Modeling COVID-19 disease biology to identify drug treatment candidates

  1. Walter J. Jessen
  2. Stefan Diaz Gaisenband
  3. M’Lissa Quintanilla
  4. Sadiq Lula
  5. Patrick McLeroth
  6. Adam Sullivan
  7. Stanley Letovsky

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Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, there are a limited number of effective treatments. A variety of drugs that have been approved for other diseases are being tested for the treatment of COVID-19, and thus far only remdesevir, dexamethasone, baricitinib, tofacitinib, tocilizumab, and sarilumab have been recommended by the National Institutes of Health (NIH) COVID-19 Treatment Guidelines Panel for the therapeutic management of hospitalized adults with COVID-19. Using a disease biology modeling approach, we constructed a protein-protein interactome network based on COVID-19- associated genes/proteins described in research literature together with known protein-protein interactions in epithelial cells. Phenotype and disease enrichment analysis of the COVID-19 disease biology model demonstrated strong statistical enrichments consistent with patients’ clinical presentation. The model was used to interrogate host biological response induced by SARS-CoV-2 and identify COVID-19 drug treatment candidates that may inform on drugs currently being evaluated or provide insight into possible targets for potential new therapeutic agents. We focused on cancer drugs as they are often used to control inflammation, inhibit cell division, and modulate the host microenvironment to control the disease. From the top 30 COVID-19 drug candidates, twelve have a role as an antineoplastic agent, seven of which are approved for human use. Altogether, nearly 40% of the drugs identified by our model have been identified by others for COVID-19 clinical trials. Disease biology modeling incorporating disease-associated genes/proteins discussed in the research literature together with known molecular interactions in relevant cell types is a useful method to better understand disease biology and identify potentially effective therapeutic interventions.

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    SciScore for 10.1101/2022.04.18.488660: (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
    Model construction: To build an interactome network, we first identified relationships between COVID-19 disease and genes/proteins in Medline and PubMed Central using PolySearch 2.0, an online text-mining system 17.
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    PolySearch
    suggested: (PolySearch, RRID:SCR_005291)
    Following both automated mapping and manual review, we identified 14 COVID-19 and 13 influenza A H1N1 genes/proteins, which were used as seeds to construct interactome networks in Ingenuity Pathway Analysis (IPA
    Ingenuity Pathway Analysis
    suggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)
    Pathway Ontologies (MSigDB C2 BIOCART (v7.3) and PantherDB), and Drugs (Comparative Toxicogenomics Database (CTD) and Stitch) using ToppFun, a module of the ToppGene Suite (https://toppgene.cchmc.org) 24.
    https://toppgene.cchmc.org
    suggested: ( ToppGene Suite , RRID:SCR_005726)
    For the top 500 GO:BP terms, we used REVIGO to find a representative subset and group semantically similar terms together 26.
    REVIGO
    suggested: (REViGO, RRID:SCR_005825)

    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:
    This is due to a limitation in the IPA knowledgebase; there are no direct physical interactions in epithelial cells up- or down-stream of ACE2 in the knowledgebase. Thus, since it does not connect to any other nodes in our model, it was not included. Host biological response induced by SARS-CoV-2: Biological process and pathway enrichment analyses provide context to better understand disease biology (Tables 1, 2). At a high level, these analyses reflect viral infection by way of xenobiotic metabolism, host cellular damage (i.e. response to wounding), defense response and cytokine production, and a change in extracellular-signal-regulated kinase (ERK) signaling, the last step of the Ras/Raf/Mitogen-activated protein kinase/ERK kinase (MEK)/ERK signal transduction pathway. Upon various extracellular stimuli, this regulatory cascade results in ERK-1 and ERK-2 activation, which phosphorylates numerous downstream substrates and leads to the expression of multiple genes essential for diverse cellular functions, such as cell proliferation, differentiation, survival or apoptosis 31, 32. Many of these functions were captured by our model, including apoptosis signaling and negative regulation of apoptotic process, the Ras pathway, leukocyte differentiation, response to oxidative stress, and P53 pathway feedback loops (Tables 1, 2). Notably, our model captures some of the hematological and neurological manifestations of COVID-19 infection, including blood microparticle formation, coagul...

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

    IdentifierStatusTitle
    NCT04407273CompletedStatin Therapy and COVID-19 Infection

    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.

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  2. Version published to 10.1101/2022.04.18.488660v1 on bioRxiv