Multiple sclerosis (MS) is an inflammatory disease that affects the nervous system and results in a wide range of signs and symptoms including physical and cognitive problems. Recent evidence demonstrates that interactions between the host immune system and the commensal gut microbiota have a key role in the development of the disease. However, the natures of these interactions are poorly studied, and the set of bacteria with pathogenic or protective potential are unknown. Here, the investigators propose a multi-pronged approach to deciphering the role of the microbiota in MS, by developing microbiome-based machine learning algorithms aimed at: (1) distinguishing healthy individuals from MS patients; (2) predicting the time since the onset of MS in relation to disease activity by predicting next relapse and neurological progression; (3) identifying microbiome signatures that characterize the relapse state; (4) distinguishing various MS phenotypes in relation to blood and microbiome transcriptome signatures; (5) predicting response to various immunomodulatory treatments in relation to blood and microbiome transcriptome signatures. Overall, these studies should establish the role of the microbiome in multiple sclerosis, resulting in a set of non-invasive tools for characterization of the disease; identification of the kinetics of MS using microbiome as a readout; and allowing the prediction of individuals prone to MS based on their microbiome and in relation to their protein expression. These new set of diagnostic and predictive tools may thus add a novel and unexplored dimension to the study of the disease that may lead in the future to new therapeutic avenues based on designing microbiome-targeted interventions.
Description: Intestinal microbiome composition and function of a cohort of 50 untreated early MS patients, up to 12 months from onset, untreated with immunomodulatory drugs or steroids for at least 3 months, as well as 50 age-, sex-, and diet-matched healthy controls (obtained from the Weizmann DataBank) will be performed.Measure: 1. Change in expression of intestinal microbiome composition between MS patients and healthy controls. Time: 5 years
Description: Intestinal microbiome composition and function and blood profiling of 100 patients with different disease phenotypes (RIS=20; CIS=30; RRMS=30; PPMS=20) will be performed.Measure: Change in microbiome expression intestinal microbiome composition between MS patients phenotypes. Time: 5 years
There is one SNP
From each patient, the investigators will obtain a multi-dimensional data from the MS database consisting, as appropriate, of a subset of: (1) Clinical metadata, including: Consent form; Medications; annual relapse rate; (2) Blood tests, including a complete blood count, complete biochemistry, lipid profile, cholesterol profile; (3) Complete neurological examination for obtaining an EDSS score, cognitive assessment, gait assessment; MRI imaging data, evoked potentials, treatment response; (4) Blood samples will be processed for protein mRNA expression and peripheral blood mononuclear cells (PBMCs) will be separated on Ficoll-Hypaque gradient, total RNA purified, labeled, hybridized to Genechip array (U133A2), and scanned (GeneArray-TM scanner G2500A; Hewlett Packard) according to the manufacturer's protocol (Affymetrix, Santa Clara, CA). --- G2500A ---