A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

Area of Science:

• Physiology
• Systems Biology
• Genomics

Background:

• Complex diseases involve interactions across multiple physiological systems.
• Understanding these interactions is crucial for preventing organ damage and treating diseases like hypertension.
• Current therapeutic options for many complex diseases remain limited.

Purpose of the Study:

• To investigate regulatory interactions within and across organs during disease progression.
• To develop data-driven dynamic network models of multi-organ gene regulatory influences.
• To identify molecular targets for complex diseases like cardiovascular disease, metabolic syndrome, and immune dysfunction.

Main Methods:

• Integrated in vivo gene expression dynamics analysis with reverse engineering.
• Developed continuous-time models to describe multi-organ network dynamics and structure.
• Estimated a sparse subset of gene regulatory interactions from experimental data.

Main Results:

• Identified an autonomic dysfunction-specific multi-organ gene expression pattern linked to a distinct regulatory network.
• Discovered disease-specific network motifs involving genes with aberrant temporal dynamics.
• Found disease-specific single nucleotide variants near transcription factor binding sites of key homeostasis genes.

Conclusions:

• The study presents a novel framework for investigating pathogenesis via model-based analysis of multi-organ system dynamics.
• Results provide candidate molecular targets for cardiovascular disease, metabolic syndrome, and immune dysfunction.
• This approach can advance the understanding and treatment of complex, multi-system diseases.