While certain autoimmune diseases share selected genetic, clinical and laboratory features, it is not clear if shared pathogenic mechanisms might link a number of SAID. One approach to the study of disease pathogenesis is the use of MZ twins as a means of controlling for the inherent genetic variability of study subjects in order to better assess the contribution of genetic, epigenetic and environmental factors [18
]. MZ twins, however, are not genetically identical owing to various post-meiotic and age-related epigenetic modifications. Despite these differences, microarray analyses suggest that RNA expression levels of polymorphic genes are more tightly controlled in MZ twins than other first degree family members or unrelated controls [19
In the present study, we have evaluated biologic pathways altered among multiple SAID by studying levels of plasma proteins using LC-ESI-MS from MZ twins discordant for SAID and unrelated, matched controls. Blood plasma is well-suited to the study of systemic or multi-organ diseases given its capacity to sample proteins from damaged tissues and detect changes in other physiologic pathways associated with complex host responses to disease processes and infectious and/or other environmental agents [11
]. The human plasma proteome is one of the most complex and better characterized human bio-fluids wherein the identity and expression levels of its approximately 1,000 distinct protein constituents are currently cataloged [3
Previous studies have examined human tissue and bio-fluid proteomes in autoimmune conditions with the goal of identifying disease-specific biomarkers to aid in improved disease diagnosis and understanding of underlying pathogeneses [4
]. These findings point consistently to coordinated changes in the levels of multiple proteins involved in such canonical pathways as immune activation, signal transduction, cell adhesion, apoptosis, and acute phase responses, in addition to various transcription factors, structural and transport proteins. In fact, composite phenotypic profiles of coordinated changes in multiple protein factors and physiologic pathways rather than solitary biomarkers may prove more reliable in differentiating complex and sometimes overlapping autoimmune syndromes.
We examined multiple SAID in an attempt to uncover shared biomarkers or proteomic profiles, with the understanding that these otherwise heterogeneous disorders often share many clinical features, immunologic abnormalities, genetic risk factors and serum autoantibodies [30
]. We hypothesized that certain proteomic profiles may be similar among patients with different SAID and that those profiles will differ from those of unaffected MZ twins. Moreover, we asked whether the proteomic profiles of unaffected twins more closely resembled that of unrelated, 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 published studies of human autoimmune diseases. Namely, the apparent coordinated regulation of multiple proteins from several canonical pathways (for example, immune regulation, acute phase response, protein and lipid homeostasis, apoptosis and signal transduction) 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 (for example, α1-microglobulin, fibrinogen, apolipoproteins A and E, complement C3 and C4B, and retinol binding protein) may exhibit altered plasma levels as a consequence of chronic inflammation as they were also reported as up-regulated in synovial fluid from osteoarthritis (OA) patients [23
]. Increased levels of apolipoprotein A were also observed in isolated peripheral blood mononuclear cells from SLE patients and muscle biopsies of patients with inclusion body myositis [21
]. Similarly, the leucine-rich α2 glycoprotein marker (LRG1) - a molecule 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 [26
]. More recently, LRG1 was identified as a novel, serum pro-inflammatory biomarker for RA and Crohn's disease [32
]. Molecular Pathways analysis of our total proteomics data set comparing SAID discordant MZ twins, helped us identify numerous acute 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 physiological pathways altered in these patients are not necessarily 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 univariate data by disregarding potential protein-protein interactions as a basis for accurate disease profiling. Investigators have employed machine learning algorithms for the multivariate analysis of large proteomic data sets derived from cancer prevention trials and human autoimmune disease studies [33
]. Liu et al
. described the use of a support vector machine algorithm to effectively classify RA patients and controls using serum proteomic component peaks [22
]. Among the several decision tree ensemble methods available, we utilized the Random Forests algorithm to create a model which accurately classified affected vs. unaffected twin pairs. Putative interactions among seven proteins (STX17, MGAM, PON1, C6, SYNE1, PLEKHG5 and AZGP1) accounted for the majority of this effect. Several of these proteins were likewise identified in our univariate analyses (STX17, MGAM, PON1 and C6). The STX17 marker was one of three proteins whose altered plasma levels was unique to the comparison of discordant MZ twins, while PON1 was the only marker identified with statistically different levels in each of the three two-group comparisons.
The PON1 gene product, paraoxonase 1, is an arylesterase that serves an important role in several physiological pathways including the detoxification of xenobiotics - most notably organophosphorus metabolites associated with pesticide exposures - as well as reducing oxidative damage when associated with circulating high and low density lipoproteins [35
]. Interestingly, functional polymorphisms in the PON1 gene influence expression levels and activity of the enzyme and have been associated with several immune-mediated conditions, atherosclerotic risk, and possibly influence responses to anti-TNF-α therapy in RA [38
Several independent lines of evidence implicate reduced plasma PON1 levels as a potential biomarker for a subset of SAID [39
]. In our present study, we observed an apparent gradient of decreasing PON1 levels among our three study groups in univariate analyses whereby PON1 levels were lowest in SAID-affected twins and highest in unrelated controls. Also, PON1 was identified as an informative marker in a multivariate RF model, which effectively segregated SAID affected vs. unaffected twins. In molecular pathway modeling, PON1 mapped as a central node in interactions predicted among all the relevant factors in the RF analysis. More recently, certain PON1 polymorphic variants were implicated as risk factors for other chronic inflammatory diseases, including RA and types 1 and 2 diabetes [44
]. Plasma protein blot analysis of our twin pairs and matched, unrelated controls demonstrated reduced plasma PON1 levels in 50% of the twin cases independent of disease phenotype. We speculate that shared or similar environmental factors, such as pesticide exposures, might influence the development of different SAID by a common mechanism [46
There are several limitations to our plasma proteomics study design. Most importantly, small sample sizes and the resulting decrease in statistical power owing to the difficulties associated with the identification and recruitment of SAID-discordant MZ twins with recent disease onset. Also, the heterogeneity of human study subjects, including variations in environmental exposures, clinical phenotypes, disease activity and duration and immunosuppressive therapies may influence plasma protein composition and present potential confounders. Additionally, given the capacity of mass spectrometric techniques to detect several thousand component peaks from individual plasma samples, higher false discovery rates (FDR) are anticipated in the absence of corrections for multiple statistical comparisons. Despite these limitations, most of the candidate markers and molecular pathways identified in our study are consistent with those identified in other studies of individual human autoimmune disease [21