In silico Drug Screening Approach Using L1000-Based Connectivity Map and Its Application to COVID-19

Abstract

Conventional drug screening methods search for a limited number of small molecules that directly interact with the target protein. This process can be slow, cumbersome and has driven the need for developing new drug screening approaches to counter rapidly emerging diseases such as COVID-19. We propose a pipeline for drug repurposing combining in silico drug candidate identification followed by in vitro characterization of these candidates. We first identified a gene target of interest, the entry receptor for the SARS-CoV-2 virus, angiotensin converting enzyme 2 (ACE2). Next, we employed a gene expression profile database, L1000-based Connectivity Map to query gene expression patterns in lung epithelial cells, which act as the primary site of SARS-CoV-2 infection. Using gene expression profiles from 5 different lung epithelial cell lines, we computationally identified 17 small molecules that were predicted to decrease ACE2 expression. We further performed a streamlined validation in the normal human epithelial cell line BEAS-2B to demonstrate that these compounds can indeed decrease ACE2 surface expression and to profile cell health and viability upon drug treatment. This proposed pipeline combining in silico drug compound identification and in vitro expression and viability characterization in relevant cell types can aid in the repurposing of FDA-approved drugs to combat rapidly emerging diseases.

Keywords: ACE2; COVID-19; L1000; connectivity map (CMap); drug repurposing; lung epithelial cell.

Figure 1

Drug screening process in L1000-based CMap.

Figure 2

Dose-response of ACE2 expression levels in each small molecule in A549. Gray vertical bars mean the optimal dosages in each small molecule indicated in Table 1. ACE2 expression levels in CORL23, H1299, NCIH596, and SKLU1 are also shown in Bortezomib, Idarubicin, Tanespimycin, SN-38, and Gemcitabine.

Figure 3

Cell toxicity assay in BEAS-2B treated with each of 17 small molecules and the control. (A) Cell viability measured by MTT assay (n = 4 in samples and n = 12 in control), (B) Mitochondrial Super Oxide production measured by MitoSOX assay (n = 4 in samples and n = 16 in control), and (C) Cytotoxicity measured by LDH assay (n = 4). Error bars mean 95% confidence interval. *p < 0.05, **p < 0.01.

Figure 4

(A) Representative image showing ACE2 staining in BEAS-2B. ACE2-high expressed (ACE2 positive) cells are detected and then ACE2 positive ratio is calculated. (B) Representative cell scoring masks indicating ACE2 expressing cells (Green) and ACE2 negative cells (Red). (C) ACE2 positive ratios in each drug treatment compared to the control (n = 8 in samples and n = 23 in control). Error bars mean 95% confidence interval. *p < 0.05, **p < 0.01.

Figure 5

Correlation between ACE2 positive ratio vs. cell toxicity. Correlation between ACE2 positive ratio and (A) cell viability measured by MTT assay, (B) MitoSOX Assay, and (C) LDH assay. The locations of the top 7 small molecules with reduced cytotoxicity in Table 2 are labeled.

Figure 6

ACE2 expression measure by FACS in BEAS-2B treated with the 5 repurposing candidates. (A) mean ACE2 expression (n = 5). (B) ACE2 positive ratio. Error bar means standard deviation (n = 5). *p < 0.05, **p < 0.01, ****p < 0.0001. (C) Representative scatter plot showing percent positive ACE2 surface expression in BEAS-2B cells.