Metabolism is a biological process involved in various diseases, not only 48metabolic diseases such as obesity and diabetes, but also autoimmune diseases, psychiatric diseases, and cancer. Biochemical pathways for metabolism consist of myriad feedback loops, thereby defying simple causation analyses fre-quently performed in other linear networks and cascades. Furthermore, metab-olism undergoes multiplexed regulation from other omic layers, e.g., phosphor-ylation of enzymes by signal transduction (phosphoproteome), transcriptional regulation (transcriptome), translational regulation (expression proteome), etc. Our research interest is to understand intracellular metabolism and its regula-tory mechanisms as a system of biochemical reactions in dynamic, macroscopic and quantitative contexts. We employ the methodology of ‘trans-omics’ that aims to reconstruct global metabolic regulatory networks spanning multiple omic layers, not as a group of indirect statistical correlations but as chains of direct mechanistic interactions on the basis of reaction kinetics (Yugi et al., Trends Biotechnol., 2016; Yugi and Kuroda, Cell Syst., 2017; Yugi and Kuroda, Curr. Opin. Syst. Biol., 2018; Yugi et al., Curr. Opin. Syst. Biol., 2019). Interdisci-plinary approaches, such as ‘wet’ biology experiments, and ‘dry’ analyses, such as databases and mathematical models, are utilized to characterize the global metabolic regulatory networks. The network reconstruction is performed based on comprehensive measurement data, public databases, and a kinetic picture of the cellular processes (Figure). The comprehensive data of multiple omic layers should be measured under identical conditions in a time-series manner so that one can construct mathematical models of the multi-layered network for subse-quent systems biological analyses. We eventually aim to reveal the chain of logic from individual biochemical reactions to omics-scale metabolic regulatory sys-tems.Figure: Differential equation representation of a trans-omic networkWe integrate multiple omic data, postulating a dynamic picture of cellular processes driven by reaction kinetics. Each reaction rate (terms represented by ‘v’) is a function of the number of molecules that belong to the same or different omic layers. Characteristic time ‘τ’ emphasizes time scales for each omic layer (PTM: post-translational modification such as phosphorylation of an enzyme; Emod: modification enzyme; Substrate: substrate for the modi-fication reaction; Donor: chemical group donor such as acetyl-CoA for histone acetylation; Edemod: demodification enzyme; Active TF: active transcription factor; Open chr: open chromatin; ncRNA: noncoding RNA; RBP: RNA bind-ing proteins; Prot: protein abundance; Ub: ubiquitin; S: reactant metabolites; I: activators or inhibitors; E: enzyme abundance).Recent Major PublicationsKokaji T, Hatano A, Ito Y, Yugi K, Eto M, Morita K, Ohno S, Fujii M, Hironaka KI, Egami R, Terakawa A, Tsuchiyal T, Ozaki H, Inoue H, Uda S, Kubota H, Suzuki Y, Ikeda K, Arita M, Matsumoto M, Nakayama KI, Hirayama A, Soga T, Kuroda S. Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity. Sci Signal 13, eaaz1236 (2020)Hoshino D, Kawata K, Kunida K, Hatano A, Yugi K, Wada T, Fujii M, Sano T, Ito Y, Furuichi Y, Manabe Y, Suzuki Y, Fujii NL, Soga T, Kuroda S. Trans-omic Analysis Reveals ROS-dependent pentose phosphate pathway activation after high-frequency electrical stimulation in C2C12 myotubes. iScience 23, 101558 (2020)Ohno S, Quek LE, Krycer JR, Yugi K, Hirayama A, Ikeda S, Shoji F, Suzuki K, Soga T, James DE, Kuroda S. Kinetic trans-omic analysis reveals key regulatory mechanisms for insulin-regulated glucose metabolism in adipocytes. iScience 23, 101479 (2020)Invited presentationsYugi K. “Trans-omics: Integration of multiple omic data on the basis of reaction kinetics” Informatics in Biology, Medicine and Pharmacology 2020 (Online) September 2020Yugi K. “Unwritten tips and secrets of trans-omics: experi-mental designs, and inconspicuous technologies” Techni-cal seminar for young researchers of the MEXT Grant-in-Aid for Scientific Research on Innovative Areas “Transomic Analysis of Metabolic Adaptation” (Online) August 2020Yugi K. “Development of next-generation trans-omics technologies for the characterization of psychiatric disorders” Annual Meeting of the MEXT Grant-in-Aid for Scientific Research on Innovative Areas “Constructive un-derstanding of multi-scale dynamism of neuropsychiatric disorders” 2020 (Online) July 2020Yugi K. “Reconstruction of insulin signal flow from phos-phoproteome and metabolome data” KI–RIKEN Joint International Doctoral Course 2020 “Bioinformatics analy-sis of gene regulation in omics data and its applications to medical problems” (Stockholm, Sweden) March 2020Laboratory for Integrated Cellular SystemsTeam Leader: Katsuyuki Yugi
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