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

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

doi:10.1016/j.isci.2020.100855

Cited by 28 publications

(42 citation statements)

References 58 publications

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“…In Krycer et al, 2017, we classified metabolites into five clusters using hierarchical clustering based on differences in metabolite concentrations between the Ins and Ctrl conditions. In all three studies, we found that reactions in the TCA cycle were classified into multiple clusters: four clusters in this study ( Figures 5C and 7) and in Krycer et al (2017) and three clusters in Quek et al (2020). Thus, collectively these three studies indicated that the TCA cycle is regulated as functionally separate blocks, rather than as a single cycle, in the insulin-stimulated adipocytes.…”

supporting

confidence: 53%

“…Thus our metabolic flux model indicates that the flux through Pfk1 in glycolysis was markedly increased by insulin stimulation, which is consistent with previous findings (Cahill et al, 1959;Katz et al, 1966). Decreased flux through Glut4 and Pfk1 from 0 to 3 min in both the Ins and Ctrl conditions is likely due to the effect of media change, because fluxes through Glut4 and Pfk1 decreased similarly in both Ins and Ctrl conditions (Quek et al, 2020).…”

Section: Overview Of Kinetic Trans-omic Analysismentioning

confidence: 92%

“…Step I: we used mass balances in the metabolic network to model flux changes from studies of adipocytes treated with or without insulin (Krycer et al, 2017;Quek et al, 2020). The data were from time-resolved 13 C-labeled metabolomic data and extracellular lactate studies;…”

Section: Overview Of Kinetic Trans-omic Analysismentioning

confidence: 99%

“…We understand that measured data are often perturbed by a Monte Carlo procedure to estimate confidence intervals of parameters in metabolic flux analysis (Hörl et al, 2013;Quek et al, 2020).…”

Section: Estimation Of Confidence Intervals Of Parametersmentioning

confidence: 99%

“…For metabolic flux analysis and key regulatory mechanisms, we used metabolomic and phosphoproteomic data from insulin-stimulated adipocytes in our previous studies (Humphrey et al, 2013;Krycer et al, 2017;Quek et al, 2020) and the experimental procedures of the previous studies are briefly described below. For metabolomic measurements (Krycer et al, 2017;Quek et al, 2020), differentiated 3T3-L1 adipocytes were labelled with [U-13 C] glucose (25 mM, 99 atom % 13 C) and treated with 100 nM insulin or vehicle (PBS) for 1, 5, 10, 20, 40, or 60 min. Labelling with 13 C and the treatment with 100 nM insulin or vehicle were started at the same time.…”

Section: Metabolomic and Phosphoproteomic Data In Insulin-stimulated mentioning

confidence: 99%

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Kinetic Trans-omic Analysis Reveals Key Regulatory Mechanisms for Insulin-Regulated Glucose Metabolism in Adipocytes

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supporting

confidence: 53%

Section: Overview Of Kinetic Trans-omic Analysismentioning

confidence: 92%

Section: Overview Of Kinetic Trans-omic Analysismentioning

confidence: 99%

Section: Estimation Of Confidence Intervals Of Parametersmentioning

confidence: 99%

Section: Metabolomic and Phosphoproteomic Data In Insulin-stimulated mentioning

confidence: 99%

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Kinetic Trans-omic Analysis Reveals Key Regulatory Mechanisms for Insulin-Regulated Glucose Metabolism in Adipocytes

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Kinetic modelling of the cellular metabolic responses underpinning in vitro glycolysis assays

Patil,

Mirveis,

Byrne

2024

FEBS Open Bio

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This study aims to demonstrate the benefits of augmenting commercially available, real‐time, in vitro glycolysis assays with phenomenological rate equation‐based kinetic models, describing the contributions of the underpinning metabolic pathways. To this end, a commercially available glycolysis assay, sensitive to changes in extracellular acidification (extracellular pH), was used to derive the glycolysis pathway kinetics. The pathway was numerically modelled using a series of ordinary differential rate equations, to simulate the obtained experimental results. The sensitivity of the model to the key equation parameters was also explored. The cellular glycolysis pathway kinetics were determined for three different cell‐lines, under nonmodulated and modulated conditions. Over the timescale studied, the assay demonstrated a two‐phase metabolic response, representing the differential kinetics of glycolysis pathway rate as a function of time, and this behaviour was faithfully reproduced by the model simulations. The model enabled quantitative comparison of the pathway kinetics of three cell lines, and also the modulating effect of two known drugs. Moreover, the modelling tool allows the subtle differences between different cell lines to be better elucidated and also allows augmentation of the assay sensitivity. A simplistic numerical model can faithfully reproduce the differential pathway kinetics for three different cell lines, with and without pathway‐modulating drugs, and furthermore provides insights into the cellular metabolism by elucidating the underlying mechanisms leading to the pathway end‐product. This study demonstrates that augmenting a relatively simple, real‐time, in vitro assay with a model of the underpinning metabolic pathway provides considerable insights into the observed differences in cellular systems.

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Investigating metabolism by mass spectrometry: From steady state to dynamic view

et al. 2020

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Metabolism is the set of life‐sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so‐called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady‐state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

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

Cited by 28 publications

References 58 publications

Kinetic Trans-omic Analysis Reveals Key Regulatory Mechanisms for Insulin-Regulated Glucose Metabolism in Adipocytes

Kinetic Trans-omic Analysis Reveals Key Regulatory Mechanisms for Insulin-Regulated Glucose Metabolism in Adipocytes

Kinetic modelling of the cellular metabolic responses underpinning in vitro glycolysis assays

Investigating metabolism by mass spectrometry: From steady state to dynamic view

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