bioRxivPhysiologyPreprint
Transcriptomic analysis of human and mouse muscle during hyperinsulinemia demonstrates insulin receptor downregulation as a mechanism for insulin resistance
H. H. Cen, J. D. Botezelli, S. Wang, N. Noursadeghi, N. Jessen, J. D. Johnson, J. A. Timmons
Abstract
Hyperinsulinemia is commonly viewed as a compensatory response to insulin resistance, yet studies have suggested that chronically elevated insulin may also drive insulin resistance. The molecular mechanisms underpinning this potentially cyclic process remain poorly defined, especially on a transcriptome-wide level. To study the direct effects of prolonged exposure to excess insulin in muscle cells, we incubated C2C12 myotubes with elevated insulin for 16 hours, followed by 6 hours of serum starvation, and established that acute AKT and ERK signaling were attenuated in this model of in vitro hyperinsulinemia. Global RNA-sequencing of cells both before and after nutrient withdrawal highlighted genes in the insulin signaling, FOXO signaling, and glucose metabolism pathways indicative of hyperinsulinemia and starvation programs. We observed that hyperinsulinemia led to a substantial reduction in insulin receptor (Insr) gene expression, and subsequently a reduced surface INSR and total INSR protein, both in vitro and in vivo. Transcriptomic meta-analysis in >450 human samples demonstrated that fasting insulin reliably and negatively correlated with insulin receptor (INSR) mRNA in skeletal muscle. Bioinformatic modeling combined with RNAi, identified SIN3A as a negative regulator of Insr mRNA (and JUND, MAX, and MXI as positive regulators of Irs2 mRNA). Together, our analysis identifies novel mechanisms which may explain the cyclic processes underlying hyperinsulinemia-induced insulin resistance in muscle, a process directly relevant to the etiology and disease progression of type 2 diabetes.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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