Translational Bioinformatics Approaches for Decoding Mechanisms and Developing Drug Repositioning Strategies in Multiple Sclerosis
Author: Astrid Manuel, BSc (2023)
Primary advisor: Zhongming Zhao, PhD
Committee members: Assaf Gottlieb, PhD; Yulin Dai, PhD
PhD thesis, McWilliams School of Biomedical Informatics at UTHealth Houston.
As the scope of biotechnology rapidly widens, disease-specific molecular datasets continue to
grow, warranting the need for advanced computational biology tools. Translational bioinformatics
methods aim to harness these emerging datasets, including genomics, transcriptomics, epigenomics, and clinical data, to investigate the underlying biological mechanisms and identify actionable therapeutic strategies for complex diseases. Multiple sclerosis (MS) serves as a model complex disease of interest, involving both genetic and environmental risk factors. It is an autoimmune disease, characterized by immune-mediated demyelination, leading to chronic neurological disability in millions of people worldwide. Despite advancements in MS treatment, breakthrough disease persists in several MS patients. Drug repositioning strategies, which explore new indications for existing drugs, offer a promising approach for discovering new treatment options. This research aimed to develop robust computational approaches for mining multi-omics data of MS, decoding underlying biological pathways, and identifying actionable drug targets for drug repositioning. We investigated the functional consequences of genetic variations in MS through network-assisted integration of genomic data with other disease-specific omics datasets (transcriptomic and epigenomic), using the human protein interactome as the reference network. The resultant MS-associated networks were enriched with relevant biological process terms, involving immune and neurological functions, and providing insights into potential viral mechanisms of MS pathogenesis. Our investigations revealed links between genetic risk factors and existing drug targets. We identified potential repurposable drug candidates for MS based on their ability to target the products of genes enriched in MS-associated networks, as well as their mechanisms of action. We evaluated a particular drug candidate of interest, montelukast, a leukotriene receptor antagonist prescribed for asthma and allergic rhinitis, by designing a retrospective case-control study utilizing de-identified administrative health claims data. We observed significant reductions in MS relapses, suggesting its therapeutic potential for MS treatment and supporting our data-driven hypothesis. Overall, we demonstrated how the integration of multi-omics data through innovative translational bioinformatics approaches can advance our understanding of complex diseases and provide valuable insights for novel therapeutic options.