Metabolism is a biological process involved in various diseases, not only 47metabolic diseases such as obesity and diabetes, but also autoimmune diseases, psychiatric diseases, and cancer. Biochemical pathways for metabo-lism consist of myriad feedback loops and branching points, thereby defy-ing simple causation analyses frequently performed in other linear cascades. Furthermore, metabolism undergoes multiplexed regulation from other omic layers: phosphorylation of enzymes by signal transduction (phosphopro-teome), transcriptional regulation (transcriptome), translational regulation (expression proteome), etc. Our research interest is to understand intracel-lular metabolism and its regulatory mechanisms as a system of biochemical reactions in dynamic, macroscopic and quantitative contexts. We employ the methodology of ‘trans-omics’ to reconstruct global metabolic regulatory net-works that come across multiple omic layers (Figure), not as a group of indi-rect statistical correlations but as chains of direct mechanistic interactions on the basis of reaction kinetics (Yugi et al., Trends Biotechnol., 2016 [https://doi.org/10.1016/j.tibtech.2015.12.013]; Yugi and Kuroda, Cell Syst., 2017 [https://doi.org/10.1016/j.cels.2017.01.007]; Yugi and Kuroda, Curr. Opin. Syst. Biol., 2018 [https://doi.org/10.1016/j.coisb.2017.12.002]; Yugi et al., Curr. Opin. Syst. Biol., 2019 [https://doi.org/10.1016/j.coisb.2019.04.005]; Okamoto et al., Neuro-sci. Res. 2022 [https://doi.org/10.1016/j.neures.2021.12.006]). Interdisciplinary approaches, such as ‘wet’ biology experiments, and ‘dry’ data analyses, such as mathematical models and statistical methods, are utilized to characterize the global metabolic regulatory networks. The network reconstruction is per-formed based on comprehensive measurement data, public databases, and a ki-netic picture of the cellular processes. The comprehensive data of multiple omic layers should be measured under identical conditions, preferably in a time-series manner, so that one can construct mathematical models of the multi-layered network for subsequent systems biological analyses. We eventually aim to reveal the chain of logic from individual biochemical reactions to omics-scale metabolic regulatory systems.Figure: The trans-omic network of the responses to glucose challenge in the liver of WT and ob/ob mice automatically produced by transomics-2cytoscapeThe top layer is the insulin signaling pathway. The sec-ond layer is for transcription factors (TFs). The third to fifth layers are the global metabolic pathways of mice from KEGG, representing enzyme genes, metabolic reac-tions, and metabolites, respectively. The edges between each layer are trans-omic interactions from signaling molecules to transcription factors (between layers 1 and 2), from TFs to target enzyme genes (between layers 2 and 3), from enzyme genes to metabolic reac-tions (between layers 3 and 4), and from metabolites to metabolic reactions (between layers 5 and 4). The edges indicate regulation functioning: only in WT (blue), only in ob/ob (red), in similar ways both in WT and ob/ob (green), and in opposite ways in WT and ob/ob (ma-genta).Recent Major PublicationsNishida K, Maruyama J, Kaizu K, Takahashi K, Yugi K. transomics2cytoscape: An automated software for in-terpretable 2.5-dimensional visualization of trans-omic networks. npj Syst Biol Appl 10, 16 (2024)Takeuchi T, Kubota T, Nakanishi Y, Tsugawa H, Suda W, Kwon AT, Yazaki J, Ikeda K, Nemoto S, Mochizuki Y, Kitami T, Yugi K, Mizuno Y, Yamamichi N, Yamazaki T, Takamoto I, Kubota N, Kadowaki T, Arner E, Carninci P, Ohara O, Arita M, Hattori M, Koyasu S, Ohno H. Gut mi-crobial carbohydrate metabolism contributes to insulin resistance. Nature 621, 389-395 (2023)Okamoto L, Watanabe S, Deno S, Nie X, Maruyama J, Tomita M, Hatano A, Yugi K. Meta-analysis of transcrip-tional regulatory networks for lipid metabolism in neu-ral cells from schizophrenia patients based on an open-source intelligence approach. Neurosci Res 175, 82-97 (2022)Invited presentationsYugi K. “Bridging the gap between SNPs and traits/diseases with trans-omic networks” The 9th RIKEN-KI-SciLifeLab Symposium (Yokohama, Japan) October 2023Yugi K. “Data-driven Darwinian medicine approach for exploring shared metabolic regulatory networks be-tween seasonal affective disorder and hibernation” 75th SBJ Annual Meeting (Nagoya, Japan) September 2023Yugi K. “Trans-omics of pharmacological action mecha-nism” JPrOS2023 (Niigata, Japan) July 2023Yugi K. “Systems biology of metabolic regulation” Con-ceptual Framework (Systems Biology) Seminar 2023 (Tsuruoka, Japan) April 2023Laboratory for Integrated Cellular SystemsTeam Leader: Katsuyuki Yugi
元のページ ../index.html#53