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

doi:10.1371/journal.pbio.3000970

. 2020 Nov 6;18(11):e3000970. doi: 10.1371/journal.pbio.3000970

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

Yadi Zhou ^1,^#, Yuan Hou ^1,^#, Jiayu Shen ¹, Reena Mehra ^2,³, Asha Kallianpur ^1,², Daniel A Culver ^4,⁵, Michaela U Gack ⁶, Samar Farha ^4,⁵, Joe Zein ^2,⁴, Suzy Comhair ^2,⁴, Claudio Fiocchi ^2,⁴, Thaddeus Stappenbeck ^2,⁴, Timothy Chan ^2,⁴, Charis Eng ^1,^2,^4,^7,⁸, Jae U Jung ^2,⁴, Lara Jehi ^2,³, Serpil Erzurum ^2,⁴, Feixiong Cheng ^1,^2,^8,^*

Editor: Nicole Soranzo⁹

¹Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America

²Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America

³Neurological Institute, Cleveland Clinic, Cleveland, Ohio, United States of America

⁴Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America

⁵Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States of America

⁶Florida Research and Innovation Center, Cleveland Clinic, Port Saint Lucie, Florida, United States of America

⁷Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America

⁸Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America

⁹Wellcome Trust Sanger Institute, UNITED KINGDOM

The authors have declared that no competing interests exist.

^✉

* E-mail: chengf@ccf.org

Contributed equally.

Roles

Yadi Zhou: Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft

Yuan Hou: Formal analysis, Investigation, Methodology, Validation, Writing – original draft

Jiayu Shen: Data curation, Formal analysis, Investigation

Reena Mehra: Validation

Asha Kallianpur: Validation

Daniel A Culver: Validation

Michaela U Gack: Validation

Samar Farha: Validation

Joe Zein: Validation

Suzy Comhair: Validation

Claudio Fiocchi: Validation

Thaddeus Stappenbeck: Validation

Timothy Chan: Validation

Charis Eng: Validation

Jae U Jung: Validation

Lara Jehi: Resources, Validation

Serpil Erzurum: Resources, Validation, Writing – review & editing

Feixiong Cheng: Conceptualization, Funding acquisition, Investigation, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

Nicole Soranzo: Academic Editor

PMCID: PMC7728249 PMID: 33156843

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.

This study uses network medicine methodologies along with multiomic observations (including SARS-CoV-2 and human interactome, transcriptome, and proteome) to identify widespread disease manifestations and drug repurposing candidates (including melatonin) for COVID-19, validating these findings using a large-scale patient registry database.

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.

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 [7,8]. SARS-CoV-2 is a newly discovered member of the coronavirus (CoV) family [9]. 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 [10]. This binding is primed by transmembrane protease serine 2 (TMPRSS2) [10] and the host cell protease furin [11] (Fig 1A). Studies have shown that ACE2 and TMPRSS2 are highly co-expressed in alveolar type II (AT2) epithelial cells in the lung [12], nasal mucosa [13], bronchial secretory cells [14], and absorptive enterocytes in the ileum [15]. 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 [16–20]. Studies using transcriptomics [21], proteomics [22], and interactomics (PPIs) methods [8] 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 1B).

Fig 1 — (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 [23,24]. Remdesivir, an agent originally developed for treatment of Ebola virus, was reported to shorten the time to recovery in adults who were hospitalized with COVID-19 [25]; 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 [26]. 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 [27]; yet, dexamethasone did not reduce death in COVID-19 patients not receiving respiratory support [27]. 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 [28,29]. 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 [30]. Yet, the answer to the key question of why an approved drug 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, malignant 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) HCoV-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 HCoV-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 HCoV-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 are 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.

Fig 2 — (A) Pathway and Gene Ontology (biological process) enrichment analysis results of the SARS-CoV-2 host genes/proteins across 5 different datasets. We assembled 5 gene/protein datasets from SARS-CoV-2 host protein–protein interactions, transcriptomics, and proteomics (S2 Table). (B–D) Network and biological characteristics of the SARS-CoV-2 host genes/proteins. The proteins in PanCoV-PPI have higher node degrees (B), lower *dN/dS* ratios (C), and lower evolutionary ratios (D) compared to randomly selected proteins (grey, mean ± standard deviation of 100 repeats). (E) Among the 460 proteins in PanCoV-PPI, 450 (98%) are expressed in lungs, and 317 (69%) have lung-specific expression (Z > 0). (F and G) The distribution of the node degrees in the human interactome and *dN/dS* ratios of PanCoV-PPI and 4 published virus-related host protein sets (S3 Table). (H) The shared target human proteins (blue) of SARS-CoV-2 (red) and other viruses (green). (I) SARS-CoV-2 target proteins overlap significantly with disease-associated genes (Mendelian disease and orphan disease), cancer genes, cell cycle genes, and innate immune genes. The data underlying this figure can be found in S1 Data. Co-IP+LCMS, co-immunoprecipitation and liquid chromatography–mass spectrometry; CoV, coronavirus; *dN/dS* ratio, nonsynonymous to synonymous substitution rate ratio; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes; RNAi, RNA interference; viORF, viral open reading frame.

Network and biological characteristics of virus–host interactome for SARS-CoV-2

In addition to identifying the functions that these viral gene/protein sets represent, we next characterized the network patterns (node degree in the human PPI network) and bioinformatics features of these SARS-CoV-2 datasets, including dN/dS ratio, evolutionary rate ratio, and lung expression specificity (Figs 2B–2G and S6). To find common as well as unique network and bioinformatic characteristics of SARS-CoV-2, we further compiled 4 additional virus–host gene/protein networks, identified by different methods, for comparison (S3 Table): (1) 900 virus–host interactions connecting 10 other viruses and 712 host genes identified by gene-trap insertional mutagenesis, (2) 2,855 known virus–host interactions connecting 2,443 host genes and 55 pathogens identified from RNA interference (RNAi), (3) 579 host proteins mediating translation of 70 innate immune-modulating viral open reading frames (viORFs), and (4) 1,292 host genes mediating influenza–host interactions identified by co-immunoprecipitation and liquid chromatography-mass spectrometry (Co-IP+LC/MS). These virus–host gene/protein networks have been well characterized [35–37] and offer high-quality datasets (S3 Table) for comparisons. We found that host proteins in PanCoV-PPI (Fig 2B) and 4 other datasets (SARS2-DEG, SARS2-DEP, HCoV-PPI, and SARS2-PPI) (S6 Fig) were more likely to be highly connected in the human PPI network. Several hub genes, such as JUN, XPO1, MOV10, NPM1, VCP, and HNRNPA1, have the highest degree (connectivity) in the PanCoV-PPI network (S2 Table). PanCoV-PPI has a comparable degree distribution with host genes/proteins identified by viORFs and Co-IP+LC/MS, although marginally higher than that identified by RNAi and gene-trap insertional mutagenesis assay (Fig 2F).

Expression patterns of genes in a specific disease-related tissue play a crucial role for elucidation of disease pathogenesis and drug discovery [31,32]. Given the major impact of SARS-CoV-2 on pulmonary function and lung injury [2], we inspected the lung-specific expression of genes in PanCoV-PPI using a Z score measure (see Materials and Methods—Tissue specificity analysis) compared to expression in other tissues from the GTEx database [38]. We found that most host genes for SARS-CoV-2 have high expression in lung (Fig 2E; S2 Table) compared to other tissues; yet, ACE2 has a low expression in lung compared to other tissues. A recent study showed that ACE2 was primarily expressed in the epithelial cells in lungs, and only 3.8% of AT2 pneumocytes expressed both ACE2 and TMPRSS2, but ACE2 is significantly upregulated in smokers and 24–48 hours following SARS-CoV-2 infection [12,39]. Another study also showed that despite relatively low expression of ACE2 in the lung, ACE2 was expressed in multiple epithelial cell types along the airway [13]. Therefore, it is important to understand the cell-type-specific SARS-CoV-2 pathogenesis at the single-cell level.

To inspect the evolutionary factors underlying the SARS-CoV-2–human PPIs, we investigated the selective pressure and evolutionary rates quantified by the dN/dS ratio using human–mouse orthologous gene pairs. We found that PanCoV-PPI shows stronger purifying selection (lower dN/dS ratio [Fig 2C] and evolutionary rate ratio [Fig 2D]) compared to the same number of random genes. PanCoV-PPI is also comparable to 4 other virus–host genes/protein datasets identified by different assays (Fig 2F and 2G) in terms of node degrees and dN/dS ratios. Altogether, these observations suggest that the virus–host PPIs assembled in this study offer a high-quality interactome map for SARS-CoV-2 for identifying pathogenesis and potential treatments for COVID-19.

To inspect shared viral pathways across different viruses and SARS-CoV-2, we further performed network overlap analysis of PanCoV-PPI with 712 host genes across 10 types of other viruses identified by gene-trap insertional mutagenesis assays [35]. We found a significant overlap of the SARS-CoV-2 host proteins with non-coronaviruses (P < 0.002, Fisher’s exact test; Fig 2H). For example, BRD2, a transcriptional regulator that belongs to the Bromodomain and Extra-Terminal motif family, is connected to SARS-CoV-2 and 2 other viruses, herpes simplex virus 2 (HSV-2) and reovirus. RHOA, encoding a small GTPase protein in the Rho family of GTPases, is connected to SARS-CoV-2, cowpox, and HSV-2 as well. RHOA has been reported to be involved in multiple human diseases, including cardiovascular disease [40] and cancer [41]. These observations indicate possible disease manifestations associated with SARS-CoV-2.

SARS-CoV-2 cellular network perturbations of disease manifestations

Investigation of the relationships between human host proteins targeted by SARS-CoV-2 and disease susceptibility genes may offer crucial information for identifying COVID-19-associated disease manifestations. We thus inspected the overlap between SARS-CoV-2 host genes/proteins and the susceptibility gene sets implicated in different diseases and biological events (Fig 2I). We found that host genes/proteins targeted by SARS-CoV-2 are significantly enriched in Mendelian disease (P = 0.002, Fisher’s exact test), orphan disease (P = 0.044), and cancer (P < 0.001). Mechanistically, SARS-CoV-2 target host genes are significantly enriched in cell cycle genes (P < 0.001) and innate immune genes (P < 0.001).

Using the 5 SARS-CoV-2 host gene/protein sets, we next tried to identify potential COVID-19 comorbidities. To achieve this, we assembled the disease–protein network of COVID-19 and 6 disease categories (S4 Table). For pan-cancer analysis, the somatic driver genes were retrieved from the Cancer Gene Census Tier 1 gene table [42,43]. The putative somatic driver genes for individual cancer types were identified from The Cancer Genome Atlas projects and were downloaded from a previous study [44]. For autoimmune, pulmonary, neurological, cardiovascular, and metabolic categories, we extracted their associated genes/proteins from the Human Gene Mutation Database (HGMD) [45,46]. Each gene from HGMD has at least 1 reported mutation associated with the disease. This information on the genes, in addition to the sources, counts, and terms used in HGMD to identify the diseases, can be found in S4 Table. Using the disease–protein network together with the SARS2-PPI (SARS-CoV-2 virus–host interactome) and HCoV-PPI (HCoV–host interactome) sets, we examined the overall connectivity of the disease-associated proteins and the SARS-CoV-2 target host proteins in the human PPI network (Fig 3A; S1 File; S2 Data). To build the global network for the disease comorbidities, we extracted the PPIs from the human interactome for the virus target proteins and disease-associated proteins. Each node indicates a virus target host protein (blue) or a disease-associated protein (green). Some disease-associated proteins can be directly targeted by the viruses, as shown in orange. Edges among these protein nodes indicate PPIs. For SARS-CoV-2, the targets of its individual viral proteins are shown based on the SARS2-PPI dataset. Due to their tendency of having common disease-associated proteins, some disease categories tend to cluster closely, e.g., cancer and neurological. Diseases from other categories, such as autoimmune and pulmonary, are scattered. Most of the virus target proteins are connected with the disease-associated proteins, which suggests shared pathobiological pathways of COVID-19 and these diseases. Various cancer types formed a relatively distant module from the virus targets, compared to other disease categories.

Shown in Fig 3A, the disease genes can interact with the SARS-CoV-2 viral proteins either directly or indirectly in the human protein–protein interactome. For example, among the 4 chronic obstructive pulmonary disease (COPD)–associated proteins shown in the network, TGFB1 is the direct target of HCoV-229E, and all 4 proteins (TGFB1, DEFB1, SNAI1, and ADAM33) interact with at least 1 SARS-CoV-2 target protein. The risk of various cardiovascular diseases was found to be increased in COVID-19 patients, including heart block, coronary artery disease, congestive heart failure, and arrhythmia, which is consistent with clinically reported myocardial injury [47] and cardiac arrest [48]. These observations reveal a common network relationship between COVID-19 and human diseases (Fig 3A). We then performed meta-analysis of 34 COVID-19 clinical studies (S5 Table) to evaluate the pooled risk ratios of 10 comorbidities among 4,973 COVID-19-positive patients (including 2,268 with mild and 731 with severe COVID-19). The random effects model was used to estimate the pooled risk ratio of disease severity. The tau² and I² statistics were used to evaluate the heterogeneity among studies (see Materials and Methods—Risk ratio analysis for COVID-19 patients). We found that patients with several disease comorbidities or risk factors—including COPD, cardiovascular disease, stroke, diabetes mellitus, chronic kidney disease, hypertension, cancer, and history of smoking—have significantly higher risks of severe COVID-19 (Fig 3B). The overall pooled risk ratio for patients with COPD was 4.33 (95% CI 2.42–7.74, P = 0.001) in 12 low heterogeneous clinical studies (I² = 0.0%, P = 0.6). The COVID-19 patients with cardiovascular diseases had a risk ratio of 3.87 (95% CI 1.97–7.59, P = 0.001), and there was slightly higher heterogeneity across the 13 studies (I² = 57.6%, P = 0.005). We next turned to quantify the network-based relationships between COVID-19 and human diseases in the human interactome model using state-of-the-art network proximity analysis [32,33,49].

Network-based measurement of COVID-19-associated disease manifestations

We systematically evaluated the network-based relationships of the 64 diseases across the 6 categories to COVID-19 (Fig 4A; S3 Data). We used the state-of-the-art network proximity measure to evaluate the connectivity and the closeness of the disease proteins and SARS-CoV-2 host proteins, taking the topology of the human interactome network into consideration. To test the significance of the proximity, Z scores and P values were calculated based on permutation tests (see Materials and Methods—Network proximity measure) and are shown in Fig 4A. We found that each disease–disease pair has a well-defined network-based footprint. If the footprint between the COVID-19 module and another disease module is significantly close (low Z score and P < 0.05), the magnitude of the proximity is indicative of their biological relationship: Closer network proximity (Fig 4A) of SARS-CoV-2 host genes/proteins with a disease module indicates higher potential of manifestation between COVID-19 and a specific disease. We first noticed that immunological, pulmonary, and neurological diseases showed significant network proximity to different SARS-CoV-2 maps more frequently than did cancer, cardiovascular, and metabolic diseases. Several diseases have significant network proximities to more than 1 SARS-CoV-2 dataset, most notably inflammatory bowel disease (IBD), attention-deficit/hyperactivity disorder, and stroke, which achieved significant P values for all 5 SARS-CoV-2 protein sets. Pulmonary diseases, including COPD, lung injury, pulmonary fibrosis, and respiratory failure, achieved 4 significant proximities. Some diseases have significant proximities to certain SARS-CoV-2 datasets, indicating associations at certain levels, e.g., asthma (transcriptomic), respiratory distress syndrome (proteomic), and hypertension (HCoV-PPI and PanCoV-PPI).

Network visualization can further show the connections between SARS-CoV-2 and other diseases, for example, respiratory distress syndrome (Fig 4B), sepsis (Fig 4C), and COPD (S7 Fig). Respiratory distress syndrome and sepsis are the 2 main causes of mortality in patients with severe COVID-19 [50,51]. We found that multiple SARS-CoV-2 host proteins are directly connected with the disease-associated proteins (Fig 4B). ABCA3 is a lipid transporter located in the outer membrane of lamellar bodies in AT2 cells; mutations of the ABCA3 gene can disrupt pulmonary surfactant homeostasis and lead to inherited pulmonary diseases [52]. Another membrane surface protein, the pulmonary-associated surfactant protein C encoded by SFTPC, can cause lung injury when misfolded [53]. For sepsis, we noticed several inflammatory and immune-related proteins, such as IRAK1, IRAK3, IKBKB, and STAT3, in the network, suggesting overlap of the inflammatory response activated in COVID-19 and sepsis (Fig 4C). It has been reported that an overzealous production of certain cytokines, such as IL-6, caused by dysregulation of innate immune responses to SARS-CoV-2 infection, can result in a “cytokine storm,” better known as cytokine release syndrome (CRS) [54]. The potential prognoses of acute respiratory distress syndrome and sepsis using IL-6 expression levels have also been established [55–57]. IL-6 also has a significantly increased expression level in the human bronchial epithelial cells infected with SARS-CoV-2 from the SARS2-DEG dataset [21]. In addition, it is also potentially affected by SARS-CoV-2 through multiple PPIs (Fig 4B and 4C, IL-6 neighbors), such as IL-6R, endophilin A1 (encoded by SH3GL2), and parathyroid hormone like hormone (encoded by PTHLH). Our random effects meta-analysis of 5 clinical studies of COVID-19 revealed that there was an increase of IL-6 levels in patients with severe COVID-19 compared to those with non-severe COVID-19 (Fig 4D). The mean difference was 33.0 pg/ml (95% CI 0.58–65.3; Fig 4D), with high literature heterogeneity (I² = 94%, P < 0.001). These results indicate that IL-6 plays a critical role in COVID-19-associated respiratory distress syndrome and sepsis. Due to the importance of IL-6 in SARS-CoV-2 infection, IL-6 antagonists including tocilizumab [58] (NCT04315480) and sarilumab (NCT04327388) are being investigated in clinical trials for treatment of patients with severe COVID-19.

As an illustration of the shared pathobiology and inflammatory pathways of COVID-19 with various disease manifestations (Fig 4), we next turned to focus on 2 inflammation-driven diseases, asthma and IBD.

Inflammatory molecular profile shared by COVID-19 and asthma

Patients with severe COVID-19 symptoms showed a higher prevalence of dyspnea (S8A Fig; P < 0.001). To understand the associations between COVID-19 and respiratory disease (including asthma), we adopted a multimodal analysis utilizing bulk and single-cell transcriptomics data and metabolomics data under the human interactome network model. To be specific, we identified the enzymes in the network that are associated with altered metabolites in COVID-19 patients. Comparing the DEGs from asthma patients and DEGs from COVID-19 patients, we aimed to find the common genes/proteins or interacting proteins in these patient groups. Using network analyses (degree enrichment and eigenvector centrality), we can rank the importance of these genes. Last, we examined their expression in the cell types that are more susceptible to SARS-CoV-2 (i.e., cells expressing more ACE2 and TMPRSS2).

Fig 5A shows the subnetwork of the connections among SARS-CoV-2 target host proteins and asthma-associated proteins. Most of these proteins have enriched connections within the subnetwork (Fig 5B; degree enrichment), i.e., more connections in the subnetwork than in a random network of the same size in the human interactome. To evaluate the potential influence of the nodes in the network, we also computed the eigenvector centrality of these proteins. A higher eigenvector centrality suggests a higher network influence of a specific protein node. Six overlapped proteins (orange) from both groups were identified: NFKBIA, IRAK3, TNC, IL-6, ADRB2, and CD86. A recent study showed that glucose metabolism plays a key role in influenza A–regulated cytokine storm [59]. The plasma metabolome of asthma patients versus healthy controls also suggests activated inflammatory and immune pathways [60]. Therefore, in addition to PPIs, we also integrated metabolomics data generated in a previously assembled asthma cohort [61]. By matching the enzymes of the differential metabolites and the proteins in the PPI network, we found 3 key metabolites: arachidonate, L-arginine, and L-citrulline. L-arginine and L-citrulline were decreased in the sera of COVID-19 patients [62]. These metabolites were also decreased in asthma patients [61]. Arachidonate, the precursor of a variety of products that regulate inflammatory pathways [63], was found to have an increased level in the inflamed airways of asthma patients [64]. Arachidonate can be converted by 5-lipoxygenase encoded by ALOX5 to leukotriene, which is released during an asthma attack and is responsible for the bronchoconstriction [65].

We further examined the gene expression data from 2 asthma cohorts from the Severe Asthma Research Program [66,67]. Utilizing 2 bulk RNA-Seq datasets (GSE63142 and GSE130499) of asthma patients compared to healthy controls, we identified that IRAK3 and ADRB2 had significantly elevated expression (false discovery rate [FDR] < 0.05) in asthma patients. Both genes also have significantly elevated expression in SARS-CoV-2-infected human bronchial epithelial cells (Fig 5B). IRAK-M, encoded by IRAK3, regulates the toll-like receptor/interleukin-1 receptor pathway and NF-κB pathway, and IRAK3 was identified as an asthma susceptibility gene [68]. ADRB2 encodes the beta2-adrenergic receptor. The polymorphisms of ADRB2 (p.Arg16Gly and p.Gln27Glu) increase the risk of asthma occurrence, and p.Gln27Glu is associated with asthma severity [69]. IRAK3 and ADRB2 also have high eigenvector centrality scores (top 5 and top 2 among these genes, respectively; Fig 5B, eigenvector centrality). Altogether, altered IRAK3 and ADRB2 expression may explain relationships between COVID-19 and asthma, though these findings require experimental and clinical validation in patients with these disorders.

To understand the expressions of the proteins in the asthma–COVID-19 network across different cell types, especially cells that express ACE2, we analyzed the single-cell RNA-Seq data from bronchial epithelium (Fig 5C) and lung (Fig 5F) [14]. Consistent with previous studies, ACE2 and TMPRSS2 have higher expression in a subtype of the secretory cells (secretory 3 cells) compared to other bronchial epithelial cell types (Figs 5D and S8B–S8F). In the lung, ACE2 and TMPRSS2 have relatively higher expression in AT2 cells (Fig 5G and S8G–S8K). We further examined the expression of the genes in the asthma–COVID-19 network (Fig 5A) in these cell types (S9 Fig). Several genes were also more highly expressed in secretory 3 cells (Fig 5E; ALOX5, IRAK3, ADRB2, TNIP1, BID, CXCL5, and NFKBIZ) and in AT2 cells (Fig 5H; CFTR, NFKBIA, NFKBIZ, and TNC), than in other cell types. IRAK3 and ADRB2 are among the 6 overlapped genes, potentially implicating the roles of IRAK3 and ADRB2 in COVID-19-associated asthma at the single-cell level as well.

Immune pathobiology shared by COVID-19 and IBD

It has been shown, using confocal and electron microscopy, that human small intestine is an additional SARS-CoV-2 target organ [70]. Diarrhea is now well described as an occasional presenting symptom of COVID-19 [71]. Our network proximity analysis showed a significant association of COVID-19 and IBD across all 5 SARS-CoV-2 datasets (Fig 4A). In addition, patients with severe COVID-19 had higher risks of abdominal pain and diarrhea (Figs 6A and S10). To understand these associations at the cellular level, we integrated network analysis and single-cell RNA-Seq analysis using publicly available data [72]. As shown in Fig 6B (S5 Data), although only 1 IBD-associated protein, HEATR3, was found to be the target of the SARS-CoV-2 protein Orf7a, other IBD-associated proteins showed an enriched number of connections to the SARS-CoV-2 target proteins (Fig 6I, degree enrichment).

Fig 6 — (A) Patients with severe COVID-19 have higher risks of abdominal pain and diarrhea by meta-analysis. (B) A highlighted subnetwork between the IBD-associated genes, the SARS-CoV-2 virus proteins, and virus target proteins under the human interactome network model. (C) UMAP visualization of non-epithelial cells from the ileal tissues of patients with Crohn disease. (D) UMAP visualization of epithelial cells from the ileal tissues of patients with Crohn disease. (E) Cell-type-specific expression of *ACE2* and *TMPRSS2* in non-epithelial cells (C). (F) Cell-type-specific expression of *ACE2* and *TMPRSS2* in epithelial cells (D). (G) The co-expression of *ACE2* and *TMPRSS2* is elevated in absorptive enterocytes of inflamed ileal tissues compared to uninflamed tissues in patients with Crohn disease. (H) Box plot showing the expression of *ACE2* and *TMPRSS2* in absorptive enterocytes expressing *ACE2* and *TMPRSS2*, respectively. (I) Co-expression analysis for the genes in the subnetwork with *ACE2* and *TMPRSS2*. Heatmap shows the Pearson correlation coefficients (PCCs) of *ACE2* and *TMPRSS2* with other genes (labeled in [B]) in the absorptive enterocytes. Blue bars show the degree enrichment of the genes in the subnetwork compared to a random network of the same size (left) and the eigenvector centrality (right). Asterisks indicate that these genes may play important roles in COVID-19-associated IBD. The data underlying this figure can be found in S5 Data. Single-cell data were retrieved from the NCBI GEO database using the accession number GSE134809. See S1 Table for more details of the datasets.

Using single-cell data from the ileum (distal small bowel) in Crohn disease patients [72], we found that ACE2 and TMPRSS2 had low to undetectable expression in the non-epithelial cells (Figs 6C, 6E, and S11A–S11E). However, they showed higher expression levels in the epithelial cells, especially absorptive enterocytes (Figs 6D, 6F and S11F–S11J). We further found that both ACE2 and TMPRSS2 had elevated expression levels in inflamed cells compared to uninflamed cells in the absorptive enterocytes (Figs 6G, S11K and S11L). The Pearson correlation coefficient (PCC) of ACE2 and TMPRSS2 was also increased in the inflamed cells compared to uninflamed cells (PCC = 0.165 versus PCC = −0.006) in the absorptive enterocytes. In absorptive enterocytes expressing ACE2, the expression of ACE2 was significantly increased (Fig 6H; P = 0.016) in the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues. These observations prompted us to investigate the co-expression of the network genes in the absorptive enterocytes (Fig 6I). Several genes showed elevated co-expression with ACE2 in inflamed cells, such as XIAP, SMAD3, DLG5, SLC15A1, RAC1, STOM, RAB18, and AKAP8.

We next turned to highlight 2 potential associations between COVID-19 and IBD. First, SARS-CoV-2 protein Orf7a can directly interact with HEATR3 (top 2 eigenvector centrality; Fig 6I), whose variant was shown to be associated with increased risk of IBD by genome-wide association study [73]. Second, SARS-CoV-2 infection may impact RAC1 (top 4 eigenvector centrality; Fig 6I) signal transduction pathways. RAC proteins play important roles in many inflammatory pathways, and their dysregulation can be pathogenic. Increased RAC1 expression by single nucleotide polymorphisms promotes an inflammatory response in the colon [74]. Mercaptopurine, an effective treatment for IBD, was found to lower RAC1 expression in IBD patients [75]. Since our results show that RAC1 and ACE2 had higher co-expression in inflamed enterocytes (Fig 6I), it is highly possible that these inflamed cells are more susceptible to SARS-CoV-2 infection, and that the infection could lead to an altered RAC1 expression level through PPIs with virus target proteins STOM, HDAC2, POLA2, CIT, and RAP1GDS1 (Fig 6B). Notably STOM (top 12 eigenvector centrality) was also more co-expressed with ACE2 in inflamed cells compared to uninflamed cells (Fig 6I).

Network-based drug repurposing for COVID-19

Knowledge of the complex interplays between SARS-CoV-2 targets and human diseases indicate possibilities of drug repurposing, as the drugs that target other diseases could potentially target SARS-CoV-2 through the shared functional PPI networks [23]. In addition, drug repurposing efforts may also reveal unrecognized biological connections between originally approved indications/diseases and COVID-19. For example, the aforementioned anti-inflammatory drugs tocilizumab and sarilumab that are now being tested for COVID-19 were originally used for rheumatoid arthritis. Although not significant, our network proximity results show that rheumatoid arthritis has small network proximities (negative Z scores) across all 5 SARS-CoV-2 datasets (Fig 4A). Another drug, the thiopurine mercaptopurine, which has been used to treat IBD [76], was one of the top repurposable drugs for COVID-19 proposed in our previous work [30].

Therefore, we next performed network-based drug repurposing modeling using the existing knowledge of the drug–target network and the global map of the SARS-CoV-2 interactome built in this study. The basis for the network-based drug repurposing methodologies is the observation that for a drug with multiple targets to be effective against a disease, its target proteins should be within or in the immediate vicinity of the corresponding subnetwork of the disease in the human interactome, as we have demonstrated in multiple diseases previously [31,32]. Using our state-of-the-art network proximity framework, we measured the “closest” proximities of nearly 3,000 drugs and the 4 SARS-CoV-2 host gene/protein profiles (SARS2-DEG, SARS2-DEP, HCoV-PPI, and SARS2-PPI; S6 Table). Additionally, we performed gene set enrichment analysis (GSEA) using 5 gene/protein expression datasets: 1 SARS-CoV-2 transcriptomics dataset, 1 SARS-CoV-2 proteomics dataset, 1 MERS-CoV dataset, and 2 SARS-CoV-1 transcriptomics datasets. GSEA was used to evaluate the individual drugs for their potential to reverse the expression at the transcriptome or proteome level altered by the viruses [35].

We next prioritized the drug candidates using subject matter expertise based on a combination of factors: (1) strength of the network-based and bioinformatics-based predictions (a higher network proximity score and significant GSEA score; Fig 7; S6 Data), (2) literature-reported antiviral activities or ongoing clinical trial information, (3) availability of sufficient patient data for meaningful evaluation (exclusion of infrequently used medications) from our COVID-19 registry database, and (4) well-defined antiviral mechanisms of action (such as anti-inflammatory or immune modulators).

Fig 7 — Thirty-four drugs from the top predicted list are highlighted, with the disease category they are approved for by the US Food and Drug Administration. We highlight 3 types of evidence: (1) network proximities of drugs’ targets across the 4 SARS-CoV-2 datasets (SARS2-DEG, SARS2-DEP, HCoV-PPI, and SARS2-PPI) in the human interactome, (2) gene set enrichment analysis (GSEA) scores across 5 coronavirus transcriptomics and proteomics datasets, and (3) literature-reported antivirus profiles. GSEA scores shown in grey indicate that these drugs cannot be evaluated due to the lack of data. Eight drugs that are currently being or have been tested in COVID-19 clinical trials are highlighted. Horizontal bars in boxes indicate P < 0.05. The data underlying this figure can be found in S6 Data. ANDV, Andes virus; CHIKV, chikungunya virus; CMV, cytomegalovirus; DENV, dengue virus; EBOV, Zaire ebolavirus; EMCV, encephalomyocarditis virus; ES, enrichment score; HAV, hepatitis A virus; HBV, hepatitis B virus; HCoV-229E, human coronavirus 229E; HCV, hepatitis C virus; HDV, hepatitis D virus; HIV, human immunodeficiency virus; IAV, influenza A virus; IBV, influenza B virus; MERS-CoV, Middle East respiratory syndrome coronavirus; MHV, mouse hepatitis virus; MV, measles virus; RSV, respiratory syncytial virus; RV, rhinovirus; SARS-CoV, severe acute respiratory syndrome coronavirus; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SVCV, spring viremia of carp virus; WNV, West Nile virus; ZIKV, Zika virus.

In total, we computationally identified 34 drugs that were associated (Z < −1.5 and P < 0.05, permutation test) with the SARS-CoV-2 datasets (SARS2-DEG, SARS2-DEP, HCoV-PPI, and SARS2-PPI) using the above criteria. These drugs were significantly proximal to 2 or more SARS-CoV-2 host protein sets (Fig 7; S6 Data). We manually curated their reported antiviral profiles. The disease categories that these drugs have been used to treat are also shown in Fig 7. Ten drugs have been used to treat respiratory-related diseases, and the most common categories for these drugs are antibiotic and β2 agonist. The next most common disease category is cardiovascular diseases, for which 7 drugs were predicted. Among the 34 drugs, 3 drugs achieved significant network proximity with all 4 SARS-CoV-2 datasets investigated here. These drugs are (1) the antibiotic drug cefdinir, which is a cephalosporin for the treatment of bacterial infections [77]; (2) the antineoplastic drug toremifene, a selective estrogen receptor modulator that shows striking activities in blocking various viral infections at low micromolar levels, including Ebola virus [78] (50% inhibitive concentration [IC₅₀] = approximately 1 μM), MERS-CoV [79] (50% effective concentration [EC₅₀] = 12.9 μM), SARS-CoV-1 [80] (EC₅₀ = 11.97 μM), and SARS-CoV-2 [81] (IC₅₀ = 3.58 μM); and (3) the antihypertensive drug irbesartan, an angiotensin II receptor blocker (ARB) that can inhibit viral entry by inhibiting sodium/bile acid cotransporters [82].

Evidence from the COVID-19 registry data that supports the predicted drug repurposing strategies

We next evaluated drug–outcome relationships using a large-scale patient dataset from the Cleveland Clinic COVID-19 patient registry (see Materials and Methods—Patient data validation of the network-identified drugs using a COVID-19 registry). Applying subject matter expertise to the 34 repurposed drugs resulted in identifying melatonin, a physiologic hormone common to many living organisms, and carvedilol, approved for both hypertension and heart failure. A retrospective COVID-19 cohort analysis was conducted to validate the potential prevention effect of melatonin and carvedilol (Fig 8A and 8B). Among a total of 26,779 patients tested for COVID-19 in the Cleveland Clinic Health System in Ohio and Florida, 8,274 patients were diagnosed as SARS-CoV-2 positive confirmed by reverse transcription–polymerase chain reaction (RT-PCR) between March 8 and July 27, 2020 (Table 1).

Fig 8 — Validation for (A) melatonin and (B) carvedilol using the whole COVID-19 registry (all combined population). Validation for melatonin (C) in the black American (African American) subgroup and (D) in the white American subgroup. Patient groups were matched using propensity score matching. Four models were evaluated: (1) model 1 was matched using age, sex, race, and smoking without adjustment for the odds ratio; (2) model 2 was matched using age, sex, race, and smoking, and the odds ratio of COVID-19 was adjusted by age, sex, race, and smoking; (3) model 3 was matched using age, sex, race, smoking, coronary artery disease, diabetes, hypertension, and COPD without adjustment for the odds ratio; and (4) model 4 was matched using age, sex, race, smoking, coronary artery disease, diabetes, hypertension, and COPD, and the odds ratio of COVID-19 was adjusted by age, sex, race, smoking, coronary artery disease, diabetes, hypertension, and COPD. These models were adjusted for different variables using the propensity score matching approach (see Materials and Methods—Patient data validation of the network-identified drugs using a COVID-19 registry). ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; COPD, chronic obstructive pulmonary disease; OR, odds ratio.

Table 1. Baseline characteristics of the melatonin and carvedilol use groups from the COVID-19 registry.

Use group and characteristic	All individuals			SARS-CoV-2-positive patients
Use group and characteristic	Drug not used	Drug used	P value	Drug not used	Drug used	P value
Melatonin
Total patients	25,724	1,055		8,052	222
Age	48.75 ± 20.58	63.42 ± 19.77	<0.001	49.44 ± 20.54	67.75 ± 18.92	<0.001
Sex (female)	15,274 (59.4)	549 (52.0)	<0.001	4,528 (56.2)	114 (51.4)	0.168
Race other	1,376 (5.3)	48 (4.5)	0.287	462 (5.7)	3 (1.4)	0.008
Race black	5,848 (22.7)	273 (25.9)	0.019	2,721 (33.8)	70 (31.5)	0.528
Race white	16,182 (62.9)	695 (65.9)	0.054	4,290 (53.3)	142 (64.0)	0.002
Smoking	3,050 (13.8)	165 (16.1)	0.042	603 (9.0)	19 (8.8)	1
COPD & emphysema	1,768 (10.1)	257 (29.0)	<0.001	441 (10.4)	45 (26.2)	<0.001
Diabetes	4,681 (25.4)	417 (44.9)	<0.001	1,453 (31.4)	78 (42.9)	0.001
Hypertension	9,810 (49.7)	764 (76.4)	<0.001	3,148 (59.1)	170 (83.3)	<0.001
Coronary artery disease	2,699 (15.1)	330 (36.3)	<0.001	744 (17.1)	59 (34.7)	<0.001
Asthma	5,075 (27.5)	265 (29.5)	0.211	1,243 (26.9)	38 (22.2)	0.203
ACEIs	1,873 (7.3)	208 (19.7)	<0.001	607 (7.5)	48 (21.6)	<0.001
ARBs	1,371 (5.3)	148 (14.0)	<0.001	447 (5.6)	21 (9.5)	0.019
Carvedilol
Total patients	25,994	785		8,070	204
Age	48.81 ± 20.67	66.38 ± 15.40	<0.001	49.47 ± 20.65	68.28 ± 13.08	<0.001
Sex (female)	15,485 (59.6)	338 (43.1)	<0.001	4,550 (56.4)	92 (45.1)	0.002
Race other	1,385 (5.3)	39 (5.0)	0.717	460 (5.7)	5 (2.5)	0.066
Race black	5,841 (22.5)	280 (35.7)	<0.001	2,704 (33.5)	87 (42.6)	0.008
Race white	16,432 (63.2)	445 (56.7)	<0.001	4,328 (53.6)	104 (51.0)	0.497
Smoking	3,080 (13.8)	135 (17.8)	0.002	604 (9.0)	18 (9.1)	1
COPD & emphysema	1,862 (10.5)	163 (26.8)	<0.001	447 (10.5)	39 (27.1)	<0.001
Diabetes	4,656 (25.0)	442 (63.4)	<0.001	1,422 (30.6)	109 (66.9)	<0.001
Hypertension	9,837 (49.2)	737 (95.7)	<0.001	3,124 (58.6)	194 (97.5)	<0.001
Coronary artery disease	2,615 (14.5)	414 (60.7)	<0.001	704 (16.2)	99 (61.5)	<0.001
Asthma	5,184 (27.7)	156 (25.1)	0.169	1,243 (26.7)	38 (26.6)	1
ACEIs	1,866 (7.2)	215 (27.4)	<0.001	597 (7.4)	58 (28.4)	<0.001
ARBs	1,324 (5.1)	195 (24.8)	<0.001	418 (5.2)	50 (24.5)	<0.001

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Age is shown as mean ± standard deviation. All other characteristics are shown as number of cases (percentage). Percentages were calculated using the total number of patients with known information for each variable. P values were calculated by 2-sided t test for age and Fisher’s exact test for other variables.

ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; COPD, chronic obstructive pulmonary disease.

We found that melatonin usage was associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 (odds ratio [OR] = 0.72, 95% CI 0.56–0.91; Fig 8A) after adjusting for age, sex, race, smoking history, and various disease comorbidities (diabetes, hypertension, coronary artery disease, and COPD) using a propensity score (PS) matching method. Angiotensin-converting enzyme inhibitors (ACEIs) and ARBs are 2 common types of drugs for treatment of hypertension. A recent study showed that inpatient use of ACEI/ARB was associated with lower risk of all-cause mortality compared with ACEI/ARB non-use among hospitalized COVID-19 patients with hypertension [83]. Several recent studies also showed that there was no association of ARBs and ACEIs with the risk of SARS-CoV-2 infection [84–86]. We further performed an observational study for 3 cohorts using user active comparator design with ARBs and ACEIs used as comparators and PS adjustment for confounding factors as described in our previous study [32]. We found that melatonin usage was significantly associated with a reduced likelihood of a positive laboratory test result for SARS-CoV-2 compared to use of ARBs (OR = 0.70, 95% CI 0.54–0.92) and ACEIs (OR = 0.69, 95% CI 0.52–0.90) after adjusting for age, sex, race, smoking history, and various disease comorbidities (Fig 8A). Altogether, network-based prediction (Fig 7) and multiple observational analyses (Fig 8A) suggest that melatonin usage offers a potential prevention and treatment strategy for COVID-19; yet, randomized controlled clinical trials are urgently needed to test meaningfully the effect of melatonin for COVID-19.

We found that carvedilol use was significantly associated with a reduced likelihood of a positive laboratory test result for SARS-CoV-2 (OR = 0.74, 95% CI 0.56–0.97) after adjusting for age, sex, race, smoking history, and various disease comorbidities. Yet, carvedilol did not show a significant advantage compared to ARBs (OR = 0.90, 95% CI 0.65–1.25) or ACEIs (OR = 0.73, 95% CI 0.53–1.01).

We therefore tested whether a clinically meaningful effect of melatonin and carvedilol can be observed in different subgroups of patients. To be specific, we generated 5 different subgroups: asthma patients, hypertension patients, diabetes patients, black Americans (African Americans), and white Americans. We found that melatonin was significantly associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in black Americans (OR = 0.48, 95% CI 0.31–0.75; Fig 8C) after adjusting for age, sex, race, smoking, and various disease comorbidities, which is stronger than the association in white Americans (OR = 0.77, 95% CI 0.57–1.04; Fig 8D). In addition, in black Americans, melatonin usage was significantly associated with a reduced likelihood of a positive laboratory test result for SARS-CoV-2 compared to ARB usage (OR = 0.57, 95% CI 0.34–0.96; Fig 8C), while there was no significant difference compared to ACEI usage (OR = 0.65, 95% CI 0.39–1.11; Fig 8C). Yet, melatonin usage was not significantly associated with a reduced likelihood of a positive laboratory test result for SARS-CoV-2 compared to use of ARBs (OR = 0.80, 95% CI 0.57–1.13; Fig 8D) and ACEIs (OR = 0.85, 95% CI 0.60–1.20; Fig 8D) in white Americans. Among the 3 comorbid disease subgroup analyses, melatonin usage was significantly associated with a reduced risk of SARS-CoV-2 positive test in diabetes patients only (OR = 0.52, 95% CI 0.36–0.75; S12B Fig); there was no significant association for asthma (OR = 0.61, 95% CI 0.36–1.06; S12A Fig) or hypertension (OR = 0.80, 95% CI 0.61–1.05; S12C Fig) patients. Carvedilol usage was not significantly associated with a reduced likelihood of a positive laboratory test result for SARS-CoV-2 among the 5 subgroups after adjusting for age, sex, race, smoking, and various disease comorbidities (S12 and S13 Figs). Thus, further observations using large-scale independent cohorts to test the meaningful effect of carvedilol in reducing risk of COVID-19 are highly needed.

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.

We further investigated the molecular determinants of the association of COVID-19 and the comorbidities by multimodal analyses of large-scale bulk and single-cell transcriptomic profiles, metabolomic data, and the PPI networks. We identified the cell types that have the highest expression levels of ACE2 and TMPRSS2: the lung AT2 cells, secretory bronchial epithelial cells, and absorptive enterocytes in the ileum. We examined the expression of asthma- and IBD-associated genes in their relevant cell types. Combining these findings with the results of differential expression analysis, network analysis, and differential metabolites, we identified several key pathogenic pathways for asthma (including IRAK3 and ADRB2) that can be altered by the viral infection.

For asthma, our network-based findings suggest several possible shared pathobiological pathways associated with COVID-19. First, SARS-CoV-2 infection might alter the expression of several key inflammatory genes: IRAK3, which is associated with asthma [68,89,90]; ADRB2, which is an essential genetic factor for asthma [69,91]; and NFKBIA, which shows critical transcriptional responses in childhood asthma [92]. These genes show high expression in secretory 3 and AT2 cell types, suggesting a higher susceptibility to be impacted by SARS-CoV-2 infection through ACE2. SARS-CoV-2 increased the expression of IRAK3 and ADRB2, which lead to a higher risk of asthma (Fig 5B). Second, decreased levels of L-arginine and L-citrulline were found in SARS-CoV-2-infected patients [62], while it has been shown that higher levels of these metabolites are protective against asthma [61]. L-arginine can be converted to nitric oxide by the nitric oxide synthases, and it was shown that nitric oxide protects against viral infection through multiple potential mechanisms [93].

Our network proximity results show a strong connection between IBD and COVID-19 (Fig 4A). We have also shown that IBD-related pathways can potentially be affected by SARS-CoV-2 infection (Fig 6B). However, we should also note that the meta-analysis does not show a significant risk ratio of digestive system disease in COVID-19 patients (Fig 3B), while 2 specific symptoms, abdominal pain and diarrhea, showed increased risks in patients with severe COVID-19 (Fig 6A). Digestive system disease covers a broad range of gastrointestinal diseases. Future studies are needed to reveal and validate the associations of COVID-19 and individual gastrointestinal diseases.

Finally, we computationally prioritized nearly 3,000 FDA-approved/investigational drugs for their potential anti-SARS-CoV-2 effects using network proximity measurement and GSEA analysis. A list of 34 repurposable drugs with their reported antiviral profiles are highlighted, among which 8 drugs are currently in ongoing COVID-19 clinical trials (Fig 7). We further explored drug–disease outcome relationships for melatonin using a large-scale COVID-19 patient registry database. We found that among individuals who received testing for SARS-CoV-2, melatonin usage was associated with a 28% and 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in the all combined population (Fig 8A) and black Americans (Fig 8C), respectively, after adjusting for age, sex, race, smoking, and various disease comorbidities. Using user active comparator design, we further found that melatonin usage was associated with a reduced likelihood of a positive laboratory test result for SARS-CoV-2 compared to use of ARBs and ACEIs as well. Exogenous melatonin may be of benefit in older patients with COVID-19, given the aging-related reduction of endogenous melatonin and the greater vulnerability of older individuals to mortality from SARS-CoV-2 [94], the latter potentially due to declining immunity, i.e., immunosenescence [95]. Moreover, melatonin suppresses NLRP3 inflammasome activation induced by cigarette smoking and attenuates pulmonary inflammation [96], not only via reduction of NF-κB p65 and tumor necrosis factor-α (TNF-α) expression, but also via increase in anti-inflammatory cytokines such as IL-10 or IL-6, which can also have anti-inflammatory effects [97,98]. Thus, large-scale observational studies and randomized controlled trials are needed to validate the clinical benefit of melatonin for patients with COVID-19. It would be important, however, that the trials be designed with the understanding of the mechanism of the drug to be repurposed. For example, it would be obvious that drugs that decrease viral entry, e.g., part of melatonin’s action, would be beneficial in preventing infection or very early in the COVID-19 course, but may be inconsequential when utilized in severe or end-stage infection. Several randomized controlled trials are being performed to test the clinical benefits of melatonin in patients with COVID-19 (ClinicalTrials.gov NCT04409522 and NCT04353128). In addition, the Selective Estrogen Modulation and Melatonin in Early COVID-19 (SENTINEL) trial is underway to test the combination therapy [99] of melatonin with toremifene (an approved selective estrogen receptor modulator [100]) for patients with early and mild COVID-19 (ClinicalTrials.gov NCT04531748).

We acknowledge several potential limitations. First, although we integrated data from multiple sources to build the human interactome and the drug–target network, they are still incomplete. Second, this study relied on the SARS-CoV-2 target host gene/protein datasets, and their quality and literature bias may influence the performance of our network analysis. The genes in these datasets can differ significantly. DEGs identified from transcriptomics profiles can be very different from DEPs from proteomics data by multiple factors, which may influence the results of network proximity analysis as well. These datasets were from different cell types, and the COVID-19-relevant cell types may not be representative. PPIs and DEGs/proteins in different cell lines or tissues may contain false positives as well. Two recent studies suggested that chloroquine or hydroxychloroquine showed ideal antiviral activities in African green monkey kidney cells (VeroE6) but not in a model of reconstituted human airway epithelium [101] or the TMPRSS2-positive lung cell line Calu-3 [102]. These studies showed that cell lines mimicking important aspects of respiratory epithelial cells should be used when analyzing the antiviral activity of drugs targeting host cell functions. In the original SARS2-PPI dataset based on the VeroE6 cell line, a key PPI for ACE2–spike protein was absent. We posited that combining transcriptomics profiles and proteomics data derived from diverse COVID-19-relevant cell lines or tissues may provide complementary molecular information to overcome the high disease heterogeneities of COVID-19 [103]. In addition, through creating a pan-coronavirus PPI network by combining HCoV-PPI and SARS2-PPI, we aimed to identify broad-spectrum antiviral medications for SARS-CoV-2—and other emerging coronaviruses, if broadly applied—with our network medicine framework.

Third, our method can only apply to diseases with well-characterized genetic information and may not be applicable for diseases that lack such information, such as rare diseases (i.e., cerebral palsy or mental conditions). Potential literature bias of disease-associated genes and the human interactome may also influence our findings. For example, well-studied genes associated with both COVID-19 and other diseases may explain the similarity of COVID-19 with other diseases, while the understudied genes associated with both diseases may not be uncovered. Fourth, the patient data analysis is retrospective, may have selection bias, and is limited for commonly used drugs due to patient data availability. Dose and period of the medications were also missing in our current COVID-19 patient registry database. Although we performed multiple types of PS matching, residual confounding is possible despite high-dimensional covariate adjustment. Carvedilol use did not show a significant advantage compared to use of ARBs (OR = 0.90, 95% CI 0.65–1.25) or ACEIs (OR = 0.73, 95% CI 0.53–1.01) after adjusting for age, sex, race, smoking history, and multiple disease comorbidities (Fig 8B). In addition, carvedilol was not significantly associated with a reduced likelihood of a positive laboratory test result for SARS-CoV-2 among 5 subgroup analyses (S12 and S13 Figs). There are 2 possible explanations: (1) the small number of patients using carvedilol (Table 1) may yield insufficient statistical power for the current observational study or (2) carvedilol may only reduce the likelihood of a positive laboratory test result for SARS-CoV-2 for patients having existing health conditions, such as hypertension. Replication of the associations and causal inference using large-scale independent cohorts may rule out treatment effect heterogeneity and possible confounding further. Finally, although we made the intriguing observation that the use of melatonin was much less prevalent in individuals testing positive for SARS-CoV-2, we recognize that many asymptomatic or minimally symptomatic persons with the virus were not tested and, therefore, their use of melatonin was not evaluable; melatonin use in this latter group might also have been high. It should therefore be noted that all drugs we identified as therapeutic candidates in this study must be validated using experimental assays and randomized clinical trials before they can be recommended for use in patients with COVID-19.

Recent studies have suggested that COVID-19 is a systemic disease that has impacts on multiple cell types, tissues, and organs [104]. Our network methodology could potentially be improved by using tissue-specific genes for the diseases and incorporating directionality of the gene/protein biological effects. We recomputed network proximity using only genes that have a tissue specificity ≥ 0 in the associated disease: We found overall consistent results (S15 Fig) compared to lung-specific network analysis (Fig 4A). For example, IBD achieved significant proximities across all 5 SARS-CoV-2 gene/protein datasets in Fig 4A. When using genes with tissue specificity ≥ 0, IBD showed the same results in small intestine, but not in colon (S15 and S16 Figs). Type 2 diabetes showed significant network proximities across all 5 SARS-CoV-2 gene/protein datasets using pancreas-specific genes (S15 Fig), which is consistent with recent clinical observations [105]. Yet, the GTEx database used in this study was from healthy tissues, which may not represent the gene expression state in the disease condition. In addition, the disease-associated genes used this study were based on somatic or germline genetic evidence. For example, we did not observe significant association of COVID-19 with cancer. One possible explanation is that cancer is a more somatic-mutation-driven, chronic disease than COVID-19, which involves an acute immune response and inflammation-driven heterogeneous disease.

We also experimented with incorporating the directionality of the gene expression using the 2 asthma expression datasets and SARS2-DEG, since the direction of the DEGs are available in those 2 datasets. We found more significant network proximities and smaller Z scores between asthma and COVID-19 when using the up- or down-expressed genes separately (S17 Fig), suggesting that incorporating the directionalities of genes/proteins may improve the performance of network analysis. A previous study has shown that integration of the directionality of the human interactome didn’t change the results of network proximity measurement [49]. Owing to the lack of a systematic human interactome with well-documented directionalities, and without comprehensive information about whether a viral protein activates or inhibits a host protein, we didn’t test the influence of the directionality of proteins and the human interactomes on our findings in a systematic way. Therefore, future work is needed to explore well-documented directionalities in human interactome network analysis that integrates precise perturbation effects of disease-causing variants and viral proteins.

In conclusion, our study provides a powerful, integrative network medicine strategy for advancing understanding of COVID-19-associated comorbidities and facilitating the identification of drug candidates for COVID-19. This approach also promises to address the translational gap between genomic studies and clinical outcomes, which poses a significant problem when rapid development of effective therapeutic interventions is critical during a pandemic. From a translational perspective, if broadly applied, the network medicine tools applied here could prove helpful in developing effective treatment strategies for other complex human diseases as well, including other emerging infectious diseases.

Materials and methods

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

Building the datasets of SARS-CoV-2 target host genes/proteins

We assembled 4 SARS-CoV-2 datasets of target host genes/proteins: (1) 246 DEGs in human bronchial epithelial cells infected with SARS-CoV-2 [21] (GSE147507), denoted as SARS2-DEG; (2) 293 DEPs in human Caco-2 cells infected with SARS-CoV-2 [22], denoted as SARS2-DEP; (3) 134 strong literature-evidence-based pan-human coronavirus target host proteins from our recent study [30] with 15 newly curated proteins, denoted as HCoV-PPI; and (4) 332 proteins involved in PPIs with 26 SARS-CoV-2 viral proteins identified by affinity purification–mass spectrometry (AP-MS) [8], denoted as SARS2-PPI. Finally, due to the interactome nature of HCoV-PPI and SARS2-PPI, we combined these datasets as the fifth SARS-CoV-2 dataset, which has 460 proteins and is denoted as PanCoV-PPI. Details of these datasets can be found in S2 Table.

SARS2-DEG

In the original study, the primary human bronchial epithelial cells were infected with SARS-CoV-2 for 24 hours. The transcriptome profiles of infected (3 replicates) and uninfected cells (3 replicates) were characterized, and the fold change (FC) and FDR for each gene were calculated by DESeq2 and provided in the original study. We applied a cutoff of |log₂FC| > 0.5 and FDR < 0.05 to identify the DEGs.

SARS2-DEP

As described in the previous study [22], human Caco-2 cells were infected with SARS-CoV-2 for up to 24 hours. Proteomics assays of the infected and uninfected cells were measured at 24 hours in triplicates. We used the results at 24 hours, as the original study showed most DEPs at 24 hours. The P values were computed using 2-sided unpaired Student t tests with equal variance assumed in this study. We converted the P value to FDR using the “fdrcorrection” function in the Python package statsmodels v0.11.1 and used a cutoff of FDR < 0.05 to identify the DEPs.

Collection of 4 additional virus–host gene/protein networks

To characterize the SARS-CoV-2 datasets, we downloaded 4 virus–host gene/protein networks from previous studies for comparison: (1) 900 virus–host interactions identified by gene-trap insertional mutagenesis connecting 10 other viruses and 712 host genes [35]; (2) 2,855 virus–host interactions identified from RNAi connecting 2,443 host genes and 55 pathogens [35]; (3) 579 host proteins mediating translation of 70 innate immune-modulating viORFs [36]; and (4) 1,292 host genes identified by Co-IP+LC/MS that mediate influenza–host interactions [37]. All details for these 4 virus–host gene/protein networks are provided in S3 Table.

Building the disease gene profiles

We compiled the disease-associated gene sets from various sources. All databases were accessed on March 26, 2020.

Cancer

We defined a driver gene as a gene that had significantly enriched driver mutations based on whole-genome or whole-exome sequencing data or reported experimental data from the Cancer Gene Census [42,43] or the original publications from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). The pan-cancer driver genes were retrieved from the Cancer Gene Census [42,43]. Driver genes for individual cancer types were from a previous study [44].

Mendelian disease genes (MDGs)

A set of 2,272 MDGs were retrieved from the Online Mendelian Inheritance in Man (OMIM) database [106].

Orphan disease-causing mutant genes (ODMGs)

A set of 2,124 ODMGs were retrieved from a previous study [107].

Cell cycle genes

A set of 910 human cell cycle genes were downloaded from a previous study in which they were identified by a genome-wide RNAi screening [108].

Innate immune genes

A set of 1,031 human innate immune genes were collected from InnateDB [109].

Genes associated with autoimmune, pulmonary, neurological, cardiovascular, and metabolic diseases

The disease-associated genes/proteins were extracted from HGMD [45]. HGMD is a well-documented database, and we downloaded the whole database for data analysis and extraction using well-documented disease ontology terms [46]. We defined a disease-associated gene as a gene that has at least 1 disease-associated mutation in original publications provided in HGMD. The details, including the sources, number of genes, mutations associated with the disease, and terms used to identify diseases in HGMD, are provided in S4 Table.

Functional enrichment analysis

We performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) biological process enrichment analyses to reveal the biological relevance and functional pathways of the 5 SARS-CoV-2 datasets. All functional enrichment analyses were performed using Enrichr [110]. An overview of the virus infection-related pathways and ontology terms shared by 1 or more datasets was generated by searching for significant pathways or terms (FDR < 0.05). The enrichment analysis results for the 5 SARS-CoV-2 gene/protein sets can be found in S1–S5 Figs.

Selective pressure and evolutionary rate characterization

We calculated the dN/dS ratio [111] and the evolutionary rate ratio [112] as described in our previous study [113]. A dN/dS ratio below, equal to, or above 1 suggests purifying selection, neutral evolution, or positive Darwinian selection, respectively [114]. The evolutionary rate ratio was computed using the criterion that a ratio > 1 indicates a fast rate and a ratio < 1 indicates a slow rate [112]. The dN/dS and evolutionary rate ratios of the genes in the 5 SARS-CoV-2 datasets and 4 additional virus gene/protein sets can be found in S2 and S3 Tables.

Tissue specificity analysis

The RNA-Seq data (transcripts per million [TPM]) of 33 tissues from the GTEx V8 release (accessed on March 31, 2020; https://www.gtexportal.org/home/) were downloaded. Genes with count per million (CPM) ≥ 0.5 in over 90% of samples in a tissue were considered tissue-expressed genes, and otherwise tissue-unexpressed. To quantify the expression specificity of gene i in tissue t, we calculated the mean expression E_i and the standard deviation σ_i of a gene’s expression across all considered tissues. The significance of gene expression specificity in a tissue is defined as

z_{i t} = \frac{E_{i t} - E_{i}}{σ_{i}}

(1)

Risk ratio analysis for COVID-19 patients

PubMed, Embase, and medRxiv databases were searched for publications as of April 25, 2020 (S18 Fig). The search was limited to articles in English describing the demographic and clinical features of SARS-CoV-2 cases. We used the search term (“SARS-COV-2” OR “COVID-19” OR “nCoV 19” OR “2019 novel coronavirus” OR “coronavirus disease 2019”) AND (“clinical characteristics” OR “clinical outcome” OR “comorbidities”). Only research articles were included; reviews, case reports, comments, editorials, and expert opinions were excluded. Three criteria were used to select studies from a total of 1,054 initial hits: (1) studies that had ≥20 COVID-19 patients; (2) studies that grouped the outcomes by degree of severity of COVID-19 (e.g., severe versus non-severe) according to the American Thoracic Society guidelines for community-acquired pneumonia; and (3) studies that were from different institutions. Two criteria were used for exclusion: (1) studies that focused on specific populations (e.g., only death cases, pregnant women, children, or family clusters) and (2) basic molecular biology research. Finally, 34 studies meeting these criteria were used for further analyses.

We performed random effects meta-analysis to estimate the pooled risk ratio with 95% CI of 10 comorbidities for patients with severe versus non-severe COVID-19. The Mantel–Haenszel method was used to estimate the pooled effects of results [115]. The DerSimonian–Laird method was used to estimate the variance among studies [116]. Continuous data such as IL-6 levels were transformed to mean and standard deviation first using Wan’s approach based on sample size, median, and interquartile range [117]. Next, we used the inverse variance method to estimate the pooled mean difference and estimated the variance among studies using the DerSimonian–Laird method. We estimated the pooled prevalence of 3 COVID-19 symptoms (abdominal pain, diarrhea, and dyspnea) and 1 comorbidity (COPD) in 3 COVID-19 patient groups (severe, non-severe, and all). A random intercept logistic regression model was used to estimate pooled prevalence, and a maximum-likelihood estimator was used to quantify the heterogeneity of studies [118]. The tau² and I² statistics were calculated for the heterogeneity among studies. We considered I² ≤ 50% as low heterogeneity among studies, 50% < I² ≤ 75% as moderate heterogeneity, and I² > 75% as high heterogeneity. All meta-analyses were conducted using the meta and dmetar packages in the R v3.6.3 platform.

Building the human protein–protein interactome

A total of 18 bioinformatics and systems biology databases were assembled to build a comprehensive list of human PPIs with 5 types of experimental evidence: (1) protein complexes data identified by a robust AP-MS methodology collected from BioPlex V2.016 [119]; (2) binary PPIs tested by high-throughput yeast-two-hybrid (Y2H) systems from 2 publicly available high-quality Y2H datasets [120,121] and 1 in-house dataset [32]; (3) kinase–substrate interactions identified by literature-derived low-throughput or high-throughput experiments from Kinome NetworkX [122], Human Protein Resource Database (HPRD) [123], PhosphoNetworks [124], PhosphoSitePlus [125], DbPTM 3.0 [126], and Phospho.ELM [127]; (4) signaling networks identified by literature-derived low-throughput experiments from SignaLink 2.0 [128]; and (5) literature-curated PPIs identified by AP-MS, Y2H, literature-derived low-throughput experiments, or protein 3D structures from BioGRID [129], PINA [130], INstruct [131], MINT [132], IntAct [133], and InnateDB [109]. Inferred PPIs based on gene expression data, evolutionary analysis, and metabolic associations were excluded. Genes were mapped to their Entrez ID based on the NCBI database [134]. The official gene symbols were based on GeneCards (https://www.genecards.org/). The final human protein–protein interactome used in this study included 351,444 unique PPIs (edges or links) connecting 17,706 proteins (nodes). Detailed descriptions for building the human protein–protein interactome are provided in our previous studies [31–33,135]. An overview of the human protein–protein interactome can be found in S19 Fig.

Network proximity measure

We used the “closest” network proximity measure throughout this study. For 2 gene/protein sets A and B, their closest distance d_AB was calculated as

〈 d_{A B} 〉 = \frac{1}{‖ A ‖ + ‖ B ‖} (\sum_{a \in A} {m i n}_{b \in B} d (a, b) + \sum_{b \in B} {m i n}_{a \in A} d (a, b))

(2)

where d(a,b) is the shortest distance of a and b in the human interactome. To evaluate the significance, we performed a permutation test using randomly selected proteins from the whole interactome that were representative of the 2 protein sets being evaluated in terms of their degree distributions. We then calculated the Z score as

Z_{d_{A B}} = \frac{d_{A B} - \bar{d_{r}}}{σ_{r}}

(3)

where $\bar{d_{r}}$ and σ_r were the mean and standard deviation of the permutation test. All network proximity permutation tests in this study were repeated 1,000 times.

Network-based comorbidity analysis

To reveal potential COVID-19 comorbidities, we computed the network proximity of the disease-associated proteins for each disease and the 5 SARS-CoV-2 datasets. SARS-CoV-2 target proteins with a non-negative tissue specificity in lung were used in the computation. The degree enrichment for protein i in a subnetwork was calculated as

e_{i} = \frac{d_{i} / n}{D_{i} / N}

(4)

where d_i is the degree of i in the subnetwork, n is number of nodes in the subnetwork, D_i is the degree in the complete human protein interactome, and N is the total number of nodes in the interactome. The log₁₀ e_i value is reported.

We also computed the eigenvector centrality [136] of the nodes to evaluate their influence in the network topology while also considering the importance of their neighbors. A high eigenvector centrality value suggests that the node is connected to many other nodes with high eigenvector centrality scores as well. The computation was performed using Gephi 0.9.2 (https://gephi.org/).

Bulk and single-cell RNA-Seq data analysis

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

Bulk RNA-Seq datasets for asthma patients were retrieved from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/) using the accession numbers GSE63142 [66] and GSE130499 [67]. Differential expression of 3 comparisons—severe versus control, mild versus control, and severe versus mild—were performed using the GEO2R function (https://www.ncbi.nlm.nih.gov/geo/geo2r/) [137]. In GSE63142 [66], bronchial epithelial cells of 27 control samples, 72 mild asthma samples, and 56 severe asthma samples were obtained by bronchoscopy with endobronchial epithelial brushing. In GSE130499 [67], bronchial epithelial cells of 38 control samples, 72 mild asthma samples, and 44 severe asthma samples were available by bronchoscopy with endobronchial epithelial brushing as well. The differential expression analysis was performed by defining the groups in GEO2R first, then by selecting the 2 groups to compare. Genes with |log₂FC| > 0.5 and FDR < 0.05 were considered significantly differentially expressed.

Single-cell data of normal lung and primary human bronchial epithelial cells were downloaded from https://data.mendeley.com/datasets/7r2cwbw44m/1 [14]. These datasets contain 39,778 lung cells and 17,451 bronchial epithelial cells with cell type annotated. GSE134809 [72] was downloaded from the NCBI GEO database. This dataset contains 67,050 inflamed and uninflamed cells from the ileal samples of 8 patients with Crohn disease. Qualifying cells based on the criteria from the original paper were used for the single-cell analysis. We used the cell type gene markers from a previous study [72] (CD3D, CD2, CD7, TNFRSF17, MZB1, BANK1, CD79B, CD22, MS4A1, HLA-DRB1, HLA-DQA1, LYZ, IL3RA, IRF7, GZMB, LILRA4, CLEC4C, TPSAB1, CMA1, KIT, PLVAP, VWF, LYVE1, CCL21, COL3A1, COL1A1, ACTA2, GPM6B, S100B) for the non-epithelial cells. We used markers from Zhang et al. [15] (DEFA5, REG3A, DEFA6, SOX4, CDCA7, KIAA0101, TOP2A, MKI67, HMGB2, STMN1, SPINK4, ITLN1, REG4, CLCA1, FCGBP, HMGA1, EIF3F, ETHE1, ADH1C, C1QBP, RBP2, APOB, APOC3, APOA1, APOA4) for the epithelial cells. The expression of these markers in the cells can be found in S20 and S21 Figs. All single-cell data analyses and visualizations were performed with the R package Seurat v3.1.4 [138]. “NormalizeData” was used to normalize the data. “FindIntegrationAnchors” and “IntegrateData” functions were used to integrate cells from different samples. UMAP was used as the dimension reduction method for visualization.

Building the metabolite–enzyme network

We built a comprehensive metabolite–enzyme network by assembling data from 4 commonly used metabolism databases: KEGG [139], Recon3D [140], the Human Metabolic Atlas (HMA) [141], and the Human Metabolome Database (HMDB) [142]. The metabolite–enzyme network contains 60,822 records of 6,725 reactions among 3,808 metabolites and 3,446 genes. Four types of enzyme functions were included in the network: biosynthesis, degradation, transformation, and transportation.

Building the drug–target network

To evaluate whether a drug is closely associated with SARS-CoV-2 target proteins in the human interactome, we gathered the drug–target interaction information from several databases: DrugBank database (v4.3) [143], Therapeutic Target Database (TTD) [144], PharmGKB database, ChEMBL (v20) [145], BindingDB [146], and the IUPHAR/BPS Guide to Pharmacology [147]. We included the interactions that have binding affinities K_i, K_d, IC₅₀, or EC₅₀ ≤ 10 μM and a unique UniProt accession number with “reviewed” status. The details for building the experimentally validated drug–target network can be found in our recent studies [31–33].

Network-based drug repurposing

We computed the closest network proximity as described before for 2,938 FDA-approved or investigational drugs and the 5 SARS-CoV-2 datasets. For prioritization, we ranked the drugs by their distance to the datasets (D < 2, network distance using the closest measure) and Z score (Z < −1.5) from the network proximity analysis. The antiviral profiles of the highlighted drugs were manually curated. COVID-19-related clinical trials were retrieved on August 28, 2020.

Gene set enrichment analysis (GSEA)

The GSEA was conducted as described in our recent work [30] as an additional source of evidence for drug repurposing. Briefly, for each drug and coronavirus target gene set, we computed an enrichment score (ES) to indicate whether the drug can reverse the effect of SARS-CoV-2 at the transcriptome or proteome level. Gene expression profiles for the drugs were retrieved from the Connectivity Map (CMAP) database [148]. Five gene sets were evaluated: (1) the DEGs in human bronchial epithelial cells infected with SARS-CoV-2 [21] (GSE147507); (2) the DEPs in human Caco-2 cells infected with SARS-CoV-2 [22]; (3 and 4) 2 transcriptome datasets of SARS-CoV-1-infected samples from patient’s peripheral blood [149] (GSE1739) and Calu-3 cells [150] (GSE33267), respectively; and (5) the DEGs in MERS-CoV-infected Calu-3 cells [151] (GSE122876).

The ES was calculated for up- and down-regulated genes separately first. The overall ES was calculated as

E S = {\begin{matrix} {E S}_{u p} - {E S}_{d o w n}, s g n ({E S}_{u p}) \neq s g n ({E S}_{d o w n}) \\ 0, e l s e \end{matrix}

(5)

where ES_up and ES_down were calculated using a_up/down and b_up/down as

a = \max_{1 \leq j \leq s} (\frac{j}{s} - \frac{V (j)}{r})

(6)

b = \max_{1 \leq j \leq s} (\frac{V (j)}{r} - \frac{j - 1}{s})

(7)

j = 1,2,⋯,s were the genes in the gene sets sorted in ascending order by their rank in the drug profiles. V(j) indicates the rank of gene j, where 1≤V(j)≤r, with r being the number of genes from the drug profile. Then, ES_up/down was set to a_up/down if a_up/down>b_up/down, and was set to −b_up/down if b_up/down>a_up/down. A permutation test was performed to evaluate the significance. Drugs were prioritized and selected if ES > 0 and P < 0.05.

Patient data validation of the network-identified drugs using a COVID-19 registry

We used institutional-review-board-approved COVID-19 registry data, including 26,779 individuals (8,274 SARS-CoV-2 positive) tested during March 8 to July 27, 2020, from the Cleveland Clinic Health System in Ohio and Florida. The pooled nasopharyngeal and oropharyngeal swab specimens were tested. SARS-CoV-2 positivity was confirmed by reverse transcription–polymerase chain reaction assay in the Cleveland Clinic Robert J. Tomsich Pathology and Laboratory Medicine Institute. All SARS-CoV-2 testing was authorized by the FDA under an Emergency Use Authorization and complied with the guidelines established by the Centers for Disease Control and Prevention. Data include COVID-19 test results, baseline demographic information, medications, and all recorded disease conditions. We conducted a series of retrospective case–control studies with a new user active comparator design to test the drug–outcome relationships for COVID-19. Patients were actively taking the evaluated drugs (carvedilol and melatonin) at the time of testing. Data were extracted from electronic health records (EPIC Systems) and were manually checked by a study team trained on uniform sources for the study variables. We collected and managed all patient data using REDCap electronic data capture tools. The exposures of drugs (including carvedilol and melatonin) were used as recorded in the medication list in the electronic medical records at the time of testing for SARS-CoV-2. A positive laboratory test result for SARS-CoV-2 was used as the primary outcome. PS was used to match patients to reduce various confounding factors. Four models, from less to more stringent in terms of patient matching and OR adjustment, were performed: (1) model 1 was matched using age, sex, race, and smoking without adjustment for the OR; (2) model 2 was matched using age, sex, race, and smoking, and the OR of COVID-19 was adjusted by age, sex, race, and smoking; (3) model 3 was matched using age, sex, race, smoking, coronary artery disease, diabetes, hypertension, and COPD without adjustment for the OR; and (4) model 4 was matched using age, sex, race, smoking, coronary artery disease, diabetes, hypertension, and COPD, and the OR of COVID-19 was adjusted by age, sex, race, smoking, coronary artery disease, diabetes, hypertension, and COPD. All analyses were conducted using the matchit package in the R v3.6.3 platform.

Statistical analysis and network visualization

Statistical tests were performed with the Python package SciPy v1.3.0 (https://www.scipy.org/). P < 0.05 was considered statistically significant throughout this study. Networks were visualized using Gephi 0.9.2 (https://gephi.org/).

Ethics statement

Procedures follow institutional guidelines for research on the COVID-19 registry database and were approved by the Cleveland Clinic Foundation Institutional Review Board.

Supporting information

S1 Data. Fig 2 data.

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S2 Data. Fig 3 data.

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S3 Data. Fig 4 data.

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S4 Data. Fig 5 data.

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S5 Data. Fig 6 data.

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S6 Data. Fig 7 data.

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S7 Data. S1–S5 Figs data.

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S8 Data. S6 Fig data.

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S9 Data. S7 Fig data.

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S10 Data. S14 Fig data.

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S11 Data. S15 Fig data.

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S1 Fig. Functional enrichment analysis for SARS2-DEG.

In total, 246 differentially expressed genes (DEGs) in human bronchial epithelial cells infected with SARS-CoV-2 were obtained from the NCBI GEO database with the accession number GSE147507, denoted as SARS2-DEG. The data underlying this figure can be found in S7 Data.

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S2 Fig. Functional enrichment analysis for SARS2-DEP.

In total, 293 differentially expressed proteins (DEPs) in human Caco-2 cells infected with SARS-CoV-2 were obtained from Bojkova et al. [22], denoted as SARS2-DEP. The data underlying this figure can be found in S7 Data.

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S3 Fig. Functional enrichment analysis for HCoV-PPI.

This dataset contains 134 strong literature-evidence-based pan-human coronavirus target host proteins from Zhou et al. [30] with 15 newly curated proteins, denoted as HCoV-PPI. The data underlying this figure can be found in S7 Data.

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S4 Fig. Functional enrichment analysis for SARS2-PPI.

This dataset contains 332 proteins involved in protein–protein interactions with 26 SARS-CoV-2 viral proteins identified by affinity purification–mass spectrometry from Gordon et al. [8], denoted as SARS2-PPI. The data underlying this figure can be found in S7 Data.

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S5 Fig. Functional enrichment analysis for PanCoV-PPI.

Due to the interactome nature of HCoV-PPI and SARS2-PPI, we combined these datasets as the fifth SARS-CoV-2 dataset, which has 460 proteins and is denoted as PanCoV-PPI. The data underlying this figure can be found in S7 Data.

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S6 Fig. Characteristics of the 4 SARS-CoV-2 target datasets.

Node degree (blue), dN/dS ratio (orange), evolutionary ratio (green), and lung expression specificity (purple) are shown for each dataset. Grey areas indicate mean ± standard deviation of 100 repeats using randomly selected genes. The data underlying this figure can be found in S8 Data.

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S7 Fig. Chronic obstructive pulmonary disease and COVID-19.

(A) The risk of chronic obstructive pulmonary disease (COPD) is increased in severe COVID-19 patients. (B) Subnetwork shows the proteins potentially involved in the interaction between COPD and COVID-19. The data underlying this figure can be found in S9 Data.

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S8 Fig. Asthma and COVID-19.

(A) The risk of dyspnea is increased in patients with severe COVID-19. (B) UMAP visualization for human bronchial epithelial cells. (C and D) Expression levels of ACE2 across 14 cell types. (E and F) Expression levels of TMPRSS2 across 14 cell types. (G) UMAP visualization for lung cells. (H and I) Expression levels of ACE2 across 9 cell types. (J and K) Expression levels of TMPRSS2 across 9 cell types. The single-cell data with cell type annotation were retrieved from Lukassen et al. [14], which contains 39,778 lung cells and 17,451 bronchial epithelial cells.

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S9 Fig. The expression of asthma genes and SARS-CoV-2 targets.

The expression levels of the genes from the asthma–COVID-19 subnetwork in bronchial epithelial cells (A) and lung cells (B) are shown.

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S10 Fig. Risk ratios for abdominal pain and diarrhea in COVID-19 patients.

Abdominal pain (A) and diarrhea (B) have increased risks in patients with severe COVID-19.

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S11 Fig. Inflammatory bowel disease and COVID-19.

(A) UMAP visualization of non-epithelial cells from the ileal tissues of patients with Crohn disease. (B and D) The expression of ACE2 in the non-epithelial cells in (A). (C and E) The expression of TMPRSS2 in the non-epithelial cells in (A). (F) UMAP visualization of epithelial cells from the ileal tissues of patients with Crohn disease. (G and I) The expression of ACE2 in the epithelial cells in (F). (H and J) The expression of TMPRSS2 in the epithelial cells in (F). (K and L) The expression levels of ACE2 and TMPRSS2 in inflamed versus uninflamed ileal absorptive enterocytes in Crohn disease patients. The single-cell data were retrieved from Martin et al. [72], which contains 67,050 inflamed and uninflamed cells from the ileal samples of 8 patients with Crohn disease.

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S12 Fig. Patient-based validation of drug repurposing for COVID-19 using 3 different disease subgroups.

The disease subgroups are (A) asthma, (B) diabetes, and (C) hypertension. Four models were evaluated. These models were matched and adjusted using different variables, as shown in the table. The variable that was used to extract each patient subgroup was not used for propensity score matching or odds ratio adjustment. ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker.

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S13 Fig. Comparison of the patient validation results of carvedilol use in black Americans and white Americans.

In black Americans, carvedilol use showed a lowered risk of a positive SARS-CoV-2 test when propensity score was matched with basic variables (age, sex, and smoking).

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S14 Fig. Analysis of the effect of the number of genes associated with diseases on the network proximity Z scores.

Each dot represents a disease (Z score versus number of genes). No significant bias was observed for the number of genes. The maximum R² is 0.1468 from the SARS2-PPI dataset. The data underlying this figure can be found in S10 Data.

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S15 Fig. Disease manifestations associated with COVID-19 quantified by network proximity measurement using tissue-specific genes for each disease.

The disease-associated genes were filtered by their tissue specificity. Tissues considered are shown after the disease names. Only genes with positive specificity were retained for the network analysis. After filtering, diseases with fewer than 5 genes were removed from the evaluation. The data underlying this figure can be found in S11 Data.

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S16 Fig. The subnetwork between the IBD-associated genes, the SARS-CoV-2 virus proteins, and virus target proteins.

Node sizes show their tissue specificity in colon.

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S17 Fig. Network proximity analysis of asthma and COVID-19 taking into consideration the directionalities of the differential gene expression.

The up- and down-expressed genes in the 2 asthma datasets (GSE63142 and GSE130499, severe versus control) were computed against the up- and down-expressed genes from the SARS2-DEG dataset. Overall, the results show more significant network proximities and smaller Z scores than when the direction is not considered, as in Fig 4.

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S18 Fig. Workflow of clinical study search.

We searched PubMed, Embase, and medRxiv databases for publications as of April 25, 2020, using the search term (“SARS-COV-2” OR “COVID-19” OR “nCoV 19” OR “2019 novel coronavirus” OR “coronavirus disease 2019”) AND (“clinical characteristics” OR “clinical outcome” OR “comorbidities”). Only research articles were included. Several criteria were used to filter the initial 1,054 articles to a final sample of 34 studies for meta-analyses.

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S19 Fig. Overview of the human protein interactome.

Cytoscape 3.7.1 was used for the visualization and for generating the statistics. Clustering coefficient (ranges from 0 to 1) measures the extent to which the nodes in the network tend to cluster together. Network centralization (ranges from 0 to 1) measures the extent to which the topology resembles a star. Network density (ranges from 0 to 1) shows how densely the nodes are connected in the network. Network heterogeneity shows the tendency of the network to contain hub nodes.

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S20 Fig. Cell type markers and their expressions in dot plot used to identify the ileal non-epithelial cells.

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S21 Fig. Cell type markers and their expressions in dot plot used to identify the ileal epithelial cells.

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S1 File. Network file for the global network of disease manifestations associated with human coronavirus.

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S1 Table. Summary of the datasets used in this study.

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S2 Table. Five SARS-CoV-2 target datasets used in this study.

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S3 Table. Additional virus target lists for comparisons with SARS-CoV-2 targets.

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S4 Table. Disease-associated genes.

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S5 Table. COVID-19 clinical studies used in the meta-analysis.

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S6 Table. Network proximity results for 2,938 drugs against the SARS-CoV-2 datasets.

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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.

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

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 Statement

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.

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PLoS Biol. doi: 10.1371/journal.pbio.3000970.r001

Decision Letter 0

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21 Jun 2020

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Senior Editor

PLOS Biology

PLoS Biol. 2020 Nov 6;18(11):e3000970. doi: 10.1371/journal.pbio.3000970.r002

Author response to Decision Letter 0

22 Jun 2020

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PLoS Biol. doi: 10.1371/journal.pbio.3000970.r003

Decision Letter 1

Roland G Roberts

30 Jul 2020

Dear Dr Cheng,

Thank you very much for submitting your manuscript "A Network Medicine Approach to Investigation and Population-based Validation of Disease Manifestations and Therapeutics for COVID-19" for consideration as a Research Article at PLOS Biology. Your manuscript has been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by several independent reviewers.

As you will see in the reviews below, the referees raise a substantial number of concerns with the analyses performed, the reporting and the ultimate relevance of the drug screening results. All of the issues raised are pertinent and should be thoroughly addressed. Given the interest of the topic and approach, we would like to invite you to submit a thoroughly revised manuscript that takes into account all of the reviewers' comments. Please note that we will not be able to make a decision about publication until we have seen the revised manuscript, your response to the reviewers' comments and how they received the revised manuscript.

We expect to receive your revised manuscript within 2 months.

Please email us (plosbiology@plos.org) if you have any questions or concerns, or would like to request an extension. At this stage, your manuscript remains formally under active consideration at our journal; please notify us by email if you do not intend to submit a revision so that we may end consideration of the manuscript at PLOS Biology.

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Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Roli Roberts

Roland G Roberts, PhD,

Senior Editor,

rroberts@plos.org,

PLOS Biology

*****************************************************

REVIEWS:

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #1: Overall Summary:

Zhou et al developed network-based methodologies to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies. They incorporated SARSCoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measure was used to identify the underlying pathogenesis for broad COVID-19-associated manifestations. Multi-modal analyses of single-cell RNA-seq data identified the co-expression pattern of ACE2 and TMPRSS2 in absorptive enterocytes from the inflamed ileal tissues of Crohn's disease patients compared to uninflamed tissues, revealing shared pathobiology by COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicated that COVID-19 shared intermediate inflammatory endophenotypes with asthma. By combing network-based prediction and propensity score matching observation study of 18118 patients from a COVID-19 registry, the authors identified that melatonin was associated with 64% reduced likelihood of a positive lab test for SARS-CoV-2 and can have better efficacy than angiotensin II receptor blockers or angiotensin converting enzyme inhibitors for treating SARS-CoV-2. However, details about how to identify the differential expressed genes, PPI-networks and drug-target network construction, and single-cell RNA-seq analysis are missing; the rationale about the construction of PanCoV-PPI and the network proximity measure need to be clarified. Most of the results are computational discoveries. It may provide valuable insight if the following concerns are addressed.

Major Comments:

1. Details about the RNA-seq data analysis are missing. I am not sure how the authors generated SARS2-DEP. For example, which samples in GSE147507 are used to identify differentially expressed genes? Which samples are used as controls? What kind of software is used to analyze those samples? How to identify differentially expressed genes? The same problem arises elsewhere in the manuscript.

2. In line 194 of page 10, the authors mentioned that "For cancer, the driver 195 genes for pan-cancer and individual cancer types were retrieved from the Cancer Gene 196 Census [34] and a previous study [35]. For autoimmune, pulmonary, neurological, 197 cardiovascular, and metabolic categories, we extracted their associated genes/proteins 198 from the Human Gene Mutation Database" I am not sure how those genes were retrieved from corresponding databases? Did the authors use any keywords for searching?

3. In line 675 of page 31, the authors mentioned that "The final human protein-protein interactome used in this study included 351,444 unique PPIs connecting 17,706 proteins". However, would the authors put those results in their GitHub page for the reader's reference? Several methods adopted are described as "as described in our previous study". Some statistics/overview of the network features/methods, although published somewhere else, should be provided for a better understanding of the proposed methods. For example, how did the authors build human protein-protein interactome? What are active comparator design and PS adjustment? How did the authors obtain the experimentally validated drug-target network?

4. Source code and supporting data cannot be found from the Github link provided by the authors.

5. In line 138-142, the authors mentioned that "PanCoV-PPI (Fig. 2B) and four other data sets (SARS2-139 DEG, SARS2-DEP, HCoV-PPI, and SARS2-PPI) (S6 Fig) were more likely to be highly connected (high degree or connectivity) in the human PPI network, including several hubs, such as JUN, XPO1, MOV10, NPM1, VCP, and HNRNPA1". According to Fig. 2B and S6, I cannot see anything supporting data to explain that JUN, XPO1, MOV10, NPM1, VCP, and HNRNPA1 are the hubs.

6. The authors performed functional enrichment analyses for five different PPIs, i.e., SARS2-DEG, SARS2-DEP, HCoV-PPI, SARS2-PPI, and PanCoV-PPI, generated from transcriptomic and proteomic data of SARS-CoV-2 as well as literature-based virus-host protein-protein interactions. They found different PPIs differ considerably in terms of enriched pathways, and then claimed "These observations suggest that these different SARS-CoV-2 data sets capture complementary aspects of the biological and cellular states of the viral life cycle and host immunity". However, many factors can cause the complementary effects. For example, 1. These data are derived from different cells or tissues, and not representative; 2. How did the authors perform functional enrichment analysis for SARS2-PPI and HCoV-PPI? 3. The differential expressed genes identified from transcriptome profiles can be very different from proteomic data.

7. In Figs 2C, 2D, and 2G, what is the physical meaning of the ratio of nonsynonymous to synonymous substitutions (dN/dS) and evolutionary rate ratio?

8. In line 130 of page 7, the authors mentioned that "further compiled four additional virus-host gene/protein networks identified by different methods for comparisons". However, I am not sure how those virus-host gene/protein networks are identified, details regarding how to generate those networks and relevant references are missing.

9. The network proximity measure was used to measure the distance of two genes/proteins in a protein-protein interaction network, however, the importance of the centrality of the nodes/proteins in the PPI network was ignored.

10. The authors mentioned that "We next performed network-based drug repurposing using the existing knowledge of the drug-target network." and "Using our state-of-the-art network proximity framework, we measured the "closest" proximities of nearly 3,000 drugs". However, details about the drug-target network, the network proximity framework, and the nearly 3,000 drugs are missing. The authors then computationally found 34 drugs that are associated with SARS-CoV-2 data sets, how did the authors rank those 3,000 drugs? how many of those 34 drugs are being tested, or have been tested in clinical trials and have positive effects for COVID-19 patients? The authors validated the efficacy of melatonin, one of those 34 drugs, on COVID-19 patients using their medical records. I am not sure if melatonin was ranked as the top-one among those 34 drugs? Not sure what are the differences between the drug-repositioning method proposed in this paper and in the authors' previous publication (ref 27), which used a similar network-based approach.

11. I am not sure how cell types in Figs 5C, 5F, 6C, and 6D are annotated? Are there any control samples in the single-cell RNA-seq analysis?

12. In Fig7.B there are four different bars with different colors, what does the PS-matched model 1 and PS-matched model 2 mean? There are two different OR model1s indicated by different colors, however, are these OR model1s independent? Besides, are four different models (variables) in Fig7.B independent?

Reviewer #2: Review report on Zhou et al PLoS Biology paper: PBIOLOGY-D-20-01653R1

Comments for the authors:

This paper addresses the network interpretation of higher risk of morbidity and mortality of COVID-19 patients with one or more other common diseases utilizing integrative network analysis of transcriptomics, proteomics, and human interactome. Utilizing bulk and single cell RNA-seq data together with differential metabolite information (only for asthma patients), the authors provided insights on shared pathobiology of COVID-19 patients with asthma and inflammatory bowel diseases. The authors of this paper utilized their earlier developed in-silico drug repurposing approaches on COVID-19 clinical registry database and prioritized existing FDA-approved drugs as potential therapeutic candidates.

Overall authors utilized all possible data sources and network-inference state of the art methods in their integrative analysis. The question remains however, with regard to whether these methods are good enough to yield substantial predictions. Below are the major concerns that need to be carefully further addressed before this paper can be considered for publication:

Major Comments:

1. One intrinsic limitation of the authors' method is that directionalities are in general not being taken into account in the various networks they built and/or used. For example, it seems that whether a viral protein activates or inhibits a host protein, or whether a gene is upregulated or downregulated in a disease is not being considered, and this can make the interpretation of results difficult or give rise to ambiguities. As an example of this issue, although the authors have identified proximity between the SARS-CoV-2 network and the asthma network with several shared nodes (Fig. 5A), when comparing the differential expression (DE) profiles in asthma to that in SARS-CoV-2 infection (Fig. 5B), there does not seem to be significant concordance in terms of the direction of DE. Notably, IL6 increased in SARS-CoV-2 infection but decreased in asthma. Will the same findings still hold if the directionality is taken into account properly? We think that this is an important issue that should be addressed appropriately.

2. The different coronavirus datasets were from different cell types, for example the SARS2-DEG was based on data in primary bronchial epithelial cells, while the SARS2-DEP was based on Caco-2 colorectal cancer cell line, and the SARS2-PPI was based on HEK273T cell line. Further, the genes associated with each disease should all act in a context-specific manner in the corresponding tissue types where each of the diseases manifest. It's questionable whether the network within one context can still largely hold if transferred to a different context (e.g. tissue type). The authors have implicitly studied the related issue of cell/tissue type-specific expression. If the tissue type-specific expression information is explicitly used to refine the various SARS2 network separately and specifically for each tissue type, will the findings on proximity with disease networks still hold? This potentially serious confounding factor cannot be ignored and needs to be carefully addressed.

3. In the 'Validating drug-outcome relationships on COVID-19 using patient data' under Results section, authors utilized their earlier developed approaches of network proximity, GSEA analysis and PS-score matching methods to prioritize the drug candidates. The basis for selection of the final drug melatonin seems weak. The authors didn't mention anywhere how many drugs they finally considered in their analysis and with what frequency each of them was used by the patients. It is not clear whether the drugs (including melatonin) are being taken by the patients before or at the time of being tested positive, if before then what is the time interval between the drug consumption and testing, for how long have the patient been taking the drug, and how such information are being used in terms of selecting the samples to include in the analysis, as well as in the analytical model during the analysis. Such details are critical for the interpretation of the results, and an overview should be provided in the main text (Results or Methods) with comprehensive additional details in the Supplementary Materials. Whatever drug list authors provided in Figure 7A, it is very obvious that all drugs were not used by all the patients in similar frequency. Melatonin is a very common drug compared to other drugs listed in that figure. So all these statistical tests are not at all applicable for all these drugs. In other words, this validation is not very useful in the COVID-19 patients' context, where intake of medicines is not homogeneous among patients.

4. Can the authors prioritize drugs in a more context-specific manner rather than one drug for all? Likewise, can they prioritize drugs in a more similar group of patients, like for asthma patients or for IBD patients or for hypertension patients?

5. In many places, the authors just provided some numbers without any biological implication. There is no explanation of such variability in numbers or what are the biological consequences of those. For example, in Figure 2, the authors presented the data for PanCoV-PPI which is a combination of HCoV-PPI and SARS2-PPI. It is well known that SARS2-PPI and HCoV-PPI networks are not very similar. In that case, all these numbers are not very useful towards the overall theme of the paper.

6. The authors utilized the network proximity measure to evaluate the connectivity and closeness of other diseases with COVID-19. It is now well known a variety of underlying health conditions are risk factors for covid-19 patients, including children with rare diseases, like cerebral palsy or mental conditions. In such scenarios, network proximity is not a very useful measure for identifying this high morbidity and mortality risk. What is the authors take on that?

Minor Comments:

We feel that many parts of the writing are inaccurate or confusing and can be improved (examples given below). We recommend the authors to further refine the writing so that it is easier for the readers to understand:

(a) Line 214, the authors write "these diseases can be targeted directly or interact with the 215 targets of SARS-CoV-2 or other HCoVs." We think it is actually meant that "the disease genes can interact with the viral proteins either directly or indirectly via another host protein".

(b) Line 228, authors can explicitly mention which 8 comorbidities they are referring to here.

(c) Line 293, the term "endophenotype", which has a strict definition, may not be appropriate here, the authors may intend to write "molecular profile" or perhaps "molecular program".

(d) Line 297, in multi-modal analysis, there is no clear explanation what authors exactly did here? There is no clear methodology for their multi-modal analysis in the Methods section.

(e) Line 308, "matching the enzymes of the differential metabolites and the proteins in the PPI network", it's not clear whether enzymes for the synthesis, or degradation, or any transformation, or transportation, etc. of the metabolites were considered, and it seems that this is not explained elsewhere either.

(f) Line 318 "Utilizing two bulk RNA-Seq data sets from asthma patients and healthy controls, we identified elevated expression of IRAK3 and ADRB2 in SARS-CoV-2 infected human bronchial epithelial cells." -- this is confusing.

(g) Line 428 "Validating drug-outcome relationships on COVID-19 using patient data", it seems to mean "evidence from the COVID-19 registry data that supports the predicted drug repurposing strategies".

Reviewer #3: The manuscript by Zhou and colleagues is submitted for consideration for publication to PLOS Biology. In this manuscript the authors tried to investigate pathogenesis, clinical manifestations and therapies COVID-19 using network medicine approach on clinical and multi-omics data. The reason for this study is to understand molecular mechanisms of SARS-CoV-2 infection, to compare with other not-infectious diseases and to identify FDA-approved drugs as potential COVID-19 drug candidates through network-medicine findings and clinical data from a large COVID-19 clinical registry database. The paper is well structured and exhaustive in every parts, while the integrated approach, the authors have used, is original and very interesting. Importantly, this theoretical findings might have practical significance via guiding both pharmaceutical and diagnostic research with the prospect to identify potential new biological targets. It can be recommended for publication upon addressing several concerns into some not clear parts.

The main concerns are:

1. in introduction they report numbers of pandemics, but it needs to insert one or more references (e.g. John Hopkins University), while at row 54 they should add other references about network based-approach model based on comparative PPI interactomes with other HCoV and concept of Disease Map applied to COVID19 .

2. in Results they talk about S2 Table, containing "additional virus-host gene / protein networks identified by different methods for comparisons", but it is not clear how they selected these genes, where these data is from and what purpose it would serve .

3. at pag 19 they wrote: "These observations reveal common network relationship between COVID-19 and human diseases". In my opinion, this phrase could result obvious, due to wide previous literature about COVID19 produced up this moment and the pathogenic and molecular similarity with SARS-CoV.

4. at pag 20 they talk about the network-based relationships of the 64 diseases across the 6 categories to COVID-19, shown in Fig 4A, taking into account the proximity, Z scores and P values, as significance test. However this part results hard to understand: firstly, they should report the absolute numbers of how many genes they used for each Z-score test and p-value, because different sample size of genes provide the streghtness of associations. Secondly, they must explain to biological function of genes tested and the pathway involved, because it is very difficult to figure out why they found strong significant network proximity with attention-deficit / hyperactivity disorder and not with other cardiovascular diseases, since vascular damage is one of most featured manifestations in severe COVID19 cases, or asthma. Moreover, for the networks in this figure, it is not clear why they chose sepsis and respiratory distress syndroms as example and it could result misleading.

5. at pag 28 they analyzed data by a large-scale patient data from the Cleveland Clinic COVID-19 patient registry, evaluating the charateristics of melatonin and carvedilol. I noted that in SARS-CoV-2 positive patients there was a wide diversity in sample size between cases and controls (cases are ~ 2% of controls). I understand it was due to availability of COVID19 cases tested, but it should much more report in the limitation section.

PLoS Biol. 2020 Nov 6;18(11):e3000970. doi: 10.1371/journal.pbio.3000970.r004

Author response to Decision Letter 1

3 Sep 2020

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PLoS Biol. doi: 10.1371/journal.pbio.3000970.r005

Decision Letter 2

Roland G Roberts

13 Oct 2020

Dear Dr Cheng,

Thank you for submitting your revised Research Article entitled "A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19" for publication in PLOS Biology. I have now obtained advice from the original reviewers and have discussed their comments with the Academic Editor.

Based on the reviews, we will probably accept this manuscript for publication, assuming that you will modify the manuscript to address the following points:

a) Please address my Data Policy requests (see further down).

b) Please clarify the funding information (it currently appears that you had no specific funding for this project, but this is unclear).

c) Please go very carefully through the text and especially the Abstract, ensuring that you do *not* overstate your claims regarding melatonin or any other treatments herein. This is particularly important given the high-profile disinformation that has occurred during recent months. You must only claim what is supported by the evidence presented. The final clause ("and identifying melatonin for potential prevention and treatment of COVID-19.") seems excessively strong. Also, given that melatonin is an endogenous chemical, the mentions of melatonin (e.g. "melatonin is associated with a reduced likelihood....") presumably should read "melatonin usage" or similar, to avoid the impression that endogenous levels were measured.

d) Please update any assessment of the current state of COVID-19 treatments to ensure that any fast-moving developments are captured.

We expect to receive your revised manuscript within two weeks. Your revisions should address the specific points made by each reviewer. In addition to the remaining revisions and before we will be able to formally accept your manuscript and consider it "in press", we also need to ensure that your article conforms to our guidelines. A member of our team will be in touch shortly with a set of requests. As we can't proceed until these requirements are met, your swift response will help prevent delays to publication.

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*Protocols deposition*

Please do not hesitate to contact me should you have any questions.

Sincerely,

Roli Roberts

Roland G Roberts, PhD,

Senior Editor,

rroberts@plos.org,

PLOS Biology

------------------------------------------------------------------------

DATA POLICY:

You may be aware of the PLOS Data Policy, which requires that all data be made available without restriction: http://journals.plos.org/plosbiology/s/data-availability. For more information, please also see this editorial: http://dx.doi.org/10.1371/journal.pbio.1001797

Note that we do not require all raw data. Rather, we ask that all individual quantitative observations that underlie the data summarized in the figures and results of your paper be made available in one of the following forms:

1) Supplementary files (e.g., excel). Please ensure that all data files are uploaded as 'Supporting Information' and are invariably referred to (in the manuscript, figure legends, and the Description field when uploading your files) using the following format verbatim: S1 Data, S2 Data, etc. Multiple panels of a single or even several figures can be included as multiple sheets in one excel file that is saved using exactly the following convention: S1_Data.xlsx (using an underscore).

2) Deposition in a publicly available repository. Please also provide the accession code or a reviewer link so that we may view your data before publication.

Regardless of the method selected, please ensure that you provide the individual numerical values that underlie the summary data displayed in the following figure panels as they are essential for readers to assess your analysis and to reproduce it: All main and supplementary figure panels except Fig 1. NOTE: the numerical data provided should include all replicates AND the way in which the plotted mean and errors were derived (it should not present only the mean/average values).

IMPORTANT: I note that your supplementary files (Tables S1-S7) and Github deposition contain a significant amount of data; however their relationship to the specific Figure panels is unclear; please either supply the data that immediately underlies the Figures, or specify where it can be found; each main and supplementary Figure should have a clear citation to the underlying data (e.g. "The data underlying this Figure can be found in https://github.com/ChengF-Lab/COVID-19_Map"). It may be more appropriate to re-name Tables S2-S7 as Supplementary Data files.

Please also ensure that figure legends in your manuscript include information on where the underlying data can be found, and ensure your supplemental data file/s has a legend.

Please ensure that your Data Statement in the submission system accurately describes where your data can be found.

------------------------------------------------------------------------

REVIEWERS' COMMENTS:

Reviewer #2:

We thank the authors for addressing our comments. We think the manuscript has now been largely improved and is suitable for publication.

Reviewer #3:

No comments submitted.

PLoS Biol. 2020 Nov 6;18(11):e3000970. doi: 10.1371/journal.pbio.3000970.r006

Author response to Decision Letter 2

21 Oct 2020

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PLoS Biol. doi: 10.1371/journal.pbio.3000970.r007

Decision Letter 3

Roland G Roberts

28 Oct 2020

Dear Dr Cheng,

On behalf of my colleagues and the Academic Editor, Nicole Soranzo, I am pleased to inform you that we will be delighted to publish your Research Article in PLOS Biology.

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Before publication you will see the copyedited word document (within 5 business days) and a PDF proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors. Please return the copyedited file within 2 business days in order to ensure timely delivery of the PDF proof.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point. Given the disruptions resulting from the ongoing COVID-19 pandemic, there may be delays in the production process. We apologise in advance for any inconvenience caused and will do our best to minimize impact as far as possible.

EARLY VERSION

The version of your manuscript submitted at the copyedit stage will be posted online ahead of the final proof version, unless you have already opted out of the process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

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We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with biologypress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

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Thank you again for submitting your manuscript to PLOS Biology and for your support of Open Access publishing. Please do not hesitate to contact me if I can provide any assistance during the production process.

Kind regards,

Alice Musson

Publishing Editor,

PLOS Biology

on behalf of

Roland Roberts,

Senior Editor

PLOS Biology

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data. Fig 2 data.

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S2 Data. Fig 3 data.

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S3 Data. Fig 4 data.

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S4 Data. Fig 5 data.

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S5 Data. Fig 6 data.

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S6 Data. Fig 7 data.

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S7 Data. S1–S5 Figs data.

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S8 Data. S6 Fig data.

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S9 Data. S7 Fig data.

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S10 Data. S14 Fig data.

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S11 Data. S15 Fig data.

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S1 Fig. Functional enrichment analysis for SARS2-DEG.

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S2 Fig. Functional enrichment analysis for SARS2-DEP.

(PDF)

Click here for additional data file.^{(422.8KB, pdf)}

S3 Fig. Functional enrichment analysis for HCoV-PPI.

(PDF)

Click here for additional data file.^{(408.5KB, pdf)}

S4 Fig. Functional enrichment analysis for SARS2-PPI.

(PDF)

Click here for additional data file.^{(424.1KB, pdf)}

S5 Fig. Functional enrichment analysis for PanCoV-PPI.

(PDF)

Click here for additional data file.^{(379.5KB, pdf)}

S6 Fig. Characteristics of the 4 SARS-CoV-2 target datasets.

(PDF)

Click here for additional data file.^{(273.3KB, pdf)}

S7 Fig. Chronic obstructive pulmonary disease and COVID-19.

(PDF)

Click here for additional data file.^{(1.2MB, pdf)}

S8 Fig. Asthma and COVID-19.

(PDF)

Click here for additional data file.^{(3.2MB, pdf)}

S9 Fig. The expression of asthma genes and SARS-CoV-2 targets.

The expression levels of the genes from the asthma–COVID-19 subnetwork in bronchial epithelial cells (A) and lung cells (B) are shown.

(PDF)

Click here for additional data file.^{(761.2KB, pdf)}

S10 Fig. Risk ratios for abdominal pain and diarrhea in COVID-19 patients.

Abdominal pain (A) and diarrhea (B) have increased risks in patients with severe COVID-19.

(PDF)

Click here for additional data file.^{(136.6KB, pdf)}

S11 Fig. Inflammatory bowel disease and COVID-19.

(PDF)

Click here for additional data file.^{(1.9MB, pdf)}

S12 Fig. Patient-based validation of drug repurposing for COVID-19 using 3 different disease subgroups.

(PDF)

Click here for additional data file.^{(326.7KB, pdf)}

S13 Fig. Comparison of the patient validation results of carvedilol use in black Americans and white Americans.

In black Americans, carvedilol use showed a lowered risk of a positive SARS-CoV-2 test when propensity score was matched with basic variables (age, sex, and smoking).

(PDF)

Click here for additional data file.^{(241.9KB, pdf)}

S14 Fig. Analysis of the effect of the number of genes associated with diseases on the network proximity Z scores.

(PDF)

Click here for additional data file.^{(24.3KB, pdf)}

S15 Fig. Disease manifestations associated with COVID-19 quantified by network proximity measurement using tissue-specific genes for each disease.

(PDF)

Click here for additional data file.^{(51.9KB, pdf)}

S16 Fig. The subnetwork between the IBD-associated genes, the SARS-CoV-2 virus proteins, and virus target proteins.

Node sizes show their tissue specificity in colon.

(PDF)

Click here for additional data file.^{(1.6MB, pdf)}

S17 Fig. Network proximity analysis of asthma and COVID-19 taking into consideration the directionalities of the differential gene expression.

(PDF)

Click here for additional data file.^{(35.3KB, pdf)}

S18 Fig. Workflow of clinical study search.

(PDF)

Click here for additional data file.^{(87.6KB, pdf)}

S19 Fig. Overview of the human protein interactome.

(PDF)

Click here for additional data file.^{(890.7KB, pdf)}

S20 Fig. Cell type markers and their expressions in dot plot used to identify the ileal non-epithelial cells.

(PDF)

Click here for additional data file.^{(91.3KB, pdf)}

S21 Fig. Cell type markers and their expressions in dot plot used to identify the ileal epithelial cells.

(PDF)

Click here for additional data file.^{(104.5KB, pdf)}

S1 File. Network file for the global network of disease manifestations associated with human coronavirus.

(ZIP)

Click here for additional data file.^{(56.1KB, zip)}

S1 Table. Summary of the datasets used in this study.

(PDF)

Click here for additional data file.^{(86.9KB, pdf)}

S2 Table. Five SARS-CoV-2 target datasets used in this study.

(XLSX)

Click here for additional data file.^{(111.4KB, xlsx)}

S3 Table. Additional virus target lists for comparisons with SARS-CoV-2 targets.

(XLSX)

Click here for additional data file.^{(241.7KB, xlsx)}

S4 Table. Disease-associated genes.

(XLSX)

Click here for additional data file.^{(1.2MB, xlsx)}

S5 Table. COVID-19 clinical studies used in the meta-analysis.

(XLSX)

Click here for additional data file.^{(13.1KB, xlsx)}

S6 Table. Network proximity results for 2,938 drugs against the SARS-CoV-2 datasets.

(XLSX)

Click here for additional data file.^{(514.3KB, xlsx)}

Attachment

Submitted filename: renamed_9304e.pdf

Click here for additional data file.^{(19.4KB, pdf)}

Attachment

Submitted filename: Response_to_Reviewers_Sep2.pdf

Click here for additional data file.^{(3.3MB, pdf)}

Attachment

Submitted filename: Response_to Editors comments.docx

Click here for additional data file.^{(18.7KB, docx)}

Data Availability Statement

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PERMALINK

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

Roles

Abstract

Introduction

Fig 1. Overall workflow of this study.

Results

A global map of the SARS-CoV-2 virus–host interactome

Fig 2. Network and biological characteristics of the SARS-CoV-2 interactome map.

Network and biological characteristics of virus–host interactome for SARS-CoV-2

SARS-CoV-2 cellular network perturbations of disease manifestations

Fig 3. A global network illustrating disease manifestations associated with human coronavirus.

Network-based measurement of COVID-19-associated disease manifestations

Fig 4. A landscape of disease manifestations associated with COVID-19 quantified by network proximity measure.

Inflammatory molecular profile shared by COVID-19 and asthma

Fig 5. Shared molecular profile between asthma and COVID-19.

Immune pathobiology shared by COVID-19 and IBD

Fig 6. Inflammatory molecular profile between inflammatory bowel disease (IBD) and COVID-19.

Network-based drug repurposing for COVID-19

Fig 7. Network-based prediction of drug repurposing for COVID-19.

Evidence from the COVID-19 registry data that supports the predicted drug repurposing strategies

Fig 8. Patient-based validation of drug repurposing for COVID-19.

Table 1. Baseline characteristics of the melatonin and carvedilol use groups from the COVID-19 registry.

Discussion

Materials and methods

Building the datasets of SARS-CoV-2 target host genes/proteins

SARS2-DEG

SARS2-DEP

Collection of 4 additional virus–host gene/protein networks

Building the disease gene profiles

Cancer

Mendelian disease genes (MDGs)

Orphan disease-causing mutant genes (ODMGs)

Cell cycle genes

Innate immune genes

Genes associated with autoimmune, pulmonary, neurological, cardiovascular, and metabolic diseases

Functional enrichment analysis

Selective pressure and evolutionary rate characterization

Tissue specificity analysis

Risk ratio analysis for COVID-19 patients

Building the human protein–protein interactome

Network proximity measure

Network-based comorbidity analysis

Bulk and single-cell RNA-Seq data analysis

Building the metabolite–enzyme network

Building the drug–target network

Network-based drug repurposing

Gene set enrichment analysis (GSEA)

Patient data validation of the network-identified drugs using a COVID-19 registry

Statistical analysis and network visualization

Ethics statement

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Roland G Roberts

Roles

Author response to Decision Letter 0

Decision Letter 1