Long non-coding RNA signatures to classify multiple sclerosis

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Diagnosis of relapsing remitting multiple sclerosis (MS) rests on clinical symptoms and examinations as outlined in the revised McDonald’s criteria supported by appropriate magnetic resonance imaging findings and other laboratory tests. The need for early diagnosis is clearly emphasized in a position paper produced in 2015 by MS Brain Health organization called “Brain health, Time matters in multiple sclerosis’ which is endorsed by the major organizations and foundations that advocate for MS research, providers and patients including Accelerated Cure Project (ACP), Americans Committed for Treatment and Research in Multiple Sclerosis (ACTRIMS), The Consortium of Multiple Sclerosis Centers (CMSC), European Brain Council (EBC), European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), European Multiple Sclerosis Platform (EMSP), International Society of Neuroimmunology (ISNI), International Organization of Multiple Sclerosis Nurses (IOMSN), National Multiple Sclerosis Society (NMSS), and Multiple Sclerosis Trust (MS). To cite from their executive summary page: (1) “A therapeutic strategy that offers the best chance of preserving brain and spinal cord tissue early in the disease course needs to be widely accepted – and urgently adopted.” (2) “Significant delays often occur before a person with symptoms suggestive of MS sees a neurologist for diagnosis and treatment.” (3) “Early intervention is vital.” (bold type face is theirs, not ours). The question of whether or not disease classifiers capable of providing clinically useful information could be built based upon disease-specific expression levels of mRNAs in whole blood has been a subject of research for several years. Long non-coding RNAs (lncRNA) are recently discovered regulatory RNA molecules that do not code for proteins but influence a vast array of biological processes. It is also thought that lncRNAs drive biologic complexity observed in vertebrates that may also be reflected by the greater array of complex idiopathic diseases that humans develop. As such, our data obtained in the phase 1 portion of this work, support the notion that disease-associated lncRNAs exhibit far greater differences in expression than disease-associated mRNAs. In this application, we propose to explore the hypothesis that lncRNAs are better biomarkers of human disease than mRNAs. Here, we will focus on MS as a disease category and have identified and validated MS associated differentially expressed lncRNAs. Study of lncRNAs in human autoimmune disease is in its infancy and exploration of lncRNAs as biomarkers of autoimmune disease has not been previously addressed. We propose to determine expression levels of target lncRNAs in blood obtained from larger cohorts of subjects that include 1) subjects with RRMS, 2) healthy controls, 3) neurologic disease controls including both inflammatory and non- inflammatory disorders, and 4) peripheral autoimmune disease controls obtained from various sites in the U.S. and Europe to establish a wide geographic distribution and to identify optimum machine learning classifiers to distinguish the MS cohorts from healthy and disease control cohorts with greatest overall accuracy.