet the lysosome-enriched fractions that contained no ER markers do not present spatacsin staining, suggesting that spatacsin is either in the ER or in lysosomes associated with the ER that are not positive for cathepsin D. We reformulated the text of Figure 1 according to the new included data (p. 5-6).

The authors generally do a good job of quantifying their results. However, this is lacking for the biochemical experiments (immunoblotting and IP) in figures 4 and 5, and I would prefer to see these quantified (the quantification should include data from repeat experiments so that we can judge the reproducibility of the results).

Authors response : We agree that our presentation did not indicate that the western blots were repeated several times. We have added quantifications for the western blots present in Figures 4 and 5.

On page 10, referring to the proximity ligation results, the authors comment: "This suggests that the spatacsin-spastizin interaction occurs at contact sites between the ER and lysosomes to allow spastizin recruitment to lysosomes". I'm not sure this statement is fully supported, as mentioned at point 1 above it is possible that some steady state spatacsin is at lysosomes. To fully support this, we'd need to see the PLA signal also convincingly co-localise with an ER marker.

Authors response : We will perform extra PLA experiment to indeed show that the spots where spatacsin and spastizin colocalize with an ER marker. This data will be added in Figure 5.

In figure 6C and D the effect of spastizin on lysosomal tubulation and dynamics is investigated. Wartmannin treatment is used to do this, as it is known to remove spastizin from lysosomes. However, this is a very indirect manipulation that could have many other consequences and it would be better to demonstrate this directly by showing the effect of depletion of spastizin on lysosomal morphology/dynamics. I also think the role of AP5Z1 in tubulation/dynamics would be better supported with additional experiments to deplete the protein - at present only over-expression is examined.

Authors response: *We added new data to answer this comment. Downregulation of spastizin using siRNA led to lower number of tubular lysosomes and decreased the proportion of dynamic lysosomes, showing that spastizin is required to regulate lysosome motility (Figure 6B-6C Supplementary Figure 7B). We have also added new data regarding downregulation of AP5Z1 (Figure 6A-6C-Supplementary 7A). Both overexpression and downregulation of AP5Z1 using siRNA decreased the number of tubular lysosomes and decreased the proportion of dynamic lysosomes (Figure 6A-6C-Supplementary Figure 6C-D). *

This observation suggests that the levels of AP5Z1 must be tightly regulated to control lysosome motility. We added discussion about this point as well (p.12-13).

While the experiments showing that over-expression of dominant negative forms of KIF13A and p150Glued affect lysosomal tubulation/dynamics provide good circumstantial evidence that spatacsin influences these lysosomal properties via its interactions with spastizin and AP5Z1 (which bind to these motor proteins), the authors have not shown that the interaction of the motor proteins with spastizin and AP5Z1 is required for this ability to regulate lysosome tubulation/dynamics. This means that the model presented in figure 7 is not fully supported by the data. If the authors have been able to map the binding regions for these interactions then perhaps this could be investigated with rescue experiments, although I appreciate that this is potentially a major piece of work and perhaps outside the scope of this paper. An alternative would be that the authors acknowledged this part of the model as somewhat speculative.

Authors response : We agree with the reviewer that our data do not show that KIF13A and p150Glued interact directly with spastizin and AP5Z1 to regulate lysosome dynamics. It has previously been shown that the adaptor complex AP2 interacts with p150glued via the ear domain of AP2 b subunit (Kononenko et al, 2017). It is therefore likely that the interaction of adaptor complex 5 with p150-Glued also occurs via AP5B1 subunit, and thus interaction of AP5Z1 with p150 glued would be indirect. *We discussed this point carefully (p.16). *

*Regarding the interaction of Spastizin with KIF13A, it was identified by yeast-two hybrid screen and validated by GST-pulldown (Sagona et al, 2010). This showed that KIF13A interacts with the C-terminal domain of Spastizin, and we discussed this point. To confirm that KIF13A interaction with spastizin is required to promote its role in tubular lysosome formation and dynamics, we can perform an experiment where we downregulate endogenous mouse spastizin using siRNA and express either full length human spastizin to rescue the effect of the siRNA, or overexpress a human spastizin lacking its C-terminal domain required for the interaction with KIF13A (where we would expect no rescue). This would strengthen our conclusion on the role of KIF13A in link with spastizin to regulate the formation and dynamics of tubular lysosomes. We could add these data in Figure 6 (or Supplementary Figure 7). *

• Are the experiments adequately replicated and statistical analysis adequate?

In general I am not convinced that the statistical tests are applied rigorously in this paper. Most experiments are done three times, but the "n" used for statistical testing is typically chosen as, e.g. the number of cells, number of lysosomes, rather than number of biological repeat experiments. This means that inter-experimental variability is not rigorously taken into account. A more rigorous practice would be to use the mean measures for each of three biological repeats and apply the statistical tests to the three means, so n=3 if three repeats were done. Superplots would be a nice way to graphically display these data.

Authors response : We agree with the comments of the reviewer regarding data presentation. We have therefore changed the presentation of all graphs of the manuscript using superplots that allow us to show all the points that were analyzed as well as the mean value for each biological replicate, and performed statistical analyses by comparing the biological replicates as proposed in Lord et al, JCB 2020 (10.1083/jcb.202001064).

Minor comments:

1. In supplementary figure 3D I cannot honestly say that I see the smaller band.

Authors response : We agree that this western blot is not clear. We will provide a new western blot.

When first called out, I expected supplementary tables 1 and 2 to show the list of interactors with wild-type spatacsin and spatacsind32-34 respectively, but this is not what they show.

Author response : We have added two supplementary data tables (Now Supplementary Tables 1 and 2) to give the list of interactors of wild-type C-terminal domain of spatacsin and spatacsinD32-34, respectively.

Supplementary Tables 3 and 4 now refer to the analysis of the downregulation experiments by respectively the neural network method and the tubular lysosome detection method.

The experiments in Figure 4A are a little problematic in the way that they are called out. The first call refers to just a small subset of the data in the figure, and the figure is then called out at various points later in the paper. This is quite confusing. Is there any way this could be simplified?

Authors response :We agree with the reviewer that Figure 4A was called at various points of the manuscript. This was to avoid duplicating data into two separate figures. However, we have modified the presentation of Figure 4 and Figure 5. We have included new Figure 4C to show that downregulation of UBR4 prevents the degradation of AP5Z1 upon overexpression of Spatacsin-GFP, but also in basal conditions in wild-type fibroblasts. The co-IP that was originally presented in Figure 4A has now been moved into Supplementary Figure 6A.

The section on page 10: "Spatacsin also interacts with spastizin, and is required to recruit spastizin to lysosomes (Hirst et al., 2021). ........ We hypothesized that spatacsin interaction with spastizin was required for spastizin localization to lysosomes." Is odd, as the authors seem to be hypothesising an observation that they have just said has already been demonstrated.

Authors response : We agree that these sentences were odd. We have rephrased the paragraph (p. 11).

Can the authors explain why there is so little interaction between wild-type KIF13A and spastizin?

Authors response : The interaction domain of spastizin with KIF13A is close to the motor domain according to the two-hybrid data published by Sagona et al (2010). The dominant negative construct of KIF13A that is devoid of the motor domain (KIF13A-ST) may thus facilitate access of spastizin to binding domain. We have commented on this point in the text (p.13).

In figure 6G p150Glued signal is also present in the control IP lane, which casts doubt on the specificity of the interaction. Could the authors generate a cleaner result?

Authors response : We have repeated the experiment 3 times, always with some p150Glued signal present in the control IP. Of note, as stated in the method section, we have increased the concentration of NaCl in the washing of this co-IP to decrease non-specific binding of p150glued to control beads, but we could not get cleaner results so far. We will try to get cleaner western blot to illustrate Figure 6G.

I would be interested to see how AP5Z1 expression differs between neurons with and without spatacsin- we would expect similar results to those shown in the MEFS.

*Authors response : We have not checked the levels of AP5Z1 in neurons with and without spatacsin yet. However, the complete knockout of spatacsin strongly modifies the levels of its partners. We previously showed that spastizin levels are decreased by >90% in Spg11 knockout brain (Branchu et al, 2017). Furthermore, the levels of AP5Z1 have been shown to be decreased by ~50% in fibroblasts of SPG11 and SPG15 patients (Hirst et al, 2015). *

*Our work shows that spatacsin promotes the degradation of AP5Z1 by lysosomes. It is possible that other degradation mechanism(s) may exist and could explain the lower levels of AP5Z1 in knockout cells. We discussed this point (p.15). *

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

In this study Pierga et al. report that SPG11 (spatacsin) is an ER-resident protein involved in the regulation of ER-lysosome contact sites (in particular tubular lysosomes) and subsequent faster motility of tubular lysosomes, as well in the degradation of AP5Z1 (SPG48), which forms a heterotrimeric complex with SPG15 (spastizin) and SPG11. This complex has been localized by several groups on the cytoplasmic side of LAMP-1-positive lysosomes. In addition, mutations in SPG11, SPG15, and SPG48 patients share various clinical features and were supported by biochemical/cell biological data from Spg11 and Spg15 KO mouse models and cultured cells both from patients and mice, respectively, demonstrating e.g. accumulation of autolysosome storage material, defects in the autophagic lysosome reformation process, and the loss of cortical motoneurons and Purkinje cells.

Major concerns:

i) Fig. 1, 2, 3: major disadvantage of this study is the analysis of overexpressed proteins (SPG11-V5, GFP-Sec61, and Lamp1-mCherry) which might contribute to the observed strong expression of SPG11-V5 in the ER/ER-enriched fraction. The results should be compared with the endogenous expressed proteins.

Authors response :* As stated by reviewer 1, there are no good antibodies to endogenous spatacsin, and therefore we have to rely on expression of tagged spatacsin to study its localization by immunohistochemistry. For the colocalization with the ER, we stained the latter by GFP-Sec61 that is a widely used marker for this compartment. To confirm our results, we plan to try to perform new STED imaging with REEP5 antibody to stain the ER, and Lamp1 antibody to label lysosomes, avoiding overexpression of proteins to label the subcellular compartments. Furthermore, as it is not possible to localize endogenous spatacsin by immunostaining, we addressed its localization by biochemical fractionation and western blots comparing wild-type and Spg11 knockout samples. *

For Figure 2, the data presented were indeed obtained using transfection of Lamp1-mCherry. However, we confirmed our observation of Figure 2A using alternative staining of lysosomes (Lysotracker or loading of lysosomes with Texas-Red Dextran). We therefore think that our data presented in figure 2 are valid, and that the effect we observed on tubular lysosomes was not affected by expression of Lamp1-mCherry.

In Figure 3, the lysosome were labelled with Texas-Red Dextran, and thus all the data presented in figure 3 do not rely on overexpression.

In Fig. 1C the lack of the mature Cathepsin D form which is proteolytically generated only in lysosomes from the higher molecular mass precursor is misleading and should be related to presence of lysosomal membrane proteins.

Authors response: As requested, we have performed a western blot to show the lysosomal membrane protein Lamp2 on the ER- and lysosome-enriched fractions (Figure 1B). This western blot shows that a significant proportion of Lamp2 is actually present in the ER-enriched fraction, which may be explained by the strong association of ER with late endosomes and lysosomes previously described (Friedman et al, 2013). Yet the lysosome-enriched fractions that contained no ER markers do not present spatacsin staining, suggesting that spatacsin is either in the ER or in lysosomes associated with the ER. We reformulated the text of Figure 1 according to the new included data (p 5-6). The 3-colours STED experiment that we plan to perform to answer reviewer 1 comments will help discriminate between these possibilities.

Fig. 1D: the TEM image shows only a single lysosome and proposed ER contact zones in wt-MEFs without comparison with Spg11 KO MEFs (only in the quantification). Without double immunogold labeling of SPG11 (and their lack on SPG11 KO cell lysosomes) and known ER contact-site proteins this image and the conclusion are insufficient.

Authors response : We have added an image of a lysosome taken from a knockout fibroblast (Figure 1E). As stated above there are no good antibodies to spatacsin for immunostaining, so it will not be possible to perform double immunogold labelling. This prevents us from claiming that spatacsin is a protein enriched at contact site. We therefore modulated our result section and discussion accordingly (p.5-6 and p.16).

ii) The rationale for the selection of the deleted Spg11 region D32-34 is not clear. What are the symptoms of this Spg11 knock-in mouse? A more detailed description of the phenotype is required Is the phenotype including the accumulation of LC3-positive material similar to the phenotype of the SPG11 KO mouse which has been published by Varga et al.(2015) and Branchu et al. (2017) ? If not, is the new mechanisms reported here not so important?

Author response : We have added new data (Supplementary Figure 3E-F) showing motor and cognitive impairment in mice expressing truncating spatacsin, although the motor dysfunction is slightly less marked than in Spg11 knockout animals. We also checked for accumulation of autophagy markers. We did not use LC3, but p62 that labels substrates to be degraded by autophagy. We observed accumulation of p62 in Spg11 knockout and in Spg11D32-34/D32-34 mouse neurons (Supplementary Figure 3G). These data support the functional importance of the domain encoded by exons 32 to 34 of Spg11. We commented on this in the text (p.9).

iii) p8/Fig. 3F/Suppl.Fig.3F- the most important part of the manuscript: what are the parameters of lysosomal staining in images that were used to identify genes important for lysosome tubulation by the neural network?

Authors response : For screening in Figure 3, lysosomes were stained by loading fibroblasts with Texas-Red Dextran overnight, followed by a wash of at least 4 hours. The neural network was first trained to discriminate between control and Spg11-/- fibroblasts, using any parameters of the lysosomal staining, not necessarily lysosome tubulation. This is a completely unsupervised and unbiased method, but one of its drawbacks is that we do not know which parameters were used by the network to discriminate between control and Spg11-/- fibroblasts. Therefore, we validated the classification performed by the neural network on a data set independent from the training set before using it for the screening. We rephrased the paragraph to make it clearer (p.9).

I cannot understand how the authors predict the probability of the cell to be considered as an Spg11 KO fibroblast (why not as an Spg11 D32-34 knock-in fibroblast?) as the basis for the selection of interaction candidates.

Author response : The neural network was trained on sets of images obtained from wild-type and Spg11 KO fibroblasts, which were expected to represent extreme lysosomal phenotypes linked to spatacsin function. We could therefore predict the probability of cells to be considered as Spg11 KO, not as Spg11 *D32-34 fibroblasts. We clarified this in the text (p9). *

A simple statement that the neural network approach identified those genes is too weak and requires more convincing experimental data. It has to be shown at least for the 8 positive genes in both approaches how the siRNA treatments of these genes phenocopied the lysosomal changes and of course the effect of the downregulation on the protein level of their products both in wild-type control and Spg11 D32-34 knock-in MEF. The Suppl. Fig.3F is completely unclear. How were the Y2H interaction partner validated? Did the authors use the identified 8 interaction candidates as full length bait to demonstrate the interaction with the Spg11 exons 32-34 ?

Author response : The purpose of the siRNA screen was to quickly identify putative candidates important for the regulation of lysosome dynamics. We identified 8 candidates possibly implicated in lysosomes dynamics based on the two analysis methods. We have added in Supplementary Figure 4 C-D the effect of both siRNA on lysosomal function by the two methods of analysis compared to the effect of siSPG11. However, here we aimed to identify candidates and we do not claim that every one of these eight proteins were indeed implicated in the regulation of lysosome dynamics. We corrected the text, accordingly, stating that the products of the 8 identified genes are good candidates to regulate lysosomal function (p.10). We validated the role of one of the identified candidates, UBR4, and we showed that the UBR4 siRNA indeed downregulates the protein level (Figure 4C). We only validated the interaction of spatacsin Cter with UBR4 by co-immunoprecipitation (Figure 4B).

*For the 7 remaining candidates, full characterization would indeed be required to validate their role and elucidate their mechanisms of action, but this is out of the scope of this manuscript. *

p8/Fig.3F: the genes identified in both approaches have to be listed in the Fig. 3F-Table.

Authors response : We have added in new Figure 3F the list of the 8 candidate genes that could contribute to regulate lysosome function.

The GO process- ubiquitin-dependent protein catabolic process is neither positive for the neural network nor for the directed analysis but positive for both analyses? Please explain. Similarly, the GO process proteolysis involved in cellular protein catabolic process -is not positive for the neural network analysis but again positive for both analyses.

Authors response : We agree with the reviewer that Table 3F in its older version could be a bit confusing. GO analysis is based on “enrichment” of biological processes within a list of proteins. As we did not have the same number of proteins in the 3 analyses provided in original Table 3F, we got variability in the identified biological processes. To simplify, we have therefore chosen to present only the GO analysis for the 8 candidates that were most likely implicated in lysosomal dynamics according to our two analyses of the siRNA screen which is the most relevant for our study (new Figure 3G).

For Fig. 3G the mutant ubiquitin-K0 staining in wild-type MEF cells has to be shown as well as for the Spg11 ki/KO MEFs (+ quantification of the respective data)

Authors response : As stated by Reviewer 4, the expression of lysine-null ubiquitin may impact many different cellular pathways. We therefore removed this part of the data in order to simplify the manuscript (p.10)

iv) The interpretation of the Y2H-interactome analysis by the authors is hard to follow. They searched with the exon 32-34 cDNA for binding partner, selected 3 degradative GO processes and showed by overexpression of a mutant Ub-K0 plasmid in wild-type MEFs a decreased number of tubular lysosomes, as well as their dynamics (without showing the control data in Spg11 KO or ki-MEFs). Thus, poly-ub of proteins should be in some way responsible for a lysosomal phenotype of Spg11ki MEFs.

Now they went to AP5Z1, the second binding partner of SPG11, which is reduced in its abundance upon overexpression of Spg11-GFP. I would expect to do the respective control experiment to show that in the absence of SPG11 or in the knock-in cells the amount of AP5Z1 has to increase. However, in the studies by the Huebner group by deletion of Spg11 or the other binding partner Spg15, no increase of AP5Z1 protein levels has been observed. The authors have to comment on this discrepancy.

*Authors response : We agree that this is an important point to discuss, and we failed to do it in our first version. *

*The complete knockout of spatacsin strongly modifies the levels of its partners. We previously showed that spastizin levels are decreased by >90% in Spg11 knockout (Branchu 2017). Furthermore, the levels of AP5Z1 have been shown to be decreased by ~50% in fibroblasts of SPG11 and SPG15 patients (Hirst et al, 2015). *

Our work shows that spatacsin promotes the degradation of AP5Z1 by lysosomes. It is possible that other degradation mechanism may exist, and could explain the lower levels of AP5Z1 in knockout cells. Furthermore, it was proposed that AP5Z1 stability may depend on the presence of spatacsin and spastizin (Hirst et al., 2013)*. Therefore spatacsin may contribute to tightly regulate AP5Z1 levels by contributing both to its stability, and to its degradation. We have carefully discussed this point (p.16). Furthermore, the experiments requested by reviewer 2 in point (vi) that we are planning to perform will help clarify the mechanisms of AP5Z1 degradation both in presence and absence of spatacsin. *

Then the authors found that the selected interaction partner of the exon 32-34 sequence, UBR4, does not bind to the Spg11-GFP construct lacking the domain encoded by exons 32-34 but to the C-terminal domain of Spg11-GFP. Unfortunately, all these IP-experiments were shown as cut and paste figures, preventing the direct comparison between the input and the IP protein amounts (since the information is missing what percentage of the input and the IP has been loaded per lane, the evaluation and significance of these Co-IPs are unclear).

Authors response : We have added in the Figure legend the fact that the input represents 5% of lysate added to the immunoprecipitation assays

v) p9: AP5 (Z1) is a cytoplasmic protein and can be localized on the cytoplasmic surface of lysosomes. How should the GFP-mcherry-AP5Z1 protein enter the lumen of lysosomes justifying the quenching of the GFP signal? A positive control has to be included in the experiment shown in Fig. 4E demonstrating the effect of MG132 under identical conditions of a protein substrate for proteasomal degradation.

Authors response :* We agree this is an important control. We plan to add a control showing accumulation of ubiquitin in lysates upon MG132 treatment to show it was indeed effective. *

vi) Fig. 5A: In contrast to GFP-mcherry-AP5Z1, spastizin-GFP is localized at the cytoplasmic surface of lysosomes (co-staining with LAMP1-mcherry) in wild-type MEFs. In regard to the incomplete data commented under "minor points Fig.4/Suppl.Fig.4", I suggest to perform a simple control experiment with overexpressed GFP-spastizin and mCherry-AP5Z1 in wild-type MEFs (at the best also in Spg11 KO MEF) with and without bafA treatment, which will clearly demonstrate whether single components of the trimeric Spg11, spastizin-AP5Z1 complex are degraded independently of each other in lysosomes.

*Authors response : As stated above, we will perform this control experiment, and will add the data in Figure 5 in future revision. This will help clarify the mechanism of degradation of AP5Z1 and spastizin both in presence and absence of spatacsin. Discussion of this point will also help to clarify the point iv raised by reviewer #2. *

vii) why did the authors neither mention nor discuss the described role of SPG11 in autophago-lysosome reformation (ALR)?

*Authors response : We did not discuss ALR in our first version as we did not investigate autophagic conditions. However, due to the well-described role of spatacsin in ALR, we agree that we should discuss ALR in our manuscript, and we added a paragraph (p.15). *

Minor points

• Figure 1 A, B, D, and G: ER-lysosome contact sites. The quantification of the co-localization of spatacsin-V5 with the ER marker protein GFP-Sec61b has to be given.

Authors response :* We plan to add quantification data performed on STED images showing localization of Spatacsin-GFP together with ER and lysosomal markers. This data will be added in Figure 1. *

Moreover, the authors analyzed overexpressed tagged-proteins only. The results should be compared with the endogenous proteins.

Authors response :* As stated above, there are no good antibodies to endogenous spatacsin for immunostaining. We will add new STED images with antibodies against endogenous Reep5 and Lamp1 to label the ER and lysosomes together with overexpressed spatacsin. Regarding endogenous spatacsin, we could only investigate its localization by subcellular fractionation and western blots comparing wild-type and Spg11 knockout samples. We added biochemical data suggesting that spatacsin is enriched either in the ER or in lysosome membrane associated with the ER. These data have been added in Figure 1 and in text (p.5) and we added a paragraph in discussion regarding spatacsin subcellular localization (p.15). *

p8/Figure 3: what does the 'analysis of trained neural networks' mean?

Authors response : We did not analyzed the trained neural network, but we used this trained neural network to perform image analysis. We clarified the text (p.10).

Figure 4: what happens with the other AP5 subunits?

Authors response : This is a very interesting question. We will test whether overexpression of spatacsin-GFP induces a degradation of some other AP5 subunit, provided we get specific antibody. We will add the data in Figure 4A.

Fig.4F/Suppl.Fig4: live images of GFP-mcherry-AP5Z1 + lysotracker staining have to be shown both for wild-type MEFs with and without bafilomycin A treatment(as in Fig.4F), and in Spg11 KO and Ki MEFs +/- bafA.

Authors response : We will add these data in Figure 4 (WT Mefs +/- Baf A) and in Supplementary Figure 5 (Spg11KO and SPG11D32-34 Mefs +/- Baf).

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

This manuscript highlights an interesting localization of spatacsin in the endoplasmic reticulum (ER)-lysosomes contact sites. In addition, it implicates spatacsin in regulating tubular dynamic lysosomes. Mechanistically, the authors propose that spatacsin interacts with UBR4 to promote the autophagic degradation of its binding partner AP5Z1 at the lysosomes. In turn, this would also regulate the amount of spastizin at the lysosomes, which is known to interact with anterograde motors. The authors further show that AP5Z1 interacts with p150Glued. Thus, the balance between AP5Z1 and spastizin at the lysosomes would determine lysosomal trafficking directionality.

Major Comments

1. Several crucial results of the manuscript are based on quantifications performed on immunofluorescence stainings. Data points in graphs show individual cells or individual lysosomes and the authors apply statistical tests on replicates that cannot be considered biologically independent, since they come from the same experiment or even the same cell. It is recommended to show superplots where both the individual data and the average of each independent experiment is indicated as recommended by Lord et al. (J Cell Biol 2020 219 (6): e202001064.). Statistics should be performed only on independent biological replicates.

Authors response : We agree with the comments of the reviewer regarding data presentation. We have therefore changed the presentation of all graphs of the manuscript using superplots that allow us to show all the points that were analyzed as well as the mean value for each biological replicate, and performed statistical analyses by comparing the biological replicates as proposed in Lord et al, JCB 2020 (10.1083/jcb.202001064).

The authors have used yeast two-hybrid to search for spatacsin interactors. Although in the manuscript they refer to supplementary tables that should show these interactors, the available Tables are confusing and refer to the following downregulation experiments.

Author response : We have added two supplementary data tables (Now Supplementary Tables 1 and 2) to give the list of interactors of wild-type C-terminal domain of spatacsin and spatacsinD32-34, respectively.

Supplementary Tables 3 and 4 now refer to the analysis of the downregulation experiments by respectively the neural network method and the tubular lysosome detection method.

An experiment to demonstrate that endogenous UBR4 and spatacsin interact by co-immunoprecipitation would be crucial.

Authors response : We agree with the reviewer that it would be important to test whether endogenous spatacsin and UBR4 are interacting by co-immunoprecipitation. So far we have not managed to immunoprecipitate either endogenous spatacsin or endogenous UBR4 with the antibodies we tested, which prevents us to test the interactions of endogenous proteins by co-immunoprecipitation. We are not sure we can provide this result.

Several important experiments to unravel the mechanistic role of spatacsin (Figure 4 and 5) are performed upon overexpression. This is a major limitation of the study and the authors should address it as much as possible. Western blots and immunoprecipitations are shown that appear to have been performed only once and have no quantification. As an example, in Fig 4A the difference in levels of AP5Z1 upon spatacsin overexpression or UBR4 downregulation are very minor. I would be very careful in drawing big conclusions, without additional repetitions and additional experiments in an endogenous setting.

*Authors response : We agree that a lot of our experiments used overexpression. We have now added to the manuscript new data obtained in MEFs where we downregulated spastizin or AP5Z1 (Figure 6). They confirm the role of spastizin in the regulation of lysosome dynamics. Furthermore, our new data show that levels of AP5Z1 must be tightly regulated as both overexpression and downregulation of AP5Z1 affects lysosome dynamics (p.12). We also discussed these data carefully (p.16 ). *

Furthermore, we agree that our presentation did not indicate that the western blots were repeated several times. We have now added quantifications for the western blots presented in Figures 4 and 5. Furthermore, we have also added the data showing that downregulation of UBR4 led to higher levels of AP5Z1 in control fibroblasts (Figure 4C).

The authors suggest a model by which UBR4 recruited by spatacsin is involved in autophagic degradation of AP5Z1. The data shown do not support this conclusion. First, in Figure 4A downregulation of UBR4 does not increase levels of AP5Z1 above the control in lane 1, but only when spatacsin is overexpressed. The effect of downregulation of UBR4 in wilt-type cells on AP5Z1 should be investigated. Secondly, there is no experiment directly proving that the stability of AP5Z1 depends on UBR4.

Authors response : We have added new western blots (and quantification) in Figure 4C showing that downregulation of UBR4 increased levels of AP5Z1 in control conditions. The fact that downregulation of UBR4 increased levels of AP5Z1 in control conditions suggests that UBR4 contributes to regulating the levels of AP5Z1. However, we do not show whether UBR4 directly promotes the degradation of UBR4, which has been added in the discussion (p15). To test whether UBR4 affects the stability of AP5Z1, we will monitor whether downregulation of UBR4 by siRNA increases the half-life of AP5Z1. These data will be added on Figure 4.

The authors suggest that the interaction of spatacsin with spastizin or AP5Z1 are in competition. This is an interesting hypothesis, however to conclusively demonstrate this, pull-down experiments in KO cells and not upon extreme overexpression should be performed.

Authors response : We agree that testing the interaction of spatacsin with its partners in SPG15 KO or AP5Z1 KO fibroblasts would be a very good control of our hypothesis. However, we previously showed that the levels of AP5Z1 are lower in SPG15 KO than in control fibroblasts (Hirst et al, 2015), which introduces a bias in the analysis. We therefore plan to concentrate on AP5Z1 fibroblasts and investigate whether interaction of spatacsin with spastizin is modified in these cells. An alternative would be to monitor the effect of siRNA downregulating AP5Z1 on the interaction between spatacsin and spastizin. We will add these data in Figure 5.

Minor comments

1. In figure 1G and 1H the overlapping area between lysosomes and ER is quantified. Considering that the ER occupies a large portion of the field a 90{degree sign} flipped control for both WT and KO would be important to sort out random colocalization. In this direction, it would be also essential to show that the total amount of lysosomes is not different in WT and KO, especially because in figure 1A the lysosomes in WT and KO seem to be different not just in shape but also in number and size. A different number or size of lysosomes affects this analysis.

Authors response :* We added quantifications in Supplementary Figure 1F showing that 90° flipped controls are indeed not capturing the same proportion of contacts between the ER and lysosomes. We also added quantifications in Supplementary Figure 1D-E showing that the average size of lysosomes and the number of lysosomes per unit area are similar in control and Spg11 KO fibroblasts and mentioned it in the text (p.6). If the lysosomal staining appears different in Spg11 KO fibroblasts it is because lysosomes are clustered around the nucleus, an observation that we reported previously (Boutry et al, 2019). *

In the second chapter of the Results, the authors state: "we observed by live imaging a higher number of lysosomes with tubular shape in Spg11+/+ compared to Spg11-/- cells", however the number of elongated lysosomes is quantified per area. Why the number of elongated lysosomes is not quantified over the total amount of lysosomes?

Authors response : The point raised by the reviewer is a fair point. The purpose of our analysis was to compare the number of lysosomes with tubular shape in control and Spg11 KO cells. As the number of lysosomes per unit area is invariant between control and Spg11 KO cells as shown in new data included in Supplementary Figure 1D, normalization to total number of lysosomes or to cell surface reflects the same difference in phenotype.

The In the fourth chapter of the Results, the authors state:" In wild-type MEFs, mCherry was colocalized with lysosomes. In contrast, GFP that is sensitive to pH was poorly colocalized with lysosomes, suggesting that AP5Z1 was mainly inside the acidic subcellular compartment (Figure 4F)." If the aim of the authors is to shown that AP5Z1 is mainly into the lysosome, the amount AP5Z1-mcherry inside and outside the lysosome need to be compared, with a proper statistical analysis. There is also a lot of GFP signal in the cytosol. Why is that?

*Authors response : We agree with the reviewer, we will add quantification of the proportion of AP5Z1-mCherry inside lysosomes on Supplementary Figure 5. *

Regarding the GFP-AP5Z1 signal in the cytosol, AP5Z1 has no transmembrane domain and may thus exist as a cytosolic protein. Since GFP is quenched in the acidic environment of lysosomes, the GFP fluorescence of the mCherry-GFP-AP5Z1 protein is outside lysosomes, and it appears partly cytosolic. Of note, there is also some cytosolic mCherry signal that is less visible due to the high level of mCherry fluorescence in lysosomes. We will clarify this point with the quantification of the proportion of mCherry signal compared to GFP inside the lysosomes and add it in Figure 4.

construct used in the paper is a C-terminal tagged version of spatacsin. The authors should consider to test an N-terminal tagged construct at least for the localization experiments.

Authors response : We added an immunostaining image of Spatacsin with an N-terminal tag (Supplementary Figure 1B) and mentioned it in the text (p.6). As spatacsin with a C-terminal tag, it presents a diffuse distribution that poorly co-localizes with lysosomes.

Figure 5C: a negative control and the quantification are missing.

Authors response : A non-transfected cell is present on Figure 5C, visible thanks to the Lamp1 immunostaining, and that we considered as a negative control. In this non-transfected cell, we detected no PLA signal. We added an asterisk to point the non-transfected cell on Figure 5C. Quantification will also be added in the revised version after we have performed the PLA experiment required by Reviewer 1.

Reviewer #3 (Significance (Required)):

Since spatacsin, AP5Z1 and spastizin are all implicated in hereditary spastic paraplegia, the data are of potential interest not only for basic cell biology, but also to understand the pathogenesis of the disease. In addition, the manuscript proposes a novel model regulating trafficking of dynamic lysosomes.

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

Pierga et al. reveal subtle differences in lysosome morphology, ER-contact, and trafficking in the absence of Spatascin. These data are replicated with a truncated Spatascin, presumably a loss of function. Two-hybrid screening of the deleted sequence from this truncation for interactors and then asked whether these hits could phenocopy the lysosome morphology changes. This led to an assertion for a role for ubiquitination in these effects. Rather than these hits the group then investigates previously known Spatascin interactors and reports similar complex but subtle abnormalities via overexpression or knockdown of these. While data show overlapping phenotypes by modulation Spatascin, AP5z1, and Spastizin, the manuscript is confusing, leaps from experiment to experiment, and does not provide novel rigorous mechanisitic insight. It conflates all the discrete lysosomes aspects into a collective to link them. The title is over-stated and not appropriate for the experiments.

The localization of endogenous Spatascin is lacking - over-expression is prone to artifact and the punctate data on the V5 suggests much more work is needed to understand where in the cell it is. It would seem much more work is needed here.

Authors response : As stated by reviewer 1, there are no good antibody to endogenous spatacsin, and therefore we have to rely on expression of tagged spatacsin to study its localization by immunofluorescence. When performing the images, we avoided the cells with the highest ovexpression of tagged spatacsin. Yet, we agree that this is still overexpression. That’s why we included subcellular fractionation data where we can detect endogenous spatacsin (Figure 1A-1B). These data confirmed that spatacsin is enriched in the ER or in lysosome fraction tightly associated with the ER.

Furthermore, the EM data (1E) would suggest the far majority of lysosomes are in contact with ER - these seems uncharacteristic.

Authors response : The EM data in figure 1E indeed shows that the majority of lysosomes are in contact with the ER, as previously shown by other groups (Friedman et al, 2013, Höglinger et al, 2019).

The phenotypes analyzed are very subtle, and while statistically significant the biological impact is unclear - in many cases individual lysosomes (or lysosome-ER contacts) are considered as an 'n'. While these results are probed across multiple independent experiments the batch effects and how uniform per cell the events are is unclear.

Authors response : We agree with the comments of the reviewer regarding data presentation. ‘n’ represented individual cells, but did not actually take into account the variability across experiments. We have therefore changed the presentation of all graphs of the manuscript using superplots that allow us to show all the points that were analyzed as well as the mean value for each biological replicate, and performed statistical analyses by comparing the biological replicates as proposed in Lord et al, JCB 2020 (10.1083/jcb.202001064).

In fig 2H critical data are missing - the effect of Spatascin KO on the transition between these morphologies should be considered as in G. Otherwise the relevance is unclear.

Authors response : We have added this quantification on Figure 2I. It shows that transition of morphology of lysosomes from round to tubular in Spg11 KO cells is still associated with a change of speed, although the average speed attained is halved compared to conditions where spatacsin is present. This shows that loss of spatacsin does not abolish morphological transition of lysosomes but limit their speed in the tubular shape. We commented on this new data in the text (p.8).

The impact of over-expressing a lysine-null Ub ( Fig 3) is far too crude and non-specific to have meaning here. It is assumed that the only proteins affected are those of interest. This is consistent with much of the paper where "true-true-and unrelated" is more likely than the presumption of causality.

Authors response : It is true that the expression of lysine-null ubiquitin is really crude and may impact many different cellular pathways. Furthermore, the results obtained with the lysine-null ubiquitin do not contribute to the rest of the paper. We therefore removed the original Fig3G, H, I and Fig 4B and updated the text accordingly (p.10).

The blots in Fig4 are a relatively poor quality and not quantified over repetition.

*Authors response :Spatacsin and spastizin are large proteins, and there is not much choice for antibodies able to detect these proteins. Yet we have validated their specificity by western blot using knockout cells (spatacsin) (Supplementary Figure 4 A-B) or siRNA (spastizin) (Supplementary Figure 7B). We agree that our presentation did not indicate that the western blots were repeated several times. We have added quantifications for the western blots present in Figures 4 and 5. We also changed some illustrative western blots to improve quality. *

Controls are missing and Fig5 suffers from a reliance on over-expression - there is a massive over-expression of AP5Z1 which may be affected the stoichiometry of these overall interactions, but with an n=1 its hard to know and its not clear what these data add. Again, while statistically significant (5E and F) due to the nature of data analysis (every lysosome=n of 1) it is not clear how biologically significant UBR4 siRNA or AP5Z1 over-expression is - as the accumulation of AP5Z1 in these two conditions is orders of magnitude apart - again likely unrelated.

Authors response : We added quantification for this western blot (Supplementary Figure 6A).

*As stated above we have changed the representation of the graphs. Each point represents one cell, and we included the mean value for each biological replicate. *

Preventing degradation of AP5Z1 by UBR4 siRNA or overexpression of AP5Z1 do not indeed have the same effect on total AP5Z1 but do have a similar effect on the interaction of spatacsin with its partners evaluated by co-immunoprecipitation, as illustrated by the quantifications that we have added. We clarified this in the text (p.12). As requested by reviewer 3, we will also investigate the effect of AP5Z1 knockout or downregulation on the interaction between spatacsin and spastizin assessed by co-immunoprecipitation. These data will be added in Figure 5 and will strengthen our conclusions.

Fig 6 begins to conflate the fact that different lysosome morphologies appear to have different trafficking properties even in WT cells and that many of these targets affect morphology - therefore to conclude a direct effect on trafficking seems inappropriate.

Authors response : In original Figure 6, we showed that Kif13A-ST and p150CC1 changed the proportion of tubular lysosomes (previous Figure 6 and H), and the data showing that these constructs changed the trafficking of lysosomes were presented in Supplementary Figure 5 B-C. We have now moved the data showing the effect of Kif13A-ST and p150CC1 in the main Figure (Figure 6F and 6I) to facilitate the interpretation of the data. Therefore, expression of Kif13A-ST and p150CC1 do not only affect the morphology of lysosomes, but also impaired their trafficking. We thus do not extrapolate lysosome dynamics from their morphology, we actually quantify lysosome dynamics.

Fig 7 extends this into polar cells (neurons) but still it is not clear whether form (morphology) dictates function (likelihood of trafficking or directionality.

Authors response : We did not only analyzed neurons because they are polarized cells, but because neurons are the main cells affected by neurodegeneration observed in absence of spatacsin (Branchu et al, 2017). We added new data on Figure 7 showing that tubular lysosomes in axons are actually more dynamic than round lysosomes, as observed in fibroblasts. We added these data in Figure 7 and text (p.13).

Investigation of lysosome trafficking in axons also allowed us to investigate the directionality of movement, which is difficult in MEFs. We clarified this point in the text (p.13).

In sum, there is a lot of data that collectively points to a partial localization of Spatascin at Er-lysosome contacts and an influence on morphology and trafficking of lysosomes in the cell, but at the end of the day very new mechanism is brought to light.

Authors response : The mechanisms regulating trafficking of lysosomes are far from being fully resolved. Our manuscript shows that spatacsin contributes to this regulation by modulating the degradation of AP5Z1. This in turn regulate the lysosomal association of AP5Z1 and spastizin that interact with motor proteins to control lysosomal dynamics.

Reviewer #4 (Significance (Required)):

This manuscript is directed to the basic cell biology community - involving ER, lysosome, and microtubule dependent trafficking. There are some new analytical tools employed and many co-factors and binding partners of Spatascin considered but frankly too many to adequately and rigorously control for. Because of this the manuscript is very unfocused, hard to follow and makes too many assumptions about shared dynamics ? necessarily arising from shared morphology - lysosomes are highly dynamic and can be affected by virtually any change in intracellular trafficking or protein/membrane transport. This is not appropriately considered.

Authors response : We have clarified our manuscript to show that dynamics is not necessarily arising from a tubular morphology. It turns out that lysosomes with a tubular morphology indeed are more dynamic that lysosomes with a round morphology. Importantly, in all our experiments dealing with lysosomal dynamics, we have actually included a quantification of lysosome dynamics using time lapse imaging as detailed in methods (p.21).

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Referee #4

Evidence, reproducibility and clarity

Pierga et al. reveal subtle differences in lysosome morphology, ER-contact, and trafficking in the absence of Spatascin. These data are replicated with a truncated Spatascin, presumably a loss of function. Two-hybrid screening of the deleted sequence from this truncation for interactors and then asked whether these hits could phenocopy the lysosome morphology changes. This led to an assertion for a role for ubiquitination in these effects. Rather than these hits the group then investigates previously known Spatascin interactors and reports similar complex but subtle abnormalities via overexpression or knockdown of these. While data show overlapping phenotypes by modulation Spatascin, AP5z1, and Spastizin, the manuscript is confusing, leaps from experiment to experiment, and does not provide novel rigorous mechanisitic insight. It conflates all the discrete lysosomes aspects into a collective to link them. The title is over-stated and not appropriate for the experiments.

The localization of endogenous Spatascin is lacking - over-expression is prone to artifact and the punctate data on the V5 suggests much more work is needed to understand where in the cell it is. It would seem much more work is needed here. Furthermore, the EM data (1E) would suggest the far majority of lysosomes are in contact with ER - these seems uncharacteristic.

The phenotypes analyzed are very subtle, and while statistically significant the biological impact is unclear - in many cases individual lysosomes (or lysosome-ER contacts) are considered as an 'n'. While these results are probed across multiple independent experiments the batch effects and how uniform per cell the events are is unclear.

In fig 2H critical data are missing - the effect of Spatascin KO on the transition between these morphologies should be considered as in G. Otherwise the relevance is unclear.

The impact of over-expressing a lysine-null Ub ( Fig 3) is far too crude and non-specific to have meaning here. It is assumed that the only proteins affected are those of interest. This is consistent with much of the paper where "true-true-and unrelated" is more likely than the presumption of causality.

The blots in Fig4 are a relatively poor quality and not quantified over repetition.

Controls are missing and Fig5 suffers from a reliance on over-expression - there is a massive over-expression of AP5Z1 which may be affected the stoichiometry of these overall interactions, but with an n=1 its hard to know and its not clear what these data add. Again, while statistically significant (5E and F) due to the nature of data analysis (every lysosome=n of 1) it is not clear how biologically significant UBR4 siRNA or AP5Z1 over-expression is - as the accumulation of AP5Z1 in these two conditions is orders of magnitude apart - again likely unrelated.

Fig 6 begins to conflate the fact that different lysosome morphologies appear to have different trafficking properties even in WT cells and that man of these targets affect morphology - therefore to conclude a direct effect on trafficking seems inappropriate. Fig 7 extends this into polar cells (neurons) but still it is not clear whether form (morphology) dictates function (likelihood of trafficking or directionality.

In sum, there is a lot of data that collectively points to a partial localization of Spatascin at Er-lysosome contacts and an influence on morphology and trafficking of lysosomes in the cell, but at the end of the day very new mechanism is brought to light.

Significance

This manuscript is directed to the basic cell biology community - involving ER, lysosome, and microtubule dependent trafficking. There are some new analytical tools employed and many co-factors and binding partners of Spatascin considered but frankly too many to adequately and rigorously control for. Because of this the manuscript is very unfocused, hard to follow and makes too many assumptions about shared morphology necessarily arising from shared morphology - lysosomes are highly dynamic and can be affected by virtually any change in intracellular trafficking or protein/membrane transport. This is not appropriately considered.

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Referee #3

Evidence, reproducibility and clarity

This manuscript highlights an interesting localization of spatacsin in the endoplasmic reticulum (ER)-lysosomes contact sites. In addition, it implicates spatacsin in regulating tubular dynamic lysosomes. Mechanistically, the authors propose that spatacsin interacts with UBR4 to promote the autophagic degradation of its binding partner AP5Z1 at the lysosomes. In turn, this would also regulate the amount of spastizin at the lysosomes, which is known to interact with anterograde motors. The authors further show that AP5Z1 interacts with p150Glued. Thus, the balance between AP5Z1 and spastizin at the lysosomes would determine lysosomal trafficking directionality.

Major Comments

1. Several crucial results of the manuscript are based on quantifications performed on immunofluorescence stainings. Data points in graphs show individual cells or individual lysosomes and the authors apply statistical tests on replicates that cannot be considered biologically independent, since they come from the same experiment or even the same cell. It is recommended to show superplots where both the individual data and the average of each independent experiment is indicated as recommended by Lord et al. (J Cell Biol 2020 219 (6): e202001064.). Statistics should be performed only on independent biological replicates.
2. The authors have used yeast two-hybrid to search for spatacsin interactors. Although in the manuscript they refer to supplementary tables that should show these interactors, the available Tables are confusing and refer to the following downregulation experiments. An experiment to demonstrate that endogenous UBR4 and spatacsin interact by co-immunoprecipitation would be crucial.
3. Several important experiments to unravel the mechanistic role of spatacsin (Figure 4 and 5) are performed upon overexpression. This is a major limitation of the study and the authors should address it as much as possible. Western blots and immunoprecipitations are shown that appear to have been performed only once and have no quantification. As an example, in Fig 4A the difference in levels of AP5Z1 upon spatacsin overexpression or UBR4 downregulation are very minor. I would be very careful in drawing big conclusions, without additional repetitions and additional experiments in an endogenous setting.
4. The authors suggest a model by which UBR4 recruited by spatacsin is involved in autophagic degradation of AP5Z1. The data shown do not support this conclusion. First, in Figure 4A downregulation of UBR4 does not increase levels of AP5Z1 above the control in lane 1, but only when spatacsin is overexpressed. The effect of downregulation of UBR4 in wilt-type cells on AP5Z1 should be investigated. Secondly, there is no experiment directly proving that the stability of AP5Z1 depends on UBR4.
5. The authors suggest that the interaction of spatacsin with spastizin or AP5Z1 are in competition. This is an interesting hypothesis, however to conclusively demonstrate this, pull-down experiments in KO cells and not upon extreme overexpression should be performed.

Minor comments

1. In figure 1G and 1H the overlapping area between lysosomes and ER is quantified. Considering that the ER occupies a large portion of the field a 90{degree sign} flipped control for both WT and KO would be important to sort out random colocalization. In this direction, it would be also essential to show that the total amount of lysosomes is not different in WT and KO, especially because in figure 1A the lysosomes in WT and KO seem to be different not just in shape but also in number and size. A different number or size of lysosomes affects this analysis.
2. In the second chapter of the Results, the authors state: "we observed by live imaging a higher number of lysosomes with tubular shape in Spg11+/+ compared to Spg11-/- cells", however the number of elongated lysosomes is quantified per area. Why the number of elongated lysosomes is not quantified over the total amount of lysosomes?
3. The In the fourth chapter of the Results, the authors state:" In wild-type MEFs, mCherry was colocalized with lysosomes. In contrast, GFP that is sensitive to pH was poorly colocalized with lysosomes, suggesting that AP5Z1 was mainly inside the acidic subcellular compartment (Figure 4F)." If the aim of the authors is to shown that AP5Z1 is mainly into the lysosome, the amount AP5Z1-mcherry inside and outside the lysosome need to be compared, with a proper statistical analysis. There is also a lot of GFP signal in the cytosol. Why is that?
4. construct used in the paper is a C-terminal tagged version of spatacsin. The authors should consider to test an N-terminal tagged construct at least for the localization experiments.
5. Figure 5C: a negative control and the quantification are missing.

Significance

Since spatacsin, AP5Z1 and spastizin are all implicated in hereditary spastic paraplegia, the data are of potential interest not only for basic cell biology, but also to understand the pathogenesis of the disease. In addition, the manuscript proposes a novel model regulating trafficking of dynamic lysosomes.

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Referee #2

Evidence, reproducibility and clarity

In this study Pierga et al. report that SPG11 (spatacsin) is an ER-resident protein involved in the regulation of ER-lysosome contact sites (in particular tubular lysosomes) and subsequent faster motility of tubular lysosomes, as well in the degradation of AP5Z1 (SPG48), which forms a heterotrimeric complex with SPG15 (spastizin) and SPG11. This complex has been localized by several groups on the cytoplasmic side of LAMP-1-positive lysosomes. In addition, mutations in SPG11, SPG15, and SPG48 patients share various clinical features and were supported by biochemical/cell biological data from Spg11 and Spg15 KO mouse models and cultured cells both from patients and mice, respectively, demonstrating e.g. accumulation of autolysosome storage material, defects in the autophagic lysosome reformation process, and the loss of cortical motoneurons and Purkinje cells.

Major concerns:

1. Fig. 1, 2, 3: major disadvantage of this study is the analysis of overexpressed proteins (SPG11-V5, GFP-Sec61,and Lamp1-mCherry) which might contribute to the observed strong expression of SPG11-V5 in the ER/ER-enriched fraction. The results should be compared with the endogenous expressed proteins. In Fig. 1C the lack of the mature Cathepsin D form which is proteolytically generated only in lysosomes from the higher molecular mass precursor is misleading and should be related to presence of lysosomal membrane proteins. Fig. 1D: the TEM image shows only a single lysosome and proposed ER contact zones in wt-MEFs without comparison with Spg11 KO MEFs (only in the quantification). Without double immunogold labeling of SPG11 (and their lack on SPG11 KO cell lysosomes) and known ER contact-site proteins this image and the conclusion are insufficient.
2. The rationale for the selection of the deleted Spg11 region 32-34 is not clear. What are the symptoms of this Spg11 knock-in mouse? A more detailed description of the phenotype is required! Is the phenotype including the accumulation of LC3-positive material similar to the phenotype of the SPG11 KO mouse which has been published by Varga et al.(2015) and Branchu et al. (2017) ? If not, is the new mechanisms reported here not so important?
3. p8/Fig. 3F/Suppl.Fig.3F- the most important part of the manuscript: what are the parameters of lysosomal staining in images that were used to identify genes important for lysosome tubulation by the neural network? I cannot understand how the authors predict the probability of the cell to be considered as an Spg11 KO fibroblast (why not as an Spg11 32-34 knock-in fibroblast?) as the basis for the selection of interaction candidates. A simple statement that the neural network approach identified those genes is too weak and requires more convincing experimental data. It has to be shown at least for the 8 positive genes in both approaches how the siRNA treatments of these genes phenocopied the lysosomal changes and of course the effect of the downregulation on the protein level of their products both in wild-type control and Spg11 32-34 knock-in MEF. The Suppl. Fig.3F is completely unclear. How were the Y2H interaction partner validated? Did the authors use the identified 8 interaction candidates as full length bait to demonstrate the interaction with the Spg11 exons 32-34 ? p8/Fig.3F: the genes identified in both approaches have to be listed in the Fig. 3F-Table. The GO process- ubiquitin-dependent protein catabolic process is neither positive for the neural network nor for the directed analysis but positive for both analyses? Please explain. Similarly, the GO process proteolysis involved in cellular protein catabolic process -is not positive for the neural network analysis but again positive for both analyses. For Fig. 3G the mutant ubiquitin-K0 staining in wild-type MEF cells has to be shown as well as for the Spg11 ki/KO MEFs (+ quantification of the respective data)
4. The interpretation of the Y2H-interactome analysis by the authors is hard to follow. They searched with the exon 32-34 cDNA for binding partner, selected 3 degradative GO processes and showed by overexpression of a mutant Ub-K0 plasmid in wild-type MEFs a decreased number of tubular lysosomes, as well as their dynamics (without showing the control data in Spg11 KO or ki-MEFs). Thus, poly-ub of proteins should be in some way responsible for a lysosomal phenotype of Spg11ki MEFs. Now they went to AP5Z1, the second binding partner of SPG11, which is reduced in its abundance upon overexpression of Spg11-GFP. I would expect to do the respective control experiment to show that in the absence of SPG11 or in the knock-in cells the amount of AP5Z1 has to increase. However, in the studies by the Huebner group by deletion of Spg11 or the other binding partner Spg15, no increase of AP5Z1 protein levels has been observed. The authors have to comment on this discrepancy. Then the authors found that the selected interaction partner of the exon 32-34 sequence, UBR4, does not bind to the Spg11-GFP construct lacking the domain encoded by exons 32-34 but to the C-terminal domain of Spg11-GFP. Unfortunately, all these IP-experiments were shown as cut and paste figures, preventing the direct comparison between the input and the IP protein amounts (since the information is missing what percentage of the input and the IP has been loaded per lane, the evaluation and significance of these Co-IPs are unclear).
5. p9: AP5 (Z1) is a cytoplasmic protein and can be localized on the cytoplasmic surface of lysosomes. How should the GFP-mcherry-AP5Z1 protein enter the lumen of lysosomes justifying the quenching of the GFP signal? A positive control has to be included in the experiment shown in Fig. 4E demonstrating the effect of MG132 under identical conditions of a protein substrate for proteasomal degradation.
6. Fig. 5A: In contrast to GFP-mcherry-AP5Z1, spastizin-GFP is localized at the cytoplasmic surface of lysosomes (co-staining with LAMP1-mcherry) in wild-type MEFs. In regard to the incomplete data commented under "minor points Fig.4/Suppl.Fig.4", I suggest to perform a simple control experiment with overexpressed GFP-spastizin and mCherry-AP5Z1 in wild-type MEFs (at the best also in Spg11 KO MEF) with and without bafA treatment, which will clearly demonstrate whether single components of the trimeric Spg11, spastizin-AP5Z1 complex are degraded independently of each other in lysosomes.
7. why did the authors neither mention nor discuss the described role of SPG11 in autophago-lysosome reformation (ALR)?

Minor points

• Figure 1 A, B, D, and G: ER-lysosome contact sites. The quantification of the co-localization of spatacsin-V5 with the ER marker protein GFP-Sec61b has to be given. Moreover, the authors analyzed overexpressed tagged-proteins only. The results should be compared with the endogenous proteins.
• p8/Figure 3: what does the 'analysis of trained neural networks' mean?
• Figure 4: what happens with the other AP5 subunits?
• Fig.4F/Suppl.Fig4: live images of GFP-mcherry-AP5Z1 + lysotracker staining have to be shown both for wild-type MEFs with and without bafilomycin A treatment(as in Fig.4F), and in Spg11 KO and Ki MEFs +/- bafA.

Significance

see above.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This paper identifies a role for the hereditary spastic paraplegia protein spatacsin in lysosome morphology, positioning and dynamics, and undertakes detailed mechanistic studies to try to identify the mechanism for this effect. In doing so the paper elucidates further mechanistic information about the properties of two other hereditary spastic paraplegia proteins, spastizin and AP5Z1. The work is done in mammalian cells and uses a combination of over-expression, depletion and biochemical studies. The main findings are:

1. The authors present evidence that spatacsin is an ER-localised protein.
2. Murine embryonic fibroblasts lacking spatacsin have a reduced number of tubular lysosomes and the remaining lysosomes are less motile. In general, a relationship between tubular lysosome morphology and lysosome motility, often in association with the endoplasmic reticulum (ER), is demonstrated. These tubular lysosomes are catalytically active and acidic.
3. In terms of mechanism of this effect, by combining a yeast-two hybrid and siRNA phenotypic screen, the authors identify a number of spatacsin-interacting proteins that also regulate lysosomal tubulation. The most important of these for the purposes of this paper is UBR4, an E3 ubiquitin ligase.
4. The authors show that spatacsin and UBR4 promote degradation of AP5Z1, and that this property required the ability of spatacsin to interact with UBR4. Somewhat surprisingly, as AP5Z1 is a coat protein, this degradation appeared to occur within the lumen of the lysosome - the authors speculate how this could be in the discussion.
5. The authors then demonstrate that AP5Z1 and spastizin, both hereditary spastic paraplegia proteins, compete for binding with spatacsin.
6. The relationship between spatacsin, spastizin, AP5Z1 and motor proteins in then examined. There is a known interaction between spastizin and KIF13A and expression of a dominant negative KIF13A protein reduced lysosomal tubulation. The authors then demonstrate an interaction between AP5Z1 and the p150Glued dynein/dynactin complex member, then showed that expression of a dominant negative p150Glued protein reduced lysosomal tubulation.
7. Finally, that authors demonstrate the relevance of these findings to neurons, the target cells of hereditary spastic paraplegia, by showing that lysosomal tubulation and axonal transport are reduced in mouse neurons lacking spastacsin, and that depletion of UBR4 or AP5Z1 affected these as expected from the experiments above.

Major comments:

Overall I believe that the key conclusions of this paper are generally convincing and that the work is of high quality. However, I do have some reservations:

1. The localisation of spatacsin on the ER. It is always difficult to be convinced about colocalization of a diffuse punctate marker and the ER. From the STED experiments in figure 1, while it definitely seems that there is some spatacsin on the ER, there also appears to be some spatacsin puncta that are not. I'd like to know if these puncta represent lysosome-associated spatacsin. This is important for interpretation of the subsequent experiments (see point 3 below). I also think quantification of these co-localisation will increase confidence in the results. In addition, a caveat of the immunofluorescence studies is that they use over-expressed spatacsin. I appreciate that there are no good antibodies to endogenous spatacsin, but I don't think this limitation is sufficiently acknowledged. As the claim of ER-localisation is critical for the proposed mechanistic model, and in the absence of experiments with endogenously tagged spatacsin, this makes the biochemical fractionation studies of figure 1C very important. To make these more convincing I would prefer to see additional control markers to verify the separation of lysosomal and ER compartments - e.g. lamp1, lamp2, an ER tubular marker such as a REEP5 or a reticulon.
2. The authors generally do a good job of quantifying their results. However, this is lacking for the biochemical experiments (immunoblotting and IP) in figures 4 and 5, and I would prefer to see these quantified (the quantification should include data from repeat experiments so that we can judge the reproducibility of the results).
3. On page 10, referring to the proximity ligation results, the authors comment: "This suggests that the spatacsin-spastizin interaction occurs at contact sites between the ER and lysosomes to allow spastizin recruitment to lysosomes". I'm not sure this statement is fully supported, as mentioned at point 1 above it is possible that some steady state spatacsin is at lysosomes. To fully support this, we'd need to see the PLA signal also convincingly co-localise with an ER marker.
4. In figure 6C and D the effect of spastizin on lysosomal tubulation and dynamics is investigated. Wartmannin treatment is used to do this, as it is known to remove spastizin from lysosomes. However, this is a very indirect manipulation that could have many other consequences and it would be better to demonstrate this directly by showing the effect of depletion of spastizin on lysosomal morphology/dynamics. I also think the role of AP5Z1 in tubulation/dynamics would be better supported with additional experiments to deplete the protein - at present only over-expression is examined.

5. While the experiments showing that over-expression of dominant negative forms of KIF13A and p150Glued affect lysosomal tubulation/dynamics provide good circumstantial evidence that spatacsin influences these lysosomal properties via its interactions with spastizin and AP5Z1 (which bind to these motor proteins), the authors have not shown that the interaction of the motor proteins with spastizin and AP5Z1 is required for this ability to regulate lysosome tubulation/dynamics. This means that the model presented in figure 7 is not fully supported by the data. If the authors have been able to map the binding regions for these interactions then perhaps this could be investigated with rescue experiments, although I appreciate that this is potentially a major piece of work and perhaps outside the scope of this paper. An alternative would be that the authors acknowledged this part of the model as somewhat speculative.

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

Yes - Are the experiments adequately replicated and statistical analysis adequate?

In general I am not convinced that the statistical tests are applied rigorously in this paper. Most experiments are done three times, but the "n" used for statistical testing is typically chosen as, e.g. the number of cells, number of lysosomes, rather than number of biological repeat experiments. This means that inter-experimental variability is not rigorously taken into account. A more rigorous practice would be to use the mean measures for each of three biological repeats and apply the statistical tests to the three means, so n=3 if three repeats were done. Superplots would be a nice way to graphically display these data.

Minor comments:

1. In supplementary figure 3D I cannot honestly say that I see the smaller band.
2. When first called out, I expected supplementary tables 1 and 2 to show the list of interactors with wild-type spatacsin and spatacsind32-34 respectively, but this is not what they show.
3. The experiments in Figure 4A are a little problematic in the way that they are called out. The first call refers to just a small subset of the data in the figure, and the figure is then called out at various points later in the paper. This is quite confusing. Is there any way this could be simplified?
4. The section on page 10: "Spatacsin also interacts with spastizin, and is required to recruit spastizin to lysosomes (Hirst et al., 2021). ........ We hypothesized that spatacsin interaction with spastizin was required for spastizin localization to lysosomes." Is odd, as the authors seem to be hypothesising an observation that they have just said has already been demonstrated.
5. Can the authors explain why there is so little interaction between wild-type KIF13A and spastizin?
6. In figure 6G p150Glued signal is also present in the control IP lane, which casts doubt on the specificity of the interaction. Could the authors generate a cleaner result?

7. I would be interested to see how AP5Z1 expression differs between neurons with and without spatacsin- we would expect similar results to those shown in the MEFS.

8. Are prior studies referenced appropriately?

Yes. - Are the text and figures clear and accurate?

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

Overall I thought the presentation was good. However, this is a complex paper and anything that the authors can do to simplify the textual descriptions of the experiments would be helpful. There are quite a few long multiphrase/multiclause sentences that could perhaps be broken up or simplified, e.g. I had to read the following three or four times to understand it: "Downregulation of UBR4 that prevented degradation of AP5Z1mediated by spatacsin (Figure 4A) led to higher interaction of spatacsin with AP5Z1 and decreased the interaction of spatacsin with spastizin (Figure 4A)."

Referees cross-commenting

Thanks for the opportunity to comment on the other reviews. It does seem that there is a consistent theme that reviewers are concerned about the over-reliance on over-expression experiments and the need for additional experiments using endogenous antibodies or protein depletion methodologies to strengthen the data. In addition, I and at least one other reviewer feel that it is not adequate to use number of cells as the "n" for statistical testing, and that true biological repeats are needed.

Significance

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

I think this paper represents a significant conceptual advance in our understanding of the mechanisms by which lysosomal dynamics are controlled in non-polarised cells and neurons. In addition, it elucidates mechanisms that may underlie multiple forms of hereditary spastic paraplegia, a hereditary form of motor neuron disease.<br /> - Place the work in the context of the existing literature (provide references, where appropriate).

This is a significant conceptual advance on the current literature on spatacsin and on the molecular mechanisms controlling lysosomal morphology/dynamics. The paper elucidates important mechanistic details of the relationship between three key proteins involved in hereditary spastic paraplegia, while also shedding light on the basic biology of lysosomal morphology and dynamics. - State what audience might be interested in and influenced by the reported findings.

Basic cell biologists interested in the ER, in lysosomes, in ER-organelle contacts. Scientists interested in the causation of hereditary spastic paraplegias. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Membrane traffic, lysosome function, ER-endosome contacts, hereditary spastic paraplegia.

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Reply to the reviewers

Manuscript number: RC-2022-01683

Corresponding author(s): Prof Andrew Macdonald, Dr Ethan Morgan

1. General Statements [optional]

We are very appreciative of the helpful and insightful comments provided by the reviewers, which will greatly aid in improving this manuscript. We were delighted by the many positive comments we received, highlighting the “high quality” data and praising our “detailed and carefully constructed” experiments, which together reveal an “interesting and novel mechanism” of HPV-driven cervical carcinogenesis.

To summarise our key findings, we identify the host ATP-sensitive potassium ion (KATP) channel as a critical driver of proliferation and HPV oncoprotein expression in HPV+ cervical cancer cells. Use of pharmacological inhibitors and activators of KATP channels revealed that HPV oncoprotein expression correlates with channel activity, findings that were validated via siRNA/shRNA knockdown and overexpression strategies. Indeed, HPV was found to enhance expression of the ABCC8 gene (encoding the SUR1 KATP channel subunit), likely in a manner dependent on the E7 oncoprotein. We also reveal that channel knockdown impeded HPV+ cervical cancer cell proliferation, both in cell culture monolayer and, importantly, in tumour xenograft experiments. This loss of proliferation may be associated with induction of a G1 cell cycle arrest. Finally, we demonstrate that KATP channels are capable of activating ERK1/2 signalling and, in turn, the AP-1 transcription factor, leading to recruitment of AP-1 to the HPV18 promoter. Significantly, this study is the first to our knowledge to explicitly demonstrate modulation of ion channel expression and activity by HPV, and that this can contribute to host cell transformation. We believe the potential for use of the clinically available KATP channel inhibitors as novel therapies for HPV-associated malignancies should therefore be evaluated in future studies.

Outlined below, in a point-by-point manner, are the changes we have already incorporated to improve the precision and clarity of the manuscript, as well as the additional data we intend to add in order to strengthen our findings.

2. Description of the planned revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Addressing the following major points would help to strengthen the impact of the work:

1. The paper would be greatly strengthened by addressing whether knockdown of SUR1 and knockdown of E6/E7 are affecting cell viability. siRNA depletion of E6 and E7 will cause HeLa and SiHa cells to senesce; at what time point post knockdown were the experiments performed? Is it possible to perform CellTiterGlo or other cell viability assays to confirm that the phenotypes observed upon E6/E7 depletion and upon SUR1 depletion or drug treatment are not the result of cell death/senescence/toxicity?

We agree that an assessment of cell viability following the treatments/transfections performed will strengthen the manuscript. We will therefore perform the suggested CellTiterGlo assay using both HeLa and SiHa cells after glibenclamide treatment, SUR1 knockdown, and E6/E7 knockdown.

1. There is a major concern regarding whether SUR1 protein is produced at a biologically relevant level in SiHa and HeLa cells, in which most of the experiments in the paper were conducted. Protein levels are assessed in Fig 2 by immunostaining in raft cultures and in a cervical cancer tissue microarray. However, protein levels are otherwise not examined, especially in SiHa and HeLa cells. Is SUR1 protein produced in these cells? Are its levels reduced by the knockdown approaches? The fold change RNA data presented in figure 2A does not convincingly address this question, since even an 8-fold increase of ABCC8 mRNA over a low background level might not have biological significance. It would be very helpful to measure SUR1 protein in several of the experiments in HeLa and SiHa cells.

We accept the concern of the reviewer regarding the absence of an assessment of SUR1 protein levels in HeLa and SiHa cells. There is a critical lack of high-quality antibody reagents available for the detection of SUR1, a common phenomenon within the ion channel field. We have therefore been unable to reliably detect SUR1 via immunoblot using the antibodies we have tested thus far. Nevertheless, we would argue that our other experiments in these cell lines demonstrate that not only are KATP channels expressed at biologically relevant levels but, more importantly, are active in these cell lines. Patch clamping electrophysiology, the gold-standard technique for assessing ion channel functionality, was performed in HeLa cells and the changes in current observed following inhibitor/activator application suggests that the channels are active, and by extension, that SUR1 protein must be present. Furthermore, DiBAC4(3) assays were performed following inhibitor treatments, channel stimulation, SUR1 knockdown, and HPV E7 knockdown. Although this involves an assessment of plasma membrane polarisation, and therefore is not a direct measurement of KATP channel activity, the changes observed are consistent with our expectations (e.g. increased DiBAC4(3) fluorescence with glibenclamide treatment, indicative of membrane depolarisation, is consistent with a loss of K+ ion efflux via KATP channels). However, we recognise that providing protein expression data in HeLa and SiHa cells would make our conclusions more convincing. We will therefore continue our search for antibody reagents that will allow us to reliably detect SUR1 protein in HPV+ cell lines by western blot. We will also pursue other detection methods in these cell lines, including immunofluorescence and immunohistochemistry. We hope that we are successful in our optimisation, allowing us to validate that SUR1 protein levels are reduced following our knockdown approaches.

1. The authors should address the idea of off-target effects, either experimentally or, more feasibly, by discussing the possibility of non-specific effects of SUR1 knockdown. They use a pool of four siRNAs to SUR1 and the risk of off-target effects would be greatly reduced if individual siRNAs were tested and shown to have the same effect as one another. Similarly, several experiments use just one shRNA, limiting the ability to draw conclusions.

To address off-target effects, we will repeat some of the experiments performed in this manuscript using individual siRNAs. HeLa and SiHa cells will be transfected with each of the four siRNAs individually and the impact on HPV E6 and E7 expression examined by RT-qPCR and western blot. Further, we will also repeat colony formation assays and DiBAC4(3) assays to ensure that each siRNA has a similar effect on proliferation and membrane polarisation respectively.

1. Finally, since many of the experiments rely on knockdown approaches that show similar readouts, a rescue experiment (restore sh or si-resistant SUR1 and assess the impact on the phenotype) would confirm that the effects being observed are due to changes in SUR1 levels and not to off-target effects.

We agree that rescue experiments would greatly strengthen the manuscript. Our laboratory has significant experience in carrying out this type of experiment and as such we would be very happy to perform them. We will reintroduce siRNA-resistant SUR1 following knockdown of endogenous SUR1 levels and confirm that E6/E7 expression and proliferation are restored.

CROSS-CONSULTATION COMMENTS I note several areas of common feedback among the reviews. Several reviewers commented on the large number of experiments and that the work is of interest to researchers working on HPV and cancer therapeutics. Several reviewers shared concerns about cell viability upon HPV oncoprotein knockdown and about toxicity in various experiments. Several reviewers also raised concerns about the validation of SUR1 protein levels in several experiments. These concerns seem to me to be critical to address to strengthen the manuscript. I note that Reviewer #3's suggestion of making E7 knockout cells (presumably in HPV+ cancer cell lines) is unlikely to be possible because the cells require E7 for survival.

We agree that the two main areas of concern shared among the reviewers (cell viability assays and validation of SUR1 protein levels) are issues that should be addressed in a revised version of this manuscript. We will endeavour to perform the experiments described above, which we hope will significantly enhance the impact of our work.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Major Comments: Authors did not explain how HPV E7 would upregulate ABCC8 transcription or elevate SUR1 protein (Figure 4). Depletion of E7 is known to produce lethal effect in cervical cancer cell lines. No experiment was done to assess cytotoxicity. Hence it is not clear from the available evidence if the SUR1 is reduced by direct E7 mediated event or indirectly by general cytotoxicity induced by E7 knock down.

We thank the reviewer for their suggestions. We agree that it would be useful to provide some mechanistic insight into how E7 upregulates ABCC8 transcription. We therefore plan to analyse the promoter region of ABCC8 to identify potential transcriptional regulators. ChIP-qPCR and luciferase reporter constructs containing the ABCC8 promoter region will be used to unravel the importance of any candidate TFs identified. An assessment of the impact of E7 on the expression and/or activity of these TFs may also be performed. Finally, we will combine E7 overexpression in a HPV- cell line with knockdown/inhibition of a candidate TF to confirm our findings.

Experiments to assess cell viability/cytotoxicity will be performed as outlined in our response to Reviewer #1.

Authors did not analyze expression level and role of p53, pRB proteins, the direct targets of E6 and E7 proteins, on cell cycle regulation following SUR1 siRNA or Glibenclamide-treatment in cervical cancer cell lines.

We agree that the protein levels of p53 and pRb, the primary targets of E6 and E7 respectively, should be analysed in HeLa and SiHa cells following SUR1 knockdown and glibenclamide treatment and will endeavour to perform this.

Minor Comments:

Additional immunofluorescence or histological analysis is necessary to assess the potential cytotoxic effects of E7 siRNA, SUR1 siRNA or KATP inhibitors (Glibenclamide) in cervical cancer cell lines.

We agree that an assessment of cell viability will strengthen the manuscript and we plan to address this as outlined in our response to Reviewer #1.

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

In addition to the points raised by the reviewers, we identified a minor error that occurred during assembly of the manuscript. Incorrect images of colony formation assays were provided in the original version of Figure 5B. This has now been amended in the transferred manuscript.

__Reviewer #1 (Evidence, reproducibility and clarity (Required)):____ __Overall, the data are of high quality and the individual results are consistent with each other and are convincing. However, the authors have understandably focused on two HPV-positive cancer cell lines (affected by modulating KATP levels) and one HPV-negative cancer cell line (which is not affected in the same way). The ability to extrapolate to conclusions about cervical or HPV-positive cancers in general is therefore limited and many of the authors' statements should be tempered to reflect the experiments they have conducted.

We thank the reviewer for their positive comments regarding our data. We agree that by mainly focussing our studies on cervical cancer cell lines, we limit the potential for extrapolation to other HPV-associated malignancies. We were careful to qualify many of our statements in the original submission to reflect this: e.g. lines 314-315: “Collectively, these data demonstrate that KATP channels are important drivers of proliferation in HPV+ cervical cancer cells.” Post review, we have altered references to HPV+ cancers in general to more accurately reflect the data presented, as outlined below:

• Lines 112-113: We hope that the targeting of KATP channels may prove to be beneficial in the treatment of HPV-associated __cervical __neoplasia.____

• Lines 430-431: As such, we believe that the clinically available inhibitors of KATP channels could constitute a potential novel therapy for HPV-associated malignancies HPV+ cervical cancer.

  Addressing the following major points would help to strengthen the impact of the work:

• The paper would be greatly strengthened by addressing whether knockdown of SUR1 and knockdown of E6/E7 are affecting cell viability. siRNA depletion of E6 and E7 will cause HeLa and SiHa cells to senesce; at what time point post knockdown were the experiments performed? Is it possible to perform CellTiterGlo or other cell viability assays to confirm that the phenotypes observed upon E6/E7 depletion and upon SUR1 depletion or drug treatment are not the result of cell death/senescence/toxicity?

All experiments involving siRNA-mediated knockdown were performed at 72 hours post-transfection. We apologise for omitting this information, and it has now been added to the ‘Materials and Methods’ section.

Minor comments: The text and figures are clear and statistics are appropriate. The authors should include at what time point post siRNA transfection the experiments were conducted.

As above, this information has been added to the ‘Materials and Methods’ section.

__Reviewer #2 (Evidence, reproducibility and clarity (Required)):____ __E6 and E7 protein bands in DMSO treated HeLa and SiHa cells are not consistent between Figures 1 E, H and J, hence confound the interpretation. There is no information on biological replicates. It is not clear why the data from inhibitor treatments were not corroborated by genetic knock down or knock out experiments.

Regarding the number of biological replicates, as described under the ‘Statistical analysis’ subheading of the ‘Materials and Methods’ section, “all experiments were performed a minimum of three times, unless stated otherwise”. Where data is presented as bar graphs, this information is also included in the figure legend and each biological replicate is represented by a single data point where possible. In the case of western blots, a statement of the number of biological repeats has been added under the ‘Western blot analysis’ subheading of the ‘Materials and Methods’ section:

• Lines 614-615: “A minimum of three biological repeats were performed in all cases and representative blot images are displayed.”

  Minor Comments: They did not provide physiological functions of K+ATP channel. I consider this information should be important part of the introduction.

We agree that this would provide important contextual information and apologise for omitting this in the original submission. Details of some well-characterised functions of KATP channels, outside of their potential role in regulating cell proliferation, have been added to the introduction (lines 99-102).

The evidence for elevated expression of SUR1 in raft cultures of uninfected and HPV-18 infected HFK, CINs, and HSIL like cultures of W12E cells (Figure 2) is not of good quality. Moreover, in the absence of histological evidence (hematoxylin and eosin staining) and markers for HPV E6 E7 activity it is difficult to interpret about the location of SUR1 signals in spatial relationship to E7 functions.

In an attempt to resolve the issue highlighted, we have removed a reference in the text to the layers of the epithelium in which SUR1 expression is upregulated, as detailed below:

• Lines 195-197: This demonstrated a marked increase in SUR1 protein expression in __the suprabasal layers of __HPV18+ rafts in comparison to NHK raft cultures, consistent across both donors (Fig 2C).

  There is no physical evidence that HPV-18 transfected HFK indeed harbored HPV-18 plasmid in this experiment. What is the effect of glibenclamide on HPV-18 episome maintenance or replication?

The HPV18-containing keratinocytes used in this study are the same model system we have used in previous investigations (Wasson et al. (2017) Oncotarget 8(61): 103581–103600; Morgan et al (2018) PLoS Pathog 14(4): e1006975). In these studies, which focus on the HPV life cycle rather than HPV+ cancer, Southern blot analysis of HPV episomes and western blots for E6/E7 protein levels were performed, providing validation of the presence of HPV in these cells. We have added a reference to our previous work in the text (line 191).

As this manuscript primarily focusses on HPV+ cancer rather than HR-HPV infection, we believe an assessment of the role of KATP channels on HPV episome maintenance and/or genome replication to be beyond the scope of this study.

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

__Reviewer #1 (Evidence, reproducibility and clarity (Required)):____ __Addressing the following major points would help to strengthen the impact of the work:

The authors should address the idea of off-target effects, either experimentally or, more feasibly, by discussing the possibility of non-specific effects of SUR1 knockdown. They use a pool of four siRNAs to SUR1 and the risk of off-target effects would be greatly reduced if individual siRNAs were tested and shown to have the same effect as one another. Similarly, several experiments use just one shRNA, limiting the ability to draw conclusions.

Our plan to address potential off-target effects of the siRNAs used is outlined above. Regarding the shRNA data, we use two different shRNAs in the majority of experiments presented. These were found to have highly similar impacts on E6/E7 expression (Figure 3E & 3G) and proliferation (Supp Figure 4). We therefore do not believe that further experiments to eliminate off-target effects of the shRNA are necessary.

__Reviewer #2 (Evidence, reproducibility and clarity (Required)):____ __Major Comments: Overall, the authors performed many experiments to reveal an interesting and novel mechanism. (1) SUR1 expression and activity is necessary for HPV16 and-18 E6 and E7 expression. (2) HPV-16/18 E7 upregulates expression of ABCC8/SUR1 transcription. (3) SUR1 containing K+ATP channel then phosphorylates ERK. (4) Activated ERK then phosphorylates JUN/AP1. (5) Next, activated JUN/AP1 promotes E7 or E6E7 expression from HPV URR. However, in this cyclic feedforward regulation of these genes there is no control mechanism. Then how is homeostasis maintained in HPV infected lesions?

We thank the reviewer for praising the “interesting and novel mechanism” we have uncovered. The primary focus of our study is HPV+ cervical cancers, rather than HPV-infected lesions. The data we present in Figure 2 indicates that upregulation of KATP channel expression and/or activity is likely also occurring during HR-HPV infection, given we see e.g. increased SUR1 staining in HPV18-containing organotypic rafts. However, given our primary focus, we believe a more thorough assessment of KATP channels in HR-HPV infection, including any potential homeostatic regulation mechanisms, is outside the scope of our current study and inclusion of additional life cycle data would dilute the main conclusions of this manuscript. It is nonetheless a potentially exciting area for future work, and could form part of a separate, focussed manuscript.

E6 and E7 protein bands in DMSO treated HeLa and SiHa cells are not consistent between Figures 1 E, H and J, hence confound the interpretation. There is no information on biological replicates. It is not clear why the data from inhibitor treatments were not corroborated by genetic knock down or knock out experiments.

We believe that bands for E6 and E7 protein in DMSO-treated samples in Figure 1E and 1J are broadly consistent. However, we appreciate that E6 and E7 protein expression appears to be lower in DMSO samples in Figure 1H. All experiments involving diazoxide stimulation were performed under conditions of serum starvation, as indicated in the legends for Figures 1 and 8. Oncoprotein expression is known to be regulated by a series of host transcription factors, many of which become active in response to growth factor stimulation, such as AP-1 and SP1 (see: Tan et al. (1992) Nucleic Acids Res. 20(2):251-6; Hoppe-Seyler et al. (1992) Nucleic Acids Res. 20(24):6701-6; Butz K et al. (1993) J Virol. 67(11):6476-86). Serum starvation was therefore used to disentangle the upregulation of HPV gene expression by KATP channels from the myriad of other host signalling pathways demonstrated to drive E6/E7 expression. A side effect of this was a reduction in basal E6 and E7 protein levels in DMSO-treated cells. In addition, shorter exposure times were deliberately selected to prevent overexposure of protein bands corresponding to 50uM diazoxide treated cells. We apologise for any confusion caused.

Data from inhibitor treatments has been corroborated by knockdown experiments throughout the manuscript. The loss of E6/E7 expression with glibenclamide treatment was confirmed by SUR1 siRNA and shRNA knockdowns (Figure 3D-G) and Kir6.2 knockdown (Supp Figure 3C-D). The glibenclamide-induced loss of proliferation observed in HeLa and SiHa cells was also validated using the same approaches (Figure 5D-F, Supp Figure 3E-G, Supp Figure 4). Indeed, the cell cycle dysregulation experiments in Figure 7 were all performed with both inhibitor treatments and SUR1 siRNA knockdown.

The increase of G1 population, determined by flow cytometry, of HeLa cells treated with Glib or SUR1 siRNA is relative to controls appears to be small and not supported by similar study on other HPV+ or HPV_ vervical cancer cell lines. Importantly the mechanism of this increased G1 in HeLa cell line is not clear. The immunoblot data about the role of cyclins are not sufficient.

The flow cytometry experiments with glibenclamide treatment and SUR1 knockdown in HeLa cells were also performed with HPV16+ SiHa cells (Figure 7A-B) and the effects are highly consistent between the two cell lines. We believe that elucidating a more detailed mechanism of the observed G1 arrest is beyond the scope of this manuscript. Analysis of the mRNA and protein expression of cyclins was intended to provide corroboration of our findings via flow cytometry (i.e. a specific reduction in G1-phase cyclins corresponds to an accumulation of cells in G1 phase), rather than provide mechanistic insight.

What is the physiological effect of cyclin D1 in the context of HR-HPV infection (Figure 7)? In the event of HPV E7 mediated pRB degradation in cervical cancer cell lines, the inactivation of pRB by cyclin D1 does not appear to be physiologically relevant, may not account for difference in growth. It is known in literature that Cyclins A2 and B1 are often elevated by E7 activity. If SUR1 siRNA reduces E7-transcription and protein levels as shown in earlier results, why cyclinB1 and A2 protein level did not change?

As discussed, this manuscript primarily focusses on the role of KATP channels in HPV+ cervical cancer cells. An investigation into the effects on cyclin D1 expression in the context of HR-HPV infection, whilst an important question, is beyond the scope of this study and should form part of a standalone manuscript.

Regarding the expression of cyclins A2 and B1, we repeatedly observed very little impact following glibenclamide treatment and SUR1 knockdown in both cell lines examined. As highlighted above, given we observe a G1 arrest after KATP channel knockdown/inhibition, it would perhaps be unusual to observe changes in the expression of cyclins which drive G2 and M phase progression, respectively.

If activated ERK1/2 and c-Jun is required for URR activity, why are not they detectable in DSO or scrRNA treated HeLa cells (Fig 8A, B)? Why there is no 18 E7 in DMSO treated HeLa cells (Fig. 8A)? Authors also did not explain how inhibition of KATP channel regulates ERK phosphorylation in cervical cancer cell lines. There is no data from additional cervical cancer cell lines or HSIL mimicking W12E.

As mentioned above and as referred to in the figure legend, the experiment presented in Figure 8A was performed under serum starved conditions. Thus, the levels of phosphorylated ERK1/2 and cJun are much reduced in the unstimulated, DMSO-treated cells. Importantly, cJun/AP-1 is capable of activating its own expression, explaining the low total protein level of cJun in this sample. Further, as has widely been reported, MAPK signalling and AP-1 activity are critical drivers of HPV URR-driven transcription (e.g., see: Morgan et al. (2021) Cell Death Differ. 28(5):1669-87), so the reduced signalling activity resulting from serum starvation will consequently lower 18E7 expression. Shorter exposure times were selected for the pERK1/2, p-cJun and cJun western blots in Figure 8B to highlight the dramatic increase in pathway activation following KATP channel overexpression and for consistency with Figure 8A. We apologise for any confusion caused but do not believe that the potential issue raised here significantly alters any of the conclusions drawn.

We believe that an explanation of the mechanism by which KATP channel activity contributes to the activation of ERK1/2 signalling in HPV+ cervical cancer cells is beyond the scope of this initial publication. In the course of acquiring the data presented here, we aimed to provide some evidence of how KATP channels regulate proliferation in these cells, and therefore decided to investigate what impact they had on host cell signalling pathways known to be critical for proliferation. This led to us identifying enhanced ERK1/2 activity in response to KATP channel stimulation/overexpression. We agree that the critical next step will be to elucidate how channels promote ERK1/2 signalling, but this would require a significant body of work and as such would warrant being in a standalone or follow-up publication.

Throughout the manuscript, we endeavoured to validate our discoveries in the HPV18+ HeLa cell line in an additional cervical cancer cell line (HPV16+ SiHa cells). We provide evidence that the requirement for KATP channel activity is shared by both cell lines, and the impacts on oncoprotein expression, proliferation and cell cycle progression are highly concordant between the two cell lines. Thus, it is reasonable to assume that the mechanism by which KATP channels drive proliferation and HPV URR activity, elucidated in Figure 8 using HeLa cells, will be common to other HPV+ cervical cancer cell lines. Given the wealth of evidence supporting our proposal in Figure 8, and the highly concordant data presented earlier in the manuscript, we do not believe repeating all of the experiments in Figure 8 in a HPV16+ cell line is warranted. Similarly, as this manuscript focusses on HPV+ cancer, we believe that a study of the activation of MAPK/AP-1 signalling by KATP channels in HSIL mimicking W12E cells is beyond the scope of this paper and should constitute part of a standalone manuscript.

Minor Comments: In introduction, the authors mentioned that high risk HPV E6 and E7 deregulate cell cycle in host cells, and current limitations in cervical cancer treatments. Then they introduced importance of K+ ion channels in cell cycle regulation by sighting published literature not related to HPV, immediately followed by their proposed study on role of K+ATP channels in HPV infection. However, authors did not sufficiently clarify the rational of taking up a study on K+ channels in the context of HPV infection or E6 and E7 expression. If K+ATP channel proteins are elevated by E7, it is highly likely that there are some prior information on status of these transporters in the published literature or data from RNAseq analyses.

As the reviewer acknowledges, we described in the introduction the importance of K+ channels in regulating cell proliferation and the absence of effective HPV-specific therapies. We also stated that “ion channels may represent ideal candidates for … novel therapies given the abundance of licensed and clinically available drugs targeting the complexes which could be repurposed…”. Thus, the rationale for studying K+ channels in HPV+ cancers was to see if any of the available inhibitors of K+ channels could potentially be repurposed to be used in treating HPV-driven cervical cancer. We believe this logic is explained with sufficient clarity in the original draft.

Regarding prior analysis of KATP channel expression, in Figure 2H of the manuscript we analyse a publically available microarray dataset, which provides mRNA expression data for 128 cervical tissue specimens (24 normal, 14 CIN1 lesions, 22 CIN2 lesions, 40 CIN3 lesions, and 28 cancers specimens). ABCC8 expression was found to be significantly higher in the CIN3 and cervical cancer specimens, compared to normal samples. Details of the dataset used are provided in the ‘Materials and Methods’ section.

The evidence for elevated expression of SUR1 in raft cultures of uninfected and HPV-18 infected HFK, CINs, and HSIL like cultures of W12E cells (Figure 2) is not of good quality. Moreover, in the absence of histological evidence (hematoxylin and eosin staining) and markers for HPV E6 E7 activity it is difficult to interpret about the location of SUR1 signals in spatial relationship to E7 functions.

Organotypic raft culture data was included to demonstrate increased SUR1 expression in primary keratinocytes containing HR-HPV to show broader upregulation of the host factor and strengthen data from cell lines and clinical samples. Our data shows an upregulation of SUR1 in the HPV-containing rafts compared to controls. However, raft culture is a highly complex, time-consuming and expensive technique to perform. Therefore, whilst we agree, in principal, that being able to more closely correlate the increase in SUR1 protein levels to specific layers of the epithelium (e.g. via H&E staining and for markers of E6/E7 activity) would be of value, we would not be able to perform these assays within a reasonable time frame. Moreover, we feel that it would not add significant new knowledge to the study of SUR1 in the context of HPV-driven cancers.

There is no physical evidence that HPV-18 transfected HFK indeed harbored HPV-18 plasmid in this experiment. What is the effect of glibenclamide on HPV-18 episome maintenance or replication?

As this manuscript primarily focusses on HPV+ cancer rather than HR-HPV infection, we believe an assessment of the role of KATP channels on HPV episome maintenance and/or genome replication to be beyond the scope of this study.

Reviewer #2 (Significance (Required)):____ (1) General Assessment: Strengths and limitations This study identified KATP channel components as novel regulators of transcriptional activity of high-risk HPV-18 URR through ERK1/2-c-Jun/AP1 pathway. Authors revealed that HPV E7 regulates expression of ABCC8, the gene for channel component SUR1 protein.

There are important limitations. (1) Lack of any information about homeostatic regulation of SUR1 in HPV infection, (2) Lack of sufficient evidence about potential confounding cytotoxic effects of SUR1 inhibition or E7 down regulation on cervical cancer cells. (3) Immunoblot experiments are not consistent. (4) There is no mechanism of how E7 regulates ABCC8 transcription. (5) What is the mechanism for SUR1 regulate cell cycle in HPV+ cells? (6) There is no mention of effect of SUR1 on cell cycle regulators p53 and pRB, which are direct targets of HPV E6 and E7 proteins. (7) There is no evidence for the role of Kir6.2 /SUR1 in the regulation of HPV-16 URR, which causes most of HPV-attributed cancers. (8) Authors did not analyze spatial relationship between HPV E6 and E7 activity and expression of SUR1 protein in raft cultures of human foreskin keratinocytes with or without E6E7 expression and in cervical cancer tissue.

We thank the reviewer for summarising in a concise manner the areas of this manuscript they believe could be improved. Our responses to points 1-6 and point 8 have been detailed above. Regarding (7), we present data showing that KATP channel inhibition/knockdown negatively affects E6 and E7 expression in HPV16+ SiHa cells (Figure 1D-E, Figure 3D and F, Supp Figure 3C-D). Furthermore, we present evidence that SUR1 knockdown in SiHa cells correlates with a reduction in HPV16 URR-driven luciferase activity (Figure 3H). We therefore believe that this issue was adequately addressed in the original manuscript.

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

Knockout SUR1 stable cell lines and knockout HPV E7 stable cell lines should be established to test all the related data.

We have performed experiments using two well characterised inhibitors of KATP channels (glibenclamide and tolbutamide) and both siRNA- and shRNA-mediated knockdown of SUR1, all of which result in similar reductions in HPV E6/E7 expression. Further, the reductions in proliferation observed in HPV+ cell lines following glibenclamide treatment or siRNA/shRNA knockdown of SUR1 are also highly concordant. Thus, we do not believe the establishment of knock__out__ cell lines, using e.g. CRISPR/Cas9 technology, would significantly enhance the manuscript, particularly given the time and expense involved in this.

As noted by Reviewer #1 following cross-consultation, the establishment of E7 knockout cells lines is “unlikely to be possible because the cells require E7 for survival”. It has been previously demonstrated that in the absence of E7 expression, HeLa cells cease to proliferate and undergo senescence within 10 days (see: DeFelippis et al. (2003) J.Virol 77(2): 1551–1563). We therefore agree with Reviewer #1 and believe that, unfortunately, we would be unable to carry out the suggested experiment.

Tumor weights of the in vivo experiment should be indicated.

Tumour weights were not collected following the conclusion of the in vivo experiment, so we are unable to provide this information. The experiment was designed such that animals would be sacrificed upon the tumours reaching a set measurement (15 mm in either direction), rather than concluding the experiment at a set end point. Therefore, many of the tumours would have been of similar weight upon sacrifice, but critically the SUR1-depleted tumours took significantly longer to reach that size. We therefore believe that, given the experimental set-up, adding the tumour weights would not add significant value, even if we were able to provide this information.

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Referee #3

Evidence, reproducibility and clarity

Summary:

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

Authors present the evidence that HPV can target ATP-sensitive potassium ion (KATP) channels of a host to promote cervical carcinogenesis. They indicate that these channels are active in HPV-positive cells and that this activity is required for HPV oncoprotein expression by using activators and inhibitors of KATP channels. Furthermore, they verified SUR1 was upregulated in both HPV+ cervical cancer cells and in clinical samples in a manner dependent on the E7 oncoprotein. Knockdown of SUR1 or KATP channel inhibition significantly impeded cell proliferation via induction of a G1 cell cycle phase arrest. They propose that tumorgenesis effect of KATP channels is mediated via the activation of a MAPK/AP-1 signalling axis. Overall, It is an interesting research to unveil the mechanism how HPV promote cervical carcinogenesis through ATP-sensitive potassium ion channels. However，some major concerns should be addressed.

Major comments:

• Are the key conclusions convincing?

I think the key conclusions are convincing. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

NO - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

NO - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. - Are the data and the methods presented in such a way that they can be reproduced?

Yes - Are the experiments adequately replicated and statistical analysis adequate?

The experiments are adequately replicated and statistical analysis

Minor comments:

• Specific experimental issues that are easily addressable.

Yes - Are prior studies referenced appropriately?

Yes - Are the text and figures clear and accurate?

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

Knockout SUR1 stable cell lines and knockout HPV E7 stable cell lines should be established to test all the related data.

Tumor weights of the in vivo experiment should be indicated.

Significance

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

The author explain for the first time that HPV can upregulate host ATP-sensitive potassium ion channels, consequently activate MAPK/AP-1 signalling contribute to cervical carcinogenesis. This is a new mechanism how HPV cause cervical carcinogenesis. However, the MAPK/AP-1 signalling contributes to carcinogenesis is well known. The technical in the experiment is commonly used. - Place the work in the context of the existing literature (provide references, where appropriate).

HPV can promote cervical carcinogenesis through different pathway including MAPK/AP-1(1: Wang M, Qiao X, Cooper T, Pan W, Liu L, Hayball J, Lin J, Cui X, Zhou Y,Zhang S, Zou Y, Zhang R, Wang X. HPV E7-mediated NCAPH ectopic expression regulates the carcinogenesis of cervical carcinoma via PI3K/AKT/SGK pathway. Cell Death Dis. 2020 Dec 11;11(12):1049. doi: 10.1038/s41419-020-03244-9. PMID:33311486; PMCID: PMC7732835. 2. Singh T, Chhokar A, Thakur K, Aggarwal N, Pragya P, Yadav J, Tripathi T, Jadli M, Bhat A, Gupta P, Khurana A, Chandra Bharti A. Targeting Aberrant Expression of STAT3 and AP-1 Oncogenic Transcription Factors and HPV Oncoproteins in Cervical Cancer by Berberis aquifolium. Front Pharmacol.2021 Oct 28;12:757414. doi: 10.3389/fphar.2021.757414. PMID: 34776976; PMCID: PMC8580881.). However, the detail mechanism is still elusive. Here, authors indicated E7 can upregulate SUR1, one component of ATP-sensitive potassium ion channels and activate MAPK/AP-1. SUR1 can also upregulat E7 levels. They set up a positive feedback loop to contribute cervical cancer. - State what audience might be interested in and influenced by the reported findings.

cervical carcinogenesis researchers and cancer drug researches. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I familiar with cancer related signaling pathway and cancer chemoprevention research. Especially MAPK signaling pathway and related drugs.

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Referee #2

Evidence, reproducibility and clarity

Summary: Authors explored the role of k+ channel transporters in HPV induced cervical cancers. They used several inhibitors and gene knockdowns in biochemical and cell biology experiments to show that SUR1 and Kir6.2 components of K+ ATP channel, via activating pERK1/2 and c-Jun/AP1, up-regulate HPV URR promoter mediated expression of E6 and E7 proteins. They showed that SUR1 knockdown inhibited growth of HeLa cells in vivo.

Major Comments:

Overall, the authors performed many experiments to reveal an interesting and novel mechanism. (1) SUR1 expression and activity is necessary for HPV16 and-18 E6 and E7 expression. (2) HPV-16/18 E7 upregulates expression of ABCC8/SUR1 transcription. (3) SUR1 containing K+ATP channel then phosphorylates ERK. (4) Activated ERK then phosphorylates JUN/AP1. (5) Next, activated JUN/AP1 promotes E7 or E6E7 expression from HPV URR. However, in this cyclic feedforward regulation of these genes there is no control mechanism. Then how is homeostasis maintained in HPV infected lesions?

E6 and E7 protein bands in DMSO treated HeLa and SiHa cells are not consistent between Figures 1 E, H and J, hence confound the interpretation. There is no information on biological replicates. It is not clear why the data from inhibitor treatments were not corroborated by genetic knock down or knock out experiments.

Authors did not explain how HPV E7 would upregulate ABCC8 transcription or elevate SUR1 protein (Figure 4). Depletion of E7 is known to produce lethal effect in cervical cancer cell lines. No experiment was done to assess cytotoxicity. Hence it is not clear from the available evidence if the SUR1 is reduced by direct E7 mediated event or indirectly by general cytotoxicity induced by E7 knock down.

The increase of G1 population, determined by flow cytometry, of HeLa cells treated with Glib or SUR1 siRNA is relative to controls appears to be small and not supported by similar study on other HPV+ or HPV_ vervical cancer cell lines. Importantly the mechanism of this increased G1 in HeLa cell line is not clear. The immunoblot data about the role of cyclins are not sufficient.

What is the physiological effect of cyclin D1 in the context of HR-HPV infection (Figure 7)? In the event of HPV E7 mediated pRB degradation in cervical cancer cell lines, the inactivation of pRB by cyclin D1 does not appear to be physiologically relevant, may not account for difference in growth. It is known in literature that Cyclins A2 and B1 are often elevated by E7 activity. If SUR1 siRNA reduces E7-transcription and protein levels as shown in earlier results, why cyclinB1 and A2 protein level did not change?

Authors did not analyze expression level and role of p53, pRB proteins, the direct targets of E6 and E7 proteins, on cell cycle regulation following SUR1 siRNA or Glibenclamide-treatment in cervical cancer cell lines.

If activated ERK1/2 and c-Jun is required for URR activity, why are not they detectable in DSO or scrRNA treated HeLa cells (Fig 8A, B)? Why there is no 18 E7 in DMSO treated HeLa cells (Fig. 8A)? Authors also did not explain how inhibition of KATP channel regulates ERK phosphorylation in cervical cancer cell lines. There is no data from additional cervical cancer cell lines or HSIL mimicking W12E.

Minor Comments:

In introduction, the authors mentioned that high risk HPV E6 and E7 deregulate cell cycle in host cells, and current limitations in cervical cancer treatments. Then they introduced importance of K+ ion channels in cell cycle regulation by sighting published literature not related to HPV, immediately followed by their proposed study on role of K+ATP channels in HPV infection. However, authors did not sufficiently clarify the rational of taking up a study on K+ channels in the context of HPV infection or E6 and E7 expression. If K+ATP channel proteins are elevated by E7, it is highly likely that there are some prior information on status of these transporters in the published literature or data from RNAseq analyses. They did not provide physiological functions of K+ATP channel. I consider this information should be important part of the introduction.

The evidence for elevated expression of SUR1 in raft cultures of uninfected and HPV-18 infected HFK, CINs, and HSIL like cultures of W12E cells (Figure 2) is not of good quality. Moreover, in the absence of histological evidence (hematoxylin and eosin staining) and markers for HPV E6 E7 activity it is difficult to interpret about the location of SUR1 signals in spatial relationship to E7 functions.

Additional immunofluorescence or histological analysis is necessary to assess the potential cytotoxic effects of E7 siRNA, SUR1 siRNA or KATP inhibitors (Glibenclamide) in cervical cancer cell lines

There is no physical evidence that HPV-18 transfected HFK indeed harbored HPV-18 plasmid in this experiment. What is the effect of glibenclamide on HPV-18 episome maintenance or replication?

Significance

(1) General Assessment: Strengths and limitations

This study identified KATP channel components as novel regulators of transcriptional activity of high-risk HPV-18 URR through ERK1/2-c-Jun/AP1 pathway. Authors revealed that HPV E7 regulates expression of ABCC8, the gene for channel component SUR1 protein.

There are important limitations. (1) Lack of any information about homeostatic regulation of SUR1 in HPV infection, (2) Lack of sufficient evidence about potential confounding cytotoxic effects of SUR1 inhibition or E7 down regulation on cervical cancer cells. (3) Immunoblot experiments are not consistent. (4) There is no mechanism of how E7 regulates ABCC8 transcription. (5) What is the mechanism for SUR1 regulate cell cycle in HPV+ cells? (6) There is no mention of effect of SUR1 on cell cycle regulators p53 and pRB, which are direct targets of HPV E6 and E7 proteins. (7) There is no evidence for the role of Kir6.2 /SUR1 in the regulation of HPV-16 URR, which causes most of HPV-attributed cancers. (8) Authors did not analyze spatial relationship between HPV E6 and E7 activity and expression of SUR1 protein in raft cultures of human foreskin keratinocytes with or without E6E7 expression and in cervical cancer tissue.

(2) Advance: This study identifies SUR1/Kir6.2 as new targets to intervene HPV-18 URR activity and demonstrates potential to inhibit growth of cervical cancer tumors using HeLa xenograft model. This study did not develop any new methodology, novel mutation or model system.

(3) Audience: This study is aimed at basic scientists involved in the field of HPV research.

(4) Describe your expertise. I have long experience in HPV research. I study regulation of HR-HPV18 life cycle in 3D organotypic raft cultures of HPV-18 infected neonatal foreskin keratinocytes. A major part of my research is focused on identification of novel therapeutics against cervical cancers using in vitro 3D organoids and in vivo PDX models.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript by Scarth and colleagues, the authors investigate the relationship between ATP-sensitive potassium ion channels (KATP) and the viability and growth of certain HPV-positive cancer cell lines. In a series of detailed and carefully conducted experiments, they determine that there is a correlation between KATP channel activity and levels of certain HPV E6 and E7 RNA and protein. KATP channels are active in HeLa cells. In HeLa and SiHa cells, inhibiting KATP activity decreases HPV oncoprotein levels. HPV-positive status in the cell lines examined is found to be associated with an upregulation of the ABCC8 gene, which encodes the SUR1 KATP subunit, and some of the data supports that SUR1 protein levels increase with cervical cancer CIN grade. Depleting SUR1 with shRNA or siRNA reduces KATP activity and decreases HPV E6 and E7 levels in HeLa and SiHa cells. The opposite is also true; depleting ABCC8 reduces the levels of HPV E6 and E7 transcripts. The effect on ABCC8 appears to be due mainly to the effects of HPV E7. Decreased KATP activity is associated with decreased growth of HeLa and SiHa cells in monolayer and in anchorage independent growth assays, perhaps not surprising given that E6/E7 levels are reduced in the cells under the treatment conditions. SUR1 overexpression itself promotes cell growth even in the absence of HPV oncoproteins. The growth defect upon KATP inhibition or siSUR1 is associated with some modest cell cycle dysregulation and, impressively, with reduced tumor growth in a mouse model. Finally, the authors present evidence that increased KATP activity is associated with increased recruitment of the transcription factor AP-1 to the HPV18 promoter and enhancer.

Overall, the data are of high quality and the individual results are consistent with each other and are convincing. However, the authors have understandably focused on two HPV-positive cancer cell lines (affected by modulating KATP levels) and one HPV-negative cancer cell line (which is not affected in the same way). The ability to extrapolate to conclusions about cervical or HPV-positive cancers in general is therefore limited and many of the authors' statements should be tempered to reflect the experiments they have conducted.

Addressing the following major points would help to strengthen the impact of the work:

1. The paper would be greatly strengthened by addressing whether knockdown of SUR1 and knockdown of E6/E7 are affecting cell viability. siRNA depletion of E6 and E7 will cause HeLa and SiHa cells to senesce; at what time point post knockdown were the experiments performed? Is it possible to perform CellTiterGlo or other cell viability assays to confirm that the phenotypes observed upon E6/E7 depletion and upon SUR1 depletion or drug treatment are not the result of cell death/senescence/toxicity?
2. There is a major concern regarding whether SUR1 protein is produced at a biologically relevant level in SiHa and HeLa cells, in which most of the experiments in the paper were conducted. Protein levels are assessed in Fig 2 by immunostaining in raft cultures and in a cervical cancer tissue microarray. However, protein levels are otherwise not examined, especially in SiHa and HeLa cells. Is SUR1 protein produced in these cells? Are its levels reduced by the knockdown approaches? The fold change RNA data presented in figure 2A does not convincingly address this question, since even an 8-fold increase of ABCC8 mRNA over a low background level might not have biological significance. It would be very helpful to measure SUR1 protein in several of the experiments in HeLa and SiHa cells.
3. The authors should address the idea of off-target effects, either experimentally or, more feasibly, by discussing the possibility of non-specific effects of SUR1 knockdown. They use a pool of four siRNAs to SUR1 and the risk of off-target effects would be greatly reduced if individual siRNAs were tested and shown to have the same effect as one another. Similarly, several experiments use just one shRNA, limiting the ability to draw conclusions.
4. Finally, since many of the experiments rely on knockdown approaches that show similar readouts, a rescue experiment (restore sh or si-resistant SUR1 and assess the impact on the phenotype) would confirm that the effects being observed are due to changes in SUR1 levels and not to off-target effects.

It is recognized that some of these experiments would be lengthy and technically challenging to perform. Measuring cell viability and SUR1 protein levels in SiHa and HeLa cells should be relatively straightforward. The experiments to address off-target effects (rescue experiment, deconvolving siRNA pool) are more involved. If it is not possible to complete such experiments, the possibility of off-target effects should be discussed in the text.

Minor comments:

The text and figures are clear and statistics are appropriate. The authors should include at what time point post siRNA transfection the experiments were conducted.

Referees cross-commenting

I note several areas of common feedback among the reviews. Several reviewers commented on the large number of experiments and that the work is of interest to researchers working on HPV and cancer therapeutics. Several reviewers shared concerns about cell viability upon HPV oncoprotein knockdown and about toxicity in various experiments. Several reviewers also raised concerns about the validation of SUR1 protein levels in several experiments. These concerns seem to me to be critical to address to strengthen the manuscript. I note that Reviewer #3's suggestion of making E7 knockout cells (presumably in HPV+ cancer cell lines) is unlikely to be possible because the cells require E7 for survival.

Significance

The work connects the biology of certain cervical cancer cell lines to KATP channels. It will be of interest to HPV researchers and to cancer researchers whose interests involve KATP signaling. As a reviewer, I have expertise in HPV biology but not in KATP signaling.

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