Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/281062
Title: Identification of diagnostic biomarkers For multiple sclerosis A systems biological approach
Researcher: GnanaKKumaar P
Guide(s): Shiek Fareeth Ahmed
Keywords: Life Sciences,Molecular Biology and Genetics,Genetics and Heredity
Multiple sclerosis
University: Chettinad Academy of Research and Education
Completed Date: 2019
Abstract: newlineMultiple sclerosis (MS) is an inflammatory neurodegenerative disorder of central nervous system. Nearly 2.5 million people were affected by MS worldwide in the year 2012. The etiology of the disease is largely unknown. MS exhibits various symptoms such as fatigue blurred vision, muscle weakness and walking imbalance, which help in clinical diagnosis. It is believed that immune mediated inflammatory damage of myelinated neurons causes the disease progression. However, the antigen which triggers the autoimmune response against the myelinated neurons is largely unclear due to complexity of disease pathophysiology and lack of integrative approach. To overcome this problem, a novel systems biological approach is adopted in this thesis to construct gene regulatory network to identify disease mechanism that may help in detection of biomarkers for feasible diagnosis. The gene regulatory network (GRN) of peripheral mononuclear cells (PBMC) associated with multiple sclerosis were constructed and ranked to identify significant GRNs based on indigenous scoring algorithm. Among the top ranked GRNs, POU3F2_CDK6_hsa-miR-590-3p, MEIS1_CASC3_hsa-miR-1261, STAT3_OGG1_hsa-miR-298 and TCF4_FMR1_hsa-miR-301b were commonly identified in relapsing remitting multiple sclerosis (RRMS), primary progressive multiple sclerosis (PPMS) and secondary progressive multiple sclerosis (SPMS) and expression pattern were studied using real time PCR. Further, the selected GRNs were subjected to pathway analysis which showed inflammatory, fatty acid metabolism and neuronal signaling growth are the key pathways associated with multiple sclerosis. In addition, serum metabolomic profiling was carried out which identifies significant perturbation in metabolites participating in energy and lipid metabolism in MS. Furthermore, meta analysis of gene expression showed STAT3, OGG1, CASC3 and TCF4 to be significant contributors of MS across various population. Of significant genes, variation of OGG1 (rs1052133) is highly susceptible to MS. Hence,
Pagination: 
URI: http://hdl.handle.net/10603/281062
Appears in Departments:Department of Medical Biotechnology FAHS



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