Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness

Ivan Carcamo-Orive, Marc Y.R. Henrion, Kuixi Zhu, Noam D. Beckmann, Paige Cundiff, Sara Moein, Zenan Zhang, Melissa Alamprese, Sunita L. D’Souza, Martin Wabitsch, Eric E. Schadt, Thomas Quertermous, Rui Chang, Joshua W. Knowles

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Article numbere1008491
JournalPLoS computational biology
Issue number12 December
StatePublished - Dec 23 2020
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


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