TY - JOUR
T1 - Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity
AU - Kokaji, Toshiya
AU - Hatano, Atsushi
AU - Ito, Yuki
AU - Yugi, Katsuyuki
AU - Eto, Miki
AU - Morita, Keigo
AU - Ohno, Satoshi
AU - Fujii, Masashi
AU - Hironaka, Ken Ichi
AU - Egami, Riku
AU - Terakawa, Akira
AU - Tsuchiya, Takaho
AU - Ozaki, Haruka
AU - Inoue, Hiroshi
AU - Uda, Shinsuke
AU - Kubota, Hiroyuki
AU - Suzuki, Yutaka
AU - Ikeda, Kazutaka
AU - Arita, Makoto
AU - Matsumoto, Masaki
AU - Nakayama, Keiichi I.
AU - Hirayama, Akiyoshi
AU - Soga, Tomoyoshi
AU - Kuroda, Shinya
N1 - Funding Information:
This work was supported by the Creation of Fundamental Technologies for Understanding and Control of Biosystem Dynamics, CREST (JPMJCR12W3) from the Japan Science and Technology Agency (JST) and by the Japan Society for the Promotion of Science (JSPS) KAKENHI grant numbers JP17H06300, JP17H06299, and JP18H03979. K.Y. receives funding from JSPS KAKENHI grant numbers JP15H05582 and JP18H05431 and “Creation of Innovative Technology for Medical Applications Based on the Global Analyses and Regulation of Disease-Related Metabolites”, PRESTO (JPMJPR1538) from JST. S.O. receives funding from a Grant-in-Aid for Young Scientists (B) (JP17K14864). M.F. was supported by JSPS KAKENHI grant numbers JP16K12508 and JP19K20382. T.T. was supported by JSPS KAKENHI grant numbers JP19K24361 and JP20K19915. H.O. was supported by JSPS KAKENHI grant numbers JP19H03696 and JP19K20394. H.I. was supported by JSPS KAKENHI grant numbers JP18KT0020 and JP17H05499, and by the Adaptable and Seamless Technology transfer Program through Target-driven R&D (A-STEP) from JST. S.U. was supported by JSPS KAKENHI grant numbers JP18H02431 and JP18H04801. H.K. was supported by JSPS KAKENHI grant numbers JP16H06577. Y.S. was supported by JSPS KAKENHI grant number JP17H06306. K.I.N. was supported by JSPS KAKENHI grant numbers JP17H06301 and JP18H05215. A. Hirayama was supported by JSPS KAKENHI grant number JP18H04804. T.S. receives funding from the AMED-CREST from the Japan Agency for Medical Research and Development (AMED) under grant number JP18gm0710003. Author
Publisher Copyright:
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.
AB - Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.
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U2 - 10.1126/SCISIGNAL.AAZ1236
DO - 10.1126/SCISIGNAL.AAZ1236
M3 - Article
C2 - 33262292
AN - SCOPUS:85097034104
SN - 1945-0877
VL - 13
JO - Science Signaling
JF - Science Signaling
IS - 660
M1 - eaaz1236
ER -