期刊
METABOLIC ENGINEERING
卷 69, 期 -, 页码 275-285出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymben.2021.12.009
关键词
Metabolic flux analysis; Metabolomics; Metabolic modeling software; Mass spectrometry; Nuclear magnetic resonance; INST-MFA
资金
- NIH [R01 DK106348, U01 CA235508]
- Robert A. Welch Foundation [I-1804]
- [R01 DK105346]
- [U2C DK119889]
- [P41 GM122698]
- [R01 DK 078184]
- [R01 DK128168]
- [P41 EB015908]
Metabolic flux analysis is a method that combines experimental measurements and computational modeling to determine reaction rates in biological systems. This study introduces a software tool called INCA 2.0, which can integrate isotopomer measurements from both MS and NMR platforms and estimate metabolic fluxes with flexibility. The results demonstrate that INCA 2.0 accurately simulates and analyzes NMR datasets, outperforming other established tools. Additionally, combining NMR and MS datasets improves the precision of estimated fluxes.
Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state 13C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different 13C acetate tracers, we show that MFA based on dynamic 13C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined 13C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.
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