Stable Isotope Tracing and Metabolomics to Study In Vivo Brown Adipose Tissue Metabolic Fluxes

Jung, Sung-Min; Le, Johnny; Doxsey, Will G.; Haley, John A.; Park, Grace; Guertin, David A.; Jang, Cholsoon

doi:10.1007/978-1-0716-2087-8_8

Cited by 4 publications

(5 citation statements)

References 36 publications

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“…In recent years, a powerful approach, Stable Isotope Resolved Metabolomics (SIRM) [1][2][3][4][5][6][7][8][9][10][11] has been developed to decipher metabolic networks and changes in disease states, which can be linked to altered regulation at gene and protein expression levels. Based on mass spectrometry and nuclear magnetic resonance techniques, SIRM uses stable isotope tracers such as uniformly 13 C-enriched glucose ( 13 C 6 -Glc ) to trace metabolic pathways or networks at the atomic level [1,7,9,[11][12][13][14][15][16] in cells, tissues, living organisms, or even human patients, that respond to disease development or drug treatment. By determining the atomic position (isotopomers) of stable isotope incorporation into relevant metabolites, the enzymes that are involved in the altered biochemical conversion of parent tracers into these metabolites can be systematically identified along with their possible allosteric regulator(s).…”

Section: Introductionmentioning

confidence: 99%

Bayesian kinetic modeling for tracer-based metabolomic data

Zhang

Lane

et al. 2023

BMC Bioinformatics

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Background Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly $$^{13}C$$ 13 C -enriched glucose ($$^{13}C_6$$ 13 C 6 -Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. Results We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts’ knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux. Conclusions Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

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Section: Introductionmentioning

confidence: 99%

Bayesian kinetic modeling for tracer-based metabolomic data

Zhang

Lane

et al. 2023

BMC Bioinformatics

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“…The advances in MS coupled with stable isotope tracing methods allowed also to improve the knowledge of the metabolism in several cells and tissues (Jung et al, 2022). The use of stable isotopes, such as labeled 13 C‐glucose combined with targeted metabolomic analysis, allows to perform the so‐called metabolic flux analysis, which provides an overview of the dynamic movement of glucose carbon atoms through a metabolic network and, more generally, the activity of several metabolic pathways (Ruiz et al, 2015; Schnelle et al, 2020).…”

Section: Methods For the Study Of The Secretome: Role Of Msmentioning

confidence: 99%

“…Thanks to the advances in MS coupled with stable isotope tracing methods BAT was demonstrated to have a great metabolic capacity (Jung et al, 2022). Several studies have been performed to study brown adipocyte metabolism using stable isotope tracing (Panic et al, 2020; Winther et al, 2018).…”

Section: The Brown Adipose Tissue: Batokinesmentioning

confidence: 99%

“…However, in vitro culture conditions do not often reflect the more complex in vivo environment. Recently, Jung et al applied isotope tracing coupled to LC‐MS metabolomics to study in vivo BAT metabolic fluxes and quantify metabolite levels (Jung et al, 2022). They used universally labeled 13 C‐glucose provided by oral gavage to study BAT from mice acclimated to different temperatures to achieve diverse levels of brown fat activity.…”

Section: The Brown Adipose Tissue: Batokinesmentioning

confidence: 99%

“…After labeled 13 C‐glucose administration, serum and tissues were harvested and the metabolites were extracted from the samples and loaded onto the LC‐MS to measure the metabolic flux. This method could be also applied to examining other tracers and to assess the pathway fluxes for each of them within BAT facilitating the discovery of new metabolic pathways not previously appreciated in BAT physiology (Jung et al, 2022).…”

Section: The Brown Adipose Tissue: Batokinesmentioning

confidence: 99%

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Mass spectrometry for the study of adipocyte cell secretome in cardiovascular diseases

Gianazza

Brioschi

Eligini

et al. 2022

Mass Spectrometry Reviews

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Adipose tissue is classically considered the primary site of lipid storage, but in recent years has garnered appreciation for its broad role as an endocrine organ, capable of remotely signaling to other tissues to alter their metabolic program. The adipose tissue is now recognized as a crucial regulator of cardiovascular health, mediated by the secretion of several bioactive products, with a wide range of endocrine and paracrine effects on the cardiovascular system. Thanks to the development and improvement of high‐throughput mass spectrometry, the size and components of the human secretome have been characterized. In this review, we summarized the recent advances in mass spectrometry‐based studies of the cell and tissue secretome for the understanding of adipose tissue biology, which may help to decipher the complex molecular mechanisms controlling the crosstalk between the adipose tissue and the cardiovascular system, and their possible clinical translation.

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A Novel Strategy for the Development of Functional Foods to Improve Energy Metabolism Disorders: Stable Isotope-Resolved Metabolomics

Meng

et al. 2023

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Stable Isotope Tracing and Metabolomics to Study In Vivo Brown Adipose Tissue Metabolic Fluxes

Cited by 4 publications

References 36 publications

Bayesian kinetic modeling for tracer-based metabolomic data

Bayesian kinetic modeling for tracer-based metabolomic data

Mass spectrometry for the study of adipocyte cell secretome in cardiovascular diseases

A Novel Strategy for the Development of Functional Foods to Improve Energy Metabolism Disorders: Stable Isotope-Resolved Metabolomics

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