A network medicine approach to investigation and population-based validation of disease...

Updated: Sep 19, 2021

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

Abstract The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn's disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56–0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54–0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52–0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31–0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.


A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

Citation: Zhou Y, Hou Y, Shen J, Mehra R, Kallianpur A, Culver DA, et al. (2020) A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19. PLoS Biol 18(11): e3000970. https://doi.org/10.1371/journal.pbio.3000970 Academic Editor: Nicole Soranzo, Wellcome Trust Sanger Institute, UNITED KINGDOM Received: June 5, 2020; Accepted: October 28, 2020; Published: November 6, 2020 Copyright: © 2020 Zhou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All data sets used in this study and their sources for downloading can be found in the S1 Table. The bulk and single-cell RNA-Seq data used in this study were downloaded from the NCBI GEO database with accession numbers GSE63142, GSE130499, and GSE134809. The lung and human bronchial epithelial single-cell data were downloaded from https://data.mendeley.com/datasets/7r2cwbw44m/1. Source code, the human protein-protein interactome, and drug-target network can be downloaded from https://github.com/ChengF-Lab/COVID-19_Map. All other relevant data are within the paper and its Supporting Information files. Funding: This work was supported by the National Institute of Aging (R01AG066707 and 3R01AG066707-01S1) and the National Heart, Lung, and Blood Institute (R00HL138272) to F.C. This work has been also supported in part by the VeloSano Pilot Program (Cleveland Clinic Taussig Cancer Institute) to F.C. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; AP-MS, affinity purification–mass spectrometry; ARB, angiotensin II receptor blocker; AT2, alveolar type II; Co-IP+LC/MS, co-immunoprecipitation and liquid chromatography-mass spectrometry; COPD, chronic obstructive pulmonary disease; COVID-19, coronavirus disease 2019; DEG, differentially expressed gene; DEP, differentially expressed protein; dN/dS ratio, nonsynonymous to synonymous substitution rate ratio; ES, enrichment score; FDA, US Food and Drug Administration; FDR, false discovery rate; GSEA, gene set enrichment analysis; HCoV, human coronavirus; HGMD, Human Gene Mutation Database; IBD, inflammatory bowel disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; OR, odds ratio; PPI, protein-protein interaction; PS, propensity score; RNA-Seq, RNA sequencing; RNAi, RNA interference; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; via, viral open reading frame

Introduction The ongoing global coronavirus disease 2019 (COVID-19) pandemic has led to 38 million confirmed cases and 1 million deaths worldwide as of October 14, 2020. The United States alone has recorded nearly 8 million confirmed cases, with a death toll of more than 216,000. Several retrospective studies have reported the clinical characteristics of individuals with symptomatic COVID-19, and an emerging theme has been the significantly higher risk of morbidity and mortality among individuals with 1 or more comorbid health conditions, such as hypertension, asthma, diabetes mellitus, cardiovascular or cerebrovascular disease, chronic kidney disease, and malignancy. However, these retrospective clinical studies are limited by small sample sizes and unmeasured confounding factors, leaving the underlying pathomechanisms largely unknown. More specifically, it is unclear whether associations of disease manifestations and COVID-19 severity are merely a reflection of poorer health in general or a clue to shared pathobiological mechanisms. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, is an enveloped virus that carries a single-stranded positive-sense RNA genome. SARS-CoV-2 is a newly discovered member of the coronavirus (CoV) family []. SARS-CoV-2 enters host cells via binding of its spike protein to the angiotensin-converting enzyme 2 (ACE2) receptor on the surfaces of many cell types []. This binding is primed by transmembrane protease serine 2 (TMPRSS2) and the host cell protease furin [. Studies have shown that ACE2 and TMPRSS2 are highly co-expressed in alveolar type II (AT2) epithelial cells in the lung nasal mucosa [ bronchial secretory cells, and absorptive enterocytes in the ileum Yet, much remains to be learned about how these critical human proteins involved in the infection and replication of SARS-CoV-2 are associated with various disease comorbidities and complications. Systematic identification of the host factors involved in the protein-protein interactions (PPIs) of SARS-CoV-2 and the human host will facilitate identification of drug targets and advance understanding of the complications and comorbidities resulting from COVID-19 [Studies using transcriptomics ], proteomics [, and interactomics (PPIs) methods have contributed to a better understanding of the SARS-CoV-2–host interactome, which has enabled the investigation of the complications and comorbidities of SARS-CoV-2 and a facilitated search for effective treatment Fig 1. The overall workflow of this study. (A) A diagram illustrating the basic pathogenesis of SARS-CoV-2. (B) A diagram illustrating how to build a global interactome map for SARS-CoV-2. We compiled the SARS-CoV-2 human target gene/protein sets from multi-omics data from the transcriptome, proteome, and human interactome, and validated network-based findings using patient data from a COVID-19 registry. (C) A diagram illustrating network-based measurement of disease manifestations associated with COVID-19. We systematically evaluated the network proximities of the SARS-CoV-2 human target genes/proteins with 64 diseases across 6 main categories: autoimmune, cancer, cardiovascular, metabolic, neurological, and pulmonary. (D) A workflow illustrating validation of network-based findings. We performed single-cell analyses to further investigate the underlying mechanisms of COVID-19 with asthma and inflammatory bowel disease. We prioritized nearly 3,000 US Food and Drug Administration–approved/investigational drugs for their potential anti-SARS-CoV-2 effects from network-based findings and validated drug–COVID-19 outcomes using an institutional review board-approved COVID-19 patient registry. Major efforts are underway to develop safe and effective drugs to treat COVID-19: Preventive and therapeutic strategies currently being explored include vaccination, SARS-CoV-2-specific antibodies, novel nucleoside analogs such as remdesivir, and repurposed drugs []. Remdesivir, an agent originally developed for the treatment of the Ebola virus, was reported to shorten the time to recovery in adults who were hospitalized with COVID-19 []; yet, a 10-day course of remdesivir did not show a statistically significant difference in clinical status compared with standard care for patients with moderate COVID-19 ]. Dexamethasone, an FDA-approved glucocorticoid receptor (GR) agonist, has been shown to reduce mortality by one-third in hospitalized COVID-19 patients requiring ventilation and by one-fifth in individuals requiring oxygen; yet, dexamethasone did not reduce death in COVID-19 patients not receiving respiratory support [. Many existing drugs are currently being or have been tested in clinical trials, such as the antimalarial drug hydroxychloroquine and protease inhibitor combination lopinavir/ritonavir; results from these trials have not yet shown significant clinical benefits for COVID-19 patients [. We recently evaluated nearly 3,000 FDA-approved/investigational drugs using a network-based method and prioritized 16 drug candidates and 3 drug combinations for COVID-19 [ Yet, the answer to the key question of why an approved drug was originally documented for other diseases might be beneficial for COVID-19 remains unclear. One possible explanation is that COVID-19 shares common disease pathobiology or functional pathways elucidated by the human PPIs [30–32]. Systematic identification of common disease pathobiological pathways shared by COVID-19 and other diseases would offer novel targets and therapies for COVID-19. In this study, we present an integrative network medicine platform that quantifies the association of COVID-19 with other diseases across 6 categories, including autoimmune, cancer, cardiovascular, metabolic, neurological, and pulmonary (Fig 1C). The rationale for these analyses rests on the notions that (1) the proteins that functionally associate with a disease (such as COVID-19) are localized in the corresponding subnetwork within the comprehensive human PPI network [31–34] and (2) proteins that are associated with a specific disease may be directly targeted by the virus or are in the close vicinity of the target host proteins. We first performed network analysis followed by single-cell RNA sequencing (RNA-Seq) data analysis to identify the underlying pathobiological relationships between COVID-19 and its associated comorbidities. Additionally, we use our network medicine findings and patient data from a large COVID-19 patient registry database to identify and prioritize existing FDA-approved drugs as potential COVID-19 drug candidates (Fig 1D).

Results A global map of the SARS-CoV-2 virus-host interactome We assembled 4 host gene/protein sets for SARS-CoV-2 (S1 and S2 Tables): (1) SARS2-DEG, representing the differentially expressed genes (DEGs) from the transcriptomic data of SARS-CoV-2-infected primary human bronchial epithelial cells; (2) SARS2-DEP, representing the differentially expressed proteins (DEPs) from the proteomic data of SARS-CoV-2-infected human Caco-2 cells; (3) HCV-PPI, representing the literature-based virus-host proteins across multiple human coronaviruses (HCoVs), including SARS-CoV-1 (from the 2002–2003 pandemic) and MERS-CoV; and (4) SARS2-PPI (SARS-CoV-2-specific virus-host PPIs). Since HCV-PPI and SARS2-PPI both involve physical virus-host PPIs, we further combined them as the fifth dataset, PanCoV-PPI. We first performed functional enrichment analyses for the 5 different gene/protein datasets. We found that these datasets share several common pathways and ontology terms (Fig 2A; S1 Data), such as phagosome, measles, apoptosis, NF-κB signaling pathway, neutrophil-related immunity, apoptotic processes, virus transport, viral genome replication, and response to interferon, yet they differ considerably in terms of their most significantly enriched pathways (S1–S5 Figs). This is especially noticeable for SARS2-DEP and SARS2-PPI. While SARS2-DEG (S1 Fig) and HCV-PPI (S3 Fig) show more enrichment in immune responses and viral pathways, SARS2-DEP (S2 Fig) is more related to various cellular metabolic pathways, and SARS2-PPI (S4 Fig) is more enriched in DNA replication, RNA transcription, and protein translation. These observations suggest that these different SARS-CoV-2 viral–host gene/protein sets capture complementary aspects of the biological and cellular states of the viral life cycle and host immunity. Therefore, building a global virus-host map (including interactome, transcriptome, and proteome) that incorporates data from transcriptomics, proteomics, and physical virus-host PPIs is essential for a better understanding of the pathogenesis of COVID-19. This global virus-host map for SARS-CoV-2 can offer a more complete picture of the interconnected functional pathways involved in viral pathogenesis, thereby facilitating the discovery of therapeutic targets.


Recent studies indicated that SARS-CoV-2 infection was detected in multiple organs in addition to the lungs, including the heart, pharynx, liver, kidneys, brain, and intestine [70,87]. SARS-CoV-2 RNA was also found in patient stool [88]. Therefore, investigation of how SARS-CoV-2 associates with other diseases could help reveal and understand its impact on systems and organs in addition to lungs. In this study, we systematically evaluated 64 diseases across 6 categories for their potential manifestations with COVID-19. We started with assembling and characterizing 5 SARS-CoV-2 datasets representing different cellular event levels including transcriptome, proteome, and interactome. Using state-of-the-art network proximity measurement, we identified broad disease manifestations (such as autoimmune, neurological, and pulmonary; Fig 4A) associated with COVID-19. Although the number of genes associated with each disease is different (S4 Table), we did not notice any significant bias in the network proximity Z scores by different numbers of genes (S14 Fig). Retrospective meta-analyses using the clinical data of 4,973 patients across 34 studies confirmed our network-based findings.

Credited to Yadi Zhou


A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

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