Integrated lipidomics and transcriptomic analysis of peripheral blood reveals significantly enriched pathways in type 2 diabetes mellitus

• Published in: BMC medical genomics Vol. 6 Suppl 1; no. Suppl 1; p. S12
• Main Authors: Zhao, Chen, Mao, Jinghe, Ai, Junmei, Shenwu, Ming, Shi, Tieliu, Zhang, Daqing, Wang, Xiaonan, Wang, Yunliang, Deng, Youping
• Format: Journal Article
• Language: English
• Published: England BioMed Central 2013; BioMed Central Ltd
• Subjects: Adult; Aged; Cluster Analysis; Diabetes Mellitus, Type 2 - blood; Diabetes Mellitus, Type 2 - genetics; Diabetes Mellitus, Type 2 - metabolism; Female; Gene Expression Profiling; Humans; Insulin - metabolism; Insulin Resistance; Lipid Metabolism - genetics; Male; Middle Aged; Oligonucleotide Array Sequence Analysis; Phenotype; Sex Factors; Signal Transduction
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/1755-8794-6-s1-s12

Insulin resistance is a key element in the pathogenesis of type 2 diabetes mellitus. Plasma free fatty acids were assumed to mediate the insulin resistance, while the relationship between lipid and glucose disposal remains to be demonstrated across liver, skeletal muscle and blood. We profiled both lipidomics and gene expression of 144 total peripheral blood samples, 84 from patients with T2D and 60 from healthy controls. Then, factor and partial least squares models were used to perform a combined analysis of lipidomics and gene expression profiles to uncover the bioprocesses that are associated with lipidomic profiles in type 2 diabetes. According to factor analysis of the lipidomic profile, several species of lipids were found to be correlated with different phenotypes, including diabetes-related C23:2CE, C23:3CE, C23:4CE, ePE36:4, ePE36:5, ePE36:6; race-related (African-American) PI36:1; and sex-related PE34:1 and LPC18:2. The major variance of gene expression profile was not caused by known factors and no significant difference can be directly derived from differential gene expression profile. However, the combination of lipidomic and gene expression analyses allows us to reveal the correlation between the altered lipid profile with significantly enriched pathways, such as one carbon pool by folate, arachidonic acid metabolism, insulin signaling pathway, amino sugar and nucleotide sugar metabolism, propanoate metabolism, and starch and sucrose metabolism. The genes in these pathways showed a good capability to classify diabetes samples. Combined analysis of gene expression and lipidomic profiling reveals type 2 diabetes-associated lipid species and enriched biological pathways in peripheral blood, while gene expression profile does not show direct correlation. Our findings provide a new clue to better understand the mechanism of disordered lipid metabolism in association with type 2 diabetes.