DCcov

DCcov predicts candidate single-agent and combination therapeutics for SARS-CoV-2 infection by applying Flux Balance Analysis to genome-scale metabolic models of infected human lung cells to support drug repositioning and identification of host metabolic vulnerabilities.

Key Features:

• Flux Balance Analysis (FBA): Employs FBA to model steady-state metabolite fluxes in metabolic networks and to simulate the effects of gene perturbations on viral biomass production and host cell viability.
• Metabolic Network Modeling: Reconstructs genome-scale COVID-19-specific metabolic models using expression datasets from SARS-CoV-2–infected human lung tissue.
• In Silico Knockouts: Performs virtual knockouts of individual genes and gene pairs to identify host-specific essential genes and combinations that reduce viral biomass while preserving host viability.
• Pathway Analysis: Highlights metabolic pathways associated with COVID-19 severity in lung tissue, including oxidative stress, ferroptosis, and pyrimidine metabolism.
• Drug Repositioning: Screens FDA-approved drugs against identified essential genes and gene pairs to predict candidate single drugs and drug combinations (reporting 85 single drugs and 52 combinations).

Scientific Applications:

• Drug Repositioning for COVID-19: Prioritizes FDA-approved single agents and combinations predicted to inhibit SARS-CoV-2 replication in host lung tissue.
• Antiviral Target Discovery: Identifies host-specific essential genes and gene pairs as candidate antiviral targets using metabolic-model-based perturbation analysis.
• Pathway-level Mechanistic Insight: Maps metabolic pathways (oxidative stress, ferroptosis, pyrimidine metabolism) linked to COVID-19 severity to inform therapeutic hypotheses.

Methodology:

Reconstructs genome-scale COVID-19-specific metabolic models from infected lung expression datasets, applies Flux Balance Analysis, conducts in silico single and pairwise gene knockouts, performs pathway-level analysis, and screens FDA-approved drugs against identified targets.