Proteomic understanding of SARS-CoV-2 infection and COVID-19: Biological, diagnostic, and therapeutic perspectives

The research community responded rapidly to the SARS-CoV-2 pandemic with a burst of proteomic studies to understand this new virus. We focus in this chapter on proteomic approaches and advances in the areas of the viral proteome and the viral-host protein-protein interactions exploited to facilitate the pathogenic life cycle of SARS-CoV2. We also outline the proteome and posttranslational protein modifications of infected cells and blood proteomics of COVID-19 patients in relation to pathogenesis, host response, and disease severity. Finally, tools and application for COVID-19 proteomics and implementations in diagnostics and therapies are surveyed before summarizing the present achievements and future perspectives. © 2023 Elsevier Inc. All rights reserved.

Future of digital health and precision medicine in rheumatology practice in India

With the digitalization of the services across various sectors in an Indian setting, health care is also influenced by the same. It was evidenced during the COVID-19 pandemic that Indian patients were orienting themselves toward more teleconsultations and digital and smartphone-based health care. This not only saves time and money but also reduces the chances of hospital-acquired cross infections. This is more important for patients with rheumatic diseases who try to avoid frequent hospital visits despite the need for regular health-care consultations due to the aforementioned reasons. Apart from the telemedicine and smartphone apps, health care is expanding to robotics and artificial intelligence-based machine learning. Healthcare digitalization will lead to the expansion of precision based medicine. When more robust genomics, proteomics, metabolomics, and transcriptomics data become available for Indian patients with rheumatic diseases, management then would be more personalized than blanket therapy. However, such futuristic advancements face challenges of their own which are neither time nor knowledge bound. We are currently just at the tip of this massive iceberg. We describe various aspects of the future of digital health and precision medicine in rheumatology in an Indian setting. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Salivary proteomic analysis in asymptomatic and symptomatic SARS-CoV-2 infection: Innate immunity, taste perception and FABP5 proteins make the difference.

BACKGROUND AND AIM: SARS-CoV-2 infection spawns from an asymptomatic condition to a fatal disease. Age, comorbidities, and several blood biomarkers are associated with infection outcome. We searched for biomarkers by untargeted and targeted proteomic analysis of saliva, a source of viral particles and host proteins. METHODS: Saliva samples from 19 asymptomatic and 16 symptomatic SARS-CoV-2 infected subjects, and 20 controls were analyzed by LC-MS/MS for untargeted peptidomic (flow through of 10 kDa filter) and proteomic (trypsin digestion of filter retained proteins) profiling. RESULTS: Peptides from 53 salivary proteins were identified. ADF was detected only in controls, while IL1RA only in infected subjects. PRPs, DSC2, FABP5, his-1, IL1RA, PRH1, STATH, SMR3B, ANXA1, MUC7, ACTN4, IGKV1-33 and TGM3 were significantly different between asymptomatic and symptomatic subjects. Retained proteins were 117, being 11 highly different between asymptomatic and symptomatic (fold change ≥2 or ≤-2). After validation by LC-MS/MS-SRM (selected reaction monitoring analysis), the most significant discriminant proteins at PCA were IL1RA, CYSTB, S100A8, S100A9, CA6, and FABP5. CONCLUSIONS: The differentially abundant proteins involved in innate immunity (S100 proteins), taste (CA6 and cystatins), and viral binding to the host (FABP5), appear to be of interest for use as potential biomarkers and drugs targets.

Correlates with Vaccine Protective Capacity and COVID-19 Disease Symptoms Identified by Serum Proteomics in Vaccinated Individuals.

In the last two years, the coronavirus disease 19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a scientific and social challenge worldwide. Vaccines have been the most effective intervention for reducing virus transmission and disease severity. However, genetic virus variants are still circulating among vaccinated individuals with different disease symptomatology. Understanding the protective- or disease-associated mechanisms in vaccinated individuals is relevant to advances in vaccine development and implementation. To address this objective, serum-protein profiles were characterized by quantitative proteomics and data-analysis algorithms in four cohorts of uninfected and SARS-CoV-2-infected vaccinated individuals with asymptomatic, non-severe, and severe disease symptomatology. The results show that immunoglobulins were the most overrepresented proteins in infected cohorts when compared to PCR-negative individuals. The immunoglobulin profile varied between different infected cohorts and correlated with protective- or disease-associated capacity. Overrepresented immunoglobulins in PCR-positive individuals correlated with protective response against SARS-CoV-2, other viruses, and thrombosis in asymptomatic cases. In non-severe cases, correlates of protection against SARS-CoV-2 and HBV together with risk of myasthenia gravis and allergy and autoantibodies were observed. Patients with severe symptoms presented risk for allergy, chronic idiopathic thrombocytopenic purpura, and autoantibodies. The analysis of underrepresented immunoglobulins in PCR-positive compared to PCR-negative individuals identified vaccine-induced protective epitopes in various coronavirus proteins, including the spike receptor-binding domain RBD. Non-immunoglobulin proteins were associated with COVID-19 symptoms and biological processes. These results evidence host-associated differences in response to vaccination and the possibility of improving vaccine efficacy against SARS-CoV-2.

CREATING BIOINFORMATICS MATRIX OF MOLECULAR MARKERS TO PREDICT RISK-ASSOCIATED HEALTH DISORDERS

Long-term or permanent chemical ambient air pollution in residential areas is among priority factors that cause medical and demographic losses. It is necessary to achieve greater precision when assessing risks of changes in homeostasis at their early reversible stage (molecular level). These changes are highly likely to transform into pathological processes at an older age in case the exposure persists. Our research goal was to create a bioinformatics matrix of molecular markers to predict risk-associated health disorders (exemplified by a marker of exposure). We introduced a stepwise research algorithm that involved using the proteome technology to identify expressed proteins and cause-effect relations between them and influencing factors;revealing molecular-cellular and functional relationships within the “exposure factor – gene – protein – negative outcome” system to predict risk-associated health disorders. The algorithm was implemented to examine the proteomic blood plasma profile of children aged 3–6 years living under long-term aerogenic exposure to fluoride-containing compounds. We established certain changes in the proteomic profiles of the exposed children in comparison with non-exposed ones as per 27 identified proteins. A bioinformatics matrix was created on the example of cathepsin L1;we established that changes in the level of this protein had a cause-effect relationship with fluoride ion concentrations in urine. Qualitative synthesis of molecular-cellular localization, functional and tissue belonging showed that cathepsin L1 expression caused by elevated fluoride ion levels in urine could affect extracellular matrix remodeling, degradation and post-translation modification of proteins in cells of the lungs, large intestine, and pancreas, in cardiomyocytes and in glomerular podocytes. It also mediated proteolysis of the subunits of the SARS-CoV-2 S1 protein necessary for the virus penetration into a cell and its replication. This created bioinformatics matrix exemplified by cathepsin L1 made it possible to predict risk-associated negative outcomes in exposed people including cardiomyopathy, colitis, glomerulonephritis, diabetes mellitus, atherosclerosis, and coronavirus infection. These predictive estimates raise effectiveness of early detection and development of preventive measures aimed at minimizing possible negative outcomes. © Zemlyanova M.A., Zaitseva N.V., Koldibekova Yu.V., Peskova E.V., Bulatova N.I., 2022

Proteomic analysis reveals zinc-finger CCHC-type containing protein 3 as a factor inhibiting virus infection by promoting innate signaling.

Influenza a virus exploits host machinery to benefit its replication in host cells. Knowledge of host factors reveals the complicated interaction and provides potential targets for antiviral treatment. Here, instead of the traditional proteomic analysis, we employed a 4D label free proteomic method to identify cellular factors in A549 cells treated with avian H9N2 virus. We observed that 425 proteins were upregulated and 502 proteins were downregulated. Western blotting and quantitative real-time PCR results showed that the zinc-finger CCHC-type containing protein 3 (ZCCHC3) levels were markedly induced by H9N2 infection. Transient expression assay showed that ZCCHC3 expression decreased NP protein levels and viral titers, whereas knockdown of ZCCHC3 enhanced viral growth. Specifically, ZCCHC3 promoted the expression of IFN-ß, leading to the increased transcription of IFN downstream antiviral factors. Surprisingly, viral NS1 protein was able to antagonize the antiviral effect of ZCCHC3 by downregulating IFN-ß. Eventually, we observed that chicken finger CCCH-type containing protein 3, named ZC3H3, could also suppress the replication of H9N2 virus and the coronavirus-infectious bronchitis virus (IBV) in DF-1 cells. Together, our results showed the cellular proteomic response to H9N2 infection and identified ZCCHC3 as a novel antiviral factor against H9N2 infection, contributing to the understanding of host-virus interaction.

Competition for dominance within replicating quasispecies during prolonged SARS-CoV-2 infection in an immunocompromised host.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) emerge for their capability to better adapt to the human host aimed and enhance human-to-human transmission. Mutations in spike largely contributed to adaptation. Viral persistence is a prerequisite for intra-host virus evolution, and this likely occurred in immunocompromised patients who allow intra-host long-term viral replication. The underlying mechanism leading to the emergence of variants during viral persistence in the immunocompromised host is still unknown. Here, we show the existence of an ensemble of minor mutants in the early biological samples obtained from an immunocompromised patient and their dynamic interplay with the master mutant during a persistent and productive long-term infection. In particular, after 222 days of active viral replication, the original master mutant, named MB610, was replaced by a minor quasispecies (MB61222) expressing two critical mutations in spike, namely Q493K and N501T. Isolation of the two viruses allowed us to show that MB61222 entry into target cells occurred mainly by the fusion at the plasma membrane (PM), whereas endocytosis characterized the entry mechanism used by MB610. Interestingly, coinfection of two human cell lines of different origin with the SARS-CoV-2 isolates highlighted the early and dramatic predominance of MB61222 over MB610 replication. This finding may be explained by a faster replicative activity of MB61222 as compared to MB610 as well as by the capability of MB61222 to induce peculiar viral RNA-sensing mechanisms leading to an increased production of interferons (IFNs) and, in particular, of IFN-induced transmembrane protein 1 (IFITM1) and IFITM2. Indeed, it has been recently shown that IFITM2 is able to restrict SARS-CoV-2 entry occurring by endocytosis. In this regard, MB61222 may escape the antiviral activity of IFITMs by using the PM fusion pathway for entry into the target cell, whereas MB610 cannot escape this host antiviral response during MB61222 coinfection, since it has endocytosis as the main pathway of entry. Altogether, our data support the evidence of quasispecies fighting for host dominance by taking benefit from the cell machinery to restrict the productive infection of competitors in the viral ensemble. This finding may explain, at least in part, the extraordinary rapid worldwide turnover of VOCs that use the PM fusion pathway to enter into target cells over the original pandemic strain.

Microvascular and proteomic signatures overlap in COVID-19 and bacterial sepsis: the MICROCODE study.

AIMS: Although coronavirus disease 2019 (COVID-19) and bacterial sepsis are distinct conditions, both are known to trigger endothelial dysfunction with corresponding microcirculatory impairment. The purpose of this study was to compare microvascular injury patterns and proteomic signatures in COVID-19 and bacterial sepsis patients. METHODS AND RESULTS: This multi-center, observational study included 22 hospitalized adult COVID-19 patients, 43 hospitalized bacterial sepsis patients, and 10 healthy controls from 4 hospitals. Microcirculation and glycocalyx dimensions were quantified via intravital sublingual microscopy. Plasma proteins were measured using targeted proteomics (Olink). Coregulation and cluster analysis of plasma proteins was performed using a training-set and confirmed in a test-set. An independent external cohort of 219 COVID-19 patients was used for validation and outcome analysis. Microcirculation and plasma proteome analysis found substantial overlap between COVID-19 and bacterial sepsis. Severity, but not disease entity explained most data variation. Unsupervised correlation analysis identified two main coregulated plasma protein signatures in both diseases that strictly counteract each other. They were associated with microvascular dysfunction and several established markers of clinical severity. The signatures were used to derive new composite biomarkers of microvascular injury that allow to predict 28-day mortality or/and intubation (area under the curve 0.90, p < 0.0001) in COVID-19. CONCLUSION: Our data imply a common biological host response of microvascular injury in both bacterial sepsis and COVID-19. A distinct plasma signature correlates with endothelial health and improved outcomes, while a counteracting response is associated with glycocalyx breakdown and high mortality. Microvascular health biomarkers are powerful predictors of clinical outcomes.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Gut microbiota, inflammation, and molecular signatures of host response to infection.

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.

Proteomic profiling reveals a distinctive molecular signature for critically ill COVID-19 patients compared with asthma and chronic obstructive pulmonary disease.

OBJECTIVE: The mortality rate for critically ill COVID-19 cases was more than 80%. Nonetheless, research about the effect of common respiratory diseases on critically ill COVID-19 expression and outcomes is scarce. DESIGN: We performed proteomic analyses on airway mucus obtained by bronchoscopy from patients with severe COVID-19, or induced sputum from patients with chronic obstructive pulmonary disease (COPD), asthma, and healthy controls. RESULTS: Of the total identified and quantified proteins, 445 differentially expressed proteins (DEPs) were found in different comparison groups. In comparison with COPD, asthma, and controls, 11 proteins were uniquely present in COVID-19 patients. Apart from DEPs associated with COPD versus controls and asthma versus controls, there was a total of 59 DEPs specific to COVID-19 patients. Finally, the findings revealed that there were 8 overlapping proteins in COVID-19 patients, including C9, FGB, FGG, PRTN3, HBB, HBA1, IGLV3-19, and COTL1. Functional analyses revealed that most of them were associated with complement and coagulation cascades, platelet activation, or iron metabolism, and anemia-related pathways. CONCLUSIONS: This study provides fundamental data for identifying COVID-19-specific proteomic changes in comparison with COPD and asthma, which may suggest molecular targets for specialized therapy.

Therapeutic potential of induced iron depletion using iron chelators in Covid-19.

Ferritin, which includes twenty-four light and heavy chains in varying proportions in different tissues, is primarily responsible for maintaining the body's iron metabolism. Its normal value is between 10 and 200 ngmL-1 in men and between 30 and 300 ngmL-1 in women. Iron is delivered to the tissue via them, and they act as immunomodulators, signaling molecules, and inflammatory markers. When ferritin level exceeds 1000 µgL-1, the patient is categorized as having hyperferritinemia. Iron chelators such as deferiprone, deferirox, and deferoxamine are currently FDA approved to treat iron overload. The inflammation cascade and poor prognosis of COVID-19 may be attributed to high ferritin levels. Critically ill patients can benefit from deferasirox, an iron chelator administered orally at 20-40 mgkg-1 once daily, as well as intravenous deferoxamine at 1000 mg initially followed by 500 mg every 4 to 12 h. It can be combined with monoclonal antibodies, antioxidants, corticosteroids, and lactoferrin to make iron chelation therapy effective for COVID-19 victims. In this article, we analyze the antiviral and antifibrotic activity of iron chelators, thereby promoting iron depletion therapy as a potentially innovative treatment strategy for COVID-19.

Characterization by Quantitative Serum Proteomics of Immune-Related Prognostic Biomarkers for COVID-19 Symptomatology.

The COVID-19 pandemic caused by SARS-CoV-2 challenges the understanding of factors affecting disease progression and severity. The identification of prognostic biomarkers and physiological processes associated with disease symptoms is relevant for the development of new diagnostic and therapeutic interventions to contribute to the control of this pandemic. To address this challenge, in this study, we used a quantitative proteomics together with multiple data analysis algorithms to characterize serum protein profiles in five cohorts from healthy to SARS-CoV-2-infected recovered (hospital discharge), nonsevere (hospitalized), and severe [at the intensive care unit (ICU)] cases with increasing systemic inflammation in comparison with healthy individuals sampled prior to the COVID-19 pandemic. The results showed significantly dysregulated proteins and associated biological processes and disorders associated to COVID-19. These results corroborated previous findings in COVID-19 studies and highlighted how the representation of dysregulated serum proteins and associated BPs increases with COVID-19 disease symptomatology from asymptomatic to severe cases. The analysis was then focused on novel disease processes and biomarkers that were correlated with disease symptomatology. To contribute to translational medicine, results corroborated the predictive value of selected immune-related biomarkers for disease recovery [Selenoprotein P (SELENOP) and Serum paraoxonase/arylesterase 1 (PON1)], severity [Carboxypeptidase B2 (CBP2)], and symptomatology [Pregnancy zone protein (PZP)] using protein-specific ELISA tests. Our results contributed to the characterization of SARS-CoV-2-host molecular interactions with potential contributions to the monitoring and control of this pandemic by using immune-related biomarkers associated with disease symptomatology.

Mass Spectrometric Analysis of Urine from COVID-19 Patients for Detection of SARS-CoV-2 Viral Antigen and to Study Host Response.

SARS-CoV-2 infection has become a major public health burden and affects many organs including lungs, kidneys, the liver, and the brain. Although the virus is readily detected and diagnosed using nasopharyngeal swabs by reverse transcriptase polymerase chain reaction (RT-PCR), detection of its presence in body fluids is fraught with difficulties. A number of published studies have failed to detect viral RNA by RT-PCR methods in urine. Although microbial identification in clinical microbiology using mass spectrometry is undertaken after culture, here we undertook a mass spectrometry-based approach that employed an enrichment step to capture and detect SARS-CoV-2 nucleocapsid protein directly from urine of COVID-19 patients without any culture. We detected SARS-CoV-2 nucleocapsid protein-derived peptides from 13 out of 39 urine samples. Further, a subset of COVID-19 positive and COVID-19 negative urine samples validated by mass spectrometry were used for the quantitative proteomics analysis. Proteins with increased abundance in urine of SARS-CoV-2 positive individuals were enriched in the acute phase response, regulation of complement system, and immune response. Notably, a number of renal proteins such as podocin (NPHS2), an amino acid transporter (SLC36A2), and sodium/glucose cotransporter 5 (SLC5A10), which are intimately involved in normal kidney function, were decreased in the urine of COVID-19 patients. Overall, the detection of viral antigens in urine using mass spectrometry and alterations of the urinary proteome could provide insights into understanding the pathogenesis of COVID-19.

An overview of proteomic methods for the study of 'cytokine storms'.

Introduction: The cytokine storm is a form of excessive systemic inflammatory reaction triggered by a myriad of factors that may lead to multi-organ failure, and finally to death. The cytokine storm can occur in a number of infectious and noninfectious diseases including COVID-19, sepsis, ebola, avian influenza, and graft versus host disease, or during the severe inflammatory response syndrome.Area covered: This review mainly focuses on the most common and well-known methods of protein studies (PAGE, SDS-PAGE, and high- performance liquid chromatography). It also discusses other modern technologies in proteomics like mass spectrometry, soft ionization techniques, cytometric bead assays, and the next generation of microarrays that have been used to get an in-depth understanding of the pathomechanisms involved during the cytokine storm.Expert opinion: Overactivation of leukocytes drives the production and secretion of inflammatory cytokines fueling the cytokine storm. These events lead to a systemic hyper-inflammation, circulatory collapse and shock, and finally to multiorgan failure. Therefore, monitoring the patient's systemic cytokine levels with proteomic technologies that are redundant, economical, and require minimal sample volume for real-time assessment might help in a better clinical evaluation and management of critically ill patients.

Virological and immunological features of SARS-CoV-2-infected children who develop neutralizing antibodies.

As the global COVID-19 pandemic progresses, it is paramount to gain knowledge on adaptive immunity to SARS-CoV-2 in children to define immune correlates of protection upon immunization or infection. We analyzed anti-SARS-CoV-2 antibodies and their neutralizing activity (PRNT) in 66 COVID-19-infected children at 7 (±2) days after symptom onset. Individuals with specific humoral responses presented faster virus clearance and lower viral load associated with a reduced in vitro infectivity. We demonstrated that the frequencies of SARS-CoV-2-specific CD4+CD40L+ T cells and Spike-specific B cells were associated with the anti-SARS-CoV-2 antibodies and the magnitude of neutralizing activity. The plasma proteome confirmed the association between cellular and humoral SARS-CoV-2 immunity, and PRNT+ patients show higher viral signal transduction molecules (SLAMF1, CD244, CLEC4G). This work sheds lights on cellular and humoral anti-SARS-CoV-2 responses in children, which may drive future vaccination trial endpoints and quarantine measures policies.

The proteomic characteristics of airway mucus from critical ill COVID-19 patients.

BACKGROUND: The pandemic of the coronavirus disease 2019 (COVID-19) has brought a global public health crisis. However, the pathogenesis underlying COVID-19 are barely understood. METHODS: In this study, we performed proteomic analyses of airway mucus obtained by bronchoscopy from severe COVID-19 patients. In total, 2351 and 2073 proteins were identified and quantified in COVID-19 patients and healthy controls, respectively. RESULTS: Among them, 92 differentiated expressed proteins (DEPs) (46 up-regulated and 46 down-regulated) were found with a fold change >1.5 or <0.67 and a p-value <0.05, and 375 proteins were uniquely present in airway mucus from COVID-19 patients. Pathway and network enrichment analyses revealed that the 92 DEPs were mostly associated with metabolic, complement and coagulation cascades, lysosome, and cholesterol metabolism pathways, and the 375 COVID-19 only proteins were mainly enriched in amino acid degradation (Valine, Leucine and Isoleucine degradation), amino acid metabolism (beta-Alanine, Tryptophan, Cysteine and Methionine metabolism), oxidative phosphorylation, phagosome, and cholesterol metabolism pathways. CONCLUSIONS: This study aims to provide fundamental data for elucidating proteomic changes of COVID-19, which may implicate further investigation of molecular targets directing at specific therapy.

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2.

Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.