Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

Abstract

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

Figure 1

COVID-19 transcriptomics-based bioinformatics approach for drug repositioning. We generated lists of statistically significant differentially expressed genes from the analysis of three published studies of SARS-CoV-2 and COVID-19. The drug repositioning computational pipeline compares the ranked differential expression of the COVID-19 disease signature with that of drug profiles from CMap. A reversal score based on the Kolmogorov–Smirnov statistic is generated for each disease-drug pair. If a drug profile significantly (FDR < 0.05) reverses the disease signature, then the drug could be therapeutic for the disease. Across all datasets, a total of 102 drugs have been identified as potentially therapeutic for COVID-19. Twenty-five drugs were identified in analyses of at least two of the three datasets. We further conducted pathways analyses and targeted analyses on the results, focusing on the 25 shared hits. Finally, we validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays.

Figure 2

SARS-CoV-2 differential gene expression signatures reversed by drug profiles from CMap. (A) Enrichment analysis using GSEA reveals common pathways among input signatures. (B) DEG overlap from input signatures. Only 1 gene, DKK1, was shared by all 3 signatures. (C) Top 15 drug profiles reversing the ALV signature (109 genes). For each column, the gene expression values were ranked, with rank 1 being the most up-regulated gene (in red) and the maximum rank (109 for ALV) being the most down-regulated gene (in blue). Drug names highlighted in green were hits for a second signature, and drug hits highlighted in purple reversed all three signatures. (D) Top 15 drug profiles reversing the EXP signature (108 genes). (E) Top 15 drug profiles reversing the BALF signature (941 genes).

Figure 3

Common therapeutic hits from drug repurposing pipeline applied to SARS-CoV-2 signatures. (A) Drug profiles from CMap significantly reversed signatures from the ALV, BALF, and EXP signatures. 25 of the drugs were significant in at least 2 of the signatures. (B) Drug-protein target network. For the 25 drugs that reversed at least 2 of the signatures, target information was gathered from DrugBank to identify clusters of drugs from shared targets.

Figure 4

Haloperidol inhibits viral replication of SARS-CoV-2 in the Calu-3 lung cell line. (A) Calu-3 cells were infected with SARS-CoV-2 at an MOI of 0.05 for 72 h. Viral replication levels were determined by RT-qPCR from supernatant RNA using specific primers for the E gene. Viral RNA levels relative to DMSO are graphed. Error bars represent 3 or 4 independent experiments. One-way ANOVA analysis was used to determine significance. (B) Microscopy: Calu-3 cells were infected with SARS-CoV-2 at an MOI of 0.05 for 72 h. Cells were fixed with paraformaldehyde and used for immunofluorescence analysis with dsRNA antibody (SCICONS) and DAPI stain. Images were acquired and analyzed using ImageXpress Micro Confocal High-Content Imaging System.

Figure 5

Viral inhibition and cell viability tests of 13 compounds in 293T-ACE2 cell assays. Several drugs inhibit viral infectivity. Red, viral infectivity (anti-NP); black, cell viability. The lack of a dose response in cell viability probably reflects cytostatic and not cytotoxic effects. Data are mean ± sd; n = 3 biologically independent samples for cell viability data. Cells were incubated for 24 h at 37 °C in 5% CO₂. Then, 2 h before infection, the medium was replaced with 100 μl of DMEM (2% FBS) containing the compound of interest at concentrations 50% greater than those indicated, including a DMSO control. 100 PFU (MOI = 0.025) was added in 50 μl of DMEM (2% FBS), bringing the final compound concentration to those indicated. Plates were then incubated for 48 h at 37 °C. The cells were then immunostained for the viral NP protein.