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. 2021 Nov 5;22(6):bbab114.
doi: 10.1093/bib/bbab114.

Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19

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Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19

Marios Tomazou et al. Brief Bioinform. .

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

Keywords: COVID-19; multi-omics integrative analysis; multiplex drug repurposing.

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Figures

Figure 1
Figure 1
Overview of the workflow. (A) Data sources: multi-omics datasets were selected from various sources in order to identify differentially expressed genes, differentially abundant metabolites and proteins along with protein–protein interactions (PPIs) between SARS-CoV-2 and other pathogens with the human host. This was followed by (B) drug repurposing approaches based on (i) transcriptomics, (ii) genomics—GWAS–phenotype association analysis and (iii) pathogen–host interaction network analysis. (C) Multiplexing of repurposed drug lists: (i) integration of the multi-omic data from patients, (ii) functional analysis, (iii) drug re-ranking based on the calculated target–disease association of the integration map and (iv) structural clustering and shortlisting of drug candidates. Finally, the integrated drug list comprises 65 drugs out of which 16 were manually curated by experts for further annotation.
Figure 2
Figure 2
Taxonomy tree of all the pathogens used in the host–pathogen interaction drug repurposing approaches. The main taxonomic groups are highlighted with light blue slices showing the ancestral taxonomy groups for SARS-CoV-2 (green leaf). The yellow to red colored bars represent the number of drugs obtained from various taxa. The circular blue to yellow heat map represents the number of pathogen–host protein interactions retrieved from various databases.
Figure 3
Figure 3
The integrated multi-source information (MI) network along with the distributions of the weighted degree, weighted gene-specific information and the combined MIG scores. Node size represents the MIG score bin for each gene, whereas colors indicate the originating omics dataset. Nodes with mixed colors show genes commonly identified from different datasets. The bar plot shows the MIG score for each gene as the weighted sum of Edge (yellow) and Nodal (blue) scores. The subplots show the corresponding distributions.
Figure 4
Figure 4
Connected pathway communities related to COVID-19 on a pathway-to-pathway network as highlighted by PathWalks. The bar plot shows the odds ratio value for the top 30 KEGG pathways, found to be involved in COVID-19. The bar plot color scale represents the visit counts from less (dark blue) to more (light) frequently visited pathways.
Figure 5
Figure 5
Sankey plots of the drugs highlighted after expert curation of integrated list in Table 3. Drugs are ordered based on their normalized ranking and pathways based on their OR value obtained from PathWalks. (A) Drugs targeting human pathways, their originating list, their target genes and corresponding pathways. (B) Antiviral drugs targeting viral proteins, their originating list and known target pathogens.

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