System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology

Background: Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily accounted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insulin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tissues have been conducted in past but due to inherent noise in microarray data and heterogeneity in disease etiology; reproduction of prioritized pathways/genes is very low across various studies. Objective: In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology. Method: We used ‘R’, an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D. Result: Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology. Conclusion: Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.