Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

  1. Yadi Zhou
  2. Yuan Hou
  3. Jiayu Shen
  4. Yin Huang
  5. William Martin
  6. Feixiong Cheng

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Abstract

Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV–host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the “ Complementary Exposure ” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

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  1. ScreenIT

    SciScore for 10.1101/2020.02.03.20020263: (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

    Software and Algorithms
    SentencesResources
    The protein alignment and phylogenetic tree of HCoVs were constructed by MEGA X.
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    Functional Enrichment Analysis: Next, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses to evaluate the biological relevance and functional pathways of the HCoV-associated proteins.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Building the Drug-Target Network: Here, we collected drug-target interaction information from the DrugBank database (v4.3)[79]
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    , PharmGKB database, ChEMBL (v20)[81], BindingDB[82], and IUPHAR/BPS Guide to PHARMACOLOGY[83].
    PharmGKB
    suggested: (PharmGKB, RRID:SCR_002689)
    ChEMBL
    suggested: (ChEMBL, RRID:SCR_014042)
    Here, only drug-target interactions meeting the following three criteria were used: (i) binding affinities, including Ki, Kd, IC50 or EC50 each ≤ 10 μM; (ii) the target was marked as ‘reviewed’ in the UniProt database[84]; and (iii) the human target was represented by a unique UniProt accession number.
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    The genes were mapped to their Entrez ID based on the NCBI database[85] as well as their official gene symbols based on GeneCards (https://www.genecards.org/).
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    GeneCards
    suggested: (GeneCards, RRID:SCR_002773)
    All networks were visualized using Gephi 0.9.2 (
    Gephi
    suggested: (Gephi, RRID:SCR_004293)
    We first collected three differential gene expression data sets of hosts infected by HCoVs from the NCBI Gene Expression Omnibus (
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We acknowledge several limitations in our current study. In this study, we used a low binding affinity value of 10 μM as a threshold to define a physical drug-target interaction. However, a stronger binding affinity threshold (e.g., 1μM) may be a more suitable cut-off in drug discovery, although it will generate a smaller drug-target network. Although sizeable efforts were made for assembling large-scale, experimentally reported drug-target networks from publicly available databases, the network data may be incomplete and some drug-protein interactions may be functional associations, instead of physical bindings. We may use computational approaches to systematically predict the drug-target interactions further[24, 69]. In addition, the collected virus-host interactions are far from complete and the quality can be influenced by multiple factors, including different experimental assays and human cell line models. We may computationally predict a new virus-host interactome for HCoVs using sequence-based and structure-based approaches[70]. The current systems pharmacology model cannot separate therapeutic antiviral effects from those predictions due to lack of detailed pharmacological effects of drug targets and unknown functional consequences of virus-host interactions. Drug targets representing nodes within cellular networks are often intrinsically coupled with both therapeutic and adverse profiles[71], as drugs can inhibit or activate protein functions (including antagonists v...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    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.

    About SciScore

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  2. Version published to 10.1038/s41421-020-0153-3
  3. Version published to 10.1101/2020.02.03.20020263v1 on medRxiv