Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates

Area of Science:

• Systems biology
• Computational biology
• Genomics

Background:

• COVID-19 presents an urgent global health challenge.
• Understanding the molecular underpinnings of COVID-19 is crucial for therapeutic development.

Purpose of the Study:

• To develop and apply the integrated cell (iCell) framework for COVID-19 research.
• To identify disease-related genes and predict potential drug repurposing candidates for COVID-19.

Main Methods:

• Constructed infected and control iCells by fusing multi-omics data with human molecular interaction networks.
• Utilized patient samples and cell lines for iCell generation.
• Applied data fusion and molecular docking for drug repurposing predictions.

Main Results:

• Significant discrepancies observed between patient-based and cell line-based iCells, indicating cell lines' limitations for COVID-19 study.
• Identified highly rewired genes in patient-derived iCells, with 18 of the top 20 confirmed as COVID-19-related.
• Predicted and validated drug candidates, notably artemisinol targeting ZFP62, a novel COVID-19-associated gene.

Conclusions:

• The iCell framework is a valuable tool for integrative analysis of complex diseases like COVID-19.
• Patient-derived iCells offer a more relevant model than cell lines for studying COVID-19 molecular mechanisms.
• The study identified novel therapeutic strategies and drug repurposing opportunities for COVID-19 treatment.