Long non-coding RNA signatures to classify multiple sclerosis

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 DESCRIPTION (provided by applicant): Diagnosis of multiple sclerosis [MS] rests on clinical symptoms and examinations supported by appropriate magnetic resonance imaging findings or other laboratory tests such as oligoclonal bands in cerebrospinal fluid and evoked potential testing. Clinically isolated syndrome (CIS) is a first neurologic episode lasting at least 24 hours possibly caused by focal inflammation or demyelination. Approximately 10,000-15,000 new diagnoses of MS are made in the United States each year. Approximately 2-3 times that number experience a CIS each year indicating that a far greater number of subjects experience a CIS than develop MS. Costs to healthcare of determining if a subject with a CIS will develop MS are significant. Furthermore, misdiagnosis of MS produces a huge cost burden on our healthcare system as it is a frequent event and with the rising cost of newer as well as older therapies, the cost of managing a person with MS can exceed $50,000 per year. Thus, even a simple confirmatory test would be of significant financial benefit to the healthcare system. 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 greater than ten years. Many disease-specific gene expression signatures have been identified in the research lab. A few of these have even progressed into commercially viable diagnostic tests, notably for irritable bowel syndrome, fibromyalgia, and systemic sclerosis. 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. In vertebrates, the number of lncRNA genes greatly exceeds the number of protein-coding genes. It is also thought that lncRNAs drive greater biologic complexity between vertebrates and invertebrates. These lncRNAs also show much greater cell-type specific expression patterns than mRNAs. Humans also develop many more complex diseases than other organisms. As such, our data presented in preliminary studies, 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 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 the following specific aim: To identify annotated and novel lncRNAs differentially expressed in MS and assess their function as biomarkers to distinguish MS subjects from healthy subjects and subjects with other neurologic disorders.