Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin

Lake-Ee Quek; James R Krycer; Satoshi Ohno; Katsuyuki Yugi; Daniel J Fazakerley; Richard Scalzo; Sarah D Elkington; Ziwei Dai; Akiyoshi Hirayama; Satsuki Ikeda; Futaba Shoji; Kumi Suzuki; Jason W Locasale; Tomoyoshi Soga; David E James; Shinya Kuroda

doi:10.1016/j.isci.2020.100855

Dynamic ¹³C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin

iScience. 2020 Feb 21;23(2):100855. doi: 10.1016/j.isci.2020.100855. Epub 2020 Jan 21.

Authors

Affiliations

¹ Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia. Electronic address: lake-ee.quek@sydney.edu.au.
² Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.
³ Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.
⁴ Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; YCI Laboratory for Trans-Omics, Young Chief Investigator Program, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan.
⁵ Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia.
⁶ Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Duke University, Durham, NC 27710, USA.
⁷ Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; AMED-CREST, AMED, 1-7-1 Otemachi, Chiyoda-Ku, Tokyo 100-0004, Japan.
⁸ Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan.
⁹ Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia. Electronic address: david.james@sydney.edu.au.
¹⁰ Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan; CREST, Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan. Electronic address: skuroda@bs.s.u-tokyo.ac.jp.

Abstract

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient ¹³C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

Keywords: Biological Sciences; Flux Data; Metabolic Flux Analysis; Metabolomics.