首页 | 本学科首页   官方微博 | 高级检索  
   检索      


Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness
Authors:Ivan Carcamo-Orive  Marc Y R Henrion  Kuixi Zhu  Noam D Beckmann  Paige Cundiff  Sara Moein  Zenan Zhang  Melissa Alamprese  Sunita L D&#x;Souza  Martin Wabitsch  Eric E Schadt  Thomas Quertermous  Joshua W Knowles  Rui Chang
Abstract:Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.
Keywords:
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号