scholarly journals In Vivo Estimation of Ketogenesis Using Metabolic Flux Analysis—Technical Aspects and Model Interpretation

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 279
Author(s):  
Stanislaw Deja ◽  
Blanka Kucejova ◽  
Xiaorong Fu ◽  
Jeffrey D. Browning ◽  
Jamey D. Young ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Accurate Determination ◽  
Liver Mitochondria ◽  
Flux Analysis ◽  
Peripheral Tissues ◽  
Model Interpretation ◽  
Liquid Chromatography Tandem Mass ◽  
Pool Model

Ketogenesis occurs in liver mitochondria where acetyl-CoA molecules, derived from lipid oxidation, are condensed into acetoacetate (AcAc) and reduced to β-hydroxybutyrate (BHB). During carbohydrate scarcity, these two ketones are released into circulation at high rates and used as oxidative fuels in peripheral tissues. Despite their physiological relevance and emerging roles in a variety of diseases, endogenous ketone production is rarely measured in vivo using tracer approaches. Accurate determination of this flux requires a two-pool model, simultaneous BHB and AcAc tracers, and special consideration for the stability of the AcAc tracer and analyte. We describe the implementation of a two-pool model using a metabolic flux analysis (MFA) approach that simultaneously regresses liquid chromatography-tandem mass spectrometry (LC-MS/MS) ketone isotopologues and tracer infusion rates. Additionally, 1H NMR real-time reaction monitoring was used to evaluate AcAc tracer and analyte stability during infusion and sample analysis, which were critical for accurate flux calculations. The approach quantifies AcAc and BHB pool sizes and their rates of appearance, disposal, and exchange. Regression analysis provides confidence intervals and detects potential errors in experimental data. Complications for the physiological interpretation of individual ketone fluxes are discussed.

scholarly journals 2H and 13C metabolic flux analysis elucidates in vivo thermodynamics of the ED pathway in Zymomonas mobilis

2019 ◽  
Vol 54 ◽  
pp. 301-316 ◽  
Author(s):  
Tyler B. Jacobson ◽  
Paul A. Adamczyk ◽  
David M. Stevenson ◽  
Matthew Regner ◽  
John Ralph ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Zymomonas Mobilis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis

Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis

2019 ◽  
Vol 36 (1) ◽  
pp. 232-240
Author(s):  
Axel Theorell ◽  
Katharina Nöh
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Bayesian Model Averaging ◽  
Linear Models ◽  
Model Averaging ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Large Set ◽  
Vast Number

Abstract Motivation The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Results Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary information Supplementary data are available at Bioinformatics online.

Assessment and Expansion of Models for13C-Metabolic Flux Analysis (MFA) In Vivo

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 258-LB
Author(s):  
CLINTON HASENOUR ◽  
MOHSIN RAHIM ◽  
JAMEY YOUNG
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis

scholarly journals OpenMebius: An Open Source Software for Isotopically Nonstationary13C-Based Metabolic Flux Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shuichi Kajihata ◽  
Chikara Furusawa ◽  
Fumio Matsuda ◽  
Hiroshi Shimizu
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Simulated Data ◽  
Isotopic Labeling ◽  
Metabolic Model ◽  
Cell Physiology ◽  
Flux Analysis

Thein vivomeasurement of metabolic flux by13C-based metabolic flux analysis (13C-MFA) provides valuable information regarding cell physiology. Bioinformatics tools have been developed to estimate metabolic flux distributions from the results of tracer isotopic labeling experiments using a13C-labeled carbon source. Metabolic flux is determined by nonlinear fitting of a metabolic model to the isotopic labeling enrichment of intracellular metabolites measured by mass spectrometry. Whereas13C-MFA is conventionally performed under isotopically constant conditions, isotopically nonstationary13C metabolic flux analysis (INST-13C-MFA) has recently been developed for flux analysis of cells with photosynthetic activity and cells at a quasi-steady metabolic state (e.g., primary cells or microorganisms under stationary phase). Here, the development of a novel open source software for INST-13C-MFA on the Windows platform is reported. OpenMebius (Open source software for Metabolic flux analysis) provides the function of autogenerating metabolic models for simulating isotopic labeling enrichment from a user-defined configuration worksheet. Analysis using simulated data demonstrated the applicability of OpenMebius for INST-13C-MFA. Confidence intervals determined by INST-13C-MFA were less than those determined by conventional methods, indicating the potential of INST-13C-MFA for precise metabolic flux analysis. OpenMebius is the open source software for the general application of INST-13C-MFA.

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