An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver

Abstract

An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we perform phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine liver for 4 h after insulin administration and integrate the resulting time series. Structural characteristics and dynamic nature of the network are analyzed to elucidate the impact of insulin. Early and prominent changes in protein phosphorylation and persistent and asynchronous changes in mRNA and protein levels through non-transcriptional mechanisms indicate enhanced crosstalk between phosphorylation-mediated signaling and protein expression regulation. Metabolic response shows different temporal regulation with transient increases at early time points across categories and enhanced response in the amino acid and nucleotide categories at later time points as a result of process convergence. This extensive and dynamic view of the trans-omic network elucidates prominent regulatory mechanisms that drive insulin responses through intricate interlayer coordination.

Bistable insulin response: The win-win solution for glycemic control

“…66 , 67 , 68 This phenomenon, if associated with disorders characterized by their postprandial onset, adrenergic mediated symptoms, and other relatively benign causes, is called “reactive hypoglycemia.” 69 Reactive hypoglycemia is discovered in mice after the intraperitoneal injection of insulin. 70 Although ubiquitous, the transitional low blood-glucose state is poorly understood.…”

“…Fortunately, recently there emerged several excellent trans-omics analyses on insulin signaling and the related pathways and tissues. 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 These studies have provided a holistic perspective, which led to important discoveries. They even shed light on the present study, such as the phenomenon of reactive hypoglycemia.…”

Trans-omics analysis of insulin action reveals a cell growth subnetwork which co-regulates anabolic processes

“…There have been many attempts to characterize the insulin-regulated network of signaling molecules, transcription factors (TFs), metabolic enzymes, and other proteins regulating cellular functions such as proteins synthesis, and metabolites. Various “omic” studies of insulin action have been reported focusing on the phosphoproteome ( Humphrey et al., 2013 , 2015 ; Kawata et al., 2018 , 2019 ; Krüger et al., 2008 ; Krycer et al., 2017 ; Matsuzaki et al., 2021 ; Monetti et al., 2011 ; Ohno et al., 2020 ; Vinayagam et al., 2016 ; Zhang et al., 2017 ), PPIs ( Friedman et al., 2011 ; Glatter et al., 2011 ; Vinayagam et al., 2016 ), the transcriptome ( Dupont et al., 2001 ; Hectors et al., 2012 ; Kawata et al., 2018 ; Kim and Lee, 2014 ; Matsuzaki et al., 2021 ; Rome et al., 2003 ; Sano et al., 2016 ; Versteyhe et al., 2013 ), and the metabolome ( Everman et al., 2016 ; Kawata et al., 2018 ; Krycer et al., 2017 ; Matsuzaki et al., 2021 ; Noguchi et al., 2013 ; Ohno et al., 2020 ; Yugi et al., 2014 ). To provide a more comprehensive view than what can be gained from a single type of omic data alone, we have previously proposed “trans-omics” as a discipline for constructing molecular interaction networks from multi-omic data sets using direct molecular interactions rather than indirect statistical relationships ( Yugi and Kuroda, 2017 ; Yugi et al., 2014 , 2016 ).…”

“…To provide a more comprehensive view than what can be gained from a single type of omic data alone, we have previously proposed “trans-omics” as a discipline for constructing molecular interaction networks from multi-omic data sets using direct molecular interactions rather than indirect statistical relationships ( Yugi and Kuroda, 2017 ; Yugi et al., 2014 , 2016 ). For example, in our previous studies, we constructed trans-omic networks responding to insulin stimulation in mammalian cells ( Kawata et al., 2018 ; Kokaji et al., 2020 ; Matsuzaki et al., 2021 ; Ohno et al., 2020 ; Yugi et al., 2014 ), and responding to glucose administration in the liver of healthy and obese mice ( Kokaji et al., 2020 ). Although these studies have expanded our view of insulin signaling, functional aspects (i.e., effects on phenotypes) remain to be explored for a large part of the network components (i.e., molecules and the regulatory relationships between them) and their relationships with phenotypes.…”

Comparison of hepatic responses to glucose perturbation between healthy and obese mice based on the edge type of network structures

“…The liver plays an essential role in maintaining blood glucose levels, and part of the control of the metabolic response depends on the regulation of hepatic gene expression [19][20][21] . A recent study examined hepatic glucose metabolism using multiomics measurements 22,23 . However, the experimental design was inadequate for the examination of statistical independence.…”