A Novel Graph Based Semi-Supervised Learning Approach to Identify Pathways Contributing to the Development of Diabetes and Obesity

Abstract

\n Background: Gestational diabetes (GDM) has profound effects on the intrauterine metabolic milieu, induces marked abnormalities in fetal glucose and insulin secretion and is linked to obesity and diabetes in the offspring, but the mechanisms remain largely unknown. Epigenetic modifications in stems cells may be one mechanism by which an in utero exposure can lead to the development of diabetes and obesity later in life.\n Objective: To identify novel pathways contributing to the development of diabetes and obesity in offspring exposed to GDM in utero by integrating data generated from transcriptome and methylome analysis from second trimester human amniocytes exposed to GDM in utero.\n Methods: We analyzed RNAseq and genome wide DNA methylation data (ERRBS) generated from second trimester amniocytes obtained from women with GDM (n=14). Amniocytes have stem cells-like characteristics and are derived from the fetus. Expression data of 22,271 genes were retrieved from RNAseq data. CpGs with significant changes in DNA methylation were mapped into 20,028 genes by collapsing methylation probes into promoter and gene regions. To better understand the associations among diverse gene sets or among gene sets and GDM,we first constructed two weighted co-expression networks from RNAseq and DNA methylation data, respectively. Then, two co-expression networks were integrated using a linear combination. With the integrated co-expression network, graph-based label propagation algorithm was employed to prioritize GDM-associated genes.\n Results: From the differential expression analysis between GDM and control, the top 20 query genes, including 11 genes and 9 methylated genes, were selected for label propagation. Finally, the top 100 genes were picked up for the pathway enrichment testusing an over-representation analysis approach. Significantly enriched pathways included: Interferon Signaling, N-glycan Antennae Elongation, Sphingolipid Pathway and Metabolism, Classical Complement Pathway, Complement and Coagulation Cascades, Tryptophan Metabolism, Peroxisomal Protein Import, Unsaturated Fatty Acid Metabolism, Complement Activation, Human Innate Immune Response, Ceramide Metabolism, Fertilization Pathway, Keratan Sulfate Biosynthesis Pathway, Transport to the Golgi and Modification Pathway (FDR q<0.05 for all pathways).\n Conclusion: Using a novel bioinformatic approach to synthesize transcriptome and methylome data derived from human amniocytes exposed to GDM in utero, we identified potential pathways involved in programming of diabetes and obesity in offspring including pathways involving the immune response, complex lipid metabolism, the complement pathway, and protein transport and processing. Further investigation of these pathways may yield new mechanisms contributing to diabetes and obesity.