The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.
Animals ; Antirheumatic Agents/therapeutic use ; Biomedical Research/*methods ; Cytokines/genetics/metabolism ; Genetic Markers ; Genetic Predisposition to Disease ; Humans ; Inflammation Mediators/metabolism ; Molecular Targeted Therapy ; Phenotype ; Prognosis ; *Rheumatic Diseases/drug therapy/genetics/metabolism/physiopathology ; Rheumatology/*methods ; Risk Factors ; Signal Transduction ; *Systems Biology ; Systems Integration

OBJECTIVE: We undertook this study to investigate the discriminant metabolites in urine from patients with established rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and from healthy individuals. METHODS: Urine samples were collected from 148 RA patients, 41 SLE patients and 104 healthy participants. The urinary metabolomic profiles were assessed using 1H nuclear magnetic resonance spectroscopy. The relationships between discriminant metabolites and clinical variables were assessed. Collagen-induced arthritis was induced in mice to determine if a choline-rich diet reduces arthritis progression. RESULTS: The urinary metabolic fingerprint of patients with established RA differs from that of healthy controls and SLE patients. Markers of altered gut microbiota (trimethylamine-N-oxide, TMAO), and oxidative stress (dimethylamine) were upregulated in patients with RA. In contrast, markers of mitochondrial dysfunction (citrate and succinate) and metabolic waste products (p-cresol sulfate, p-CS) were downregulated in patients with RA. TMAO and dimethylamine were negatively associated with serum inflammatory markers in RA patients. In particular, patients with lower p-CS levels exhibited a more rapid radiographic progression over two years than did those with higher p-CS levels. The in vivo functional study demonstrated that mice fed with 1% choline, a source of TMAO experienced a less severe form of collagen-induced arthritis than did those fed a control diet. CONCLUSION: Patients with RA showed a distinct urinary metabolomics pattern. Urinary metabolites can reflect a pattern indicative of inflammation and accelerated radiographic progression of RA. A choline-rich diet reduces experimentally-induced arthritis. This finding suggests that the interaction between diet and the intestinal microbiota contributes to the RA phenotype.
Animals ; Arthritis* ; Arthritis, Experimental ; Arthritis, Rheumatoid* ; Choline ; Dermatoglyphics ; Diet* ; Gastrointestinal Microbiome ; Healthy Volunteers ; Humans ; Inflammation ; Lupus Erythematosus, Systemic ; Magnetic Resonance Spectroscopy ; Metabolome* ; Metabolomics ; Mice ; Oxidative Stress ; Phenotype* ; Spectrum Analysis ; Waste Products