key: cord-0747150-dapo536f
authors: Nain, Zulkar; Rana, Humayan Kabir; Liò, Pietro; Islam, Sheikh Mohammed Shariful; Summers, Matthew A; Moni, Mohammad Ali
title: Pathogenetic profiling of COVID-19 and SARS-like viruses
date: 2020-08-11
journal: Brief Bioinform
DOI: 10.1093/bib/bbaa173
sha: 0f786f3c5c8033877686accdc974147aa61cc447
doc_id: 747150
cord_uid: dapo536f

The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA–DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein–chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein–chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic.

During the past century, several human coronaviruses have emerged, causing severe respiratory illness and global outbreaks in humans [1] . Coronaviruses (CoVs) are a large group of positive-sense single-stranded RNA viruses belonging to the Coronaviridae family [1, 2] . Six species of coronavirus have been identified as human pathogens [1, 3] . The spectrum of clinical manifestations ranges from mild to severe infection of the respiratory tract [3] . Four species OC43, HKU1, NL63 and 229E cause common cold in immune-compromised patients [1, 3] . Notably, two other coronaviruses, namely, severe acute the respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), have plagued the general public and caused global outbreaks in 2003 and 2012, respectively [1, 2] . In the late December 2019, however, a novel coronavirus (2019-nCoV), which may have evolved through host-induced natural selection [4] , was first identified in patients with viral pneumonia in Wuhan City, China [1, 2] . As of 30 April 2020, there has been a total of 3 200 414 confirmed 2019-nCoV cases and a death toll of 226 893 across 210 countries and territories (https://worldometers.info/coronavirus/). At the time of article writing, Bangladesh had 7103 confirmed cases with 163 deaths (http://corona.gov.bd/). The number of cases is still increasing worldwide with 4.5% global mortality rate [5, 6] . On 7 January 2020, newly evolved SARS-CoV-2 was first identified in Wuhan City, China [1] . The virus was initially named as 2019 novel coronavirus (2019-nCoV) by the World Health Organization [7] . However, based on established taxonomic methods, the virus has been recognized as a sister to SARS-CoV, hence, formally designated as SARS-CoV-2 by the Coronavirus Study Group (CSG) of the International Committee on Taxonomy of Viruses. The disease outbreak is named as coronavirus disease [7] .

The SARS-CoV-2 genome encodes for four major proteins, i.e. spike (S), envelope (E), membrane (M) and nucleocapsid (N) proteins. The S-protein of SARS-CoVs facilitates the viral entry upon binding to angiotensin-converting enzyme 2 (ACE2) expressed in human lung cells [8, 9] . However, the binding affinity of S-protein towards ACE2 is higher in SARS-CoV-2 due to the translational alteration of five out of six vital amino acids in the active site of its S-protein allowing improved hydrophobic and saltbridge interactions [8] . Upon binding, viral membrane fuses with host cells. After fusion occurs, transmembrane serine protease 2 (TMPRSS2) present on the host cell surface activates the ACE2-S-protein complex followed by the conformational changes that allow the virus to enter the cells [10] .

The viral entry induces the host's immune response by exposing itself to antigen-presenting cells (APCs) which have different types of pattern recognition receptors (PRRs) such as toll-like receptors (TLRs) [11] . For instance, TLR4 recognizes the S-protein and activate NF-κB transcription factors and pathogen-activated protein kinase (MAPK) pathway to induce proinflammatory proteins [10] . Meanwhile, other TLRs such as TLR3 could detect an RNA genome that eventually ends up activating the IRF3 and NF-κB transcription factors to induce proinflammatory cytokines such as IFN-α which in turn form complexes with its receptor, IFNAR, thereby, initiating the JAK-STAT pathways [10] . Further, JAK1 and TYK2 kinases phosphorylate STAT1 and 2 followed by its complexation with IRF9, and together they migrate into the nucleus to initiate the transcription of IFN-stimulated genes (ISGs) and lead to suppression of viral replication and prevent the severity of the disease [12] . However, excess releasing of pro-inflammatory cytokines (i.e. IFN-α, IFN-γ , IL-6, IL-18, TNF-α, TGFβ, etc.) and chemokines (i.e. CCL2, CCL5, CXCL8, CXCL10, etc.) from immune effector cells causes cytokine storms which will eventually lead to acute respiratory distress syndrome (ARDS) [10, 13] . However, for better survival and host infections, SARS-CoVs developed immune invasion strategies such as the formation of double vesicles during APC recognition by PRRs. In addition, it has several other proteins such as Nsp1 which can suppress INF-α activity through host translational machinery inactivation and inhibition of STAT1 phosphorylation [12] .

Human coronavirus disease is a zoonosis, infection of animal origins. Most coronaviruses are originated from bats where they are non-virulent [14] . SARS-CoV-2 shares 88-92% genomic similarities with the most bat SARS-like coronaviruses, except W1V1 which showed ∼96% sequence homology [14] . This bat coronavirus (W1V1) can use bat, civet and human ACE2 as a receptor for cell entry. Intriguingly, convalescent sera from SARS patients were able to neutralize WIV1 [14] . Therefore, it has been proposed that the bat is the original host of SARS-CoV-2 [15] . However, viral transmission from bats to humans requires several intermediate reservoirs [14] . In support, several studies have linked pangolins, cats, dogs and hamsters with SARS-CoV-2 infection and transmission [16] [17] [18] [19] [20] . These indicate the widespread prevalence of SARS-CoV-2 across animals as well as the potential threats to humans [21] . Preliminary evidence indicated that the COVID-19 outbreak started at the Wuhan seafood market. However, several recent studies suggest otherwise. For instance, among the first 41 cases, 14 had no prior contact with the market. In another study, five out of seven cases had no link with the market. Therefore, the virus could have amplified in the market, but it might neither have been the site of origin nor the only source of the outbreak [15] .

The COVID-19 outbreak in China reiterates the significant and continued threat of zoonotic diseases caused by coronaviruses to the global population [1] . Unlike most other strains, the newly emerged 2019-nCoV causes SARS-like severe respiratory illness [2] . Like SARS-CoV infection, most infected patients develop pneumonia with prominent fever, shortness of breath, invasive lung lesions, anosmia and sometimes diarrhea [22] [23] [24] [25] . The rate of severity and recovery from the COVID-19 is involved with the age, biological sex and other health conditions of the infected patients [6] . For instance, the majority of the COVID-19 patients are male (54.3%), while the fatality is more frequent (15%) in older age groups (above 80 years old) than in the younger population [6, 26] . In most cases, however, symptoms are similar to seasonal flu [27] . Nevertheless, in a recent Chinese study, 80% of cases were identified as asymptomatic, making early diagnosis difficult [28] . As a result, these undetected asymptomatic patients could be a major source of contagion [28] . Furthermore, the transmission of SARS-CoV-2 from humans to humans is highly frequent in family, health care and community setting [15] . The transmission occurs via respiratory tract droplets or fomites while fecal-oral spread is also possible. The estimated basic reproduction number (R O ) of SARS-CoV-2 ranged between 2.0 and 2.8 [15] . Therefore, early detection is crucial for reducing viral shedding as well as for effective treatment [28] . Despite the knowledge and experience related to SARS-CoV and MERS-CoV, major gaps related to the epidemiology, clinical manifestations and pathogenetic behaviors of this emerging 2019-nCoV remain to be fulfilled [2] . Since no vaccine or antiviral drugs are available, present treatments are based on managing symptoms and complications. Therefore, insight into biomarkers and molecular mechanisms that transform the 2019-nCoV infection into COVID-19 disease and related complications is necessary to Figure 1 . The overall experimental workflow applied in this study. Following establishing SARS-CoV as the closest relative of 2019-nCoV through whole-genome alignment (1000 bootstrap tests), the microarray datasets related to SARS-CoV infection and four COVID-19 complications (i.e. SARS, PNA, SHOB and DRA) were retrieved and analyzed to identify differentially expressed genes (DEGs). Herein, Limma R package was employed where logFC≥|1| was used to distinguish differential expression, and adjusted P-value ≤0.05 was set as cutoff for statistical significance. Then, the DEGs from SARS-CoV infection were subjected to validation with gold-standard databases (i.e. OMIM disease, OMIM expanded and dbGaP) for comorbidity and risk factor assessment followed by comparative analysis with the disease datasets.

Finally, we used the shared DEGs for network-based interactome analysis and functional enrichments in terms of protein-protein interactions (PPI confidence cutoff = 980), gene-biomarker associations (DC≥5 and BC≥35) and protein-chemical interactions (DC≥20 and BC≥20). For pathway analysis, we considered BioCarta (2016), Reactome (2016), KEGG (human; 2019) and WikiPathways (human; 2019) databases, while Biological Process (2018), Molecular Function (2018) and Human Phenotype Ontology databases were used for gene ontological evaluation. In both cases, adjusted P-value ≤0.05 was considered as significant. evaluate the clinical significance and in assessing the severity and prognosis of the disease.

In this study, we aimed to explore the molecular pathogenesis of COVID-19. As the 2019-nCoV shares characteristics with SARS-CoV in terms of genomic and pathogenic features, existing SARS-CoV experimental data is used to study molecular aspects of 2019-nCoV. To accomplish this, we first validated the genetic linkage among 2019-nCoV and other coronaviruses using phylogenetic analysis. We employed a multistep statistical method to determine differentially expressed genes (DEGs), regulatory networks and gene-disease associations as illustrated in Figure 1 . Using the shared DEGs, we identified hubproteins and regulatory biomarkers, i.e. transcription factors (TFs) and microRNAs (miRNAs). Furthermore, gene ontology and pathway analyses were performed to determine significant regulatory checkpoints related to disease complications. Finally, we screened potential chemical antagonists useful in the fight against COVID-19 disease.

The complete genomes used in this study were collected from the Virus Pathogen Database and Analysis Resource (ViPR; https://viprbrc.org/). To investigate the impact of COVID-19 at the molecular level, we employed gene expression microarray datasets that were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/). Due to the genetic closeness and unavailability of COVID-19-related datasets, however, we used microarray datasets related to SARS-CoV infection and four COVID-19 complications, i.e. pneumonia (PNA), severe acute respiratory syndrome (SARS), shortness of breath (SHOB) and diarrhea (DRA). In this study, five different datasets with accession numbers GSE30589, GSE103119, GSE14841, GSE137268 and GSE1739 were used [29] [30] [31] [32] [33] . The SARS-CoV dataset (GSE30589) is an Affymetrix microarray data involving 33 infected and 9 mock-infected patients with 3 hybridizations. The SARS dataset (GSE1739) is a gene expression profile derived from peripheral blood mononuclear cells (PBMCs) of 10 patients and 4 healthy controls using Affymetrix HG-Focus target array platform. The PNA dataset (GSE103119) is an Illumina humanHT-12 v4.0 gene expression array of 152 patients with communityacquired pneumonia and 20 healthy patients. The SHOB dataset (GSE137268) is a gene expression array of 54 asthma patients and 15 healthy individuals developed using Illumina humanRef-8 v2.0 bead chip. The DRA dataset (GSE14841) is an Affymetrix human genome U133 plus 2.0 array from jejunal mucosa of five patients with diarrhea-irritable bowel syndrome and four healthy volunteers.

To determine the relationship between 2019-nCoV and other coronaviruses, we created a comprehensive phylogram that includes the complete genomes of 389 human beta-coronaviruses (Supplementary File S1). The tree was inferred with the FastME program integrated with ViPR database using genomic data with 1000 bootstrap replications. Then, Newick-formatted phylogram was redesigned with the interactive tree of life (iTOL) online tool (https://itol.embl.de/). In addition, the genomic distance and G+C difference were calculated through digital DNA-DNA hybridization (dDDH) using Genome-to-Genome Distance Calculator (GGDC) server v2.1 with default settings [34] . For this analysis, we considered reference genomes of 2019-nCoV (NC_045512.2), SARS-CoV (NC_004718.3), MERS-CoV (NC_019843.3), CoV-OC43 (NC_006213.1) and CoV-HKU1 (NC_006577.2); and formula-2 derived dDDH and G+C difference scores were emphasized as per the server's recommendation [34] .

Evaluation of gene expression microarray is very efficient when it comes to determining underlying molecular mechanisms that lead to the development of disease [35] . Using the Limma R package [36] , we analyzed the selected microarray datasets as described earlier [35, 37] . Briefly, we categorized the samples in each dataset into two groups (treatment and control) followed by log transformation with a series of statistical methods (i.e. B-and t-statistics) and P-value adjustment with Benjamini and Hochberg method (false discovery rate). Herein, genes were considered as significantly upregulated or downregulated when expressed at adjusted P-value ≤0.05 with logFC score greater or lesser than (or equal to) +1 or −1, respectively.

To determine gene-disease associations, we applied neighborhood-based benchmark and topological methods. Thus, we built a comorbidity network from the gene-disease association where the node in the network can be either a disease or a gene. This network is a type of bipartite graph in which diseases are connected when they share at least 1 significant dysregulated gene. Let a particular set of human diseases be D and a set of human genes be G; a comorbidity network then attempts to find whether gene g G is associated with disease d D.

If G i and G j , the sets of significant upand downregulated genes, are associated with diseases i and j, respectively, then the number of shared dysregulated genes (n g ij ) associated with both diseases i and j is as follows:

Co-occurrence indicates the number of common genes in the comorbidity network. Based on the Jaccard similarity index, the common neighbors are found. For each node pair with the edge (E), the prediction point is as follows:

The protein-protein interaction (PPI), regulatory biomarkers (i.e. TFs and miRNAs), protein-drug interaction (PDI) and protein-chemical interaction (PCI) networks were constructed using NetworkAnalyst tool [38] . Experimentally validated STRING interactome with 980 confidence cutoff was used for PPI network. For TFs and miRNAs, we employed experimentally verified TF target genes from the JASPAR database [39] and miRNA target genes from TarBase [40] and miRTarBase [41] via NetworkAnalyst [38] . Similarly, PDI and PCI were evaluated based on DrugBank v5.0 and Comparative Toxicogenomics Database, respectively [42, 43] . All interactions were visualized and customized in Cytoscape v3.7.2 [44] .

The degree centrality (DC) and betweenness centrality (BC) were applied to filter out potential regulatory biomarkers and chemical antagonists. Nodes with a high degree act as important 'hubs' in a network, while high betweenness indicates nodes as important 'bottlenecks' in a network. Briefly, the DC of a node v in a network can be defined as the total number of nodes that are directly connected to node v in that network which can also be expressed as follows:

where n represents the total number of nodes in the network and a vj represents that node v and j are directly connected. BC represents the total number of times node v appears in the shortest path between other nodes. It is also defined as follows:

where σ ij is the total number of shortest paths from node i to node j and σ ivj is the total number of paths through node v. Furthermore, we analyzed the pathways and gene ontology (GO) of the shared DEGs with the Enrichr web application [45] Herein, an adjusted P-value ≤0.05 was considered as statistically significant.

Whole-genome phylogenetic analysis revealed the relation among the different human beta-coronaviruses as shown in Figure 2 . Importantly, the recent epidemic 2019-nCoV formed a sister clade with human SARS-CoV indicating their closest relationship with SARS-CoV at the genomic level. Moreover, as per the phylogenetic position, both SARS-CoV and 2019-nCoV share a common ancestral origin with MERS-CoV, which happened to be closely related to the CoV-HKU1 ( Figure 2 ). This result coincides with the estimated genomic distance and G+C content difference. In our study, the taxonomic distance between SARS-CoV and 2019-nCoV was 0.1985 rendering 22.10% of DNA-DNA hybridization. Moreover, the G+C content of 2019-nCoV was different from SARS-CoV, MERS-CoV and CoV-HKU1 by 2.79, 3.26 and 5.91%, respectively. Interestingly, the G+C difference between 2019-nCoV and CoV-OC43 was lower (1.18%) compared to the closest neighbors. Since the G+C difference is bound to ≤1% within the same species, all these results suggest 2019-nCoV as a distinct coronavirus species.

To investigate the host response upon 2019-nCoV infection, we analyzed the gene expression pattern of SARS-CoV-infected tissues and healthy tissues using microarray data. We found that 1300 genes were differentially expressed as compared to healthy controls in which 562 genes were upregulated while 738 genes were downregulated significantly with infection (adj. P ≤ 0.05). Similarly, we also analyzed the diseasome of four COVID-19 complications to observe their association with SARS-CoV infection. We identified a large number of DEGs for each condition, i.e. 427 in DRA, 581 in SHOB, 997 in PNA and 728 in SARS (Supplementary File S2). Then, we performed a crosscomparative analysis to find common significant DEGs between each condition and SARS-CoV infection. We found a total of 87 unique shared DEGs in which SARS-CoV infection shares 18, 15, 21 and 40 genes with DRA, SHOB, PNA and SARS, respectively. To visualize their association, we construct infectome-diseasome relationship networks centered on the SARS-CoV infection, in which two diseases are comorbid if there exists one or more genes that are associated with both diseases (Figure 3 ). Table 1 includes comprehensive details of important DEGs in terms of their expression pattern, associated conditions and pathogenic roles in disease development. Interestingly, SARS-CoV infection shared ≤19 upregulated and ≤22 downregulated genes with each different COVID-19 complication. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, PNA and SARS ( Figure 3A ) while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation ( Figure 3B ). Nevertheless, less frequent COVID-19 conditions such as DRA 

We used genes that are significantly dysregulated upon SARS-CoV infection to construct gene-disease association networks (GDN) to explore the shared pathogenetic projections and comorbidities in COVID-19, as shown in Figure 4 . This multi-relational GDN is a bipartite graph consisting of two disjointed sets of nodes in which one set represents the known genetic disorders while others correspond to the significantly altered genes for SARS-CoV infection. The information related to disorders, genes and their possible association that we embedded into the GDN was obtained from OMIM and dbGaP databases (Supplementary File S3). Based on the physiological system affected, we classified the associated disorders into 13 categories including SARS-CoV. Herein, the nodes in the GDN indicate either disease categories or involved genes, and two diseases are related to each other if they have at least one gene in common (Figure 4 ). The number of interconnected genes between SARS-CoV and other diseases suggested that cardiac (20) , immunological (18), cancer (23) and respiratory (19) diseases are greatly influenced by SARS-CoV and vice versa due to their higher number of shared genes ( Figure 4) . Interestingly, 14 genes were also common to all included categories of disease such as APC, KPNA2, CTNNB1, CCND1, NFKB1, FOS, HDAC2, PTPRK, LHB, FUS, CTBP1, THRA, CDX2 and BUB1 which further indicate their strong association with these diseases.

In complex pathological conditions, analysis of signaling pathways can shed light on the underlying molecular mechanisms linking multiple diseases to each other. Therefore, we analyzed a total of 87 unique shared DEGs from four COVID-19 presentations to determine the significantly involved pathways (Supplementary File S4) . Table 2 shows a disease-wise total of 22 significant pathways that are relevant and shared among 4 COVID-19 presentations and SARS-CoV disease with their associated genes and adjusted P-values. For example, we identified five pathways including chemokine signaling pathway NRF2 pathway, non-small cell lung cancer, FoxO signaling pathway and hypoxia and p53 in the cardiovascular system that are significantly expressed in SARS condition (Table 2 ). In addition, we observed five pathways related to SHOB that are FOSB gene expression and drug abuse, EGFR downregulation, MAPK signaling pathway, TGF-β signaling pathway and oxidative damage. In this study, PNA-related significant pathways were mostly associated with cytokine signaling ( Table 2) ; and DRA shared five pathways with SARS-CoV disease. In addition to pathways, GO properties were anticipated to determine the overrepresented ontological groups among the DEGs (Supplementary File S5) . We found 25 significant GO groups that are linked to the highly expressed genes shared by SARS-CoV and COVID-19 diseases. The genes associated with their GO terms and adjusted P-values are presented in Table 3 . Each of the complication PNA, SHOB and DRA shared six significant pathways with SARS-CoV, while SARS had seven pathways related to SARS-CoV disease (Table 3) .

Among the 25 ontological features, notable GO terms were regulation of p38MAPK cascade (GO:1900744), regulation of NF-κB import into the nucleus (GO:0042345), protein kinase binding (GO:0019901), negative regulation of JAK-STAT cascade (GO:0046426), leukotriene receptor activity (GO:0004974), respiratory insufficiency due to muscle weakness (HP:0002747), respiratory failure (HP:0002878), regulation of epidermal growth factor receptor signaling pathway (GO:0042058), MAPK cascade (GO:0000165) and regulation of ERK1 and ERK2 cascade (GO:0070372). These ontological features were common in SARS-CoV disease and COVID-19 complications. Therefore, they could either be risk factors or regulatory checkpoints in COVID-19 disease.

Using the shared DEGs, 12 proteins having experimental evidence were characterized as seed and formed an interaction network with the other 122 proteins. These proteins are SOCS3, BUB3, EIF2S3, MET, MCL1, NFKBIA, GADD45A, ATF3, EGR1, FOSB, ATP2A and KLF5. Among these 12 proteins, 7 proteins have interactions with 8 or more other proteins and are identified as hub-proteins ( Figure 5 ). In our study, the majority of hubproteins were related to SARS such as NFKBIA, BUB3, EIF2S3 and GADD45A, while two proteins (MET and MCL1) belonged to DRA and a single protein, SOCS3, was associated with PNA. However, no hub-protein was found in the shared DEGs for SHOB complication. From Figure 5 , we can see the interrelationships among the different diseases, hub-proteins and their protein networks. However, EGR1 of SARS and ATF3 of PNA were intermediately connected, while FOSB, KLF5 and ATP2A2 were non-hub proteins.

We found 47 miRNAs and 28 TFs that might influence the differential expression of these genes thereby regulating disease progression. Among the 47 miRNAs, four human miRNAs (i.e. let-7c-5p, miR-27b-3p, miR-98-5p and miR-125a-5p) were found to be targeting the SARS-CoV-2 genome, while many others were associated with the development of COVID-19 symptoms, i.e. let-7b-5p, miR-155-5p, miR-186-5p, miR-16-5p, miR-27b-3p, miR-29a-3p, miR-30a-5p, etc., as depicted in Figure 6A . Among the 28 TFs, notable TFs are RELA, E2F1, STAT3, TP53, NFKB1, GATA3 and CREB1 as shown in Figure 6B .

To identify potent drugs and antiviral agents, we used shared DEGs to analyze their interactions with different drugs and chemical agents. Using PDI approach, we found 13 drugs that act against the MET gene (Supplementary File S6) . Besides, the PCI method predicted 31 potential chemical agents related to 52 genes as shown in Figure 7 . Among the 13 drugs, most of them were kinase inhibitors such as antibiotic K-252A, cabozantinib, amuvatinib, crizotinib and SGX-523. Out of the total 31 chemicals, relevant chemical agents were cyclosporine, quercetin, resveratrol, (+)-JQ1 compound, SB-431542 and dorsomorphin.

To understand the genetics of COVID-19, we developed a network-based framework where intra-and interconnections among genes and proteins may provide valuable information on their roles in developing certain diseases or conditions. In this study, we first established the genomic closeness and position of 2019-nCoV within the Coronaviridae family using the phylogenic approach. In the phylogram, both 2019-nCoV and SARS-CoV formed two clades in a single node which confirms their close relationship. However, it also revealed that 2019-nCoV is not descended from SARS-CoV; rather they have a common ancestral origin. This observation is in line with another recent study that led to the naming of 2019-nCoV as SARS-CoV-2 [7] . Due to higher genetic similarity, all SARS-like coronaviruses (i.e. 2019-nCoV, SARS-CoV, MERS-CoV and CoV-HKU1) formed a separate branch from all other coronaviruses included in Beta-CoV 1, such as HCoV-OC43. This relationship is in line with an earlier report [108] . Furthermore, a recent study reveals that 2019-nCoV shares the highest (79.7%) nucleotide sequence identity with SARS-CoV [109] . This high degree of genomic similarity could be responsible for their analogous pathogenic behavior and clinical disease presentation. Therefore, SARS-CoV might share pathogenic signatures and pathways involved in the progression of COVID-19 upon 2019-nCoV infection, thereby providing the basis for further investigation. Based on this guiding principle, we identified 87 significant DEGs shared among SARS-CoV infection and 4 COVID-19related disorders. These genes could be associated with 2019-nCoV infections as well. Therefore, we reviewed the molecular pathogenesis of 44 genes (out of 87 DEGs) that are involved in COVID-19 and other viral infections, especially SARS-like viruses. Intriguingly, the expression pattern and pathogenetic roles of most genes are indifferent to COVID-19 and other viral infections, except for few. In a recent study, for example, the increased level of inflammatory chemokines such as IL-1 and TNF-α has been observed in COVID-19 patients [22] . As we speculated, the upregulation of NFKBIA and FOXO3 could be involved in this process by being the key modulators of IL-1 and TNF-α-related chemokine signaling [81, 110, 111] . In addition, the association of NFKBIA gene in SARS-CoV and MERS-CoV infections was found in a previous study [79] . Another gene Abl2 is important for cell entry and replication of SARS-like viruses [47, 48] . Furthermore, LTB4 is highly prevalent in type 1 diabetic patients, and overexpression of this gene triggers acute asthma attacks in SARS-related diseases [61, 62] . This also explains the increased susceptibility of diabetic patients to COVID-19. Therefore, they are not merely associated with SARS-CoV rather may have broader roles that might include their involvement in COVID-19 pathogenesis. In addition, we have characterized GO and pathways that are regulated by these genes. About 22 pathways were identified that are linked to SARS-CoV infection and could possibly be involved in COVID-19 as well. For instance, high expression of IL-6 signaling [22, 112] and IFN-γ production [113] was observed in COVID-19 sufferers. Therefore, these pathways could be linked to COVID-19 and may reveal important checkpoints for drug targets. We also identified 25 important GO terms that are related to the clinical conditions of COVID-19. Therefore, the regulation of these pathways might be important in COVID-19 progression. For example, interferon-MAPK pathway-mediated adaptive immune response against COVID-19 was observed through blood single-cell immune profiling [2] . Therefore, it is probable that COVID-19 disease progression might be mediated by those and other significant pathways.

To validate our findings, significant DEGs were employed to predict gene-disease association using benchmark databases. In doing so, we created a GDN network as comorbidities of SARS-CoV, to understand the possible COVID-19 projections and risk factors. Comorbidities may result from pathogenic relationships or common risk factors among concomitant disorders [114] . Apart from the respiratory disease, the anticipated comorbid profiles suggested a wide array of diseases involved in 2019-nCoV infection including cardiac, renal, brain and blood-related problems as well as different types of cancer. For example, 17 genes were found to be associated with blood diseases including thalassemia, thrombocytopenia, hypertension, thrombophilia and leukemia. Strikingly, a recent meta-analysis included thrombocytopenia as a comorbidity of COVID-19 [115] . Very recently, unusual blood clots have been found in multiple organs causing strokes and organ failures [116, 117] . This could be due to thrombophilia, an abnormal blood clotting disorder caused by altered expression of the associated genes upon 2019-nCoV infection. On the other hand, comorbidities related to brain diseases include encephalopathy, dementia and Leigh syndrome. Interestingly, a recent study suggested that COVID-19 could lead to acute hemorrhagic necrotizing encephalopathy, which is in line with our findings [118] .

Furthermore, a study suggested hypertension as a potential risk factor in 2019-nCoV infection [119] . Two recent studies from China have shown a strong association between hypertension and COVID-19 deaths (Hazard ratio 1.70 to 3.05), raising concerns that hypertension is confounded by treatment with specific antihypertensive medications such as ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) [120, 121] . In response, the European Society of Cardiology Council on Hypertension made the following statement, 'The Council on Hypertension strongly recommends that physicians and patients should continue treatment with their usual anti-hypertensive therapy because there is no clinical or scientific evidence to suggest that treatment with ACEIs or ARBs should be discontinued because of the COVID-19 infection [123] '. According to the American College of Cardiology (ACC) clinical bulletin, the fatality rate in COVID-19 is higher in patients with preexisting conditions such as cancer (5.6%), hypertension (6.0%), cardiovascular diseases (10.5%) and diabetes (7.3%) [123] [124] [125] . Furthermore, about 16.7% of patients developed arrhythmia, and 7.2% experienced an acute cardiac injury in addition to 2019-nCoV-associated complications [123] . These cardiovascular complications may result from various mechanisms, including relative ischemia, systemic inflammation and pathogen-mediated damage [126] . Interestingly, we identified 20 genes that are involved in different cardiovascular diseases and 16 genes related to diabetes mellitus (type 1 and 2). Evidence to date suggests that people who died of COVID-19 were more likely to have comorbidities such as hypertension, diabetes, cardiovascular disease or chronic lung disease [127, 128] . Further research on these genes could reveal long-term cardiovascular implications of viral infection, thus closing the significant knowledge gaps in the recent COVID-19 outbreak.

Using the shared genes, a PPI network was constructed to pinpoint the key disease-modifying agents in COVID-19 in light of SARS-CoV pathobiology. Hubs are defined as proteins with eight or more interactions, while proteins with less than four interactions are named non-hubs and the rest are intermediately connected [129] . We identified seven hub-proteins (MCL1, NFKBIA, BUB3, MET, EIF2S3, GADD45A and SOCS3) involved in SARS-CoV and COVID-19 diseases. The protein NFKBIA regulates inflammatory responses and plays an important role in the pathogenesis of acute respiratory distress syndrome [130] . Another hub-protein GADD45A is known to play a significant role in lung injury due to inflammation [131, 132] . Furthermore, the expression of SOCS3 downregulated JAK2/STAT3 pathway to promote macrophage polarization that plays a key role in lung inflammation [133] . Therefore, these hub-proteins can be regarded as candidate biomarkers or, if their biological role in COVID-19 is confirmed, as potential drug targets. Furthermore, TFs control the rate of transcription [134] , while miRNAs are key players in RNA silencing and regulation of gene expression at the posttranscription level [135] . Hence, both are essential to understand particular disease development. In this regard, this study uncovered relationships among the shared DEGs and their respective TFs and regulatory miRNAs. Herein, we identified several TFs, such as STAT3, NFKB1A, E2F1, TP53, GATA3 CREB1, which are known to involve in viral-mediated acute respiratory diseases [86, 136, 137] . Further, some miRNAs associated with lung diseases (i.e. let-7b-5p and let-7c-5p), asthma (i.e. miR-155-5p, miR-186-5p, miR-16-5p, miR-27b-3p) and pneumonia (i.e. miR-29a-3p and miR-30a-5p) were also found highly prevalent in COVID-19 [86, [138] [139] [140] .

TFs and miRNAs usually target host proteins to alter their expression during the development of specific diseases. For coronavirus, for instance, miR-98 modulates the host inflammatory response by interacting with STAT3. For other respiratory viruses, let-7b and let-7c target STAT3, while miR-30a influences TP53 [86] . Both are regulatory TFs that are identified as important biomarkers in this study. Furthermore, miR-16 and miR-155 modulate inflammatory responses by targeting the MCL1 and NFKBIA hub-proteins [86] . Interestingly, we also identified four miRNAs (let-7c-5p, miR-125a-5p, miR-27b-3p and miR-98-5p) that are known to target different genes of 2019-nCoV [141, 142] . For instance, let-7c-5p targets ORF1ab gene of 2019-nCoV and inhibits viral replication [142] . Conversely, the spike protein from SARS-CoV may downregulate the expression of miR-98 in bronchoalveolar stem cells to control their differentiation and inflammatory cytokine production. Moreover, we identified two long noncoding RNA (lncRNA) in our shared DEGs, i.e. MALAT1 and LINC00265. These lncRNAs may act in concert with miRNAs to orchestrate the competing endogenous RNA (ceRNA) network in disease progression. For instance, upregulation of MALAT1 ceases miR-155 expression which promotes GATA3 and Th2 cytokine production leading to severe asthma and loss of lung function [90] . On the other hand, LINC00265 inhibits let-7a activity using ceRNA mechanism, which results in the upregulation of IL6 [143] . All these eventually lead to the deterioration of lung tissues and successful viral replication [144] .

In addition to regulatory molecules, we identified 13 MET gene-targeting drugs using PDI method that are mostly kinase inhibitors. Kinases play a key role in the airway muscle contractions and in the release of pro-inflammatory mediators [145] . Therefore, our results suggest plausible mechanisms, since most of the proteins are related to inflammatory responses. Nevertheless, we excluded these drugs since they have multiple known side effects including hypertension, diarrhea, anorexia and nausea [146] . Furthermore, we anticipated PCI network to identify the chemical agents that could act as potential antagonists. Among the total 31 chemical agents, we could find the potent antagonists by targeting the hub-proteins that are linked to COVID-19 progression as stated above. Previously, several drugs and chemical agents showed potent therapeutic action against 2019-nCoV. For example, remdesivir and chloroquine have been reported to inhibit 2019-nCoV and other SARS-like viruses [147] . Furthermore, a Japanese flu-drug favipiravir showed a significant protective effect against COVID-19 [148] . Moreover, a recent nonrandomized clinical trial showed a promising effect of hydroxychloroquine in concert with azithromycin against 2019-nCoV by inhibiting their genomic replication [149, 150] . Although hydroxychloroquine possesses less toxicity, prolonged and overdose usage of this antiinflammatory agent can still cause poisoning [151] . Alternatively, the chemical agents identified in this study can be checked against 2019-nCoV or its disease form COVID-19. For example, we identified quercetin which is a potent antiviral and antiinflammatory agent active against different strains of influenza virus (i.e. H1N1 and H3N2) [152] . Another identified chemical antagonist was resveratrol in our study. Interestingly, resveratrol is a prominent antiviral agent which showed antiviral activity against several human and animal viruses [153, 154] . Most importantly, Lin et al. showed resveratrol-mediated effective inhibition of MERS-CoV due to the downregulation of apoptosis [155] . As we stated earlier, overexpression of NF-κB is central to asthma and other pulmonary diseases [156] . Resveratrol inhibits the expression of NF-κB and related pathways, thereby subduing the conditions [154] . Another major identified compound is cyclosporine, which is known to inhibit the replication of RNA viruses including SARS-CoV, MERS-CoV and CoV-229E by targeting cyclophilins [157, 158] . Cyclophilins regulate the replication of RNA viruses and are usually overexpressed in viral infections as well as in inflammatory diseases such as asthma [157] [158] [159] . Importantly, a recent study includes resveratrol and cyclosporine A in the list of therapeutic options for 2019-nCoV [160] . Overall, these data are of clinical interest and may shed light on the cause and progression of COVID-19, as well as any new prospective therapeutic strategies.

The present study highlights molecular insight on potential biomarkers, regulatory components and molecular checkpoints that may help in developing therapeutics to combat the current 2019-nCoV pandemic. Based on gene expression analysis, we highlight significantly expressed genes with possible projections and risk factors associated with COVID-19, in the form of comorbidities. These identified biomarkers could be potential drug targets based on their role in regulatory checkpoints and disease progression. Furthermore, potent chemical agents that are identified could be used to target-specific disease-modifying factors. The comorbidity analysis presented here provides disease mechanistic insight and prognostic features relevant in COVID-19 disease. These data will aid in unlocking new opportunities for novel therapeutics to control this, as well as future viral outbreaks.

• The 2019-nCoV and SARS-CoV have a common ancestral origin, and their pathobiological profiles could have a number of significantly dysregulated genes in common. These could be compared with upcoming COVID-19 transcriptomes to assess the distinct genetic mechanisms involved in a disease. • Comorbidity profiles and associated genes could be evaluated for their roles in recently observed clinical presentations and future implications of COVID-19.

• In protein-protein network analyses, identified hubproteins can lead us to co-expression partners related to normal and disease states, which can help in the assessment of risk factors related to COVID-19 complications.

• TFs and miRNAs identified as biomarkers can be used as prognostic signatures to differentiate patients and healthy individuals. Their role as disease-modifying traits can further be explored.

• Signaling pathways that are common to SARS-CoV and COVID-19 presentations can be used as molecular checkpoints to develop therapeutics against 2019-nCoV infection.

• Protein-chemical interactome analysis suggests cyclosporine, resveratrol and quercetin as potential chemical antagonists against COVID-19 as well as SARS-like viral infections.

A novel coronavirus from patients with pneumonia in China

Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

Epidemiology characteristics of human coronaviruses in patients with respiratory infection symptoms and phylogenetic analysis of HCoV-OC43 during 2010-2015 in Guangzhou

The proximal origin of SARS-CoV-2

Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

COVID-19: herd immunity and convalescent plasma transfer therapy

The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2

Angiotensinconverting enzyme 2: SARS-CoV-2 receptor and regulator of the renin-angiotensin system

Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): an overview of viral structure and host response

SARS-CoV-2 and coronavirus disease 2019: what we know so far

Immune responses in COVID-19 and potential vaccines: lessons learned from SARS and MERS epidemic

Molecular immune pathogenesis and diagnosis of COVID-19

Zoonotic origins of human coronaviruses

COVID-19: a novel zoonotic disease caused by a coronavirus from China: what we know and what we don't

Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins

Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARScoronavirus 2

Pathogenesis and transmission of SARS-CoV-2 in golden hamsters

Infection of dogs with SARS-CoV-2

Transmission of SARS-CoV-2 in domestic cats

Single-cell screening of SARS-CoV-2 target cells in pets, livestock, poultry and wildlife

The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak -an update on the status

Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia

A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster

Non-neural expression of SARS-CoV-2 entry genes in the olfactory epithelium suggests mechanisms underlying anosmia in COVID-19 patients

Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan

Differential diagnosis of illness in patients under investigation for the novel coronavirus

Covid-19: four fifths of cases are asymptomatic, China figures indicate

Severe acute respiratory syndrome coronavirus envelope protein regulates cell stress response and apoptosis

Molecular distance to health transcriptional score and disease severity in children hospitalized with community-acquired pneumonia

Decreased TESK1-mediated cofilin 1 phosphorylation in the jejunum of IBS-D patients may explain increased female predisposition to epithelial dysfunction

Sputum transcriptomics implicates increased p38 signalling activity in severe asthma

Expression profile of immune response genes in patients with severe acute respiratory syndrome

Taxonomic use of DNA G+C content and DNA-DNA hybridization in the genomic age

Machine learning and bioinformatics models to identify pathways that mediate influences of welding fumes on cancer progression

Use of within-array replicate spots for assessing differential expression in microarray experiments

Genetic profiling and comorbidities of Zika infection

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A database curates experimentally validated microRNA-target interactions

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Impact of viral presence in tumor on gene expression in non-small cell lung cancer

A comprehensive review on drug repositioning against coronavirus disease 2019 (COVID19)

Abelson kinase inhibitors are potent inhibitors of severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus fusion

In Silico discovery of candidate drugs against Covid-19

MERS coronavirus induces apoptosis in kidney and lung by upregulating Smad7 and FGF2

Single-cell longitudinal analysis of SARS-CoV-2 infection in human bronchial epithelial cells

Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19

BCL6 represses antiviral resistance in follicular T helper cells

Identification and functional characterization of DR6, a novel death domaincontaining TNF receptor

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Homozygous mutation of STXBP5L explains an autosomal recessive infantile-onset neurodegenerative disorder

The role of PI3K/Akt in human herpesvirus infection: from the bench to the bedside

Activation of the PI3K/Akt pathway early during Vaccinia and cowpox virus infections is required for both host survival and viral replication

Long noncoding RNA LINC00265 predicts the prognosis of acute myeloid leukemia PATIENTS and functions as a promoter by activating pi3k-akt pathway

Leukotriene A4 hydrolase activation and leukotriene B4 production by eosinophils in severe asthma

Leukotriene pathway activation associates with poor glycemic control and with cardiovascular autonomic neuropathy in type 1 diabetes

Networkbased analysis of fatal comorbidities of COVID-19 and potential therapeutics

Calmodulin antagonists inhibit human immunodeficiency virus-induced cell fusion but not virus replication

Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: structural genomics approach

Lung biopsy cells transcriptional landscape from COVID-19 patient stratified lung injury in SARS-CoV-2 infection through impaired pulmonary surfactant metabolism

SARS coronavirus papainlike protease induces Egr-1-dependent up-regulation of TGF-β1 via ROS/p38 MAPK/STAT3 pathway

COVID-19 and crosstalk between the hallmarks of aging

MicroRNA-223 promotes type I interferon production in antiviral innate immunity by targeting forkhead box protein O3 (FOXO3)

Prediction of repurposed drugs for treating lung injury in COVID-19

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Imbalanced host response to SARS-CoV-2 drives development of COVID-19

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KLF5 is involved in regulation of IFITM1, 2, and 3 genes during H5N1 virus infection in A549 cells

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Elucidating the molecular physiopathology of acute respiratory distress syndrome in severe acute respiratory syndrome patients

Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies

Chemokine expression in inflamed adipose tissue is mainly mediated by NF-κB

Exuberant expression of chemokine genes by adult human articular chondrocytes in response to IL-1β

The NF-κB-dependent and -independent transcriptome and chromatin landscapes of human coronavirus 229E-infected cells

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Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro

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Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro

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Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro

Quercetin as an antiviral agent inhibits influenza a virus (IAV) entry

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Cyclosporin A inhibits the replication of diverse coronaviruses

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Cyclophilin A: a key player for human disease

Therapeutic options for the 2019 novel coronavirus (2019-nCoV)

We thank all those who have contributed in 2019-nCoV genome sequences deposited to the GISAID database (https://www.gisaid.org/) and provided ideas to the Virological website (http://virological.org/).

The authors declare no conf lict of interests.

Not applicable.

Supplementary data are available online at https://academic. oup.com/bib.