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

Yadi Zhou ,Yuan Hou ,Jiayu Shen,Reena Mehra,Asha Kallianpur,Daniel A. Culver,Michaela U. Gack,Samar Farha,Joe Zein,Suzy Comhair,Claudio Fiocchi,Thaddeus Stappenbeck,Timothy Chan,Charis Eng,Jae U. Jung,Lara Jehi,Serpil Erzurum,Feixiong Cheng

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 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.

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 [1]. 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 [2–7]. However, these retrospective clinical studies are limited by small sample sizes and unmeasured confounding factors, leaving the underlying patho-mechanisms 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.

Materials & Methods

A list of the sources of all the datasets used in this study can be found in S1 Table.

Discussion

Recent studies indicated that SARS-CoV-2 infection was detected in multiple organs in addition to lungs, including 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 number of genes (S14 Fig). Retrospective meta-analyses using the clinical data of 4,973 patients across 34 studies confirmed our network-based findings.

Acknowledgments

We thank all helpful discussions and critical comments regarding this manuscript from the COVID-19 Research Intervention Advisory Committee members at the Cleveland Clinic.

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. doi: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 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;viORF, viral open reading frame.

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