Gene Expression Profiling of Apoptotic Proteins in Circulating Peripheral Blood Mononuclear Cells in Type II Diabetes Mellitus and Modulation by Metformin

Dysregulation of Gene Expression of Key Signaling Mediators in PBMCs from People with Type 2 Diabetes Mellitus

Diabetes is currently the fifth leading cause of death by disease in the USA. The underlying mechanisms for type 2 Diabetes Mellitus (DM2) and the enhanced susceptibility of such patients to inflammatory disorders and infections remain to be fully defined. We have recently shown that peripheral blood mononuclear cells (PBMCs) from non-diabetic people upregulate expression of inflammatory genes in response to proteasome modulators, such as bacterial lipopolysaccharide (LPS) and soybean lectin (LEC); in contrast, resveratrol (RES) downregulates this response. We hypothesized that LPS and LEC will also elicit a similar upregulation of gene expression of key signaling mediators in (PBMCs) from people with type 2 diabetes (PwD2, with chronic inflammation) ex vivo. Unexpectedly, using next generation sequencing (NGS), we show for the first time, that PBMCs from PwD2 failed to elicit a robust LPS- and LEC-induced gene expression of proteasome subunit LMP7 (PSMB8) and mediators of T cell signaling that were observed in non-diabetic controls. These repressed genes included: PSMB8, PSMB9, interferon-γ, interferon-λ, signal-transducer-and-activator-of-transcription-1 (STAT1), human leukocyte antigen (HLA DQB1, HLA DQA1) molecules, interleukin 12A, tumor necrosis factor-α, transporter associated with antigen processing 1 (TAP1), and several others, which showed a markedly weak upregulation with toxins in PBMCs from PwD2, as compared to those from non-diabetics. Resveratrol (proteasome inhibitor) further downregulated the gene expression of these inflammatory mediators in PBMCs from PwD2. These results might explain why PwD2 may be susceptible to infectious disease. LPS and toxins may be leading to inflammation, insulin resistance, and thus, metabolic changes in the host cells.

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.