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Reply to the reviewers

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

Marta Sanvicente-García et al and colleague developed a comprehensive and versatile genome editing web application tool and a nextflow pipeline to give support to gene editing experimental design and analysis.

The manuscript is well written and all data are clearly shown.

While I did not tested extensively, the software seems to work well and I have no reason to doubt the authors' claims.

I usually prefer ready to use web applications like outknocker, they are in general easier to use for rookies (it would be good if the author could cite it, since it is very well implemented) but the nextflow implementation is anyway well suited.

We have been able to analyze the testing dataset that they provide, but we have tried to run it with our dataset and we have not been able to obtain results. We have also tried to run it with the testing dataset of CRISPRnano and CRISPResso2 without obtaining results. The error message has been in all the cases: “No reads mapping to the reference sequence were found.”

Few minor points:

Regarding the methods to assess whether the genome editing is working or not, I would definitely include High Resolution Melt Analysis, which is by far the fastest and probably more sensitive amongst the others.

Following the Reviewer 1 suggestion, we have added this technique in the introduction: “Another genotyping method that has been successfully used to evaluate genome editing is high-resolution melting analysis (HRMA) [REFERENCE]. This is a simple and efficient real-time polymerase chain reaction-based technique.”

Another point that would important to taclke is that often these pipelines do nto define the system they are working with (eg diploid, aploid vs etc). This will change the number of reads needed ato unambigously call the genotypes detected and to perform the downstream analysis (the CRISPRnano authors mentioned this point).

In the introduction, it is already said: " it is capable of analyzing edited bulk cell populations as well as individual clones". In addition, following this suggestion we have added in the help page of CRISPR-A web application and in the documentation of the nextflow pipeline a recommended sample coverage to orient the users on that.

I am also wondering whether the name CRISPR-A is appropriate since someone could confuse it with CRISPRa.

CRISPR-A is an abbreviation for CRISPR-Analytics. Even if it is true that it can be pronounced in the same way that CRISPRa screening libraries, it is spelled differently and would be easily differentiated by context.

CROSS-CONSULTATION COMMENTS

Reviewer 2 made an excellent work and raised important concerns about the software they need to be addressed carefully.

In the meantime we had more time to test the software and can confirm some of the findings of Reviewer 1:

1) We spent hours running (unsuccessfully) CRISPR A on Nextflow. The software does not seem to run properly.

2) No manual or instruction can be found on both their repositories (https://bitbucket.org/synbiolab/crispr-a_nextflow/

https://bitbucket.org/synbiolab/crispr-a_figures/)

We have added a readme.md file to both repositories and we hope that with the new documentation the software can be downloaded and run easily. We have also added an example test in CRISPR-A nextflow pipeline to facilitate the testing of the software. Currently, the software is implemented in DLS1 instead of DLS2, making it impossible to be run with the latest version of nextflow. We are planning to make the update soon, but we want to do it while moving the pipeline to crisprseq nf-core pipeline to follow better standards and make it fully reproducible and reusable.

Few more points to be considered:

• UMI clustering is not proper terminology. Barcode multiplexing/demultiplexing (SQK-LSK109 from Oxford Nanopore).

We have added more details in the methods section “Library prep and Illumina sequencing with Unique Molecular Identifiers (UMIs)” to clarify the process and used terminology: “Uni-Molecular Identifiers are added through a 2 cycles PCR, called UMI tagging, to ensure that each identifier comes just from one molecule. Barcodes to demultiplex by sample are added later, after the UMI tagging, in the early and late PCR.”

We had already explained the computational pipeline through which these UMIs are clustered together to obtain a consensus of the amplified sequences in “CRISPR-A gene editing analysis pipeline” section in methods:

“An adapted version of extract_umis.py script from pipeline_umi_amplicon pipeline (distributed by ONT https://github.com/nanoporetech/ pipeline-umi-amplicon) is used to get UMI sequences from the reads, when the three PCRs experimental protocol is applied. Then vsearch⁴⁸ is used to cluster UMI sequences. UMIs are polished using minimap2³² and racon⁴⁹ and consensus sequences are obtained using minialign (https://github.com/ocxtal/minialign) and medaka (https://github.com/nanoporetech/medaka).”

We also have added the following in “CRISPR-A gene editing analysis pipeline” methods section to help to understand differences between the barcodes that can be used: “In case of working with pooled samples, the demultiplexing of the samples has to be done before running CRISPR-A analysis pipeline using the proper software in function of the sequencing used platform. The resulting FASTQ files are the main input of the pipeline.”

Then, SQK-LSK109 from Oxford Nanopore is followed through the steps specified in methods: “The Custom PCR UMI (with SQK-LSK109), version CPU_9107_v109_revA_09Oct2020 (Nanopore Protocol) was followed from UMI tagging step to the late PCR and clean-up step.”

Finally, we want to highlight that, as can be seen in methods as well as in discussion, UMIs are used to group sequences that have been amplified from the same genome and not to identify different samples: “Precision has been enhanced in CRISPR-A through three different approaches. [...] We also removed indels in noisy positions when the consensus of clusterized sequences by UMI are used after filtering by UBS.” As well as in results (Fig. 5C).

• Text in Figure 5 is hard to read.

We have increased the letter size of Figure 5.

• They should test the software based on the ground truth data

We have added a human classified dataset to do the final benchmarking. And we can see that for all examined samples CRISPR-A has an accuracy higher than 0.9. As has been shown in the figure with manual curated data, CRISPR-A shows good results in noisy samples using the empiric noise removal algorithm, without need of filtering by edition windows.

• The alignment algorithm is not the best one, I think minimap2 would be better for general purpose (at least it work better for ONT).

As can be seen in figure 2A, minimap is one of the alignment methods that gives better results for the aim of the pipeline. In addition, we have tuned the parameters (Figure 2B) for a better detection of CRISPR-based long deletions, which can be more difficult to report in a single open gap of the alignment.

• The minimum configuration for installation was not mentioned (for their Docker/next flow pipeline).

Proper documentation to indicate the configuration requirements for installation has been added to the readme.md of the repository·

• Fig 2: why do they use PC4/PC1?

Principal Component Analysis is used to reduce the number of dimensions in a dataset and help to understand the effect of the explainable variables, detect trends or samples that are labeled in incorrect groups, simplify data visualization… Even PC4 explains less variability than PC2 or PC3, this helps us to understand and better decipher the effect of the 4 different analyzed parameters even if the differences are not big. We have decided to include as a supplementary figure other PCs to show these.

• There are still typos and unclear statements thorughout the whole manuscript.

One more drawback is that the software seems to only support single FASTQ uploading (or we cannot see the option to add more FASTQ).

In the case of paired-end reads instead of single-end reads, in the web application, these can be selected at the beginning answering the question “How should we analyze your reads? Type of Analysis: Single-end Reads; Paired-end Reads”. In the case of the pipeline, now it is explained in the documentation how to mark if the data is paired-end or single-end. It has to be indicated in “input” and “r2file” configuration variables.

In the case of multiple samples, and for that reason multiple FASTQ files, there is the button to add more samples in the web application. In the pipeline, multiple samples can be analyzed in a single run by putting all together in a folder and indicating it with variable “input”.

Since usually people analyze more than one clone at the time (we usually analyze 96 clones together) this would mean that I have to upload manually each one of them.

All files can be added in the same folder and analyzed in a single run using the nextflow pipeline. Web application has a limit of ten samples that can be added clicking the button “Add more”.

Also, the software (the webserver, the docker does not work) works with Illumina data in our hands but not with ONT.

This should be clarified in the manuscript.

If a fastq is uploaded to CRISPR-A, the analysis can be done even if we haven't specifically optimized the tool for long reads sequencing platforms. We have checked the performance of CRISPR-A with CRISPRnano nanopore testing dataset and we have succeeded in the analysis. See results here: https://synbio.upf.edu/crispr-a/RUNS/tmp_1118819937/.

Summary of the results:

Sample

CRISPRnano

CRISPR-A

'rep_3_test_800'

42.60 % (-1del); 12.72 % (-10del)

71% (-1del);

16% (-10del)

– 36 (logo)

'rep_3_test_400'

37.50 % (-1del); 15.63 % (-10 del)

65% (-1del);

28% (-10del)

– 38 (logo)

'rep_1_test_200'

39.29 % (-1del); 8.33 % (-17del)

10del; 17del; 1del

'rep_1_test_400'

80.11 % (-17 del)

del17; del20; del18; del16;del 16

'rep_0_test_400'

80.11% (-17 del)

del17; del20; del 18; del16; del16

'rep_0_test_200'

71.91% (-17 del)

del17; del18

As we can see from these exemple, CRISPR-A reports all indels in general without classifying them as edits or noise. Since nanopore data has a high number of indels as sequencing errors the percentages of CRISPR-A are not accurate. Eventhat, CRISPR-A reports more diverse outcomes, which are probably edits, than CRISPRnano.

Therefore, we have added the following text in results:

“Even single-molecule sequencing (eg. PacBio, Nanopore..) can be analyzed by CRISPR-A, targeted sequencing by synthesis data is required for precise quantification.”

Reviewer #1 (Significance (Required)):

As I mentioned above I think this could be a useful software for those people that are screening genome editing cells. Since CRISPR is widely used i assume that the audience is broad.

There are many other software that perform similarly to CRISPR-A but it seems that this software adds few more things and seems to be more precise. It is hard to understand if everything the author claims is accurate since it requires a lot of testing and time and the reviewing time is of just two weeks. But 1) I have no reason to doubt the authors and 2) the software works

Broad audience (people using CRISPR)

Genetics, Genome Engineering, software development (we develop a very similar software), genetic compensation, stem cell biology

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

Summary:

CRISPR-Analytics, abbreviated as CRISPR-A, is a web application implementing a tool for analyzing editing experiments. The tool can analyze various experiment types - single cleavage experiments, base editing, prime editing, and HDR. The required data for the analysis consists of NGS raw data or simulated data, in fastq, protospacer sequence and cut site. Amplicon sequence is also needed in cases where the amplified genome is absent from the genome reference list. The tool pipeline is implemented in NextFlow and has an interactive web application for visualizing the results of the analysis, including embedding the results into an IGV browser.

The authors developed a gene editing simulation mechanism that enables the user to assess an experiment design and to predict expected outcomes. Simulated data was generated by SimGE over primary T-cells. The parameters and distributions were also fitted for 3 cell lines to make it more generalized (Hek293, K562, and HCT116). The process simulated CRISPR-CAS9 activity and the resulting insertions, deletions, and substitutions. The simulation results are then compared to the experimental results. The authors report the Jensen-Shannon (JS) divergence between the results. The exact distributions that served as input to the JS are not well defined in the manuscript (see below).

To clarify the used distributions in the JS divergence calculation, we have changed the following piece of text in section “Simulations evaluation” of methods:

“ Afterward, we tested the performance on the fifth fold, generating the simulated sequences with the same target and gRNA as the samples that belong to the fifth fold, in order to calculate the distance between these. The final validation, with the mean parameters of the different training interactions, was performed on a testing data set that was not used in the training. Validation was done with samples that had never taken place in the training process. Jensen distance is used to compare the characterization of real samples and simulated samples since this is the explored distance that differentiates better replicates among samples. In order to obtain the different distributions, the T cell data, including 1.521 unique cut sites, was split into different datasets based on the different classes: deletions, insertions and substitutions. For each of these classes, giving as input the datasets with only that class, we obtained the distribution for size and then for position of indels. The same was done for the other three cell lines: K562, HEK293 and HCT116, which included 96 unique cut sites, with three replicates each. The whole datasets (with 1521 and 96 unique cut sites) were split into five-folds (4 for training and one for test) and validation, in order to train and validate the simulator. Using the parameters obtained during the training-test iterations (the average value of the 5 iterations), we generate simulated sequences with the same target and gRNA as the samples that are assigned to the test subset to calculate the Jensen-Shannon (JS) divergence between the simulated and real samples of that subset. Finally, the same was performed for validation. The input for the distance calculations were the generated simulated subset and its real equivalent (same target and gRNA) distributions of the classes. ”

The authors also report an investigation of different alignment approaches and how they may affect the resulting characterization of editing activity.

The authors examine three different approaches to increase what they call "edit quantification accuracy" (aka, in a different place - "precise allele counts determination" - what is this???): (1) spike-in controls (2) UMI's and (3) using mock to denoise the results. See below for our comments about these approaches.

Moreover, the authors developed an empirical model to reduce noise in the detection of editing activity. This is done by using mock (control), and by normalization and alignment of reads with indels, with the notion and observation that indels that are far from the cut site tend to classify as noise.

The authors then perform a comparison between 6 different tools, in the context of determining and quantifying editing activities. One important comparison approach uses manually curated data. However - the description of how this dataset was created is far from being sufficiently clear. The comparison is also performed for HDR experiment type, which can be compared only to 2 other tools.

We have changed alleles by editing outcomes in the title section “Three different approaches to increase precise editing outcomes counts determination” trying to be more clear.

There is already a section in methods “Manual curation of 30 edited samples” explaining how the manual curation was done.

We see the potential contribution aspects of the paper to be the following:

1. NextFlow pipeline implementation is an important engineering contribution. Same is true for the interactive web application
2. The option to simulate an experiment to assess it is a nice feature and can help experiment design
3. Identification of amplicons when not provided as input
4. CRISPR-A seeks substitutions along the entire amplicon sequence and is less dependent on the quantification window and on the putative cutsite
5. Analysis of the difference, in edit activity, comparing different cell lines
6. CRISPR-A supports the use of UMIs
7. Interesting sequence pattern insights - like "...found certain patterns associated with low diversity outcomes: free thymine or adenine at the 3' nucleotide upstream of the cut site that leads to insertions of the same nucleotide, a free cytosine at the same place that leads to its loss, and strong micro-homology patterns that lead to long deletions " We further comment on the soundness of these contributions in our comments below and on their significance in our comments related to the general potential significance of the paper.

Major comments:

• Upon attempting to run an analysis from the web interface (https://synbio.upf.edu/crispr-a) and using: fastq of Tx and mock (control), the human genome and the gRNA sequence provided as input for the protospacer field, our run was not successful. In fact the site crashed with no interpretable error message from CRISPR-A. We have improved the error handling together with the explanations in the help page, where you will find a video. Hopefully these improvements will avoid unexpected crashings.

• Moreover, there should be more clear context. There is no information regarding the type of experiments that can be analyzed with the tool. We figure it is multiplex PCR and NGS but can the tool also be used for GUIDESeq, Capture, CircleSeq etc.? Experiments that could be analyzed are specified in Results: “CRISPR-A analyzes a great variety of experiments with minimal input. Single cleavage experiments, base editing (BE), prime editing (PE), predicted off-target sites or homology directed repair (HDR) can be analyzed without the need of specifying the experimental approach.” We have also specified this in the nextflow pipeline documentation as well as in the web application help page.

• No off target analysis. Only on-target The accuracy of the tool allows checking if edits in predicted off-target sites are produced, this being an off-target analysis with some restrictions, since just variants of the predicted off-target sites are assessed. Translocations or other structural off-targets will not be detected by CRISPR-A since the input data analyzed by this tool are demultiplexed amplicon or targeted sequencing samples.

• No translocations and long/complex deletions The source of used data as input does not allow us to do this. There are other tools like CRISPECTOR available for this kind of analysis. We have added this to supplementary table 1.

• We view the use of a mock experiment as control as a must for any sound attempt to measure edit activity. This is even more so when off-target events need to be assessed (any rigorous application of GE, certainly any application aiming for clinical or crop engineering purposes). We therefore think that all investigation of other approaches should be put in this context. We agree with the necessity of using negative controls to assess editing. For that reason we have included the possibility of using mocks in the quantification. In addition, there are few tools that include this functionality.

• It's a nice feature to have simulated data, however, it is not a good approach to rely on it. As can be seen in the manuscript we highlight the support that simulations can give without pretending to substitute experimental data by just simulated data. Simulated data has been useful in the development and benchmarking of CRISPR-A, but we are aware of the limitations of simulations. Here some examples from the manuscripts explaining how we have used or can be used simulated data:

“Analytical tools, and simulations are needed to help in the experimental design.”

“simulations to help in design or benchmarking”

“We developed CRISPR-A, a gene editing analyzer that can provide simulations to assess experimental design and outcomes prediction.”

“Gene editing simulations obtained with SimGE were used to develop the edits calling algorithm as well as for benchmarking CRISPR-A with other tools that have similar applications.”

Even simulated data has been useful for the development and benchmarking of CRISPR-A, we have also used real data and human validated data.

• In p7 the authors indicate the implementation of three approaches to improve quantification. They should be clear as to the fact that many other tools and experimental protocols are also using these approaches. for example, ampliCan, CRipresso2 and CRISPECTOR all take into account a mock experiment run in parallel to the treatment. Even in page 7 (results) we don’t mention the other tools that also use mocks for noise correction, we detail this information in Supplementary Table 1. CRISPResso2 was not included since they can run mocks in parallel but only to compare results qualitatively, i.e. there is not noise reduction in their pipeline. It has been added to the table.

• Figure1: ○ The figure certainly provides what seems to be a positive indication of the simulations approach being close to measured results. Much more details are needed, however, to fully understand the results.

We have added more details.

○ Squema = scheme ??

We have changed the word “schema” by diagram.

○ What was the clustering approach?

As is said in the caption of Figure 1 the clustering is hierarchical: “hierarchical clustering of real samples and their simulations from validation data set.” And we have added that “The clustering distance used is the JS divergence between the two subsets.”

○ What is the input to the JS calculation? What is the dimension of the distributions compared? These details need to be precisely provided.

The distribution has two dimensions, sizes and counts or positions and counts.

As said before, to clarify the used distributions in the JS divergence calculation, we have changed the following piece of text in section “Simulations evaluation” of methods:

“ Afterward, we tested the performance on the fifth fold, generating the simulated sequences with the same target and gRNA as the samples that belong to the fifth fold, in order to calculate the distance between these. The final validation, with the mean parameters of the different training interactions, was performed on a testing data set that was not used in the training. Validation was done with samples that had never taken place in the training process. Jensen distance is used to compare the characterization of real samples and simulated samples since this is the explored distance that differentiates better replicates among samples. In order to obtain the different distributions, the T cell data, including 1.521 unique cut sites, was split into different datasets based on the different classes: deletions, insertions and substitutions. For each of these classes, giving as input the datasets with only that class, we obtained the distribution for size and then for position of indels. The same was done for the other three cell lines: K562, HEK293 and HCT116, which included 96 unique cut sites, with three replicates each. The whole datasets (with 1521 and 96 unique cut sites) were split into five-folds (4 for training and one for test) and validation, in order to train and validate the simulator. Using the parameters obtained during the training-test iterations (the average value of the 5 iterations), we generate simulated sequences with the same target and gRNA as the samples that are assigned to the test subset to calculate the Jensen-Shannon (JS) divergence between the simulated and real samples of that subset. Finally, the same was performed for validation. The input for the distance calculations were the generated simulated subset and its real equivalent (same target and gRNA) distributions of the classes. ”

○ What clustering/aggregation approach did the authors use here (average dist, min dist, dist of centers?)

Hierarchical clustering.

○ 5 pairs were selected out of how many? Call that number K.

We have 100 samples in the validation set. Following the suggestion of indicating the total number of samples in the testing set, we have added this information to the figure caption.

○ What does the order of the samples in 1C mean? Is 98_real closer to 22_sim than to 98_sim? If so then state it. If not - what is the meaning of the order? Furthermore - how often, over K choose 2 pairs does this mis-matching occur for the CRISPR-A simulator??

Exactly, it is a hierarchical clustering, where samples are sorted by JS divergence. It was already stated in Results: “In addition, on top of comparing the distance between the experimental sample and the simulated, we have included two experimental samples, SRR7737722 and SRR7737698, which are replicates. These two and their simulated samples show a low distance between them and a higher distance with other samples.” As well as in Figure 1 caption: “For instance, SRR7737722 and SRR7737698, which cluster together, are the real sample and its simulated sample for two replicates.” Then, since these samples are replicates, its simulations will come from the same input and is expectable to find low distance between these two real samples as well as between both of them and their simulation. We have stated it in the discussion.

• "From the characterized data we obtained the probability distribution of each class" (page 3) - How is this done? how many guides? how many replicates? what is class? where do you elabore regarding it? how you obtain the distributions? More details of the methods need to be provided. Added in methods.

• The 96 samples used for development here - where are they taken from? This should be indicated in the first time these samples are mentioned. Namely - bottom of P6 Added: “The 96 samples, from these cell lines, are obtained from a public dataset BioProject PRJNA326019.”

• CRISPECTOR is not mentioned in the comparison in the section: "CRISPR-A effectively calls indels in simulated and edited samples" (Table S2). Is there a specific reason for having left it out? CRISPECTOR, as well as ampliCan, is not in Table S2, since in this table is shown detailed data from Figure 2. CRISPECTOR is compared with CRISPR-A in figure 5, where the different approaches to enhance precision, like using a negative control, are explored.

• In the section "Improved discovery and characterization of template-based alleles or objective modifications" - part of the analysis was made over simulated data and then over real data. The authors state "it is difficult to explain the origin of these differences...". Thus, needs to be investigated in more detail ... :) (P5) Moreover - the performance over real data is, at the end of the day, the more interesting one for comparison purposes. We have added this sample to the human validated dataset to understand better what was happening in this case and the results and pertinent discussion have been added in the manuscript: “CRISPResso2 is detecting a 2% more of reads classified as WT. These 2% correspond with the percentage classified as indels by CRISPR-A. In total, the percentage difference between CRISPResso2 and CRISPR-A template-based class is 0.6%, higher in CRISPR-A. CRISPR-A percentage is closer to the ground truth data than CRISPResso2.”

• We found no explanation of "spike-in"/"spike experimental data" across the entire article. There is some general language about lengths but the scheme is still totally unclear. We have indicated in methods section when we were talking about the spike-in controls.

• Description of the 96 gRNAs? Is this data from REF26? If so - where do you state this? If so - how do the methods described herein avoid the unique characteristics of the data of REF26? We have added the reference: “The 96 samples, from these cell lines, are obtained from a public dataset BioProject PRJNA326019.” In addition, there are other sources of data, simulations and now even human validated data.

• "distance between the percentage of microhomology mediated end-joining deletions of samples with the same target was calculated and the mean of all these distances was used to reduce the information of the 96 different targets to a single one." (P6) What is the exact calculation used? which distance? How was clustering performed? What is the connection for gene expression? The used distance was euclidean distance and the clustering was performed using hierarchical clustering. We have added this information to the manuscript. Regarding the connection of gene expression, we are exploring the correlation of two phenotypes: the gene expression of the proteins differentially related with NHEJ and MMEJ pathways, and the gene editing landscape (indel patterns that are related with MMEJ and those that are more prone to be generated with NHEJ). We have tried to improve this explanation in the manuscript.

• "we have fitted a linear model to transform the indels count depending on its difference in relation to the reference amplicon" (P7) - needs more explanation. Is this part of the pipeline? We have explained better how we have fitted the linear model in methods: “A linear regression model was fitted to obtain the parameters of Equation 1 using spike-in controls experimental data (original count, observed count and size of the change in the synthetic molecules). We have used the lm function from R. Parameter m in Equation 1 is equivalent to the obtained coefficient estimate of x which was 0.156 and n is the intercept (n=10). ”.

The model is optionally used as part of the pipeline as explained at the end of section “CRISPR-A gene editing analysis pipeline” to correct amplification biases due to differences in amplicon size. Then, what is part of the pipeline is the use of this model to make the transformation of counts from the observed counts to the predicted original counts. This is done with Equation 1 and can be found in the pipeline (VC_parser-cigar.R).

• What is it "...manually curated data set"? (page 8) This is explained in “Manual curation of 30 edited samples” in methods.

• Section "CRISPR-A empiric model removes more noise than other approaches" - with what data were the comparisons performed? Moreover, how were the comparison criteria selected (efficiency and sensitivity)? The literature already used several approaches to compare data analysis tools for editing experiments. See for example ampliCan, Crispresso (1 and 2) and CRISPECTOR. Maybe the authors should follow similar lines. The data used in this comparison comes from the reference 26:“26. van Overbeek, M. et al. DNA Repair Profiling Reveals Nonrandom Outcomes at Cas9-Mediated Breaks. Mol. Cell 63, 633–646 (2016).” We have added it to the manuscript.

The values of efficiency and sensitivity were not used directly for the comparison. We wanted to firstly evaluate our own algorithm. For that we obtained the values of efficiency and sensitivity for the previous mentioned dataset. These values were chosen to firstly have an idea of firstly, how much noise the algorithm is able to detect, and secondly, how much of it is able to be reduced after the Tx vs M process. That established a framework of comparison in which we can then compare directly the reported percentage of edition of the different tools.

Regarding the approaches used to compare data analysis tools for editing experiments, we are going to explain why we haven’t followed similar lines or how we have now included it:

In the case of ampliCan, the comparison that they do is with a synthetic dataset with introduced errors:

"synthetic benchmarking previously used to assess these tools (Lindsay et al. 2016), in which experiments were contaminated with simulated off-target reads that resemble the real on-target reads but have a mismatch rate of 30% per base pair".

In CRISPResso2, they benchmarked the efficiency against an inhouse dataset but this dataset is not published. Finally, for the benchmarking of CRISPECTOR, a manual curated dataset is used as a standard: "Assessment of such classification requires the use of a gold standard dataset of validated editing rates. In this analysis, we define the validated percent indels as the value determined through a detailed human investigation of the individual raw alignment results". In this sense, we have added a human validated dataset to do something similar to complement the analysis that we had already done.

In the end, we consider that simulated or synthetic datasets, as those used by ampliCan or CRISPResso2, does not capture the complete landscape of confounding events that can be detrimental to the analysis results. Similar limitations are found in the use of a gold standard dataset of validated editing rates, since the amount of reads or samples that can be validated by humans is not big since it is time consuming. In addition, humans can also make errors and have biases. Eventhogh, we have found very valuable talking into consideration adding a human validated dataset to complete our exploration.

• In the section "CRISPR-A empiric model removes more noise than other approaches" the authors state, incorrectly, that CRISPECTOR only reports the percentage of editing activity per site (there is much more information reported in the HTML report, including the type of edit event detected - deletion, of various lengths, insertions, substitutions etc). (P8) We thank the reviewer for the observation, as indeed the state is incorrect. What we wanted to express is that with CRISPECTOR we cannot trace individually each of the called indels, as any sort of excel or file with this content is given in the output. Therefore we cannot investigate which events have been corrected. To be precise in our statement we changed this sentence to the following:

“CRISPECTOR, although providing extensive information on the statistics and information about the indels, is not possible to track the reads along their pipeline, thus we cannot know which have been corrected and which have not.”

• Section "CRISPR-A noise subtraction pipeline" describes a pretty naive method for noise subtraction (P12). Should be rigorously compared, for Tx vs Mock experiments, to CRISPECTOR and to CRISPResso2. In the section "CRISPR-A empiric model removes more noise than other approaches", we perform an exhaustive comparison with a dataset that contains 288 Mock Files vs 864 Tx files. This can be better appreciated in the, now included, figure Sup. 13A. CRISPResso2 was intentionally left out since their pipeline does not use a model to reduce noise but other approaches like reducing the quantification window.

• "recalculated using a size bias correction model based on spike-in controls empiric data.." (P14). Where is the formula? The formula comes from Equation 1. Now it is correctly referenced.

• Section "Noise subtraction comparison with ampliCan and CRISPECTOR" - fake mock was generated for comparison. We consider the avoidance of a Mock control in experiments designed to measure editing activity to not be best practice. It is OK to support this approach in CRISPR-A. However - the comparison to tools that predominantly work using a Mock control (including ampliCan and CRISPECTOR) should be done with actual Mock. Not with fake Mock .... (P15) We understand the claims of the reviewer for this point as the use of a “fake” mock may not be the best practice for general comparisons. Nevertheless here what we wanted to compare is the difference in the edition percentages using mock and not using it. Since to make a run for on-target data CRISPECTOR requires a mock, the only way to replicate the conditions of “no mock” was to use a synthetic file with the same characteristics of the treated files in terms of depth, but with no edition/noise events to avoid any correction outside this framework. The other run was made with the 288 real Mocks. This was a solution ad Hoc for CRISPECTOR, with ampliCan we used only real mock since they allow to make runs without a mock for on-target.

We changed the word fake for synthetic in the Noise subtraction comparison with ampliCan and CRISPECTOR section:

“As for CRISPECTOR, since it requires a mock file to perform on-target analysis, synthetic mock files were generated”.

Minor comments:

• "Also, most of these tools lack important functionalities like reference identification, clustering, or noise subtraction" - bold part incorrect for CRISPECTOR, although it is not aiming only for CRISPECTOR In supplementary table 1, it is already elucidated which are the functionalities that each tool has. We have also added more context to that statement to highlight the differences between different tools:

“Even not all of them have the same missing functionalities, as can be seen in the Supplementary table 1, CRISPR-A is the only tool that can identifies the amplicon reference from in a reference genome, correct errors through UMI clustering and sequence consensus, correct quantification errors due to differences in amplicon size, and includes interactive plots and a genome browser representation of the alignment.”

• "Same parameters and probability distributions were fitted for three other cell lines: Hek293, K562, and HCT11626, to make SimGE more generalizable and increase its applicability" (page 3) - how was fitted? It was fitted in the same way as the t-cell samples as specified in methods. We have detailed more methods explaining how SimGE is built.

• What is the "nature of modification"? (P5) We have changed nature by type for a better understanding.

• In the section "CRISPR-A effectively calls indels in simulated and edited samples" (P5) towards the end, the authors write that the CRISPR-A algorithm did not give good results for a few examples. They then state that this was corrected and then yielded good results. There is no explanation of what correction was done, if it was implemented in the code and how to avoid/detect it in further cases. The problem was that the used reference sequence was too short. There is no modification in CRISPR-A code, we have just used the whole amplicon reference sequence obtained with the amplicon reference identification functionality of CRISPR-A. We have tried to explain it better in the manuscript: “Once the reference sequence is corrected used is the one corresponding to the whole reference amplicon, obtained with CRISPR-A amplicon sequence discovery function, CRISPR-A shows a perfect edition profile”

• Cell culture, transfection, and electroporation - explanation only for HEK293, what about the others? (P15) We already had explained it for HEK293 and for C2C12, that are the experiments done by use. In the case of the analysis of the three cell lines and 96 targets we reference the source of the data as this data was not produced in our lab.

• Typos and unclear wording: ○ "obtention" (P8) → changed by obtaining

○ "mico" >> micro (P 7,10) → changed

○ "Squema" >> scheme (Fig.1) → changed

○ "decombuled" (P10) → changed by separated

○ "empiric" >> empirical (P8 and other places) → changed

○ "Delins" (P14) → this is not a typo, it is used to indicate that a deletion and insertion has take place (http://varnomen.hgvs.org/recommendations/DNA/variant/delins/)

○ "performancer" (P9) → Change to performance

○ Change word across all article - "edition" to "editing" → changed. In the case of edition windows it has been changed by quantification windows.

○ "...has enough precision to find" (P6) not related to "results" section → We have moved to discussion.

• Comments on figures: ○ Fig. 2C:

■ No CRISPECTOR in the analysis

It is not included because for on-target analysis this tool requires a mock control sample. For this reason, it is compared in Figure 5D, where samples using negative controls are compared, and in Figure 5E where all tools and their different analysis options are compared.

■ It is simulated data only

Yes, it is. Comparison with real data is done in Figure 2D and 2E. And now we also have added a ground truth data in our comparisons obtained from human validation of the classification of more than 3,000 different reads.

■ It is not violin plot as mentioned in the description

It is a violin plot, but in general there is not much dispersion of the data points making the density curves flat.

○ Fig 3A - Is it significant? Yes, it is. We have added this information in the caption of the figure.

○ Fig. 4:

■ A

• Each row/column is a vector of 96 guides? No, as it is said in the caption of the figure, it is the “mean between the distances calculated for each of the 96 different targets.”

• How is the replicate number decided? Is it a different experiment by date? What is separating between experiments? Rep numbers? All this information should be found in the referenced paper from which this dataset comes from as already referenced.

■ B - Differential expression:

We have realized that the caption was not correct, missing the explanations for Fig. 4B and all the following ones moved to a previous letter.

• How? did you measure RNA? It is already stated in methods that RNAseq data was obtained from SRA database and the analysis was done using nf-core/rnaseq pipeline: “RNAseq differential expression analysis of samples from BioProject PRJNA208620 and PRJNA304717 was performed using nf-core/rnaseq pipeline⁵².”

• Is the observed data in the figure sufficiently strong in terms of P-value? Yes, at is it is highlighted in the plot with ** and ***. We have also added the p-value in the cation of the figure.

• Where is the third cell-line? As mentioned in the text, we have just chosen the cell lines that show us higher differences in the the percentage of MMEJ: “HCT116 than in K562, which are the cell lines with the major and minor ratios of MMEJ compared with NHEJ, respectively”.

○ Fig.13 - There is no A and B as mentioned in the text

We thank the reviewer for the observation as we mistakenly uploaded the wrong figure. We corrected it.

Reviewer #2 (Significance (Required)):

We repeat the aspects of contribution, as listed in the first part of the review, and comment about significance:

• NextFlow pipeline implementation is an important engineering contribution. Same is true for the interactive web application

  Significant engineering contribution. Nonetheless, we were not able to run the analysis. So - needs to be checked.

Hopefully now that the documentation is properly added to the repository it will be easier to run analysis.

• The option to simulate an experiment to assess it is a nice feature and can help experiment design

  An important methodology contribution

• Identification of amplicons when not provided as input

  Not important in the context of multiplex PCR and NGS measurement assays, as amplicons will be known. Not clear what other contexts the authors were aiming at.

It is useful to save time, no need to look for the sequence of each amplicon and add it as input. Also, it can help to detect unspecific amplification, since all amplicons of the same genome can be retrieved from the discovery amplicon process. In addition, we have already found one example where this avoids getting incorrect results: “Once the reference sequence used is the one corresponding to the whole reference amplicon, obtained with CRISPR-A amplicon sequence discovery function, CRISPR-A shows a perfect edition profile”. We have added this to the discussion of the manuscript.

• CRISPR-A seeks substitutions along the entire amplicon sequence and is less dependent on the quantification window and on the putative cutsite

  Importance/significance needs to be demonstrated

In figure 3 are shown the results of template-based and substitutions detection. CRISPR-A is a versatile and agnostic tool for gene editing analysis. This means that it can be prepared for the analysis of gene editing of future tools, since the cut site or other elements of experiment design are not required. In addition, it has been shown that when a mock is used its performance is comparable to filtering by edition windows, avoiding the loss of edits when the cut site is slided.

• Analysis of the difference, in edit activity, comparing different cell lines

  Significant contribution. However - the methods need to be much better explained and the results better described in order for this to be useful to the community.

We have made an effort to try to be more clear in the description of the results.

• CRISPR-A supports the use of UMIs

  Mildly significant technical contribution. However - only addresses on-target. Also addressing off-target would have been significant.

The use of UMIs is something that has never been done before in this context. Sequencing biases are not taken into account and editing percentages are reported as observed. Being able to differentiate between different molecules at the beginning of the amplification sequence, allows a higher precision avoiding under or overestimation of each of the species in a bulk of cells.

In the case of off-targets, can be for sure done using sequencing the predicted off-target sites. In addition, there are other methods, like GuideSeq that can be used to discover off-targets, but this kind of data is out of the scope of CRISPR-A. Even that, we are aware of the importance of being able to analyse off-targets when in a context of a broad analysis platform and we will take these into consideration when participating in the building of crisprseq pipeline from nf-core.

• Interesting sequence pattern insights - like "...found certain patterns associated with low diversity outcomes: free thymine or adenine at the 3' nucleotide upstream of the cut site that leads to insertions of the same nucleotide, a free cytosine at the same place that leads to its loss, and strong micro-homology patterns that lead to long deletions "

  As stated - interesting.

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Referee #2

Evidence, reproducibility and clarity

Summary:

CRISPR-Analytics, abbreviated as CRISPR-A, is a web application implementing a tool for analyzing editing experiments. The tool can analyze various experiment types - single cleavage experiments, base editing, prime editing, and HDR. The required data for the analysis consists of NGS raw data or simulated data, in fastq, protospacer sequence and cut site. Amplicon sequence is also needed in cases where the amplified genome is absent from the genome reference list. The tool pipeline is implemented in NextFlow and has an interactive web application for visualizing the results of the analysis, including embedding the results into an IGV browser. The authors developed a gene editing simulation mechanism that enables the user to assess an experiment design and to predict expected outcomes. Simulated data was generated by SimGE over primary T-cells. The parameters and distributions were also fitted for 3 cell lines to make it more generalized (Hek293, K562, and HCT116). The process simulated CRISPR-CAS9 activity and the resulting insertions, deletions, and substitutions. The simulation results are then compared to the experimental results. The authors report the Jensen-Shannon (JS) divergence between the results. The exact distributions that served as input to the JS are not well defined in the manuscript (see below).

The authors also report an investigation of different alignment approaches and how they may affect the resulting characterization of editing activity. The authors examine three different approaches to increase what they call "edit quantification accuracy" (aka, in a different place - "precise allele counts determination" - what is this???): (1) spike-in controls (2) UMI's and (3) using mock to denoise the results. See below for our comments about these approaches. Moreover, the authors developed an empirical model to reduce noise in the detection of editing activity. This is done by using mock (control), and by normalization and alignment of reads with indels, with the notion and observation that indels that are far from the cut site tend to classify as noise. The authors then perform a comparison between 6 different tools, in the context of determining and quantifying editing activities. One important comparison approach uses manually curated data. However - the description of how this dataset was created is far from being sufficiently clear. The comparison is also performed for HDR experiment type, which can be compared only to 2 other tools. We see the potential contribution aspects of the paper to be the following:

1. NextFlow pipeline implementation is an important engineering contribution. Same is true for the interactive web application
2. The option to simulate an experiment to assess it is a nice feature and can help experiment design
3. Identification of amplicons when not provided as input
4. CRISPR-A seeks substitutions along the entire amplicon sequence and is less dependent on the quantification window and on the putative cutsite
5. Analysis of the difference, in edit activity, comparing different cell lines
6. CRISPR-A supports the use of UMIs
7. Interesting sequence pattern insights - like "...found certain patterns associated with low diversity outcomes: free thymine or adenine at the 3' nucleotide upstream of the cut site that leads to insertions of the same nucleotide, a free cytosine at the same place that leads to its loss, and strong micro-homology patterns that lead to long deletions " We further comment on the soundness of these contributions in our comments below and on their significance in our comments related to the general potential significance of the paper.

Major comments:

• Upon attempting to run an analysis from the web interface (https://synbio.upf.edu/crispr-a) and using: fastq of Tx and mock (control), the human genome and the gRNA sequence provided as input for the protospacer field, our run was not successful. In fact the site crashed with no interpretable error message from CRISPR-A.
• Moreover, there should be more clear context. There is no information regarding the type of experiments that can be analyzed with the tool. We figure it is multiplex PCR and NGS but can the tool also be used for GUIDESeq, Capture, CircleSeq etc.?
• No off target analysis. Only on-target
• No translocations and long/complex deletions
• We view the use of a mock experiment as control as a must for any sound attempt to measure edit activity. This is even more so when off-target events need to be assessed (any rigorous application of GE, certainly any application aiming for clinical or crop engineering purposes). We therefore think that all investigation of other approaches should be put in this context.
• It's a nice feature to have simulated data, however, it is not a good approach to rely on it.
• In p7 the authors indicate the implementation of three approaches to improve quantification. They should be clear as to the fact that many other tools and experimental protocols are also using these approaches. for example, ampliCan, CRipresso2 and CRISPECTOR all take into account a mock experiment run in parallel to the treatment.
• Figure1:
  • The figure certainly provides what seems to be a positive indication of the simulations approach being close to measured results. Much more details are needed, however, to fully understand the results.
  • Squema = scheme ??
  • What was the clustering approach?
  • What is the input to the JS calculation? What is the dimension of the distributions compared? These details need to be precisely provided.
  • What clustering/aggregation approach did the authors use here (average dist, min dist, dist of centers?)
  • 5 pairs were selected out of how many? Call that number K.
  • What does the order of the samples in 1C mean? Is 98_real closer to 22_sim than to 98_sim? If so then state it. If not - what is the meaning of the order? Furthermore - how often, over K choose 2 pairs does this mis-matching occur for the CRISPR-A simulator??
• "From the characterized data we obtained the probability distribution of each class" (page 3) - How is this done? how many guides? how many replicates? what is class? where do you elabore regarding it? how you obtain the distributions? More details of the methods need to be provided.
• The 96 samples used for development here - where are they taken from? This should be indicated in the first time these samples are mentioned. Namely - bottom of P6
• CRISPECTOR is not mentioned in the comparison in the section: "CRISPR-A effectively calls indels in simulated and edited samples" (Table S2). Is there a specific reason for having left it out?
• In the section "Improved discovery and characterization of template-based alleles or objective modifications" - part of the analysis was made over simulated data and then over real data. The authors state "it is difficult to explain the origin of these differences...". Thus, needs to be investigated in more detail ... :) (P5) Moreover - the performance over real data is, at the end of the day, the more interesting one for comparison purposes.
• We found no explanation of "spike-in"/"spike experimental data" across the entire article. There is some general language about lengths but the scheme is still totally unclear.
• Description of the 96 gRNAs? Is this data from REF26? If so - where do you state this? If so - how do the methods described herein avoid the unique characteristics of the data of REF26?
• "distance between the percentage of microhomology mediated end-joining deletions of samples with the same target was calculated and the mean of all these distances was used to reduce the information of the 96 different targets to a single one." (P6) What is the exact calculation used? which distance? How was clustering performed? What is the connection for gene expression?
• "we have fitted a linear model to transform the indels count depending on its difference in relation to the reference amplicon" (P7) - needs more explanation. Is this part of the pipeline?
• What is it "...manually curated data set"? (page 8)
• Section "CRISPR-A empiric model removes more noise than other approaches" - with what data were the comparisons performed? Moreover, how were the comparison criteria selected (efficiency and sensitivity)? The literature already used several approaches to compare data analysis tools for editing experiments. See for example ampliCan, Crispresso (1 and 2) and CRISPECTOR. Maybe the authors should follow similar lines.
• In the section "CRISPR-A empiric model removes more noise than other approaches" the authors state, incorrectly, that CRISPECTOR only reports the percentage of editing activity per site (there is much more information reported in the HTML report, including the type of edit event detected - deletion, of various lengths, insertions, substitutions etc). (P8)
• Section "CRISPR-A noise subtraction pipeline" describes a pretty naive method for noise subtraction (P12). Should be rigorously compared, for Tx vs Mock experiments, to CRISPECTOR and to CRISPResso2.
• "recalculated using a size bias correction model based on spike-in controls empiric data.." (P14). Where is the formula?
• Section "Noise subtraction comparison with ampliCan and CRISPECTOR" - fake mock was generated for comparison. We consider the avoidance of a Mock control in experiments designed to measure editing activity to not be best practice. It is OK to support this approach in CRISPR-A. However - the comparison to tools that predominantly work using a Mock control (including ampliCan and CRISPECTOR) should be done with actual Mock. Not with fake Mock .... (P15)

Minor comments:

• "Also, most of these tools lack important functionalities like reference identification, clustering, or noise subtraction" - bold part incorrect for CRISPECTOR, although it is not aiming only for CRISPECTOR
• "Same parameters and probability distributions were fitted for three other cell lines: Hek293, K562, and HCT11626, to make SimGE more generalizable and increase its applicability" (page 3) - how was fitted?
• What is the "nature of modification"? (P5)
• In the section "CRISPR-A effectively calls indels in simulated and edited samples" (P5) towards the end, the authors write that the CRISPR-A algorithm did not give good results for a few examples. They then state that this was corrected and then yielded good results. There is no explanation of what correction was done, if it was implemented in the code and how to avoid/detect it in further cases.
• Cell culture, transfection, and electroporation - explanation only for HEK293, what about the others? (P15)
• Typos and unclear wording:
  • "obtention" (P8)
  • "mico" >> micro (P 7,10)
  • "Squema" >> scheme (Fig.1)
  • "decombuled" (P10)
  • "empiric" >> empirical (P8 and other places)
  • "Delins" (P14)
  • "performancer" (P9)
  • Change word across all article - "edition" to "editing"
  • "...has enough precision to find" (P6) not related to "results" section
• Comments on figures:
  • Fig. 2C:
• No CRISPECTOR in the analysis
• It is simulated data only
• It is not violin plot as mentioned in the description
  • Fig 3A - Is it significant?
  • Fig. 4:
• A
• Each row/column is a vector of 96 guides?
• How is the replicate number decided? Is it a different experiment by date? What is separating between experiments? Rep numbers?
• B - Differential expression:
• How? did you measure RNA?
• Is the observed data in the figure sufficiently strong in terms of P-value?
• Where is the third cell-line?
  • Fig.13 - There is no A and B as mentioned in the text

Significance

We repeat the aspects of contribution, as listed in the first part of the review, and comment about significance:

• NextFlow pipeline implementation is an important engineering contribution. Same is true for the interactive web application
  • Significant engineering contribution. Nonetheless, we were not able to run the analysis. So - needs to be checked.
• The option to simulate an experiment to assess it is a nice feature and can help experiment design
  • An important methodology contribution
• Identification of amplicons when not provided as input
  • Not important in the context of multiplex PCR and NGS measurement assays, as amplicons will be known. Not clear what other contexts the authors were aiming at.
• CRISPR-A seeks substitutions along the entire amplicon sequence and is less dependent on the quantification window and on the putative cutsite
  • Importance/significance needs to be demonstrated
• Analysis of the difference, in edit activity, comparing different cell lines
  • Significant contribution. However - the methods need to be much better explained and the results better described in order for this to be useful to the community.
• CRISPR-A supports the use of UMIs
  • Mildly significant technical contribution. However - only addresses on-target. Also addressing off-target would have been significant.
• Interesting sequence pattern insights - like "...found certain patterns associated with low diversity outcomes: free thymine or adenine at the 3' nucleotide upstream of the cut site that leads to insertions of the same nucleotide, a free cytosine at the same place that leads to its loss, and strong micro-homology patterns that lead to long deletions "
  • As stated - interesting.

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Referee #1

Evidence, reproducibility and clarity

Marta Sanvicente-García et al and colleague developed a comprehensive and versatile genome editing web application tool and a nextflow pipeline to give support to gene editing experimental design and analysis.

The manuscript is well written and all data are clearly shown. While I did not tested extensively, the software seems to work well and I have no reason to doubt the authors' claims. I usually prefer ready to use web applications like outknocker, they are in general easier to use for rookies (it would be good if the author could cite it, since it is very well implemented) but the nextflow implementation is anyway well suited.

Few minor points:

Reagording the methods to assess whether the genome editing is working or not, i would definetely include High Resolution Melt Analysis, which is by far the fastest and probably more sensitive amongst the others.

Another point that would important to taclke is that often these pipelines do nto define the system they are working with (eg diploid, aploid vs etc). This will change the number of reads needed ato unambigously call the genotypes detected and to perform the downstream analysis (the CRISPRnano authors mentioned this point).

I am also wondering whether the name CRISPR-A is appropriate since someone could confuse it with CRISPRa.

Referees cross-commenting

Reviewer 2 made an excellent work and raised important concerns about the software they need to be addressed carefully.

In the meantime we had more time to test the software and can confirm some of the findings of Reviewer 1:

1. We spent hours running (unsuccessfully) CRISPR A on Nextflow. The software does not seem to run properly.
2. No manual or instruction can be found on both their repositories (https://bitbucket.org/synbiolab/crispr-a_nextflow/ https://bitbucket.org/synbiolab/crispr-a_figures/)

Few more points to be considered

• UMI clustering is not proper terminology. Barcode multiplexing/demultiplexing (SQK-LSK109 from Oxford Nanopore).
• Text in Figure 5 is hard to read.
• They should test the software based on the ground truth data
• The alignment algorithm is not the best one, I think minimap2 would be better for general purpose (at least it work better for ONT).
• The minimum configuration for installation was not mentioned (for their Docker/next flow pipeline).
• Fig 2: why do they use PC4/PC1?
• There are still typos and unclear statements thorughout the whole manuscript.

One more drawback is that the software seems to only support single FASTQ uploading (or we cannot see the option to add more FASTQ). Since usually people analyze more than one clone at the time (we usually analyze 96 clones together) this would mean that I have to upload manually each one of them.

Also, the software (the webserver, the docker does not work) works with Illumina data in our hands but not with ONT. This should be clarified in the manuscript.

Significance

As I mentioned above I think this could be a useful software for those people that are screening genome editing cells. Since CRISPR is widely used i assume that the audience is broad.

There are many other software that perform similarly to CRISPR-A but it seems that this software adds few more things and seems to be more precise. It is hard to understand if everything the author claims is accurate since it requires a lot of testing and time and the reviewing time is of just two weeks. But 1) I have no reason to doubt the authors and 2) the software works

Broad audience (people using CRISPR)

Genetics, Genome Engineering, software development (we develop a very similar software), genetic compensation, stem cell biology

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Reply to the reviewers

Dear Editor,

Please find below our detailed responses (in black font) to the Reviewer's comments (in blue). In addition, and to the request of Reviewer #1, we added a PDF file called “Reply to the reviewers MS data” that shows MS/MS and quantification information of representative peptides which were selected based on their (different) caspase/control abundance ratios. We thank the reviewers for their time and valuable comments.

NOTE: our original reply includes several tables and graphs that were not incorporated into our reply shown below

Reviewer #1

Page 4 - In contrast to the hindrance of N-terminal amine ionization by Nt-acetyl groups concluded by the authors, previous studies reported an improved MS-scoring if α-amino-acetylated (tryptic) peptides by the higher numbers of b and y fragment ions observed as compared to α-amino-free (tryptic) peptides (e.g. (Staes et al., 2008)). It is rather the lack of any N-/C-terminal charged residue in case of Lys-N type N-termini which makes LATE less suitable for studying N-terminal protein acetylation.

We thank the reviewer for this comment. In the HYTANE and LATE workflows, only peptides with modified N-termini (by dimethylation or acetylation) are observed after negative selection, hence we argue that the important comparison here is between Nt-acetylated peptides and Nt-dimethylated peptides with (as in HYTANE) or without basic residue (as in LATE). While we are aware of the study by Staes et al 2008 (PMID: 18318009), we do not believe it contradicts our claim as it discusses the difference between Nt-acetylated peptides and peptides with free N-termini.

As we indicated in the manuscript (page 5 in the last sentence of 1st paragraph), we observed less overall peptide identifications in LATE, which was expected due the lack of basic C-term residue. The reduction of identification was more pronounced for Nt-acetylated peptides. However, this still does not exclude LATE as a useful tool for identification of such peptides.

Of note, the overall fragmentation coverage we obtained by LATE and HYTANE for Nt-acetylated and Nt-dimethylated peptides was similar. See the figure below.

Hence, following Cho et al 2016 (PMID: 26889926), we suggest that the difference in ionization of Nt-dimethylated peptides vs Nt-acetylated peptides is the more dominant factor in peptide identifications.

Figure 1:relative Ion coverage for modified peptides in LATE and HYTANE

Page 4 - Besides indication the retained N-termini with high relative caspase-3/control abundance ratio's as putative caspase-3 proteolytic products, also indicate that unique peptides were retained, as many such singletons were reported in previous (caspase-focussed) degradomics studies making use of differential proteomics (e.g. (Van Damme et al., 2005)). Therefore the cut-off ratio of 2 rather seems unsubstantiated, unless the cellular proteomes of so-called control cells were affected by caspase activation. As such, showing some representative MS-spectra of neo-N-termini would be informative.

We thank the reviewer for this comment. We agree that caspase-3 cleavage generates many singletons. This is indeed what we observed in the in vitro experiment as shown in Figure 2B by the long straight lines at Log2(caspase-3/control) >10. We also add here histograms of the obtained ratios that we hope will make this clearer. We chose a cut-off of 2 due to the basal activity of proteases (including caspase-3) as we did not add caspase-3 inhibitors to the cell lysate. In addition, peptides derived from the putative caspase-3 cleavages in the in vitro experiment were required to be identified only in the caspase-3-treated samples (i.e. to appear only with the heavy labeling). Minor changes to Figure 3 legend have been introduced accordingly. As can be seen in the table below, with a cut-off ratio of 2 (Log2=1) and selection of cleavage sites after D or E we identified >98% of the cleavage sites that were identified only in the caspase-3 treated samples (column text in blue). This rate did not change when the cut-off was set to 8 (Log2=3). Therefore, we have chosen to maintain our selection criteria and cut-off ratio as used before for both experiments.

Figure 2: Histograms of Log2(Caspase/control) ratios indicating the large number of singleton peptides (marked with arrows)

Table 1: In vitro experiment selection ratio

Method

Cutoff

Time

Sites

Sites identified only caspase-3 treated samples

% of caspase-treated only sites (singleton)

Sites D/E with light

Sites after D/E no light

% of singleton

LATE

Log2=1

18H

936

906

96.8%

798

786

98.5%

LATE

Log2=2

18H

884

866

98.0%

767

759

99.0%

LATE

Log2=3

18H

819

810

98.9%

722

716

99.2%

HYTANE

Log2=1

18H

1186

1159

97.7%

1037

1032

99.5%

HYTANE

Log2=2

18H

1128

1110

98.4%

998

993

99.5%

HYTANE

Log2=3

18H

1035

1025

99.0%

924

919

99.5%

LATE

Log2=1

6H

755

732

97.0%

656

645

98.3%

LATE

Log2=2

6H

711

700

98.5%

630

623

98.9%

LATE

Log2=3

6H

671

666

99.3%

601

597

99.3%

HYTANE

Log2=1

6H

1028

988

96.1%

899

890

99.0%

HYTANE

Log2=2

6H

955

931

97.5%

851

844

99.2%

HYTANE

Log2=3

6H

882

871

98.8%

795

791

99.5%

LATE

Log2=1

1H

445

423

95.1%

380

372

97.9%

LATE

Log2=2

1H

411

402

97.8%

361

355

98.3%

LATE

Log2=3

1H

386

380

98.4%

344

338

98.3%

HYTANE

Log2=1

1H

593

559

94.3%

513

506

98.6%

HYTANE

Log2=2

1H

544

532

97.8%

488

482

98.8%

HYTANE

Log2=3

1H

508

498

98.0%

461

455

98.7%

In the cell-based experiments of caspase-3, we induced apoptosis on both cell types (over-expressing caspase-3 and the control). Therefore, in this case, as the reviewer has also mentioned, a cut-off of 2 is appropriate because the control cells are also affected by caspase activation. Following the reviewer’s request we have added (in a separate PDF file) several representative MS/MS spectra of neo-N-term peptides and their corresponding quantification data.

Page 4 - replace 'without labelling of lysine residues (epsilon-amines)' to 'without notable labelling of lysine residues (epsilon-amines)', as residual labelling of lysine side-chains was observed. Also in case of the latter, do note that reduced MS-ionization potential might impact labelling efficiency calculation, and chromatographic detection of labelling efficiency should be considered to conclusify this finding.

We thank the reviewer for this comment. We have changed the sentence as requested (Page 4 marked in red). Regarding the labeling efficiency calculations, it is true that ionization potential might affect them. We used a common way to test this aspect (see e.g. Hurtado Silva et al 2019 (PMID: 30934878)) and we are not aware of any reduction in ionization potential following lysine dimethylation. Although we did not study this aspect thoroughly, we frequently observe the opposite: that dimethylation improves MS detections.

Page 6 - The experimental setup comparing caspase-3 overexpressing and ABT-199 induced versus ABT-199 induced cells will be highly biased as it will not be able to detect efficient caspase-3 cleavages (Plasman et al., 2011), as such cleavage events are complete and thus do not require any additional overexpressed capase-3. I see this as an important flaw and the authors should demonstrate that the list also includes efficient caspase-3 cleavages.

We thank the reviewer for highlighting this important aspect. We agree that with our setup, we can miss some efficient cleavages of caspases-3. We acknowledged this caveat in the original text (page 6), but chose to perform our experiments this way in order to highlight cleavages that are affected by caspase-3 expression. To address the reviewer’s comment we have added new experiment and data on caspase cleavages that occur following ABT-199 treatment in HCT116 cells without overexpression of caspase-3. The focus of this experiment was on the relatively short time points following the ABT-199 treatment when no cell death is observed based on XTT assay (see Supplement Figure 6B). This experiment was used to prove that neo-Nt-acetylation of NACA is an early event in apoptosis (Figure 5 E-F page 12). We also used this experiment as an indication of the appearance of efficient cleavages. As can be seen from Supplement Table S10, if we consider all 3 time points of the ABT-199 treatment, we quantified 106 cleavages with free neo-Nt that were cleavages after D and were identified only in the ABT-treated samples. We refer to such cleavages, which appeared prior to noticeable cell death, as "efficient cleavages". Out of these efficient cleavages, 82 were also identified and quantified in the cell-based experiment with overexpression of caspase-3. Twenty efficient cleavages show a high ratio (≥2) in both experiments. Fifty six efficient cleavages had a high ratio in the new experiment and ratio below 2 in the cell-based experiment with overexpression of caspase-3. This supports our original claim regarding efficient cleavages and addresses the reviewer’s concern regarding our ability to identify efficient caspase-3 cleavages with the experimental setup of HCT116 cells overexpressing caspase-3.

Page 12 - The setup doesn't permit ORF N-terminal stability per se, rather the cleavage susceptibly of N-termini holding (a) putative caspase-3 cleavage site(s). Please adjust accordingly. Again since the setup might have missed efficient cleavages, the assessment might be biased.

Thanks for the comment. As requested, the word “stability” has been deleted. As discussed above, we demonstrate that our setup does allow the identification of efficient cleavages and hence our basis for believing that the assessment is not biased. Please also refer to our reply to the next comment.

The claim that Nt-acetylation is protective for caspase-3 cleavage should be validated by monitoring cleavage efficiency of an Nt-acetylated versus an Nt-free variant (e.g. by introducing a Pro residue at AA position 2, or comparing cleavage efficiencies in corresponding NAT knockdown versus control cells) of an identified caspase substrate (i.e. a substrate holding a caspase-3 cleavage site in its N-terminal sequence) versus its Nt-free counterpart

Thanks for raising this point. The reviewer's suggestions have some caveats: a mutation at a protein’s N-terminus in order to generate an Nt-free variant can alter its stability or function and NAT knockdown might have a profound biological impact on the cells. Therefore we chose a different way to study this aspect by selecting from our data ORF N-terminal peptides that were identified with both free N-termini and acetylated N-termini (i.e. the same peptide was identified in some PSMs as acetylated and in other as dimethylated). We managed to find 136 ORF N-terminal peptides that were quantified in both forms, and out of these, 122 contained Asp or Glu residues (the putative caspase cleavage motifs). We added the comparison of the abundance ratios of these peptides in Figure 4C (see also below). It shows a remarkable difference between the groups when the Nt-acetylated peptides ratios did not change as a result of caspase-3 overexpression while the peptides with free Nt were identified mostly in the control cells (negative Log2(caspase-3/control)). Comparison of the 14 ORF Nt-peptides that do not have Glu or Asp in their sequence shows no difference (see below).

Figure 3: Abundance ratio distributions of the ORF Nt peptides identified with both Nt-acetylated and free Nt in HCT116 cells overexpressing caspase-3 and in the control. A. Comparison of peptides that contain putative caspase cleavage in their sequence (D or E) B. comparison of peptides without putative caspase cleavage

These results provide additional support for the notion of the protective or shielding effect of Nt-acetylation against caspase-3 cleavage.

Page 12 - Since post-translational Nt-acetylation of neo-N-termini could be reproduced in vitro in the non-dialyzed sample, enzymatic over chemical Nt-acetylation should be demonstrated (e.g. by the use of a (bisubstrate) NAT inhibitor).

We think this is an interesting idea for future work. Yet, in our opinion, the fact that only very few neo-Nt-acetylated peptides were affected in vitro and that a similar trend of few selected neo-Nt-acetylation targets was shown in the cell-based experiments indicates that this process is enzymatic and not chemical in nature.

Other concerns:

Abstract - The abstracts holds complex/incorrect sentence constructions (e.g. simply indicate 'Protein N-termini', '... undergo ... processing by proteases' (currently: 'not be processed by proteases').

Thanks for pointing this out. We have changed the abstract accordingly.

Abstract - 'To expand the coverage of the N-terminome' only applies when this is used in conjunction with other negative enrichment strategies as by itself, LATE doesn't intrinsically provide a better coverage of the N-terminome (this is also noted at page 2).

We thank the reviewer for pointing this out. We have changed the abstract accordingly.

Change 'that cannot be identified by other methods' to 'that cannot be identified by other negative selection methods'

Thanks for pointing this out. We believe that our description here is appropriate as we explicitly state “some of which cannot be identified by other methods”.

Page 1 - Suggestion to change 'Proteases are typically described as degradative enzymes' to 'Proteases used to be described as degradative enzymes'

Changed as suggested.

Page 1 - Not really correct how written; 'N-terminomics methods highlight the N-terminal fragment of every protein (N-terminome)'

Changed as suggested.

Page 2 - Positive selection techniques .... Enrichment of unblocked (or Nt-free) N-termini

We are not sure what the reviewer had in mind here but have added the text in the brackets

Page 2 - Besides altering charge, Nt-acetylation also alters hydrophobicity ...

Changed as suggested.

Page 2 - remove 'to better chart'

Changed as suggested.

Page 2 etc. - Do note that caspase-3 can potentially activate downstream caspases in vitro

Following this comment, we have added a sentence on Page 5 with this reservation

Page 3 - functional crosstalk between proteolysis and neo-Nt-acetylation has already been demonstrated in the case of co-translational acting methionine aminopeptidases and chloroplast N-terminal acetyltransferases. Adjust accordingly.

We thank the reviewer for highlighting this aspect, although we used the term “neo-Nt-acetylation” which we used to mark that this is not the common (co-translational) acetylation. To assure that this is more clear we have added the words “post-translational” to better define the novelty of our findings.

Page 3 - when discussing the identification of ORF N-termini, note that some of the strategies of which note when used to enrich for in vivo blocked N-termini, can also be used without blocking/labelling of Lys residues, and thus trypsin will also result in Lys-ending peptides. This is important to consider in this context.

Following the reviewer's remark we have changed the sentence so it now states: “Many of these N-terminomics methods……”

Page 3 - remove the following sentence part; '... or run individually which can be useful for quantifying naturally modified N-termini.', since also a differential/labelled proteomics setup enables such assessment. Related to this, the authors should comment on the observation that much fewer (i.e. less than 40%) Nt-acetylated N-termini were identified by LATE as compared to HYTANE. How is this reflected in the number of PSMs? Probably these difference are further intensified when considering PSMs.

We have changed the sentence as suggested.

Regarding the reduction of Nt-acetylation, we thank the reviewer for this question as it led us to find typos in the numbers in Figure 1E which are now corrected. These typos did not change the overall observation that with LATE we identify fewer Nt-acetylated peptides than Nt-free (dimethylated) peptides. As the reviewer anticipated (see below), the reduction in LATE-based “contribution” to the identification of Nt-acetylated peptides as opposed to the identification of dimethylated peptides, is pronounced when considering PSMs but this is not much different than the peptide-based data. Therefore, we prefer to keep the current presentation of Figure 1E.

Figure 4: Comparison of HACAT cells N-terminal peptides identification with LATE and HYTANE when considering peptide sequences and PSMs. Peptides identified with both methods are in green and those that are unique to one method are in blue. Shared peptides were determined based on the sequence of the first 7 amino acids of the identified peptides. A. comparison for peptides with dimethylated N-terminal (free Nt) B. comparison for Nt-acetylated peptides.

Page 6 - Informative to indicate how many of the in silico predicted putative DEVD P4-P1 cleavages were actually present in the list of 2049 putative cleavages identified.

In our dataset, we identified 17 cleavages after DEVD motif. 11 were identified only with HYTANE, 3 were identified by both methods, and 3 more were identified only with LATE. Of note, it seems that in large-scale proteomic studies of apoptosis, the number of caspase cleavages after DEVD motif is quite low. For example, in the CASBAH database (PMID: 17273173__) __there are 10 reports of such cleavage out of 391 reported sites, and in DegraBase (PMID: 23264352) that combined many different apoptotic experiments there are 64 reported DEVD sites out of a total of 6896 P1-Asp sites.

Page 6 - Unclear if any of the of 2049 putative cleavages, included non-canonical P1 cleavages besides the P1 Asp and Glu cleavages identified.

These are 2049 putative cleavage sites with P1 Asp or Glu. We have changed the text to make it clearer.

Page 6 - Were the 'regular' cells mock transfected?

No. The control cells used for the cell-based experiments were the non-transfected cells from the same culture of HCT166. We chose this option to guarantee that exactly the same cells that were grown in the same dish went through the same FACS sorting as a control.

Page 6 -Important to note that an ORF can have multiple N-termini besides neo-N-termini (e.g. in the case of alternative translation initiation)

Thanks for the great point. We have added an indication if the neo-N-termini site has been reported as an alternative translation initiation site to all of the results of the cell-based experiments (Supplementary Tables S4, S5, S6, S9). We also changed the Figures and text accordingly. Our analysis of reported/unreported neo-N-temini is based on the TopFind database which includes information about alternative translation initiation sites from TISdb. Of note, since our focus is on caspase cleavages and we further select putative cleavages based on D/E in P1 and fold change, out of 973 peptides that we reported as putative caspase cleavage (Table S6) only one is in the vicinity of an alternative initiation site.

Page 6 - The authors should be more careful with generalization when comparing LATE and HYTANE (and other degradomics approaches) as in this study LATE was only applied for the identification of caspase-3 neo-N-termini, which by its extended substrate specificity might hold specific features enabling the preferred detection by one technique over the other. Also note that as compared to less recent studies, evidently the MS instrument used is a key factor in the increase in cleavages reported in the current study.

It is conceivable that caspase cleavage may differ from other proteases and thus theoretically work better with LATE, but we fail to see why this would also be the case for other N-terminomics method (like TAILS, Subtiligase, CoFRADIC, ChaFRADIC etc). We showed that LATE provides additional ORF Nt peptides identifications and demonstrated its effectiveness in E. coli (Supplement Figure S2) also, which has a proteome with a different amino acid composition to the human proteome. Furthermore, using LATE in the cell-based experiment led to the identification of many neo-Nt-peptides that do not match caspase cleavage patterns (as indicated for both HYATNE and LATE in Figures 3E and 3F). We reviewed the text again, and believe that we have used a fair description of the results especially when we compared them to previous studies.

Page 9 - The authors should provide some info/supporting statistics in the text regarding the new putative substrates showing GO-enrichments (compared to which control?) similar to previously reported caspase-3 substrates.

The results of the GO enrichment analysis are presented in Fig. S8 and details about how the test was performed are provided in the Materials & Methods. In the revised version, we are including the numerical data that include results of the statistical tests per GO term as Table S12. The enrichment analysis was performed with respect to the whole human proteome.

Page 11 - Indicate that the 11 neo-N-terminal peptides of which note are the neo-Nt-peptides matching (signal peptide) cleavages indicated in the Uniprot database. Were any corresponding di-methylated neo-N-termini of these cleavages identified? In case of the 'other' proteolytic cleavages of which note, refer to these as not-annotated in UniProt.

We thank the reviewer for pointing this out. We have added an indication that this analysis is based on UniProt annotations. Yes, all of the reported 11 neo-Nt-Acet peptides shown in Figure 4 were also found as neo-Nt-DiMet peptides.

Page 11 - post-translational Nt-acetylation is abundant in plant and the responsible NAT has been identified, please reference these studies as well.

We thank the reviewer for pointing this out regarding page 11. A relevant reference has been added in Page 11. In the discussion, we already referenced Nt-acetylation in plants in the discussion as well (see page 14).

Page 12 - Define 'undoubtedly dependent on caspase-3 cleavage'

We thank the reviewer for pointing this out. The word ‘undoubtedly’ has been deleted.

Page 14 - The NAA30 discussion is not really relevant for the discussion of the post-translational Nt-acetylation of mitochondrial neo-N-termini.

We thank the reviewer for pointing this out. This sentence has been deleted.

Viewing the harsh in vitro caspase-3 cleavage condition used, namely 1 µg caspase 3 over 20 µg protein, the P1 specificities of all identified neo-N-termini should clearly be shown.

The P1 specificities of all neo-N-termini found in the in vitro experiment are listed in the supplementary tables S2 and S3. For the reviewer’s convenience, we are providing the table with the P1 specificities below:

Since acetylation of serine and threonine residues are reported forms of post-translational modification, and many so-called past-translational Nt-acetylated neo-N-termini harbour such AA residues in their N-terminal sequence, b-ion coverage for these neo-N-termini should be provided/inspected.

We are not sure that we understand this comment. O-Acetylation of amino acids refers to their side chain. Since we are using Di-methylation labeling in both HYTANE and LATE, if we have a peptide with O-acetylated Ser or Thr at its first position, it is possible to distinguish it from the same peptide with Nt-acetylation by MS1 as illustrated in the following table for a hypothetical peptide SAAANPELKR (mass is MH+1)

Regardless we include in the manuscript MS/MS spectra of NACA Neo-Nt-acetylated peptide by HYTANE and LATE

Reviewer #2

Major suggestions:

• The LATE method relies on digestion with LysN. Can the authors comment on the digestion efficiency of the samples where the LATE workflow was applied?

The LysN digestion details that we used were based on vendor (Promega) instructions combined with details from the Nature Protocol paper by Giansanti et al 2016 (PMID: 27123950__)__ that describes optimized digestion protocol for LysN. We tested LysN efficiency in terms of the identification of missed cleavage and found that it performed very well with a missed-cleavage rate of

• The authors state that the number of peptides with acetylated N-termini was lower compared with HYTANE. Yet, the Nt-acetylation can occur co-translationally in approximately 85% of human proteins.

Did the authors consider optimizing the method (e.g. by fractionating the sample) for better identification of such peptides?

We thank the reviewer for this important comment. We are certain that it is possible to improve the output of LATE by fractionation and/or optimization by changes to the LC gradient as it is well established for most, if not all, bottom-up proteomics methods. In this work, we concentrated more on the proof of concept of the methodology and hence chose to work without fractionation. We performed one attempt to optimize the LC gradient but found that the results were not significantly different, and we thus used the same LC-MS methods that have been optimized for trypsin.

Regarding the reduced identification of Nt-acetylated peptides, as we state in the manuscript following Cho et al 2016 (PMID: 26889926), we believe that this is mainly due to the reduced ionization efficiency of Nt-acetylated peptides compared to Nt-dimethylated peptides which is more pronounced when a C-terminal positive charge is missing (due to LysN digestion).

Also, were the results of the study compared with searches done using other proteomic pipelines (e.g. FragPipe)?

Unfortunately, when we started this project, MS-Fragger did not support LysN as the digesting enzyme. At the time TPP also provided better visualization and quantification support than FragPipe. Recently, we found that MSFragger is faster while providing similar identifications but we are not convinced of the quantification output via FragPipe. In addition, we performed comparisons of Comet to X!Tandem and while the searches took longer than with Comet, the total number of IDs did not improve significantly.

Can the authors provide details on the settings used for searches done in COMET, especially for the samples treated with LysN?

The settings are provided in Table S10 in the supplementary information (Page 14 of the PDF file).

"Fractions containing relatively pure caspase-3 were pooled together and dialyzed against 20 mM HEPES 7.5, and 80 mM NaCl. Aliquots of the protein were stored at -80{degree sign}C"

o What exactly is meant by 'relatively pure'?

We apologize for the inaccurate description. The relevant text has been updated (Page 17) and now explains that this was based on Coommasse stain SDS-PAGE.

Minor suggestions:

• Please check the link for the Github as this reviewer could not open it.

We thank the reviewer for pointing this out. We corrected the link. In any case, the relevant scripts can be found here: https://github.com/OKLAB2016

• Please correct the spelling.

The manuscript was proofread.

Comments regarding figures:

• Figure 2:

o All figures comparing LATE and HYTANE utilize color green for LATE. Yet, in figure 2G, HYTANE is depicted in green-like color. Please consider staying consistent with the color scheme.

We thank the reviewer for this comment. Done as suggested.

Reviewer #2 (Significance (Required)):

Significance:

• The LATE method provides an excellent way to study proteases in vitro or in cell-based experiments. It enables deep investigation of N-terminome based on a simple and cost-effective workflow that utilizes digestion with LysN followed by chemical derivatization of α-amines. This approach allows for the identification of N-terminal peptides that may escape detection by other N-terminomics methods. With LATE, proteases' cleavage sites that might not so far be reporter due to technical limitations, can be studied and characterized. Hence, LATE is a useful addition to the N-terminomic toolbox.

We thank the reviewer for the positive comments and general assessment of LATE.

Reviewer #3

In this manuscript, Hanna et al. report LATE, an N terminomics method similar to N-TAILS and HYTANE, with modifications that enhance or change coverages of the N-terminal proteome in proteomics datasets. LATE relies on selective N-terminal modification of protease-treated, LysN digested samples, enabling internal peptides to be depleted based on the presence of the unblocked lysine epsilon amine. Using LATE in comparison with HYTANE, the authors identified a large number of both known and unknown caspase-3 cleavage sites, both in vitro and in vivo. Because LATE enables identification of both proteolytic neo-N termini and natively blocked N termini such as those that are acetylated, the authors were able to discover a number of post-translationally acetylated proteolytic neo-N termini. This finding points to potential functional cross talk between apoptotic proteolysis and Nt-acetylation. Overall, this is a very nice manuscript that adds a valuable new tool to the N-terminal proteomics toolbox. However, the manuscript could be improved by addressing the following questions and comments.

We thank the reviewer for this assessment.

1. One of the benchmark points used to describe the need for a new technology such as LATE is the idea that there are 134 putative caspase-3 substrates in the human proteome, of which only about half can be identified based on ArgC cleavages. However, the 134 substrates seem to include only those that have the exact canonical DEVD motif. Many more substrates than this are already known for caspase-3. For example, >900 caspase-3 substrates were identified by Araya et al. alone. It might make more sense to apply a position-specific scoring matric to the human proteome to predict a maximum number of possible caspase-2 cleavage sites and how many would be expected to be identified using other technologies. Otherwise, please provide a rationale for why these 134 putative caspase-3 sites are representative.

The reviewer is correct. Indeed, most of the identified caspase-3 cleavage are not exact matches to the DEVD motif. We used the DEVD as an example to illustrate the added value of using lysine-based digestion together with ArgC. We obtained a similar trend with some variations when we tested the feasibility of the identification of the human ORF Nt-peptides, E. coli ORF Nt-peptides and more. We are quite confident that any prediction will show a relatively similar distribution. To demonstrate this, we show here the relative contribution of each method for the identification of any peptide that begins after Asp in the human proteome.

While the distributions are not identical, they are very similar, and the specific additions from LATE (LysN) are between 20% to 22% out of the total and it can help to expand the coverage by 42% to 45%.

It is definitely plausible and have been previously demonstrated that selective N-terminal demethylation can be achieved under the right reaction conditions, and I do not doubt that it has been achieved here. However, I do not understand how the authors were able to conclude that alpha-amines are blocked with 95% efficiency and lysines are blocked at

This is a very good point. The reviewer is correct and indeed we don’t have a way to establish if the dimethylation is on the side chain amine of lysine or on its N-terminal amine. A partial support for our claim is from labeling experiments that we (and others) conducted with tryptic and LysC peptides that clearly demonstrate that under the specified labeling conditions, 95% of the N-terminal amines are labeled and not the lysine side chain amines. However, at the end of the day, this does not change the outcome of LATE.

Related to the above comment, Table S10 seems to indicate that MS/MS data from LATE were searched with dimethylation as a fixed modification at the N terminus. Were LATE samples searched with different parameters to generate Figure 1C? Are the dimethylated Ks identified mostly from missed cleavages and therefore not at the N terminus?

We thank the reviewer for pointing this out. The search parameters used for the generation of Figure 1C have been added to Table S10. The reviewer is correct, the few dimethylated Ks identified in the search used for Figure 1C are mostly from missed cleavages.

For both the in vitro and in vivo experiments, how many of the new caspase-3 cleavage sites occurred in proteins that were not previously known to be caspase substrates?

In the in vitro experiments, we identified cleavages of 372 proteins that were not reported as caspase-3 substrates based on the databases we used as references. A line specifying this number was also added to text on page 7. In the cell-based experiment, we identified putative caspase-3 cleavages of 67 proteins that were not reported so far as caspase-3 substrates. This information has been added to the main text on page 10. We have added columns indicating the known/unreported protein substrates to Tables S2, S3, S4, S5, and S6.

For the experiment in cells, can the authors explain the rationale for comparing cells in which apoptosis is induced with ABT-199 to ABT-199-treated cells with caspase-3 overexpression? What is the advantage over comparing ABT-199 treated cells to untreated cells

Great question. An N-terminomics study of “common” apoptosis would lead mainly to the identification of effector caspases (caspase-3 and -7) substrates. Our aim was to focus mostly on the caspase-3 cleavages that occur in the cell during apoptosis. In choosing this gain-of-function approach we were motivated by the idea that it couldprovide new insights that would otherwise go undetected when using knockout or other loss-of-function approaches. The advantage of this system over comparing ABT-199 treated to non-treated cells (which we have now added as well) is that it can enhance the identification of caspase-3 specific cleavages.

Can the authors discuss the timescale of cell death in ABT-199 treated cells vs. ABT-199 treated caspase-3-overexpressing cells. Ideally, data showing cell viability over time (e.g. Cell Titer Glo or MTT assays) would be presented, but if the authors could at least describe whether apoptosis is accelerated in the caspase-3 overexpressing cells, it would be helpful.

Great suggestion. Following the reviewer’s suggestion we have characterized the effect of caspase-3 overexpression of the cells by XTT assay, and indeed caspase-3 overexpressing cells do show accelerated cell-death in response to ABT199 compared to non-transfected cells. These results are now presented as Supplement Figure S6B and are mentioned in the results section.

The authors say that in their experimental design, they expect to see no difference between ABT-199 only and ABT-199/caspase-3 overexpression for substrates that are cleaved efficiently by endogenous caspases. If the new caspase-3 substrates are not cleaved efficiently by endogenous caspase-3, this seems to call into question their physiological relevance. Can the authors explain more thoroughly how these new substrates fit into the apoptotic program?

We thank the reviewer for raising this issue. We are aware that our original cell-based experimental design may have some limitations, yet we chose this gain-of-function setup in order to identify caspase-3 substrates in a cell-based system. We believe that this setup does allow identification of substrates that are efficiently cleaved by endogenous caspase-3, such as cleavage and acetylation of NACA at Ser34 (and neo-Nt-acetylation after caspase-3 cleavage in general). To study the physiological relevance of the neo-Nt-acetylation, we have added to the revised manuscript a time-course N-terminomics characterization of early apoptosis events conducted in HCT116 cells (without caspase-3 overexpression). The results of these experiments are now shown in Figure 5C and also in the Supplementary Table

The authors convincingly show that cleaved NACA is a neo-substrate for Nt-acetylation, suggesting functional crosstalk between proteolysis and acetylation. However, it is not clear if this acetylation event has a functional consequence, so it seems inaccurate to say at the top of page 3 that "This is the first demonstration of functional crosstalk between neo-Nt-acetylation and proteolytic pathways."

The author is correct. We changed the text accordingly.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Hanna et al. report LATE, an N terminomics method similar to N-TAILS and HYTANE, with modifications that enhance or change coverages of the N-terminal proteome in proteomics datasets. LATE relies on selective N-terminal modification of protease-treated, LysN digested samples, enabling internal peptides to be depleted based on the presence of the unblocked lysine epsilon amine. Using LATE in comparison with HYTANE, the authors identified a large number of both known and unknown caspase-3 cleavage sites, both in vitro and in vivo. Because LATE enables identification of both proteolytic neo-N termini and natively blocked N termini such as those that are acetylated, the authors were able to discover a number of post-translationally acetylated proteolytic neo-N termini. This finding points to potential functional cross talk between apoptotic proteolysis and Nt-acetylation. Overall, this is a very nice manuscript that adds a valuable new tool to the N-terminal proteomics toolbox. However, the manuscript could be improved by addressing the following questions and comments.

1. One of the benchmark points used to describe the need for a new technology such as LATE is the idea that there are 134 putative caspase-3 substrates in the human proteome, of which only about half can be identified based on ArgC cleavages. However, the 134 substrates seem to include only those that have the exact canonical DEVD motif. Many more substrates than this are already known for caspase-3. For example, >900 caspase-3 substrates were identified by Araya et al. alone. It might make more sense to apply a position-specific scoring matric to the human proteome to predict a maximum number of possible caspase-2 cleavage sites and how many would be expected to be identified using other technologies. Otherwise, please provide a rationale for why these 134 putative caspase-3 sites are representative.
2. It is definitely plausible and have been previously demonstrated that selective N-terminal demethylation can be achieved under the right reaction conditions, and I do not doubt that it has been achieved here. However, I do not understand how the authors were able to conclude that alpha-amines are blocked with 95% efficiency and lysines are blocked at <5%. This claim seems to be based on PSMs for each type of modification. However, in a LysN digested sample, we would expect the vast majority of peptides to begin with K and the vast majority of Ks to be found at the N terminus of a peptide. In this situation, how is it possible to distinguish whether demethylation has occurred on the alpha-amine or the epsilon-amine? With N-terminal K, all of the MS2 fragments containing the N-terminal a-amine would also contain the lysine epsilon-amine. The m/z values for the y-ions, b-ions, and a-ions containing this residue would be the same. I may be misunderstanding, so it would be helpful if the authors could explain how they are able to distinguish these.
3. Related to the above comment, Table S10 seems to indicate that MS/MS data from LATE were searched with dimethylation as a fixed modification at the N terminus. Were LATE samples searched with different parameters to generate Figure 1C? Are the dimethylated Ks identified mostly from missed cleavages and therefore not at the N terminus?
4. For both the in vitro and in vivo experiments, how many of the new caspase-3 cleavage sites occurred in proteins that were not previously known to be caspase substrates?
5. For the experiment in cells, can the authors explain the rationale for comparing cells in which apoptosis is induced with ABT-199 to ABT-199-treated cells with caspase-3 overexpression? What is the advantage over comparing ABT-199 treated cells to untreated cells
6. Can the authors discuss the timescale of cell death in ABT-199 treated cells vs. ABT-199 treated caspase-3-overexpressing cells. Ideally, data showing cell viability over time (e.g. Cell Titer Glo or MTT assays) would be presented, but if the authors could at least describe whether apoptosis is accelerated in the caspase-3 overexpressing cells, it would be helpful.
7. The authors say that in their experimental design, they expect to see no difference between ABT-199 only and ABT-199/caspase-3 overexpression for substrates that are cleaved efficiently by endogenous caspases. If the new caspase-3 substrates are not cleaved efficiently by endogenous caspase-3, this seems to call into question their physiological relevance. Can the authors explain more thoroughly how these new substrates fit into the apoptotic program?
8. The authors convincingly show that cleaved NACA is a neo-substrate for Nt-acetylation, suggesting functional crosstalk between proteolysis and acetylation. However, it is not clear if this acetylation event has a functional consequence, so it seems inaccurate to say at the top of page 3 that "This is the first demonstration of functional crosstalk between neo-Nt-acetylation and proteolytic pathways."

Significance

See above.

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors present a novel N-terminal enrichment method named LATE (LysN Amino Terminal Enrichment) that utilizes chemical derivatization of α-amines that enables characterization of the N-terminome. Using LATE as well as the already established HYTANE method, Hanna et al conducted a study of caspase-3 mediated proteolysis both in vitro and in cell-based apoptosis experiments, which led to the discovery of new potential caspase-3 cleavages. The results are well presented and nicely highlight that LATE is an efficient and inexpensive method that can be used to identify cleavage sites that cannot be found by other N-terminomics workflows.

Major suggestions:

• The LATE method relies on digestion with LysN. Can the authors comment on the digestion efficiency of the samples where the LATE workflow was applied?
• The authors state that the number of peptides with acetylated N-termini was lower compared with HYTANE. Yet, the Nt-acetylation can occur co-translationally in approximately 85% of human proteins. Did the authors consider optimizing the method (e.g. by fractionating the sample) for better identification of such peptides? Also, were the results of the study compared with searches done using other proteomic pipelines (e.g. FragPipe)?
• Can the authors provide details on the settings used for searches done in COMET, especially for the samples treated with LysN?
• "Fractions containing relatively pure caspase-3 were pooled together and dialyzed against 20 mM HEPES 7.5, and 80 mM NaCl. Aliquots of the protein were stored at -80{degree sign}C"
  • What exactly is meant by 'relatively pure'?

Minor suggestions:

• Please check the link for the Github as this reviewer could not open it.
• Please correct the spelling. Comments regarding figures:
• Figure 2:
  • All figures comparing LATE and HYTANE utilize color green for LATE. Yet, in figure 2G, HYTANE is depicted in green-like color. Please consider staying consistent with the color scheme.

Significance

• The LATE method provides an excellent way to study proteases in vitro or in cell-based experiments. It enables deep investigation of N-terminome based on a simple and cost-effective workflow that utilizes digestion with LysN followed by chemical derivatization of α-amines. This approach allows for the identification of N-terminal peptides that may escape detection by other N-terminomics methods. With LATE, proteases' cleavage sites that might not so far be reporter due to technical limitations, can be studied and characterized. Hence, LATE is a useful addition to the N-terminomic toolbox.

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Referee #1

Evidence, reproducibility and clarity

Manuscript Reference: RC-2022-01676

TITLE: In-depth characterization of apoptosis N-terminome reveals a link between caspase-3 cleavage and post-translational N-terminal acetylation By Rawad Hanna, Andrey Rozenberg, Daniel Ben-Yosef, Tali Lavy, and Oded Kleifeld

Summary of key results:

The manuscript "In-depth characterization of apoptosis N-terminome reveals a link between caspase-3 cleavage and post-translational N-terminal acetylation" by Rawad and co-authors reports on a negative enrichment strategy, named LysN Amino Terminal Enrichment (LATE) to perform N-terminome analysis, a strategy which complements the cohort of existing negative enrichment strategies thereby jointly permitting a more comprehensive capture of the (neo-)N-terminome by additionally enabling the capture of (neo-)N-termini with (semi-)Lys-N specificity. The authors provide preliminary evidence that Nt-acetylation is protective for a proteins' N-terminus to be cleaved by caspase-3 besides the occurence of putative post-translational Nt-acetylation occurring on neo-N-termini generated upon caspase-3 cleavage.

Concerns:

Page 4 - In contrast to the hindrance of N-terminal amine ionization by Nt-acetyl groups concluded by the authors, previous studies reported an improved MS-scoring if α-amino-acetylated (tryptic) peptides by the higher numbers of b and y fragment ions observed as compared to α-amino-free (tryptic) peptides (e.g. (Staes et al., 2008)). It is rather the lack of any N-/C-terminal charged residue in case of Lys-N type N-termini which makes LATE less suitable for studying N-terminal protein acetylation.

Page 4 - Besides indication the retained N-termini with high relative caspase-3/control abundance ratio's as putative caspase-3 proteolytic products, also indicate that unique peptides were retained, as many such singletons were reported in previous (caspase-focussed) degradomics studies making use of differential proteomics (e.g. (Van Damme et al., 2005)). Therefore the cut-off ratio of 2 rather seems unsubstantiated, unless the cellular proteomes of so-called control cells were affected by caspase activation. As such, showing some representative MS-spectra of neo-N-termini would be informative.

Page 4 - replace 'without labelling of lysine residues (epsilon-amines)' to 'without notable labelling of lysine residues (epsilon-amines)', as residual labelling of lysine side-chains was observed. Also in case of the latter, do note that reduced MS-ionization potential might impact labelling efficiency calculation, and chromatographic detection of labelling efficiency should be considered to conclusify this finding.

Page 6 - The experimental setup comparing caspase-3 overexpressing and ABT-199 induced versus ABT-199 induced cells will be highly biased as it will not be able to detect efficient caspase-3 cleavages (Plasman et al., 2011), as such cleavage events are complete and thus do not require any additional overexpressed capase-3. I see this as an important flaw and the authors should demonstrate that the list also includes efficient caspase-3 cleavages.

Page 12 - The setup doesn't permit ORF N-terminal stability per se, rather the cleavage susceptibly of N-termini holding (a) putative caspase-3 cleavage site(s). Please adjust accordingly. Again since the setup might have missed efficient cleavages, the assessment might be biased.

The claim that Nt-acetylation is protective for caspase-3 cleavage should be validated by monitoring cleavage efficiency of an Nt-acetylated versus an Nt-free variant (e.g. by introducing a Pro residue at AA position 2, or comparing cleavage efficiencies in corresponding NAT knockdown versus control cells) of an identified caspase substrate (i.e. a substrate holding a caspase-3 cleavage site in its N-terminal sequence) versus its Nt-free counterpart

Page 12 - Since post-translational Nt-acetylation of neo-N-termini could be reproduced in vitro in the non-dialyzed sample, enzymatic over chemical Nt-acetylation should be demonstrated (e.g. by the use of a (bisubstrate) NAT inhibitor).

Other concerns:

Abstract - The abstracts holds complex/incorrect sentence constructions (e.g. simply indicate 'Protein N-termini', '... undergo ... processing by proteases' (currently: 'not be processed by proteases').

Abstract - 'To expand the coverage of the N-terminome' only applies when this is used in conjunction with other negative enrichment strategies as by itself, LATE doesn't intrinsically provide a better coverage of the N-terminome (this is also noted at page 2).

Change 'that cannot be identified by other methods' to 'that cannot be identified by other negative selection methods'

Page 1 - Suggestion to change 'Proteases are typically described as degradative enzymes' to 'Proteases used to be described as degradative enzymes'

Page 1 - Not really correct how written; 'N-terminomics methods highlight the N-terminal fragment of every protein (N-terminome)'

Page 2 - Positive selection techniques .... Enrichment of unblocked (or Nt-free) N-termini

Page 2 - Besides altering charge, Nt-acetylation also alters hydrophobicity ...

Page 2 - remove 'to better chart'

Page 2 etc. - Do note that caspase-3 can potentially activate downstream caspases in vitro

Page 3 - functional crosstalk between proteolysis and neo-Nt-acetylation has already been demonstrated in the case of co-translational acting methionine aminopeptidases and chloroplast N-terminal acetyltransferases. Adjust accordingly.

Page 3 - when discussing the identification of ORF N-termini, note that some of the strategies of which note when used to enrich for in vivo blocked N-termini, can also be used without blocking/labelling of Lys residues, and thus trypsin will also result in Lys-ending peptides. This is important to consider in this context.

Page 3 - remove the following sentence part; '... or run individually which can be useful for quantifying naturally modified N-termini.', since also a differential/labelled proteomics setup enables such assessment. Related to this, the authors should comment on the observation that much fewer (i.e. less than 40%) Nt-acetylated N-termini were identified by LATE as compared to HYTANE. How is this reflected in the number of PSMs? Probably these difference are further intensified when considering PSMs.

Page 6 - Informative to indicate how many of the in silico predicted putative DEVD P4-P1 cleavages were actually present in the list of 2049 putative cleavages identified.

Page 6 - Unclear if any of the of 2049 putative cleavages, included non-canonical P1 cleavages besides the P1 Asp and Glu cleavages identified.

Page 6 - Were the 'regular' cells mock transfected?

Page 6 -Important to note that an ORF can have multiple N-termini besides neo-N-termini (e.g. in the case of alternative translation initiation)

Page 6 - The authors should be more careful with generalization when comparing LATE and HYTANE (and other degradomics approaches) as in this study LATE was only applied for the identification of caspase-3 neo-N-termini, which by its extended substrate specificity might hold specific features enabling the preferred detection by one technique over the other. Also note that as compared to less recent studies, evidently the MS instrument used is a key factor in the increase in cleavages reported in the current study.

Page 9 - The authors should provide some info/supporting statistics in the text regarding the new putative substrates showing GO-enrichments (compared to which control?) similar to previously reported caspase-3 substrates.

Page 11 - Indicate that the 11 neo-N-terminal peptides of which note are the neo-Nt-peptides matching (signal peptide) cleavages indicated in the Uniprot database. Were any corresponding di-methylated neo-N-termini of these cleavages identified? In case of the 'other' proteolytic cleavages of which note, refer to these as not-annotated in UniProt.

Page 11 - post-translational Nt-acetylation is abundant in plant and the responsible NAT has been identified, please reference these studies as well.

Page 12 - Define 'undoubtedly dependent on caspase-3 cleavage'

Page 14 - The NAA30 discussion is not really relevant for the discussion of the post-translational Nt-acetylation of mitochondrial neo-N-termini.

Viewing the harsh in vitro caspase-3 cleavage condition used, namely 1 µg caspase 3 over 20 µg protein, the P1 specificities of all identified neo-N-termini should clearly be shown.

Since acetylation of serine and threonine residues are reported forms of post-translational modification, and many so-called past-translational Nt-acetylated neo-N-termini harbour such AA residues in their N-terminal sequence, b-ion coverage for these neo-N-termini should be provided/inspected.

References

Plasman, K., Van Damme, P., Kaiserman, D., Impens, F., Demeyer, K., Helsens, K., . . . Gevaert, K. (2011). Probing the efficiency of proteolytic events by positional proteomics. Mol Cell Proteomics, 10(2), M110 003301. doi:M110.003301 [pii] 10.1074/mcp.M110.003301

Staes, A., Van Damme, P., Helsens, K., Demol, H., Vandekerckhove, J., & Gevaert, K. (2008). Improved recovery of proteome-informative, protein N-terminal peptides by combined fractional diagonal chromatography (COFRADIC). Proteomics, 8(7), 1362-1370. doi:10.1002/pmic.200700950

Van Damme, P., Martens, L., Van Damme, J., Hugelier, K., Staes, A., Vandekerckhove, J., & Gevaert, K. (2005). Caspase-specific and nonspecific in vivo protein processing during Fas-induced apoptosis. Nat Methods, 2(10), 771-777. doi:nmeth792 [pii] 10.1038/nmeth792

Significance

The manuscript "In-depth characterization of apoptosis N-terminome reveals a link between caspase-3 cleavage and post-translational N-terminal acetylation" by Rawad and co-authors reports on a negative enrichment strategy, named LysN Amino Terminal Enrichment (LATE) to perform N-terminome analysis, a strategy which complements the cohort of existing negative enrichment strategies thereby jointly permitting a more comprehensive capture of the (neo-)N-terminome by additionally enabling the capture of (neo-)N-termini with (semi-)Lys-N specificity. The authors provide preliminary evidence that Nt-acetylation is protective for a proteins' N-terminus to be cleaved by caspase-3 besides the occurence of putative post-translational Nt-acetylation occurring on neo-N-termini generated upon caspase-3 cleavage.

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Reply to the reviewers

Manuscript number: RC-2022-01588

Corresponding author(s): Erh-Min, LAI

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1. General Statements [optional]

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The authors thank the reviewers for the positive and valuable comments, which have helped us to improve the quality of this work. We have addressed all comments by providing additional data and/or explanation with a detailed point-by-point response. The revised manuscript included new data: 1) viable cell counts of growth inhibition assay (Fig. 2A), 2) Quantitative data of microscope data (Fig. 2C, Fig. 4), 3) quantitative data of interabacterial competition (Fig. 5A, 5B), western blotting data of growth inhibition (Fig. S1A and S1B), secretion assay of single glycine-zipper mutants (Fig. 5C), and inclusion of full gel of western blot results (Fig. S3 and S5). By integrating these new results, we have substantially strengthened the findings that a glycine zipper motif of a type VI secretion effector T6SS Tde1contributes to its translocation across the cytoplasmic membrane of target cells.

2. Point-by-point description of the revisions

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

Summary: In this manuscript, Ali et al. propose that a glycine zipper motif located at the N-terminus of the Agrobacterium tumefaciens T6SS DNase effector, Tde1, can transport the toxin across the cytoplasmic membrane and into the cytoplasm, where its target is found. To support these claims, they perform a series of secretion, competition, toxicity, and fluorescence microscopy assays showing that a mutation in two glycine residues affects toxicity of the effector during competition and its ability to enter a target cell, but not its secretion through the T6SS or its binding to the adaptor protein Tap1. The concept brought forth in this study is quite interesting and important - the notion that T6SS effectors have domains that aid in their transport into the cytoplasm of the target cell. This is similar to a recent finding that a domain common to bacterial pyocins and T6SS effectors can mediate DNase toxin transport through the target cell's cytoplasmic membrane (Atanaskovic et al., mBio, 2022); the authors should mention and discuss this recent work. Nevertheless, it is my impression that the results do not fully support the conclusions and proposed mechanism, even though the general idea seems correct.

Ans: We thank this reviewer found this work interesting and important. We hope the revised manuscript including the new data and careful interpretation have substantialized the conclusions and proposed mechanisms. We also included the excellent work by Atanaskovic et al., 2022 and discussed the findings in the revision (see lines 344-349).

Major comments:

• An experiment that directly demonstrates the ability of the glycine zipper to mediate transport of a toxin across a membrane would greatly support and solidify the conclusions of this work. For example, showing the ability of a purified protein to enter spheroplasts or liposomes in a glycine zipper dependent fashion. Currently, the authors perform experiments that can only indirectly support the proposed function of the glycine zipper to enable the effector to cross the membrane, and as detailed below, some of these experiments are over-interpreted in my opinion. Ans: We agree that the direct evidence for the ability of the glycine zipper to mediate Tde1 transport across target cell membrane is to perform the in vitro translocation assay. Unfortunately, the attempts to purify sufficeint amounts of full-length or N-termial version of Tde1 have not been successful. Therefore, we are unable to perform this experiment. Accoringly, we have tried our best to carefully interpret the data and rephrase the statements accordingly.

• Lines 153-159: It is not clear how much these results are relevant to the activity of the glycine zipper motif during effector delivery by the T6SS. If I understand correctly, the described experiments are of over-expression of the proteins in the E. coli cytoplasm, where glycine zipper-dependent membrane permeability and toxicity are detected. However, one would expect that if the effector is to be transported from the periplasm to the cytoplasm during T6SS delivery, then the glycine zipper should function from the periplasmic face of the cytoplasmic membrane, and not from its cytoplasmic face, as is the case in these experiments. Is it possible that the observed toxicity and membrane permeability be the result of over-expression in the "wrong place"? Ans: The reviewer is right that Tde1 should permealize cytoplasmic membrane from periplasmic side upon injection from the attacker based on our proposed model. The purpose of ectopic expression of Tde1 and its variant in E. coli is to dissect the region and motif of Tde1 DNase-independent toxicity and the ability in enhancing membrane permeability regardless of which sides of cytoplasmic membrane the Tde1 mediates toxicity and permeability. The results of glycine zipper-dependent toxicity and membrane permeability provide a ground work for the experiments of secretion and interabacterial compeittion in the context of active T6SS action to determine the role of glycine zipper in Tde1 export and translocation.

• Fig. 4B: This figure appears to be very important, and the authors base a large part of their main conclusion regarding the role of the glycine zipper in membrane crossing on it. However, some controls are missing and part of the results observed in the figure do not match their description in the text. • Lines 233-237 - While the authors state in the text that GFP and mCherry signals did not overlap in E. coli cells co-cultured with Agrobacterium cells expressing Tde1(M)-GLGL, I see many double-colored cells in this sample (bottom panels in Fig. 4B). Actually, all cells appear to have both green and blue colors, except for a few cells that are only green but that also seem to be dead judging by their ghostly appearance in the phase contrast channel.

Ans: We thank the reviewer pointed this out. By looking at this particular image more carefully, it is striking that the majority of cells seem to emit both green and blue colors from this Tde1(M)GLGL sample. We have performed a total of three indepenent experiments for this translocation assay and all results except this particular sample in this particular experiment are consistent in all three independent experiments. Honestly, we could not explain this result and a possibility is this sample might be accidentally mixed with another sample. Because this is the only sample with inconsistent result with another two independent experiments, we decided NOT to use the results from this independent experiment and instead performed another independent experiment. We now have included the quantitative data from three effective independent experiments and show the representative images in Figure 4.

How is it that all cells in the bottom panels are blue (indicating that they are E. coli target cells)? Shouldn't a large portion of the cells be Agrobacterium cells that should not be blue, since these are added at the beginning of the competition assay at a 10:1 ratio in their favor? Ans: As explained above, we have no defined answer and decided to perform additional repeats, which are consistent with results of another two independent experiments.

It is quite remarkable that so much GFP signal is transported into the E. coli target cells so that it is so clearly visible under the microscope. How do the authors know that the GFP signal overlapping with the mCherry is really inside the cell and not outside (for example, proteins secreted to the media that attach to the cell envelope)? Will the GFP signal remain if trypsin is added to the media before visualization under the microscope? Ans: Indeed, our quantitative data show there are ~50% cells have GFP overlapping with mCherry in the translocation positive samples. The signals should be inside the cells because no overlay signals were observed from N-Tde1GLGL or Tde1(M)GLGLeven though they are secreted.

Can the authors quantify the ratio of E. coli cells that have overlapping green and blue colors over several experiments for each sample, to show that this phenomenon repeats and is statistically significant? Ans: Yes, see quantitative data in Figure 4.

Can the authors explain why at least some of these E. coli cells should not be dead due to the toxicity mediated by the third effector of the Agrobacterium T6SS, Tae? Ans: In Agrobacterium tumefaciens C58, Tde1/2 are the major effectors contributing to antibacterial activity. Tae effector has little impact on interbacterial competition outcome (see previous publications Ma et al., 2014 doi: 10.1016/j.chom.2014.06.002.; Yu et al., 2020 doi.org/10.1128/JB.00490-20)

Why were the microscopy competitions performed differently than the regular competition assays? Why wasn't AK media used in these competitions? How active is the T6SS under these conditions compared to the AK media? Ans: We have tried to use AK medium for the translocation assay but only very weak fluorescent signals can be observed likely due to the low expression when grown on this nutrient poor medium. In order to correlate the results of the compeittion assay with translcoation experiment, we have performed E. coli killing assay using LB medium that is used for translocation experiment now. For the interbacterial competition against agrobacterial siblings, we still used AK medium for competition because no detectable interbacterial compettion activity could be observed between two A. tumefaciens strains on LB agar. As reported earlier, stronger interbacterial competition outcome was detected from co-culture on AK than other nutrient rich medium while the secretion activity grown in AK medium is lower (Yu et al., 2020 doi.org/10.1128/JB.00490-20). These results indicate that the factors other than secretion activity also impacted recipient cell susceptibity, which however is not the main focous of this work.

In the N-Tde1 sample, many Agrobacterium cells appear to have the GFP signal in foci rather than distributed throughout the cell (as it is in other samples), while the E. coli cells have a uniform and strong GFP signal. Can the authors comment on that? Ans: Thanks the reviewer for raising this question.We are also curious about the Tde1 glycine zipper-dependent GFP foci and now include this potential explanation in the Discussion of revised manuscript (line 387-406). To this end, we do not have an answer for it. Because glycine zipper repeats are known to interact with membrane, it is possible that Tde1 proteins may preferntially bind to microdomain of cytoplasmic membrane, which was recently found in A. tumefaciens (Czolkoss et al., 2021). We also found that Tde1 proteins (either tagged with HA or GFP) are proned for truncation when they are ectopically expressed in E. coli or when Tdi1 is absent or not equivalent. Thus, it is possible that Tde1-GFP proteins are truncated after translocation into E. coli cells, in which most GFP signals are emitted from free GFP instead of Tde1-GFP. The stability of free GFP derived from translocated Tde1-GFP may also explain the high percentage of E. coli cells exhibiting overlayed GFP/mCherry signals.

It might be easier for readers to visualize this figure and see the signal distribution in the different cells if the authors show a zoomed in version in the main text, and provide the wide field images as a supplementary figure. Ans: We have tried to include zoom-in images but the resolution is not good. We have improved the quality of images in the Figure 4 and believe the images are clear to see individual and overlayed fluorescence signals.

• Fig. 5C-D: The reduced expression and secretion of the GLGL mutant is considerable. How can the authors rule out that this reduction was the cause for the reduced observed toxicity of the mutant in 5A-B? Moreover, the results show that the GLGL double mutant is hampered in expression, secretion, and DNase activity, and it negatively affects overall T6SS activity. Since this mutant was used throughout the paper, and in the absence of a direct assay showing membrane transport mediated by the glycine zipper motif, the claim of the role of this motif in membrane crossing is not well substantiated by the results. If the authors were to show that the single glycine mutants used in Fig. 5D, which are stable and have an intact DNase activity, behave as claimed in the final conclusion sentence (lines 279-283), then the conclusions will be better substantiated by the results. Ans: Thank you very much for suggesting this important experiment. We have now constructed the single G39L and G43L variants expressed together with Tdi1 in A. tumefaciens tdei mutant for both secretion and interbacterial competition assays (see description in lines 259-280 and Fig. 5). As shown in Figure 5, both G39L and G43L variants are expressed and secreted at similar or even higher levels than wild type Tde1 but have no detectable antibacterial activity against either E. coli or A. tumefaciens 1D1609. This result substantializes the role of this glycine zipper motif in translocation.

Minor comments:

• Line 93: I am not sure that Ntox15 should still be referred to as a "novel" domain.

Ans: despite the evidence of this domain as DNase, the name of Ntox15 is used. We think to keep this nomenclatture as it will be easier to be ditinquished from other nuclease or toxin domain.

• Line 105: The section's heading does not actually describe its content. The results here only show toxicity upon over-expression of the effector or its mutant forms in E. coli. Therefore, this cannot be referred to as a "prey cell" since the effector was not transported into it during competition. Moreover, the results in Fig. 5A do not support DNase-independent toxicity during competition. Ans: The heading is changed to “Tde1 exhibits DNase-independent growth inhibition in E. coli” (line 115).

• Please consider making all of the symbols in the growth assays semi-transparent. It is impossible to discern between the different, overlapping curves. Ans: The growth curve results are improved by changing line colors and reducing size bars (Fig. 1B, 1C; Fig. 2A, 2D)

• Please consider making the size bars in all microscopy images more pronounced. They are barely visible in their current form. Also, it would be better to show images of the same magnification/zoom for the different samples, since the current presentation shows cells from different samples at different sizes, and it can be confusing to the readers. Ans: Amended (Fig. 2C; Fig. 4).

• In Fig. 1B and in Fig. 2A the authors show that expression of Tde1(M) in cells is toxic, yet in Fig. 2D they see no toxicity. Can the authors please comment on this discrepancy? Ans: Fig. 2D showed the viability of E. coli cells after Tde1 variants were induced for 1 hr before ONPG uptake assay to indicate the increased membrane permeability is not due to cell death. In Fig. 1B, the growth inhibition of Tde1(M) is also not evident at 1 hr. So, the results are consistent.

• I am not convinced that the assay in Fig. 2E can be used to determine bacteriostatic/bacteriolytic effect. It is not clear how such a distinction can be made from OD measurements, since an increase in OD can result from the entire population growing after removal of the stressor, or just part of the population that did not lyse/die. To make such a claim, the authors can spot bacteria on repressing media at different timepoints after protein induction, and then determine CFU.

Ans: The increased OD600 value during recovery could be caused by either resumed cell division or cell elongation. Based on the newly added growth inhibition assay of all Tde1 variants which we showed nice correlation between CFU counting and OD600value (Fig. 2A, S2) and no increased cell size/length of E. coli cells expressing N-Tde1 or Tde1(M), we think the recovered OD600 value is supportive of N-Tde1 or Tde1(M) exhibiting bacteriostatic toxicity. In addition to that, our interbacterial competition data showed that Tde1(M)-Tdi1 which is still having intact glycine zipper doesn’t show significant detectable killing, supporting the bacteriostatic function of Tde1 glycine zippers. In fact, we performed this experiment based on Mariano et al.(Nat. Commun. 2019 doi: 10.1038/s41467-019-13439-0), which showed the recovery of OD600 value after removal of inducer as the evidence that the Ssp6 toxin is not bacteriolytic.

• Fig. 3A: A control is missing. To verify that the N-terminal part of Tde1 is not promiscuously interacting with proteins, the authors should include a control sample testing its inability to precipitate a protein other than Tap1 in the same experiment. Ans: Our previous study has showed that Tde1 can co-immunprecipiate Tap1 but not a non-T6SS protein RpoA (Bondage et al., 2016 doi:10.1073/pnas.1600428113), indicating that Tde1 is not promiscuously interacting with proteins. Considering the tight biochemical interaction between Tap1 with N-Tde1 but not C-Tde1 that correlate with their ability for secretion upon loading onto VgrG1, N-Tde1 is unlikely to bind proteins non-specifically. This is also supported by the non-specific protein bands from cellular fractions recognized by anti-Tap1 are not co-immunoprecipitated by any of Tde1 variants (Fig. S3). We could repeat the experiments to include additional proteins as negative controls but we chose to use time for other more critical experiments during the limited revision time.

• Fig. 3B: the blots are very "dirty". It is not clear how the authors were able to determine expression and precipitation of some truncations (for example, C2-Tde1 in the E. coli IP panel looks like a background band found in other lanes too).

Ans: We agree that western blots of co-IP experiments in E. coli are not very clear due to the weak signals of some Tde1 variants and background. As pointed out by the reviewer 3, this result is not conclusive and rovide little additional information other than the co-IP results from A. tumefaciens. Because the interaction between Tde1 variants and Tap1 when expressed in E. coli are not physiologically relevant and not the main focus of this work, we have removed the E. coli co-IP results from this manuscript as suggested by the reviewer 3.

• Lines 222-225 (Fig. 4A): I can't see C-1-Tde1(M)-sfGFP in the cellular blot. All the bands in this lane look like background bands that are also present in all other lanes. Therefore, I am not sure how the conclusion regarding this truncation's ability to be secreted was reached. Ans: We agree that C1-Tde1(M)-sfGFP is barely detectable due to its weak signal overlapping with cross-reacted bands. Since several attemps to improve the western blot quality by changing antibody and pre-blocking with protein lysages of vector control strain did not produce convincing results for detection of C1-Tde1(M)-sfGFP, we have rephrased the description of this result as “However, C1-Tde1(M)-sfGFP protein signal could not be unambiguously determined in the cellular fraction due to the overlapping of its predicted protein band with cross-reacted proteins, and no corresponding C1-Tde1(M)-sfGFP band was detected in the extracellular fraction.” (line 234-237).

• Fig. 4A: the protein names above the lanes should include the sfGFP that is fused to them. Ans: Amended.

• It would be preferable to show quantitative competition assays with statistics rather than pictures of a plate showing a single competition result, if conclusions or observations on minor differences in toxicity are made (for example, line 253: "The killing activity of Δtdei(Tde1GLGL-Tdi1) was largely compromised"). Since the authors performed each competition assay more than once, these data should be available to them. Ans: Amended. We have repeated the interbacterial competition experiments including single G39L and G43L variants for multiple biological repeats (see detailed in legends of Fig. 5A, 5B). The quantitative data with statistical analysis were added, which show no statistical difference of any glycine zipper mutants as comapred to Tde1(M) or when expressed in the T6SS mutant. Thus, there are no detectable antibacterial activity of glycine zipper mutants against either E. coli or A. tumefaciens siblings.

• Fig. 5A: The author claim at the beginning of the manuscript (first results section heading: "Tde1 can cause DNase-independent growth inhibition of prey cells") that the N-terminal region of Tde1 is toxic on its own in the prey cell, yet in this competition assay Tdi1(M) shows no toxicity against the E. coli target cells. In the microscopy assay (Fig. 4B), it appears that a lot of Tdi1(M) enters the prey cell, since we can visualize it under the microscope. Can the authors clarify this discrepancy and explain why they do not expect to see target killing by this mutant even though they claimed it is toxic earlier? Ans: As describbed in earlier response, N-Tde1 amd Tde1(M) toxicity can exhibit toxicity by ectopic expression in E. coli. We mainly used this ectopic expression assay to dissect the region and motif contributing the toxicity. Compared to the interbacterial competiton process where Tde1(M) may only transiently permealze cytoplasmic membrane transiently as the final destination is cytoplasm where wild type Tde1 but not Tde1(M) exerts DNase toxicity. Thus, the toxicity of N-Tde1 and Tde1(M) can be only observed when the proteins are continuously produced in the cytoplasm. The role of N-Tde1, specifically the glycine zipper motifs, is to mediate Tde1 translocation across inner membrane, instead of exerting toxicity during the context of interbacterial competition.

• Fig. 5B: the GLGL mutant seems to have some residual toxicity, not dissimilar to what is shown in 5A. Why are these similar results interpreted differently (in 5A they are "largely compromised", while in 5B "killing activity... was not detectable")? Also, why was Tde(M)1-Tdi1 used in Fig. 5A but Tdi1(M) without the immunity gene used in Fig. 5B? Ans: As described above, to better quantify the interbacterial competition outcomes, we have repeated the interbacterial competition experiments and used Tde(M)1-Tdi1 instead of Tdi1(M) for at least three biological replicates. The quantitative data with statistical analysis were carried out to clarify this ambiguity (Fig. 5A, 5B).

• Fig. 5: Does the remaining third effector, Tae, not play a role in these competition assays? If, as shown in Fig. 5C, the entire T6SS is less active when a GLGL mutant is expressed, couldn't the different in toxicity shown in Figs. 5A-B be the result of lack of Tae secretion and toxicity?

Ans: As decribed above, Tae effector has little impact on interbacterial competition outcome. The quantatitive interbacterial competition results (Fig. 5A, 5B) also clarify the ambiguity because single G39L and G43L variants are expressed and secreted at similar or even higher levels than wild type Tde1 but have no detectable antibacterial activity against either E. coli or A. tumefaciens 1D1609.

• Lines 359-362: T6SS effectors that bind the inner Hcp tube were suggested to be only partially folded. Ans: Amended.

Reviewer #1 (Significance (Required)):

The concept of T6SS effectors providing their own mechanism of transport from the cytoplasm to the periplasm is very interesting. It will appeal to audience in a wide range of microbiology disciplines, including those interested in toxins, membrane transport, and even translational applications. A similar concept was recently proposed and demonstrated for a domain that is also found in T6SS effectors (Atanaskovic et al., mBio, 2022).

Expertise: I have been studying the different aspects of T6SS for the past decade.

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

This manuscript is focused on understanding how the Agrobacterium tumefaciens T6SS effector, Tde1, is translocated across the cell envelope of target cells and how this effector binds to the adapter Tap1. The authors show that GxxxG motifs in the N terminal region of Tde1 are required for delivery into the cytoplasm of target cells and permeabilising the cytoplasmic membrane. Given that these GxxxG motifs resemble glycine zipper structures that are found in proteins involved in membrane channel formation, the authors propose that these Tde1 motifs are involved in channel formation in the target cell. The authors also show that the N terminal region of Tde1 binds to Tap1 to facilitate loading onto the T6SS machinery but that the GxxxG motifs are not involved in this binding. Overall the manuscript was easy to read and followed a logical presentation of the findings. There are a few major comments that this reviewer has below - addressing these would allow the authors' claims to be more robustly supported. Ans: Thank you very much for the positive comments and valuable suggestion. We hope the revised manuscript including the new data and careful interpretation have substantialized the conclusions and proposed mechanisms.

Major comments:

1. Fig 1B: Why is this such a short growth experiment (5 hrs total with 2 hr pre and 3 hrs post induction)? Reporting on a growth experiment would normally be at least until the cells reach stationary phase but here the cells are still clearly in exponential phase. This reviewer would query what happens to growth rate in later exponential growth and into stationary phase? Is the toxic effect lessened in later stages of growth? Ans: We have indeed performed the growth curve analysis with longer time period. However, we noted that the growth at later time points are not always consistent and our interpretation is that the continuous expression of toxins may lead to the selection of mutants. Since the 3 or 4 hr time period already showed the toxicity phenotype, we have focused on this time frame for the growh experiments.

2. It is indeed surprising that C2-Tde1(WT) does not inhibit growth despite it having a functional DNase domain and being expressed in the cytoplasm. Did the authors confirm that this protein variant was expressed by Western blot or other means? This should be done to confirm that this variant is indeed not impacting upon growth instead of it not impacting growth simply because it is not being expressed.

Ans: Amended. All Tde1 variants including C2-Tde1 are expressed (data included in Fig S1)

1. The letters used to report significance are not clear to this reviewer. The authors say that "The significant differences were shown by the different letters (p value

For all fluorescence microscopy experiments how many fields of view were imaged for each biological replicate? Were the fields selected at random or was the field selection biased to what was present in the field before taking the image? The answers to all of these questions should be stated in the methods. Also the microscopy data presented in the manuscript is not quantitative. Quantification of the number of cells with PI vs Hoechst signal (in Fig 2C) and mcherry vs gfp signal (in Fig 4B) for all fields of view and for all biological replicates would be very informative and convince the reader that the authors have not just "cherry picked" the images they are showing in the manuscript. This could be performed manually or the authors could use the freely available image analysis program Fiji (https://imagej.net/software/fiji/) to perform these analysis in a semi-automated manner.

Ans: The number of images and experiments were now described in the figure legends and the quantititive data are included (Fig. 2C).

1. For the co-IP experiments in Fig 3 where interaction between HA tagged Tde1 and Tap1 is demonstrated the authors should also show that Tap1 does not interact with a different HA-tagged protein i.e. that the interaction is specific to Tde1 and not the HA motif. Ans: All Tde1 variants were tagged with HA. As shown in Fig. 3A, Tap1was not co-precipitated by C2-Tde1 and C1-Tde1(M), indicating that Tap1 specifically interacts with N-terminal region of Tde1.

For all Western blot images there should be at least 2 protein standard markers present in each individual blot - i.e. for Fig 3A and B the bottom panel showing Tap1 detection only has the 35 kDa marker, it should have at least one more marker in it. The same is true for other panels in Fig 2, 3 and 4. Having at least two molecular weight markers in a panel is now standard for most journals when presenting Western blot images. Ans: Amended. We have now included the full gel of western blot results in Fig. S3 and S5 of those shown in main figures.

For the competition assay serial dilution images in Fig 5A-B the images are a nice way to visually represent the experimental outcome but they should accompany graphs showing the competitive index of CFU/ml of the input prey and attacker vs the output prey and attacker for all biological replicates. This will convince the reader that the authors had equivalent amounts of the prey and the attacker going into the experiment and also that all attackers grew at the same rate and so were equally able to target the prey cell. This quantification could also provide more convincing out competition of ID1609 prey by C58 attacker (Fig 5B). Ans: Amended. As indicated above, we have repeated the interbacterial competition experiments for at leaset three biological replicates and show that quantitative data with statistical analysis (Fig. 5A, 5B).

Minor comments:

Line 40: should read "...demonstrate that the effector itself..." Ans: The sentence has been rephrased (line 40) .

Line 41: "...we propose..." instead of "...we proposed..." since present tense makes more sense for this statement.

Ans: Amended (line 42).

Line 51: "Each specialized protein secretion system" instead of "Each of...." Ans: Amended (line 52).

Line 76: "A glycine zipper structure..."

Ans: Amended (line 83).

Line 79: "For example..."

Ans: Amended (line 86).

Lines 96-100: The present tense should be used here as the current usage of past tense implies that this has been done in previous work and not in the current study - eg "we revealed", "we showed" would be better as "we reveal", "we show".

Ans: Thanks for the advice. We have made changes throughout the manuscript.

Fig 5B - The competition assay serial dilution images look a bit blurry, are there images the authors could use that are not blurry?

Ans: Amended. As indicated above, we now show quantitative data with statistical analysis (Fig. 5A, 5B).

Reviewer #2 (Significance (Required)):

This work is significant in as while there is a great deal known about how T6SS effectors cause toxicity there is less known about how these effectors are loaded onto the T6SS machinery and very little known about how T6SS effectors are able to translocate across the cytoplasmic membrane of target cells to reach a cellular component that is in the cytoplasm. This work would be of wide general interest to researchers in the T6SS field as well as those interested in bacterial secretion systems.

Reviewer expertise key words: Molecular microbiology, T6SS, interbacterial competition

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

EVIDENCE, REPRODUCIBILITY AND CLARITY

Summary:

In this work, Ali et al. demonstrate that the N-terminal GxxxG motif of the T6SS DNase effector Tde1 of Agrobacterium tumefaciens is required for interbacterial intoxication. Using a combination of cell viability, reporter, and microscopy assays, the authors demonstrate that over-expression of the N-terminus of Tde1 results in inner membrane permeability. Moreover, the authors show that both the interaction between Tde1 and its adaptor Tap1 as well as the T6SS-mediated secretion of Tde1 are dependent on the N-terminus of Tde1. Finally, using a combination of in vitro and in vivo experiments, the authors determine that the N-terminal GxxxG motif is essential to Tde1-dependent interbacterial killing by enabling effector entry into competing bacterial cells.

Major comments:

If N-tde1 is 1-97 aa, the predicted size is 9 kDa, but it shows up as ~17 kDa? Can the authors comment on this? Does N-tde1 or tde1 dimerize? Ans: The theoretical Mw of N-Tde1-HA is 10.64 KDa, which indeed migrated at higer position ~17 kDa. It is notable that full-length Tde1 with theoretical 29.5-kDa migrated slower in SDS-PAGE with a observed size ~36 kDa as observed previously (Ma et al., 2014 doi:10.1016/j.chom.2014.06.002). Similarly, the full-length HA-tagged Tde1(M) with theoretical 30.89 kDa migrated at a position ~38 kDa. Since the protein samples analyzed by SDS-PAGE including reducing agent, we cannot exclude the possibility that Tde1 or N-Tde1 may form dimer or oligomer that was disrupted by SDS-PAGE but it appears not forming dimer on SDS-PAGE.

I have many concerns with the data and conclusions drawn from the data in Fig. 3B. I recommend removing it since (1) the data are not accurately represented in the text and (2) it is difficult to ascertain whether biologically relevant conclusions can be drawn from what happens with Agrobacterium proteins in E. coli. Below is a summary of my concerns regarding this section: I disagree with the authors' statements in lines 191-198. Their pulldown with E. coli is not consistent with their pulldown in C58. In fact, given the expression problems of some of the constructs in E. coli, I believe the data shown in Fig. 3B is inconclusive. The amount of Tap1 that co-IP'ed with N-Tde1GLGL and Tde1(M) is very low even though the expression levels of N-Tde1GLGL and Tde1(M) were relatively strong. Therefore, I do not feel confident concluding that these proteins "interact". Secondly, Tde1(M)GLGL was not expressed in E. coli, so no conclusions can be drawn. Moreover, the C1 and C2 variants were also not expressed well, so I believe the authors' statement in line 191-192: "Similar to the results in A. tumefaciens, the N-Tde1 and Tde1(M) interacted with Tap1 but not the C-terminal variants", is unjustified. You cannot rule out that C1 and C2 do not interact with Tap1 because C1 and C2, like Tde1(M)GLGL, were not expressed well in E. coli. Ans: We agree with the reviewer that the E. coli co-IP result is not conclusive due to the low expression and instability of proteins mostly during the process of cell lysis and purification, and it provides little additional information other than data from co-IP in A. tumefaciens. Because the interaction between Tde1 variants and Tap1 when expressed in E. coli are not physiologically relevant and not the main focus of this work, we have removed the E. coli co-IP results from this manuscript.

Lines 211-214: It looks like C1-Tde1(M) inhibits T6SS secretion. I am aware that in Agrobacterium, it has been shown that effector loading is essential for secretion, but then why does the pTrc200 secrete Hcp? Also, in Fig. 4B, a strain expressing C1-Tde1(M) now secretes Hcp. Ans: Thanks for noting our previous finding that Tde loading is critical for secretion. Our data are indeed supportive of the effector loading in activating T6SS as only very low levels of Hcp secretion could be detected from the strain containing vector only or C1-Tde1(M). In our previous paper (Wu et al., 2020 https://doi.org/10.15252/embr.201947961), there is either little or no detection of Hcp secretions when effectors are not loaded, indicating that effector loading is important but not essential for Hcp secretion. Because overexpression of VgrG can also activate T6SS secretion in the absence of effector loading (Bondage et al., 2016 doi:10.1073/pnas.1600428113), we think the low level secretion under certain conditions could be caused by some cells with higher levels of VgrG protein concentration but more work is required to elucidate the underlying mechanisms.

Minor comments:

Fig. 2B could benefit from better labeling to indicate that most strains lack lacY. Also, why is BW25113 WT showing such a low OD420 if it has LacY? Or is WT without lacZ? Please clarify.

Ans: We apologize for not labeling clearly. The BW25113 strain lacks lacZ, therefore all the ∆lacY strains were complemented with a plasmid encoding lacZ (pYTA-lacZ). We have now added the labels to avoid confusion (Fig. 2B).

Reviewer #3 (Significance (Required)):

SIGNIFICANCE

It has been known for over a decade that T6SS effectors have both periplasmic and cytosolic targets (e.g., cell wall and DNA). However, it remains unclear (1) where within the target cell are T6SS effectors are delivered and (2) once delivered, how do effectors reach their intracellular target site. In this work, Ali et al. demonstrate that for Tde1, the N-terminal GxxxG motif is essential for Tde1 to reach its target (DNA). The authors identified Tde1 homologs in several bacteria, suggesting that this model may be relevant across a wide range of bacteria. Additional research is needed to (1) determine whether Tde1 is originally secreted into the periplasm and (2) understand how non-Tde1/non-GxxxG effectors reach their target site.

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

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this work, Ali et al. demonstrate that the N-terminal GxxxG motif of the T6SS DNase effector Tde1 of Agrobacterium tumefaciens is required for interbacterial intoxication. Using a combination of cell viability, reporter, and microscopy assays, the authors demonstrate that over-expression of the N-terminus of Tde1 results in inner membrane permeability. Moreover, the authors show that both the interaction between Tde1 and its adaptor Tap1 as well as the T6SS-mediated secretion of Tde1 are dependent on the N-terminus of Tde1. Finally, using a combination of in vitro and in vivo experiments, the authors determine that the N-terminal GxxxG motif is essential to Tde1-dependent interbacterial killing by enabling effector entry into competing bacterial cells.

Major comments:

If N-tde1 is 1-97 aa, the predicted size is 9 kDa, but it shows up as ~17 kDa? Can the authors comment on this? Does N-tde1 or tde1 dimerize?

I have many concerns with the data and conclusions drawn from the data in Fig. 3B. I recommend removing it since (1) the data are not accurately represented in the text and (2) it is difficult to ascertain whether biologically relevant conclusions can be drawn from what happens with Agrobacterium proteins in E. coli. Below is a summary of my concerns regarding this section: I disagree with the authors' statements in lines 191-198. Their pulldown with E. coli is not consistent with their pulldown in C58. In fact, given the expression problems of some of the constructs in E. coli, I believe the data shown in Fig. 3B is inconclusive. The amount of Tap1 that co-IP'ed with N-Tde1GLGL and Tde1(M) is very low even though the expression levels of N-Tde1GLGL and Tde1(M) were relatively strong. Therefore, I do not feel confident concluding that these proteins "interact". Secondly, Tde1(M)GLGL was not expressed in E. coli, so no conclusions can be drawn. Moreover, the C1 and C2 variants were also not expressed well, so I believe the authors' statement in line 191-192: "Similar to the results in A. tumefaciens, the N-Tde1 and Tde1(M) interacted with Tap1 but not the C-terminal variants", is unjustified. You cannot rule out that C1 and C2 do not interact with Tap1 because C1 and C2, like Tde1(M)GLGL, were not expressed well in E. coli.

Lines 211-214: It looks like C1-Tde1(M) inhibits T6SS secretion. I am aware that in Agrobacterium, it has been shown that effector loading is essential for secretion, but then why does the pTrc200 secrete Hcp? Also, in Fig. 4B, a strain expressing C1-Tde1(M) now secretes Hcp.

Minor comments:

Fig. 2B could benefit from better labeling to indicate that most strains lack lacY. Also, why is BW25113 WT showing such a low OD420 if it has LacY? Or is WT without lacZ? Please clarify.

Significance

It has been known for over a decade that T6SS effectors have both periplasmic and cytosolic targets (e.g., cell wall and DNA). However, it remains unclear (1) where within the target cell are T6SS effectors are delivered and (2) once delivered, how do effectors reach their intracellular target site. In this work, Ali et al. demonstrate that for Tde1, the N-terminal GxxxG motif is essential for Tde1 to reach its target (DNA). The authors identified Tde1 homologs in several bacteria, suggesting that this model may be relevant across a wide range of bacteria. Additional research is needed to (1) determine whether Tde1 is originally secreted into the periplasm and (2) understand how non-Tde1/non-GxxxG effectors reach their target site.

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

This manuscript is focused on understanding how the Agrobacterium tumefaciens T6SS effector, Tde1, is translocated across the cell envelope of target cells and how this effector binds to the adapter Tap1. The authors show that GxxxG motifs in the N terminal region of Tde1 are required for delivery into the cytoplasm of target cells and permeabilising the cytoplasmic membrane. Given that these GxxxG motifs resemble glycine zipper structures that are found in proteins involved in membrane channel formation, the authors propose that these Tde1 motifs are involved in channel formation in the target cell. The authors also show that the N terminal region of Tde1 binds to Tap1 to facilitate loading onto the T6SS machinery but that the GxxxG motifs are not involved in this binding. Overall the manuscript was easy to read and followed a logical presentation of the findings. There are a few major comments that this reviewer has below - addressing these would allow the authors' claims to be more robustly supported.

Major comments:

1. Fig 1B: Why is this such a short growth experiment (5 hrs total with 2 hr pre and 3 hrs post induction)? Reporting on a growth experiment would normally be at least until the cells reach stationary phase but here the cells are still clearly in exponential phase. This reviewer would query what happens to growth rate in later exponential growth and into stationary phase? Is the toxic effect lessened in later stages of growth?
2. It is indeed surprising that C2-Tde1(WT) does not inhibit growth despite it having a functional DNase domain and being expressed in the cytoplasm. Did the authors confirm that this protein variant was expressed by Western blot or other means? This should be done to confirm that this variant is indeed not impacting upon growth instead of it not impacting growth simply because it is not being expressed.
3. The letters used to report significance are not clear to this reviewer. The authors say that "The significant differences were shown by the different letters (p value <0.01)" and then have a, b, c etc next to lines of the growth experiments in Figure 1B, C and Fig 2A, B, E etc. Which comparisons have a p value <0.01? this is not clear.
4. For all fluorescence microscopy experiments how many fields of view were imaged for each biological replicate? Were the fields selected at random or was the field selection biased to what was present in the field before taking the image? The answers to all of these questions should be stated in the methods. Also the microscopy data presented in the manuscript is not quantitative. Quantification of the number of cells with PI vs Hoechst signal (in Fig 2C) and mcherry vs gfp signal (in Fig 4B) for all fields of view and for all biological replicates would be very informative and convince the reader that the authors have not just "cherry picked" the images they are showing in the manuscript. This could be performed manually or the authors could use the freely available image analysis program Fiji (https://imagej.net/software/fiji/) to perform these analysis in a semi-automated manner.
5. For the co-IP experiments in Fig 3 where interaction between HA tagged Tde1 and Tap1 is demonstrated the authors should also show that Tap1 does not interact with a different HA-tagged protein i.e. that the interaction is specific to Tde1 and not the HA motif.
6. For all Western blot images there should be at least 2 protein standard markers present in each individual blot - i.e. for Fig 3A and B the bottom panel showing Tap1 detection only has the 35 kDa marker, it should have at least one more marker in it. The same is true for other panels in Fig 2, 3 and 4. Having at least two molecular weight markers in a panel is now standard for most journals when presenting Western blot images.
7. For the competition assay serial dilution images in Fig 5A-B the images are a nice way to visually represent the experimental outcome but they should accompany graphs showing the competitive index of CFU/ml of the input prey and attacker vs the output prey and attacker for all biological replicates. This will convince the reader that the authors had equivalent amounts of the prey and the attacker going into the experiment and also that all attackers grew at the same rate and so were equally able to target the prey cell. This quantification could also provide more convincing out competition of ID1609 prey by C58 attacker (Fig 5B).

Minor comments:

Line 40: should read "...demonstrate that the effector itself..."

Line 41: "...we propose..." instead of "...we proposed..." since present tense makes more sense for this statement.

Line 51: "Each specialized protein secretion system" instead of "Each of...."

Line 76: "A glycine zipper structure..."

Line 79: "For example..."

Lines 96-100: The present tense should be used here as the current usage of past tense implies that this has been done in previous work and not in the current study - eg "we revealed", "we showed" would be better as "we reveal", "we show".

Fig 5B - The competition assay serial dilution images look a bit blurry, are there images the authors could use that are not blurry?

Significance

This work is significant in as while there is a great deal known about how T6SS effectors cause toxicity there is less known about how these effectors are loaded onto the T6SS machinery and very little known about how T6SS effectors are able to translocate across the cytoplasmic membrane of target cells to reach a cellular component that is in the cytoplasm. This work would be of wide general interest to researchers in the T6SS field as well as those interested in bacterial secretion systems.

Reviewer expertise key words: Molecular microbiology, T6SS, interbacterial competition

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

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript, Ali et al. propose that a glycine zipper motif located at the N-terminus of the Agrobacterium tumefaciens T6SS DNase effector, Tde1, can transport the toxin across the cytoplasmic membrane and into the cytoplasm, where its target is found. To support these claims, they perform a series of secretion, competition, toxicity, and fluorescence microscopy assays showing that a mutation in two glycine residues affects toxicity of the effector during competition and its ability to enter a target cell, but not its secretion through the T6SS or its binding to the adaptor protein Tap1. The concept brought forth in this study is quite interesting and important - the notion that T6SS effectors have domains that aid in their transport into the cytoplasm of the target cell. This is similar to a recent finding that a domain common to bacterial pyocins and T6SS effectors can mediate DNase toxin transport through the target cell's cytoplasmic membrane (Atanaskovic et al., mBio, 2022); the authors should mention and discuss this recent work. Nevertheless, it is my impression that the results do not fully support the conclusions and proposed mechanism, even though the general idea seems correct.

Major comments:

• An experiment that directly demonstrates the ability of the glycine zipper to mediate transport of a toxin across a membrane would greatly support and solidify the conclusions of this work. For example, showing the ability of a purified protein to enter spheroplasts or liposomes in a glycine zipper dependent fashion. Currently, the authors perform experiments that can only indirectly support the proposed function of the glycine zipper to enable the effector to cross the membrane, and as detailed below, some of these experiments are over-interpreted in my opinion.
• Lines 153-159: It is not clear how much these results are relevant to the activity of the glycine zipper motif during effector delivery by the T6SS. If I understand correctly, the described experiments are of over-expression of the proteins in the E. coli cytoplasm, where glycine zipper-dependent membrane permeability and toxicity are detected. However, one would expect that if the effector is to be transported from the periplasm to the cytoplasm during T6SS delivery, then the glycine zipper should function from the periplasmic face of the cytoplasmic membrane, and not from its cytoplasmic face, as is the case in these experiments. Is it possible that the observed toxicity and membrane permeability be the result of over-expression in the "wrong place"?
• Fig. 4B: This figure appears to be very important, and the authors base a large part of their main conclusion regarding the role of the glycine zipper in membrane crossing on it. However, some controls are missing and part of the results observed in the figure do not match their description in the text.
  • Lines 233-237 - While the authors state in the text that GFP and mCherry signals did not overlap in E. coli cells co-cultured with Agrobacterium cells expressing Tde1(M)-GLGL, I see many double-colored cells in this sample (bottom panels in Fig. 4B). Actually, all cells appear to have both green and blue colors, except for a few cells that are only green but that also seem to be dead judging by their ghostly appearance in the phase contrast channel.
  • How is it that all cells in the bottom panels are blue (indicating that they are E. coli target cells)? Shouldn't a large portion of the cells be Agrobacterium cells that should not be blue, since these are added at the beginning of the competition assay at a 10:1 ratio in their favor?
  • It is quite remarkable that so much GFP signal is transported into the E. coli target cells so that it is so clearly visible under the microscope. How do the authors know that the GFP signal overlapping with the mCherry is really inside the cell and not outside (for example, proteins secreted to the media that attach to the cell envelope)? Will the GFP signal remain if trypsin is added to the media before visualization under the microscope?
  • Can the authors quantify the ratio of E. coli cells that have overlapping green and blue colors over several experiments for each sample, to show that this phenomenon repeats and is statistically significant?
  • Can the authors explain why at least some of these E. coli cells should not be dead due to the toxicity mediated by the third effector of the Agrobacterium T6SS, Tae?
  • Why were the microscopy competitions performed differently than the regular competition assays? Why wasn't AK media used in these competitions? How active is the T6SS under these conditions compared to the AK media?
  • In the N-Tde1 sample, many Agrobacterium cells appear to have the GFP signal in foci rather than distributed throughout the cell (as it is in other samples), while the E. coli cells have a uniform and strong GFP signal. Can the authors comment on that?
  • It might be easier for readers to visualize this figure and see the signal distribution in the different cells if the authors show a zoomed in version in the main text, and provide the wide field images as a supplementary figure.
• Fig. 5C-D: The reduced expression and secretion of the GLGL mutant is considerable. How can the authors rule out that this reduction was the cause for the reduced observed toxicity of the mutant in 5A-B? Moreover, the results show that the GLGL double mutant is hampered in expression, secretion, and DNase activity, and it negatively affects overall T6SS activity. Since this mutant was used throughout the paper, and in the absence of a direct assay showing membrane transport mediated by the glycine zipper motif, the claim of the role of this motif in membrane crossing is not well substantiated by the results. If the authors were to show that the single glycine mutants used in Fig. 5D, which are stable and have an intact DNase activity, behave as claimed in the final conclusion sentence (lines 279-283), then the conclusions will be better substantiated by the results.

Minor comments:

• Line 93: I am not sure that Ntox15 should still be referred to as a "novel" domain.
• Line 105: The section's heading does not actually describe its content. The results here only show toxicity upon over-expression of the effector or its mutant forms in E. coli. Therefore, this cannot be referred to as a "prey cell" since the effector was not transported into it during competition. Moreover, the results in Fig. 5A do not support DNase-independent toxicity during competition.
• Please consider making all of the symbols in the growth assays semi-transparent. It is impossible to discern between the different, overlapping curves.
• Please consider making the size bars in all microscopy images more pronounced. They are barely visible in their current form. Also, it would be better to show images of the same magnification/zoom for the different samples, since the current presentation shows cells from different samples at different sizes, and it can be confusing to the readers.
• In Fig. 1B and in Fig. 2A the authors show that expression of Tde1(M) in cells is toxic, yet in Fig. 2D they see no toxicity. Can the authors please comment on this discrepancy?
• I am not convinced that the assay in Fig. 2E can be used to determine bacteriostatic/bacteriolytic effect. It is not clear how such a distinction can be made from OD measurements, since an increase in OD can result from the entire population growing after removal of the stressor, or just part of the population that did not lyse/die. To make such a claim, the authors can spot bacteria on repressing media at different timepoints after protein induction, and then determine CFU.
• Fig. 3A: A control is missing. To verify that the N-terminal part of Tde1 is not promiscuously interacting with proteins, the authors should include a control sample testing its inability to precipitate a protein other than Tap1 in the same experiment.
• Fig. 3B: the blots are very "dirty". It is not clear how the authors were able to determine expression and precipitation of some truncations (for example, C2-Tde1 in the E. coli IP panel looks like a background band found in other lanes too).
• Lines 222-225 (Fig. 4A): I can't see C-1-Tde1(M)-sfGFP in the cellular blot. All the bands in this lane look like background bands that are also present in all other lanes. Therefore, I am not sure how the conclusion regarding this truncation's ability to be secreted was reached.
• Fig. 4A: the protein names above the lanes should include the sfGFP that is fused to them.
• It would be preferable to show quantitative competition assays with statistics rather than pictures of a plate showing a single competition result, if conclusions or observations on minor differences in toxicity are made (for example, line 253: "The killing activity of Δtdei(Tde1GLGL-Tdi1) was largely compromised"). Since the authors performed each competition assay more than once, these data should be available to them.
• Fig. 5A: The author claim at the beginning of the manuscript (first results section heading: "Tde1 can cause DNase-independent growth inhibition of prey cells") that the N-terminal region of Tde1 is toxic on its own in the prey cell, yet in this competition assay Tdi1(M) shows no toxicity against the E. coli target cells. In the microscopy assay (Fig. 4B), it appears that a lot of Tdi1(M) enters the prey cell, since we can visualize it under the microscope. Can the authors clarify this discrepancy and explain why they do not expect to see target killing by this mutant even though they claimed it is toxic earlier?
• Fig. 5B: the GLGL mutant seems to have some residual toxicity, not dissimilar to what is shown in 5A. Why are these similar results interpreted differently (in 5A they are "largely compromised", while in 5B "killing activity... was not detectable")? Also, why was Tde(M)1-Tdi1 used in Fig. 5A but Tdi1(M) without the immunity gene used in Fig. 5B?
• Fig. 5: Does the remaining third effector, Tae, not play a role in these competition assays? If, as shown in Fig. 5C, the entire T6SS is less active when a GLGL mutant is expressed, couldn't the different in toxicity shown in Figs. 5A-B be the result of lack of Tae secretion and toxicity?
• Lines 359-362: T6SS effectors that bind the inner Hcp tube were suggested to be only partially folded.

Significance

The concept of T6SS effectors providing their own mechanism of transport from the cytoplasm to the periplasm is very interesting. It will appeal to audience in a wide range of microbiology disciplines, including those interested in toxins, membrane transport, and even translational applications. A similar concept was recently proposed and demonstrated for a domain that is also found in T6SS effectors (Atanaskovic et al., mBio, 2022).

Expertise: I have been studying the different aspects of T6SS for the past decade.

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Reply to the reviewers

Reviewer 1

Part of major comment 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.

The reviewer is specifically referring to our cell cycle analysis (as these are the only experiments where some of them only contain one replicate; e.g., figure 1D and figure 2E). In the coming months we will perform for each biological replicate two additional technical replicates to increase the number of measurements. Additionally, we will also perform three technical replicates for an additional GIMEN ATRX exon 2-10 clone and three technical replicates for two additional GIMEN ATRX exon 2-13 clones. Also, for SKNAS (Figure 2F) we will perform measurements for two more wildtype clones in triplicate. This will also increase the number of biological replicates where possible.

Reviewer 1

Part of major comment 1. Each data point should be indicated in bar graphs (for example in Figure 5, especially given the variability observed).

In figure 5 we now added the data points in the figures.

Reviewer 1

Major comment 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.

There are several other known isoforms of ATRX, namely around 250, around 200 and 150 kDa, we now mention them in the text (page 4) and in the main and supplementary figures we made the panels smaller (figure 1B and S3A), to remove all the non-important bands below the IFFs. A possible explanation for the faint full-length bands is that the mutant ATRX protein products are much stronger expressed and therefore during blot development the wild-type bands become faint. Previously, we already tested other ATRX antibodies, and they either showed similar patterns in bands (also many bands observed) or performed much worse.

Reviewer 1

Major comment 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.

We agree with the reviewer that the ATRX fractionation western blot have superior resolution over the stainings and therefore we decided to remove these stainings from the manuscript.

Reviewer 1

Major comment 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.

We agree with the reviewer that without zooming in in the word file it might be hard to detect the co-localizations for the telomere and PML images. To resolve this, we made zoom-ins for single cells for the merged panels (only for CHLA-90, SK-N-MM and AMC772T2 co-localization can be observed; many studies only use the TelO staining as telomeric dots are often exclusively observed in ALT lines, however sometimes false positive can be observed and therefore including PML reduces the rate of false positives). Additionally, we also performed southern blots, which confirmed the telomere and PML stainings.

Reviewer 1

Major comment 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.

To improve on these points raised by the reviewer, we will perform additional cell cycle analyses to get an adequate number of replicates and we will perform statistical analyses. We also added the cell cycle analysis of SKNAS (old figure S5A) to main figure 2 and we added the western blot for yH2A.X of SKNAS and NB139 (old figure S4C) to main figure 2. Regarding the cytoplasmic vs. nuclear distribution, these experiments will be added for the NB139 models. We won’t perform these experiments for the SKNAS models since they are ATRX knockout.

Reviewer 1

Minor comment 1. Some grammatical errors should be corrected throughout.

We re-read the entire manuscript and changed the grammatical mistakes that we could detect.

Reviewer 1

Minor comment 2. Supplementary Table 1 was mislabeled as "Supplementary Figure 1"

We corrected this.

Reviewer 1

Extra comment. 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.

We added a paragraph to the discussion addressing this (page 18), but unfortunately no domains are currently known for the region of exon 11-13. Our claim that the majority of the protein resides within the cytoplasma is supported by the paper Qadeer et al., 2019.

Reviewer 2

Minor comment 1. Can the authors generalize these observations to other cancers with ATRX mutations?

In our discussion, we already mentioned increased ribosome biogenesis in glioma tumors with nonsense mutations, but we have included an additional sentence about these observations after that sentence (page 18).

Reviewer 2

Minor comment 2. 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.

This is a nice suggestion but is beyond the scope of this study. Our manuscript already contains RNA sequencing data from neuroblastoma tumours (iTHER data), where we find decreased ribosome biogenesis for the two iTHER patients with an exon 2-10 deletion compared to ATRX wild-type neuroblastoma tumors. Nonsense mutations are rare in neuroblastoma, only ~20 patients with such a mutation have been reported in the literature, and our iTHER cohort does not contain any neuroblastoma tumors with ATRX nonsense mutations. More extensive analyses across tumor types might be difficult since many RNA-sequencing data sets only contain a few ATRX aberrant tumors (and combining distinct data sets is very challenging due to potential batch effects) and for the majority of the rare point mutations (nonsense and missense) and rare deletions no (RNA) sequencing data is available and therefore there will not be enough statistical power.

Reviewer 3

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?

This might indeed be confusing. In our material and methods section, we addressed this under header: RNA sequencing analysis. Here we mentioned: “For the GI-ME-N clones, we observe a batch effect in the wild-type clones. Therefore, we decided to compare the different GI-ME-N ATRX aberrant models only with their corresponding wild-type clones (generated by same person).” To make this more clear, we now mention this in the result section (page 9). Our GI-ME-N models were generated in two batches (each batch by a different person, while the harvest and work-up of the RNA samples was performed by the same person on the same day for all samples) and therefore we decided to send two wild-type clones belonging to one batch and two clones belonging to the other batch (wildtype clone 3 and 4 were created by the same person as the GI-ME-N ATRXΔ2-10, while clone 1 and 2 were created by the same person as all the other GI-ME-N models). In our PCA plot for the GI-ME-N models (Supplementary figure 8B) we observe separation between wildtype clones 1+2 and wildtype clones 3+4 especially on PCA1, which explains the largest proportion of the variance. This clearly indicated a batch effect and therefore we compared the GIMEN ATRX aberrant clones with the batch corresponding wild-type clones. To exclude that the difference in ribosome biogenesis gene signatures between the different models was due to the heterogeneity present in wildtype GI-ME-N cells we also conducted the RNA analysis and GSEA for the distinct isogenic GI-ME-N models using all four GI-ME-N wild-type clones. This GSEA also showed ribosome biogenesis among the enriched gene sets (again down in ATRXΔ2-10 and up in ATRXΔ2-13 and knock-out). Nevertheless, the batch effect could have influence on other terms or single genes and therefore we needed to correct for this in all our analyses. Lastly, we now included supplementary figure 15A in main figure 4F.

Reviewer 3

Major comments 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.

If we understand the reviewer correctly, the reviewer wants us to determine the differentially expressed genes (DEGs) by comparing wildtype versus the distinct ATRX mutant. This is what we already performed as DEGs are determined by comparing WT versus the distinct ATRX mutant. To make this clearer we included a new figure explaining how DEGs are determined in figure 3. Similarly, if we understand the reviewer correctly, the reviewer suggests us to perform gene set enrichment analysis (GSEA) by comparing the WT versus the different mutants, this we already did as GSEA is performed on the stat values (a value that both represents the fold change and significance and is advised for GSEA. These stat values are acquired by performing DEG analysis on wildtype versus mutant). An overview of how we used the DEG lists for GSEA is shown in figure 4A. For the last comment, we added a new sentence explaining why we compared two different cell lines for GO term analysis (page 11) in Figures 4B-E. This we specifically did because of the different genetic background, as we are only interested in DEGs that always change in ATRX aberrant models irrespective of their (epi)genetic background. The changes in expression of those overlapping genes are more likely the direct result of the ATRX aberrations.

Reviewer 3

Major comments 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.

This is an interesting suggestion for a follow-up study but would take too much time to perform within the scope of this revision. Additionally, it would be of limited added value to the patients as only two patients with an ATRXΔ2-13 have been reported world-wide. Lastly, the most commonly used RNA polymerase I inhibitor Pidnarulex was recently discovered to inhibit topoisomerase 2B (TOP2B) instead of RNA polymerase 1 (DOI:10.1038/s41467-021-26640-x).

Reviewer 3

Major comment 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.

These comments were also postulated by reviewer 1. We will increase the number of measurements for our FACS experiments and include the statistical analysis (for more detail see the response to the comments of reviewer 1).

Reviewer 3

Major comments 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.

Originally, we attempted to also create the ATRXΔ2-13 model in the NB139 cell line (we screened more than 70 clones, which were all wild-type), however generating such large deletion is extremely difficult as the efficiency is very low (several 100 kbs have to be removed). It would take too much time to generate such clones for the revision. Additionally, as mentioned above this ATRX aberration is less relevant to patients as it is very rare. However, the reviewer does have a good point about this limitation and therefore we have included a part in the discussion regarding this limitation (page 18, included in the new paragraph).

Reviewer 3

Part of major comment 6. Statistical analyses is missing from almost every Figure.

As mentioned above we will include the missing statistical analyses on the cell cycle analysis. Due to this comment, we noticed that we forgot to include the text about the statistical analyses in the legend of figure 5 (the p-values and statistics were mentioned in the result section), which we now changed in this revision.

Reviewer 3

Part of major comment 6. Statistical analyses is missing from almost every Figure.

This was also mentioned by reviewer 1. For the FACS measurements, we will first perform additional experiments before we add the statistical analysis. For figure 5 the p-values and statistics were mentioned in the result section, but we now also added this information to the figure legend.

Reviewer 3

Minor comment 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.

We changed these images and improved the labeling text. Additionally, we show a zoom-in for the merged figures to increase the interpretability. We decided not to perform confocal microscopy as we also performed telomere southern blots which are in agreements with our microscopy pictures.

Reviewer 3

Minor comment 2. Please put the appropriate color symbol in supplementary Figure 12A. Currently, the color symbol in the Figure panel does not match the Figure.

We understand that the current labeling of the ATRX status using circles might have led to confusion to the reviewer, therefore we changed the depiction of the ATRX status and also changed the order of the two legends.

Reviewer 3

Minor comments 3. The WT GI-ME-N clone should be consistent in all supplementary western blots.

The wild-types samples are only included in the western blots as a positive control and as reference to compare the mutant with. Re-performing all those western blots with the samen WT GI-M-EN would not lead to any changes in the conclusions. Therefore, we think it is not of added value to repeat these western blots.

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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.

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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.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

The 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.

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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.

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

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Referee #3

Evidence, reproducibility and clarity

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.

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.

__

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

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

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

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

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.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary

In this 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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Learn more at Review Commons

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.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

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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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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