“… Figure 4 MFA in βTC3 G6pc2 WT and KO cells shows increased absolute flux through glycolytic and mitochondrial pathways. A , flux network representing oxidative βTC3 metabolism constructed in INCA ( 58 , 59 ). Measured media metabolites are shown in blue while measured intracellular metabolites are highlighted in green .…”
Section: Resultsmentioning
confidence: 99%
“…13 C MFA was performed by minimizing the sum-of-squared residuals (SSR) between model-simulated and experimentally determined measurements. The Isotopomer Network Compartmental Analysis (INCA) software package ( 58 , 59 ) was used to develop a model of β-cell metabolism ( Table S1 ) and estimate fluxes by fitting the model to the experimental datasets. Extracellular uptake rates and metabolite enrichment measurements were provided as inputs into the INCA model for flux analysis of βTC3 cell cultures.…”
“… Figure 4 MFA in βTC3 G6pc2 WT and KO cells shows increased absolute flux through glycolytic and mitochondrial pathways. A , flux network representing oxidative βTC3 metabolism constructed in INCA ( 58 , 59 ). Measured media metabolites are shown in blue while measured intracellular metabolites are highlighted in green .…”
Section: Resultsmentioning
confidence: 99%
“…13 C MFA was performed by minimizing the sum-of-squared residuals (SSR) between model-simulated and experimentally determined measurements. The Isotopomer Network Compartmental Analysis (INCA) software package ( 58 , 59 ) was used to develop a model of β-cell metabolism ( Table S1 ) and estimate fluxes by fitting the model to the experimental datasets. Extracellular uptake rates and metabolite enrichment measurements were provided as inputs into the INCA model for flux analysis of βTC3 cell cultures.…”
“…Extracted ion chromatograms were integrated into Quan Browser to obtain the Mass Isotopomer Distribution (MID) of each identified metabolite. The MID of each metabolite was corrected for natural isotope abundance with the Isotopomer Network Compartmental Analysis software (INCA 2.0, Vanderbilt University, Nashville, TN, USA) [ 34 , 35 ]. INCA was then utilized to assemble an isotopomer network model to estimate the relative net flux values of central metabolic reactions outlined in Supplementary Materials Figure S7 and Table S2 .…”
The compound β-lapachone, a naturally derived naphthoquinone, has been utilized as a potent medicinal nutrient to improve health. Over the last twelve years, numerous reports have demonstrated distinct associations of β-lapachone and NAD(P)H: quinone oxidoreductase 1 (NQO1) protein in the amelioration of various diseases. Comprehensive research of NQO1 bioactivity has clearly confirmed the tumoricidal effects of β-lapachone action through NAD+-keresis, in which severe DNA damage from reactive oxygen species (ROS) production triggers a poly-ADP-ribose polymerase-I (PARP1) hyperactivation cascade, culminating in NAD+/ATP depletion. Here, we report a novel combination strategy with aminooxyacetic acid (AOA), an aspartate aminotransferase inhibitor that blocks the malate-aspartate shuttle (MAS) and synergistically enhances the efficacy of β-lapachone metabolic perturbation in NQO1+ breast cancer. We evaluated metabolic turnover in MDA-MB-231 NQO1+, MDA-MB-231 NQO1−, MDA-MB-468, and T47D cancer cells by measuring the isotopic labeling of metabolites from a [U-13C]glucose tracer. We show that β-lapachone treatment significantly hampers lactate secretion by ~85% in NQO1+ cells. Our data demonstrate that combinatorial treatment decreases citrate, glutamate, and succinate enrichment by ~14%, ~50%, and ~65%, respectively. Differences in citrate, glutamate, and succinate fractional enrichments indicate synergistic effects on central metabolism based on the coefficient of drug interaction. Metabolic modeling suggests that increased glutamine anaplerosis is protective in the case of MAS inhibition.
“…Reconstructions of metabolic models are valuable, even if certain aspects remain challenging such as compartmentalization and tissue specificity ( Magnúsdóttir et al, 2016 ). Informatic tools become increasingly important for metabolic flux analysis calculations ( Arita, 2003 ; Rahim et al, 2022 ).…”
During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.
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