MS involves immune-system attacks against the brain and spinal cord. The gut, including the small and large intestine, is the largest immune organ in mammals. Each of us has millions of “commensal” bacteria living within our guts. Most of these bacteria are harmless as long as they remain in the inner wall of the intestine. They play a critical role in our normal physiology, and accumulating research suggests that they are key in the establishment and maintenance of immune balance by the molecules they release.
Susceptibility to multiple sclerosis follows a complex pattern with a clear genetic component, evidenced primarily by the relatively high recurrence risk in family members of affected individuals, and the frequent occurrence of MS in some ethnic populations, particularly those of north European origin. Since 1996, more than 15 family-based linkage and population-based genome wide association studies (GWAS) were conducted in MS. Our lab is a member of the International MS genetics consortium (IMSGC), which recently conducted the largest of these studies, including close to 10,000 cases and 10,000 controls. These studies generated a wealth of data amenable for further mining using modern bioinformatics, including advanced statistics, pattern recognition, and artificial intelligence methodologies.
Transcriptional profiling provides a cost-effective way to characterize the expression of large number of genes in a sample of interest. This strategy (combined with machine learning algorithms) can be used to characterize subgroups of patients with a certain characteristic (e.g. at risk of relapses, responding to therapy, etc).
As a logical follow-up of our genomics work, we investigate select candidates for their functional properties in-vitro and in-vivo as an attempt to link statistical significance with functional relevance. We are currently conducting an immunological characterization of Tob1-/- mice in the context of EAE.
Methods: molecular biology, immunology, cell biology.
The hierarchical organization of biological complexity can be represented as a multi-layered chart, in which each layer represents a domain of knowledge. A wealth of data is publically available through databases on each of these domains, albeit rarely does data from one domain relates to data on another. Integration and mining of these datasets could prove a powerful approach to novel discoveries. However, these databases are often built using different architectures and standards, which hampers their straightforward integration. To tackle this problem, we created integrated Complex Traits Networks (iCTNet), a bioinformatic tool (Cytoscape plugin) that allows download, visualization and analysis of data from 5 different domains (genomics, protein-interactions, protein-DNA interactions, drug-target, gene expression) (Wang et al. BMC Bioinformatics; 2011). Current efforts to expand the scope of iCTNet to include more databases and functionality are underway. Methods: Bioinformatics, advanced statistics, graph theory.