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

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. [Display omitted] •A dynamic trans-omic network constructed to decipher insulin response•Network characterized by early and prominent phosphorylation-mediated signaling•Regulation of proteins is predominantly through non-transcriptional mechanisms•Metabolite temporal profiles modified by the convergence of regulatory processes Matsuzaki et al. construct a molecular interaction network of insulin response in the murine liver using multi-omic datasets, referred to as the trans-omic network. It reveals early and predominant phosphorylation-mediated signaling, significant non-transcriptional protein regulation, and key temporally regulated metabolite profiles in insulin response.