Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments.
Yulin DaiHui YuQiheng YanBingrui LiAndi LiuWendao LiuXiaoqian JiangYejin KimYan GuoZhong-Ming ZhaoPublished in: Genes (2022)
The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein-protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases.
Keyphrases
- coronavirus disease
- sars cov
- single cell
- endothelial cells
- rna seq
- protein protein
- respiratory syndrome coronavirus
- induced pluripotent stem cells
- genome wide
- pluripotent stem cells
- systematic review
- small molecule
- healthcare
- randomized controlled trial
- anti inflammatory
- drug discovery
- gene expression
- mesenchymal stem cells
- emergency department
- infectious diseases
- stem cells
- early onset
- electronic health record
- data analysis
- bioinformatics analysis