Moreover, we asked whether the proteomic profiles of unaffected twins more closely resembled that of unre lated, matched controls or possibly shared some features with their affected twins as a consequence of their genetic similarity and or shared environmental exposures. Collectively, our proteomics data from affected MZ twins was consisten with that from other find more information published stu dies of human autoimmune diseases. Namely, the appar ent coordinated regulation of multiple proteins from several canonical pathways appears to be associated with these chronic inflammatory conditions. In univariate analyses, we observed multiple proteins whose plasma levels were statistically different in affected twins compared to either unaffected twins or unrelated controls.

Some of these proteins may exhibit altered plasma levels as a consequence of chronic inflammation as they were also reported as up regulated in synovial fluid from osteoarthritis patients. Increased levels of apolipoprotein Inhibitors,Modulators,Libraries A were also observed in isolated peripheral blood mononuclear cells from SLE patients and muscle biopsies Inhibitors,Modulators,Libraries of patients with inclusion body myositis. Similarly, Inhibitors,Modulators,Libraries the leucine rich a2 glycoprotein marker a mole cule involved in signal transduction, cell adhesion, and granulocyte differentiation was elevated in plasma from our affected twins and was also found elevated in both the cerebrospinal fluid and serum proteomes from multiple sclerosis patients. More recently, LRG1 was identified as a novel, serum pro inflammatory bio marker for RA and Crohns disease.

Molecular Pathways analysis of our total proteomics data set com paring SAID discordant MZ twins, helped us identify numerous acute Inhibitors,Modulators,Libraries phase reactants, immune complement components, coagulation factors, and retinol binding proteins as potentially important mediators of disease. Together, these data suggest that many of the physiolo gical pathways altered in these patients are not necessa rily disease specific but rather may contribute to inflammatory processes shared by multiple SAID. Proteomic data sets with large and complex arrays of candidate markers mapping across multiple biologic pathways present limits to the interpretation of univari ate data by disregarding potential protein protein inter actions as a basis for accurate disease profiling.

Investigators have employed machine learning algo rithms for the multivariate analysis of large proteomic data sets derived from cancer prevention trials and human autoimmune disease studies. Liu et al. described the use Inhibitors,Modulators,Libraries of a support vector machine algorithm to effectively classify RA patients and controls using serum proteomic component peaks. Among the several decision tree ensemble methods available, we utilized the Random nearly Forests algorithm to create a model which accurately classified affected vs. unaffected twin pairs.