bioRxivSystems BiologyPreprint
Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome
Kentaro KawataiD, Katsuyuki YugiiD, Atsushi HatanoiD, Masashi FujiiiD, Yoko TomizawaiD, Toshiya KokajiiD, Takanori Sano, Kaori Y. TanakaiD, Shinsuke UdaiD, Hiroyuki KubotaiD, Yutaka SuzukiiD, Masaki MatsumotoiD, Keiichi I. NakayamaiD, Kaori SaitohiD, Keiko KatoiD, Ayano Ueno, Maki Ohishi, Tomoyoshi SogaiD, Shinya KurodaiD
Abstract
The concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks--a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals.\n\nHighlightsO_LIWe constructed a trans-omic network of insulin action using multi-omic data.\nC_LIO_LIThe trans-omic network integrates phosphorylation, transcription, and metabolism.\nC_LIO_LIWe classified signaling, transcriptome, and metabolome by sensitivity to insulin.\nC_LIO_LIWe identified fed and fasting insulin signal flow across the trans-omic network.\nC_LI
- organism: mouse
- assay: proteomics
- assay: metabolomics
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
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