Menzies Health Institute Queensland, Griffith University, QLD, Australia; School of Medical Science, Griffith University, QLD, Australia.
Menzies Health Institute Queensland, Griffith University, QLD, Australia.
Obes Res Clin Pract. 2018 Mar-Apr;12(2):204-213. doi: 10.1016/j.orcp.2017.07.001. Epub 2017 Jul 26.
Gene expression data provides one tool to gain further insight into the complex biological interactions linking obesity and metabolic disease. This study examined associations between blood gene expression profiles and metabolic disease in obesity.
Whole blood gene expression profiles, performed using the Illumina HT-12v4 Human Expression Beadchip, were compared between (i) individuals with obesity (O) or lean (L) individuals (n=21 each), (ii) individuals with (M) or without (H) Metabolic Syndrome (n=11 each) matched on age and gender. Enrichment of differentially expressed genes (DEG) into biological pathways was assessed using Ingenuity Pathway Analysis. Association between sets of genes from biological pathways considered functionally relevant and Metabolic Syndrome were further assessed using an area under the curve (AUC) and cross-validated classification rate (CR).
For OvL, only 50 genes were significantly differentially expressed based on the selected differential expression threshold (1.2-fold, p<0.05). For MvH, 582 genes were significantly differentially expressed (1.2-fold, p<0.05) and pathway analysis revealed enrichment of DEG into a diverse set of pathways including immune/inflammatory control, insulin signalling and mitochondrial function pathways. Gene sets from the mTOR signalling pathways demonstrated the strongest association with Metabolic Syndrome (p=8.1×10; AUC: 0.909, CR: 72.7%).
These results support the use of expression profiling in whole blood in the absence of more specific tissue types for investigations of metabolic disease. Using a pathway analysis approach it was possible to identify an enrichment of DEG into biological pathways that could be targeted for in vitro follow-up.
基因表达数据为深入了解肥胖和代谢疾病之间复杂的生物学相互作用提供了一种工具。本研究探讨了肥胖人群血液基因表达谱与代谢疾病之间的关系。
使用 Illumina HT-12v4 Human Expression Beadchip 进行全血基因表达谱分析,比较了(i)肥胖(O)或瘦(L)个体(n=21 例)之间,以及(ii)代谢综合征(M)或无代谢综合征(H)个体(n=11 例)之间的基因表达谱差异。使用 Ingenuity Pathway Analysis 评估差异表达基因(DEG)在生物学途径中的富集情况。进一步使用曲线下面积(AUC)和交叉验证分类率(CR)评估功能相关生物学途径的基因集与代谢综合征之间的关联。
对于 OvL,仅根据所选差异表达阈值(1.2 倍,p<0.05)鉴定出 50 个显著差异表达基因。对于 MvH,有 582 个基因显著差异表达(1.2 倍,p<0.05),通路分析显示 DEG 富集到多种途径,包括免疫/炎症控制、胰岛素信号和线粒体功能途径。mTOR 信号通路的基因集与代谢综合征相关性最强(p=8.1×10;AUC:0.909,CR:72.7%)。
这些结果支持在缺乏更具体组织类型的情况下,使用全血表达谱 profiling 方法研究代谢疾病。使用通路分析方法,可以鉴定出 DEG 富集到的生物学途径,这些途径可作为体外后续研究的靶点。