Tamara Raschka
Fraunhofer Institute for Algorithms and Scientific Computing, Germany
Title: Analytical strategy to unravel novel candidates from Alzheimer's disease gene regulatory networks using public transcriptomic studies
Biography
Biography: Tamara Raschka
Abstract
Alzheimer's disease (AD) is the most common type of dementia, progressively destroying cognitive capabilities. Despite recent progress, there are no available curable drugs; questioning our current knowledge of AD pathology. Gene regulatory networks (GRNs), generated from meta-analysis of existing studies, could help us revisit our mechanistic understanding. However, they do not elaborate on the context specificity and additionally, miss out on lesser studied genes given the tendency to focus on differential expressed genes and prior knowledge. In this poster, we present a novel strategy to determine common mechanistic patterns across all publicly available AD gene expression datasets. An optimized method of BC3Net and WGCNA were used to get robust and coherent gene regulatory patterns. This approach leverages the power of literature and iterative functional enrichment approach (derived from the data) to define context specificity. The results show significant enrichment for pathways across disparate datasets that are involved in key signaling mechanisms, like neurotrophin and calcium signaling. Interestingly, there are no common genes involved in these pathways across datasets. Among these, genetic variant and linkage disequilibrium analysis prioritized novel candidate genes, which are less studied in AD, but prominent in AD comorbidity. Some of these genes encode for cytokines, which are part of the immune response, responsible for the cell growth and differentiation and inflammation. For the first time we show that functional enrichment to generate GRNs in neurodegeneration reveal unknown yet novel biomarkers. This may lead the way to mitigate the black-box pathogenesis of AD.