Genomics-guided identification of potential modulators of SARS-CoV-2 entry proteases, TMPRSS2 and Cathepsins B/L.
Kartikay PrasadSuliman Yousef AlOmarEman Abdullah AlmuqriHassan Ahmed RudayniVijay KumarPublished in: PloS one (2021)
SARS-CoV-2 requires serine protease, transmembrane serine protease 2 (TMPRSS2), and cysteine proteases, cathepsins B, L (CTSB/L) for entry into host cells. These host proteases activate the spike protein and enable SARS-CoV-2 entry. We herein performed genomic-guided gene set enrichment analysis (GSEA) to identify upstream regulatory elements altering the expression of TMPRSS2 and CTSB/L. Further, medicinal compounds were identified based on their effects on gene expression signatures of the modulators of TMPRSS2 and CTSB/L genes. Using this strategy, estradiol and retinoic acid have been identified as putative SARS-CoV-2 alleviation agents. Next, we analyzed drug-gene and gene-gene interaction networks using 809 human targets of SARS-CoV-2 proteins. The network results indicate that estradiol interacts with 370 (45%) and retinoic acid interacts with 251 (31%) human proteins. Interestingly, a combination of estradiol and retinoic acid interacts with 461 (56%) of human proteins, indicating the therapeutic benefits of drug combination therapy. Finally, molecular docking analysis suggests that both the drugs bind to TMPRSS2 and CTSL with the nanomolar to low micromolar affinity. The results suggest that these drugs can simultaneously target both the entry pathways of SARS-CoV-2 and thus can be considered as a potential treatment option for COVID-19.
Keyphrases
- sars cov
- genome wide
- respiratory syndrome coronavirus
- endothelial cells
- combination therapy
- copy number
- molecular docking
- gene expression
- genome wide identification
- binding protein
- induced pluripotent stem cells
- small molecule
- dna methylation
- poor prognosis
- emergency department
- estrogen receptor
- transcription factor
- climate change
- long non coding rna
- single cell
- protein kinase
- mass spectrometry
- data analysis
- cell cycle arrest