ComplexDnet: A Network-Based Strategy to Discover Critical Targets and Screen Active Compounds for Complex Diseases.

Area of Science:

• Computational biology
• Drug discovery
• Genomics

Background:

• Complex diseases like metabolic-associated steatohepatitis (MASH) pose significant challenges for identifying therapeutic targets.
• Existing methods often struggle to effectively prioritize disease-relevant genes and pathways.

Purpose of the Study:

• To develop and validate ComplexDnet, a novel computational framework for prioritizing therapeutic targets in complex diseases.
• To identify key regulators and potential therapeutic compounds for MASH.
• To provide an open-source tool for the research community.

Main Methods:

• Developed ComplexDnet, integrating transcriptomic data with biological network analysis.
• Applied ComplexDnet to eight cancer types, comparing its performance against existing methods.
• Utilized ComplexDnet to analyze MASH, identifying retinoid-related orphan receptor γt (RORγt) as a key regulator.
• Performed network-based virtual screening to identify RORγt inverse agonists, followed by experimental validation including X-ray crystallography and murine models.

Main Results:

• ComplexDnet demonstrated superior performance in target prioritization across cancer types, achieving an average recall of 77.63%.
• Identified RORγt as a central regulator in MASH-associated inflammation and fibrosis.
• Discovered panaxatriol (PXT) as a potent RORγt inverse agonist with confirmed structural and functional activity.
• PXT significantly attenuated fibrosis in preclinical MASH models.

Conclusions:

• ComplexDnet is an effective framework for discovering functionally and structurally relevant therapeutic targets in complex diseases.
• The study identified RORγt and PXT as promising targets/therapeutics for MASH.
• The open-source release of ComplexDnet software facilitates broader application in drug discovery.