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

doi:10.1371/journal.pcbi.1008491

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

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11 Scopus citations

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.

Original language	English
Article number	e1008491
Journal	PLoS Computational Biology
Volume	16
Issue number	12 December
DOIs	https://doi.org/10.1371/journal.pcbi.1008491
State	Published - 23 Dec 2020

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Carcamo-Orive, I., Henrion, M. Y. R., Zhu, K., Beckmann, N. D., Cundiff, P., Moein, S., Zhang, Z., Alamprese, M., D’Souza, S. L., Wabitsch, M., Schadt, E. E., Quertermous, T., Chang, R., & Knowles, J. W. (2020). Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness. PLoS Computational Biology, 16(12 December), Article e1008491. https://doi.org/10.1371/journal.pcbi.1008491

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

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.",

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

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Carcamo-Orive, I, Henrion, MYR, Zhu, K, Beckmann, ND, Cundiff, P, Moein, S, Zhang, Z, Alamprese, M, D’Souza, SL, Wabitsch, M, Schadt, EE, Quertermous, T, Chang, R & Knowles, JW 2020, 'Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness', PLoS Computational Biology, vol. 16, no. 12 December, e1008491. https://doi.org/10.1371/journal.pcbi.1008491

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AU - Carcamo-Orive, Ivan

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AU - Cundiff, Paige

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AU - D’Souza, Sunita L.

AU - Wabitsch, Martin

AU - Schadt, Eric E.

AU - Quertermous, Thomas

AU - Chang, Rui

AU - Knowles, Joshua W.

N1 - Publisher Copyright: © 2020 Carcamo-Orive et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2020/12/23

Y1 - 2020/12/23

N2 - 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.

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

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