Potential Involvement of Protein Phosphatase PPP2CA on Protein Synthesis and Cell Cycle During SARS-CoV-2 Infection: A Meta-Analysis Investigation

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

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Author Response

Thank you for accepting our manuscript for publication.

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

In this Otvos et al. analyzed published mass spectrometry data utilizing different publicly-available data analysis tools to examine possible roles of protein phosphatases in relation to SARS-CoV-2 infection and pathogenesis . Interactome analyses revealed that several cellular proteins interacting with viral proteins also interacted with protein phosphatase 2 catalytic subunit alpha (PP2CA) and Phosphatase And Tensin Homolog (PTEN). Based on their analysis, the authors appear to speculate that cell division is important during SARS-CoV-2 infection. Some of the analyses in manuscript are flawed and some of the information is erroneous. Furthermore, the manuscript did not appear to be thoroughly proofread and would benefit from better organization. Some (not all) examples of problems with this manuscript are as follows:

 

Lines 39-40: “COVID-19, with its higher potential for viral transmission and high lethality world-wide, has no effective treatment.”  Higher than what? There are antiviral drugs against SARS-CoV-2.

 

No need to cite Pubmed search terms (l43).

 

 “The SARS-CoV-2 virus belongs to genus betacoronavirus (β-COVs) and consists of a single stranded positive sense RNA molecule surrounded by an envelope.” (line 44) This description of the virion is oversimplistic.

 

References 13, 14, 36 and 45 are cited as preprints but have been published.

 

Lines 63-67: “Multi-level analysis of transcripts, protein and post-translational modifications in cultured cells, human biopsies and bodily fluids revealed a strong correlation between the extent of immune response activation and disease severity [6–15].” Severe disease is actually associated with an impaired T-cell response.  

 

For figures in which mass spec data was analyzed, there are no data set references in legends nor is there information for data sets and repositories in the Materials and Methods.

 

Combining data from different studies (e.g., Fig. 2) especially when there is no consistency is not useful. Whether certain families of phosphatase in these plots are more or less represented is probably irrelevant. Regarding conclusions about the cell cycle, phosphatases in the urine are probably irrelevant.

 

Log10(P-value) should be -Log10(P-value) in Table S2 given that there are no negative values.

 

References often appear to be out of place or missing. For example, reference 4 and 11 and some others on line 96 do not appear to be relevant to SARS-CoV-2 protein interaction data for protein phosphatases. The reference on line 200 is for influenza virus and not SARS-CoV-2 (this is a major error).

 

It is unclear why portions of text are italicized.

 

Lines 349-351: “One of the key functions of PTEN is to regulate mitosis, along with proteins involved in the anaphase promoting complex-dependent catabolic process during infection.” The opposite is true: APC promotes degradation of PTEN; PTEN mainly targets phosphoinositides like phosphatidylinositol-3,4,5-trisphosphate to restrict cell growth and survival.

 

The PP2AB56 complex is not well described (functions and components); Craney A et al. Proc Natl Acad Sci U S A. 2016 Feb 9;113(6):1540-5 is not cited.

 

As this was a bioinformatics-only-based study, data from Brewer et al. iScience. 2024 Feb 19;27(3):109302 should have been analyzed/included.

Author Response

Comment 1: In this Otvos et al. analyzed published mass spectrometry data utilizing different publicly-available data analysis tools to examine possible roles of protein phosphatases in relation to SARS-CoV-2 infection and pathogenesis . Interactome analyses revealed that several cellular proteins interacting with viral proteins also interacted with protein phosphatase 2 catalytic subunit alpha (PP2CA) and Phosphatase And Tensin Homolog (PTEN). Based on their analysis, the authors appear to speculate that cell division is important during SARS-CoV-2 infection. Some of the analyses in manuscript are flawed and some of the information is erroneous. Furthermore, the manuscript did not appear to be thoroughly proofread and would benefit from better organization. Some (not all) examples of problems with this manuscript are as follows:

Thank you for reviewing our manuscript. Many papers have been published in the “multi-omics” field (genomics, epigenomics, transcriptomics, proteomics, phosphoproteome, metabolomic), utilizing multiple high-throughput screening methods that play an important role in studying human diseases. These analysis generate a huge amount of data, making it challenging to understand the specific involvement of each identified molecule, which requires further exploration. Typically, these data are made available in public repositories. Our meta analysis paper aims to understand the potential involvement of protein phosphatases during SARS-CoV-2 infection. Protein phosphatases are essential regulators of cell signaling in both physiological and pathological contexts, interacting with different proteins and contributing to processes such as cell division, cell survival and more. Numerous studies have demonstrated the role of specific protein phosphatases in controlling cell division and other functions during viral infections, as referenced in this paper. Based on our analysis and the existing literature, we emphasize that protein phosphatases play a significant role in regulating various processes during SARS-CoV-2 infection, as evidenced by the protein-protein interaction network, levels of protein phosphatase expression, and the differential phosphorylation of many proteins. The goal of this paper was not only to highlight all potential signaling pathways involving protein phosphatases, but also to identify a key pathway based on our analyzed data and the literature. We believe this paper provides valuable insights and a foundation for designing specific experiments to address the findings presented here.

Comment 2: Lines 39-40: “COVID-19, with its higher potential for viral transmission and high lethality world-wide, has no effective treatment.”  Higher than what? There are antiviral drugs against SARS-CoV-2.

“Higher” means greater than something. Thank you for your attention. We have changed it to “high”.

We believe there may have been a misunderstanding regarding the term “no effective treatment”. The word “effective” does not means “nonexistent”. Furthermore, the FDA-authorized antivirals currently available, such as Paxlovid, Lagevrio and Remdesivir, can help reduce COVID-19 complications. However, these antivirals are recommended for patients with early or moderate symptoms and are not indicated for those with severe symptoms. Additionally, these treatments come at a high cost (USD $ 300 to $1000), making them inaccessible to a large portion of the population, especially in developing and low-income countries.

Comment 3: No need to cite Pubmed search terms (l43).

I removed Pubmed from the citation.

Comment 4: “The SARS-CoV-2 virus belongs to genus betacoronavirus (β-COVs) and consists of a single stranded positive sense RNA molecule surrounded by an envelope.” (line 44) This description of the virion is oversimplistic.

Our intention is to provide a brief introduction to the composition of the SARS-CoV-2 virus, highlighting the structural, non-structural and accessory proteins examined in our analysis.

Comment 5: References 13, 14, 36 and 45 are cited as preprints but have been published.

Thank you for your valuable observation. Both papers are in my reference software, but I mistakenly selected the preprint version. I have now updated the citations to refer to the published papers.

Comment 6: Lines 63-67: “Multi-level analysis of transcripts, protein and post-translational modifications in cultured cells, human biopsies and bodily fluids revealed a strong correlation between the extent of immune response activation and disease severity [6–15].” Severe disease is actually associated with an impaired T-cell response.  

Several immune-related pathways, including NF-kB, upstream receptors, members of theTRAF protein family, and cytokines, were enriched in the COVID-19 patients, especially in those with severe complications. These patients have been experiencing inflammatory storms, which we described as immune response activation (this can refer to dysregulation, damage, or an impaired immune response). However, these terms do not fully capture the changes. We have replaced the word “activation” with a more appropriate term “overactivation” (where inflammation is excessively stimulated, causing it to function abnormally).

Some examples:

Reference 7: “The severe COVID-19 patients showed ground-glass opacity in the lungs on Computed Tomography (CT) scanning (Fig. 1B). After treatment, the lung shadow disappeared and gradually recovered (Fig. 1B). Because the patient 4 (P4) had multiple metastases of colon cancer, only X-ray test was obtained (Figure S2). Interleukin-6 (IL-6) is an indicator of inflammatory storms.24 We found the level of IL-6 in mild patients was 4.73 ​± ​2.03 ​pg/mL (mean ​± ​standard deviation), while the expression level of IL-6 in severe patients was significantly higher than the normal standard (≤7.0 ​pg/mL) and drastically fluctuated during the infection, indicating that the stress response to viral infection in S-COVID patients was more severe (Fig. 1C and S3).”

Reference 5 in our manuscript: “We detected most of the molecules associated with the nuclear factor-κB (NF-κB) pathways, including the upstream proteins such as receptors (TLR4, CD40, and BAFF) and TRAF protein family members (TRAF2, TRAF3, and TRAF6), and downstream cytokines (interleukin-6 (IL-6), IL-8, TNFα, and IFNα, and ICAM1) or chemokines (CXCL12), finding them all upregulated in the COVID-19 lungs (Fig. 3). Among them, canonical NFκB signals (p65) and non-canonical signals (p52) were both found activated after SARS-CoV-2 infection (Fig. 3). Taken together, these results indicate that the dysfunction of biological processes and signal pathways of the protein group with close functional correlation may play an important role in the pathogenesis of COVID-19.”

Reference 6 in our manuscript: “In total, we characterized 58 distinct intraviral PPIs among 28 SARS-CoV-2 genes that are potentially involved in virus replication and can be expected to be important to immune evasion and viral pathogenesis (Figure 1A).”

“Notably, our AP-LC-MS/MS analysis uncovered additional host targets that are pertinent to inflammatory and innate immune responses (Figure 2) and may help contextualize the unique immune signature identified in SARS-CoV-2-infected cells.7,9 Key viral-cellular PPIs that may influence the pathogenesis of SARS-CoV-2 include interactions between nuclear factor-κB-repressing factor (NKRF) and nsp10, TBK1 and ORF6, TANK and ORF6, TRAF2 and ORF6, and KIT and ORF3a. NKRF is a transcriptional repressor and controls the induction of IL-8 to stimulate neutrophil chemotaxis and recruitment to the site of infection.40,41 Nsp10 may regulate IL-8 levels by targeting NKRF and shapes the unique immune signature in COVID-19. TBK1 plays critical roles for type I IFN and NF-κB pathways upon viral infection: it phosphorylates the key transcriptional factors, IFN regulatory factor 3 (IRF3) and IRF7, to activate IFN promoters38; it also forms a ternary complex with TANK and tumor necrosis factor (TNF) receptor-associated factor 2 (TRAF2) to facilitate NF-κB activation.42 ORF6 may interact with the TBK1-TANK-TRAF2 ternary complex to modulate both type I IFN and NF-κB pathways upon infection, facilitating SARS-CoV-2 to escape from host innate immune surveillance. The tyrosine-protein kinase KIT has been shown to facilitate the activation of signal transducers and activators of transcription (STATs),43 such as STAT1, STAT3, and STAT5, which are involved in signaling transduction upon IFN or IL-6 stimulation. ORF3a may regulate IFN or IL-6 signaling pathways by association with KIT.”

Comment 7: For figures in which mass spec data was analyzed, there are no data set references in legends nor is there information for data sets and repositories in the Materials and Methods.

 All datasets were extracted from the supplementary files of the cited references. The references were cited in the Methods and in each Results section. Additionally, all data analyzed from these references have been compiled into the supplementary files (Tables S1-S4) included in this paper.

Comment 8: Combining data from different studies (e.g., Fig. 2) especially when there is no consistency is not useful. Whether certain families of phosphatase in these plots are more or less represented is probably irrelevant. Regarding conclusions about the cell cycle, phosphatases in the urine are probably irrelevant.

Our analysis compiled data on protein phosphatases, including expression levels, protein-protein interaction and the phosphorylation levels of their substrates, from different cell models and samples from confirmed SARS-CoV-2 patients. Figure 2 presents data on protein phosphatase expression levels. Figure 3, 4 and 5 display data on protein-protein interactions involving protein phosphatases and the associated functional clusters. Finally, Figure 6 illustrates the phosphorylation levels of intermediate proteins (proteins that interact with both protein phosphatase and viral protein). These findings highlight the involvement of multiple proteins during SARS-CoV-2 infection, not only in cell models but also in patient samples. Additionally, we also highlight a specific signaling pathway involving one of the protein phosphatase (PPP2CA), based on our analysis and in the literature. We present a predict complex (PPP2CA-B56-CDC20) with its simulation results. We believe these analysis offers valuable insights for future research in the field.

Comment 9: Log10(P-value) should be -Log10(P-value) in Table S2 given that there are no negative values.

 Thank you for your comment. The data are correct. I have simply added the signal (-) into the parentheses (-)Log10(P-value) in Table S2 and Table S4.

Comment 10: References often appear to be out of place or missing. For example, reference 4 and 11 and some others on line 96 do not appear to be relevant to SARS-CoV-2 protein interaction data for protein phosphatases. The reference on line 200 is for influenza virus and not SARS-CoV-2 (this is a major error).

Reference 4 is an excellent review that describes structure of the SARS-CoV-2 virus. Since I included an introduction about the virus, it should be cited. Reference 11 was used in our analysis (see Table 1). The reference on line 200 was not update in the reference list when I refreshed the reference software used for this paper. Thank you for bringing this to my attention; I have now updated it.

Comment 11: It is unclear why portions of text are italicized.

This paper was reviewed by other referees when I first submitted it to Kinases and Phosphatases. Therefore, the instruction was to resubmit it as a new submission, highlighting the changes. I explained this in the response letter. Apologies if you did not have access to the response letter.

Comment 12: Lines 349-351: “One of the key functions of PTEN is to regulate mitosis, along with proteins involved in the anaphase promoting complex-dependent catabolic process during infection.” The opposite is true: APC promotes degradation of PTEN; PTEN mainly targets phosphoinositides like phosphatidylinositol-3,4,5-trisphosphate to restrict cell growth and survival.

Please see the text added to the paper:

One of the key functions of PTEN is to regulate mitosis, along with proteins involved in the anaphase promoting complex-dependent catabolic process [54]. On the other hand, dephosphorylated PTEN associated with chromatin is removed by APC/C during mitotic exit [55].

Comment 13: The PP2AB56 complex is not well described (functions and components); Craney A et al. Proc Natl Acad Sci U S A. 2016 Feb 9;113(6):1540-5 is not cited.

 Please see the revised text (in italic) for the complex, as suggested by the reviewer in the penultimate paragraph of the section “Phosphorylation level of intermediate proteins”:

CDC20 plays a crucial role in the mitotic checkpoint, regulating cell division through ubiquitination. The Ube2S (E2) protein extends the ubiquitin chain on CDC20, which is required for APC/C substrate degradation in a dependent-manner of phosphorylation [50,63,64].

In fact, the depletion of PPP2CA reduced the interaction of Ube2S with CDC20 and APC/C, thereby interfering with its activation. Thus, the interaction of PPP2CA with APC/C requires the presence of CDC20, Ube2S and kinetochore protein Knl1, which is important for the proper binding of the PPP2CA-B56-CDC20-Ube-2S-APC/C complex at the kinetochore [50].

Comment 14: As this was a bioinformatics-only-based study, data from Brewer et al. iScience. 2024 Feb 19;27(3):109302 should have been analyzed/included.

I understand that the reviewer suggests adding references that include data on the substrates identified for PPP2CA through various approaches. In fact, by using the Biogrid program and searching for PPP2CA in Homo Sapiens, we can find many papers detailing the methodologies used to identify these substrates. The paper proposed by the reviewer is a more recent one. In our manuscript our goal was to identify these substrates specifically during SARS-CoV-2 infection. Therefore, we have decided to add the following sentence to the fourth paragraph of the section “Protein phosphatases interact with intermediate proteins”:

Interesting, many of these intermediate proteins have been also identified to interact with the human proteins phosphatases listed in the Biogrid database.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

The revised manuscript should show ALL modifications made to it.

A meta-analysis should be comprehensive rather than selecting only a subset of data sets; such selection can introduce bias. Not analyzed for in this study:

Brewer et al. iScience. 2024 Feb 19;27(3):109302

Zhou et al. Nature Biotechnology volume 41, pages 128–139 (2023

While expensive, the current antivirals are effect. The text should be corrected. The authors are welcome to explain why additional treatments are needed.

The viral RNA genome within the envelope of the virion is bound by the nucleocapsid protein to form a ribonucleoprotein complex; this is a fundamental to the composition of the virion.

The authors must explain what is meant by overactivation of the immune system (e.g., cytokine storm, dysregulated inflammatory response). Abnormal activation of T-cells in responsible for some autoimmune diseases (one could call it overactivation). For COVID, the T-cell response is impaired, and disease is associated with T-cell death.

In addition to citations, the authors still have not supplied reference codes/numbers for the data sets they used.

“To achieve this, we searched scientific literature for protein interaction data during infection and identified protein phosphatases that directly interact with the virus or with other intermediate proteins that interact with viral proteins [4,11,23,29–34].” While reference 4 may be an excellent review, it is not relevant to the statement, nor is reference 11.

There may still be a lack of understanding regarding the role of PTEN. It is considered a tumor suppressor as it antagonizes PI3K/Akt signal and therefor has a negative role in cell growth and survival.

The relevance of analyzing a data set for proteins in the urine has not been addressed.

Author Response

Comment 1: The revised manuscript should show ALL modifications made to it.

The modifications made in the revised manuscript was indicated in italic. The references were updated and not in italic, as the order changed and the software automatically added the numbers as a citation in the text and in the References section. As mentioned in my previous response letter, these changes were explained. To make the text modifications more visible, they are now highlighted in red.

 

Comment 2: A meta-analysis should be comprehensive rather than selecting only a subset of data sets; such selection can introduce bias. Not analyzed for in this study:

Brewer et al. iScience. 2024 Feb 19;27(3):109302

 

We have included the requested reference (Brewer et al., 2024) in the revised version of our manuscript in the fourth paragraph of “Phosphorylation level of intermediate proteins” and in the Table S5.

 

Furthermore, a recent analysis searching for PPP2CA substrates used dTAG proteolysis-targeting chimeras to selectively degrade dTAG-PPP2CA in HEK293 cells. In this study, they identified 7,589 proteins, with 5,829 phosphorylated and 3,353 showing significant increases in abundance. Only 11 proteins showed a significant decrease in abundance in dTAG-13-treated cells compared to DMSO-treated controls. These proteins are involved in cell cycle, RNA transport, ubiquitin-mediated proteolysis, and the spliceosome [67]. We identified 141 intermediate proteins that bind to both PPP2CA and SARS-CoV-2 virus in our meta-analysis, which were also observed in that study (Table S5). Of these, 59 proteins showed a significant increase in phosphorylation abundance in dTAG-13-treated cells compared to DMSO-treated controls (Table S5), with particular emphasis on the RPS, RPL and CDC20 proteins.

 

Zhou et al. Nature Biotechnology volume 41, pages 128–139 (2023

 

We also have included the requested reference (Zhou et al., 2023) in the revised version of our manuscript in the fourth paragraph of “Protein phosphatases interact with intermediate proteins” and in the Table S1 and S3.

 

Additionally, a recent study transfected 28 SARS-CoV-2 proteins, each with a specific N-terminal-tag, into Saccharomyces cerevisiae for a high-throughput yeast two-hybrid (Y2H) screen, and also transfected them into Caco-2 cells for tandem mass tag affinity purification followed by mass spectrometry (TMT-AP-MS) to analyze the human protein-protein interactome. They reported 299 and 472 human-SARS-CoV-2 PPIs via Y2H screen and TMT-AP-MS methodologies, respectively [49]. From these, we searched for the intermediate proteins identified in our meta-analysis and found 33 intermediates proteins that interact with viral proteins (Table S3).

 

Comment 3: While expensive, the current antivirals are effect. The text should be corrected. The authors are welcome to explain why additional treatments are needed.

 

We have revised the previous sentence by removing the statement on antiviral effectiveness as stated below:

The high transmission rate and global lethality of COVID-19 has driven the research efforts towards the understanding of disease mechanisms to allow drugs and vaccine development to prevent and treat the disease.

 

Comment 4: The viral RNA genome within the envelope of the virion is bound by the nucleocapsid protein to form a ribonucleoprotein complex; this is a fundamental to the composition of the virion.

 

In the fourth paragraph of Introduction section, there is a concise description of each viral protein. We have added the phrase “and form a ribonucleoprotein complex” to the sentence, as folllows:

“’…and the N protein, which wraps the RNA genome and forms a ribonucleoprotein complex.”

 

Comment 5: The authors must explain what is meant by overactivation of the immune system (e.g., cytokine storm, dysregulated inflammatory response). Abnormal activation of T-cells in responsible for some autoimmune diseases (one could call it overactivation). For COVID, the T-cell response is impaired, and disease is associated with T-cell death.

 

We have revised the sentence in the fifth paragraph of the Introduction section as follows:

…a strong correlation between the inflammatory response, T-cell activity, and the severity of COVID-19 disease. Pro-inflammatory cytokines such as IL-6 and TNFα, along with chemokines like CXCL10 and CXCL12, and the transcription factor NFκB signaling pathway, all increase with disease severity. [5–14]. In contrast, T-cell activation is observed in mild cases but appears to be impaired in severe disease [15,16].

 

Comment 6: In addition to citations, the authors still have not supplied reference codes/numbers for the data sets they used.

 

The references have been added to the manuscript (note: we did not alter the color for expression data) and are highlighted in red in “sheet 2” of Table S2 and Table S4, covering both expression and phosphorylation data analysis.

 

Comment 7: “To achieve this, we searched scientific literature for protein interaction data during infection and identified protein phosphatases that directly interact with the virus or with other intermediate proteins that interact with viral proteins [4,11,23,29–34].” While reference 4 may be an excellent review, it is not relevant to the statement, nor is reference 11.

 

The references 4 and 11 are not cited in this sentence of the revised manuscript: “To achieve this, we searched scientific literature for protein interaction data during infection and identified protein phosphatases that directly interact with the virus or with other intermediate proteins that interact with viral proteins [7,14,35,41–46].”

 

Due to the addition of other references, it has changed again to [7,14,37,43–48].

 

Comment 8: There may still be a lack of understanding regarding the role of PTEN. It is considered a tumor suppressor as it antagonizes PI3K/Akt signal and therefor has a negative role in cell growth and survival.

 

We added a sentence to the penultimate paragraph of “Protein phosphatases interact with intermediate proteins” to enhance clarity:

Among the key players, we can highlight NEDD4 (Figure 5B), which is known to be a major E3 ubiquitin ligase for PTEN, promoting its ubiquitination and degradation, thereby leading to activation of PI3K activation [60].

 

Comment 9: The relevance of analyzing a data set for proteins in the urine has not been addressed.

The urine sample is a non-invasive approach that has been used in various studies to identify biomarkers of human diseases in proteomic analyses. Therefore, detecting proteins in urine samples could aid in disease diagnostic and assess disease severity. Please see Table S1 (which summarizes information on the references used for each analysis) and Table S2 (protein phosphatase expression) in our manuscript. Note that only the reference Li et al., 2020 used urine sample for the analysis, identifying one protein phosphatase – PTPRN. In fact, the other reference, Li et al., 2021, analyzed PMBC cells, which we have now corrected in the Table S1 and S2 (in red).

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

Most comments were addressed. Data set information is still missing. As an example, for Stukalov et al., in the appropriate figure legend the data set identifier (e.g., PXD022282 if you used this one) must be included. In the Materials and Methods, need to know where to find the data sets that were analyzed (e.g., ProteomeXchange Consortium). 

Author Response

Comment 1: Most comments were addressed.

Thank you for your feedback. We have carefully addressed most of the comments and made the necessary revisions accordingly.

Comment 2: Data set information is still missing. As an example, for Stukalov et al., in the appropriate figure legend the data set identifier (e.g., PXD022282 if you used this one) must be included. In the Materials and Methods, need to know where to find the data sets that were analyzed (e.g., ProteomeXchange Consortium). 

We have included the requested information in the revised version of our manuscript, specifically in Table S1 and the following sentence in the Material and methods section – In silico analysis.

Information regarding the source data (PRIDE identifier, website links for supplementary tables, and others dataset) is summarized in Table S1.

Author Response File: Author Response.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is a very useful Article focusing on the involvement of a critical group of cellular proteins in SARS-CoV-2 infection. It is quite comprehensive and may be of interest to researchers in the area of cell signaling, cell biology, virology and infectious diseases. However, there are several issues to be addressed in order to improve this manuscript.

1. The article sometimes lacks focus. Protein phosphatases (PPs) are very important regulatory proteins and probably participate in all cellular events, hence conclusions that PPs interact with multiple proteins involved in various cellular processes are trivial. It is more interesting to focus on how these PPs are involved in specific phenomena directly related to SARS-CoV-2 infection. 

2. The authors hypothesis is stated as follows; "we hypothesized that protein phosphorylation is promoted during SARS-CoV-2 infection by down-regulating protein phosphatases, amplifying the signaling duration and maximum signaling amplitude leading to cell death". Does the presented material support this hypothesis? The data in Fig. 6 indicate that some intermediate proteins increase their phosphorylation level in infected cells, while others decrease. Moreover, Fig. 2 shows that the levels of PP expression move in response both up and down. It would help if the hypothesis and the conclusions were more specific.

3. Frequently, the nature of observed protein-protein interactions is not specified. Sure, a manuscript has to be succinct, but the method to detect interactions are sometimes very important. There is a big difference if co-immunoprecipitation was used or just so-called 'pathway analysis', when physical interactions are not even detected. Also, it is unclear sometimes what exactly proteins were studied. For example, "Li et al (2021) overexpressed the SARS-CoV-2 gene in HEK293 cell and identified 286 cellular proteins interaction using affinity purification and mass spectrometry". What is SARS-CoV-2 gene? The entire genome sequence or some specific ORFs? Affinity purification of what protein(s) was used? What was identified - cellular proteins interacting to each other, or cellular proteins interacting to viral proteins? Which viral proteins?

4. PTEN is always referred to as a protein phosphatase. However, while it has some protein phosphatase activity, it is mostly a lipid phosphatase, so its effects on PIP3-dependent events, which are mentioned in the manuscript, are probably linked to its lipid phosphatase activity, which is well-known and considered critical in cell signaling. It would be very helpful to specify that.

5. According to its legend, Fig. 3D shows both intermediate (magenta) and viral (yellow) proteins. The figure shows only magenta-colored symbols.

6. The model presented in Fig. 7 is not quite clear. Do the two threonine residues mentioned here belong to PPP2CA? Does it mean the role of this complex is to regulate (auto)dephosphorylation of these residues by the phosphatase? What is the role of CDC20? Does this dephosphorylation occur only in this complex? It is unclear from the description.

 

 

Comments on the Quality of English Language

Minor editing is needed for unclear prepositions (example - 'by infection from', should not it be 'with'?) and nouns (example - 'the code gene to four structural proteins', should it be 'genes encoding four structural proteins' or 'four genes encoding structural proteins?). Some inconsistencies between nouns and verbs used as plural and singular in one sentence are also present.

Reviewer 2 Report

Comments and Suggestions for Authors

The study identifies protein phosphatases, particularly PP2CA and PTEN, as critical regulators of the cell cycle and apoptosis during SARS-CoV-2 infection, highlighting their interaction with the CDC20 protein. It utilized a meta-analysis of proteomic databases from COVID-19 patients to explore the role of protein phosphatases in SARS-CoV-2 infection dynamics. Although the work comprises only computational studies, it can be considered as providing initial insights. However, there are certain points the authors might consider for improvement:

It would be beneficial if the authors added more details to Figure 1, particularly regarding the criteria used for excluding studies.

The legends for Figures 2 and 6 are overly lengthy, a more concise presentation would appear more professional.

In Figure 3E, please elaborate on the direct interactions between the SARS proteins and CDC20. If targeting CDC20 is pivotal, this should be emphasized.

The docking study lacks clarity. Employing two or three docking methods to validate the work would strengthen the findings.

Additionally, Molecular Dynamics (MD) simulations could provide deeper insights into the interactions and stability of the protein complexes.

Regarding the novelty of the work: The role of PTEN has already been reported in other computational studies (https://doi.org/10.1038/s41598-023-31380-7). Please comment on how this study's findings differ or advance our understanding.

It would be useful to mention any experimental work designed to target these proteins for COVID-19 treatment.

Limit the conclusion section to key findings to enhance clarity and impact.

Comments on the Quality of English Language

Minor english editing is needed