Metabolism is a biological process involved in various diseases, not only 51metabolic diseases such as obesity and diabetes, but also autoimmune diseases, psychiatric disorders and cancer. Biochemical pathways for me-tabolism consist of myriad feedback loops, thereby defying simple causation analyses frequently performed in other linear networks and cascades. Further-more, metabolism undergoes multiplexed regulation from other omic layers, e.g., phosphorylation of enzymes by signal transduction (phosphoproteome), transcriptional regulation (transcriptome), translational regulation (expression proteome), etc. Our research interest is to understand intracellular metabolism and its regulatory 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; Okatomo et al., Neurosci Res., 2022). Interdisciplinary 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 measure-ment 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 subsequent systems bio-logical analyses. We eventually aim to reveal the chain of logic from individual biochemical reactions to omics-scale metabolic regulatory systems.Figure: Differential equation representation of a trans-omic network and its application to re-lated fieldsWe integrate multiple omic data with 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 other 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 modification 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 binding proteins; Prot: protein abun-dance; Ub: ubiquitin; S: reactant metabolites; I: activa-tors or inhibitors; E: enzyme abundance). We apply this methodology to characterizing complex metabolic regulatory systems related to drug action, the nervous system, the immune system, human genetics, etc.Recent Major PublicationsMiyauchi K, Adachi Y, Tonouchi K, Yajima T, Harada Y, Fukuyama H, Deno S, Iwakura Y, Yoshimura A, Hasegawa H, Yugi K, Fujii S.I, Ohara O, Takahashi Y, Kubo M. Influ-enza virus infection expands the breadth of antibody responses through IL-4 signalling in B cells. Nat Commun 12, 3789 (2021)Egami R, Kokaji T, Hatano A, Yugi K, Eto M, Morita K, Ohno S, Fujii M, Hironaka K, Uematsu S, Terakawa A, Bai U, Pan Y, Tsuchiya T, Ozaki H, Inoue H, Uda S, Kubota H, Suzuki Y, Matsumoto M, Nakayama K.I, Hirayama A, Soga T, Kuroda S. Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle. iScience 24, 102217 (2021)Kokaji T, Hatano A, Ito Y, Yugi K, Eto M, Morita K, Ohno S, Fujii M, Hironaka KI, Egami R, Terakawa A, Tsuchiya 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)Invited presentationsYugi K. “A data-driven and a hypothesis-driven omics inte-gration for systems biology of metabolism” NARA Institute of Science and Technology (Online) November 2021Yugi K. “Trans-omic data integration for reconstructing metabolic regulatory networks” Current Trends in Bioinfor-matics, Yokohama City University (Online) November 2021Yugi K. “Trans-omics: systems biology based on integra-tion of multiple omic data” Lectures on Molecular Biol-ogy, Niigata University, (Online) October 2021Laboratory for Integrated Cellular SystemsTeam Leader: Katsuyuki Yugi
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