Auto-antigenic Properties of the Spliceosome as a Molecular Tool for Diagnosing Systemic Lupus Erythematosus and Mixed Connective Tissue Disease Patients

• Main Author: Mesa, Annia
• Format: Others
• Published: FIU Digital Commons 2014
• Subjects: Lupus; autoimmune disorders; patient classification; mixed connetive tissue disease; U1 snRNP; Bioinformatics; Biology; Immunity; Other Immunology and Infectious Disease
• Online Access: http://digitalcommons.fiu.edu/etd/1126; http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=2233&context=etd

Systemic Lupus Erythematosus (SLE) and Mixed Connective Tissue Disease (MCTD) are chronic, autoimmune disorders that target overlapping autoantigens and exhibit similar clinical manifestations. Despite 40 years of research, a reliable biomarker capable of diagnosing these syndromes has yet to be identified. Previous studies have confirmed that components of the U1 small nuclear ribonucleoprotein complex (U1 snRNP) such as U1A are 1000 fold more autoantigenic than any other nuclear component in SLE patients. Based on these findings, I hypothesize that models derived from the U1 snRNP autoantigenic properties could distinguish SLE from MCTD patients. To test this hypothesis, 30 peptides corresponding to protein regions of the U1 snRNP were tested in triplicates by indirect ELISA in sera from SLE or MCTD subjects. In addition laboratory tests and clinical manifestations data from these patients were included and analyzed in this investigation. Statistical classification methods as well as bioinformatics pattern recognition strategy were employed to determine which combination, if any, of all the variables included in this study provide the best segregation power for SLE and MCTD. The results confirmed that the IgM reactivity for U1 snRNP and U1A have the power to significantly distinguish SLE from MTCD patients as well as identify kidney and lung malfunctions for these subjects (p ≤ 0.05). Furthermore, the data analysis revealed eight novel classification rules for the segregation of SLE and MCTD which are a better classification tool than any of the currently available methods (p ≤ 0.05). Consequently, the results derived from this study support that SLE and MCTD are indeed separate disorders and pioneer the description of eight novel classification criteria capable of significantly discerning between SLE and MCTD patients (p ≤ 0.05).