Dear Mick:

Thank you very much for submitting your manuscript "Organising the cell cycle in the absence of transcriptional control: Dynamic phosphorylation co-ordinates the Trypanosoma brucei cell cycle post-transcriptionally" (PPATHOGENS-D-19-01221) for review by PLOS Pathogens. Your manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important topic but identified some aspects of the manuscript that should be improved.

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Sincerely,

Jeremy C. Mottram

Guest Editor

PLOS Pathogens

David Horn

Section Editor

PLOS Pathogens

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Grant McFadden

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-2556-3526

***********************

The referees find the manuscript to be interesting, of a very high standard and likely to be a valuable resource to the community. As you can see, the referees have raised several concerns. No further experimental work is required and no sections need to be removed, however, but it would be helpful to include some comparison with previously published proteomics datasets (including your own) and to address the issue of "thresholds". Reference to the data in Saldivia et al. (2019) bioRxiv 10.1101/616417 could provide the experimental validation for the role of phosphorylation in kinetochore function, which has been requested by one of the referees.

Reviewer's Responses to Questions

Part I - Summary

Please use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.

Reviewer #1: Benz and Urbaniak present quantitative proteomics and phosphoproteomics on procyclic Trypanosoma brucei cells synchronised by elutriation. They follow ~3600 proteins and ~6000 phosphosites through the full cell cycle, showing that phosphorylation changes are more numerous and of greater magnitude than protein level changes (or mRNA by comparison to previous work). Ordering the profiles according to time stage of maximum abundance, they describe the burst of phosphosites peaking during entry into S and M phases in particular and define 29 and 30 clusters of protein and phosphosite behaviours, respectively, some of which are associated with different gene ontologies. 2 phosphosite clusters with high-G2/M profiles are significantly enriched in likely CDK sites, while possible PLK sites are enriched in one of these and another which peaks during S phase.

The authors describe the detected protein/phosphosite changes occurring on particular sets of proteins: protein kinases, kinetochore proteins, RNA-binding proteins and components of the translation machinery. Based on some of these observations, they knockdown a protein with putative role in translation initiation (TbDED1.2) and show reduction in cells with DNA content expected for S and G2/M, and accumulation of cells with less than 2C content, concluding that might be due to this protein regulating transcripts in a phospho-dependent manner.

Finally, they describe a class of CCR proteins/phosphosites in trypanosomes, including previously described proteins associated with cycling transcripts, that areunited by the presence of a PCP1 domain. Analysis of these proteins recapitulated profiles from the proteomics data, but produced no defect on depletion. Immunopurification of 2 of them (CSBPII-33 and -45) did not isolate the same complex as described in Crithidia, but showed a possible interaction with translation machinery.

How trypanosomes control the cell cycle is of clear scientific importance – especially given the divergent nature of many of the systems involved and that changes in mRNA (and even protein levels) probably don't contribute to the same degree as in other systems. The proteomics and phosphoproteomics data here appear to be of very high quality, and the extent and analysis of these data make a really substantial contribution to mapping and understanding this system. There are previous (non-cell cycle) phosphoproteomes published for trypanosomes [Nett et al. (2009) Mol Cell Proteomics, Urbaniak et al. (2013) J Proteome Res] and also a cell cycle proteome [Crozier et al. (2018) Mol Cell Proteomics], but these data are clearly distinct in scale and bringing together protein level changes with the first quantitation of phosphosite changes across the cell cycle makes this a dataset of considerable interest in an important model parasite that I can see gathering a lot of citations.

After the presentation of the proteomic data, however, I found the analyses of the specific protein sets increasingly descriptive and 'bitty'. The analysis of cell cycle regulation of kinases (and cyclins) was reasonably compelling, particularly as there was evidence of association of specific kinase motifs with specific cell cycle profiles (is there evidence of other motifs associated with unknown kinases that can be seen from these clusters?). The analysis of the kinetochore also seemed to add to the understanding of the system – although the analysis needs to incorporate recent data on KKT2 phosphorylation by CLK1/KKT10 deposited on bioRxiv (Saldivia et al. (2019) bioRxiv 10.1101/616417). However, data on RNA binding proteins and much of that on translation machinery was purely descriptive and should in my opinion be removed or moved to supplement. The data on TbDED1.2 added little to the manuscript and the conclusions are very speculative. The discovery of the association of PCP1 and cell cycle regulation is an interesting finding, although the lack of an observable defect on knockdown obviously leaves something of a mechanistic gap.

I realise that the attempt here is to add 'new biology' to avoid the proteomic data being dismissed as descriptive or too specialist. In my opinion, the quantitative proteomics here are of sufficient importance for understanding trypanosome (and leishmania) growth, that they cannot be dismissed in this way, and that some of the add-ons are actually detracting from these findings. I recommend that if these proteomics data are bolstered (see below) and some of the more speculative/descriptive elements later removed, this work is suitable for publication in PLoS Pathogens and likely to attract a good readership.

Reviewer #2: In the manuscript "Organising the cell cycle in the absence of transcriptional control: Dynamic phosphorylation co-ordinates the Trypanosoma brucei cell cycle post-transcriptionally", Benz & Urbaniak systematically study the contribution of protein phosphorylation to cell cycle regulation in African trypanosomes. Several studies that addressed changes in protein or transcription profiles during cell cycle progression exist already but to my knowledge, this is the first approach to evaluate the function of dynamic phosphorylation during this process. Synchronisation of the cells by elutriation is a well-established and suitable method for this purpose. The authors identified more than 900 cell cycle-regulated phosphorylation sites, some of them in proteins that have not been associated with cell cycle control, yet. Although this study is mainly descriptive, this set of data is without doubt a valuable resource for the community.

The manuscript is well written and concise. The design of the study is in general well thought though and performed with high experimental quality and adequate sample size. Since I am not a MS expert, I cannot comment on technical details or suitability of bioinformatic methods.

There is nothing wrong with the descriptive part of the study (clustering of the phosphorylation site, cell cycle regulation of kinases and dynamic phosphorylation of specific protein groups). However, an experiment to confirm that any the novel CCR phosphorylation sites are indeed involved in cell cycle control would improve the manuscript substantially (see below).

Reviewer #3: The manuscript describes:

1. An analysis of changes in protein abundance over a cell cycle in elutriation synchronised cells.

2. The indentification of protein phosphorylation sites that vary over the cell cycle

3. Some tests of the observations (or hypotheses drawn from the measurements) using reverse genetics.

The work is meticulous and the measurements described with clarity. The validity of the measurements are discussed.

I enjoyed reading the manuscript and it an exemplary mass spec analysis of an very interesting biological phenomenon.

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Part II – Major Issues: Key Experiments Required for Acceptance

Please use this section to detail the key new experiments or modifications of existing experiments that should be absolutely required to validate study conclusions.

Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".

Reviewer #1: The thresholds for being ‘cell cycle regulated’ appear arbitrary (3-fold for phosphosites and 1.5-fold for protein). To a certain extent these will always be the case, but a substantial portion of the conclusions in the manuscript are based on comparisons of either protein or phosphosites that are ‘CCR’ (either overall numbers or specific proteins) – or comparison to other sets which use different thresholds (e.g. Crozier et al. (2018) Mol Cell Proteomics wherein a 1.3-fold threshold is used). As such, some rationale should be provided as to why these thresholds have been used. Or at least some exploration of how the conclusions might be affected by implementation of alternative thresholds. It would also be very helpful to see some representation of the distribution of the data with respect to the threshold somewhere in the manuscript rather than only presenting already filtered data.

The proteomics data add to/supersede three related (phospho)proteomic datasets [Nett et al. (2009) Mol Cell Proteomics, Urbaniak et al. (2013) J Proteome Res, Crozier et al. (2018) Mol Cell Proteomics]. Some comparison to Crozier et al. is made in the discussion, and it is not necessarily easy to make direct comparisons due to difference in methodology, but it would be extremely useful to the reader to see more information on how these data relate to previous. Which of the phosphosites seen in the non-cell cycle data are also seen in these data? The number of observed sites has increased, but what is the overlap? Is there any link between CCR phosphosites and those changing between lifecycle stage? As mentioned, there is little overlap between the CCR protein set here and in Crozier et al., but how much of this might be thresholding effects? Is their correlation in CCR? It is not necessary to address all these question, but some consideration would greatly aid the reader in navigating between the sets.

The profiles of phosphosite/proteins are really useful in understanding the cell cycle and being able to conceptualise them as archetypes is in my opinion a substantial contribution – as exemplified by the ability to see associate specific phosphosite motifs to particular archetypes (although this is not explored further). A small number of these profile clusters are shown in Fig.4, but I recommend that they are all liberated from the supplement and brought to the main manuscript. In addition, there is clearly information in the relationships between the clusters (for example in the relative similarity of 855 and 874, and their dissimilarity to 862/884) and between protein-level and phosphosite clusters. It would be really useful to see the heirarchical structure of the similarities between the clusters, and especially in the similarities between phosphosite behaviours and protein profiles. How do the clusters relate to the ordering based on peak time seen in Fig 2?

In my opinion, the sections “Dynamic phosphorylation of RNA binding proteins” and “Dynamic phosphorylation of translation machinery” should be removed or, if not removed, substantially augmented to produce more tangible mechanistic insight. If the data are kept, some measure of significance for the observed changes must be included. Given that classification of DNA content into G1, S, G2/M by flow cytometry is highly dependent on the sample preparation, gating, etc. histograms of the raw data should also be included in supplement. Surely the distinction between 'failure of replication or stalled mitosis' can be made from the flow cytometry data?

Non-essentiality cannot be inferred from knockdown [line 471/2] as it is unclear how much protein would be required to fulfil any (unknown) function.

CSBPII-33 shows clear co-IP with PCD4, but other than that it is unclear which interactions are genuine and which noise. Interaction with ribosomal proteins is consistent with the proposed role in translation, but these are also very high abundance proteins that commonly contaminate IP. Since neither the anticipated interaction between CSBPII-33 and -45 or clear binding to PABP2 is seen, more data will be needed to either discriminate more clear signal/noise or show that tagging has not disrupted function (an alternative tag at the C-terminus?).

Reviewer #2: line123: the authors claim that cells "maintain synchrony into subsequent cell cycles". This is a little misleading. To my knowledge, cells lose synchrony quickly within a few cell cycles. The authors should show the data or state this more precisely.

line 287 As mentioned above, ONE experiment to confirm that the identified CCR phosphorylation sites are indeed involved in cell cycle control would improve the manuscript substantially. For example, would over expression of MAPK6 or RCK mutants without their CCR phosphorylation sites show a dominant negative (cytokinesis) phenotype?

line 302: A similar experiments is possible with the identified kinetochore protein phosphorylation sites, which may play a role in kinetochore assembly (or maybe not).

Reviewer #3: There are no major issues

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Part III – Minor Issues: Editorial and Data Presentation Modifications

Please use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.

Reviewer #1: The naming of the clusters is unhelpful - e.g. 30 clusters for phosphosite profiles with ids in the range 735-886. I assume these are in some way linked to something “under the hood” of the analysis, but it doesn't aid in comprehension. Could the authors consider changing? It would be really useful to have some obvious distinction in the naming between clusters at the protein level and those at the phosphosite.

Given the apparent usefulness of the clusters, why are they only used in the section on protein kinases? To which clusters do the kinetochore kinases belong? Do most kinetochore proteins belong to the same cluster?

What are the 5 sites with >100-fold change excluded from Fig 2 and where in the cycle do they fall?

Currently, the PRIDE ID does not resolve to any data. I’m assuming this is only that the data are embargoed, but would need resolving before publication.

There is a weird distinction between “cell cycle” (e.g. RNA binding) and “unrelated” (e.g. microtubule organizing center) in the GO terms in Fig.4. How is this distinction being made?

Reviewer #2: Fig1B I assume that the percentage of cells in a specific cell cycle is based on the gates set in Fig1A. Has that been confirmed by nuclei/kinetoplast configuration?

line 173/174: It is an over-interpretation to claim that changes in phosphorylation are more important that changes in in protein abundance (based on on the profiles) without functional data

Reviewer #3: Line 121: ‘high quality population’ - what does this mean? Give a percentage of cells that fitted within the defined parameters for G1

Line 241: a few words to explain T loop

Line 248: how might the lack of the 3 phosphorylation sites contribute to the metaphase arrest in caused by the destruction box deleted cyclin

Line 262: reference needed

Line 403: delete ‘are’

Line 452: why is a punctuate pattern in the cytoplasm consistent with mRNA binding?

Line 550: the discussion of the differences between datasets from different labs is very useful. These dirty little secrets need more open discussion. Perhaps the authors could give the overlap between the phosphorylation sites detected here and their earlier paper to provide information on contributors to variation.

Very minor points

The authors often use ‘results’ when they mean measurements or observations and ‘Our data show’ when they mean their interpretation of the data

Finally, and to be very pedantic, data is plural.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Dear Mick,

We are pleased to inform that your manuscript, "Organising the cell cycle in the absence of transcriptional control: Dynamic phosphorylation co-ordinates the Trypanosoma brucei cell cycle post-transcriptionally", has been editorially accepted for publication at PLOS Pathogens. 

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Best regards,

Jeremy C. Mottram

Guest Editor

PLOS Pathogens

David Horn

Section Editor

PLOS Pathogens

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Grant McFadden

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-2556-3526

***********************************************************

One reviewer made the following comment: Non-essentiality cannot be inferred from knockdown [line 471/2] as it is unclear how much protein

would be required to fulfil any (unknown) function.

Authors' Response: We have demonstrated that knockdown of the five PSP1-C domain containing proteins

using inducible RNAi ablates the tagged proteins to levels undetectable by western blotting (Fig 9

& S7), yet do not observe any significant growth defect or cell cycle defect for 7 days (~21 cell

cycles). Therefore, we feel it is justified to infer that the individual proteins are not essential for

successful completion of the cell cycle.

The editors feel that the reviewer is correct and the authors have not addressed this adequately. A simple solution for the authors would be to insert "likely" not essential, as non-essentality cannot be proven by RNAi. This could be done by submitting a revised manuscript, or at the proof stage.

Reviewer Comments (if any, and for reference):