Synthetic lethality-based prediction of anti-SARS-CoV-2 targets

  1. Lipika R. Pal
  2. Kuoyuan Cheng
  3. Nishanth Ulhas Nair
  4. Laura Martin-Sancho
  5. Sanju Sinha
  6. Yuan Pu
  7. Laura Riva
  8. Xin Yin
  9. Fiorella Schischlik
  10. Joo Sang Lee
  11. Sumit K. Chanda
  12. Eytan Ruppin

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Abstract

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  1. Version published to 10.1016/j.isci.2022.104311
  2. ScreenIT

    SciScore for 10.1101/2021.09.14.460408: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Cells were then incubated for 2 h at room temperature with primary anti-SARS-CoV-2 N protein rabbit polyclonal antibody (kind gift from Dr. Adolfo Garcia-Sastre), followed by three PBS washes and 1 h incubation with Alexa Fluor 488-conjugated anti-rabbit secondary antibody (A-11008, ThermoFisher) diluted in 3% BSA.
    anti-SARS-CoV-2 N protein
    suggested: None
    anti-rabbit
    suggested: (Molecular Probes Cat# A-11008, RRID:AB_143165)
    Experimental Models: Cell Lines
    SentencesResources
    Differential expression analysis of transcriptomic data on SARS-CoV-2 infection: Several RNA-sequencing datasets of SARS-CoV-2-infected and matched control samples were obtained as follows: Vero E6 cells (Riva et al. 2020; from the GEO database, dataset ID GSE153940), A549 cells with exogenous ACE2 (A549ACE2; Blanco-Melo et al. 2020; from GEO, GSE147507), nasopharyngeal swab samples from human COVID-19 patients (Butler et al. 2020 and Lieberman et al. 2020, from the Supplementary Materials of the respective publications), single-cell data of nasopharyngeal and bronchial samples from COVID-19 patients (Chua et al. 2020; from FigShare https://doi.org/10.6084/m9.figshare.12436517), and single-cell data of bronchoalveolar lavage fluid from COVID-19 patients (Liao et al. 2020; obtained following instructions in https://github.com/zhangzlab/covid_balf).
    A549
    suggested: None
    For the Vero E6 and A549ACE2 data, we performed differential expression (DE) analysis of virus-infected vs control samples with the DESeq2 R package (Love et al. 2014).
    Vero E6
    suggested: None
    Calculation of weighted DE score: Each of 140 candidates have the same or different significant DE genes from six different transcriptomic datasets - in vitro Vero E6 (Riva et al. 2020), in vitro A549ACE2 cells (Blanco-Melo et al. 2020), two in vivo Bulk Swab datasets (Butler et al. 2020 and Lieberman et al. 2020) and two in vivo single-cell RNA-seq datasets (Chua et al. 2020 and Liao et al. 2020).
    A549ACE2
    suggested: None
    Targeted siRNA screen: Screen: To evaluate the effect of the 26 predicted targets on viral replication and cell viability, a targeted siRNA screen was conducted using human Caco-2 cells (ATCC)
    Caco-2
    suggested: CLS Cat# 300137/p1665_CaCo-2, RRID:CVCL_0025)
    Software and Algorithms
    SentencesResources
    Differential expression analysis of transcriptomic data on SARS-CoV-2 infection: Several RNA-sequencing datasets of SARS-CoV-2-infected and matched control samples were obtained as follows: Vero E6 cells (Riva et al. 2020; from the GEO database, dataset ID GSE153940), A549 cells with exogenous ACE2 (A549ACE2; Blanco-Melo et al. 2020; from GEO, GSE147507), nasopharyngeal swab samples from human COVID-19 patients (Butler et al. 2020 and Lieberman et al. 2020, from the Supplementary Materials of the respective publications), single-cell data of nasopharyngeal and bronchial samples from COVID-19 patients (Chua et al. 2020; from FigShare https://doi.org/10.6084/m9.figshare.12436517), and single-cell data of bronchoalveolar lavage fluid from COVID-19 patients (Liao et al. 2020; obtained following instructions in https://github.com/zhangzlab/covid_balf).
    FigShare
    suggested: (FigShare, RRID:SCR_004328)
    For the Vero E6 and A549ACE2 data, we performed differential expression (DE) analysis of virus-infected vs control samples with the DESeq2 R package (Love et al. 2014).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Annotation of the final SL-based candidate target gene list: The predicted targets (140 candidates) were mapped to known drugs (clinically approved and experimental/ investigational) that target (i.e. inhibit) them using drug target annotation from DrugBank (Wishart et al. 2018).
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04518735CompletedEvolution of COVID-19 in Anticoagulated or Antiaggregated Pa…
    NCT04844658RecruitingCovid-19, Hospitalized, PatIents, Nasafytol
    NCT04578158CompletedTrial to Study the Adjuvant Benefits of Quercetin Phytosome …
    NCT04377789CompletedEffect of Quercetin on Prophylaxis and Treatment of COVID-19

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.09.14.460408v1 on bioRxiv